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Wen H, Ouyang H, Shang H, Da C, Zhang T. Helix-to-sheet transition of the Aβ42 peptide revealed using an enhanced sampling strategy and Markov state model. Comput Struct Biotechnol J 2024; 23:688-699. [PMID: 38292476 PMCID: PMC10825278 DOI: 10.1016/j.csbj.2023.12.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 12/14/2023] [Accepted: 12/16/2023] [Indexed: 02/01/2024] Open
Abstract
The self-assembly of Aβ peptides into toxic oligomers and fibrils is the primary cause of Alzheimer's disease. Moreover, the conformational transition from helix to sheet is considered a crucial step in the aggregation of Aβ peptides. However, the structural details of this process still remain unclear due to the heterogeneity and transient nature of the Aβ peptides. In this study, we developed an enhanced sampling strategy that combines artificial neural networks (ANN) with metadynamics to explore the conformational space of the Aβ42 peptides. The strategy consists of two parts: applying ANN to optimize CVs and conducting metadynamics based on the resulting CVs to sample conformations. The results showed that this strategy achieved better sampling performance in terms of the distribution of sampled conformations. The sampling efficiency is increased by 10-fold compared to our previous Hamiltonian Exchange Molecular Dynamics (MD) and by 1000-fold compared to ordinary MD. Based on the sampled conformations, we constructed a Markov state model to understand the detailed transition process. The intermediate states in this process are identified, and the connecting paths are analyzed. The conformational transitions in D23-K28 and M35-V40 are proven to be crucial for aggregation. These results are helpful in clarifying the mechanism and process of Aβ42 peptide aggregation. D23-K28 and M35-V40 can be identified as potential targets for screening and designing inhibitors of Aβ peptide aggregation.
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Affiliation(s)
- Huilin Wen
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin 300070, PR China
- The Third Hospital of Hebei Medical University, Shijiazhuang 050051, PR China
| | - Hao Ouyang
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin 300070, PR China
| | - Hao Shang
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin 300070, PR China
| | - Chaohong Da
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin 300070, PR China
| | - Tao Zhang
- School of Biomedical Engineering and Technology, Tianjin Medical University, Tianjin 300070, PR China
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Xie X, Yu W, Wang L, Yang J, Tu X, Liu X, Liu S, Zhou H, Chi R, Huang Y. SERS-based AI diagnosis of lung and gastric cancer via exhaled breath. Spectrochim Acta A Mol Biomol Spectrosc 2024; 314:124181. [PMID: 38527410 DOI: 10.1016/j.saa.2024.124181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 03/13/2024] [Accepted: 03/20/2024] [Indexed: 03/27/2024]
Abstract
Distinct diagnosis between Lung cancer (LC) and gastric cancer (GC) according to the same biomarkers (e.g. aldehydes) in exhaled breath based on surface-enhanced Raman spectroscopy (SERS) remains a challenge in current studies. Here, an accurate diagnosis of LC and GC is demonstrated, using artificial intelligence technologies (AI) based on SERS spectrum of exhaled breath in plasmonic metal organic frameworks nanoparticle (PMN) film. In the PMN film with optimal structure parameters, 1780 SERS spectra are collected, in which 940 spectra come from healthy people (n = 49), another 440 come from LC patients (n = 22) and the rest 400 come from GC patients (n = 8). The SERS spectra are trained through artificial neural network (ANN) model with the deep learning (DL) algorithm, and the result exhibits a good identification accuracy of LC and GC with an accuracy over 89 %. Furthermore, combined with information of SERS peaks, the data mining in ANN model is successfully employed to explore the subtle compositional difference in exhaled breath from healthy people (H) and L/GC patients. This work achieves excellent noninvasive diagnosis of multiple cancer diseases in breath analysis and provides a new avenue to explore the feature of disease based on SERS spectrum.
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Affiliation(s)
- Xin Xie
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China
| | - Wenrou Yu
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China
| | - Li Wang
- School of Optoelectronics Engineering, Chongqing University, Chongqing 401331, China
| | - Junjun Yang
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China
| | - Xiaobin Tu
- Department of Oncology and Department of Hematology, Chongqing Wulong People's Hospital, Chongqing 408500, China
| | - Xiaochun Liu
- Department of Oncology and Department of Hematology, Chongqing Wulong People's Hospital, Chongqing 408500, China
| | - Shihong Liu
- Department of Geriatric Oncology and Department of Palliative Care, Chongqing University Cancer Hospital, Chongqing 400030, China.
| | - Han Zhou
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China
| | - Runwei Chi
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China
| | - Yingzhou Huang
- Chongqing Key Laboratory of Interface Physics in Energy Conversion, College of Physics, Chongqing University, Chongqing 400044, China.
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Basri KN, Yazid F, Mohd Zain MN, Md Yusof Z, Abdul Rani R, Zoolfakar AS. Artificial neural network and convolutional neural network for prediction of dental caries. Spectrochim Acta A Mol Biomol Spectrosc 2024; 312:124063. [PMID: 38394882 DOI: 10.1016/j.saa.2024.124063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 01/12/2024] [Accepted: 02/18/2024] [Indexed: 02/25/2024]
Abstract
Dental caries has high prevalence among kids and adults thus it has become one of the global health concerns. The current modern dentistry focused on the preventives measures to reduce the number of dental caries cases. The employment of machine learning coupled with UV spectroscopy plays a crucial role to detect the early stage of caries. Artificial neural network with hyperparameter tuning was employed to train spectral data for the classification based on the International Caries Detection and Assesment System (ICDAS). Spectra preprocessing namely mean center (MC), autoscale (AS) and Savitzky Golay smoothing (SG) were applied on the data for spectra correction. The best performance of ANN model obtained has accuracy of 0.85 with precision of 1.00. Convolutional neural network (CNN) combined with Savitzky Golay smoothing performed on the spectral data has accuracy, precision, sensitivity and specificity for validation data of 1.00 respectively. The result obtained shows that the application of ANN and CNN capable to produce robust model to be used as an early screening of dental caries.
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Affiliation(s)
- Katrul Nadia Basri
- School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia; Photonics Technology Lab, MIMOS Berhad, Technology Park Malaysia, 57000 Kuala Lumpur, Malaysia
| | - Farinawati Yazid
- Faculty of Dentistry, Universiti Kebangsaan Malaysia, 50300 Kuala Lumpur, Malaysia
| | | | - Zalhan Md Yusof
- Photonics Technology Lab, MIMOS Berhad, Technology Park Malaysia, 57000 Kuala Lumpur, Malaysia
| | - Rozina Abdul Rani
- School of Mechanical Engineering, College of Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
| | - Ahmad Sabirin Zoolfakar
- School of Electrical Engineering, College of Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia.
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Orang A, Berke O, Poljak Z, Greer AL, Rees EE, Ng V. Forecasting seasonal influenza activity in Canada-Comparing seasonal Auto-Regressive integrated moving average and artificial neural network approaches for public health preparedness. Zoonoses Public Health 2024; 71:304-313. [PMID: 38331569 DOI: 10.1111/zph.13114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 11/28/2023] [Accepted: 01/25/2024] [Indexed: 02/10/2024]
Abstract
INTRODUCTION Public health preparedness is based on timely and accurate information. Time series forecasting using disease surveillance data is an important aspect of preparedness. This study compared two approaches of time series forecasting: seasonal auto-regressive integrated moving average (SARIMA) modelling and the artificial neural network (ANN) algorithm. The goal was to model weekly seasonal influenza activity in Canada using SARIMA and compares its predictive accuracy, based on root mean square prediction error (RMSE) and mean absolute prediction error (MAE), to that of an ANN. METHODS An initial SARIMA model was fit using automated model selection by minimizing the Akaike information criterion (AIC). Further inspection of the autocorrelation function and partial autocorrelation function led to 'manual' model improvements. ANNs were trained iteratively, using an automated process to minimize the RMSE and MAE. RESULTS A total of 378, 462 cases of influenza was reported in Canada from the 2010-2011 influenza season to the end of the 2019-2020 influenza season, with an average yearly incidence risk of 20.02 per 100,000 population. Automated SARIMA modelling was the better method in terms of forecasting accuracy (per RMSE and MAE). However, the ANN correctly predicted the peak week of disease incidence while the other models did not. CONCLUSION Both the ANN and SARIMA models have shown to be capable tools in forecasting seasonal influenza activity in Canada. It was shown that applying both in tandem is beneficial, SARIMA better forecasted overall incidence while ANN correctly predicted the peak week.
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Affiliation(s)
- Armin Orang
- Department of Population Medicine, University of Guelph, Guelph, Ontario, Canada
| | - Olaf Berke
- Department of Population Medicine, University of Guelph, Guelph, Ontario, Canada
- Centre for Public Health and Zoonoses, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - Zvonimir Poljak
- Department of Population Medicine, University of Guelph, Guelph, Ontario, Canada
- Centre for Public Health and Zoonoses, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - Amy L Greer
- Department of Population Medicine, University of Guelph, Guelph, Ontario, Canada
- Centre for Public Health and Zoonoses, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
| | - Erin E Rees
- Public Health Risk Sciences Division, National Microbiology Laboratory Branch, Public Health Agency of Canada, Saint-Hyacinthe, Québec, Canada
| | - Victoria Ng
- Centre for Public Health and Zoonoses, Ontario Veterinary College, University of Guelph, Guelph, Ontario, Canada
- Public Health Risk Sciences Division, National Microbiology Laboratory Branch, Public Health Agency of Canada, Guelph, Ontario, Canada
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Aigbe UO, Lebepe TC, Oluwafemi OS, Osibote OA. Prediction and optimizing of methylene blue sequestration to activated charcoal/magnetic nanocomposites using artificial neutral network and response surface methodology. Chemosphere 2024; 355:141751. [PMID: 38522674 DOI: 10.1016/j.chemosphere.2024.141751] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 01/18/2024] [Accepted: 03/16/2024] [Indexed: 03/26/2024]
Abstract
Green synthesized magnetic nanoparticles (MNPs) linked with activated charcoal (AC) (AC/Fe3O4 NCs) were exploited for methylene blue (MB) confiscation in this study. The AC/Fe3O4 NCs produced were characterized using TEM, FTIR, UV/Vis and XRD spectrometry. The Response-Surface-Methodology (RSM) was utilized to improve the experimental data for the MB sorption to AC/Fe3O4 NCs, with 20 experimental runs implemented through a central composite design (CCD) to assess the effect of sorption factors-initial MB concentration, pH and sorbent dosage effects on the response (removal-effectiveness). The quadratic model was discovered to ideally describe the sorption process, with an R2 value of 0.9857. The theoretical prediction of the experimental data using the Artificial-Neural-Network (ANN) model showed that the Levenberg-Marquardt (LM) had a better performance criterion. Comparison between the modelled experimental and predicted data showed also that the LM algorithm had a high R2 of 0.9922, which showed NN model applicability for defining the sorption of MB to AC/Fe3O4 NCs with practical precision. The results of the non-linear fitting (NLF) of both isotherm and kinetic models, showed that the sorption of MB to AC/Fe3O4 NCs was perfectly described using the pseudo-second-order (PSOM) and Freundlich (FRHM) models. The estimated optimum sorption capacity was 455 mg g-1. Thermodynamically, the sorption of MB to AC/Fe3O4 NCs was shown to be non-spontaneous and endothermic.
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Affiliation(s)
- Uyiosa Osagie Aigbe
- Department of Mathematics and Physics, Faculty of Applied Sciences, Cape Peninsula University of Technology, Cape Town, South Africa.
| | - Thabang Calvin Lebepe
- Department of Chemical Sciences (Formerly Applied Chemistry), University of Johannesburg, Doornfontein Campus, Johannesburg, South Africa
| | - Oluwatobi Samuel Oluwafemi
- Department of Chemical Sciences (Formerly Applied Chemistry), University of Johannesburg, Doornfontein Campus, Johannesburg, South Africa; Centre for Nanomaterials Science Research, University of Johannesburg, P. O. Box 17011, Doornfontein, 2028, Johannesburg, South Africa
| | - Otolorin Adelaja Osibote
- Department of Mathematics and Physics, Faculty of Applied Sciences, Cape Peninsula University of Technology, Cape Town, South Africa
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Rai AK, Malakar S, Goswami S. Evaluating seismic risk by MCDM and machine learning for the eastern coast of India. Environ Monit Assess 2024; 196:471. [PMID: 38658399 DOI: 10.1007/s10661-024-12615-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 04/05/2024] [Indexed: 04/26/2024]
Abstract
Natural disasters such as earthquakes endanger human lives and infrastructure, particularly in urban areas. With the advancements in science and technology in understanding natural hazards, recent studies have attempted to mitigate them by mapping the risks using geospatial technology. In this paper, we attempt to integrate the multi-criteria decision-making (MCDM) models, namely the Analytical Hierarchy Process (AHP) and the Criteria Importance Through Inter-criteria Correlation (CRITIC), besides using the artificial neural network (ANN) to assess the seismic risk in the eastern coast of India. The AHP-CRITIC technique is used to evaluate the earthquake coping capacity and vulnerability and has been further used to generate a training base for earthquake probability mapping by ANN. The earthquake probability and spatial intensity information are used to develop the hazard map. Following that, integrating vulnerability, hazard and coping capacity spatial information assessed earthquake risk. Our results indicate that approximately 5% of the study area is at high risk, whilst more than 11% of the population is at high risk due to seismic induced hazards. The area under the curve of the receiver operating characteristic curve is 0.85, which indicates reliable results. The results of this study may help various agencies involved in planning, development and disaster mitigation to develop seismic hazard mitigation methods by better understanding their impacts on the eastern coastal region of India.
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Affiliation(s)
- Abhishek K Rai
- Centre for Ocean, River, Atmosphere and Land Sciences (CORAL), Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India.
| | - Sukanta Malakar
- Centre for Ocean, River, Atmosphere and Land Sciences (CORAL), Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India
| | - Susmita Goswami
- Centre for Ocean, River, Atmosphere and Land Sciences (CORAL), Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India
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Jovanović M, Radan M, Čarapić M, Filipović N, Nikolic K, Crevar M. Application of parallel artificial membrane permeability assay technique and chemometric modeling for blood-brain barrier permeability prediction of protein kinase inhibitors. Future Med Chem 2024. [PMID: 38639375 DOI: 10.4155/fmc-2023-0390] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/20/2024] Open
Abstract
Aim: This study aims to investigate the passive diffusion of protein kinase inhibitors through the blood-brain barrier (BBB) and to develop a model for their permeability prediction. Materials & methods: We used the parallel artificial membrane permeability assay to obtain logPe values of each of 34 compounds and calculated descriptors for these structures to perform quantitative structure-property relationship modeling, creating different regression models. Results: The logPe values have been calculated for all 34 compounds. Support vector machine regression was considered the most reliable, and CATS2D_09_DA, CATS2D_04_AA, B04[N-S] and F07[C-N] descriptors were identified as the most influential to passive BBB permeability. Conclusion: The quantitative structure-property relationship-support vector machine regression model that has been generated can serve as an efficient method for preliminary screening of BBB permeability of new analogs.
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Affiliation(s)
- Milan Jovanović
- University of Belgrade - Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Vojvode Stepe 450, P.O.Box 146, 11221, Belgrade, Serbia
- University of Belgrade - "VINCA" Institute of Nuclear Sciences - National Institute of the Republic of Serbia, Department of Molecular Biology & Endocrinology, Mike Petrovica Alasa 12-14, Vinca, 11351, Belgrade, Serbia
| | - Milica Radan
- Institute for Medicinal Plant Research "Dr. Josif Pančić", Tadeuša Košćuška 1, Belgrade, 11000, Serbia
| | - Marija Čarapić
- Medicines & Medical Devices Agency of Serbia, Vojvode Stepe 458, 11000, Belgrade, Serbia
| | - Nenad Filipović
- University of Belgrade - Faculty of Agriculture, Nemanjina 6, 11000, Belgrade, Serbia
| | - Katarina Nikolic
- University of Belgrade - Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Vojvode Stepe 450, P.O.Box 146, 11221, Belgrade, Serbia
| | - Milkica Crevar
- University of Belgrade - Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Vojvode Stepe 450, P.O.Box 146, 11221, Belgrade, Serbia
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Umar M, Khan H, Hussain S, Arshad M, Choi H, Lima EC. Integrating DFT and machine learning for the design and optimization of sodium alginate-based hydrogel adsorbents: Efficient removal of pollutants from wastewater. Environ Res 2024; 247:118219. [PMID: 38253197 DOI: 10.1016/j.envres.2024.118219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 01/01/2024] [Accepted: 01/14/2024] [Indexed: 01/24/2024]
Abstract
This study presents a novel approach to design and optimize a sodium alginate-based hydrogel (SAH) for efficient adsorption of the model water pollutant methylene blue (MB) dye. Utilizing density functional theory (DFT) calculations, sodium alginate-g-poly (acrylamide-co-itaconic acid) was identified with the lowest adsorption energy (Eads) for MB dye among 14 different clusters. SAHs were prepared using selected monomers and sodium alginate combinations through graft co-polymerization, and swelling studies were conducted to optimize grafting conditions. Advanced characterization techniques, including FTIR, XRD, XPS, SEM, EDS, and TGA, were employed, and the process was optimized using statistical and machine learning tools. Screening tests demonstrated that Eads serves as an effective predicting indicator for adsorption capacity (qe) and MB removal efficiency (RRMB,%), with reasonable agreement between Eads and both responses under given conditions. Process modeling and optimization revealed that 5 mg of selected SAH achieves a maximum qe of 3244 mg g-1 at 84.4% RRMB under pH 8.05, 98.8 min, and MB concentration of 383.3 mg L-1, as identified by the desirability function approach. Moreover, SAH effectively eliminated various contaminants from aqueous solutions, including sulfasalazine (SFZ) and dibenzothiophene (DBT). MB adsorption onto selected SAH was exothermic, spontaneous, and followed the pseudo-first-order and Langmuir-Freundlich isotherm models. The remarkable ability of SAH to adsorb MB is attributed to its well-designed structure predicted through DFT and optimal operational conditions achieved by AI-based parametric optimization. By integrating DFT-based computations and machine-learning tools, this study contributes to the efficient design of adsorbent materials and optimization of adsorption processes, also showcasing the potential of SAH as an efficient adsorbent for the abatement of aqueous pollution.
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Affiliation(s)
- Muhammad Umar
- Faculty of Materials and Chemical Engineering, GIK Institute of Engineering Sciences and Technology, Topi, Pakistan
| | - Hammad Khan
- Faculty of Materials and Chemical Engineering, GIK Institute of Engineering Sciences and Technology, Topi, Pakistan.
| | - Sajjad Hussain
- Faculty of Materials and Chemical Engineering, GIK Institute of Engineering Sciences and Technology, Topi, Pakistan
| | - Muhammad Arshad
- Department of Chemical Engineering, College of Engineering, King Khalid University, Abha, Saudi Arabia
| | - Hyeok Choi
- Department of Civil Engineering, The University of Texas at Arlington, 416 Yates Street, Arlington, TX, 76019-0308, USA
| | - Eder C Lima
- Institute of Chemistry, Federal University of Rio Grande do Sul (UFRGS), Av. Bento Gonçalves 9500, PO. Box 15003, 91501-970, Porto Alegre, RS, Brazil
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Ayeleru OO, Fajimi LI, Onu MA, Nyam TT, Dlova S, Ameh VI, Olubambi PA. Estimating plastic waste generation using supervised time-series learning techniques in Joh annesburg, South Africa. Heliyon 2024; 10:e28199. [PMID: 38571638 PMCID: PMC10987939 DOI: 10.1016/j.heliyon.2024.e28199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2023] [Revised: 02/28/2024] [Accepted: 03/13/2024] [Indexed: 04/05/2024] Open
Abstract
In recent times, many investigators have delved into plastic waste (PW) research, both locally and internationally. Many of these studies have focused on problems related to land-based and marine-based PW management with its attendant impact on public health and the ecosystem. Hitherto, there have been little or no studies on forecasting PW quantities in developing countries (DCs). The key objective of this study is to provide a forecast on PW generation in the city of Johannesburg (CoJ), South Africa over the next three decades. The data used for the forecasting were historical data obtained from Statistics South Africa (StatsSA). For effective prediction and comparison, three-time series models were employed in this study. They include exponential smoothing (ETS), Artificial Neural Network (ANN), and the Gaussian Process Regression (GPR). The exponential kernel GPR model performed best on the overall plastic prediction with a determination coefficient (R2) of 0.96, however, on individual PW estimation, ANN was better with an overall R2 of 0.93. From the result, it is predicted that between 2021 and 2050, the total PW generated in CoJ is forecasted to be around 6.7 megatonnes with an average of 0.22 megatonnes/year. In addition, the estimated plastic composition is 17,910 tonnes PS per year; 13,433 tonnes PP per year; 59,440 tonnes HDPE per year; 4478 tonnes PVC per year; 85,074 tonnes PET per year; 34,590 tonnes LDPE per year and 8955 tonnes other PWs per year.
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Affiliation(s)
- Olusola Olaitan Ayeleru
- Centre for Nanoengineering and Advanced Material, University of Johannesburg, Doornfontein Campus, Johannesburg, 2028, South Africa
- Conserve Africa Initiative (CAI), Osogbo, Osun State, Nigeria
| | - Lanre Ibrahim Fajimi
- Centre for Nanoengineering and Advanced Material, University of Johannesburg, Doornfontein Campus, Johannesburg, 2028, South Africa
| | - Matthew Adah Onu
- Centre for Nanoengineering and Advanced Material, University of Johannesburg, Doornfontein Campus, Johannesburg, 2028, South Africa
| | - Tarhemba Tobias Nyam
- Centre for Nanoengineering and Advanced Material, University of Johannesburg, Doornfontein Campus, Johannesburg, 2028, South Africa
| | - Sisanda Dlova
- Centre for Nanoengineering and Advanced Material, University of Johannesburg, Doornfontein Campus, Johannesburg, 2028, South Africa
| | - Victor Idankpo Ameh
- Centre for Nanoengineering and Advanced Material, University of Johannesburg, Doornfontein Campus, Johannesburg, 2028, South Africa
| | - Peter Apata Olubambi
- Centre for Nanoengineering and Advanced Material, University of Johannesburg, Doornfontein Campus, Johannesburg, 2028, South Africa
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Claude BJ, Onyango MS. Predictive modeling of copper (II) adsorption from aqueous solutions by sawdust: a comparative analysis of adaptive neuro-fuzzy interference system (ANFIS) and artificial neural network ( ANN) approaches. J Environ Sci Health A Tox Hazard Subst Environ Eng 2024:1-8. [PMID: 38613163 DOI: 10.1080/10934529.2024.2339775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 03/22/2024] [Indexed: 04/14/2024]
Abstract
Heavy metal ions are considered to be the most prevalent and toxic water contaminants. The objective of thois work was to investigate the effectiveness of employing the adsorption technique in a laboratory-size reactor to remove copper (II) ions from an aqueous medium. An adaptive neuro-fuzzy inference system (ANFIS) and a feed-forward artificial neural network (ANN) were used in this study. Four operational factors were chosen to examine their influence on the adsorption study: pH, contact duration, initial Cu (II) ions concentration, and adsorbent dosage. Using sawdust from wood, prediction models of copper (II) ions adsorption were optimized, created, and developed using the ANN and ANFIS models for tests. The result indicates that the determination coefficient for copper (II) metal ions in the training dataset was 0.987. Additionally, the ANFIS model's R2 value for both pollutants was 0.992. The findings demonstrate that the models presented a promising predictive approach that can be applied to successfully and accurately anticipate the simultaneous elimination of copper (II) and dye from the aqueous solution.
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Affiliation(s)
- Banza Jean Claude
- Department of Chemical, Metallurgical and Materials Engineering, Tshwane University of Technology, Pretoria, South Africa
| | - Maurice Stephane Onyango
- Department of Chemical, Metallurgical and Materials Engineering, Tshwane University of Technology, Pretoria, South Africa
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Abrofarakh M, Moghadam H, Abdulrahim HK. Investigation of direct contact membrane distillation (DCMD) performance using CFD and machine learning approaches. Chemosphere 2024; 357:141969. [PMID: 38604515 DOI: 10.1016/j.chemosphere.2024.141969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/24/2024] [Accepted: 04/08/2024] [Indexed: 04/13/2024]
Abstract
Direct Contact Membrane Distillation (DCMD) is emerging as an effective method for water desalination, known for its efficiency and adaptability. This study delves into the performance of DCMD by integrating two powerful analytical tools: Computational Fluid Dynamics (CFD) and Artificial Neural Networks (ANN). The research thoroughly examines the impact of various factors, such as inlet temperatures, velocities, channel heights, salt concentration, and membrane characteristics, on the process's efficiency, specifically calculating the water vapor flux. A rigorous validation of the CFD model aligns well with established studies, ensuring reliability. Subsequently, over 1000 data points reflecting variations in input factors are utilized to train and validate the ANN. The training phase demonstrated high accuracy, with near-zero mean squared errors and R2 values close to one, indicating a strong predictive capability. Further analysis post-ANN training shed light on key relationships: higher membrane porosity boosts water vapor flux, whereas thicker membranes reduce it. Additionally, it was detailed how salt concentration, channel dimensions, inlet temperatures, and velocities significantly influence the distillation process. Finally, a mathematical model was proposed for water vapor flux as a function of key input factors. The results highlighted that salt mole fraction and hot water inlet temperature have the most effect on the water vapor flux. This comprehensive investigation contributes to the understanding of DCMD and emphasizes the potential of combining CFD and ANN for optimizing and innovating water desalination technology.
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Affiliation(s)
- Moslem Abrofarakh
- Department of Chemical Engineering, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran
| | - Hamid Moghadam
- Department of Chemical Engineering, Faculty of Engineering, University of Sistan and Baluchestan, Zahedan, Iran.
| | - Hassan K Abdulrahim
- Water Research Center (WRC), Kuwait Institute for Scientific Research (KISR), P.O. Box 24885, 13109, Safat, Kuwait
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12
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Lytou A, Fengou LC, Koukourikos A, Karampiperis P, Zervas P, Schultz Carstensen A, Del Genio A, Michael Carstensen J, Schultz N, Chorianopoulos N, Nychas GJ. Seabream quality monitoring throughout the supply chain using a portable multispectral imaging device. J Food Prot 2024:100274. [PMID: 38583716 DOI: 10.1016/j.jfp.2024.100274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 03/29/2024] [Accepted: 04/02/2024] [Indexed: 04/09/2024]
Abstract
Monitoring food quality throughout the supply chain in a rapid and cost-effective way allows on-time decision making, reducing food waste and increasing sustainability. In that framework, a portable multispectral imaging sensor was used, while the acquired data in combination with neural networks were evaluated for the prediction of fish fillets quality. Images of fish fillets were acquired using samples from both aquaculture and retail stores of different packaging and fish parts. The obtained products (air or vacuum packaged) were further stored at different temperature conditions. In parallel to image acquisition, microbial quality was estimated as well. The data were used for the training of predictive neural models that aimed to estimate total aerobic counts (TAC). The models were developed and validated using data from aquaculture and were externally validated with samples purchased from the retail stores. The set up allowed the evaluation of models for the different parts of the fish and conditions. The performance for the validation set was similar for flesh (RMSE: 0.402-0.547) and skin side (RMSE: 0.500-0.533) of the fish fillets. The performance for the different packaging conditions was also similar, however, in the external validation, the vacuum-packaged samples showed better performance in terms of RMSE compared to the air-packaged ones. Models irrespective of packaging condition are very important for cases where the products' history is unknown although the prediction capability was not as high as in the models per packaging condition individually. The models tested with unknown samples (i.e., from retail stores) showed poorer performance (RMSE: 1.061-1.414) compared to the models validated with data partitioning (RMSE: 0.402-0.547). Multispectral imaging sensor appeared to be efficient for the rapid assessment of the microbiological quality of fish fillets for all the different cases evaluated.
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Affiliation(s)
- Anastasia Lytou
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
| | - Lemonia-Christina Fengou
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
| | - Antonis Koukourikos
- SCiO P.C., Technology Park Lefkippos, P. Grigoriou & Neapoleos Str., Agia Paraskevi, Greece, GR-15310
| | - Pythagoras Karampiperis
- SCiO P.C., Technology Park Lefkippos, P. Grigoriou & Neapoleos Str., Agia Paraskevi, Greece, GR-15310
| | - Panagiotis Zervas
- SCiO P.C., Technology Park Lefkippos, P. Grigoriou & Neapoleos Str., Agia Paraskevi, Greece, GR-15310
| | | | | | | | - Nette Schultz
- Videometer A/S, Hørkær 12B 3., DK-2730 Herlev, Denmark
| | - Nikos Chorianopoulos
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece
| | - George-John Nychas
- Laboratory of Microbiology and Biotechnology of Foods, Department of Food Science and Human Nutrition, School of Food and Nutritional Sciences, Agricultural University of Athens, Iera Odos 75, 11855 Athens, Greece.
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13
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Gholizadeh M, Saeedi R, Bagheri A, Paeezi M. Machine learning-based prediction of effluent total suspended solids in a wastewater treatment plant using different feature selection approaches: A comparative study. Environ Res 2024; 246:118146. [PMID: 38215928 DOI: 10.1016/j.envres.2024.118146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 12/31/2023] [Accepted: 01/05/2024] [Indexed: 01/14/2024]
Abstract
Accurately predicting the characteristics of effluent, discharged from wastewater treatment plants (WWTPs) is crucial for reducing sampling requirements, labor, costs, and environmental pollution. Machine learning (ML) techniques can be effective in achieving this goal. To optimize ML-based models, various feature selection (FS) methods are employed. This study aims to investigate the impact of six FS methods (categorized as Wrapper, Filter, and Embedded methods) on the accuracy of three supervised ML algorithms in predicting total suspended solids (TSS) concentration in the effluent of a municipal wastewater treatment plant. Based on the features proposed by each FS method, five distinct scenarios were defined. Within each scenario, three ML algorithms, namely artificial neural network-multi layer perceptron (ANN-MLP), K-nearest neighbors (KNN), and adaptive boosting (AdaBoost) were applied. The features utilized for predicting TSS concentration in the WWTP effluent included BOD5, COD, TSS, TN, NH3 in the influent, and BOD5, COD, residual Cl2, NO3, TN, NH4 in the effluent. To construct the models, the dataset was randomly divided into training and testing subsets, and K-fold cross-validation was employed to control overfitting and underfitting. The evaluation metrics that are used are root mean squared error (RMSE), mean absolute error (MAE), and correlation coefficient (R2). The most efficient scenario was identified as Scenario IV, with the Sequential Backward Selection FS method. The features selected by this method were CODe, BOD5e, BOD5i, TNi. Furthermore, the ANN-MLP algorithm demonstrated the best performance, achieving the highest R2 value. This algorithm exhibited acceptable performance in both the training and testing subsets (R2 = 0.78 and R2 = 0.8, respectively).
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Affiliation(s)
- Mahdi Gholizadeh
- Environmental and Occupational Hazards Control Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Department of Health, Safety and Environment, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Reza Saeedi
- Department of Health, Safety and Environment, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Workplace Health Promotion Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Amin Bagheri
- Environmental and Occupational Hazards Control Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Department of Health, Safety and Environment, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
| | - Mohammad Paeezi
- Department of Health, Safety and Environment, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Workplace Health Promotion Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran
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14
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Marimuthu V, Ramasamy A. Mechanical characteristics of waste-printed circuit board-reinforced concrete with silica fume and prediction modelling using ANN. Environ Sci Pollut Res Int 2024:10.1007/s11356-024-33099-y. [PMID: 38558342 DOI: 10.1007/s11356-024-33099-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Accepted: 03/22/2024] [Indexed: 04/04/2024]
Abstract
The use of electronic waste in cement concrete as a fibre additive has proven to be very promising for improving mechanical characteristics and developing sustainable construction materials to reduce the waste dumped in landfills. The following study investigated the effect of electronic waste (printed circuit boards (PCBs)) on the mechanical properties of concrete and predicted the same properties with an appropriate machine learning technique. PCB fibres 45 mm in length and 1.5 mm in width were manufactured and added as fibre additions to two sets of concrete mixes with and without silica fume. A 10% volume replacement of cement was substituted with silica fume (SF) to enhance the characteristics of PCB fibre-reinforced concrete and minimize cement consumption. The study included an evaluation of the fresh properties and mechanical characteristics after a 28-day curing period; thereafter, the results were compared and studied using the Levenberg-Marquardt backpropagation algorithm for predictions. The results show that the mechanical properties improved up to a 5% addition of PCB fibres, resulting in strengths of 63.55 MPa and 69.92 MPa for mixtures of PCB5% and SFPCB5%, respectively. A similar trend was achieved for other properties, such as the tensile and flexural strengths. The results of the ANN model predicted values with R2 values ranging from 0.94 to 0.99, indicating the efficacy of the model.
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Affiliation(s)
- VishnuPriyan Marimuthu
- Department of Civil Engineering, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, Chengalpattu, India, 603203.
| | - Annadurai Ramasamy
- Department of Civil Engineering, SRM Institute of Science and Technology, Tamil Nadu, Kattankulathur, Chengalpattu, India, 603203
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15
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Momina M, Ahmad K. Synthesis of biodegradable sodium alginate-based carbon dot-nanomagnetic composite (SA-FOCD) for enhanced water remediation using ANN modelling, RSM optimization, and economic analysis. Int J Biol Macromol 2024; 263:130253. [PMID: 38368976 DOI: 10.1016/j.ijbiomac.2024.130253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 12/04/2023] [Accepted: 02/15/2024] [Indexed: 02/20/2024]
Abstract
This study involves the synthesis of a magnetic‑sodium alginate bio-composite embedded with carbon dots, designed to eliminate pollutants like dyes and metal ions and tackle environmental issues. The modified particles are effectively incorporated into the biopolymers for improved adsorption and regeneration performance using an economically viable and environmentally sustainable process. The composite's surface morphology and chemical structure have been extensively characterized through various analytical techniques. It has been found that CD-modified nanoparticles demonstrate good dispersion, abundance in functional groups, and excellent adsorption performance. The adsorption process variables have been optimized using Response Surface Methodology (RSM), resulting in a maximum adsorption capacity of 232.44 mg/g achieved under optimal conditions. An Artificial Neural Network (ANN) model with a topology of 3-5-5-1 is constructed to predict the adsorption capacity of composite, exhibiting superior predictive performance. The statistical physical model was also performed to understand the adsorption mechanism and orientation of dye molecules attached to the surface of the composite. The adsorption capacity using statistical physical method was found to be 467.57 mg/g. The composite exhibits good adsorption and regeneration performance in the column adsorption study. Furthermore, a detailed cost analysis of the synthesized composite was performed, ensuring its economic viability in real-world applications.
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Affiliation(s)
- Momina Momina
- Department of Civil Engineering, Jamia Millia Islamia, New Delhi-110025, India.
| | - Kafeel Ahmad
- Department of Civil Engineering, Jamia Millia Islamia, New Delhi-110025, India
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16
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Mansoor MS, Mishra A, Lokhat D, Meikap BC. Application of Artificial Neural Network ( ANN) as a predictive tool for the removal of pharmaceutical from wastewater streams using biochar: a multifunctional technology for environment sustainability. J Environ Sci Health A Tox Hazard Subst Environ Eng 2024; 59:40-53. [PMID: 38525556 DOI: 10.1080/10934529.2024.2329033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 03/05/2024] [Indexed: 03/26/2024]
Abstract
This study investigates biochar as an attractive option for removing pharmaceuticals from wastewater streams utilizing data from various literature sources and also explores the sensitivity of the characteristics and implementation of biochar. ANN 1 was designed to determine the optimal biochar characteristics (Surface Area, Pore Volume) to achieve the maximum percentage removal of pharmaceuticals in wastewater streams. ANN 2 was developed to identify the optimal biomass feedstock composition, pyrolysis conditions (temperature and time), and chemical activation (acid or base) to produce the optimal biochar from ANN 1. ANN 3 was developed to investigate the effectiveness of the biochar produced in ANN 1 and 2 in removing dye from water. Biomass feedstock with a high lignin content and high volatile matter at a high pyrolysis temperature, whether using an acid or base, achieves a high mesopore volume and high surface area. The biochar with the highest surface area and mesopore volume achieved the highest removal percentage. Regardless of hydrophobicity conditions, at low dosages (0.2), a high surface area and pore volume are required for a high percent removal. And with a higher dosage, a lower surface area and pore volume is necessary to achieve a high percent removal.
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Affiliation(s)
- Mohammed Saleem Mansoor
- Discipline of Chemical Engineering, School of Engineering, Howard College Campus, University of Kwazulu Natal, Durban, South Africa
| | - Asmita Mishra
- Department of Chemical Engineering, Parala Maharaja Engineering College (PMEC), Berhampur, India
| | - David Lokhat
- Discipline of Chemical Engineering, School of Engineering, Howard College Campus, University of Kwazulu Natal, Durban, South Africa
| | - B C Meikap
- Discipline of Chemical Engineering, School of Engineering, Howard College Campus, University of Kwazulu Natal, Durban, South Africa
- Department of Chemical Engineering, Indian Institute of Technology (IIT) Kharagpur, Kharagpur, India
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17
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Das CR, Das S. Coastal groundwater quality prediction using objective-weighted WQI and machine learning approach. Environ Sci Pollut Res Int 2024; 31:19439-19457. [PMID: 38355860 DOI: 10.1007/s11356-024-32415-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 02/07/2024] [Indexed: 02/16/2024]
Abstract
The water quality index (WQI) is a globally accepted guideline to indicate the water quality standard of any groundwater resource. Water levels in existing groundwater sources are declining in several coastal zones. Therefore, for monitoring water quality and improving water management, the prediction and identification of groundwater status by an effective technique with higher accuracy is urgently needed. Therefore, this research aims to find an effective model for WQI prediction by comparing entropy and critic weight-based WQI (ENW-WQI and CRITIC-WQI) with multi-layer perceptron artificial neural network (MLP-ANN) technique and also to identify contaminated zones using GIS. Initially, 1000 water sampling datasets with concentrations of several water quality parameters of different coastal blocks of eastern India during 2018 to 2022 are considered for the estimation of ENW-WQI and CRITIC-WQI. It shows 65% and 67% of the samples are excellent to good for drinking. ENW-WQI and CRITIC-WQI-based MLP-ANN models have been established considering different data portioning and hidden neuron numbers. Input variables and appropriate dataset partitioning with hidden neurons for models obtained from correlation and trial-error analysis. Spatial distribution maps are also produced for calculated WQIs using inverse distance weighted interpolation approaches. Three fitting models are obtained: ENW-WQI-MLP-ANN, CRITIC-WQI-MLP-ANN-I and CRITIC-WQI-MLP-ANN-II. CRITIC-WQI-MLP-ANN-II model (data ratio 85:15, network structure 6-12-1, R2 = 0.986, NSE = 0.98, and error rate 0.49%) provides the best accuracy in WQI prediction. The GIS-based WQI maps record several areas related to drinking water quality. The results of this research can help in planning the provision of safe drinking water in the future.
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Affiliation(s)
- Chinmoy Ranjan Das
- School of Water Resources Engineering, Jadavpur University, Kolkata, India
- Civil Engineering Department, Global Institute of Science & Technology, Purba Medinipur 721657, Haldia, West Bengal, India
| | - Subhasish Das
- School of Water Resources Engineering, Jadavpur University, Kolkata, India.
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18
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Kartal V. Prediction of monthly evapotranspiration by artificial neural network model development with Levenberg-Marquardt method in Elazig, Turkey. Environ Sci Pollut Res Int 2024; 31:20953-20969. [PMID: 38381292 PMCID: PMC10948580 DOI: 10.1007/s11356-024-32464-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 02/09/2024] [Indexed: 02/22/2024]
Abstract
The phenomenon of evapotranspiration (ET) is closely linked to the issue of water scarcity, as it involves water loss through both evaporation and plant transpiration. Accurate prediction of evapotranspiration is of utmost importance in the strategic planning of agricultural irrigation, effective management of water resources, and precise hydrological modeling. The current investigation aims to predict the monthly ET values in the Elazig province by developing an artificial neural network (ANN) model utilizing the Levenberg-Marquardt method. Consequently, the values of temperature, precipitation, relative humidity, solar hour, and mean wind speed were utilized in forecasting evapotranspiration values by implementing ANN algorithms. This research makes a valuable contribution to the existing body of literature by utilizing an ANN model developed with the Levenberg-Marquardt method to estimate evapotranspiration. It has been discovered that evapotranspiration values are impacted by various factors such as temperature (minimum, average, maximum), relative humidity (minimum, average, maximum), wind speed, solar hour, and precipitation values, which are taken into consideration for prediction. The findings indicated that Elazig, Keban, Baskil, and Agin sites had R values of 0.9995, 0.9948, 0.9898, and 0.9994 in the proposed model. It was found that Elazig's MAPE ranged from 0 to 0.2288, Keban's was 0.0001 to 0.3703, Baskil's was between 0 and 0.4453, and Agin's was both 0 and 0.2784. The findings obtained from the proposed model are compatible with evapotranspiration values computed from the Hargreaves method (R2 = 0.996). The study's findings provide significant insights for planners and decision-makers involved in the planning and managing water resources and agricultural irrigation.
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Affiliation(s)
- Veysi Kartal
- Department of Civil Engineering, Siirt University, Siirt, 56000, Turkey.
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19
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Khoshraftar Z, Ghaemi A, Hemmati A. Comprehensive investigation of isotherm, RSM, and ANN modeling of CO 2 capture by multi-walled carbon nanotube. Sci Rep 2024; 14:5130. [PMID: 38429340 PMCID: PMC10907356 DOI: 10.1038/s41598-024-55836-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Accepted: 02/28/2024] [Indexed: 03/03/2024] Open
Abstract
Chemical vapor deposition was used to produce multi-walled carbon nanotubes (MWCNTs), which were modified by Fe-Ni/AC catalysts to enhance CO2 adsorption. In this study, a new realm of possibilities and potential advancements in CO2 capture technology is unveiled through the unique combination of cutting-edge modeling techniques and utilization of the recently synthesized Fe-Ni/AC catalyst adsorbent. SEM, BET, and FTIR were used to analyze their structure and morphology. The surface area of MWCNT was found to be 240 m2/g, but after modification, it was reduced to 11 m2/g. The modified MWCNT showed increased adsorption capacity with higher pressure and lower temperature, due to the introduction of new adsorption sites and favorable interactions at lower temperatures. At 25 °C and 10 bar, it reached a maximum adsorption capacity of 424.08 mg/g. The optimal values of the pressure, time, and temperature parameters were achieved at 7 bar, 2646 S and 313 K. The Freundlich and Hill models had the highest correlation with the experimental data. The Second-Order and Fractional Order kinetic models fit the adsorption results well. The adsorption process was found to be exothermic and spontaneous. The modified MWCNT has the potential for efficient gas adsorption in fields like gas storage or separation. The regenerated M-MWCNT adsorbent demonstrated the ability to be reused multiple times for the CO2 adsorption process, as evidenced by the study. In this study, a feed-forward MLP artificial neural network model was created using a back-propagation training approach to predict CO2 adsorption. The most suitable and efficient MLP network structure, selected for optimization, consisted of two hidden layers with 25 and 10 neurons, respectively. This network was trained using the Levenberg-Marquardt backpropagation algorithm. An MLP artificial neural network model was created, with a minimum MSE performance of 0.0004247 and an R2 value of 0.99904, indicating its accuracy. The experiment also utilized the blank spreadsheet design within the framework of response surface methodology to predict CO2 adsorption. The proximity between the Predicted R2 value of 0.8899 and the Adjusted R2 value of 0.9016, with a difference of less than 0.2, indicates a high level of similarity. This suggests that the model is exceptionally reliable in its ability to predict future observations, highlighting its robustness.
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Affiliation(s)
- Zohreh Khoshraftar
- School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, P.O. Box 16765-163, Tehran, Iran.
| | - Ahad Ghaemi
- School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, P.O. Box 16765-163, Tehran, Iran.
| | - Alireza Hemmati
- School of Chemical, Petroleum and Gas Engineering, Iran University of Science and Technology, P.O. Box 16765-163, Tehran, Iran
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Liu D, Qiao Y, Wang S, Fan S, Liu D, Shi C, Shi J. A reliability estimation method based on combination of failure mechanism and ANN supported wiener processes. Heliyon 2024; 10:e26230. [PMID: 38390134 PMCID: PMC10882046 DOI: 10.1016/j.heliyon.2024.e26230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 11/22/2023] [Accepted: 02/08/2024] [Indexed: 02/24/2024] Open
Abstract
For some engineering application, accurately estimating reliability only depend on the history data or failure mechanism is difficult to implement, due to the lack of data and imperfect theory of failure mechanism. Namely, both history data and failure mechanism should be utilized to improve the reliability estimation accuracy for engineering applications. Hence, we construct a reliability estimation method by fusing the failure mechanism and artificial neural network (ANN) supported Wiener processes for utilizing both history data and failure mechanism. ANN and failure mechanism are integrated into Wiener process with random effects, respectively. Bayesian model averaging (BMA) method is adapted to combine the failure mechanism with ANN supported Wiener processes, as well as to update the model parameters by fusing data. Based on a typical aviation hydraulic pump's actual dataset, we illustrate the advantages of our approach by comparing to Wiener process supported only by ANN or failure mechanism in engineering practices. The proposed method shows superiorities on reliability estimation considering the estimation accuracies comparing the other two models.
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Affiliation(s)
- Di Liu
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
- State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
- Tianmushan Laboratory, Xixi Octagon City, Yuhang District, Hangzhou 310023, China
- Key Laboratory of Flight Techniques and Flight Safety, CAAC, Guanghan 618307, China
| | - Yajing Qiao
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
| | - Shaoping Wang
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
- Tianmushan Laboratory, Xixi Octagon City, Yuhang District, Hangzhou 310023, China
| | - Siming Fan
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
| | - Dong Liu
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
| | - Cun Shi
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
| | - Jian Shi
- School of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China
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Obi CC, Nwabanne JT, Igwegbe CA, Abonyi MN, Umembamalu CJ, Kamuche TT. Intelligent algorithms-aided modeling and optimization of the deturbidization of abattoir wastewater by electrocoagulation using aluminium electrodes. J Environ Manage 2024; 353:120161. [PMID: 38290261 DOI: 10.1016/j.jenvman.2024.120161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 01/05/2024] [Accepted: 01/20/2024] [Indexed: 02/01/2024]
Abstract
The removal of turbidity from abattoir wastewater (AWW) by electrocoagulation (EC) was modeled and optimized using Artificial Intelligence (AI) algorithms. Artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), particle swarm optimization (PSO), and genetic algorithms (GA) were the AI tools employed. Five input variables were considered: pH, current intensity, electrolysis time, settling time, and temperature. The ANN model was evaluated using the Levenberg-Marquardt (trainlm) algorithm, while the ANFIS modeling was accomplished using the Sugeno-type FIS. The ANN and ANFIS models demonstrated linear adequacy with the experimental data, with an R2 value of 0.9993 in both cases. The corresponding statistical error indices were RMSE (ANN = 5.65685E-05; ANFIS = 2.82843E-05), SSE (ANN = 1.60E-07; ANFIS = 3.4E-08), and MSE (ANN = 3.2E-09; ANFIS = 8E-10). The error indices revealed that the ANFIS model had the least performance error and is considered the most reliable of the two. The process optimization performed with GA and PSO considered turbidity removal efficiency, energy requirement, and electrode material loss. An optimal turbidity removal efficiency of 99.39 % was predicted at pH (3.1), current intensity (2 A), electrolysis time (20 min), settling time (50 min), and operating temperature (50 °C). This represents a potential for the delivery of cleaner water without the use of chemicals. The estimated power consumption and the theoretical mass of the aluminium electrode dissolved at the optimum condition were 293.33 kW h/m3 and 0.2237 g, respectively. The work successfully affirmed the effectiveness of the EC process in the removal of finely divided suspended particles from AWW and demonstrated the suitability of the AI algorithms in the modeling and optimization of the process.
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Affiliation(s)
| | - Joseph Tagbo Nwabanne
- Department of Chemical Engineering, Nnamdi Azikiwe University, P.M.B. 5025, Awka, 420218, Nigeria.
| | - Chinenye Adaobi Igwegbe
- Department of Chemical Engineering, Nnamdi Azikiwe University, P.M.B. 5025, Awka, 420218, Nigeria; Department of Applied Bioeconomy, Wroclaw University of Environmental and Life Sciences, Wrocław, Poland.
| | - Matthew Ndubuisi Abonyi
- Department of Chemical Engineering, Nnamdi Azikiwe University, P.M.B. 5025, Awka, 420218, Nigeria.
| | | | - Toochukwu ThankGod Kamuche
- Department of Chemical Engineering, Chukwuemeka Odumegwu Ojukwu University, Uli, Anambra State, Nigeria.
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Zhao X, Peng Q, Hu D, Li W, Ji Q, Dong Q, Huang L, Piao M, Ding Y, Wang J. Prediction of risk factors for linezolid-induced thrombocytopenia based on neural network model. Front Pharmacol 2024; 15:1292828. [PMID: 38449807 PMCID: PMC10915059 DOI: 10.3389/fphar.2024.1292828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 01/29/2024] [Indexed: 03/08/2024] Open
Abstract
Background: Based on real-world medical data, the artificial neural network model was used to predict the risk factors of linezolid-induced thrombocytopenia to provide a reference for better clinical use of this drug and achieve the timely prevention of adverse reactions. Methods: The artificial neural network algorithm was used to construct the prediction model of the risk factors of linezolid-induced thrombocytopenia and further evaluate the effectiveness of the artificial neural network model compared with the traditional Logistic regression model. Results: A total of 1,837 patients receiving linezolid treatment in a hospital in Xi 'an, Shaanxi Province from 1 January 2011 to 1 January 2021 were recruited. According to the exclusion criteria, 1,273 cases that did not meet the requirements of the study were excluded. A total of 564 valid cases were included in the study, with 89 (15.78%) having thrombocytopenia. The prediction accuracy of the artificial neural network model was 96.32%, and the AUROC was 0.944, which was significantly higher than that of the Logistic regression model, which was 86.14%, and the AUROC was 0.796. In the artificial neural network model, urea, platelet baseline value and serum albumin were among the top three important risk factors. Conclusion: The predictive performance of the artificial neural network model is better than that of the traditional Logistic regression model, and it can well predict the risk factors of linezolid-induced thrombocytopenia.
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Affiliation(s)
- Xian Zhao
- Department of Pharmacy, First Affiliated Hospital of Air Force Medical University, Xi’an, Shaanxi, China
| | - Qin Peng
- Department of Hepatobiliary Surgery, First Affiliated Hospital of Air Force Medical University, Xi’an, Shaanxi, China
| | - Dongmei Hu
- Department of Pharmacy, First Affiliated Hospital of Air Force Medical University, Xi’an, Shaanxi, China
| | - Weiwei Li
- Department of Pharmacy, First Affiliated Hospital of Air Force Medical University, Xi’an, Shaanxi, China
| | - Qing Ji
- Department of Pharmacy, First Affiliated Hospital of Air Force Medical University, Xi’an, Shaanxi, China
| | - Qianqian Dong
- Department of Pharmacy, First Affiliated Hospital of Air Force Medical University, Xi’an, Shaanxi, China
| | - Luguang Huang
- Department of Information, First Affiliated Hospital of Air Force Medical University, Xi’an, Shaanxi, China
| | - Miyang Piao
- Department of Pharmacy, First Affiliated Hospital of Air Force Medical University, Xi’an, Shaanxi, China
| | - Yi Ding
- Department of Pharmacy, First Affiliated Hospital of Air Force Medical University, Xi’an, Shaanxi, China
| | - Jingwen Wang
- Department of Pharmacy, First Affiliated Hospital of Air Force Medical University, Xi’an, Shaanxi, China
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23
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Islam R, Majurski P, Kwon J, Sharma A, Tummala SRSK. Benchmarking Artificial Neural Network Architectures for High-Performance Spiking Neural Networks. Sensors (Basel) 2024; 24:1329. [PMID: 38400487 PMCID: PMC10892219 DOI: 10.3390/s24041329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 02/13/2024] [Accepted: 02/17/2024] [Indexed: 02/25/2024]
Abstract
Organizations managing high-performance computing systems face a multitude of challenges, including overarching concerns such as overall energy consumption, microprocessor clock frequency limitations, and the escalating costs associated with chip production. Evidently, processor speeds have plateaued over the last decade, persisting within the range of 2 GHz to 5 GHz. Scholars assert that brain-inspired computing holds substantial promise for mitigating these challenges. The spiking neural network (SNN) particularly stands out for its commendable power efficiency when juxtaposed with conventional design paradigms. Nevertheless, our scrutiny has brought to light several pivotal challenges impeding the seamless implementation of large-scale neural networks (NNs) on silicon. These challenges encompass the absence of automated tools, the need for multifaceted domain expertise, and the inadequacy of existing algorithms to efficiently partition and place extensive SNN computations onto hardware infrastructure. In this paper, we posit the development of an automated tool flow capable of transmuting any NN into an SNN. This undertaking involves the creation of a novel graph-partitioning algorithm designed to strategically place SNNs on a network-on-chip (NoC), thereby paving the way for future energy-efficient and high-performance computing paradigms. The presented methodology showcases its effectiveness by successfully transforming ANN architectures into SNNs with a marginal average error penalty of merely 2.65%. The proposed graph-partitioning algorithm enables a 14.22% decrease in inter-synaptic communication and an 87.58% reduction in intra-synaptic communication, on average, underscoring the effectiveness of the proposed algorithm in optimizing NN communication pathways. Compared to a baseline graph-partitioning algorithm, the proposed approach exhibits an average decrease of 79.74% in latency and a 14.67% reduction in energy consumption. Using existing NoC tools, the energy-latency product of SNN architectures is, on average, 82.71% lower than that of the baseline architectures.
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Affiliation(s)
- Riadul Islam
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
| | - Patrick Majurski
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
| | - Jun Kwon
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
| | - Anurag Sharma
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
| | - Sri Ranga Sai Krishna Tummala
- Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, MD 21250, USA
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24
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Elamshity MG, Alhamdan AM. Non-Destructive Evaluation of the Physiochemical Properties of Milk Drink Flavored with Date Syrup Utilizing VIS-NIR Spectroscopy and ANN Analysis. Foods 2024; 13:524. [PMID: 38397501 PMCID: PMC10888200 DOI: 10.3390/foods13040524] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 01/28/2024] [Accepted: 01/30/2024] [Indexed: 02/25/2024] Open
Abstract
A milk drink flavored with date syrup produced at a lab scale level was evaluated. The production process of date syrup involves a sequence of essential unit operations, commencing with the extraction, filtration, and concentration processes from two cultivars: Sukkary and Khlass. Date syrup was then mixed with cow's and camel's milk at four percentages to form a nutritious, natural, sweet, and energy-rich milk drink. The sensory, physical, and chemical characteristics of the milk drinks flavored with date syrup were examined. The objective of this work was to measure the physiochemical properties of date fruits and milk drinks flavored with date syrup, and then to evaluate the physical properties of milk drinks utilizing non-destructive visible-near-infrared spectra (VIS-NIR). The study assessed the characteristics of the milk drink enhanced with date syrup by employing VIS-NIR spectra and utilizing a partial least-square regression (PLSR) and artificial neural network (ANN) analysis. The VIS-NIR spectra proved to be highly effective in estimating the physiochemical attributes of the flavored milk drink. The ANN model outperformed the PLSR model in this context. RMSECV is considered a more reliable indicator of a model's future predictive performance compared to RMSEC, and the R2 value ranged between 0.946 and 0.989. Consequently, non-destructive VIS-NIR technology demonstrates significant promise for accurately predicting and contributing to the entire production process of the product's properties examined.
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Affiliation(s)
| | - Abdullah M. Alhamdan
- Chair of Dates Industry & Technology, Agricultural Engineering Department, College of Food & Agricultural Sciences, King Saud University, Riyadh 11451, Saudi Arabia;
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25
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Gogoi S, Das P, Nayak PK, Sridhar K, Sharma M, Sari TP, Kesavan RK, Bhaswant M. Optimizing Quality and Shelf-Life Extension of Bor-Thekera ( Garcinia pedunculata) Juice: A Thermosonication Approach with Artificial Neural Network Modeling. Foods 2024; 13:497. [PMID: 38338632 PMCID: PMC10855326 DOI: 10.3390/foods13030497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 01/27/2024] [Accepted: 02/01/2024] [Indexed: 02/12/2024] Open
Abstract
This study investigated the quality characteristics of pasteurized and thermosonicated bor-thekera (Garcinia pedunculata) juices (TSBTJs) during storage at 4 °C for 30 days. Various parameters, including pH, titratable acidity (TA), total soluble content (TSSs), antioxidant activity (AA), total phenolic content (TPC), total flavonoid content (TFC), ascorbic acid content (AAC), cloudiness (CI) and browning indexes (BI), and microbial activity, were analyzed at regular intervals and compared with the quality parameters of fresh bor-thekera juice (FBTJ). A multi-layer artificial neural network (ANN) was employed to model and optimize the ultrasound-assisted extraction of bor-thekera juice. The impacts of storage time, treatment time, and treatment temperature on the quality attributes were also explored. The TSBTJ demonstrated the maximum retention of nutritional attributes compared with the pasteurized bor-thekera juice (PBTJ). Additionally, the TSBTJ exhibited satisfactory results for microbiological activity, while the PBTJ showed the highest level of microbial inactivation. The designed ANN exhibited low mean squared error values and high R2 values for the training, testing, validation, and overall datasets, indicating a strong relationship between the actual and predicted results. The optimal extraction parameters generated by the ANN included a treatment time of 30 min, a frequency of 44 kHz, and a temperature of 40 °C. In conclusion, thermosonicated juices, particularly the TSBTJ, demonstrated enhanced nutritional characteristics, positioning them as valuable reservoirs of bioactive components suitable for incorporation in the food and pharmaceutical industries. The study underscores the efficacy of ANN as a predictive tool for assessing bor-thekera juice extraction efficiency. Moreover, the use of thermosonication emerged as a promising alternative to traditional thermal pasteurization methods for bor-thekera juice preservation, mitigating quality deterioration while augmenting the functional attributes of the juice.
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Affiliation(s)
- Shikhapriyom Gogoi
- Department of Food Engineering and Technology, Central Institute of Technology, Kokrajhar 783370, India; (S.G.); (P.D.); (P.K.N.)
| | - Puja Das
- Department of Food Engineering and Technology, Central Institute of Technology, Kokrajhar 783370, India; (S.G.); (P.D.); (P.K.N.)
| | - Prakash Kumar Nayak
- Department of Food Engineering and Technology, Central Institute of Technology, Kokrajhar 783370, India; (S.G.); (P.D.); (P.K.N.)
| | - Kandi Sridhar
- Department of Food Technology, Karpagam Academy of Higher Education (Deemed to Be University), Coimbatore 641021, India
| | - Minaxi Sharma
- Department of Applied Biology, University of Science and Technology Meghalaya, Baridua 793101, India;
| | - Thachappully Prabhat Sari
- Department of Food Science and Technology, National Institute of Food Technology, Entrepreneurship and Management, Kundli 131028, India;
| | - Radha krishnan Kesavan
- Department of Food Engineering and Technology, Central Institute of Technology, Kokrajhar 783370, India; (S.G.); (P.D.); (P.K.N.)
| | - Maharshi Bhaswant
- New Industry Creation Hatchery Center, Tohoku University, Sendai 980-8579, Japan
- Center for Molecular and Nanomedical Sciences, Sathyabama Institute of Science and Technology, Chennai 600119, India
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26
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Ezenkwu CP, Cannon S, Ibeke E. Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments' carbon reduction policies. Environ Monit Assess 2024; 196:231. [PMID: 38308016 PMCID: PMC10837261 DOI: 10.1007/s10661-024-12388-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 01/20/2024] [Indexed: 02/04/2024]
Abstract
Across the globe, governments are developing policies and strategies to reduce carbon emissions to address climate change. Monitoring the impact of governments' carbon reduction policies can significantly enhance our ability to combat climate change and meet emissions reduction targets. One promising area in this regard is the role of artificial intelligence (AI) in carbon reduction policy and strategy monitoring. While researchers have explored applications of AI on data from various sources, including sensors, satellites, and social media, to identify areas for carbon emissions reduction, AI applications in tracking the effect of governments' carbon reduction plans have been limited. This study presents an AI framework based on long short-term memory (LSTM) and statistical process control (SPC) for the monitoring of variations in carbon emissions, using UK annual CO2 emission (per capita) data, covering a period between 1750 and 2021. This paper used LSTM to develop a surrogate model for the UK's carbon emissions characteristics and behaviours. As observed in our experiments, LSTM has better predictive abilities than ARIMA, Exponential Smoothing and feedforward artificial neural networks (ANN) in predicting CO2 emissions on a yearly prediction horizon. Using the deviation of the recorded emission data from the surrogate process, the variations and trends in these behaviours are then analysed using SPC, specifically Shewhart individual/moving range control charts. The result shows several assignable variations between the mid-1990s and 2021, which correlate with some notable UK government commitments to lower carbon emissions within this period. The framework presented in this paper can help identify periods of significant deviations from a country's normal CO2 emissions, which can potentially result from the government's carbon reduction policies or activities that can alter the amount of CO2 emissions.
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Affiliation(s)
| | - San Cannon
- School of Creative and Cultural Business, Robert Gordon University, Aberdeen, UK
| | - Ebuka Ibeke
- School of Creative and Cultural Business, Robert Gordon University, Aberdeen, UK
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27
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Vinayagam R, Nagendran V, Goveas LC, Narasimhan MK, Varadavenkatesan T, Samanth A, Selvaraj R. Machine learning, conventional and statistical physics modeling of 2,4-Dichlorophenoxyacetic acid (2,4-D) herbicide removal using biochar prepared from Vateria indica fruit biomass. Chemosphere 2024; 350:141130. [PMID: 38185425 DOI: 10.1016/j.chemosphere.2024.141130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 12/22/2023] [Accepted: 01/03/2024] [Indexed: 01/09/2024]
Abstract
The adsorption properties of 2,4-Dichlorophenoxyacetic acid (2,4-D) onto biochar, obtained through HCl-assisted hydrothermal carbonization process of Vateria indica fruits (VI-BC), were extensively studied using traditional and statistical physics approaches. The traditional adsorption investigations encompassed kinetics, equilibrium, and thermodynamics studies. Subsequently, the Hill statistical physics model was employed to interpret the mechanism. Also, artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) machine learning tools were successfully employed to model the adsorption data wherein both models had high prediction potential (R2 > 0.99). The outcomes demonstrated that the produced VI-BC exhibited remarkable adsorptive traits, having a considerable specific surface area (111.54 m2/g), pore size (5.89 nm), a variety of functional groups, and appropriate attributes for efficiently adsorbing 2,4-D. For 10 mg/L 2,4-D, at pH 2.0 and with 0.3 g/L dose, an impressive 91.67% adsorption efficiency was achieved within a 120-min. Pseudo-second-order model aptly depicted the kinetic behavior of 2,4-D adsorption, while the Freundlich model provided a more accurate representation of the isotherms. 2,4-D maximum adsorption capacity stood at 131.39 mg/g at 303 K. The Hill statistical physics model elucidated that the adsorption primarily occurred via physisorption mechanisms, involving electrostatic attractions, π-π conjugation, and pore filling. This conclusion was further substantiated by post-adsorption characterization of the VI-BC. Thermodynamic analysis indicated that the interactions between VI-BC and 2,4-D were favorable, spontaneous, and exothermic. The calculated low energy of adsorption (1.255 kJ/mol) and ΔH° value (-20.49 kJ/mol) further supported physisorption as the dominant mechanism. In summary, this study underscores the significant potential of the newly developed biochar as a promising alternative material for efficiently removing the 2,4-D herbicide from polluted environments.
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Affiliation(s)
- Ramesh Vinayagam
- Department of Chemical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Vasundra Nagendran
- Department of Chemical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Louella Concepta Goveas
- Nitte (Deemed to Be University), Department of Biotechnology Engineering, NMAM Institute of Technology (NMAMIT), Nitte, India
| | - Manoj Kumar Narasimhan
- Department of Genetic Engineering, School of Bioengineering, College of Engineering and Technology, SRM Institute of Science and Technology, Potheri, Kattankulathur, Tamil Nadu, Chengalpattu District, 603203, India
| | - Thivaharan Varadavenkatesan
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Adithya Samanth
- Department of Chemical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Raja Selvaraj
- Department of Chemical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
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Al Karim R, Kabir MR, Rabiul MK, Kawser S, Salam A. Linking green supply chain management practices and environmental performance in the manufacturing industry: a hybrid SEM- ANN approach. Environ Sci Pollut Res Int 2024; 31:13925-13940. [PMID: 38265594 DOI: 10.1007/s11356-024-32098-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 01/16/2024] [Indexed: 01/25/2024]
Abstract
This research determines the influence of green supply chain management practices (GSCM) on environmental performance. It also investigates the moderating role of supply chain environmental cooperation on GSCM practices and environmental performance relationships. A total of 370 employees of several Bangladeshi manufacturing companies were conveniently chosen as respondents. To verify the data validity and reliability and to test the hypotheses, we used SmartPLS. Finally, we employed an artificial neural network (ANN) to examine the relationship. Green design and green manufacturing have significant positive impacts on environmental performance, while green procurement and green distribution do not. Moreover, environmental cooperation moderates the relationships of green design and green distribution with environmental performance. The moderating effect of supply chain environmental cooperation in the relationship between GSCM practices and environmental performance in the manufacturing industry adds knowledge to the existing literature by incorporating a hybrid model combining PLS-SEM and ANN. Our study adds to the current body of knowledge by delving into the literature on GSCM from the perspective of Bangladesh's industrial sector. This study fills a knowledge gap by shedding light on the interactions of GSCM and environmental performance. Indeed, this study represents a step forward from classic linear regression-based models to an ANN-based nonlinear model. It also demonstrates new contributions to the literature on green supply chain management and environmental performance.
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Affiliation(s)
- Rashed Al Karim
- School of Business Administration, East Delta University, Chattogram, Bangladesh
| | - Mohammad Rokibul Kabir
- School of Business Administration, East Delta University, Chattogram, Bangladesh
- School of Business and Entrepreneurship, Daffodil International University, Ashulia, Bangladesh
| | - Md Karim Rabiul
- Faculty of Hospitality and Tourism, Prince of Songkla University, Phuket, Thailand.
| | - Sakia Kawser
- School of Business Administration, East Delta University, Chattogram, Bangladesh
| | - Abdus Salam
- School of Business Administration, East Delta University, Chattogram, Bangladesh
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29
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Juturu R, Murty VR, Selvaraj R. Efficient adsorption of Cr (VI) onto hematite nanoparticles: ANN, ANFIS modelling, isotherm, kinetic, thermodynamic studies and mechanistic insights. Chemosphere 2024; 349:140731. [PMID: 38008295 DOI: 10.1016/j.chemosphere.2023.140731] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 11/09/2023] [Accepted: 11/13/2023] [Indexed: 11/28/2023]
Abstract
Hematite nanoparticles (AF-Fe2O3NPs) were prepared through a simple method utilizing Acacia falcata leaf extract in this investigation. The nanoparticles were extensively characterized to understand their specific properties. FESEM images revealed agglomerated surface morphology, while EDS confirmed the existence of elemental components, including Fe, O, and C. The mesoporous nature of AF-Fe2O3NPs with a pore diameter of 3.77 nm was determined through BET studies. XRD analysis indicated the crystallinity, with lattice parameters characteristic of hematite nanoparticles (a = 0.504 nm and c = 1.381 nm). Superparamagnetic property of the AF-Fe2O3NPs was affirmed from the saturation magnetization (2.98 emu/g) without any hysteresis. Subsequently, AF-Fe2O3NPs were used as adsorbent for the removal of Cr (VI) from aqueous solution. The experimental data were subjected to machine learning (ML) models, specifically ANN and ANFIS, to predict Cr (VI) removal. Both ML models exhibited excellent predictive capabilities, with high R2 values (>0.99) and low error indices such as MSE, RMSE, and MAE. Furthermore, comprehensive kinetic, isotherm, and thermodynamic studies were conducted to gain insights into the behavior and sorption mechanisms of Cr (VI). The Hill model, a statistical physics model, demonstrated an outstanding fit compared to conventional isotherms. It revealed a saturation adsorption potential of 12.91 mg/g at pH 2, 1.5 g/L dose, and a temperature of 30 °C, corroborating physisorption as the dominant mechanism. XPS results confirmed Cr (VI) reduction to Cr (III) through the appearance of specific peaks at 577.18 and 587.08 eV. Thermodynamic investigations established the endothermicity and spontaneity of the adsorption. In summary, the hematite nanoparticles synthesized in this study exhibit promising potential to remove Cr (VI) from aqueous streams, making them a viable option for water treatment applications.
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Affiliation(s)
- Rajesh Juturu
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Vytla Ramachandra Murty
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
| | - Raja Selvaraj
- Department of Chemical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
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30
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Özdoğan H, Üncü YA, Şekerci M, Kaplan A. Neural network predictions of (α,n) reaction cross sections at 18.5±3 MeV using the Levenberg-Marquardt algorithm. Appl Radiat Isot 2024; 204:111115. [PMID: 38006780 DOI: 10.1016/j.apradiso.2023.111115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 11/02/2023] [Accepted: 11/16/2023] [Indexed: 11/27/2023]
Abstract
In recent developments, artificial neural networks (ANNs) have demonstrated their capability to predict reaction cross-sections based on experimental data. Specifically, for predicting (α,n) reaction cross-sections, we meticulously fine-tuned the neural network's performance by optimizing its parameters through the Levenberg-Marquardt algorithm. The effectiveness of this approach is corroborated by notable correlation coefficients; an R-value of 0.90928 for overall correlation, 0.98194 for validation, 0.99981 for testing, and 0.94116 for the comprehensive network prediction. We conducted a rigorous comparison between the results and theoretical computations derived from the TALYS 1.95 nuclear code to validate the predictive accuracy. The mean square error value for artificial neural network results is 7620.92, whereas for TALYS 1.95 calculations, it has been found to be 50,312.74. This comprehensive evaluation process validates the reliability of the ANN based on the Levenberg-Marquardt algorithm in approximating the reaction sections, thus demonstrating its potential for comprehensive investigations. These recent developments confirm the feasibility of using ANN models to gain insight into (α,n) reaction cross-sections.
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Affiliation(s)
- Hasan Özdoğan
- Antalya Bilim University, Vocational School of Health Services, Department of Medical Imaging Techniques, 07190, Antalya, Turkey.
| | - Yiğit Ali Üncü
- Akdeniz University, Vocational School of Technical Sciences, Department of Biomedical Equipment Technology, 07070, Antalya, Turkey
| | - Mert Şekerci
- Süleyman Demirel University, Faculty of Arts and Sciences, Department of Physics, 32260, Isparta, Turkey
| | - Abdullah Kaplan
- Süleyman Demirel University, Faculty of Arts and Sciences, Department of Physics, 32260, Isparta, Turkey
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31
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Xiao X, Li T, Lin F, Li X, Hao Z, Li J. A Framework for Determining the Optimal Vibratory Frequency of Graded Gravel Fillers Using Hammering Modal Approach and ANN. Sensors (Basel) 2024; 24:689. [PMID: 38276382 PMCID: PMC10820290 DOI: 10.3390/s24020689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 01/10/2024] [Accepted: 01/19/2024] [Indexed: 01/27/2024]
Abstract
To address the uncertainty of optimal vibratory frequency fov of high-speed railway graded gravel (HRGG) and achieve high-precision prediction of the fov, the following research was conducted. Firstly, commencing with vibratory compaction experiments and the hammering modal analysis method, the resonance frequency f0 of HRGG fillers, varying in compactness K, was initially determined. The correlation between f0 and fov was revealed through vibratory compaction experiments conducted at different vibratory frequencies. This correlation was established based on the compaction physical-mechanical properties of HRGG fillers, encompassing maximum dry density ρdmax, stiffness Krd, and bearing capacity coefficient K20. Secondly, the gray relational analysis algorithm was used to determine the key feature influencing the fov based on the quantified relationship between the filler feature and fov. Finally, the key features influencing the fov were used as input parameters to establish the artificial neural network prediction model (ANN-PM) for fov. The predictive performance of ANN-PM was evaluated from the ablation study, prediction accuracy, and prediction error. The results showed that the ρdmax, Krd, and K20 all obtained optimal states when fov was set as f0 for different gradation HRGG fillers. Furthermore, it was found that the key features influencing the fov were determined to be the maximum particle diameter dmax, gradation parameters b and m, flat and elongated particles in coarse aggregate Qe, and the Los Angeles abrasion of coarse aggregate LAA. Among them, the influence of dmax on the ANN-PM predictive performance was the most significant. On the training and testing sets, the goodness-of-fit R2 of ANN-PM all exceeded 0.95, and the prediction errors were small, which indicated that the accuracy of ANN-PM predictions was relatively high. In addition, it was clear that the ANN-PM exhibited excellent robust performance. The research results provide a novel method for determining the fov of subgrade fillers and provide theoretical guidance for the intelligent construction of high-speed railway subgrades.
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Affiliation(s)
- Xianpu Xiao
- China Academy of Railway Sciences Co., Ltd., Beijing 100081, China; (X.X.); (F.L.)
- Department of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China;
| | - Taifeng Li
- China Academy of Railway Sciences Co., Ltd., Beijing 100081, China; (X.X.); (F.L.)
| | - Feng Lin
- China Academy of Railway Sciences Co., Ltd., Beijing 100081, China; (X.X.); (F.L.)
| | - Xinzhi Li
- Department of Civil Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China;
| | - Zherui Hao
- Department of Civil Engineering, Central South University, Changsha 410075, China; (Z.H.); (J.L.)
| | - Jiashen Li
- Department of Civil Engineering, Central South University, Changsha 410075, China; (Z.H.); (J.L.)
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32
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Baba A. Neural networks from biological to artificial and vice versa. Biosystems 2024; 235:105110. [PMID: 38176518 DOI: 10.1016/j.biosystems.2023.105110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 12/22/2023] [Accepted: 12/22/2023] [Indexed: 01/06/2024]
Abstract
In this paper, we examine how deep learning can be utilized to investigate neural health and the difficulties in interpreting neurological analyses within algorithmic models. The key contribution of this paper is the investigation of the impact of a dead neuron on the performance of artificial neural networks (ANNs). Therefore, we conduct several tests using different training algorithms and activation functions to identify the precise influence of the training process on neighboring neurons and the overall performance of the ANN in such cases. The aim is to assess the potential application of the findings in the biological domain, the expected results may have significant implications for the development of effective treatment strategies for neurological disorders. Successive training phases that incorporate visual and acoustic data derived from past social and familial experiences could be suggested to achieve this goal. Finally, we explore the conceptual analogy between the Adam optimizer and the learning process of the brain by delving into the specifics of both systems while acknowledging their fundamental differences.
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Affiliation(s)
- Abdullatif Baba
- Kuwait College of Science and Technology, Computer Science and Engineering Department, Kuwait; University of Turkish Aeronautical Association, Computer Engineering Department, Ankara, Turkey.
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Gani A, Singh M, Pathak S, Hussain A. Groundwater quality index development using the ANN model of Delhi Metropolitan City, India. Environ Sci Pollut Res Int 2023:10.1007/s11356-023-31584-4. [PMID: 38133760 DOI: 10.1007/s11356-023-31584-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 12/12/2023] [Indexed: 12/23/2023]
Abstract
Groundwater is widely recognized as a vital source of fresh drinking water worldwide. However, the rapid, unregulated population growth and increased industrialization, coupled with a rise in human activities, have significantly harmed the quality of groundwater. Changes in the local topography and drainage systems in an area have negative impacts on both the quality and quantity of groundwater. This underscores the critical need to assess the susceptibility of groundwater to pollution and implement measures to mitigate these risks. The water quality index (WQI) is an approach that simulates the water quality at peculiar locations for a particular period of time. The artificial neural network (ANN) model approach is such an idealistic methodology that can be utilized for WQI development and provides better results for specific locations in optimum time. Therefore, the goal of the current study is to provide a unique way for using artificial neural networks (ANN) to characterize the groundwater quality of Delhi Metropolitan City, India. In order to make the water fit for residential and drinking use, the research also pinpoints the geographical variability and spots where the contaminated region has to be sufficiently cleaned. A minimum WQI of 41.51 was obtained at the Jagatpur location while a maximum value of 779.01 was at the Peeragarhi location. During the training phase, the results obtained using the ANN model were highly favorable, demonstrating a strong association with an R-value of 98.10%, thus highlighting the program's exceptional efficiency. However, in accordance with the correlation regression findings, the prediction outcomes of the ANN model in testing are observed to be an R-value of 99.99-100%. This study confirms the promise and advantages of employing advanced artificial intelligence in managing groundwater quality in the studied area.
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Affiliation(s)
- Abdul Gani
- Department of Civil Engineering, Netaji Subhas University of Technology, New Delhi, 110073, India
| | - Mohit Singh
- Department of Civil Engineering, Netaji Subhas University of Technology, New Delhi, 110073, India
| | - Shray Pathak
- Department of Civil Engineering, Indian Institute of Technology Ropar, Rupnagar, Punjab, 140001, India.
| | - Athar Hussain
- Department of Civil Engineering, Netaji Subhas University of Technology, New Delhi, 110073, India
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Liu C, Zhou SH, Su H, Yang WQ, Lu J. An Artificial Neural Network Model Combined with Dietary Retinol Intake from Different Sources to Predict the Risk of Nonalcoholic Fatty Liver Disease. Biomed Environ Sci 2023; 36:1123-1135. [PMID: 38199224 DOI: 10.3967/bes2023.120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 06/19/2023] [Indexed: 01/12/2024]
Abstract
Objective This study aimed to develop an artificial neural network (ANN) model combined with dietary retinol intake from different sources to predict the risk of non-alcoholic fatty liver disease (NAFLD) in American adults. Methods Data from the 2007 to 2014 National Health and Nutrition Examination Survey (NHANES) 2007-2014 were analyzed. Eligible subjects ( n = 6,613) were randomly divided into a training set ( n 1 = 4,609) and a validation set ( n 2 = 2,004) at a ratio of 7:3. The training set was used to identify predictors of NAFLD risk using logistic regression analysis. An ANN was established to predict the NAFLD risk using a training set. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the accuracy of the model using the training and validation sets. Results Our study found that the odds ratios ( ORs) and 95% confidence intervals ( CIs) of NAFLD for the highest quartile of plant-derived dietary retinol intake (i.e., provitamin A carotenoids, such as β-carotene) ( OR = 0.75, 95% CI: 0.57 to 0.99) were inversely associated with NAFLD risk, compared to the lowest quartile of intake, after adjusting for potential confounders. The areas under the ROC curves were 0.874 and 0.883 for the training and validation sets, respectively. NAFLD occurs when its incidence probability is greater than 0.388. Conclusion The ANN model combined with plant-derived dietary retinol intake showed a significant effect on NAFLD. This could be applied to predict NAFLD risk in the American adult population when government departments formulate future health plans.
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Affiliation(s)
- Can Liu
- School of Public Health, Shanxi Medical University, Taiyuan 030001, Shanxi, China;School of Management, Shanxi Medical University, Taiyuan 030001, Shanxi, China
| | - Shi Hui Zhou
- Department of Medical Administration, Affiliated Qingdao Central Hospital of Qingdao University, Qingdao 266000, Shandong, China
| | - Hong Su
- Primary Medical Department, Affiliated Qingdao Central Hospital of Qingdao University, Qingdao 266000, Shandong, China
| | - Wen Qin Yang
- Digestive Endoscopy Minimally Invasive Surgery Center, Shanxi Provincial Cancer Hospital, Chinese Academy of Medical Sciences Cancer Hospital Shanxi Hospital, Affiliated Cancer Hospital of Shanxi Medical University, Taiyuan 030013, Shanxi, China
| | - Jiao Lu
- School of Public Policy and Administration, Xi'an Jiaotong University, Xi'an 710049, Shaanxi, China
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Shen X, Xu B, Shen H. Indoor Localization System Based on RSSI-APIT Algorithm. Sensors (Basel) 2023; 23:9620. [PMID: 38139466 PMCID: PMC10747929 DOI: 10.3390/s23249620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 11/26/2023] [Accepted: 11/28/2023] [Indexed: 12/24/2023]
Abstract
An indoor localization system based on the RSSI-APIT algorithm is designed in this study. Integrated RSSI (received signal strength indication) and non-ranging APIT (approximate perfect point-in-triangulation test) localization methods are fused with machine learning in order to improve the accuracy of the indoor localization system. The system focuses on the improvement of preprocessing and localization algorithms. The primary objective of the system is to enhance the preprocessing of the acquired RSSI data and optimize the localization algorithm in order to enhance the precision of the coordinates in the indoor localization system. In order to mitigate the issue of significant fluctuations in RSSI, a technique including the integration of Gaussian filtering and an artificial neural network (ANN) is employed. This approach aims to preprocess the acquired RSSI data, thus reducing the impact of multipath effects. In order to address the issue of low localization accuracy encountered by the conventional APIT localization algorithm during wide-area localization, the RSSI ranging function is incorporated into the APIT localization algorithm. This addition serves to further narrow down the localization area. Consequently, the resulting localization algorithm is referred to as the RSSI-APIT positioning algorithm. Experimental results have demonstrated the successful reduction of inherent localization errors within the system by employing the RSSI-APIT positioning algorithm. The present study aims to investigate the impact of the localization scene and the number of anchors on the RSSI-APIT localization algorithm, with the objective of enhancing the performance of the indoor localization system. The conducted experiments demonstrated that the enhanced system exhibits several advantages. Firstly, it successfully decreased the frequency of anchor calls, resulting in a reduction in the overall operating cost of the system. Additionally, it effectively enhanced the accuracy and stability of the system's localization capabilities. In a complex environment of 100 m2 in size, compared with the traditional trilateral localization method and the APIT localization algorithm, the RSSI-APIT localization algorithm reduced the localization error by about 2.9 m and 1.8 m, respectively, and the overall error was controlled within 1.55 m.
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Affiliation(s)
- Xiaoyan Shen
- School of Information Science and Technology, Nantong University, Nantong 226019, China; (X.S.); (B.X.)
- Nantong Research Institute for Advanced Communication Technologies, Nantong University, Nantong 226019, China
| | - Boyang Xu
- School of Information Science and Technology, Nantong University, Nantong 226019, China; (X.S.); (B.X.)
- Nantong Research Institute for Advanced Communication Technologies, Nantong University, Nantong 226019, China
| | - Hongming Shen
- School of Information Science and Technology, Nantong University, Nantong 226019, China; (X.S.); (B.X.)
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H S, Bhat M R, Selvaraj R. Removal of an agricultural herbicide (2,4-Dichlorophenoxyacetic acid) using magnetic nanocomposite: A combined experimental and modeling studies. Environmental Research 2023; 238:117124. [PMID: 37716397 DOI: 10.1016/j.envres.2023.117124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 08/25/2023] [Accepted: 09/11/2023] [Indexed: 09/18/2023]
Abstract
This study focused on modeling the removal of one of the widely used agricultural herbicides known as 2,4-Dichlorophenoxyacetic acid (2,4-D) using polypyrrole-coated Fe2O3 nanoparticles (Fe2O3@PPy). The Fe2O3@PPy nanocomposite was synthesized by surface-coating the Tabebuia aurea leaf extract synthesized Fe2O3 nanoparticles with polypyrrole. After characterization, the adsorptive potential of the nanocomposite for removing 2,4-D from aqueous solution was examined. Central composite design (CCD) was employed for optimizing the adsorption, revealing an adsorption efficiency of 90.65% at a 2,4-D concentration of 12 ppm, a dosage of 3.8 g/L, an agitation speed of 150 rpm, and 196 min. Adsorption dataset fitted satisfactorily to Langmuir isotherm (R2: 0.984 & χ2: 0.054) and pseudo-second-order kinetics (R2: 0.929 & χ2: 0.013) whereas the exothermic and spontaneous nature were confirmed via the thermodynamic study. The predictive models, including adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and response surface methodology (RSM), demonstrated good precision for the prediction of 2,4-D adsorption, with respective R2 of 0.9719, 0.9604, and 0.9528. Nevertheless, statistical analysis supported ANFIS as the better forecasting tool, while RSM was the least effective. The maximum adsorption capacity of 2,4-D onto the Fe2O3@PPy nanocomposite was 7.29 mg/g, significantly higher than a few reported values. Therefore, the Fe2O3@PPy nanocomposite could serve as a competent adsorbent to remove 2,4-D herbicide from aqueous streams.
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Affiliation(s)
- Sridevi H
- Department of Civil Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Ramananda Bhat M
- Department of Biotechnology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India
| | - Raja Selvaraj
- Department of Chemical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
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Alfattani R, Yunus M, Selvarajan L, Venkataramanan K. Spark erosion behavior in the machining of MoSi 2-SiC ceramic composites for improving dimensional accuracy. J Mech Behav Biomed Mater 2023; 148:106166. [PMID: 37844443 DOI: 10.1016/j.jmbbm.2023.106166] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 10/04/2023] [Accepted: 10/06/2023] [Indexed: 10/18/2023]
Abstract
The novel applications of MoSi2 and SiC as matrix and reinforcing materials in the creation of high-performance composites were investigated in this work. In particular, Spark Erosion Machining's geometric tolerances were studied in order to shed light on the technique's potential for precision manufacture in the realm of MoSi2-SiC composites. Our research focused on evaluating critical parameters and their impact on machining performance, including material removal rate, surface roughness, wear rate and drilled hole accuracy. In-depth research revealed the critical input factors that had the greatest impact on the machining procedure. Notably, parameters such as current (32%), sparking on time (23%), sparking gap voltage (12%), dielectric pressure (12%), and sparking off time (17%) emerged as the most influential factors, as determined by ANOVA analysis. These findings provide valuable insights into optimizing the Sparking EDM approach for MoSi2-SiC composite materials. This study further demonstrated the improvement in composite desirability ratings across multiple performance criteria, highlighting the effectiveness of Sparking EDM in enhancing machining outcomes (e.g., from 0.8523 to 0.9527). Correlations between the EDM's output responses were found to be quite high when geometric tolerances and the coefficient of determination (R2) were used (0.7858, 0.9625, 0.8427, 0.8678, 0.8474, 0.8368, 0.8344, 0.8671). Consider that, for the sake of a more complete understanding of the procedure's approach, the emphasis is on the methodology rather than the multifaceted metal removal mechanisms involved. This research doesn't dive further into the physical concerns of Spark Erosion Machining, but it does provide insights into the practical application of this technique in the precision manufacturing of MoSi2-SiC composite materials. For real-world medical applications such implanted devices, dental implants, surgical instruments, biological sensors and diagnostics, this study provides a valuable and encouraging approach. A validation experiment verifies the results, proving the feasibility of improved spark erosion in high-precision production. The results of this research show that EDM methods can be fine-tuned to produce ceramic composites with much greater MRR, superior surface finishes and a marked decrease in subsurface cracking and microstructural modifications. This is essential for protecting the integrity of materials used in life-saving medical equipment.
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Affiliation(s)
- Rami Alfattani
- Department of Mechanical Engineering, College of Engineering and Islamic Architecture, Umm Al-Qura University, Makkah, 24224, Saudi Arabia.
| | - Mohammed Yunus
- Department of Mechanical Engineering, College of Engineering and Islamic Architecture, Umm Al-Qura University, Makkah, 24224, Saudi Arabia.
| | - L Selvarajan
- Department of Mechanical Engineering, Mahendra Institute of Technology (Autonomous), Namakkal District, Tamil Nadu, 637 503, India.
| | - K Venkataramanan
- Department of Mechanical Engineering, Mahendra Polytechnic College, Mallasamudram, Namakkal District, 637503, Tamil Nadu, India.
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Das R, Karthika S, Bhasarkar J, Bal DK. GA-coupled ANN model for predicting porosity in alginate gel scaffolds. J Mech Behav Biomed Mater 2023; 148:106204. [PMID: 37883894 DOI: 10.1016/j.jmbbm.2023.106204] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 10/17/2023] [Accepted: 10/20/2023] [Indexed: 10/28/2023]
Abstract
Alginate gel scaffolds are biocompatible and biodegradable materials that have been used in a variety of tissue engineering applications. The porosity of alginate gel scaffolds is an important property that affects their performance. However, it is difficult to predict the porosity of alginate gel scaffolds accurately. In this study, a GA-coupled ANN model was developed to predict the porosity of alginate gel scaffolds. The model was trained on a dataset of 107 scaffolds with known porosities. The model was able to achieve a mean absolute error of 0.13, which suggests that it is able to accurately predict the porosity of alginate gel scaffolds. The alginate scaffold was fabricated by a microfluidic technique using a syringe pump and a flow device. The crosslinker solution was poured into the Petri dish to crosslink the polymer to the gel structure. The Archimedes method was used to determine the scaffold's apparent porosity. The artificial neural network has been used to model the porosity of the gel scaffold using the input parameters such as alginate-pluronic viscosity, surface tension, and contact angle etc. The maximum porosity was modelled to be 96.4 % using GA whereas the experimental value for the same was measured to be 92.8 ± 2 %. A 3.7% variation in the porosity was found from modelled value. To the best of our knowledge, this study is the first to develop an integrated ANN-coupled GA model to predict the maximum porosity of the gel scaffold. The result indicates that artificial intelligence has great potential for optimizing the parameters to fabricate the gel scaffold that can be used for tissue engineering applications.
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Affiliation(s)
- Raja Das
- School of Advanced Sciences, Vellore Institute of Technology, Tamil Nadu, India
| | - S Karthika
- Department of Chemical Engineering, Anna University, Chennai, Tamil Nadu, India
| | - Jaykumar Bhasarkar
- Department of Chemical Engineering, Laxminarayan Innovation Technological University, Nagpur, Maharashtra, India
| | - Dharmendra Kumar Bal
- Colloids and Polymer Research Group, School of Chemical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India.
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Shenbagamuthuraman V, Kasianantham N. Microwave irradiation pretreated fermentation of bioethanol production from Chlorella vulgaris Biomasses: Comparative analysis of response surface methodology and artificial neural network techniques. Bioresour Technol 2023; 390:129867. [PMID: 37832853 DOI: 10.1016/j.biortech.2023.129867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 10/10/2023] [Accepted: 10/10/2023] [Indexed: 10/15/2023]
Abstract
Bioethanol is a promising biofuel for replacing gasoline due to its sustainability. This work uses microwave irradiation and acid hydrolysis to produce bioethanol from Chlorella vulgaris. The hydrolysis procedure used 1%-3% sulfuric acid (H2SO4). The maximum output of reducing sugar was 6.773 g/L after 5 min of irradiation. This study used RSM and ANN to optimize bioethanol production. The study predicted bioethanol yield using three factors: fermentation duration (12-36 h), temperature (28-32 °C), and inoculum concentration (0.5-1.5 g/L). The highest bioethanol yield was achieved using fermentation conditions of 36 h, 30 °C temperature, and 1.5 g/L inoculum concentration. The ANN model predicted the best ethanol output compared to the RSM model. The leftover biomass from biofuel synthesis was characterized for its potential for other energy production. The current study examined the feasibility of employing biomass in an environmentally sustainable manner to enhance the production of biofuels.
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Affiliation(s)
- V Shenbagamuthuraman
- Engine Testing Laboratory, School of Mechanical Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India
| | - Nanthagopal Kasianantham
- Engine Testing Laboratory, School of Mechanical Engineering, Vellore Institute of Technology, Vellore 632014, Tamil Nadu, India.
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Li W, Zheng N, Zhou Q, Alqahtani MS, Elkamchouchi DH, Zhao H, Lin S. A state-of-the-art analysis of pharmacological delivery and artificial intelligence techniques for inner ear disease treatment. Environ Res 2023; 236:116457. [PMID: 37459944 DOI: 10.1016/j.envres.2023.116457] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 06/13/2023] [Accepted: 06/17/2023] [Indexed: 08/01/2023]
Abstract
Over the last several decades, both the academic and therapeutic fields have seen significant progress in the delivery of drugs to the inner ear due to recent delivery methods established for the systemic administration of drugs in inner ear treatment. Novel technologies such as nanoparticles and hydrogels are being investigated, in addition to the traditional treatment methods. Intracochlear devices, which utilize current developments in microsystems technology, are on the horizon of inner ear drug delivery methods and are designed to provide medicine directly into the inner ear. These devices are used for stem cell treatment, RNA interference, and the delivery of neurotrophic factors and steroids during cochlear implantation. An in-depth analysis of artificial neural networks (ANNs) in pharmaceutical research may be found in ANNs for Drug Delivery, Design, and Disposition. This prediction tool has a great deal of promise to assist researchers in more successfully designing, developing, and delivering successful medications because of its capacity to learn and self-correct in a very complicated environment. ANN achieved a high level of accuracy exceeding 0.90, along with a sensitivity of 95% and a specificity of 100%, in accurately distinguishing illness. Additionally, the ANN model provided nearly perfect measures of 0.99%. Nanoparticles exhibit potential as a viable therapeutic approach for bacterial infections that are challenging to manage, such as otitis media. The utilization of ANNs has the potential to enhance the effectiveness of nanoparticle therapy, particularly in the realm of automated identification of otitis media. Polymeric nanoparticles have demonstrated effectiveness in the treatment of prevalent bacterial infections in pediatric patients, suggesting significant potential for forthcoming therapeutic interventions. Finally, this study is based on a research of how inner ear diseases have been treated in the last ten years (2012-2022) using machine learning.
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Affiliation(s)
- Wanqing Li
- Ruian People's Hospital, The Third Affiliated Hospital of Wenzhou Medical University, Ruian, 325200, China
| | - Nan Zheng
- College of Pharmacy, Zhejiang Chinese Medical University, Hangzhou, 311402, China
| | - Qiang Zhou
- Ruian People's Hospital, The Third Affiliated Hospital of Wenzhou Medical University, Ruian, 325200, China
| | - Mohammed S Alqahtani
- Radiological Sciences Department, College of Applied Medical Sciences, King Khalid University, Abha, 61421, Saudi Arabia; BioImaging Unit, Space Research Centre, Michael Atiyah Building, University of Leicester, Leicester, LE1 7RH, UK
| | - Dalia H Elkamchouchi
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
| | - Huajun Zhao
- College of Pharmacy, Zhejiang Chinese Medical University, Hangzhou, 311402, China.
| | - Sen Lin
- Ruian People's Hospital, The Third Affiliated Hospital of Wenzhou Medical University, Ruian, 325200, China.
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Subburaman B, Thangaraj V, Balu V, Pandyan UM, Kulkarni J. Artificial Neural Network Modeling of a CMOS Differential Low-Noise Amplifier Using the Bayesian Regularization Algorithm. Sensors (Basel) 2023; 23:8790. [PMID: 37960488 PMCID: PMC10647308 DOI: 10.3390/s23218790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 10/14/2023] [Accepted: 10/18/2023] [Indexed: 11/15/2023]
Abstract
The purpose of this communication is to present the modeling of an Artificial Neural Network (ANN) for a differential Complementary Metal Oxide Semiconductor (CMOS) Low-Noise Amplifier (LNA) designed for wireless applications. For satellite transponder applications employing differential LNAs, various techniques, such as gain boosting, linearity improvement, and body bias, have been individually documented in the literature. The proposed LNA combines all three of these techniques differentially, aiming to achieve a high gain, a low noise figure, excellent linearity, and reduced power consumption. Under simulation conditions at 5 GHz using Cadence, the proposed LNA demonstrates a high gain (S21) of 29.5 dB and a low noise figure (NF) of 1.2 dB, with a reduced supply voltage of only 0.9 V. Additionally, it exhibits a reflection coefficient (S11) of less than -10 dB, a power dissipation (Pdc) of 19.3 mW, and a third-order input intercept point (IIP3) of 0.2 dBm. The performance results of the proposed LNA, combining all three techniques, outperform those of LNAs employing only two of the above techniques. The proposed LNA is modeled using PatternNet BR, and the simulation results closely align with the results of the developed ANN. In comparison to the Cadence simulation method, the proposed approach also offers accurate circuit solutions.
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Affiliation(s)
- Bhuvaneshwari Subburaman
- ECE Department, Mangayarkarasi College of Engineering, Madurai 625402, Tamil Nadu, India; (B.S.); (V.T.); (V.B.)
| | - Vignesh Thangaraj
- ECE Department, Mangayarkarasi College of Engineering, Madurai 625402, Tamil Nadu, India; (B.S.); (V.T.); (V.B.)
| | - Vadivel Balu
- ECE Department, Mangayarkarasi College of Engineering, Madurai 625402, Tamil Nadu, India; (B.S.); (V.T.); (V.B.)
| | - Uma Maheswari Pandyan
- ECE Department, Velammal College of Engineering and Technology, Madurai 625009, Tamil Nadu, India;
| | - Jayshri Kulkarni
- Department of Electrical and Computer Engineering, Baylor University, Waco, TX 76798, USA
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Maselesele TL, Molelekoa TBJ, Gbashi S, Adebo OA. The Optimisation of Bitter Gourd-Grape Beverage Fermentation Using a Consolidated Response Surface Methodology (RSM) and Artificial Neural Network ( ANN) Approach. Plants (Basel) 2023; 12:3473. [PMID: 37836213 PMCID: PMC10575144 DOI: 10.3390/plants12193473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/23/2023] [Accepted: 09/27/2023] [Indexed: 10/15/2023]
Abstract
The present study adopted a response surface methodology (RSM) approach validated by artificial neural network (ANN) models to optimise the production of a bitter gourd-grape beverage. Aset of statistically pre-designed experiments were conducted, and the RSM optimisation model fitted to the obtained data, yielding adequately fit models for the monitored control variables R2 values for alcohol (0.79), pH (0.89), and total soluble solids (TSS) (0.89). Further validation of the RSM model fit using ANN showed relatively high accuracies of 0.98, 0.88, and 0.82 for alcohol, pH, and TSS, respectively, suggesting satisfactory predictability and adequacy of the models. A clear effect of the optimised conditions, namely fermentation time at (72 h), fermentation temperature (32.50 and 45.11 °C), and starter culture concentration (3.00 v/v) on the total titratable acidity (TTA), was observed with an R2 value of (0.40) and RSM model fit using ANN overall accuracy of (0.56). However, higher TTA values were observed for samples fermented for 72 h at starter culture concentrations above 3 mL. The level of 35% bitter gourd juice was optimised in this study and was considered desirable because the goal was to make a low-alcohol beverage.
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Affiliation(s)
- Tintswalo Lindi Maselesele
- Food Innovation Research Group, Department of Biotechnology and Food Technology, Faculty of Science, University of Johannesburg, P.O. Box 17011, Johannesburg 2028, South Africa;
| | - Tumisi Beiri Jeremiah Molelekoa
- Department of Biotechnology and Food Technology, Faculty of Science, University of Johannesburg, Doornfontein Campus, P.O. Box 17011, Johannesburg 2028, South Africa; (T.B.J.M.); (S.G.)
| | - Sefater Gbashi
- Department of Biotechnology and Food Technology, Faculty of Science, University of Johannesburg, Doornfontein Campus, P.O. Box 17011, Johannesburg 2028, South Africa; (T.B.J.M.); (S.G.)
| | - Oluwafemi Ayodeji Adebo
- Food Innovation Research Group, Department of Biotechnology and Food Technology, Faculty of Science, University of Johannesburg, P.O. Box 17011, Johannesburg 2028, South Africa;
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Khan H, Hussain S, Ud Din MA, Arshad M, Wahab F, Hassan U, Khan A. Multiple design and modelling approaches for the optimisation of carbon felt electro-Fenton treatment of dye laden wastewater. Chemosphere 2023; 338:139510. [PMID: 37454991 DOI: 10.1016/j.chemosphere.2023.139510] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 06/23/2023] [Accepted: 07/13/2023] [Indexed: 07/18/2023]
Abstract
This study utilizes artificial intelligence and statistical modelling to optimize the operating parameters of a carbon-based electro-Fenton process for purifying model dye (RB19)-contaminated wastewater. Multilevel experimental Box-Behnken and uniform deisgns (BBD, UD) with four variables were analysed using polynomial regression analysis (PRA) and artificial neural networks (ANN), while the process optimisation was done using desirability function. For the given testing range but different design matrices and runs, both designs predicted a maximum RB19 removal (RB19-RR) of 90 ± 2.1% at lowest energy consumption (EC) of 0.44 ± 2.5 Wh, when voltage, Na2SO4, FeSO4, and time were maintained as follows: 4-5.3 V, 7-11 mM, 0.4-0.6 mM, and 35-40 min, respectively. All the design-model combinations portrayed the similar senitivity analyses, revealing that RB19 degradation and EC are primarily influenced by electrolysis time and voltage. The performance assessment demonstrated that all the design-model combinations also excellently predicted for unseen conditions as the maximum root mean squared error (RMSE) value for RB19-RR was 4.07, while it was 0.072 for EC, however, BBD-ANN performance proved to be slightly better than others. Having ∼57% less experimentation, UD based models managed to accurately predict the results for unseen conditions as the statistical errors were quite insignificant, even in some cases, RMSE found to be less for UD compared to BBD, elucidating the potential of uniform design as an alternative of conventional factorial designs. Nevertheless, the prediction accuracy is also dependent on modelling approach, as in some cases ANN failed to predict the response precisely specially when dealing with small data. Furthermore, techno-economic evaluation results spell out the efficacy of carbon felt based enhanced electro-Fenton process as promising environmental remediation technology and highlight its practical implication from view of operational cost.
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Affiliation(s)
- Hammad Khan
- Faculty of Materials and Chemical Engineering, GIK Institute of Engineering Sciences and Technology, Topi, Pakistan
| | - Sajjad Hussain
- Faculty of Materials and Chemical Engineering, GIK Institute of Engineering Sciences and Technology, Topi, Pakistan.
| | - Muhammad Amad Ud Din
- Faculty of Materials and Chemical Engineering, GIK Institute of Engineering Sciences and Technology, Topi, Pakistan
| | - Muhammad Arshad
- Department of Chemical Engineering, College of Engineering, King Khalid University, Abha, Saudi Arabia
| | - Fazal Wahab
- Faculty of Materials and Chemical Engineering, GIK Institute of Engineering Sciences and Technology, Topi, Pakistan
| | - Usman Hassan
- Integrated Business Planning Department, My Clinic International Medical Company, Prince Sultan Road, PO Box 260, Jeddah, Saudi Arabia
| | - Abad Khan
- EHS Department, Unilever, Dubai, United Arab Emirates
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Lai VQ, Kounlavong K, Keawsawasvong S, Wipulanusat W, Jamsawang P. Physics-based and data-driven modeling for basal stability evaluation of braced excavations in natural clays. Heliyon 2023; 9:e20902. [PMID: 37867872 PMCID: PMC10587496 DOI: 10.1016/j.heliyon.2023.e20902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 10/08/2023] [Accepted: 10/10/2023] [Indexed: 10/24/2023] Open
Abstract
The design of fully braced excavation of underground works, whether in rural or urban areas, is important to ensure that the design of fully braced support is safe, particularly in determining the depth of excavation and inserting the length into the clay of the wall, as well as a proportional excavation width. This study investigates the undrained basal stability of fully braced excavation in anisotropic clays with linearly increasing shear strength with depth employing upper and lower bound finite element limit analysis under symmetry plane conditions based on the AUS failure criterion. The dimensionless variables were used to examine the stability number (N) and the failure mechanisms selected for this problem's practical analysis. There is an anisotropic strength ratio (re), depth-wide ratio (B/H), embedded wall depth ratio (D/H), and strength gradient factor (ρH/Suc0). This study proposes design charts and failure mechanisms for fully braced excavations based on finite element limit analysis. Moreover, the artificial neural network model (ANN) was used to establish the relationship between the investigated and output variables and to conduct sensitivity analysis. Therefore, the developed ANN formula is a pragmatic approach for geotechnical engineers to calculate the basal stability of the excavations.
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Affiliation(s)
- Van Qui Lai
- Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Viet Nam
- Vietnam National University Ho Chi Minh City (VNU–HCM), Linh Trung Ward, Thu Duc District, Ho Chi Minh, City, Viet Nam
| | - Khamnoy Kounlavong
- Research Unit in Sciences and Innovative Technologies for Civil Engineering Infrastructures, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, 12120, Thailand
| | - Suraparb Keawsawasvong
- Research Unit in Sciences and Innovative Technologies for Civil Engineering Infrastructures, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, 12120, Thailand
| | - Warit Wipulanusat
- Research Unit in Data Science and Digital Transformation, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, 12120, Thailand
| | - Pitthaya Jamsawang
- Soil Engineering Research Center, Department of Civil Engineering, King Mongkut's University of Technology North Bangkok, Bangkok, 10800, Thailand
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Liang X, Guan R, Zhu J, Meng Y, Zhu J, Yang Y, Cui Y, Dai J, Mao W, Lv L, Shen D, Guo R. A clinical decision support system to predict the efficacy for EGFR-TKIs based on artificial neural network. J Cancer Res Clin Oncol 2023; 149:12265-12274. [PMID: 37434091 DOI: 10.1007/s00432-023-05104-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Accepted: 06/30/2023] [Indexed: 07/13/2023]
Abstract
BACKGROUND The efficacy of epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) was affected by numerous factors. In the study, we developed and validated an artificial neural network (ANN) system based on clinical characteristics and next-generation sequencing (NGS) to support clinical decisions. METHODS A multicenter retrospective non-interventional study was conducted. 240 patients from three hospitals with advanced non-small cell lung cancer (NSCLC) and EGFR mutation were tested by NGS before the first treatment. All patients received formal EGFR-TKIs treatment. Five different models were individually trained to predict the efficacy of EGFR-TKIs based on one medical center with 188 patients. Two independent cohorts from other medical centers were collected for external validation. RESULTS Compared with logistic regression, four machine learning methods showed better predicting abilities for EGFR-TKIs. The inclusion of NGS tests improved the predictive power of models. ANN performed best on the dataset with mutations TP53, RB1, PIK3CA, EGFR mutation sites, and tumor mutation burden (TMB). The prediction accuracy, recall and AUC were 0.82, 0.82, and 0.82, respectively in our final model. In the external validation set, ANN still showed good performance and differentiated patients with poor outcomes. Finally, a clinical decision support software based on ANN was developed and provided a visualization interface for clinicians. CONCLUSION This study provides an approach to assess the efficacy of NSCLC patients with first-line EGFR-TKI treatment. Software is developed to support clinical decisions.
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Affiliation(s)
- Xiao Liang
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Department of Oncology, Jiangyin People's Hospital, Jiangyin, China
| | - Runwei Guan
- School of Electronics and Computer Science, University of Southampton, Southampton, UK
| | - Jiamin Zhu
- Department of Oncology, Jiangyin People's Hospital, Jiangyin, China
| | - Yue Meng
- Department of Oncology, Affiliated Hospital of Nantong University, Nantong, China
| | - Jing Zhu
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
- Department of Oncology, The Affiliated Jiangning Hospital With Nanjing Medical University, Nanjing, China
| | - Yuxiang Yang
- School of Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Yanan Cui
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Jiali Dai
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Weidong Mao
- Department of Oncology, Jiangyin People's Hospital, Jiangyin, China
| | - Liting Lv
- Department of Oncology, Affiliated Hospital of Nantong University, Nantong, China.
| | - Dong Shen
- Department of Oncology, Jiangyin People's Hospital, Jiangyin, China.
| | - Renhua Guo
- Department of Oncology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
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Nkolokosa C, Stothard R, Jones CM, Stanton M, Chirombo J, Tangena JAA. Monitoring and simulating landscape changes: how do long-term changes in land use and long-term average climate affect regional biophysical conditions in southern Malawi? Environ Monit Assess 2023; 195:1247. [PMID: 37750982 PMCID: PMC10522741 DOI: 10.1007/s10661-023-11783-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 08/24/2023] [Indexed: 09/27/2023]
Abstract
We set out to reveal the effects of long-term changes in land use and long-term average climate on the regional biophysical environment in southern Malawi. Object-oriented supervised image classification was performed on Landsat 5 and 8 satellite images from 1990 to 2020 to identify and quantify past and present land use-land cover changes using a support vector machine classifier. Subsequently, using 2000 and 2010 land use-land cover in an artificial neural network, land use-land cover for 2020 driven by elevation, slope, precipitation and temperature, population density, poverty, distance to major roads, and distance to villages data was simulated. Between 1990 and 2020, area of land cover increased in built-up (209%), bare land (10%), and cropland (10%) and decreased in forest (30%), herbaceous (4%), shrubland (20%), and water area (20%). Overall, the findings reveal that southern Malawi is dominantly an agro-mosaic landscape shaped by the combined effects of urban and agricultural expansions and climate. The findings also suggest the need to enhance the machine learning algorithms to improve capacity for landscape modelling and, ultimately, prevention, preparedness, and response to environmental risks.
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Affiliation(s)
- C Nkolokosa
- Sheffield Hallam University, Howard Street, Sheffield, S1 1WB, UK.
- Malawi-Liverpool-Wellcome Programme, Blantyre, Malawi.
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Kumari S, Middey A. Prediction of glaciated area fraction over the Sikkim Himalayan Region, India: a comparative study using response surface method, random forest, and artificial neural network. Environ Monit Assess 2023; 195:1230. [PMID: 37728658 DOI: 10.1007/s10661-023-11770-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 08/21/2023] [Indexed: 09/21/2023]
Abstract
Glacier area fraction at high altitude mountains is a serious worry in today's time triggered by climate change. The current information on this natural resource is very important for the survival of humanity as it affects the water, food, and energy security of people dependent on it. Due to its problematic accessibility and tough environmental condition, ground monitoring is quite challenging. This study investigates the impact of environmental parameters and pollutants on glacier area fraction over the Eastern Himalaya region and its prediction through random forest (RF), multilayer perceptron (MLP), radial basis function analysis (RBFN), and response surface methodology (RSM) models. The data are obtained from the Goddard Earth Sciences Data and Information Services Center (GES DISC), NASA's data archive portal ( https://giovanni.gsfc.nasa.gov ). The collinearity of independent variables reveals that all selected input parameters are highly correlated with R2 value > 0.9. The RSM and RF model provided valuable insight of the predictor's significance in addition to their capability to predict the response. The model performance was evaluated in terms of R2 value and the error matrices. The model's R2 value was found to be 0.843, 0.839, 0.838, and 0.743 for MLP, RBFN, RF, and RSM respectively. Although, the neural network model R2 values are the highest, but the most reliable and suitable model is RF as the error matrices for this model are much lower than others. This study encourages the investigation of the hybridization of these models for more accurate prediction.
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Affiliation(s)
- Sweta Kumari
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
- CSIR-National Environmental Engineering Research Institute, Kolkata Zonal Centre, Kolkata, 700107, India
| | - Anirban Middey
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India.
- CSIR-National Environmental Engineering Research Institute, Kolkata Zonal Centre, Kolkata, 700107, India.
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Ahmad I, Siddiqi MH, Alhujaili SF, Alrowaili ZA. Improving Alzheimer's Disease Classification in Brain MRI Images Using a Neural Network Model Enhanced with PCA and SWLDA. Healthcare (Basel) 2023; 11:2551. [PMID: 37761748 PMCID: PMC10530944 DOI: 10.3390/healthcare11182551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Revised: 09/12/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
The examination of Alzheimer's disease (AD) using adaptive machine learning algorithms has unveiled promising findings. However, achieving substantial credibility in medical contexts necessitates a combination of notable accuracy, minimal processing time, and universality across diverse populations. Therefore, we have formulated a hybrid methodology in this study to classify AD by employing a brain MRI image dataset. We incorporated an averaging filter during preprocessing in the initial stage to reduce extraneous details. Subsequently, a combined strategy was utilized, involving principal component analysis (PCA) in conjunction with stepwise linear discriminant analysis (SWLDA), followed by an artificial neural network (ANN). SWLDA employs a combination of forward and backward recursion methods to choose a restricted set of features. The forward recursion identifies the most interconnected features based on partial Z-test values. Conversely, the backward recursion method eliminates the least correlated features from the same feature space. After the extraction and selection of features, an optimized artificial neural network (ANN) was utilized to differentiate the various classes of AD. To demonstrate the significance of this hybrid approach, we utilized publicly available brain MRI datasets using a 10-fold cross-validation strategy. The proposed method excelled over existing state-of-the-art systems, attaining weighted average recognition rates of 99.35% and 96.66%, respectively, across all the datasets.
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Affiliation(s)
- Irshad Ahmad
- Department of Computer Science, Islamia College, Peshawar 25000, KPK, Pakistan
| | - Muhammad Hameed Siddiqi
- College of Computer and Information Sciences, Jouf University, Sakaka 2014, Aljouf, Saudi Arabia
| | | | - Ziyad Awadh Alrowaili
- Department of Physics, College of Science, Jouf University, Sakaka 2014, Aljouf, Saudi Arabia
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Saha P, Yang M. A Neural Network Approach to Estimate the Frequency of a Cantilever Beam with Random Multiple Damages. Sensors (Basel) 2023; 23:7867. [PMID: 37765924 PMCID: PMC10537365 DOI: 10.3390/s23187867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 08/30/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023]
Abstract
Natural frequency is an important parameter in the structural health monitoring (SHM) system. Any changes in this parameter indicate structural alteration due to damage. This study provides a neural network (NN) solution as an alternative to the finite element (FE) method to measure the natural frequencies of a cantilever beam with random multiple damage. It is based on a statistical dataset of a free vibration test obtained from the APDL (Ansys parametric design language) simulation using a MATLAB (matrix laboratory) script. The script can generate an unlimited number of possible damage combinations for any given parameters with the help of the Monte Carlo (MC) technique. MC helps to generate a random number of damages in random locations at each simulation. Damage conditions are controlled by three parameters including damage severity and damage size (in terms of the mean and standard deviation of damage). Moreover, the method proposes a curve-fitting equation to validate the predicted natural frequency for the first three modes obtained from the neural network model. Both methods are in good agreement with each other, having minimal errors in the range of 0.2-3% for each mode. The frequency result shows that the beam frequency is 8.6486 Hz if the area reduction is 10%, whereas it comes down to 7.2338 Hz if there is a 30% area reduction. A two-level factorial test shows that damage severity is the most impactful factor compared to the damage sizes on the frequency shift event. This indicates that damage alters the composition of the beam and has an impact on its frequency change with the assumed damage parameters. Therefore, the proposed NN model can estimate the frequency shift for various damage scenarios. It can be utilized in the vibration-based damage identification process to predict the frequency changes of the damaged beam without any computational burden.
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Affiliation(s)
| | - Mijia Yang
- Department of Civil, Construction and Environmental Engineering, North Dakota State University, Fargo, ND 58104, USA;
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Zhou L, Yao J, Xu H, Zhang Y, Nie P. Research on the Effects of Drying Temperature for the Detection of Soil Nitrogen by Near-Infrared Spectroscopy. Molecules 2023; 28:6507. [PMID: 37764283 PMCID: PMC10535356 DOI: 10.3390/molecules28186507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Revised: 08/24/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
Nitrogen nitrates play a significant role in the soil's nutrient cycle, and near-infrared spectroscopy can efficiently and accurately detect the content of nitrate-nitrogen in the soil. Accordingly, it can provide a scientific basis for soil improvement and agricultural productivity by deeply examining the cycle and transformation pattern of nutrients in the soil. To investigate the impact of drying temperature on NIR soil nitrogen detection, soil samples with different N concentrations were dried at temperatures of 50 °C, 65 °C, 80 °C, and 95 °C, respectively. Additionally, soil samples naturally air-dried at room temperature (25 °C) were used as a control group. Different drying times were modified based on the drying temperature to completely eliminate the impact of moisture. Following data collection with an NIR spectrometer, the best preprocessing method was chosen to handle the raw data. Based on the feature bands chosen by the RFFS, CARS, and SPA methods, two linear models, PLSR and SVM, and a nonlinear ANN model were then established for analysis and comparison. It was found that the drying temperature had a great effect on the detection of soil nitrogen by near-infrared spectroscopy. In the meantime, the SPA-ANN model simultaneously yielded the best and most stable accuracy, with Rc2 = 0.998, Rp2 = 0.989, RMSEC = 0.178 g/kg, and RMSEP = 0.257 g/kg. The results showed that NIR spectroscopy had the least effect and the highest accuracy in detecting nitrogen at 80 °C soil drying temperature. This work provides a theoretical foundation for agricultural production in the future.
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Affiliation(s)
- Ling Zhou
- College of Information Engineering, Tarim University, 1188 Junken Avenue, Alar 843300, China
| | - Jiangjun Yao
- Key Laboratory of Tarim Oasis Agriculture, Ministry of Education, Tarim University, 1188 Junken Avenue, Alar 843300, China
| | - Honggang Xu
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Yahui Zhang
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Pengcheng Nie
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
- Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, China
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