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Kaya C, Uğurlar F, Ashraf M, Hou D, Kirkham MB, Bolan N. Microbial consortia-mediated arsenic bioremediation in agricultural soils: Current status, challenges, and solutions. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 917:170297. [PMID: 38272079 DOI: 10.1016/j.scitotenv.2024.170297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 01/01/2024] [Accepted: 01/18/2024] [Indexed: 01/27/2024]
Abstract
Arsenic poisoning in agricultural soil is caused by both natural and man-made processes, and it poses a major risk to crop production and human health. Soil quality, agricultural production, runoff, ingestion, leaching, and absorption by plants are all influenced by these processes. Microbial consortia have become a feasible bioremediation technique in response to the urgent need for appropriate remediation solutions. These diverse microbial populations collaborate to combat arsenic poisoning in soil by facilitating mechanisms including oxidation-reduction, methylation-demethylation, volatilization, immobilization, and arsenic mobilization. The current state, problems, and remedies for employing microbial consortia in arsenic bioremediation in agricultural soils are examined in this review. Among the elements affecting their success include diversity, activity, community organization, and environmental conditions. Also, we emphasize the sensitivity and accuracy limits of existing assessment techniques. While earlier reviews have addressed a variety of arsenic remediation options, this study stands out by concentrating on microbial consortia as a viable strategy for arsenic removal and presents performance evaluation and technical problems. This work gives vital insights for tackling the major issue of arsenic pollution in agricultural soils by explaining the potential methods and components involved in microbial consortium-mediated arsenic bioremediation.
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Affiliation(s)
- Cengiz Kaya
- Soil Science and Plant Nutrition Department, Harran University, Sanliurfa, Turkey.
| | - Ferhat Uğurlar
- Soil Science and Plant Nutrition Department, Harran University, Sanliurfa, Turkey
| | - Muhammed Ashraf
- Institute of Molecular Biology and Biotechnology, The University of Lahore, Pakistan
| | - Deyi Hou
- School of Environment, Tsinghua University, Beijing 100084, People's Republic of China
| | - Mary Beth Kirkham
- Department of Agronomy, Throckmorton Plant Sciences Center, Kansas State University, Manhattan, KS, United States
| | - Nanthi Bolan
- UWA School of Agriculture and Environment, The University of Western Australia, Perth, Western Australia 6009, Australia; The UWA Institute of Agriculture, The University of Western Australia, Perth, Western Australia 6009, Australia
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2
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Janga JK, Reddy KR, Raviteja KVNS. Integrating artificial intelligence, machine learning, and deep learning approaches into remediation of contaminated sites: A review. CHEMOSPHERE 2023; 345:140476. [PMID: 37866497 DOI: 10.1016/j.chemosphere.2023.140476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/15/2023] [Accepted: 10/16/2023] [Indexed: 10/24/2023]
Abstract
The growing number of contaminated sites across the world pose a considerable threat to the environment and human health. Remediating such sites is a cumbersome process with the complexity originating from the need for extensive sampling and testing during site characterization. Selection and design of remediation technology is further complicated by the uncertainties surrounding contaminant attributes, concentration, as well as soil and groundwater properties, which influence the remediation efficiency. Additionally, challenges emerge in identifying contamination sources and monitoring the affected area. Often, these problems are overly simplified, and the data gathered is underutilized rendering the remediation process inefficient. The potential of artificial intelligence (AI), machine-learning (ML), and deep-learning (DL) to address these issues is noteworthy, as their emergence revolutionized the process of data management/analysis. Researchers across the world are increasingly leveraging AI/ML/DL to address remediation challenges. Current study aims to perform a comprehensive literature review on the integration of AI/ML/DL tools into contaminated site remediation. A brief introduction to various emerging and existing AI/ML/DL technologies is presented, followed by a comprehensive literature review. In essence, ML/DL based predictive models can facilitate a thorough understanding of contamination patterns, reducing the need for extensive soil and groundwater sampling. Additionally, AI/ML/DL algorithms can play a pivotal role in identifying optimal remediation strategies by analyzing historical data, simulating scenarios through surrogate models, parameter-optimization using nature inspired algorithms, and enhancing decision-making with AI-based tools. Overall, with supportive measures like open-data policies and data integration, AI/ML/DL possess the potential to revolutionize the practice of contaminated site remediation.
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Affiliation(s)
- Jagadeesh Kumar Janga
- University of Illinois Chicago, Department of Civil, Materials, and Environmental Engineering, 842 West Taylor Street, Chicago, IL 60607, USA.
| | - Krishna R Reddy
- University of Illinois Chicago, Department of Civil, Materials, and Environmental Engineering, 842 West Taylor Street, Chicago, IL 60607, USA.
| | - K V N S Raviteja
- SRM University AP, Department of Civil Engineering, Guntur, Andhra Pradesh 522503, India.
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3
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Meshram SG, Hasan MA, Nouraki A, Alavi M, Albaji M, Meshram C. Machine learning prediction of sediment yield index. Soft comput 2023. [DOI: 10.1007/s00500-023-07985-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/20/2023]
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4
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Miranzadeh MB, Bashardoust P, Atoof F, Rezvani Ghalhari M, Mostafaeii G, Rabbani D, Alimohammadi M, Rahmani H, Ghadami F. Effect of salinity on the potential cadmium phytoremediation from the polluted soil by carpobrotus rossii. Heliyon 2023; 9:e13858. [PMID: 36895380 PMCID: PMC9988476 DOI: 10.1016/j.heliyon.2023.e13858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 02/13/2023] [Accepted: 02/14/2023] [Indexed: 02/22/2023] Open
Abstract
Nowadays, toxic metals accumulation in soil texture due to anthropogenic activities is a major form of pollution, which can lead to worldwide concerns; however, there are many treatment methods to remove them from soil such as phytoremediation. The carpobrotus rossii, has shown great potential to tolerate high salinity and accumulate Cd from contaminated soils. The experiments, in this study, are analyzed and optimized by Central Composite Design (CCD) as method and using Response Surface Methodology (RSM) package in R software. The Cd removal by root and the whole plant followed the quadratic model and the R2 values were 94.95 and 94.81, respectively. The results showed that a decrease in NaCl concentration in Cd-containing solution can increase the phytoremediation process of Cd by carpobrotus rossii, significantly. The optimum conditions for 58% Cd removal by the whole plant, predicted through a CCD response surface methodology model were as follows: initial Cd concentration of 49 mgKg-1,NaCl concentration of 16 dSm-1, time of 17 days, and pH of 6.5. C. rossii's potential in removing 58% of Cd under the obtained optimum condition from the modelling was evaluated in real condition in the laboratory. The results revealed that around 56% of the initial added Cd concentration was removed by carpobrotus rossii. As a take home message, carpobrotus rossii can be recommended as an efficient plant to remove heavy metals especially cadmium from soil and sediments in arid area which have a salty soil.
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Affiliation(s)
- Mohammad Bagher Miranzadeh
- Department of Environmental Health Engineering, Faculty of Health, Kashan University of Medical Sciences, Kashan, Iran
| | - Parnia Bashardoust
- Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.,Student's Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Fatemeh Atoof
- Department of Biostatistics & Epidemiology, Faculty of Health, Kashan University of Medical Sciences, Kashan, Iran
| | - Mohammad Rezvani Ghalhari
- Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.,Student's Scientific Research Center, Tehran University of Medical Sciences, Tehran, Iran
| | - Gholamreza Mostafaeii
- Department of Environmental Health Engineering, Faculty of Health, Kashan University of Medical Sciences, Kashan, Iran
| | - Davarkhah Rabbani
- Department of Environmental Health Engineering, Faculty of Health, Kashan University of Medical Sciences, Kashan, Iran
| | - Mahmood Alimohammadi
- Department of Environmental Health Engineering, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.,Center for Water Quality Research, Institute for Environmental Research, Tehran University of Medical Sciences, Tehran, Iran.,Health Equity Research Center (HERC), Tehran University of Medical Sciences, Tehran, Iran
| | - Hasan Rahmani
- Department of Environmental Health Engineering, Faculty of Health, Kashan University of Medical Sciences, Kashan, Iran
| | - Faezeh Ghadami
- Department of Environmental Health Engineering, Faculty of Health, Kashan University of Medical Sciences, Kashan, Iran
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5
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Antony S, Antony S, Rebello S, George S, Biju DT, R R, Madhavan A, Binod P, Pandey A, Sindhu R, Awasthi MK. Bioremediation of Endocrine Disrupting Chemicals- Advancements and Challenges. ENVIRONMENTAL RESEARCH 2022; 213:113509. [PMID: 35660566 DOI: 10.1016/j.envres.2022.113509] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Revised: 05/08/2022] [Accepted: 05/18/2022] [Indexed: 06/15/2023]
Abstract
Endocrine Disrupting Chemicals (EDCs), major group of recalcitrant compounds, poses a serious threat to the health and future of millions of human beings, and other flora and fauna for years to come. A close analysis of various xenobiotics undermines the fact that EDC is structurally diverse chemical compounds generated as a part of anthropogenic advancements as well as part of their degradation. Regardless of such structural diversity, EDC is common in their ultimate drastic effect of impeding the proper functioning of the endocrinal system, basic physiologic systems, resulting in deregulated growth, malformations, and cancerous outcomes in animals as well as humans. The current review outlines an overview of various EDCs, their toxic effects on the ecosystem and its inhabitants. Conventional remediation methods such as physico-chemical methods and enzymatic approaches have been put into action as some form of mitigation measures. However, the last decade has seen the hunt for newer technologies and methodologies at an accelerated pace. Genetically engineered microbial degradation, gene editing strategies, metabolic and protein engineering, and in-silico predictive approaches - modern day's additions to our armamentarium in combating the EDCs are addressed. These additions have greater acceptance socially with lesser dissonance owing to reduced toxic by-products, lower health trepidations, better degradation, and ultimately the prevention of bioaccumulation. The positive impact of such new approaches on controlling the menace of EDCs has been outlaid. This review will shed light on sources of EDCs, their impact, significance, and the different remediation and bioremediation approaches, with a special emphasis on the recent trends and perspectives in using sustainable approaches for bioremediation of EDCs. Strict regulations to prevent the release of estrogenic chemicals to the ecosystem, adoption of combinatorial methods to remove EDC and prevalent use of bioremediation techniques should be followed in all future endeavors to combat EDC pollution. Moreover, the proper development, growth and functioning of future living forms relies on their non-exposure to EDCs, thus remediation of such chemicals present even in nano-concentrations should be addressed gravely.
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Affiliation(s)
- Sherly Antony
- Department of Microbiology, Pushpagiri Institute of Medical Sciences and Research Centre, Thiruvalla, 689 101, Kerala, India
| | - Sham Antony
- Pushpagiri Research Centre, Pushpagiri Institute of Medical Sciences and Research Centre, Thriuvalla, 689 101, Kerala, India
| | - Sharrel Rebello
- School of Food Science & Technology, Mahatma Gandhi University, Kottayam, India
| | - Sandhra George
- Pushpagiri Research Centre, Pushpagiri Institute of Medical Sciences and Research Centre, Thriuvalla, 689 101, Kerala, India
| | - Devika T Biju
- Pushpagiri Research Centre, Pushpagiri Institute of Medical Sciences and Research Centre, Thriuvalla, 689 101, Kerala, India
| | - Reshmy R
- Department of Science and Humanities, Providence College of Engineering, Chengannur, 689 122, Kerala, India
| | - Aravind Madhavan
- Rajiv Gandhi Centre for Biotechnology, Jagathy, Trivandrum, 695 014, India
| | - Parameswaran Binod
- Microbial Processes and Technology Division, CSIR-National Institute for Interdisciplinary Science and Technology (CSIR-NIIST), Thiruvananthapuram, 695 019, Kerala, India
| | - Ashok Pandey
- Center for Innovation and Translational Research, CSIR-Indian Institute of Toxicology Research, Lucknow, 226 001, India; Centre for Energy and Environmental Sustainability, Lucknow, 226 029, Uttar Pradesh, India
| | - Raveendran Sindhu
- Department of Food Technology, T K M Institute of Technology, Kollam, 691 505, Kerala, India.
| | - Mukesh Kumar Awasthi
- College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province, 712100, China.
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6
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Evaluating the Performance of a Solar Distillation Technology in the Desalination of Brackish Waters. Processes (Basel) 2022. [DOI: 10.3390/pr10081626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
Desalination is set to become a major source of drinking water in several Middle Eastern countries over the coming decades. Solar distillation is a simple power-independent method of water desalination, which can be carried out in active or passive modes. This study is among the first attempts to investigate the possibility of desalinating brackish groundwater resources under the threat of saltwater intrusion in the southern areas of Razavi Khorasan province in Iran. For this purpose, a pilot solar distillation unit was constructed to analyze the effects of the unit orientation, depth of the water pool, atmospheric conditions, input salinity, and flow continuity on the solar distillation performance. The results showed that the unit exhibited the highest efficiency when it had a 3 cm deep water pool. It was oriented facing southward while operating a continuous flow for at least 3 days under sunny weather conditions. It was found that among the studied parameters, the unit orientation and pool depth had the greatest impact on the water production performance for this type of water desalination system. Conversely, the water production efficiency was not very sensitive to the input salinity level. Overall, the solar distillation technology was able to reduce the salinity by 99.7% and the hardness by 94.7%.
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7
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Akbarian H, Jalali FM, Gheibi M, Hajiaghaei-Keshteli M, Akrami M, Sarmah AK. A sustainable Decision Support System for soil bioremediation of toluene incorporating UN sustainable development goals. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 307:119587. [PMID: 35680063 DOI: 10.1016/j.envpol.2022.119587] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/15/2022] [Accepted: 06/03/2022] [Indexed: 06/15/2023]
Abstract
Decision Support System (DSS) is a novel approach for smart, sustainable controlling of environmental phenomena and purification processes. Toluene is one of the most widely used petroleum products, which adversely impacts on human health. In this study, Fusarium Solani fungi are utilized as the engine of the toluene bioremediation procedure for the monitoring part of DSS. Experiments are optimized by Central Composite Design (CCD) - Response Surface Methodology (RSM), and the behavior of the mentioned fungi is estimated by M5 Pruned model tree (M5P), Gaussian Processes (GP), and Sequential Minimal Optimization (SMOreg) algorithms as the prediction section of DSS. Finally, the control stage of DSS is provided by integrated Petri Net modeling and Failure Modes and Effects Analysis (FMEA). The findings showed that Aeration Intensity (AI) and Fungi load/Biological Waste (F/BW) are the most influential mechanical and biological factors, with P-value of 0.0001 and 0.0003, respectively. Likewise, the optimal values of main mechanical parameters include AI, and the space between pipes (S) are equal to 13.76 m3/h and 15.99 cm, respectively. Also, the optimum conditions of biological features containing F/BW and pH are 0.001 mg/g and 7.56. In accordance with the kinetic study, bioremediation of toluene by Fusarium Solani is done based on a first-order reaction with a 0.034 s-1 kinetic coefficient. Finally, the machine learning practices showed that the GP (R2 = 0.98) and M5P (R2 = 0.94) have the most precision for predicting Removal Percentage (RP) for mechanical and biological factors, respectively. At the end of the present research, it is found that by controlling seven possible risk factors in bioremediation operation through the FMEA- Petri Net technique, efficiency of the process can be adjusted to optimum value.
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Affiliation(s)
- Hadi Akbarian
- Department of Civil Engineering, Ferdowsi University of Mashhad, Iran
| | - Farhad Mahmoudi Jalali
- Department of Civil Engineering, Faculty of Engineering, Islamic Azad University, Tabriz Branch, Iran
| | - Mohammad Gheibi
- Departamento de Ingeniería Industrial, Tecnologico de Monterrey, Puebla, Mexico
| | | | - Mehran Akrami
- Department of Civil Engineering, Ferdowsi University of Mashhad, Iran; Departamento de Ingeniería Industrial, Tecnologico de Monterrey, Puebla, Mexico
| | - Ajit K Sarmah
- Department of Civil & Environmental Engineering, The Faculty of Engineering, The University of Auckland, Auckland, 1142, New Zealand.
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8
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A Sustainable Decision Support System for Drinking Water Systems: Resiliency Improvement against Cyanide Contamination. INFRASTRUCTURES 2022. [DOI: 10.3390/infrastructures7070088] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Maintaining drinking water quality is considered important in building sustainable cities and societies. On the other hand, water insecurity is an obstacle to achieving sustainable development goals based on the issues of threatening human health and well-being and global peace. One of the dangers threatening water sources is cyanide contamination due to industrial wastewater leakage or sabotage. The present study investigates and provides potential strategies to remove cyanide contamination by chlorination. In this regard, the main novelty is to propose a sustainable decision support system for the dirking water system in a case study in Iran. First, three scenarios have been defined with low ([CN−] = 2.5 mg L−1), medium ([CN−] = 5 mg L−1), and high ([CN−] = 7.5 mg L−1) levels of contamination. Then, the optimal chlorine dosage has been suggested as 2.9 mg L−1, 4.7 mg L−1, and 6.1 mg L−1, respectively, for these three scenarios. In the next step, the residual cyanide was modelled with mathematical approaches, which revealed that the Gaussian distribution has the best performance accordingly. The main methodology was developing a hybrid approach based on the Gaussian model and the genetic algorithm. The outcomes of statistical evaluations illustrated that both injected chlorine and initial cyanide load have the greatest effects on residual cyanide ions. Finally, the proposed hybrid algorithm is characterized by the multilayer perceptron algorithm, which can forecast residual cyanide anion with a regression coefficient greater than 0.99 as a soft sensor. The output can demonstrate a strong positive relationship between residual cyanide- (RCN−) and injected chlorine. The main finding is that the proposed sustainable decision support system with our hybrid algorithm improves the resiliency levels of the considered drinking water system against cyanide treatments.
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9
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Study on Characteristics and Control Strategy of Diesel Particulate Filters Based on Engine Bench. Processes (Basel) 2022. [DOI: 10.3390/pr10071246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
The ignition temperature of a diesel oxidation catalyst (DOC) and the internal temperature-field distribution of the diesel particulate filter (DPF) during active regeneration are investigated during an engine bench test in this study. Based on the dropped to idle (DTI) test, a test method is developed to determine the safe regeneration temperature of the DPF. The results show that when the inlet temperature of the DOC is more than 240 °C, the DOC begins ignition and reaches the target temperature of 600 °C set for active regeneration of DPF; when the inlet exhaust temperature of the DOC is between 240 and 280 °C, a higher injection rate is required to reduce the secondary pollution of HC and thus make the DPF reach the set target temperature as soon as possible. The active regeneration process of the DPF is divided into three stages. During ignition, the temperature of the DPF inlet and outlet increases rapidly and successively. The internal and outlet temperatures of DPF during regeneration are approximately 50 °C higher than the inlet temperature. At the end of regeneration, the DPF inlet to outlet temperature drops rapidly. A feed-forward design and feedback algorithm are used to verify the change in the target regeneration temperature. The overshoot of the DPF control strategy was less than 3%, and the steady-state temperature control error was less than 20 °C. The results of this study provide a basis for the safe control of DPFs’ active regeneration temperatures.
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Hong Z, Feng Y, Li Z, Li Z, Hu B, Zhang Z, Tan J. Performance balance oriented product structure optimization involving heterogeneous uncertainties in intelligent manufacturing with an industrial network. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.03.063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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11
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A Smart Post-Processing System for Forecasting the Climate Precipitation Based on Machine Learning Computations. SUSTAINABILITY 2022. [DOI: 10.3390/su14116624] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
Although many meteorological prediction models have been developed recently, their accuracy is still unreliable. Post-processing is a task for improving meteorological predictions. This study proposes a post-processing method for the Climate Forecast System Version 2 (CFSV2) model. The applicability of the proposed method is shown in Iran for observation data from 1982 to 2017. This study designs software to perform post-processing in meteorological organizations automatically. From another point of view, this study presents a decision support system (DSS) for controlling precipitation-based natural side effects such as flood disasters or drought phenomena. It goes without saying that the proposed DSS model can meet sustainable development goals (SDGs) with regards to a grantee of human health and environmental protection issues. The present study, for the first time, implemented a platform based on a graphical user interface due to the prediction of precipitation with the application of machine learning computations. The present research developed an academic idea into an industrial tool. The final finding of this paper is to introduce a set of efficient machine learning computations where the random forest (RF) algorithm has a great level of accuracy with more than a 0.87 correlation coefficient compared with other machine learning methods.
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12
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Jiang R, Zhang J, Tang Y, Feng J, Wang C. Self-adaptive DE algorithm without niching parameters for multi-modal optimization problems. APPL INTELL 2022. [DOI: 10.1007/s10489-021-03003-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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13
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Mirmozaffari M, Yazdani R, Shadkam E, Khalili SM, Tavassoli LS, Boskabadi A. A Novel Hybrid Parametric and Non-Parametric Optimisation Model for Average Technical Efficiency Assessment in Public Hospitals during and Post-COVID-19 Pandemic. BIOENGINEERING (BASEL, SWITZERLAND) 2021; 9:bioengineering9010007. [PMID: 35049716 PMCID: PMC8772782 DOI: 10.3390/bioengineering9010007] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 12/13/2021] [Accepted: 12/21/2021] [Indexed: 11/16/2022]
Abstract
The COVID-19 pandemic has had a significant impact on hospitals and healthcare systems around the world. The cost of business disruption combined with lingering COVID-19 costs has placed many public hospitals on a course to insolvency. To quickly return to financial stability, hospitals should implement efficiency measure. An average technical efficiency (ATE) model made up of data envelopment analysis (DEA) and stochastic frontier analysis (SFA) for assessing efficiency in public hospitals during and after the COVID-19 pandemic is offered. The DEA method is a non-parametric method that requires no information other than the input and output quantities. SFA is a parametric method that considers stochastic noise in data and allows statistical testing of hypotheses about production structure and degree of inefficiency. The rationale for using these two competing approaches is to balance each method's strengths, weaknesses and introduce a novel integrated approach. To show the applicability and efficacy of the proposed hybrid VRS-CRS-SFA (VCS) model, a case study is presented.
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Affiliation(s)
- Mirpouya Mirmozaffari
- Department of Industrial Engineering, Dalhousie University, 5269 Morris Street, Halifax, NS B3H 4R2, Canada
- Correspondence:
| | - Reza Yazdani
- Department of Accounting, Technical and Vocational University (TVU), Tehran 1345120727, Iran;
| | - Elham Shadkam
- Department of Industrial Engineering, Faculty of Engineering, Khayyam University, Mashhad 9189747178, Iran; (E.S.); (S.M.K.)
| | - Seyed Mohammad Khalili
- Department of Industrial Engineering, Faculty of Engineering, Khayyam University, Mashhad 9189747178, Iran; (E.S.); (S.M.K.)
| | - Leyla Sadat Tavassoli
- Department of Industrial Manufacturing and Systems Engineering, University of Texas at Arlington, Arlington, TX 76019, USA;
| | - Azam Boskabadi
- Department of Finance and Management Science, Carson College of Business, Washington State University, Pullman, WA 99163, USA;
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14
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Mirmozaffari M, Shadkam E, Khalili SM, Yazdani M. Developing a Novel Integrated Generalised Data Envelopment Analysis (DEA) to Evaluate Hospitals Providing Stroke Care Services. Bioengineering (Basel) 2021; 8:207. [PMID: 34940361 PMCID: PMC8698969 DOI: 10.3390/bioengineering8120207] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 12/07/2021] [Accepted: 12/08/2021] [Indexed: 01/29/2023] Open
Abstract
Stroke is the biggest cause of adult disability and the third biggest cause of death in the US. Stroke is a medical emergency, and the treatment given in the early hours is important in shaping the patient's long-term recovery and prognosis. Despite the fact that substantial attention has been dedicated to this complex and difficult issue in healthcare, novel strategies such as operation research-based approaches have hardly been used to deal with the difficult challenges associated with stroke. This study proposes a novel approach with data envelopment analysis (DEA) and multi-objective linear programming (MOLP) in hospitals that provide stroke care services to select the most efficient approach, which will be a new experiment in literature perception. DEA and MOLP are widely used for performance evaluation and efficiency measurement. Despite their similarities and common concepts, the two disciplines have evolved separately. The generalised DEA (GDEA) cannot incorporate the preferences of decision-makers (DMs) preferences and historical efficiency data. In contrast, MOLP can incorporate the DM's preferences into the decision-making process. We transform the GDEA model into MOLP through the max-ordering approach to (i) solve the problem interactively; (ii) use the step method (STEM) and consider DM's preferences; (iii) eliminate the need for predetermined preference information; and (iv) apply the most preferred solution (MPS) to identify the most efficient approach. A case study of hospitals that provide stroke care services is taken as an example to illustrate the potential application of the proposed approach method.
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Affiliation(s)
- Mirpouya Mirmozaffari
- Department of Industrial Engineering, Dalhousie University, 5269 Morris Street, Halifax, NS B3H 4R2, Canada
| | - Elham Shadkam
- Department of Industrial Engineering, Faculty of Engineering, Khayyam University, Mashhad 9189747178, Iran; (E.S.); (S.M.K.)
| | - Seyed Mohammad Khalili
- Department of Industrial Engineering, Faculty of Engineering, Khayyam University, Mashhad 9189747178, Iran; (E.S.); (S.M.K.)
| | - Maziar Yazdani
- School of Built Environment, University of New South Wales, Sydney 2052, Australia;
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15
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Rafigh P, Akbari AA, Bidhendi HM, Kashan AH. A fuzzy rule-based multi-criterion approach for a cooperative green supplier selection problem. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2021:10.1007/s11356-021-17015-2. [PMID: 34687418 PMCID: PMC8536921 DOI: 10.1007/s11356-021-17015-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 10/08/2021] [Indexed: 06/13/2023]
Abstract
Multi-criterion decision-making models are widely used in supplier selection problems. This study contributes to a green supplier selection problem considering the green manufacturing, green transportation, and green procurement. This study contributes to reverse logistics, eco-design, reusing, recycling, and remanufacturing with their high impact on the industries. In addition to the logistics costs and transportation costs, the carbon emissions are considered. With regard to the game theory, this paper uses a cooperative green supplier selection model. If transportation requirements of two or more companies are combined, it will help manufacturers to have less [Formula: see text] emissions with lower cost. After creating the optimization model to consider the uncertainty, this cooperative game theory model is established in a fuzzy environment. In this regard, a fuzzy rule-based (FRB) system is deployed and the set of fuzzy IF-THEN rules is considered. The proposed FRB model is contributed for the first time in the area of green supplier selection problem. Finally, some sensitivity analyses are conducted in a numerical example to evaluate the proposed model. With regard to the findings, although the cost of CO2 emission of horizontal cooperation is increased, the cost saving of companies is increased. It means our total cost is optimal in a logistic network using the cooperative game theory. The results also indicate that horizontal cooperation in logistic network causes less cost and benefits for each company.
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Affiliation(s)
- Parisa Rafigh
- Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Ali Akbar Akbari
- Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
| | - Hadi Mohammadi Bidhendi
- Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran
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