An innovative coupled model in view of wavelet transform for predicting short-term PM10 concentration

A Hybrid Wavelet-Based Deep Learning Model for Accurate Prediction of Daily Surface PM2.5 Concentrations in Guangzhou City

Surface air pollution affects ecosystems and people's health. However, traditional models have low prediction accuracy. Therefore, a hybrid model for accurately predicting daily surface PM2.5 concentrations was integrated with wavelet (W), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and bidirectional gated recurrent unit (BiGRU). The data for meteorological factors and air pollutants in Guangzhou City from 2014 to 2020 were utilized as inputs to the models. The W-CNN-BiGRU-BiLSTM hybrid model demonstrated strong performance during the predicting phase, achieving an R (correlation coefficient) of 0.9952, a root mean square error (RMSE) of 1.4935 μg/m3, a mean absolute error (MAE) of 1.2091 μg/m3, and a mean absolute percentage error (MAPE) of 7.3782%. Correspondingly, the accurate prediction of surface PM2.5 concentrations is beneficial for air pollution control and urban planning.

Machine learning-based evolution of water quality prediction model: An integrated robust framework for comparative application on periodic return and jitter data

Accurate water quality prediction is paramount for the sustainable management of surface water resources. Current deep learning models face challenges in reliably forecasting water quality due to the non-stationarity of environmental conditions and the intricate interactions among various environmental factors. This study introduces a novel, multi-level coupled machine learning framework that integrates data denoising, feature selection, and Long Short-Term Memory (LSTM) networks to enhance predictive accuracy. The findings demonstrate that the LSTM model incorporates data denoising pre-processing, capturing non-stationary water quality patterns more effectively than the baseline model, enhancing prediction performance (R2 increased by 1.01%). The most adept model with wavelet transform exhibited superior adaptability and predictability, achieving a modest but statistically significant increase in R2 values of 0.81% and 0.51% relative to incorporate moving average and complete ensemble empirical mode decomposition with adaptive noise techniques, respectively. The integrated models varied in their suitability for time series characterized by different patterns of variability (stability vs. instability, periodicity vs. non-periodicity). We conducted multi-step ahead predictions (t+1 and t+3 days) and employed two training configurations (80-20% and 70-30% splits) for dissolved oxygen and the permanganate index across four monitoring stations within the world's largest long-distance inter-basin water diversion project, to assess the reliability and robustness of the proposed water quality prediction models under varying conditions. The integration of data denoising techniques with LSTM networks substantially improves the prediction of dynamic water quality indices in complex environmental settings. Future research should explore the scalability of this framework across different geographical and climatic conditions to further validate its effectiveness and utility in global water resource management.

A hybrid deep learning model-based LSTM and modified genetic algorithm for air quality applications

Over time, computing power and storage resource advancements have enabled the widespread accumulation and utilization of data across various domains. In the field of air quality, analyzing data and developing air quality models have become pivotal in safeguarding public health. Despite significant progress in modeling, the critical need for accurate pollutant predictions persists. In addressing this challenge, deep learning models have garnered substantial attention in research due to their outstanding performance across diverse applications. However, the optimization of hyperparameters and features remains a challenging task. This study seeks to leverage historical data to construct the long short-term memory-based model for forecasting multistep PM10. To refine its architecture, a modified genetic algorithm is employed for automatic design. Furthermore, we explore principal component analysis and exhaustive feature selection to identify the optimal feature set. This paper introduces a novel hybrid deep learning model named EFS-GA-LSTM, tailored for multistep hourly PM10 forecasting. To assess its performance, we compare it with other hyperparameter optimization algorithms, including particle swarm optimization, variable neighborhood search, and Bayesian optimization with Gaussian process. The input dataset comprises hourly PM10 concentrations, meteorological variables, and time variables. The results reveal that for 3-h-ahead forecasting tasks, the EFS-GA-LSTM network demonstrates improvements in root mean square error, mean absolute percentage error, correlation coefficient, and coefficient of determination.

Domain knowledge-enhanced multi-spatial multi-temporal PM2.5 forecasting with integrated monitoring and reanalysis data

Accurate air quality forecasting is crucial for public health, environmental monitoring and protection, and urban planning. However, existing methods fail to effectively utilize multi-scale information, both spatially and temporally. There is a lack of integration between individual monitoring stations and city-wide scales. Temporally, the periodic nature of air quality variations is often overlooked or inadequately considered. To overcome these limitations, we conduct a thorough analysis of the data and tasks, integrating spatio-temporal multi-scale domain knowledge. We present a novel Multi-spatial Multi-temporal air quality forecasting method based on Graph Convolutional Networks and Gated Recurrent Units (M2G2), bridging the gap in air quality forecasting across spatial and temporal scales. The proposed framework consists of two modules: Multi-scale Spatial GCN (MS-GCN) for spatial information fusion and Multi-scale Temporal GRU (MT-GRU) for temporal information integration. In the spatial dimension, the MS-GCN module employs a bidirectional learnable structure and a residual structure, enabling comprehensive information exchange between individual monitoring stations and the city-scale graph. Regarding the temporal dimension, the MT-GRU module adaptively combines information from different temporal scales through parallel hidden states. Leveraging meteorological indicators and four air quality indicators, we present comprehensive comparative analyses and ablation experiments, showcasing the higher accuracy of M2G2 in comparison to nine currently available advanced approaches across all aspects. The improvements of M2G2 over the second-best method on RMSE of 72-h future predictions are as follows: PM2.5: 6%∼10%; PM10: 5%∼7%; NO2: 5%∼16%; O3: 6%∼9%. Furthermore, we demonstrate the effectiveness of each module of M2G2 by ablation study. We conduct a sensitivity analysis of air quality and meteorological data, finding that the introduction of O3 adversely impacts the prediction accuracy of PM2.5.

Time-sensitive prediction of NO2 concentration in China using an ensemble machine learning model from multi-source data

Nitrogen dioxide (NO2) poses a critical potential risk to environmental quality and public health. A reliable machine learning (ML) forecasting framework will be useful to provide valuable information to support government decision-making. Based on the data from 1609 air quality monitors across China from 2014-2020, this study designed an ensemble ML model by integrating multiple types of spatial-temporal variables and three sub-models for time-sensitive prediction over a wide range. The ensemble ML model incorporates a residual connection to the gated recurrent unit (GRU) network and adopts the advantage of Transformer, extreme gradient boosting (XGBoost) and GRU with residual connection network, resulting in a 4.1%±1.0% lower root mean square error over XGBoost for the test results. The ensemble model shows great prediction performance, with coefficient of determination of 0.91, 0.86, and 0.77 for 1-hr, 3-hr, and 24-hr averages for the test results, respectively. In particular, this model has achieved excellent performance with low spatial uncertainty in Central, East, and North China, the major site-dense zones. Through the interpretability analysis based on the Shapley value for different temporal resolutions, we found that the contribution of atmospheric chemical processes is more important for hourly predictions compared with the daily scale predictions, while the impact of meteorological conditions would be ever-prominent for the latter. Compared with existing models for different spatiotemporal scales, the present model can be implemented at any air quality monitoring station across China to facilitate achieving rapid and dependable forecast of NO2, which will help developing effective control policies.

A multi-step water quality prediction model based on the Savitzky-Golay filter and Transformer optimized network

Effective water quality prediction techniques are essential for the sustainable development of water resources and implementation of emergency response mechanisms. However, the water environment conditions are complex, and the presence of a large amount of noise in the water quality data makes it difficult to reveal the long-term trends or cycles of the data, affecting the acquisition of serial correlation in the data. In addition, the loss function based on the vertical Euclidean distance will produce a prediction lag problem, and it is difficult to make an accurate multi-step prediction of water quality series. This paper presents a multi-step water quality prediction model for watersheds that combines Savitzky-Golay (SG) filter with Transformer optimized networks. Among them, the SG filter highlights data trend change and improves sequence correlation by smoothing the potential noise of original data. The transformer network adopts a sequence-to-sequence framework, which contains a position encoding module and a self-attentive mechanism to perform multi-step prediction while effectively obtaining the sequence correlation. Moreover, the DIstortion Loss including shApe and TimE (DILATE) loss function is introduced into the model to solve the problem of prediction lag from two aspects of shape error and time error to improve the model's generalization ability. An example validates the model with the benchmark model at four monitoring stations in the Lanzhou section of the Yellow River basin in China. The results show that the predictions of the proposed model have the correct shape, temporal positioning, and the best accuracy in a multi-step prediction task for four sites. It can provide a decision-making basis for comprehensive water quality control and pollutant control in the basin.

Spatiotemporal characteristics of air pollution in Chengdu-Chongqing urban agglomeration (CCUA) in Southwest, China: 2015-2021

Studying the spatiotemporal characteristics of air pollutants in urban agglomerations and their response factors will help to improve the quality of urban living. In combining air quality monitoring data and wavelet analysis from the Chengdu-Chongqing urban agglomeration (CCUA), this study assessed the spatiotemporal distribution characteristics and influential factors of air pollutants on daily, monthly and annual scales. The results showed that the concentration of air pollutants in the CCUA has decreased year by year, and air quality has improved. Except for O₃, pollutants in autumn and winter were higher than those in summer. The spatial distribution of air pollutants was obvious distributed in Chengdu, Chongqing, Zigong and Dazhou. Pollution incidents were mainly concentrated in winter. The 6 air pollutants and air quality index (AQI) have dominant periods on multiple time scales. AQI showed positive coherence with PM_2.5 and PM₁₀ on multiple time scales, and obvious positive coherence with SO₂, CO, NO₂ and O₃ in the short term scale. AQI was not strongly correlated with the fire point, but exhibited obvious negative coherence in the long term scale. In addition, AQI showed an obvious positive correlation with temperature and sunshine hours in short term, and a clear negative correlation with humidity and rainfall. The research results of this paper will provide a reference for pollution prevention and control in the CCUA.

Plant-mediated synthesis of silver-doped zinc oxide nanoparticles and evaluation of their antimicrobial activity against bacteria cause tooth decay

In this research, silver-doped zinc oxide (SdZnO) nanoparticles (NPs) were synthesized in an environmental-friendly manner. The synthesized NPs were identified by UV-vis spectroscopy, X-ray diffraction (XRD), and scanning electron microscopy (SEM). Finally, the antimicrobial activity of synthesized ZnO and SdZnO NPs was performed. It was observed that by doping silver, the size of ZnO NPs was changed. By adding silver to ZnO NPs, the antimicrobial effect of ZnO NPs was improved. Antibacterial test against gram-positive bacterium Streptococcus mutants showed that SdZnO NPs with a low density of silver had higher antibacterial activity than ZnO NPs; Therefore, SdZnO NPs can be used as a new antibacterial agent in medical applications.

Study on Accuracy of CFD Simulations of Wind Environment around High-Rise Buildings: A Comparative Study of k-ε Turbulence Models Based on Polyhedral Meshes and Wind Tunnel Experiments

It is important to create a comfortable wind environment around high-rise buildings for outdoor activities. To predict the wind environment, Computational Fluid Dynamics (CFD) has been widely used by designers and engineers. However, the simulation results of different CFD turbulence models might significantly vary. This paper researched the wind environment around a typical high-rise building and verified the accuracy of the CFD simulations based on polyhedral meshes. The differences between the simulation results of the k-ε turbulence models and those of the wind tunnel experiments were compared from the perspectives of wind speed and turbulence energy. The results show that the modified k-ε models could still not perfectly match the wind tunnel experiment results. Specifically, in the low-wind-speed areas, the simulation results of the Realizable Two-Layer K-Epsilon (RTLKE) model were the closest to the experimental results of the wind tunnels, while in the high-wind-speed areas the simulation results of the Standard Two-Layer K-Epsilon (STLKE) model were the closest to the experimental results of the wind tunnels. Therefore, it is recommended that these two k-ε turbulence models are applied under different conditions—the RTLKE model should be used to simulate low-wind areas around high-rise buildings (e.g., defining the size of the static-wind area around high-rise buildings, predicting the diffusion time of pollutants around high-rise buildings, etc.); STLKE should be used to simulate high-wind-speed areas around high-rise buildings (e.g., the high speed wind area around high-rise buildings during a typhoon, the maximum wind speed area around high-rise buildings, etc.). It is expected that findings from this research study supplement some existing high-rise building design guidance.

Nickel oxide nanoparticles synthesis using plant extract and evaluation of their antibacterial effects on Streptococcus mutans

Dental decay is known in the world as the most common human infectious disease. Ascending process of dental caries index in the world shows the failure of oral disease prevention. Streptococcus mutans bacteria cause acid damage and tooth decay by producing acid over time. Nanomaterials with suitable functionality, high permeability, extremely large surface area, significant reactivity, unique mechanical features, and non-bacterial resistance can be considered as promising agents for antimicrobial and antiviral applications. In this study, nickel oxide (NiO) nanoparticles with size range from 2 to 16 nm containing Stevia natural sweetener were eco-friendly synthesized via a simple method. Additionally, their various concentrations were evaluated on S. mutans bacteria by applying the broth dilution method. The results demonstrated that these spherical NiO nanoparticles had efficient bacteriostatic activity on this gram-positive coccus.

Development of a reliable empirical correlation to calculate hydrogen solubility in seventeen alcoholic media

This study uses the differential evolution optimization algorithm to adjust the coefficient of Arrhenius-shape correlation for calculating hydrogen (H₂) solubility in alcohol-based media. The pre-exponential and exponential parts of this correlation are the functions of pressure and absolute temperature, respectively. Since this model has been validated using seventeen alcohol/hydrogen binary mixtures, it is the most generalized correlation in this regard. The proposed Arrhenius-shape correlation predicts 285 laboratory solubility measurements with the absolute average relative deviation (AARD%) of 3.28% and regression coefficient (R²) of 0.99589. The accuracy of the developed model has also been compared with two empirical correlations and three equations of state suggested in the literature. The Arrhenius-shape model has 15% and 50% smaller AARD than the most accurate empirical correlation and equation of state, respectively. Simulation findings demonstrate that all alcohol/hydrogen mixtures thermodynamically behave based on Henry’s law. Hydrogen solubility in alcohols increases by increasing either pressure or temperature. 1-octanol has the maximum ability to absorb the H₂ molecules.

Biosynthesis of Zn-doped CuFe2O4 nanoparticles and their cytotoxic activity

Zn-doped CuFe2O4 nanoparticles (NPs) were eco-friendly synthesized using plant extract. These nanoparticles were characterized by X-ray diffraction, Fourier-transform infrared spectroscopy, scanning electron microscope (SEM), energy-dispersive X-ray spectroscopy and thermal gravimetric analysis (TGA). SEM image showed spherical NPs with size range less than 30 nm. In the EDS diagram, the elements of zinc, copper, iron, and oxygen are shown. The cytotoxicity and anticancer properties of Zn-doped CuFe2O4 NPs were evaluated on macrophage normal cells and A549 lung cancer cells. The cytotoxic effects of Zn-doped CuFe2O4 and CuFe2O4 NPs on A549 cancer cell lines were analyzed. The Zn-doped CuFe2O4 and CuFe2O4 NPs demonstrated IC50 values 95.8 and 278.4 µg/mL on A549 cancer cell, respectively. Additionally, Zn-doped CuFe2O4 and CuFe2O4 NPs had IC80 values of 8.31 and 16.1 µg/mL on A549 cancer cell, respectively. Notably, doping Zn on CuFe2O4 NPs displayed better cytotoxic effects on A549 cancer cells compared with the CuFe2O4 NPs alone. Also spinel nanocrystals of Zn-doped CuFe2O4 (~ 13 nm) had a minimum toxicity (CC50 = 136.6 µg/mL) on macrophages J774 Cell Line.

Developing an accurate empirical correlation for predicting anti-cancer drugs’ dissolution in supercritical carbon dioxide

This study introduces a universal correlation based on the modified version of the Arrhenius equation to estimate the solubility of anti-cancer drugs in supercritical carbon dioxide (CO₂). A combination of an Arrhenius-shape term and a departure function was proposed to estimate the solubility of anti-cancer drugs in supercritical CO₂. This modified Arrhenius correlation predicts the solubility of anti-cancer drugs in supercritical CO₂ from pressure, temperature, and carbon dioxide density. The pre-exponential of the Arrhenius linearly relates to the temperature and carbon dioxide density, and its exponential term is an inverse function of pressure. Moreover, the departure function linearly correlates with the natural logarithm of the ratio of carbon dioxide density to the temperature. The reliability of the proposed correlation is validated using all literature data for solubility of anti-cancer drugs in supercritical CO₂. Furthermore, the predictive performance of the modified Arrhenius correlation is compared with ten available empirical correlations in the literature. Our developed correlation presents the absolute average relative deviation (AARD) of 9.54% for predicting 316 experimental measurements. On the other hand, the most accurate correlation in the literature presents the AARD = 14.90% over the same database. Indeed, 56.2% accuracy improvement in the solubility prediction of the anti-cancer drugs in supercritical CO₂ is the primary outcome of the current study.

Assessment and Prioritize Risk Factors of Financial Measurement of Management Control System for Production Companies Using a Hybrid Z-SWARA and Z-WASPAS with FMEA Method: A Meta-Analysis

The management control system in an industry is managerial, directional, hindrance, and cohesive action in order to cohere and regulate various branches and sub-branches. In fact, it is a system that supports the real state of matters in the right way. This method is intended at assuring that the purposes and activities carried out have the desired outcomes and eventually lead to the objects and purposes of the company. In this matter, the financial and non-financial management control system is essential both when it comes to strategy community; Consequently, in this paper, the management control system is classified into financial and non-financial categories because such analysis gives a chance to get a broad assessment of a management control system relationship in organizations. In this paper, we evaluate the relationship between business strategy and management control system and their influences on financial performance measurement of a manufacturer (a case study of Maral Co.) with the use of Merchant’s theory. Furthermore, In this case, a decision-making strategy centered on the FMEA is used to identify and prioritize risk factors financial of the control system in companies. Nevertheless, because this strategy has some significant limitations, this research has presented a decision-making approach depending on Z-number theory. For tackle, some of the RPN score’s drawbacks, the suggested decision-making methodology combines the Z-SWARA and Z-WASPAS techniques with the FMEA method. The findings reveal that in the non-financial management control system element, customer satisfaction, and in the financial component, cost standards are at the largest level of weight. Furthermore, the strategic planning factor with a rate of 2.95 and the deviation analysis factor with a rate of 2.87 is at the lowest level, respectively. In sum, market or industry changes are the primary cause of risk in businesses, according to FMEA methodology and the opinions of three professionals.

FWNNet: Presentation of a New Classifier of Brain Tumor Diagnosis Based on Fuzzy Logic and the Wavelet-Based Neural Network Using Machine-Learning Methods

In this paper, we present a novel classifier based on fuzzy logic and wavelet transformation in the form of a neural network. This classifier includes a layer to predict the numerical feature corresponded to labels or classes. The presented classifier is implemented in brain tumor diagnosis. For feature extraction, a fractal model with four Gaussian functions is used. The classification is performed on 2000 MRI images. Regarding the results, the accuracy of the DT, KNN, LDA, NB, MLP, and SVM is 93.5%, 87.6%, 61.5%, 57.5%, 68.5%, and 43.6%, respectively. Based on the results, the presented FWNNet illustrates the highest accuracy of 100% with the fractal feature extraction method and brain tumor diagnosis based on MRI images. Based on the results, the best classifier for diagnosis of the brain tumor is FWNNet architecture. However, the second and third high-performance classifiers are the DT and KNN, respectively. Moreover, the presented FWNNet method is implemented for the segmentation of brain tumors. In this paper, we present a novel supervised segmentation method based on the FWNNet layer. In the training process, input images with a sweeping filter should be reshaped to vectors that correspond to reshaped ground truth images. In the training process, we performed a PSO algorithm to optimize the gradient descent algorithm. For this purpose, 80 MRI images are used to segment the brain tumor. Based on the results of the ROC curve, it can be estimated that the presented layer can segment the brain tumor with a high true-positive rate.

Predicting the Degree of Dissolved Oxygen Using Three Types of Multi-Layer Perceptron-Based Artificial Neural Networks

Predicting the level of dissolved oxygen (DO) is an important issue ensuring the sustainability of the inhabitants of a river. A prediction model can predict the DO level using a historical dataset with regard to water temperature, pH, and specific conductance for a given river. The model can be built using sophisticated computational procedures such as multi-layer perceptron-based artificial neural networks. Different types of networks can be constructed for this purpose. In this study, the authors constructed three networks, namely, multi-verse optimizer (MVO), black hole algorithm (BHA), and shuffled complex evolution (SCE). The networks were trained using the datasets collected from the Klamath River Station, Oregon, USA, for the period 2015–2018. We found that the trained networks could predict the DO level of 2019. We also found that both BHA- and SCE-based networks could predict the level of DO using a relatively simple configuration compared to that of MVO. From the viewpoints of absolute errors and Pearson’s correlation coefficient, MVO- and SCE-based networks performed better than BHA-based networks. In synopsis, the authors recommend MVO- and MLP-based artificial neural networks for predicting the DO level of a river.

On the Application of ARIMA and LSTM to Predict Order Demand Based on Short Lead Time and On-Time Delivery Requirements

Suppliers are adjusting from the order-to-order manufacturing production mode toward demand forecasting. In the meantime, customers have increased demand uncertainty due to their own considerations, such as end-product demand frustration, which leads to suppliers’ inaccurate demand forecasting and inventory wastes. Our research applies ARIMA and LSTM techniques to establish rolling forecast models, which greatly improve accuracy and efficiency of demand and inventory forecasting. The forecast models, developed through historical data, are evaluated and verified by the root mean squares and average absolute error percentages in the actual case application, i.e., the orders of IC trays for semiconductor production plants. The proposed ARIMA and LSTM are superior to the manufacturer’s empirical model prediction results, with LSTM exhibiting enhanced performance in terms of short-term forecasting. The inventory continued to decline significantly after two months of model implementation and application.