Article
Management
Pierre Durand, Gaetan Le Quang
Summary: This paper argues that banking regulation should focus on simple equity requirements instead of complex rules, and provides evidence that the equity ratio has a positive effect on banks' performance in terms of return on assets. However, the impact on return on equity is mostly negative. Strong equity requirements do not hinder banks' performance but reduce shareholder value. Random Forest is found to be a better method for analyzing banks' balance sheet data.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Critical Care Medicine
Steven J. Holfinger, M. Melanie Lyons, Brendan T. Keenan, Diego R. Mazzotti, Jesse Mindel, Greg Maislin, Peter A. Cistulli, Kate Sutherland, Nigel McArdle, Bhajan Singh, Ning-Hung Chen, Thorarinn Gislason, Thomas Penzel, Fang Han, Qing Yun Li, Richard Schwab, Allan I. Pack, Ulysses J. Magalang
Summary: Prediction tools using machine learning without patient-reported symptoms provide better diagnostic performance than logistic regression in identifying OSA. In clinical and community-based samples, the symptomless ANN tool has diagnostic performance similar to that of a widely used prediction tool that includes patient symptoms.
Article
Computer Science, Interdisciplinary Applications
Shih-Hui Huang, Chao-Yu Chu, Yu-Chia Hsu, San-Yuan Wang, Li-Na Kuo, Kuan-Jen Bai, Ming-Chih Yu, Jer-Hwa Chang, Eugene H. Liu, Hsiang-Yin Chen
Summary: Machine learning models with clinical and genomic features can be used as a preliminary tool for predicting platinum-induced nephrotoxicity in non-small cell lung cancer patients and providing preventive strategies in advance.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2022)
Article
Computer Science, Interdisciplinary Applications
Farid Khosravikia, Patricia Clayton
Summary: This paper studies the advantages and disadvantages of different machine learning techniques in predicting ground-motion intensity measures given source characteristics, source-to-site distance, and local site conditions. The study quantifies event-to-event and site-to-site variability of the ground motions by implementing them as random effect terms to reduce the aleatory uncertainty. The results indicate that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms.
COMPUTERS & GEOSCIENCES
(2021)
Article
Engineering, Civil
Zongjun Wu, Ningbo Cui, Daozhi Gong, Feiyu Zhu, Liwen Xing, Bin Zhu, Xi Chen, Shengling Wen, Quanshan Liu
Summary: This study developed machine learning models including Random Forest, Support Vector Machine, Artificial Neural Network, and Extreme Learning Machine for estimating maize evapotranspiration in northwest China. The results showed that the Support Vector Machine model achieved the highest simulation accuracy at certain growth stages, while the Extreme Learning Machine model achieved the highest simulation accuracy at different stages.
JOURNAL OF HYDROLOGY
(2023)
Article
Operations Research & Management Science
Erdinc Akyildirim, Ahmet Goncu, Ahmet Sensoy
Summary: This study analyzes the predictability of twelve cryptocurrencies using machine learning classification algorithms at different frequencies, showing that price trends can be predicted to a certain degree. Support vector machines demonstrate the best predictive accuracy, reaching an average accuracy of 55-65%.
ANNALS OF OPERATIONS RESEARCH
(2021)
Article
Chemistry, Multidisciplinary
Gonzalo Astray, Anton Soria-Lopez, Enrique Barreiro, Juan Carlos Mejuto, Antonio Cid-Samamed
Summary: Nowadays, plastic materials are widely produced and used in various industrial activities. These plastics can contaminate ecosystems with micro- and nanoplastics, either from their own degradation or from primary production sources. Once in the aquatic environment, microplastics can adsorb chemical pollutants, facilitating their dispersion and potential harm to living beings. To address the lack of information on adsorption, three machine learning models (random forest, support vector machine, and artificial neural network) were developed to predict microplastic/water partition coefficients (log K-d) using different approximations based on input variables. The selected machine learning models showed correlation coefficients above 0.92 in the query phase, indicating their potential for rapid estimation of organic contaminant adsorption on microplastics.
Article
Thermodynamics
Jun Young Kim, Dongjae Kim, Zezhong John Li, Claudio Dariva, Yankai Cao, Naoko Ellis
Summary: Machine learning was used to predict the yield of biomass gasification, and the results showed that Random Forest and Artificial Neural Network had high prediction accuracy. The Monte Carlo filtering method was used to forecast the desired products and identify significant variables for optimization.
Article
Chemistry, Applied
Reuben James Buenafe, Anjana Rathnam, Joanne Jerenice Anonuevo, Shanmugasundram Sundar, Nese Sreenivasulu
Summary: A classification tool was developed to identify high-yielding hybrids with good cooking and eating quality, achieving accuracies of 63.9%, 69.4%, and 69.4% using different models. The tool was based on routine grain quality parameters and key properties like gelatinization temperature and viscosity.
JOURNAL OF FOOD COMPOSITION AND ANALYSIS
(2021)
Article
Thermodynamics
Meng An, Kunliang Zhang, Fuxin Song, Xiangquan Chen, Swellam W. Sharshir, A. W. Kandeal, Amrit Kumar Thakur, A. S. Abdullah, Mohamed R. Elkadeem, Cheng Chi, Elbager M. A. Edreis, A. E. Kabeel, Weigang Ma
Summary: This study presents a comprehensive investigation on the freshwater productivity of a solar-driven HDH unit using experimental and machine learning methods. The average accumulated productivity was found to be 10.8 L/m2 based on 20 outdoor experiments. Machine learning models, including artificial neural network and random forest, were developed to predict the hourly freshwater productivity, cost, and GOR of the HDH system.
APPLIED THERMAL ENGINEERING
(2023)
Article
Construction & Building Technology
Hoang D. Nguyen, Nhan D. Dao, Myoungsu Shin
Summary: This study developed machine learning models to predict the peak lateral displacements of seismic isolation systems. The random forest model showed the best performance, and the normalized characteristic strength was found to be the most influential variable. Furthermore, a practical analysis and comparison demonstrated the effectiveness and limitations of the developed model.
JOURNAL OF BUILDING ENGINEERING
(2022)
Article
Environmental Sciences
Anton Soria-Lopez, Carlos Sobrido-Pouso, Juan C. Mejuto, Gonzalo Astray
Summary: This study uses machine learning techniques to predict the outflow of eight different dams in Galicia, Spain and finds that artificial neural network is the most suitable prediction technique.
Article
Agronomy
S. K. Mahmudul Hassan, Michal Jasinski, Zbigniew Leonowicz, Elzbieta Jasinska, Arnab Kumar Maji
Summary: Various plant diseases pose threats to agriculture, and automated disease identification is beneficial for timely control. Two methods were proposed to identify plant diseases, with the shallow VGG with Xgboost model showing superior performance in accuracy.
Article
Construction & Building Technology
Anna Hola, Slawomir Czarnecki
Summary: The article presents the results of experimental research and numerical analyses, and also shows the usefulness of the random forest algorithm and the support vector machine for the non-destructive identification of the moisture content of brick walls in historic buildings. To train and test the models, a representative dataset, including 290 sample sets and 7 predictor variables, was used. The analyses showed that the random forest algorithm is the most predisposed model for the non-destructive assessment of the Umc mass moisture content of brick walls in historic buildings, with the highest value of R2 and the lowest values of RMSE, MAE, and MAPE.
AUTOMATION IN CONSTRUCTION
(2023)
Article
Chemistry, Analytical
Dante Trabassi, Mariano Serrao, Tiwana Varrecchia, Alberto Ranavolo, Gianluca Coppola, Roberto De Icco, Cristina Tassorelli, Stefano Filippo Castiglia
Summary: The aim of this study was to determine the most accurate supervised machine learning algorithm for classifying people with Parkinson's disease from healthy subjects based on gait features. The study found that support vector machine, decision trees, and random forest showed the best classification performances.
Article
Environmental Sciences
Yukun Ma, Shaonan Hao, Hongtao Zhao, Jinxiu Fang, Jiang Zhao, Xuyong Li
Article
Green & Sustainable Science & Technology
Yukun Ma, Wenyan He, Hongtao Zhao, Jiang Zhao, Xiaowei Wu, Wei Wu, Xuyong Li, Chengqing Yin
JOURNAL OF CLEANER PRODUCTION
(2019)
Article
Engineering, Civil
Yan Jiang, Changming Liu, Shaonan Hao, Hongtao Zhao, Xuyong Li
JOURNAL OF HYDROLOGY
(2019)
Article
Engineering, Environmental
Y. J. Liao, H. T. Zhao, Y. Jiang, Y. K. Ma, X. Luo, X. Y. Li
Article
Environmental Sciences
Yukun Ma, Manli Gong, Hongtao Zhao, Xuyong Li
SCIENCE OF THE TOTAL ENVIRONMENT
(2020)
Article
Engineering, Civil
Yan Jiang, Xin Bao, Shaonan Hao, Hongtao Zhao, Xuyong Li, Xianing Wu
WATER RESOURCES MANAGEMENT
(2020)
Article
Green & Sustainable Science & Technology
Yunjie Liao, Hongtao Zhao, Zhihui Jiang, Jia Li, Xuyong Li
Summary: A new index model, composed of transport and source factors, was proposed to evaluate the risk of urban non-point source pollution. This index method demonstrated lower percent bias in runoff volume compared to traditional methods, offering a new approach for assessing urban-NPS risk.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Environmental Sciences
Hongtao Zhao, Yukun Ma, Jinxiu Fang, Lian Hu, Xuyong Li
Summary: An in-depth understanding of particle size distribution and total suspended solids (TSS) in surface runoff is crucial for managing urban diffuse pollution. Field experiments and model simulation were used to explore the dynamic behavior of TSS and influencing factors, showing that higher TSS concentrations in surface runoff contained coarser particles washed off from road-deposited sediments. Factors such as rainfall characteristics, urban-rural gradients, surface roughness, and climate differences affect the contribution rate of RDS to TSS by altering particle size composition.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Environmental Sciences
Ziqiao Wang, Xuyong Li, Hongtao Zhao
Summary: This study investigated the spatial influence of urban elements on road-deposited sediment (RDS) build-up load and phosphorus load in a specific district in Wuhan, China. The results showed that areas with higher density of factories had elevated levels of RDS and associated phosphorus, while areas with higher density of dwellings, catering, and entertainment elements had higher levels of certain types of phosphorus. Urban elements showed different correlations with RDS and phosphorus, with bus stations, dwellings, and factories having a strong impact on their spatial distribution. The study also demonstrated that the combination of geodetector and Bayesian Networks can be a useful tool for predicting and managing RDS pollution.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2022)
Article
Environmental Sciences
Yukun Ma, Hongtao Zhao
Summary: Landscape patterns significantly affect urban runoff pollution, and a reasonable arrangement of pervious patches is critical for controlling urban non-point-source pollution. This study found that the area ratio, circumference, shape, and connectivity of pervious surfaces influence runoff pollution, and it is recommended to retain large pervious areas close to one another. Different pervious/impervious patterns will have different effects on runoff pollution within urban catchments.
FRONTIERS IN ENVIRONMENTAL SCIENCE
(2022)
Article
Environmental Sciences
Lian Hu, Hongtao Zhao
Summary: This study observed the buildup and wash-off processes of particulate pollutants in road surface and sewer during rainfall events in a combined sewer system in Zhuhai, China. The results showed that the size distribution of particles had a significant influence on the TSS concentration, and the relationship between TSS concentrations in road runoff and combined sewer runoff varied with different rainfall intensities. The study also found that a significant portion of the pollutant loadings came from the sediments in the combined sewer and road runoff, with different contributions to different pollutants. These findings can provide insights for the design of stormwater control measures in combined sewer systems.
SCIENCE OF THE TOTAL ENVIRONMENT
(2022)
Article
Environmental Sciences
Jingjun Su, Tian Huang, Hongtao Zhao, Xuyong Li
Summary: Base flow is more difficult to predict due to the lack of hydrologically relevant information. This study aims to identify the most influential controls on base flow spatially and temporally and to elucidate the response relationships. The results show that annual base flow has significantly declined since 1999, with precipitation and underlying carbonate rocks being the primary controls on spatial variation.
ENVIRONMENTAL MONITORING AND ASSESSMENT
(2023)
Article
Engineering, Civil
Longbo Li, Haiyan Li, Hongtao Zhao
Summary: This study investigates the wash-off process of road-deposited sediments (RDS) on asphalt and concrete roads under different particle sizes and rainfall intensities. It is found that both particle size and road surface characteristics significantly influence the RDS wash-off. To improve the estimation accuracy and convenience, a new coefficient (CPSI) is introduced to the exponential wash-off equation, resulting in an increased coefficient of determination (R2) from 0.7039 to 0.8677.
JOURNAL OF HYDROLOGY
(2023)
Article
Engineering, Environmental
Lian Hu, Jintao Mao, Ronghua Zhong, Hongtao Zhao
Summary: This study examined the effects of chlorinated disinfectants sprayed during COVID-19 on road-deposited sediments (RDS) and found increased leaching of heavy metals (HMs) and redistribution of their chemical forms. Finer particle sizes dominate the contribution of HMs, and current control measures are ineffective in removing these particles.
Article
Engineering, Environmental
Hongtao Zhao, Tian Huang, Jingjun Su, Xuyong Li
Summary: Urban surface-deposited sediments (USDs) are a mixture of various pollutants and are widely distributed in urban environments. The vertical distribution pattern and pollution characteristics of USDs are influenced by their spatial position and height. In this study, we investigated the vertical distribution pattern of USDs and heavy metals in Beijing. Our findings showed that the concentrations of heavy metals were significantly higher in USDs on road surfaces, sidewalks, platforms, overbridges, and rooftops compared to the corresponding background soil. Flat rooftops were identified as hotspots for heavy metal contamination. Particle size also played a significant role in pollution distribution and their output. The classification of USDs by height was affected by particle size, and height was found to be a reliable indicator when particle size was less than 149 µm.
ENVIRONMENTAL SCIENCE-WATER RESEARCH & TECHNOLOGY
(2023)
Review
Environmental Sciences
Attila Csaba Kondor, Anna Viktoria Vancsik, Laszlo Bauer, Lili Szabo, Zoltan Szalai, Gergely Jakab, Gabor Maasz, Marta Pedrosa, Maria Jose Sampaio, Ana Rita Lado Ribeiro
Summary: This review provides a critical overview of research on the removal efficiency of priority substances and compounds of emerging concern through bank filtration, discussing influencing factors and future challenges. The findings show that the efficiency of bank filtration is influenced by multiple factors and varies for different substances.
ENVIRONMENTAL POLLUTION
(2024)
Article
Environmental Sciences
Xinyan Wang, Shuai Zhang, Huihui Yan, Zhao Ma, Yunshan Zhang, Haining Luo, Xueli Yang
Summary: This study investigated the association between ambient PM2.5, O3 pollution, and ovarian reserve in reproductive-aged Chinese women. The results showed that increased exposure to PM2.5 and O3 was associated with decreased AMH levels, indicating reduced ovarian reserve. Notably, the effects of O3 exposure on ovarian reserve were different from those of PM2.5 exposure.
ENVIRONMENTAL POLLUTION
(2024)
Article
Environmental Sciences
Tingting Ma, Yanjuan Ding, Fengjiao Xu, Chen Zhang, Min Zhou, Ya Tang, Yanrong Chen, Yating Wen, Rufei Chen, Bin Tang, Shigui Wang
Summary: The dragonfly species Orthetrum albistylum can accumulate heavy metals, and its heat shock protein genes have the potential to serve as biomarkers for monitoring environmental pollutants.
ENVIRONMENTAL POLLUTION
(2024)
Article
Environmental Sciences
Naoto Ishiwaka, Koya Hashimoto, Masayoshi K. Hiraiwa, Francisco Sanchez-Bayo, Taku Kadoya, Daisuke Hayasaka
Summary: Systemic insecticides and rising temperatures have combined effects on the abundance of Odonata nymphs in paddy fields. The standalone effect of insecticide exposure decreased the Odonata community, while nymphs decreased synergistically with temperature rise in paddy water. However, the impacts of each stressor alone varied among species.
ENVIRONMENTAL POLLUTION
(2024)
Article
Environmental Sciences
Marco Vecchiato, Carlo Barbante, Elena Barbaro, Francois Burgay, Warren R. L. Cairns, Alice Callegaro, David Cappelletti, Federico Dallo, Marianna D'Amico, Matteo Feltracco, Jean-Charles Gallet, Andrea Gambaro, Catherine Larose, Niccolo Maffezzoli, Mauro Mazzola, Ivan Sartorato, Federico Scoto, Clara Turetta, Massimiliano Varde, Zhiyong Xie, Andrea Spolaor
Summary: The Arctic region is facing contamination from long-range pollution and local human activities. Polycyclic Aromatic Hydrocarbons (PAHs) are used as environmental indicators for emission, transport, and deposition processes. Research conducted in the Arctic surface snow in Ny-Ålesund, Svalbard from October 2018 to May 2019 shows that long-range inputs of PAHs mainly occur in winter, while the most abundant analyte retene exhibits opposite seasonal trends.
ENVIRONMENTAL POLLUTION
(2024)
Review
Environmental Sciences
Maoshui Zhuo, Zhijie Chen, Xiaoqing Liu, Wei Wei, Yansong Shen, Bing-Jie Ni
Summary: This paper discusses the application of three catalytic processes (photocatalysis, electrocatalysis, and biocatalysis) in the management of microplastic pollution, and introduces the efficiency and catalytic mechanisms of different catalysts. It also proposes the development prospects for sustainable management of microplastic pollution.
ENVIRONMENTAL POLLUTION
(2024)
Article
Environmental Sciences
Shiyu Chen, Zhenzhen Shi, Qiang Zhang
Summary: In this study, a human physiologically based pharmacokinetic (PBPK) model of diethyl phthalate (DEP) was developed to assess its toxicity. The model considers the distribution and metabolism of DEP and its active metabolite monoethyl phthalate (MEP) in different tissue compartments. Sensitivity analysis and Bayesian Markov chain Monte Carlo (MCMC) simulations were performed to evaluate the uncertainty and variability of the model parameters. The results suggest that dermal absorption is an important route of exposure to DEP in the environment.
ENVIRONMENTAL POLLUTION
(2024)
Article
Environmental Sciences
Bangguo Wang, Lijing Wang, Wenxi Cen, Tao Lyu, Peter Jarvis, Yang Zhang, Yuanxun Zhang, Yinghui Han, Lei Wang, Gang Pan, Kaili Zhang, Wei Fan
Summary: This study investigates the feasibility and mechanisms of a chemical-free nanobubble-based AOP for treating organic micropollutants in water. The results show that the oxygen nanobubble AOP has a significantly higher removal efficiency compared to air and nitrogen nanobubbles. The treatment performance is not affected by pH and the presence of ions. Higher initial concentrations of the micropollutant lead to slower treatment processes, but similar removal performance is achieved in the end. The presence of organic matter reduces the removal rate of the micropollutant. The results have practical feasibility for water and wastewater treatment.
ENVIRONMENTAL POLLUTION
(2024)
Article
Environmental Sciences
Yingmei Huang, Jicai Yi, Yao Huang, Songxiong Zhong, Bin Zhao, Jing Zhou, Yuxuan Wang, Yiwen Zhu, Yanhong Du, Fangbai Li
Summary: This study investigates the impact of biochar on methylmercury (MeHg) accumulation in rice. The results show that biochar reduces MeHg levels in paddy soils by decreasing bioavailable Hg and microbial Hg methylation. Additionally, biochar decreases the uptake and translocation of MeHg in rice plants, resulting in a reduction of MeHg accumulation in rice grains.
ENVIRONMENTAL POLLUTION
(2024)
Article
Environmental Sciences
Nengde Zeng, Fei Huang, Jiani Du, Chenghao Huang, Qian Yang, Xinhua Zhan, Baoshan Xing
Summary: This study investigates the protein targets and protein-ligand interactions related to PAH contamination in crop xylem sap using computational tools. The results show that phenanthrene has a more pronounced effect on the xylem sap proteins of maize and wheat, with maize DEPs associated with lipid biosynthesis and wheat DEPs exhibiting an increase in ABC transporters. This study provides insights into the regulation and movement of PAHs within plant xylem.
ENVIRONMENTAL POLLUTION
(2024)
Article
Environmental Sciences
Xinwei Chen, Hao Ma, Run Wang, Man Wang, Binbin Zhu, Yanqing Cong, Xiayue Zhu, Guoqin Wang, Yi Zhang
Summary: Co3O4/TiO2-NRs electrodes with excellent photoresponse were prepared via plasma-assisted modification of Co3O4 on TiO2. The combination of Co3O4 and TiO2 improved the light utilization efficiency and showed potential for degradation of pollutants.
ENVIRONMENTAL POLLUTION
(2024)
Article
Environmental Sciences
Wenjing Ji, Liying Song, Jing Wang, Hongqing Song
Summary: This study conducted a life-cycle assessment to examine the correlation between natural gas consumption and carbon emissions in different end uses in China. The results showed that both natural gas consumption and life-cycle carbon emissions have been increasing since 2017. Significant variations in NG life-cycle carbon emissions were found across different provinces and sectors, highlighting the need for targeted efforts to reduce carbon emissions.
ENVIRONMENTAL POLLUTION
(2024)
Article
Environmental Sciences
Youyi Chen, Boxuan Zhang, Pojun Zhang, Guogui Shi, Hao Liang, Wu Cai, Jingyu Gao, Sumin Zhuang, Kaiyin Luo, Jiaqi Zhu, Chaoxiang Chen, Kunyu Ma, Jinrong Chen, Chun Hu, Xueci Xing
Summary: The synergistic effects of trace sulfadiazine and cast-iron corrosion scales on the formation of disinfection by-products in drinking water distribution systems were investigated. The presence of magnetite resulted in increased concentrations of DBPs due to the higher microbial activity and enhanced microbial extracellular electron transport pathway. The study highlights the importance of considering trace antibiotics pollution and corrosion scales in water sources for DBP control.
ENVIRONMENTAL POLLUTION
(2024)
Review
Environmental Sciences
Bishwa Raj Pokharel, Vijay Sheri, Manoj Kumar, Zhiyong Zhang, Baohong Zhang
Summary: This review summarizes the interactions, uptake, and transport of aluminum nanoparticles (Al-NPs) in plants, highlighting their negative effects on plant growth and development, as well as their potential to alter plant defense systems and gene expression.
ENVIRONMENTAL POLLUTION
(2024)
Article
Environmental Sciences
Yonglu Wang, Fengsong Zhang, Xiaoyong Liao, Xiao Yang, Guixiang Zhang, Liyun Zhang, Chaojun Wei, Pengge Shi, Jiongxin Wen, Xiaorong Ju, Can Xu, Yang Liu, Ying Lan
Summary: This study aims to explore the effects of thiencarbazone-methyl center dot isoxaflutole on soil microflora and the potential mitigation mechanisms to bacterial communities. It was found that increasing the application of thiencarbazone-methyl center dot isoxaflutole resulted in increased stress on soil bacterial community structure and diversity. Increasing soil pH was recognized as a key factor in improving the diversity and structure of soil microflora. Supplemental use of nitrapyrin or modified attapulgite can increase soil pH and improve bacterial diversity.
ENVIRONMENTAL POLLUTION
(2024)