Sub-seasonal Patterns of PM10 and Black Carbon in a Coastal City: A Case Study of Salé, Morocco by Anas Otmani, Abdeslam Lachhab, Abdelfettah Benchrif, Mounia Tahri, Mohamed Azougagh, Mohammed El Bouch, El Mahjoub Chakir

“…The study centered on the repercussions of fossil fuel combustion (BCFF specifically, emissions from traffic) and biomass burning (BCBB Consisting of forest fires and agricultural burning) on BC2.5 levels. …”
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Validation of In-field Calibration for Low-Cost Sensors Measuring Ambient Particulate Matter in Kolkata, India by Siddharth Nobell, Arnab Majumdar, Shovon Mukherjee, Sukumar Chakraborty, Sanjoy Chatterjee, Soumitra Bose, Anindita Dutta, Sandhya Sethuraman, Daniel M. Westervelt, Shairik Sengupta, Rakhi Basu, V. Faye McNeill

“…Data from the co-locations were used to calibrate the LCS network using random forest regression and multiple linear regression approaches. …”
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ENERGY AND ECONOMIC EFFICIENCY OF BIOETHANOL PRODUCTION DEPENDING ON THE QUALITY OF CORN GRAIN by Vitalii Palamarchuk, Roman Lohosha, Vadim Krychkovskyi

“…The results of studies of the influence of foliar fertilisation with a bacterial preparation based on beneficial symbiotic and associative microorganisms Biomag, microfertilisers "ROSTOK" corn, Ecolist Mono Zinc, carried out in the phase of 5-7 and 10-12 leaves of corn, on the level of pre-harvest grain moisture, the number of rows of grains are presented, number of grains in a row, weight of 1000 grains, starch content in grain, productivity and bioethanol yield in hybrids of early maturing group Kharkiv 195 MV (FAO 190) and DKS 2971 (FAO 200), medium early group DKS 3795 (FAO 250) and DKS 3871 (FAO 2480) and medium maturing group DK 315 (FAO 310) and DK 440 (FAO 350) in agro-ecological conditions of the Forest-Steppe of Right-Bank Ukraine. The research is grounded in an evaluation of the efficacy of optimising the supply of plant nutrients through foliar fertilisation in the formation of grain yield and quality. …”
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A novel framework to predict ADHD symptoms using irritability in adolescents and young adults with and without ADHD by Saeedeh Komijani, Dipak Ghosal, Manpreet K. Singh, Julie B. Schweitzer, Julie B. Schweitzer, Prerona Mukherjee, Prerona Mukherjee

“…Using a non-parametric-based approach, employing the Random Forest (RF) method, we found that among both adolescents and young adults, irritability in adolescent females significantly contributes to predicting impulsive symptoms in subsequent years, achieving a performance rate of 86%.ConclusionOur results corroborate and extend prior findings, allowing for an in-depth examination of longitudinal relations between irritability and ADHD symptoms, namely hyperactivity, impulsivity, and inattentiveness, and the unique association between irritability and ADHD symptoms in females.…”
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Enhancing the estimation of cadmium content in rice leaves by integrating vegetation indices and color indices using machine learning by Xiaoyun Huang, Shengxi Chen, Tianling Fu, Chengwu Fan, Hongxing Chen, Song Zhang, Hui Chen, Song Qin, Zhenran Gao

“…Notably, the LCd estimation model developed using the random forest method exhibited the highest accuracy, with a coefficient of determination (R2) of 0.81 and a root-mean-square error of 0.120. …”
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Assessing the Effects of Tourism Activities on the Preservation of Cultural Heritage Among Communities of Rubanda DistrictUganda. by Ahabwe, John Brighton

“…Tourists treks through the dense forest to observe mountain gorillas in their natural habitat, some respondents also showed the district's forests and wetlands are home to a diverse array of bird species, making it an excellent destination for bird watching enthusiasts. …”
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Soil organic carbon contents and their major influencing factors in mangrove tidal flats: a comparison between estuarine and non-estuarine areas by Ting Wu, Jia Guo, Gang Li, Yu Jin, Wei Zhao, Guangxuan Lin, Fang-Li Luo, Yaojun Zhu, Yifei Jia, Li Wen

“…We compared the SOC and soil physicochemical properties between estuarine and non-estuarine mangrove tidal flats. The Random Forest algorithm was employed to identify the main influencing factors affecting SOC. …”
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Characterisation of cardiovascular disease (CVD) incidence and machine learning risk prediction in middle-aged and elderly populations: data from the China health and retirement lo... by Qing Huang, Zihao Jiang, Bo Shi, Jiaxu Meng, Li Shu, Fuyong Hu, Jing Mi

“…Data preprocessing included missing value imputation via random forest. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (Lasso CV) method with cross-validation prior to model training. …”
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Non-pharmacological interventions for the reduction and maintenance of blood pressure in people with prehypertension: a systematic review protocol by Paul Rutter, Andrew Clegg, Valerio Benedetto, Caroline Watkins, Nefyn Williams, Joseph Spencer, Lucy Hives, Emma P Bray, Cath Harris, Rachel F Georgiou, Nafisa Iqbal

“…Heterogeneity will be assessed through visual inspection of forest plots and the calculation of the χ2 and I2 statistics and causes of heterogeneity will be assessed where sufficient data are available. …”
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Diurnal and Daily Variations of PM2.5 and its Multiple-Wavelet Coherence with Meteorological Variables in Indonesia by Nani Cholianawati, Tiin Sinatra, Ginaldi Ari Nugroho, Didin Agustian Permadi, Asri Indrawati, Halimurrahman, Meta Kallista, Moch Syarif Romadhon, Ilma Fauziah Ma’ruf, Dipo Yudhatama, Tesalonika Angela Putri Madethen, Asif Awaludin

“…Meanwhile, the investigation on the extreme rise of PM2.5 in Pontianak due to peatland forest fires using HYSPLIT shows that emission from the surrounding area significantly raises the maximum half-hourly in Pontianak to 700 μg m−3.…”
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A CNN-RF Hybrid Approach for Rice Paddy Fields Mapping in Indramayu Using Sentinel-1 and Sentinel-2 Data by Dodi Sudiana, Mia Rizkinia, Rahmat Arief, Tiara De Arifani, Anugrah Indah Lestari, Dony Kushardono, Anton Satria Prabuwono, Josaphat Tetuko Sri Sumantyo

“…This study proposes the CNN-RF method, which combines a convolutional neural network (CNN) as a feature extractor and a random forest (RF) as a classifier. The experiment used combinations of input data, including variations of single and multisource data, to achieve optimal results. …”
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Variabilidad intraespecífica en la calidad de semillas de especies forestales nativas en bosques montanos en el sur del Ecuador: Implicaciones para la restauración de bosques... by Ximena Palomeque, Andrea Maza, Juan Pablo Iñamagua, Sven Günter, Patrick Hildebrandt, Michael Weber, Bernd Stimm

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Effects of poultry by-product meal and complete replacement of fish oil with alternative oils on growth performance and gut health of rainbow trout (Oncorhynchus mykiss): a FEEDNET... by Imam Hasan, Simona Rimoldi, Biagina Chiofalo, Marianna Oteri, Micaela Antonini, Rosangela Armone, Violeta Kalemi, Laura Gasco, Genciana Terova

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Distribution Characteristics and Coupling Relationship Between Soil Erosion and Hydrologic and Sediment Connectivity in Changchong River Basin by LI Jianing, ZHANG Hongli, TIAN Changyuan, ZHANG Yi, ZHA Tonggang

“…[Results] (1) The average soil erosion modulus in the Changchong River Basin was 380 t/(hm2·a), and the soil erosion intensity was mainly slight erosion, which gradually intensified from north to south. (2) The high hydrological and sediment connectivity is mainly distributed in cultivated land, and the opposite is true in forest and grassland land. The higher value is mainly located in the low-lying flat area with low slope and easy water accumulation, while the lower value is mainly in the steep mountainous area. (3) Topographic factors and land use types significantly affected soil erosion and hydrological and sediment connectivity (p<0.01). …”
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Retrieval of Land Surface Temperature From Passive Microwave Observations Using CatBoost-Based Adaptive Feature Selection by Yang Dai, Yingbao Yang, Xin Pan, Penghua Hu, Xiangjin Meng, Fanggang Li, Zhenwei Wang

“…We compared the accuracy of the proposed method with the Holmes, multichannel, and Random Forest algorithms. Results showed that the proposed method had lowest RMSE, with the value of 3.28 K (1.95 K), 2.69 K (1.65 K), and 3.71 K (2.22 K) on grassland, cropland, and barren land at daytime (nighttime), respectively. …”
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Leveraging machine learning and rule extraction for enhanced transparency in emergency department length of stay prediction by Waqar A. Sulaiman, Charithea Stylianides, Andria Nikolaou, Andria Nikolaou, Zinonas Antoniou, Ioannis Constantinou, Lakis Palazis, Anna Vavlitou, Theodoros Kyprianou, Theodoros Kyprianou, Efthyvoulos Kyriacou, Antonis Kakas, Antonis Kakas, Marios S. Pattichis, Andreas S. Panayides, Constantinos S. Pattichis, Constantinos S. Pattichis

“…Using machine learning models, including Gradient Boosting (GB), Random Forest (RF), Logistic Regression (LR), and Multilayer Perceptron (MLP), we identified GB as the best performing model outperforming the other models with an AUC of 0.730, accuracy of 69.93%, sensitivity of 88.20%, and specificity of 40.95% on the original dataset. …”
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Engineering of SauriCas9 with enhanced specificity by Xiaoqi Zhang, Chen Tao, Miaomiao Li, Sufang Zhang, Puping Liang, Yan Huang, Huihui Liu, Yongming Wang

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Characteristics of Land Use Change and Evaluation of Ecological Sensitivity in Chongqing by XIE Xianjian, GOU Qiantao, WU Han

“…There had been a significant reduction in both cropland and grassland, while the areas of forest land and urban-rural construction land had increased markedly from 2000 to 2020. (2) Over the course of 20 years, the average value of the comprehensive ecological sensitivity index rose from 1.037 to 1.045, indicating an overall improvement in the ecological environment of the study area. …”
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Prediction of digestible energy requirement in growing finishing stage of pigs using machine learning models by Nibas Chandra Deb, Jayanta Kumar Basak, Sijan Karki, Elanchezhian Arulmozhi, Dae Yeong Kang, Niraj Tamrakar, Eun Wan Seo, Junghoo Kook, Myeong Yong Kang, Hyeon Tae Kim

“…Therefore, this study sought to predict the digestible energy requirement (DER) in the growing-finishing phase of pigs, where four machine learning (ML) models: multiple linear regression (MLR), support vector regression (SVR), random forest regression (RFR), and multilayer perceptron (MLP) were applied across four datasets, with the input parameters including body weight of pigs (BW), inside temperature (IT), inside relative humidity (IRH), and inside CO2 concentration (ICO2) of pig barns. …”
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Construction land transition in Qinghai-Tibet Plateau and its eco-environmental effects during 2000-2020 by ZHANG Zining, LUO Junqiang, ZHANG Peilei, RONG Han, LIU Haiyang, AN Shuang, CHANG Genying

“…Early expansion mainly occupied unused land, whereas later expansion predominantly encroached on ecological land such as cropland, forest, and grassland. Regional differences in land-use transitions were evident: the Northern Tibet Plateau and Qaidam Basin showed higher proportions of transitions to water bodies, water conservancy land, and unused land, while the expansion mainly occupied grasslands and unused land; in other regions, construction land predominantly shifted between cropland and grassland. (3) The <i>IRSEI</i> values of construction land in the regions of Qinghai-Tibet Plateau were ranked as follows: Sichuan-Tibet alpine canyon region &gt; Qilian Mountains region &gt; Southern Tibet valley region &gt; Qinghai Plateau &gt; Qaidam Basin &gt; Northern Tibet Plateau. (4) Overall, construction land transition demonstrated negative ecological effects. …”
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