Development of a prognostic gene signature and exploration of P4HA1 in the modulation of cuproptosis in colorectal cancer by RenJie Jiang, LinLin Ruan, Taohui Ding, Hongtao Wan, Yanglin Chen, XiaoJian Zhu, Zhijiang Huang, Dengke Yao, Ming Li, Bo Yi, Dan Liu

“…Key genes were further refined through LASSO regression and random forest approaches, culminating in the development of a prognostic model comprising six critical genes. …”
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Decoding Subjective Understanding: Using Biometric Signals to Classify Phases of Understanding by Milan Lazic, Earl Woodruff, Jenny Jun

“…Distinct AU patterns were found for all five phases, with gradient boosting machine and random forest models achieving the highest predictive accuracy. …”
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Identification and characterization of peat soils using a physiographic approach at semi-detailed scale: a case study in Bangka Belitung Islands Province, Indonesia by Sukarman Sukarman, Yiyi Sulaeman, Edi Yatno, Rachmat Abdul Gani, Budiman Minasny

“…The study highlights that deep peat soils under bushes and shrubs should be conserved for forests or reforested. Detailed spatial information on peatlands is useful for policymakers related to local peat soils planning and management.…”
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Land use and land cover classification for change detection studies using convolutional neural network by V. Pushpalatha, P.B. Mallikarjuna, H.N. Mahendra, S. Rama Subramoniam, S. Mallikarjunaswamy

“…Further, change detection analysis has been carried out using classified maps and the results show that built-up areas increased by 8.34 sq. km (0.83%), agricultural land expanded by 2.21 sq. km (0.23%), and water bodies grew by 3.31 sq. km (0.35%). Conversely, forest cover declined by 1.49 sq. km (0.15%), and other land uses reduced by 11.93 sq. km (1.22%) over the decade.…”
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Temporal variability of dissolved inorganic nitrogen and key environmental drivers in a dam-induced subtropical urban lake by Peng Tang, Boyu Ren, Tianyang Li, Qiwen Xu, Baoxiang Yang, Shunyao Zhu, Binghui He

“…The results indicated that DIN concentration was seasonally significantly different, showing higher values in winter and spring than that in summer and autumn. Random Forest modelling indicated that the temporal variations in DIN concentration were closely related to the notable seasonal fluctuations in key water quality indicators such as the temperature (T), Secchi depth (SD), and concentrations of dissolved phosphorus (DP), dissolved silica (DSi), chlorophyll-a (Chl-a), which were predominantly attributable to hydrological alterations associated with reservoir management and external pollutant inputs from agricultural fertilization. …”
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Climatic Trends in Hail Precipitation in France: Spatial, Altitudinal, and Temporal Variability by Lucía Hermida, José Luis Sánchez, Laura López, Claude Berthet, Jean Dessens, Eduardo García-Ortega, Andrés Merino

“…We found 177 pads with a negative trend, which were largely south of a pine forest in Landes. The remainder of the study area showed an elevated spatial variability with no pattern, even between relatively close hailpads. …”
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Modeling of wave-induced drift based on stepwise parameter calibration by Kui Zhu, Xueyao Chen, Lin Mu, Lin Mu, Lin Mu, Dingfeng Yu, Dingfeng Yu, Runze Yu, Zhaolong Sun, Tong Zhou, Tong Zhou

“…A force analysis method and three ML methods, long short-term memory (LSTM), back-propagation (BP) neural network, and random forest (RF), were used to fit the wave-induced drift velocity by combining eight different parameter schemes. …”
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Hydrologic responses of watershed assessment to land cover and climate change using soil and water assessment tool model by R.C.C. Puno, G.R. Puno, B.A.M. Talisay

“…Meanwhile, urbanization had influenced the increase in surface runoff, evapotranspiration, and baseflow. The increase of forest vegetation resulted in a minimal decrease in baseflow and surface runoff. …”
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Machine Learning para la Clasificación y Análisis de los Índices de Biomasa y su relación con el Cambio Climático, Desierto de Atacama by Santos Gómez, Edwin Pino-Vargas, Germán Huayna, Jorge Espinoza-Molina, Karina Acosta-Caipa, Fredy Cabrera-Olivera4

“…En este trabajo usamos Machine Learning (Randon Forest) como herramienta para clasificar la biomasa y calcular los índices de vegetación buscando identificar las características de la cobertura vegetal en la cabecera del desierto Atacama. …”
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Features of the biomorphological and geographic structure of segetal floras in a number of regions in Russia by O. G. Baranova, A. S. Tretyakova, N. N. Luneva, A. A. Zverev, P. V. Kondratkov, T. A. Terekhina, G. R. Khasanova, S. M. Yamalov, M. V. Lebedeva, N. A. Bagrikova

“…Depending on the zonal arrangement of segetal floras, the shares of boreal, forest-steppe and steppe species changed. The ratios among geographic elements in the alien fractions of the compared segetal floras were relatively stable. …”
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Machine learning based prediction models for the prognosis of COVID-19 patients with DKA by Zhongyuan Xiang, Jingyi Hu, Shengfang Bu, Jin Ding, Xi Chen, Ziyang Li

“…We developed five machine learning-based prediction models—Extreme Gradient Boosting (XGB), Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and Multilayer Perceptron (MLP)—to evaluate the prognosis of COVID-19 patients with DKA. …”
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Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods by Abhinav Kumar, Paul Rodrigues, A. K. Kareem, Tingneyuc Sekac, Sherzod Abdullaev, Jasgurpreet Singh Chohan, R. Manjunatha, Kumar Rethik, Shivakrishna Dasi, Mahmood Kiani

“…In this communication, we aim to develop intelligent data-driven models of decision tree, random forest, ensemble learning, adaptive boosting, support vector machine and multilayer perceptron artificial neural network to predict rock saturation exponent parameter in terms of rock absolute permeability, porosity, resistivity index, true resistivity, and water saturation based on acquired 1041 field data. …”
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Soil organic and phytomass carbon stocks in mountain periglacial settings of Vindelfjällen (Sweden) by Christina Fröjd, Berta González Sánchez, Peter Kuhry

“…Mean phytomass C storage is 0.36 kg C m−2, with birch forest storing on average about twenty times more phytomass C (4.41 kg C m−2) compared to the mean for the upland alpine tundra classes (0.20 kg C m−2). …”
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Machine Learning Algorithms Analysis of Synthetic Minority Oversampling Technique (SMOTE): Application to Credit Default Prediction by Emmanuel de-Graft Johnson Owusu-Ansah, Richard Doamekpor, Richard Kodzo Avuglah, Yaa Kyere Adwubi

“…Findings, with the exception of the SMOTE dataset, XGBoost consistently beat the other classifiers across the other datasets in terms of AUC. Random forest, decision tree, and logistic regression all performed well and might be utilized as alternatives to XGBoost classifiers for developing credit scoring models. …”
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An Automated Approach for Epilepsy Detection Based on Tunable Q-Wavelet and Firefly Feature Selection Algorithm by Ahmed I. Sharaf, Mohamed Abu El-Soud, Ibrahim M. El-Henawy

“…The firefly optimization reduces the original set of features and generates a reduced compact set. A random forest classifier is trained for the classification and prediction of the seizures and seizure-free signals. …”
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DRIVERS OF ECONOMIC DEPENDENCE ON WOOD FUEL IN RURAL SOUTHERN ETHIOPIA: A CASE IN ABELA ABAYA DISTRICT. by Deginet Berhanu, Biruk Birhan, Gemedo Furo, Gezahegn Seyoum

“…The findings of the study have the potential to provide policymakers with valuable insights into the development of effective energy and forest policies, which can promote sustainable wood fuel extraction practices while ensuring the preservation of the environment for future generations.…”
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Sown wildflower fields and hedgerows synergistically promote insectivorous bats by Franziska Peter, Rebecca Bleumer, Christina Martinez Christophersen, Sally Matern, Tim Diekötter

“…Finally, both bat activity as well as insect abundances shifted towards the ecotone when distance to the nearest forest patch was high. We showed that synergies of hedgerows and wildflower fields promote benefits of the latter for both edge‐ and open‐space foraging bats, particularly in homogeneous and structurally poor agricultural landscapes. …”
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Trees, seeds and seedlings analyses in the process of obtaining a quality planting material for black locust (Robinia pseudoacacia L.) by Andrea M. ROMAN, Irina M. MORAR, Alina M. TRUTA, Catalina DAN, Adriana F. SESTRAS, Liviu HOLONEC, Florin IORAS, Radu E. SESTRAS

“…In Romania, black locust has established itself as a forest tree appreciated for multiple uses. The objective of the hereby study was to identify a quality planting material at black locust using seeds from trees with superior traits from five stands geographically close, located in North-western of Romania. …”
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Air temperature estimation based on environmental parameters using remote sensing data by Chenoor Mohammadi, Manouchehr Farajzadeh, Yousef Ghavdel Rahimi, Abbas Ali Aliakbar Bidokhti

“…With considering different land uses, the highest R2 was related to waters and urban areas (96 to 99%) in warm months, and the lowest R2 was for mixed forest and grassland (between 15 and 36%) in cold months.…”
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eNSMBL-PASD: Spearheading early autism spectrum disorder detection through advanced genomic computational frameworks utilizing ensemble learning models by Ayesha Karim, Nashwan Alromema, Sharaf J Malebary, Faisal Binzagr, Amir Ahmed, Yaser Daanial Khan

“…Several ensemble classification methods, including Extreme Gradient Boosting, Random Forest, Light Gradient Boosting Machine, ExtraTrees, and a stacked ensemble of classifiers, were applied to assess the predictive power of the genomic features. …”
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