Daily Runoff Prediction Model Based on Multivariate Variational Mode Decomposition and Correlation Reconstruction by DING Jie, TU Peng-fei, FENG Yu, ZENG Huai-en

“…For the prediction of random terms, the R2 for random term prediction at Ankang and Baihe stations was 0.80 and 0.74, respectively. …”
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Unveiling the Urobiome: Getting to know Actinotignum schaalii and its role as a potential uropathogen by Ms Jamisha Francis, Ms Mollie Gidney, Mr. Seth Reasoner, Mr. Johnathan Schmidtz, Ms. Maria Hadjifrangiskou

“…Transmission electron micrographs detected the presence of an s-layer, metal storage organelles, and intracellular vesicles. …”
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A Nested Named Entity Recognition Model Robust in Few-Shot Learning Environments Using Label Description Information by Hyunsun Hwang, Youngjun Jung, Changki Lee, Wooyoung Go

“…We enhance the Biaffine nested NER model by modifying its output layer to incorporate label semantic information through a novel label description embedding (LDE) approach, improving performance with limited training data. …”
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Ransomware Family Attribution With ML: A Comprehensive Evaluation of Datasets Quality, Models Comparison, and a Simulated Deployment by Emilio Rios-Ochoa, Jesus Arturo Perez-Diaz, Enrique Garcia-Ceja, Gerardo Rodriguez-Hernandez

“…As for detection, while Random Forest (RF) achieved the highest offline accuracy (100%), surpassing Multi Layer Perceptron (MLP) with 99.48%, the latter performed better in deployment, reaching 70% accuracy. …”
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Urban Built Environment as a Predictor for Coronary Heart Disease—A Cross-Sectional Study Based on Machine Learning by Dan Jiang, Fei Guo, Ziteng Zhang, Xiaoqing Yu, Jing Dong, Hongchi Zhang, Zhen Zhang

“…This study evaluates five machine learning models, including decision tree (DT), random forest (RF), eXtreme gradient boosting (XGBoost), multi-layer perceptron (MLP), and support vector machine (SVM), and compares them with multiple linear regression models. …”
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Predicting the thickness of alpine meadow soil on headwater hillslopes of the Qinghai-Tibet Plateau by Xiaole Han, Jintao Liu, Pengfei Wu, Zhenghong Yu, Xiao Qiao, Hai Yang

“…Field investigations revealed that organic matter accumulation and freeze–thaw cycles are dominant pedogenic factors, producing a root-dense turf layer beneath a dark humus horizon. Freeze-thaw dynamics also contribute to geomorphological features such as landslides and stone stripes. …”
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Predicting high-need high-cost pediatric hospitalized patients in China based on machine learning methods by Peng Zhang, Bifan Zhu, Xing Chen, Linan Wang

“…The machine-learning-based models were developed to predict HNHC status using logistic regression, k-nearest neighbors (KNN), random forest (RF), multi-layer perceptron (MLP), and Naive Bayes. …”
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Developing a high-performance AI model for spontaneous intracerebral hemorrhage mortality prediction using machine learning in ICU settings by Xiao-Han Vivian Yap, Kuan-Chi Tu, Nai-Ching Chen, Che-Chuan Wang, Chia-Jung Chen, Chung-Feng Liu, Tee-Tau Eric Nya, Ching-Lung Kuo

“…Predictive models were developed using logistic regression, Random forest, LightGBM, XGBoost, and Multi-layer Perceptron (MLP), with assessments of feature importance, and Area under the curve (AUC). …”
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Machine Learning for Predicting Zearalenone Contamination Levels in Pet Food by Zhenlong Wang, Wei An, Jiaxue Wang, Hui Tao, Xiumin Wang, Bing Han, Jinquan Wang

“…Machine learning algorithms, including liner regression, k-nearest neighbors, support vector machines, random forests, XGBoost, and multi-layer perceptron (MLP), were used to classify the samples based on their volatile profiles. …”
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Exploring Consistent Feature Selection for Software Fault Prediction: An XAI-Based Model-Agnostic Approach by Adam Khan, Asad Ali, Jahangir Khan, Fasee Ullah, Muhammad Faheem

“…In this study we evaluated the consistency of two prominent XAI-based techniques, Permutation Feature Importance (PFI) and SHapley Additive exPlanations (SHAP), across five ML models: Linear Regression (LR), Multi-layer Perceptron (MLP), Random Forest (RF), Decision Trees (DT), and Support Vector Machines (SVM). …”
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Refining source-specific lung cancer risk assessment from PM2.5-bound PAHs: Integrating component-based potency factors and machine learning in Ningbo, China by Lord Famiyeh, Ke Chen, Fiseha Berhanu Tesema, Celeb Kelly, Dongsheng Ji, Hang Xiao, Lei Tong, Zongshuang Wang, Jun He

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Using artificial neural networks to predict indoor particulate matter and TVOC concentration in an office building: Model selection and method development by Saren Gaowa, Zhen Zhang, Jianchun Nie, Linxiao Li, Han A-ru, Zhili Yu

“…The MLNN model and the random forest (RF) classification method were further used to predict indoor TVOC concentrations. …”
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Primary Controlling Factors of Apatite Trace Element Composition and Implications for Exploration in Orogenic Gold Deposits by Genshen Cao, Huayong Chen, Yu Zhang, Weipin Sun, Junfeng Zhao, Hongtao Zhao, Hao Wang

“…Feature importance analysis (Gini decrease and hidden layer weights) suggest that Pb, As, U, Sr, Eu, Mn, and Fe are the important parameters. …”
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Short-term energy and meteorological impacts on Thanksgiving CO2 in Salt Lake City by Ju-Mee Ryoo, Inez Fung, James R Ehleringer

“…This increase is partially attributed to elevated energy-related emissions — especially residential sources — and meteorological factors such as weak wind speeds, cold temperature, and a low planetary boundary layer height (PBLH). While CO _2 emissions and mole fraction patterns align over time, notable spatial differences exist. …”
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Pengamanan Citra Digital Menggunakan Kriptografi DnaDan Modified LSB by Sabrina Adela Br Sibarani, Andreas Munthe, Ronsen Purba, Ali Akbar Lubis

“…The second layer involves DNA characteristics, utilizing nucleotide bases (A, T, C, G) to encrypt image data at the molecular level, resulting in higher randomness. …”
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Enhancing landslide susceptibility modelling through a novel non-landslide sampling method and ensemble learning technique by Chao Zhou, Yue Wang, Ying Cao, Ramesh P. Singh, Bayes Ahmed, Mahdi Motagh, Yang Wang, Ling Chen, Guangchao Tan, Shanshan Li

“…About 70% of the landslide pixels were randomly considered for training, and the remaining 30% were used for validation. …”
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Machine learning-based prediction of adverse pregnancy outcomes in antiphospholipid syndrome using pregnancy antibody levels by Wanqing Liu, Ju Huang, Jun Xiao, Shanling Yan

“…Six machine learning models were developed: Light Gradient Boosting Machine (LGBM), CatBoost, Extreme Gradient Boosting (XGBoost), Logistic Regression (LR), Random Forest (RF), and Multi-Layer Perceptron (MLP). …”
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Effects of different types of organic fertilizers on soil, growth and fruiting characteristics, and nut quality in walnut orchards by XU Yongjie, JIANG Dezhi, LI Li, WANG Ruiwen, WANG Daiquan, WANG Xiaofei, WANG Qizhu

“…The soil porosity in the >20-40 cm soil layer, the available sulfur content in the 0-40 cm soil layer, and the available potassium content in the 0-20 cm soil layer were significantly increased in the five treatments (p<0.05, the same below). ②Different organic fertilizers exhibited varying effects on soil improvement. …”
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Investigation of Geogrid Aperture Size Effects on Subbasesubgrade Stabilization of Asphalt Pavements by Tuba Sert, Muhammet Vefa Akpınar

“…For this purpose, a series of laboratory large scale pullout tests was carried out with three different aperture size geogrid samples randomly sampled from a single manufacturer. It was found that geogrids are unique in their pullout performance within pavement subbase layer structure based on their aperture sizes. …”
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RIS-assisted terahertz frequency band vehicle network capacity optimization by Fatang CHEN, Xiaoling LIU, Dan WANG, Ruofan ZHANG

“…In order to alleviate the spectrum scarcity and capacity limitation of the current wireless system, the terahertz frequency band was introduced and the reconfigurable intelligent surface (RIS) was used for auxiliary communication to construct a downlink vehicle network.Considering the constraints of limited total system power, QoS constraints of cellular mobile users, and randomness constraints of vehicle and user locations, a hybrid optimization model was established for optimal power allocation and optimal deployment of RIS with the aim of maximizing the total rate of vehicle users.Based on the balance method, linear transformation method, and element elimination method, the original NP-hard problem was transformed into a convex optimization problem with complex correlation constraints and multivariate coupling.The inner layer iteration was based on the Lagrange multiplier method to solve the optimal power allocation, and the outer layer iteratively solved the optimal deployment of RIS based on a improved genetic algorithm.The simulation results show that rationally deploying the number and distribution density of RIS nodes based on optimal power allocation can save costs while achieving a higher target total capacity.…”
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