The Immune‐Related Gene CD48 Is a Prognostic Biomarker Associated With the Breast Cancer Tumor Microenvironment by Yi Zhao, Hengheng Zhang, Wenwen Wang, Guoshuang Shen, Miaozhou Wang, Zhen Liu, Jiuda Zhao, Jinming Li

“…Conclusions CD48 may serve as an effective biomarker for predicting BCa patient prognosis and a potential immune‐related therapeutic target.…”
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Optimization of Adaptive I²H ∞ Control Method Based on Multiple Input Sensors by Yu Gu, Hanyang Li, Zeting Mei, Hao Wen, Yuanxiong Jin, Wenxuan Dong

“…The core contributions of this study include: 1) Designing a multi-sensor current reference estimator to dynamically generate the optimal electromagnetic torque through state variables such as wheel speed, acceleration, slope, and human factor database (heart rate, subjective score, fatigue index) to achieve real-time prediction of rider demand; 2) Proposing an adaptive current reference value estimation algorithm that integrates feedforward compensation and error feedback to ensure smooth switching of assistance modes and suppress sensor noise; 3) Developing an intention-induced H<inline-formula> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> robust current tracking controller that significantly enhances the system&#x2019;s robustness to parameter fluctuations and external disturbances by optimizing the H<inline-formula> <tex-math notation="LaTeX">$\infty $ </tex-math></inline-formula> norm of the closed-loop transfer function, while supporting personalized riding assistance.…”
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Assessing the Spatial Benefits of Green Roofs to Mitigate Urban Heat Island Effects in a Semi-Arid City: A Case Study in Granada, Spain by Francisco Sánchez-Cordero, Leonardo Nanía, David Hidalgo-García, Sergio Ricardo López-Chacón

“…Green roofs (GRs) may minimize land surface temperature (LST) by modifying albedo. This research predicts, assesses, and measures the impact of reducing the LST by applying green roofs in buildings by using a Random Forest algorithm and different remote sensing methods. …”
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The Water Table Model (WTM) (v2.0.1): coupled groundwater and dynamic lake modelling by K. L. Callaghan, K. L. Callaghan, K. L. Callaghan, A. D. Wickert, A. D. Wickert, A. D. Wickert, R. Barnes, J. Austermann

“…However, we currently lack the capacity to simulate and predict these terrestrial water changes across the full range of relevant spatial (watershed to global) and temporal (monthly to millennial) scales. …”
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AIpollen: An Analytic Website for Pollen Identification Through Convolutional Neural Networks by Xingchen Yu, Jiawen Zhao, Zhenxiu Xu, Junrong Wei, Qi Wang, Feng Shen, Xiaozeng Yang, Zhonglong Guo

“…For the optimization algorithm, we opted for the Adam optimizer and utilized the cross-entropy loss function. …”
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Bayesian inference of radial impurity transport in the pedestal of ASDEX Upgrade discharges using charge-exchange spectroscopy by T. Gleiter, R. Dux, F. Sciortino, T. Odstrčil, D. Fajardo, C. Angioni, J. Buchner, R.M. McDermott, T. Hayward-Schneider, G.F. Harrer, M. Faitsch, M. Griener, R. Fischer, E. Wolfrum, U. Stroth, the ASDEX Upgrade Team

“…This supports the hypothesis of additional transport associated with the predicted high-n ballooning-unstable region and the observed quasi- coherent mode.…”
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Berg Balance Scale Scoring System for Balance Evaluation by Leveraging Attention-Based Deep Learning with Wearable IMU Sensors by Zhangli Lu, Huiying Zhou, Honghao Lyu, Haiteng Wu, Shaohua Tian, Geng Yang

“…The key limitations included: a limited generalizability to severely impaired patients who were unable to walk independently, and the inability to predict the score of individual tasks.…”
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Chemical Composition, Chemometric Analysis, and Sensory Profile of <i>Santolina chamaecyparissus L.</i> (<i>Asteraceae</i>) Essential Oil: Insights from a Case Study in Serbia and... by Biljana Lončar, Mirjana Cvetković, Milica Rat, Jovana Stanković Jeremić, Jelena Filipović, Lato Pezo, Milica Aćimović

“…Chemometric analysis proved effective in predicting the oil’s composition, and sensory evaluation revealed a herbal aroma with earthy, woody, and camphoraceous notes. …”
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Efficient evaluation of osteotoxicity and mechanisms of endocrine disrupting chemicals using network toxicology and molecular docking approaches: triclosan as a model compound by Zhongyuan Wang, Jian Wang, Qiang Fu, Hui Zhao, Zaijun Wang, Yuzhong Gao

“…Subsequent analysis using STRING and Cytoscape, applying the Matthews correlation coefficient algorithm, identified five core genes: STAT3, TP53, EGFR, MYC, and JUN. …”
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Depth Integrated Multi-Task Prototypical Learning With Self Refinement for Unsupervised Domain Adaptation by Antonio Dauphin Fernando, Thumma Anirudh, Selvaraj Palanisamy, Karthika Prasad, Katia Alexander, Pandiyarasan Veluswamy, Rohini Palanisamy

“…Additionally, this pipeline integrates a Self-Refinement learning (SRL) algorithm that generates cross-domain pseudo-labels, which are leveraged to generate refined targets for further self-supervised training. …”
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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

“…A well-known outlier detection algorithm is applied on the gathered data to assess the data reliability before model development. …”
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Client Selection for Generalization in Accelerated Federated Learning: A Multi-Armed Bandit Approach by Dan Ben Ami, Kobi Cohen, Qing Zhao

“…In this paper, we present a novel multi-armed bandit (MAB)-based approach for client selection to minimize the training latency without harming the ability of the model to generalize, that is, to provide reliable predictions for new observations. We develop a novel algorithm to achieve this goal, dubbed Bandit Scheduling for FL (BSFL). …”
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Deep Learning-Based Secrecy Performance of UAV-IRS NOMA Systems With Friendly Jamming by Kajal Yadav, Prabhat K. Upadhyay, Jules M. Moualeu, Amani A. F. Osman, Pedro H. J. Nardelli

“…Subsequently, the accuracy of the proposed theoretical framework is validated through comprehensive Monte Carlo simulations. We also propose an algorithm that determines an optimal power allocation. …”
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Research on power system transformation based on edge node technology under the background of carbon neutrality by LI Jin, GAO Hongliang, LIU Kemeng, XIE Hu

“…A stable time series is established according to the historical data of the power system, and then, the medium and long-term power demand is predicted through the exponential smoothing method as the basis of system transformation design. …”
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Machine Learning-Driven Optimization of Transport Layers in MAPbI&#x2083; Perovskite Solar Cells for Enhanced Performance by Velpuri Leela Devi, Piyush Kuchhal, Debasis de, Abhinav Sharma, Neeraj Kumar Shukla, Mona Aggarwal

“…In this research work, among those eight ML models, the XGBoost algorithm shows high accuracy for predicting the power conversion efficiency (PCE) of the cell, achieving root mean square error (RMSE) of 0.052 and a coefficient of determination (R2) of 0.999. …”
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Data-driven machine learning with lattice distortion and thermodynamic parameters guided strength optimization of refractory high-entropy alloys by Shujian Ding, Yifan Zhang, Siyang Lei, Xiang Weng, Wenhui Li, Wei Ren, Jian Chen, Weili Wang

“…The LightGBM algorithm was utilized to train the model based on a RHEA yield strength dataset. …”
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Rapid screening and optimization of CO2 enhanced oil recovery operations in unconventional reservoirs: A case study by Shuqin Wen, Bing Wei, Junyu You, Yujiao He, Qihang Ye, Jun Lu

“…Based on the results of model interpretability, the genetic algorithm (GA) was coupled with RF (RF-GA model) to optimize the CO2-EOR process. …”
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Use of stepwise m5 model tree to forecast the P24max based on teleconnection indices by Golnar Ghanbarzadeh, Khalil Ghorbani, Meysam Salarijazi, Chooghi Bairam Komaki, Laleh Rezaei Ghaleh

“…The stepwise execution of the M5 model tree showed that the algorithm follows a greedy approach, and it is not necessary to use all variables to predict P24max. …”
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A hybrid model based on the photovoltaic conversion model and artificial neural network model for short-term photovoltaic power forecasting by Ran Chen, Shaowei Gao, Yao Zhao, Dongdong Li, Shunfu Lin

“…The proposed model consists of an improved artificial neural network (ANN) algorithm and a PV power conversion model. First, the ANN model is designed to forecast the plane of array (POA) irradiance and ambient temperature. …”
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Enhancing Process Control in Agriculture: Leveraging Machine Learning for Soil Fertility Assessment by Ashutosh Sarangi, Sailesh Kumar Raula, Sohamdev Ghoshal, Swadhin Kumar, Chinta Sai Kumar, Neelamadhab Padhy

“…<b>Result</b>: The results demonstrated that the machine learning classifier significantly improves prediction accuracy. We used LR, KNN, NB, and DT classifiers to increase the accuracy, as well as to increase the efficiency of the soil fertility assessment. …”
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