Adaptive meta-modeling of evapotranspiration in arid agricultural regions of Saudi Arabia using climatic factors, drought indices and MODIS data by Osama Elsherbiny, Salah Elsayed, Obaid Aldosari, Muhammad Sohail Memon, Ahmed Elbeltagi

“…Machine learning models, including the backpropagation neural network (BPNN) and XGBoost Regressor (XGB), enhanced with an adaptive meta-model (AMM) strategy, were evaluated for predicting monthly AET. …”
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Clinical prediction of intravenous immunoglobulin-resistant Kawasaki disease based on interpretable Transformer model. by Gahao Chen, Ziwei Yang

“…Local interpretability analysis revealed distinct correlation patterns with IVIG resistance:AST, CRP, and N demonstrated significant positive correlations, where elevated values corresponded to increased IVIG resistance risk; PLT and ALB showed negative correlations, with higher levels associated with reduced resistance probability. …”
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Mechanistic role of the KRTAP5-AS1/miR-199b-5p/CYP19A1 axis in polycystic ovary syndrome pathogenesis by Ping Tao, Xiaohong Yan, Zhanxiang Wang

“…Bioinformatics analyses including ceRNA network construction predicted the KRTAP5-AS1/miR-199b-5p/CYP19A1 regulatory axis, which was experimentally validated through dual-luciferase reporter assays. qRT-PCR confirmed the expression patterns of these molecules in expanded clinical cohorts (38 PCOS vs. 30 controls), with Pearson correlation analysis examining relationships between gene expression and clinical parameters. …”
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Beyond the Backbone: A Quantitative Review of Deep-Learning Architectures for Tropical Cyclone Track Forecasting by He Huang, Difei Deng, Liang Hu, Yawen Chen, Nan Sun

“…We categorize all DL-based TC tracking models according to the architecture, including recurrent neural networks (RNNs), convolutional neural networks (CNNs), Transformers, graph neural networks (GNNs), generative models, and Fourier-based operators. …”
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MultiRepPI: a cross-modal feature fusion-based multiple characterization framework for plant peptide-protein interaction prediction by Yu Zhiguo, Li Zixuan, Li Peng

“…First, most methods fail to adequately integrate multimodal information such as sequence, structure, and disorder properties, leading to inadequate characterization of complex interaction patterns. Second, existing models have difficulty in capturing cross-dependent features between peptides and proteins, limiting the prediction performance. …”
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Altered microstate C and D dynamics in high social anxiety: a resting-state EEG study by Huoyin Zhang, Huoyin Zhang, Binyu Peng, Yutong Liu, Yu Xi, Yi Lei, Yi Lei

“…IntroductionSocial anxiety is characterized by excessive fear of negative evaluation and avoidance in social situations. While its neural processing patterns are well-documented, the millisecond-level temporal dynamics of brain functional networks remain poorly understood. …”
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Spatiotemporal Forecasting of Solar and Wind Energy Production: A Robust Deep Learning Model with Attention Framework by Md. Shadman Abid, Razzaqul Ahshan, Mohammed Al-Abri, Rashid Al Abri

“…The variability in the spatiotemporal distribution of power generation is a significant challenge for accurately predicting renewable energy production patterns. Furthermore, numerous forms of unforeseen data contamination degrade the precision of forecasts since superfluous data points adversely affect the regression model. …”
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Machine learning-based model for behavioural analysis in rodents applied to the forced swim test by Andrea Della Valle, Sara De Carlo, Gregorio Sonsini, Sebastiano Pilati, Andrea Perali, Massimo Ubaldi, Roberto Ciccocioppo

“…Therefore, they are often unable to accurately differentiate the major subtypes of movement patterns, such as swimming and climbing. To address these limitations, we propose a novel approach based on machine learning (ML) using a three-dimensional residual convolutional neural network (3D RCNN) that processes video pixels directly, capturing the spatiotemporal dynamics of rodent behaviour. …”
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Mechanical Properties of Large-Volume Waste Concrete Lumps Cemented by Desert Mortar: Laboratory Tests by Hui Chen, Zhiyuan Qi, Baiyun Yu, Xinyu Li

“…DIC maps showed unidirectional energy release in pure-mortar specimens, whereas aggregate-containing specimens displayed chaotic energy patterns. This confirms that aggregates alter stress fields at crack tips and redirect energy-dissipation paths, shifting failure from single-crack propagation to a multi-scale damage network. …”
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Identification of PRKCQ-AS1 as a Keratinocyte-Derived Exosomal lncRNA That Promotes Th17 Differentiation and IL-17 secretion in Psoriasis Through Bioinformatics, Machine L... by Gao P, Gao X, Lin L, Zhang M, Luo D, Chen C, Li Y, He Y, Liu X, Shi C, Yang R

“…This study investigates key exosomal ncRNAs regulating the Th17/IL-17 axis in psoriasis and their mechanisms.Methods: We integrated bulk RNA sequencing datasets from the GEO database to construct and evaluate exosome-related patterns. Subsequently, exosome-related ncRNAs in psoriasis lesions were identified primarily through weighted gene co-expression network analysis and five machine learning algorithms. …”
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Effectiveness of unilateral lower-limb exoskeleton robot on balance and gait recovery and neuroplasticity in patients with subacute stroke: a randomized controlled trial by Congcong Huo, Guangjian Shao, Tiandi Chen, Wenhao Li, Jue Wang, Hui Xie, Yan Wang, Zengyong Li, Pengyuan Zheng, Liguo Li, Luya Li

“…In addition, the cortical activation pattern related to robot-assisted training was measured before and after intervention via functional near-infrared spectroscopy. …”
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Transformer-based heart language model with electrocardiogram annotations by Stojancho Tudjarski, Marjan Gusev, Evangelos Kanoulas

“…Abstract This paper explores the potential of transformer-based foundation models to detect Atrial Fibrillation (AFIB) in electrocardiogram (ECG) processing, an arrhythmia specified as an irregular heart rhythm without patterns. We construct a language with tokens from heartbeat locations to detect irregular heart rhythms by applying a transformers-based neural network architecture previously used only for building natural language models. …”
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Studying the Role of Vegetarianism as a Potential Strategy for Cancer Prevention and Treatment, a Bibliometric Analysis by Maria Chrysafi, Maria Gialeli, Constantinos Giaginis, Andreas Y. Troumbis, Georgios K. Vasios

“…Author keywords, their co-occurrence network, and thematic trends were studied. Conclusions: Through synthesizing and critically evaluating insights from the scientific literature, we aim to contribute to the understanding of the potential benefits of vegetarianism in cancer prevention and management. …”
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O2O Recycling Closed-Loop Supply Chain Modeling Based on Classification Process considering Environmental Index by Shidi Miao, Di Liu, Junfeng Ma

“…It is premised on the double lines of online service platform and offline recycling service system, thus forming a pattern of online disposal and offline trading. This mode is capable of integrating upstream and downstream resources on the network platform, creating a recycling and processing mode for the entire industry chain of waste sorting, and developing a circular development mode featuring “resources-products-waste-renewable resources” to realize the recycling of resources. …”
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Synthesis and Characterization of Chemically Cross-Linked Acrylic Acid/Gelatin Hydrogels: Effect of pH and Composition on Swelling and Drug Release by Syed Majid Hanif Bukhari, Samiullah Khan, Muhammad Rehanullah, Nazar Mohammad Ranjha

“…The prepared samples were evaluated for porosity and sol-gel fraction analysis. …”
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Comprehensive analysis of extensive drug-resistant Salmonella Typhi in Gujarat region, India: genomic findings and prospective alternative therapy by Sadanand Dangari Akshay, Heli Upadhyaya, Nitin Shukla, Rohit Bhattacharjee, Sunilkumar Das, Urmi Vyas, Priyank Chavda, Nimesh Patel, Dixsha Jamkhandi, Pritesh Sabara, Neeta Khandelwal, Sumeeta Soni, Kamlesh Upadhyaya, Jayesh Katira, Geeti Maheshwari, Madhvi Joshi, Devarshi Gajjar, Chaitanya Joshi

“…Typhi, in Gujarat, this study evaluated the antibiotic resistance patterns of S. …”
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Study on multi-scale oil displacement mechanism polymer/nanoparticle composite flooding by Bei Wei, Bei Wei, Ningyu Zheng, Ningyu Zheng, Yu Xue, Yu Xue, Jian Hou, Jian Hou, Yongsheng Liu, Yongsheng Liu, Zhixin Guo, Zhixin Guo, Xuwen Qin, Xuwen Qin, Qingjun Du, Qingjun Du

“…Subsequently, we conducted two-dimensional microscopic flooding experiments to evaluate sweep efficiency and analyze residual oil distribution patterns. …”
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A comparative study of multivariate CNN, BiLSTM and hybrid CNN–BiLSTM models for forecasting foreign exchange rate using deep learning by Elysee Nsengiyumva, Joseph K. Mung’atu, Charles Ruranga

“…This study evaluates the forecasting capabilities of multivariate Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and a hybrid CNN-BiLSTM model for predicting daily rate returns of USD, EUR and GBP in Rwanda’s foreign exchange market from 2012 to 2025. …”
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Optimization of Bus Rerouting to Alleviate the Impact of Rail Transit Construction by Yiran Wang, Jingxu Chen, Xinlian Yu, Qinhe An, Xu Wu

“…For each rerouting line, we design two adjustment patterns and associated candidate alternative rerouting sets. …”
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A Comparative Study of Machine Learning Algorithms for Intrusion Detection Systems using the NSL-KDD Dataset by Rulyansyah Permata Putra, Amarudin Amarudin

“…In today’s digital era, cyberattacks are becoming increasingly complex, rendering traditional rule-based Intrusion Detection Systems (IDS) often ineffective in recognizing new attack patterns. The primary objective of this study is to design and implement a machine learning model for detecting network intrusions efficiently while minimizing latency, through a comparative analysis of several algorithms: Decision Tree, Random Forest, Support Vector Machine (SVM), and Boosting. …”
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