Hadron Identification Prospects with Granular Calorimeters by Andrea De Vita, Abhishek, Max Aehle, Muhammad Awais, Alessandro Breccia, Riccardo Carroccio, Long Chen, Tommaso Dorigo, Nicolas R. Gauger, Ralf Keidel, Jan Kieseler, Enrico Lupi, Federico Nardi, Xuan Tung Nguyen, Fredrik Sandin, Kylian Schmidt, Pietro Vischia, Joseph Willmore

“…Additionally, the results highlight the importance of shower radius, energy fractions, and timing variables in distinguishing particle types. The XGBoost model demonstrated computational efficiency and interpretability advantages over deep learning for tabular data structures, while achieving similar classification performance. …”
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Simulation and Identification of the Habitat of Antarctic Krill Based on Vessel Position Data and Integrated Species Distribution Model: A Case Study of Pumping-Suction Beam Trawl... by Heng Zhang, Yuyan Sun, Hanji Zhu, Delong Xiang, Jianhua Wang, Famou Zhang, Sisi Huang, Yang Li

“…Variables of marine environment, including sea surface temperature (SST), sea surface height (SSH), chlorophyll concentration (CHL), sea ice concentration (SIC), sea surface salinity (SSS), and spatial factor Geographical Offshore Linear Distance (GLD) were combined and input into the ISDM for simulating and predicting the spatial distribution of the habitat. …”
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Improving daily reference evapotranspiration forecasts: Designing AI-enabled recurrent neural networks based long short-term memory by Mumtaz Ali, Jesu Vedha Nayahi, Erfan Abdi, Mohammad Ali Ghorbani, Farzan Mohajeri, Aitazaz Ahsan Farooque, Salman Alamery

“…During the model development stage, the optimal variables were determined successfully via heatmaps for precise assessment of ETo in both stations. …”
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Automated classification of midpalatal suture maturation stages from CBCTs using an end-to-end deep learning framework by Omid Halimi Milani, Lauren Mills, Amanda Nikho, Marouane Tliba, Veerasathpurush Allareddy, Rashid Ansari, Ahmet Enis Cetin, Mohammed H. Elnagar

“…The feature extraction integrates Convolutional Neural Networks (CNN) architectures, such as EfficientNet and ResNet18, alongside our novel Multi-Filter Convolutional Residual Attention Network (MFCRAN) enhanced with Discrete Cosine Transform (DCT) layers. …”
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Assessment of a Hyperspectral Remote Sensing Model Performance for Particulate Phosphorus in Optically Shallow Lake Water by Banglong Pan, Wuyiming Liu, Zhuo Diao, Qianfeng Gao, Lanlan Huang, Shaoru Feng, Juan Du, Qi Wang, Jiayi Li, Jiamei Cheng

“…It also serves as one of the most significant sources of phosphorus for primary productivity, serving as a possible source of soluble reactive phosphorus, and contributing a sizable amount of the total phosphorus (TP), so monitoring the spatial and temporal variability of PP is crucial for understanding eutrophication in water bodies. …”
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Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features by Ameya Harmalkar, Roshan Rao, Yuxuan Richard Xie, Jonas Honer, Wibke Deisting, Jonas Anlahr, Anja Hoenig, Julia Czwikla, Eva Sienz-Widmann, Doris Rau, Austin J. Rice, Timothy P. Riley, Danqing Li, Hannah B. Catterall, Christine E. Tinberg, Jeffrey J. Gray, Kathy Y. Wei

“…One important modular component of msAbs is the single-chain variable fragment (scFv). Despite the exquisite specificity and affinity of these scFv modules, their relatively poor thermostability often hampers their development as a potential therapeutic drug. …”
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Predicting future evapotranspiration based on remote sensing and deep learning by Xin Zheng, Sha Zhang, Shanshan Yang, Jiaojiao Huang, Xianye Meng, Jiahua Zhang, Yun Bai

“…Study focus: This study validates the efficiency of Convolutional Long Short-Term Memory Network (ConvLSTM) models for site-scale ETa prediction. …”
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Transformers for Neuroimage Segmentation: Scoping Review by Maya Iratni, Amira Abdullah, Mariam Aldhaheri, Omar Elharrouss, Alaa Abd-alrazaq, Zahiriddin Rustamov, Nazar Zaki, Rafat Damseh

“…Manual segmentation is time-consuming, apart from being prone to human error and variability. Transformers are a promising deep learning approach for automated medical image segmentation. …”
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Weed Detection Algorithms in Rice Fields Based on Improved YOLOv10n by Yan Li, Zhonghui Guo, Yan Sun, Xiaoan Chen, Yingli Cao

“…Accurate weed detection is vital for implementing variable spraying with unmanned aerial vehicles (UAV) for weed control. …”
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Advanced Hydro-Informatic Modeling Through Feedforward Neural Network, Federated Learning, and Explainable AI for Enhancing Flood Prediction by Shahariar Hossain Mahir, Md Tanjum An Tashrif, Md Ahsan Karim, Dipanjali Kundu, Anichur Rahman, Md. Amir Hamza, Fahmid Al Farid, Abu Saleh Musa Miah, Sarina Mansor

“…To address this, our research adopts the Federated Learning (FL) framework in an effort to train state-of-the-art deep learning models like Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), Feed-Forward Neural Network (FNN) and Temporal Fusion Transformer-Convolutional Neural Network (TFT -CNN) on a 78-year dataset of rainfall, river flow, and meteorological variables from Sylhet and its upstream regions in Meghalaya and Assam, India. …”
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One size does not fit all in evaluating model selection scores for image classification by Nermeen Abou Baker, Uwe Handmann

“…This study evaluates 14 transferability scores on 11 benchmark datasets. It includes both Convolutional Neural Network (CNN) and Vision Transformer (ViT) models and ensures consistency in experimental conditions to counter the variability in previous research. …”
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Machine Learning-Driven D-Glucose Prediction Using a Novel Biosensor for Non-Invasive Diabetes Management by Pardis Sadeghi, Shahriar Noroozizadeh, Rania Alshawabkeh, Nian Xiang Sun

“…To address challenges like data imbalance, limited samples, and inter-sensor variability, synthetic data generation methods like Synthetic Minority Oversampling Technique and Generative Adversarial Networks were employed. …”
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On variational formulations of inner boundary value problems for infinite systems of elliptic equations of special kind by Yu. A. Muzychuk, R. S. Chapko

“…We consider boundary value problems for infinite triangular systems of elliptic equations with variable coefficients in 3d Lipschitz domains. Variational formulations of Dirichlet, Neumann and Robin problems are received and their well posedness in corresponding Sobolev spaces is established. …”
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Artificial intelligence in acupuncture: bridging traditional knowledge and precision integrative medicine by Guo-Liang Hou, Bao-Qiang Dong, Ben-Xing Yu, Jian-Yu Dai, Xing-Xing Lin, Ze-Zhong Cheng

“…Despite their potential, current implementations are constrained by limited and heterogeneous datasets, annotation variability, and gaps in clinical validation. We analyze key methodological innovations and challenges, and recommend future directions including the construction of federated multimodal data platforms, development of explainable AI frameworks, and promotion of open science practices. …”
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Development and evaluation of deep learning models for cardiotocography interpretation by Nicole Chiou, Nichole Young-Lin, Christopher Kelly, Julie Cattiau, Tiya Tiyasirichokchai, Abdoulaye Diack, Sanmi Koyejo, Katherine Heller, Mercy Asiedu

“…Abstract The variability in the visual interpretation of cardiotocograms (CTGs) poses substantial challenges in obstetric care. …”
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Predicting Epileptic Seizures Using EfficientNet-B0 and SVMs: A Deep Learning Methodology for EEG Analysis by Yousif A. Saadoon, Mohamad Khalil, Dalia Battikh

“…The EfficientNet-B0 backbone ensures high accuracy with computational efficiency, while the SVM ensemble enhances prediction reliability by mitigating noise and variability in EEG data.…”
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Improving Hand Pose Recognition Using Localization and Zoom Normalizations over MediaPipe Landmarks by Miguel Ángel Remiro, Manuel Gil-Martín, Rubén San-Segundo

“…This can be mitigated by employing MediaPipe to facilitate the efficient extraction of representative landmarks from static images combined with the use of Convolutional Neural Networks. Extracting these landmarks from the hands mitigates the impact of lighting variability or the presence of complex backgrounds. …”
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A dual-branch deep learning model based on fNIRS for assessing 3D visual fatigue by Yan Wu, Yan Wu, Yan Wu, TianQi Mu, SongNan Qu, XiuJun Li, XiuJun Li, XiuJun Li, Qi Li, Qi Li, Qi Li

“…Given the time-series nature of fNIRS data and the variability of fatigue responses across different brain regions, a dual-branch convolutional network was constructed to separately extract temporal and spatial features. …”
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ON PRESENTATION OF GELFOND—LEONTIEV OPERATORS OF GENERALIZED DIFFERENTIATION IN SIMPLY CONNECTED REGION by Alexander Vasilyevich Bratishchev

“…It is known to be presented as an operator of general complex convolution. The convolution kernel is generated by the many-valued function of one variable. …”
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Fed-CL- an atrial fibrillation prediction system using ECG signals employing federated learning mechanism by Fayez Saud Alreshidi, Mohammad Alsaffar, Rajeswari Chengoden, Naif Khalaf Alshammari

“…In addition, the article explores the importance of analysing mean heart rate variability to differentiate between healthy and abnormal heart rhythms. …”
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