An epidemical model with nonlocal spatial infections by Su Yang, Weiqi Chu, Panayotis Kevrekidis

“…Stationary states and their stability analysis offer a perspective on the early spatial growth of the infection. Evolutionary computational dynamics enable visualization of the spatio-temporal progression of infection and recovery, allowing for an appreciation of the effect of varying parameters of the nonlocal kernel, such as, e.g., its width parameter. …”
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SYSTEMIC GENERATION OF SOLVING NECESSARY PROFESSIONAL THE TASKS by Vladimir M. Nesterenko

“…The article explains the widespread use of mathematical modeling and computational experiment in solving scientific and technical problems. …”
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Predicting the spatio-temporal reproductive potential of Aedes aegypti by Mr Tarek Alrefae

“…We use approximate Bayesian computation (ABC) and aegypti abundance data to fit two unknown scaling parameters of Index Q and propose an approximate global solution for making projections in cases where local data is unavailable. …”
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Frequency-resolved Time Lags due to X-Ray Disk Reprocessing in AGN by Christos Panagiotou, Iossif Papadakis, Erin Kara, Marios Papoutsis, Edward M. Cackett, Michal Dovčiak, Javier A. García, Elias Kammoun, Collin Lewin

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Pedestrian Detection in Fisheye Images Based on Improved YOLOv8 Algorithm by ZHU Yumin, SUN Guangling, MIAO Fei

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Keypoint Detection and 3D Localization Method for Ridge-Cultivated Strawberry Harvesting Robots by Shuo Dai, Tao Bai, Yunjie Zhao

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Quantum Kernel Machine Learning With Continuous Variables by Laura J. Henderson, Rishi Goel, Sally Shrapnel

“…We derive a general closed form solution for all CV quantum kernels and show every such kernel can be expressed as the product of a Gaussian and an algebraic function of the parameters of the feature map. Furthermore, in the multi-mode case, we present quantification of a quantum-classical separation for all quantum kernels via a hierarchical notion of the “stellar rank" of the quantum kernel feature map. …”
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Machine learning-based approach for bandwidth and frequency prediction of circular SIW antenna by Md Mahabub Alam, Nurhafizah Abu Talip Yusof, Ahmad Afif Mohd Faudzi, Md Raihanul Islam Tomal, Md Ershadul Haque, Md. Suaibur Rahman

“…Abstract Machine Learning (ML) has significantly transformed antenna design by enabling efficient optimization of geometrical parameters, modeling complex electromagnetic behavior, and accelerating performance prediction with reduced computational cost. …”
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PS-YOLO: A Lighter and Faster Network for UAV Object Detection by Han Zhong, Yan Zhang, Zhiguang Shi, Yu Zhang, Liang Zhao

“…This detection head effectively increases inference speed without significantly increasing parameter counts. The proposed PS-YOLO is validated on the Visdrone2019 dataset, and the results demonstrate that PS-YOLO provides a 2% improvement in precision, 0.5% improvement in recall, 1.3% improvement in mean average precision (mAP), 41.3% reduction in parameter counts, 6.1% reduction in computational cost, and 26.73 FPS improvement in inference speed compared to the benchmark model YOLOv11-s.…”
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Prediction of Shield Tunneling Attitude Based on WM-CTA Method by GAO Su, CHEN Cheng

“…The Convolutional Neural Network (CNN) integrated with a channel-wise attention mechanism explored parameter weight differences and extracted local data features. …”
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Fusing satellite imagery and ground-based observations for PM2.5 air pollution modeling in Iran using a deep learning approach by Zohreh Sohrabi, Jamshid Maleki

“…We utilized satellite data, ground-based observations, and meteorological parameters as input features. The models were evaluated using Root Mean Square Error (RMSE) and the coefficient of determination (R2). …”
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Fast Quality Detection of <i>Astragalus</i> Slices Using FA-SD-YOLO by Fan Zhao, Jiawei Zhang, Qiang Liu, Chen Liang, Song Zhang, Mingbao Li

“…This model introduces several novel modifications to enhance feature extraction and fusion while reducing computational complexity. …”
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Development and validation of a CT-based multi-omics nomogram for predicting hospital discharge outcomes following mechanical thrombectomy by Feifan Liu, Jiayi Hong, Yuhan Chen, Huan Liu, Yue Wang, Lijian Su, Sheng Hu, Jingjing Fu

“…ObjectiveThis study aimed to develop a multi-omics nomogram that combines clinical parameters, radiomics, and deep transfer learning (DTL) features of hyperattenuated imaging markers (HIM) from computed tomography scans immediately following mechanical thrombectomy (MT) to predict functional outcomes at discharge.MethodsThis study enrolled 246 patients with HIM who underwent MT. …”
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NeuroTutor: Neural Decoding of Student Engagement During Virtual and Robotic Tutoring by Shahd Elgammal, Shaymaa Alsereidi, Mahra Almansoori, Shamma Alblooshi, Abdelhadi Hireche, Abdelkader Nasreddine Belkacem

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An analytical approach to modeling conjunctival viral disease using fuzzy logic and time-delay dynamics by Muhammad Tashfeen, Hothefa Shaker Jassim, Fazal Dayan, Muhammad Azizur Rehman, Alwahab Dhulfiqar Zoltán, Husam A. Neamah

“…Simulation results confirm that the NSFD approach maintains the qualitative features of the model even under larger time steps. …”
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A three-dimensional morpho-volumetric similarity study of Down syndrome keratopathy vs. keratoconus by Ibrahim Toprak, Francisco Cavas, José S. Velázquez, Jorge L. Alio del Barrio, Jorge L. Alio

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Security monitoring via sound analysis and voice identification with artificial intelligence by Balabanova Ivelina, Sidorova Kristina, Georgiev Georgi

“…Subsequently, steps were taken to reduce the informative features when searching for similar levels of accuracy in order to limit the necessary computational procedures in neural training, but maintain the threshold of successful user authentication. …”
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Real-Time Corn Variety Recognition Using an Efficient DenXt Architecture with Lightweight Optimizations by Jin Zhao, Chengzhong Liu, Junying Han, Yuqian Zhou, Yongsheng Li, Linzhe Zhang

“…Representative Batch Normalization (RBN) is introduced into the DenseNet-121 model to improve the generalization ability of the model, and the SE module and deep separable convolution are integrated to enhance the feature representation and reduce the computational complexity, and the Dropout regularization is introduced to further improve the generalization ability of the model and reduce the overfitting. …”
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Eagle-YOLOv8: UAV Object Detection Inspired by the Eagle-Eye Vision System by Dianwei Wang, Zehao Gao, Jie Fang, Yuanqing Li, Zhijie Xu

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Leveraging hybrid model of ConvNextBase and LightGBM for early ASD detection via eye-gaze analysis by Ranjeet Bidwe, Sashikala Mishra, Simi Bajaj, Ketan Kotecha

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