Assessing thermal and hydrodynamic performance of non-Newtonian nano-coolant flow through a porous backward-facing step channel with non-Darcian effects by Zarin Akter, Preetom Nag, Hasina Akter, Md. Mamun Molla, Goutam Saha

“…The Darcy–Brinkman–Forchheimer (DBF) porous model, incorporating non-Newtonian viscous NC flow, has been numerically solved within the computational domain using a finite volume method with second-order accuracy. …”
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Attention Enhanced InceptionNeXt-Based Hybrid Deep Learning Model for Lung Cancer Detection by Burhanettin Ozdemir, Emrah Aslan, Ishak Pacal

“…Early diagnosis of this highly fatal and prevalent disease can significantly improve survival rates and prevent its progression. Computed tomography (CT) is the gold standard imaging modality for lung cancer diagnosis, offering critical insights into the assessment of lung nodules. …”
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YOLO-HF: Early Detection of Home Fires Using YOLO by Bo Peng, Tae-Kook Kim

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Accurately Estimate and Analyze Human Postures in Classroom Environments by Zhaoyu Shou, Yongbo Yu, Dongxu Li, Jianwen Mo, Huibing Zhang, Jingwei Zhang, Ziyong Wu

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SCL-YOLOv11: A Lightweight Object Detection Network for Low-Illumination Environments by Shulong Zhuo, Hao Bai, Lifeng Jiang, Xiaojian Zhou, Xu Duan, Yiqun Ma, Zihan Zhou

“…First, the StarNet architecture is introduced into the Backbone to enhance the extraction of shallow image features and significantly reduce computational complexity. …”
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Bayesian Optimized Enhanced Ensemble Multi-Layer Perceptron for Rear-End Collision Prediction in IoV by Dilna Vijayan, Mohamed Saad, Ahmed M. Khedr

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Real-time diagnosis of multi-category skin diseases based on IR-VGG by Ling TAN, Shanshan RONG, Jingming XIA, Sarker SAJIB, Wenjie MA

“…Malignant skin lesions have a very high cure rate in the early stage.In recent years, dermatological diagnosis research based on deep learning has been continuously promoted, with high diagnostic accuracy.However, computational resource consumption is huge and it relies on large computing equipment in hospitals.In order to realize rapid and accurate diagnosis of skin diseases on Internet of things (IoT) mobile devices, a real-time diagnosis system of multiple categories of skin diseases based on inverted residual visual geometry group (IR-VGG) was proposed.The contour detection algorithm was used to segment the lesion area of skin image.The convolutional block of the first layer of VGG16 was replaced with reverse residual block to reduce the network parameter weight and memory overhead.The original image and the segmented lesion image was inputed into IR-VGG network, and the dermatological diagnosis results after global and local feature extraction were outputed.The experimental results show that the IR-VGG network structure can achieve 94.71% and 85.28% accuracy in Skindata-1 and Skindata-2 skin diseases data sets respectively, and can effectively reduce complexity, making it easier for the diagnostic system to make real-time skin diseases diagnosis on IoT mobile devices.…”
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PlastidHub: An integrated analysis platform for plastid phylogenomics and comparative genomics by Na-Na Zhang, Gregory W. Stull, Xue-Jie Zhang, Shou-Jin Fan, Ting-Shuang Yi, Xiao-Jian Qu

“…In comparison with existing tools, key novel functionalities in PlastidHub include: (1) standardization of quadripartite structure; (2) improvement of annotation flexibility and consistency; (3) quantitative assessment of annotation completeness; (4) diverse extraction modes for canonical and specialized sequences; (5) intelligent screening of molecular markers for biodiversity studies; (6) gene-level visual comparison of structural variations and annotation completeness. PlastidHub features cloud-based web applications that do not require users to install, update, or maintain tools; detailed help documents including user guides, test examples, a static pop-up prompt box, and dynamic pop-up warning prompts when entering unreasonable parameter values; batch processing capabilities for all tools; intermediate results for secondary use; and easy-to-operate task flows between file upload and download. …”
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Diagnosis of Power Transformer On-Load Tap Changer Mechanical Faults Based on SABO-Optimized TVFEMD and TCN-GRU Hybrid Network by Shan Wang, Zhihu Hong, Qingyun Min, Dexu Zou, Yanlin Zhao, Runze Qi, Tong Zhao

“…This parameter-adaptive methodology demonstrates enhanced stability in signal decomposition and improved temporal feature discernment, proving particularly effective in handling non-stationary vibration signals under real operational conditions. …”
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Utilizing GCN-Based Deep Learning for Road Extraction from Remote Sensing Images by Yu Jiang, Jiasen Zhao, Wei Luo, Bincheng Guo, Zhulin An, Yongjun Xu

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Surface-based morphometry of the cerebral cortex in cognitive impairments of varying severity in patients with age-related cerebral small vessel disease by Elena I. Kremneva, Larisa A. Dobrynina, Kamila V. Shamtieva, Victoria V. Trubitsyna, Zukhra S. Gadzhieva, Angelina G. Makarova, Maria M. Tsypushtanova, Marina V. Krotenkova

“…The assessment included the analysis of signs of cerebral small vessel disease based on the results of magnetic resonance imaging with the computation of general cerebral small vessel disease index and processing T1 multiplanar reconstruction images by surface-based morphometry to quantify general and regional brain parameters, including the thickness of the cerebral cortex. …”
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Failure Analysis of Static Analysis Software Module Based on Big Data Tendency Prediction by Jian Zhu, Qian Li, Shi Ying

“…This method can learn features from original defect data, directly and efficiently extract required features of all levels from software defect data by setting different number of hidden layers, sparse regularization parameters, and noise ratio, and then classify and predict the extracted features by combining with big data. …”
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Lightweight vision transformer model for pine wilt disease detection using aerial RGB image and adversarial data augmentation by Qing Li, Wenhui Chen

“…The framework achieves 7.4 % mAP improvement over baseline models on our established PWD dataset, with a 51.1 % frames per second (FPS) increase and 50 % parameter reduction. Firstly, a FasterNet-based backbone reduces computational complexity while preserving high-level semantic features. …”
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A cross dataset meta-model for hepatitis C detection using multi-dimensional pre-clustering by Aryan Sharma, Tanmay Khade, Shashank Mouli Satapathy

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Concept and Design of Cutting Tools for Osseodensification in Implant Dentistry by Alexander Isaev, Maria Isaeva, Oleg Yanushevich, Natella Krikheli, Olga Kramar, Aleksandr Tsitsiashvili, Sergey Grigoriev, Catherine Sotova, Pavel Peretyagin

“…Results: The most important design features and parameters of osseodensification burs are identified. …”
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An Evolutionary Deep Reinforcement Learning-Based Framework for Efficient Anomaly Detection in Smart Power Distribution Grids by Mohammad Mehdi Sharifi Nevisi, Mehrdad Shoeibi, Francisco Hernando-Gallego, Diego Martín, Sarvenaz Sadat Khatami

“…To address these challenges, this study proposes a novel deep reinforcement learning (DRL)-based framework, integrating a convolutional neural network (CNN) for hierarchical feature extraction and a recurrent neural network (RNN) for sequential pattern recognition and time-series modeling. …”
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Exploration of Machine Learning Models for Prediction of Gene Electrotransfer Treatment Outcomes by Alex Otten, Michael Francis, Anna Bulysheva

“…All models used a maximum of 24 features as input, spread across target species, needle configuration, pulsing parameters, and plasmid parameters. …”
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Predicting Insemination Outcome in Holstein Dairy Cattle using Deep Learning by Mohammad Alishahi, Mahdi Ravakhah

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Damage prediction of rear plate in Whipple shields based on machine learning method by Chenyang Wu, Xiangbiao Liao, Lvtan Chen, Xiaowei Chen

“…This study establishes an expandable new dataset that accommodates additional parameters to improve the prediction accuracy. Results demonstrate the model's ability to overcome data imbalance limitations through debris cloud features, enabling rapid and accurate rear plate damage prediction across wider scenarios with minimal data requirements.…”
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Tomato Ripening Detection in Complex Environments Based on Improved BiAttFPN Fusion and YOLOv11-SLBA Modeling by Yan Hao, Lei Rao, Xueliang Fu, Hao Zhou, Honghui Li

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