Numerical Simulation of Turbulent Flow in River Bends and Confluences Using the k-ω SST Turbulence Model and Comparison with Standard and Realizable k-ε Models by Rawaa Shaheed, Abdolmajid Mohammadian, Alaa Mohammed Shaheed

“…The results are validated against experimental data, demonstrating the model’s ability to reasonably replicate flow features under both free- and closed-surface conditions. …”
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AIF: Infrared and Visible Image Fusion Based on Ascending–Descending Mechanism and Illumination Perception Subnetwork by Ying Liu, Xinyue Mi, Zhaofu Liu, Yu Yao

“…The image fusion model is trained in an unsupervised manner with a customized loss function. …”
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Clinical features and prognostic nomogram development for cancer-specific death in patients with dual primary lung cancer: a population-based study from SEER database by Tenghao Rong, Cheng Ai, Tong Yang, Qingchen Wu, Min Zhang

“…Abstract Objective This study aimed to develop a concise and valid clinical prediction model to assess the survival prognostic risk of cancer-specific death in patients with dual primary lung cancer (DPLC). …”
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Smart material selection strategies for sustainable and cost-effective high-performance concrete production using deep learning by T. Seethalakshmi, M. Murugan, P. Maria Antony Sebastin Vimalan

“…The actual measured value and the MOAC-ADenseNet model predictions, following 5-K-fold cross-validation and input feature improvement, shows its effectiveness. …”
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A Fault Diagnosis Method for Oil Well Electrical Power Diagrams Based on Multidimensional Clustering Performance Evaluation by Xingyu Liu, Xin Meng, Ze Hu, Hancong Duan, Min Wang, Yaping Chen

“…However, this method has severe limitations in terms of real-time performance and maintenance costs, making it difficult to meet the demands of modern extraction. …”
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A Generic AI-Based Technique for Assessing Student Performance in Conducting Online Virtual and Remote Controlled Laboratories by Ahmed M. Abd El-Haleem, Mohab Mohammed Eid, Mahmoud M. Elmesalawy, Hadeer A. Hassan Hosny

“…A comparison study has been developed between different Machine Learning (ML) models and a number of performance metrics are calculated. …”
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Design and Basic Performance Analysis of a Bionic Finger Soft Actuator with a Dual-Chamber Composite Structure by Yu Cai, Sheng Liu, Dazhong Wang, Shuai Huang, Dong Zhang, Mengyao Shi, Wenqing Dai, Shang Wang

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The performance of 76 kHz positional acoustic telemetry is challenged by acoustic conditions in the tailrace of a hydroelectric dam by C. Ryan Hill, Antóin M. O’Sullivan, R. Allen Curry, Tommi Linnansaari, Philip M. Harrison

“…Therefore, validating system performance in tailrace environments is important for study design and interpretation. …”
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Design and Performance Analysis of an All-Metal Phase-Change-Material Actuator for Enhanced Sealing and Displacement Output Characteristics by Shuaiqi Guo, Xianwei Yang, Qingwen Wu

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Development of a Multi‐Scale Meteorological Large‐Eddy Simulation Model for Urban Thermal Environmental Studies: The “City‐LES” Model Version 2.0 by Hiroyuki Kusaka, Ryosaku Ikeda, Takuto Sato, Satoru Iizuka, Taisuke Boku

“…Following the introduction of this model, the study confirms its basic performance through various numerical experiments, including simulations of thermals in the convective boundary layer, coherent structure of turbulence over urban canopy, and thermal environment and heat stress indices in urban districts. …”
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Diagnostic accuracy of MRI-based radiomic features for EGFR mutation status in non-small cell lung cancer patients with brain metastases: a meta-analysis by Yuqin Long, Rong Zhao, Xianfeng Du

“…Subgroup analysis indicated that deep learning models and studies conducted in Asian showed higher diagnostic accuracy compared to their respective counterparts.ConclusionsMRI-based radiomic features demonstrate a high potential for accurately detecting EGFR mutations in NSCLC patients with brain metastases, particularly when advanced deep learning techniques were employed. …”
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A Hybrid Deep Learning–Based Feature Selection Approach for Supporting Early Detection of Long-Term Behavioral Outcomes in Survivors of Cancer: Cross-Sectional Study by Tracy Huang, Chun-Kit Ngan, Yin Ting Cheung, Madelyn Marcotte, Benjamin Cabrera

“…ObjectiveThis study aimed to create a new feature selection algorithm to improve the performance of machine learning classifiers to predict negative long-term behavioral outcomes in survivors of cancer. …”
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A Feature Extraction Method of Ship Underwater Noise Using Enhanced Peak Cross-Correlation Empirical Mode Decomposition Method and Multi-Scale Permutation Entropy by Peng Liu, Chen Dai, Shuaiqiang Li, Hui Jin, Xinfu Liu, Guijie Liu

“…The results show that the IEMD-MPE method performs well in extracting the feature information of the signals and has a strong discriminative ability. …”
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Clinical features and genetic analysis of a family with t(5;9) (p15;p24) balanced translocation leading to Cri-du-chat syndrome in offspring by Jing Zhao, Ping Chen, Yijia Ren, Shurong Li, Weiyi Zhang, Yan Li, Fengyu Wang

“…We characterized individual clinical features and conducted a genetic analysis of the members of a family with t (5; 9) (p15; p24) balanced translocation leading to Cri-du-chat syndrome in the offspring.Study designWe performed a chromosomal karyotyping with high-resolution G-banding on the proband and her family members to detect their chromosomal configurations. …”
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CASSAD: Chroma-Augmented Semi-Supervised Anomaly Detection for Conveyor Belt Idlers by Fahad Alharbi, Suhuai Luo, Abdullah Alsaedi, Sipei Zhao, Guang Yang

“…We also compare CASSAD’s performance with other common models like the local outlier factor (LOF) and isolation forest (iForest), evaluating each with the area under the curve (AUC) to assess their ability to distinguish between normal and anomalous data. …”
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HSF-DETR: Hyper Scale Fusion Detection Transformer for Multi-Perspective UAV Object Detection by Yi Mao, Haowei Zhang, Rui Li, Feng Zhu, Rui Sun, Pingping Ji

“…Second, building upon features extracted by HPFNet, we develop MultiScaleNet, which enhances feature representation through dual-layer optimization and cross-domain feature learning, significantly improving the model’s capability to handle complex aerial scenarios with diverse object orientations. …”
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Deep supervised, but not unsupervised, models may explain IT cortical representation. by Seyed-Mahdi Khaligh-Razavi, Nikolaus Kriegeskorte

“…Computational object-vision models, although continually improving, do not yet reach human performance. …”
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Analysis of Factors Affecting the Seismic Performance of Widened Flange Connections in Mid-Flange H-Beams and Box Columns by Tigist Demssice Gemechu, Linfeng Lu

“…This study investigates the seismic performance of connections featuring widened beam-end flanges in mid-flange H-beams and box columns, an area with limited prior research compared to I-section columns and narrow-flange H-beams. …”
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Model of the malicious traffic classification based on hypergraph neural network by Wenbo ZHAO, Zitong MA, Zhe YANG

“…As the use and reliance on networks continue to grow, the prevalence of malicious network traffic poses a significant challenge in the field of network security.Cyber attackers constantly seek new ways to infiltrate systems, steal data, and disrupt network services.To address this ongoing threat, it is crucial to develop more effective intrusion detection systems that can promptly detect and counteract malicious network traffic, thereby minimizing the resulting losses.However, current methods for classifying malicious traffic have limitations, particularly in terms of excessive reliance on data feature selection.To improve the accuracy of malicious traffic classification, a novel malicious traffic classification model based on Hypergraph Neural Networks (HGNN) was proposed.The traffic data was represented as hypergraph structures and HGNN was utilized to capture the spatial features of the traffic.By considering the interrelations among traffic data, HGNN provided a more accurate representation of the characteristics of malicious traffic.Additionally, to handle the temporal features of traffic data, Recurrent Neural Networks (RNN) was introduced to further enhance the model’s classification performance.The extracted spatiotemporal features were then used for the classification of malicious traffic, aiding in the detection of potential threats within the network.Through a series of ablative experiments, the effectiveness of the HGNN+RNN method was verified.These experiments demonstrate the model’s ability to efficiently extract spatiotemporal features from traffic, resulting in improved classification performance for malicious traffic.The model achieved outstanding classification accuracy across three widely-used open-source datasets: NSL-KDD (94% accuracy), UNSW-NB15 (95.6% accuracy), and CIC-IDS-2017 (99.08% accuracy).These results underscore the potential significance of the malicious traffic classification model based on hypergraph neural networks in enhancing network security and its capacity to better address the evolving landscape of network threats within the domain of network security.…”
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Explainable models for predicting crab weight based on genetic programming by Tao Shi, Lingcheng Meng, Limiao Deng, Juan Li

“…Thanks to the explicit ability of feature selection, GP can select more important features to improve the prediction performance. …”
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