Anxiety and depression in patients with head and neck cancer: 6-month follow-up study by Wu Y, Lin P, Chien C, Fang F, Chiu N, Hung C, Lee Y, Chong M

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High-Fat Meal Increase Blonanserin Bioavailability 5-Fold in Chinese Healthy Subjects by Sun X, Song H, Zhao N, Qiu B, Ding C, Wu H, Bai W

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Environmental Resource Conflicts and Food Insecurity in Rural Southeast Nigeria: Implications for Humanitarian and Sustainable Development Policies by Samuel Okafor, Andrew Ogbochie, Chukwuka Ugwu, Chizoba Oranu, Gloria Amadi, Chigozie Ugwuanyi, Okolo Modesta

“…There should be a synergy between agricultural and security policies to address the issue of migration, which is affecting different regions and has enormous implications for food security. …”
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YOLOv8 forestry pest recognition based on improved re-parametric convolution by Lina Zhang, Shengpeng Yu, Bo Yang, Shuai Zhao, Ziyi Huang, Zhiyin Yang, Helong Yu

“…Additionally, the neck of the model was enhanced by integrating a slim-neck structure and adding a Dyhead module before the output layer. Further optimization was achieved through model pruning, which contributed to additional lightweighting of the model. …”
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Identification of Diagnostic Biomarkers and Therapeutic Targets in Sepsis-Associated ARDS via Combining Bioinformatics with Machine Learning Analysis by Liu T, Gao L, Li X

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Tensor RT optimized driver drowsiness detection system using edge device by Chandramohan Dhasarathan, Sambasivam Gnanasekaran, Arnab Pattanayak, Gourav Kumar, Kartik Vig, Vaibhav Narain, K.M. Deva Narayan, Sunidhi Garg

“…Driver drowsiness has emerged as a major issue in terms of road safety, causing dangerous and sometimes even fatal accidents. …”
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Quality of life and parental styles assessed by adolescents suffering from inflammatory bowel diseases and their parents by Zmeskalova D, Prasko J, Ociskova M, Latalova K, Karaskova E, Hruby R, Kamaradova D, Mihal V

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3L-YOLO: A Lightweight Low-Light Object Detection Algorithm by Zhenqi Han, Zhen Yue, Lizhuang Liu

“…Object detection in low-light conditions presents significant challenges due to issues such as weak contrast, high noise, and blurred boundaries. …”
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Enhanced group relation learning via aligned attention masking for fashion product captioning by Yuhao Tang, Dong Ye, Fei Tao, Guodong Du

“…This discrepancy arises when datasets contain multiple angles all paired with a single description, resulting in situations where certain textual details may not be visible in any angle. To address such issues, we propose a novel Fashion Transformer framework, which consists of two components: (1) an intra-group confusion module and an inter-group discrepancy module with dual feature interaction branches integrated into a unified model, and (2) a fashion encoder generates masks that identify the feature tokens and text tokens where information co-occurs in both modalities, ensuring precise alignment between the fashion images and texts. …”
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Inversion of Magnetic Anomaly Based on Cross Attention Transformer by Juntao Lei, Jieru Chi, Shandong Li

“…However, existing deep learning methods for magnetic anomaly inversion suffer from issues such as the lack of accuracy in some model structures, poor boundary details, and the skin effect. …”
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GCML: Geometric Correlation Encoding Network With Multi-Scale Local Feature Extraction for Accurate Point Cloud Registration by Jinlei Zhuang, Ziteng Wang, Weiqiang Ma

“…This study introduces GCML, a novel detector-free approach that tackles these issues. For the first problem, GCML develops a geometric correlation encoding module (GCEM) that draws inspiration from the Denavit-Hartenberg (DH) modeling method in robotics to effectively encode the geometric correlations between each pair of points within point clouds. …”
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Vehicle detection method based on multi-layer selective feature for UAV aerial images by Yinbao Ma, Yuyu Meng, Jiuyuan Huo

“…In the backbone, a Receptive-Field Attention Convolution (RFAConv) module is introduced to retain detailed features during the downsampling process. …”
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Geometric properties of quantum entanglement and machine learning by S. V. Zuev

“…Fast data analysis based on hidden patterns is one of the main issues for adaptive artificial intelligence systems development. …”
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RML-YOLO: An Insulator Defect Detection Method for UAV Aerial Images by Zhenrong Deng, Xiaoming Li, Rui Yang

“…The approach introduces a tiered scale fusion feature (TSFF) module to enhance multi-scale detection accuracy by fusing features across network layers. …”
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Pedestrian Re-Identification Based on Fine-Grained Feature Learning and Fusion by Anming Chen, Weiqiang Liu

“…An MTLA consists of three modules, i.e., a multimodal feature encoder, token-based cross-modal alignment, and correlation-aware fusion. …”
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VDMNet: A Deep Learning Framework with Vessel Dynamic Convolution and Multi-Scale Fusion for Retinal Vessel Segmentation by Guiwen Xu, Tao Hu, Qinghua Zhang

“…Secondly, the Vessel Dynamic Convolution (VDConv) module is designed to dynamically adapt to curved and crossing vessels, thereby improving the segmentation of complex morphologies. …”
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Multi-scale feature fusion keypoint detection network for ship draft line localization by Bo Zhang, Yumengmeng Yin, Kefu Ma, Hong Wang

“…Meanwhile, the Feature Enhancement Extraction Modules (FEEM) are employed to enhance these extracted features, and the Multi-scale Feature Weighted Integration (MFWI) module efficiently fuses the enhanced multi-scale features. …”
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DelAwareCol: Delay Aware Collaborative Perception by Ahmed N. Ahmed, Siegfried Mercelis, Ali Anwar

“…Secondly, an inter-agent information aggregation module manages inter-agent interactions and spatial relationships, addressing common vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) issues, such as spatial misalignment, asynchronous information sharing, and pose errors. …”
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High-performance reconfigurable encryption scheme for distributed storage by Zhihua FENG, Yuxuan ZHANG, Chong LUO, Jianing WANG

“…As the world embraces the digital economy and enters an information society, data has emerged as a critical production factor.The collection, processing, and storage of data have become increasingly prevalent.Distributed storage systems, known for their efficiency, are widely used in various data fields.However, as the scale of data storage continues to expand, distributed storage faces more significant security risks, such as information leakage and data destruction.These challenges drive the need for innovative advancements in big data distributed storage security technology and foster the integration of domestic cryptographic technology with computing storage technology.This work focused on addressing security issues, particularly information leakage, in distributed storage nodes.A dynamic and reconfigurable encryption storage solution was proposed, which considered the requirements for encryption performance and flexibility.A high-performance reconfigurable cryptographic module was designed based on the bio mapping framework.Based on this module, multiple storage pools equipped with different cryptographic algorithms were constructed to facilitate high-performance encryption and decryption operations on hard disk data.The scheme also enabled dynamic switching of cryptographic algorithms within the storage pools.A cryptographic protocol with remote online loading functions for cryptographic algorithms and keys was developed to meet the unified management and convenient security update requirements of reconfigurable cryptographic modules in various storage nodes.Furthermore, the scheme implemented fine-grained data encryption protection and logical security isolation functions based on cryptographic reconstruction technology.Experimental results demonstrate that the performance loss of this scheme for encryption protection and security isolation of stored data is approximately 10%.It provides a technical approach for distributed storage systems to meet the cryptographic application technology requirements outlined in GB/T 39786-2021 “Information Security Technology-Basic Requirements for Cryptography Applications” Level 3 and above in terms of device and computing security, application and data security.…”
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AURA-Depth: Attention-Based Uncertainty Reduction and Feature Aggregation Depth Network by Youngtak Na, Sungho Woo, Soonyoung Lee, Byung Jun Kang

“…The output of each encoder branch’s residual module is fused using a Multimodal Cross Attention (MCA) module, guided by the uncertainty calculated from the absolute difference between dense depth features and triangulated depth features. …”
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