AFENet: An Attention-Focused Feature Enhancement Network for the Efficient Semantic Segmentation of Remote Sensing Images by Jiarui Li, Shuli Cheng

“…The semantic segmentation of high-resolution remote sensing images (HRRSIs) faces persistent challenges in handling complex architectural structures and shadow occlusions, limiting the effectiveness of existing deep learning approaches. …”
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A randomised controlled trial comparing the outcomes of arteriovenous fistula for dialysis created under local vs regional anaesthesia: reflections on recruitment and process evalu... by Emma Aitken, Cecilia Vindrola-Padros, Nicolas Hall, the ACCess study collaborators

“…Abstract Background Despite the crucial role that randomised controlled trials (RCTs) play in establishing the efficacy of new treatments, many are terminated early due to difficulty with subject recruitment. Trials of complex perioperative interventions are notoriously difficult to deliver. …”
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RRBM-YOLO: Research on Efficient and Lightweight Convolutional Neural Networks for Underground Coal Gangue Identification by Yutong Wang, Ziming Kou, Cong Han, Yuchen Qin

“…Coal gangue identification is the primary step in coal flow initial screening, which mainly faces problems such as low identification efficiency, complex algorithms, and high hardware requirements. …”
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Construction and correction of a 3D digital twin for a complex gangue-bearing coal seam by Meichen ZHANG, Lijuan ZHAO, Yadong WANG, Kai ZHANG

“…Objective3D digital twin for complex gangue-bearing coal seams are designed to achieve an information-based description of physical coal seams and faithfully map their virtual forms. digital twin is identified as a prerequisite for exploring the efficient cutting of intelligent shearers while providing an effective data guarantee for achieving unmanned mining. …”
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Zwitterionic Poly(sulfobetaine methacrylate) Brushes Functionalized Threads for DNA Extraction from Complex Cell Lysates by Xianlong Shi, Liang Wu, Ke Ning, Xinmei Li, Lingke Feng, Yirong Chen, Ling Yu

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Effect of age on complexity and causality of the cardiovascular control: comparison between model-based and model-free approaches. by Alberto Porta, Luca Faes, Vlasta Bari, Andrea Marchi, Tito Bassani, Giandomenico Nollo, Natália Maria Perseguini, Juliana Milan, Vinícius Minatel, Audrey Borghi-Silva, Anielle C M Takahashi, Aparecida M Catai

“…We found that: 1) MF approaches are more efficient than the MB method when nonlinear components are present, while the reverse situation holds in presence of high dimensional embedding spaces; 2) the CE method is the least powerful in detecting age-related trends; 3) the association of HP complexity on age suggests an impairment of cardiac regulation and response to STAND; 4) the relation of SAP complexity on age indicates a gradual increase of sympathetic activity and a reduced responsiveness of vasomotor control to STAND; 5) the association from SAP to HP on age during STAND reveals a progressive inefficiency of baroreflex; 6) the reduced connection from HP to SAP with age might be linked to the progressive exploitation of Frank-Starling mechanism at REST and to the progressive increase of peripheral resistances during STAND; 7) at REST the diminished association from RESP to HP with age suggests a vagal withdrawal and a gradual uncoupling between respiratory activity and heart; 8) the weakened connection from RESP to SAP with age might be related to the progressive increase of left ventricular thickness and vascular stiffness and to the gradual decrease of respiratory sinus arrhythmia.…”
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Efficient collision-avoidance navigation strategy for autonomous surface vehicles in unstructured and constricted marine environments by Wenlong Meng, Rongdian Ku, Yanbo Pu, Xiaoxiao Shi, Ya Gong

“…In this study, we tackle the aforementioned complexities by incorporating progressive sampling and point cloud clustering, which jointly expedite the detection of constrained waterways in unstructured marine environments. …”
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Tunable Energy-Efficient Approximate Circuits for Self-Powered AI and Autonomous Edge Computing Systems by Shubham Garg, Kanika Monga, Nitin Chaturvedi, S. Gurunarayanan

“…Moreover, this problem becomes more complex while deploying computationally intensive heavy machine learning (ML) models on energy-constrained edge devices. …”
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Collaborative Optimization of Model Pruning and Knowledge Distillation for Efficient and Lightweight Multi-Behavior Recognition in Piglets by Yizhi Luo, Kai Lin, Zixuan Xiao, Yuankai Chen, Chen Yang, Deqin Xiao

“…In modern intensive pig farming, accurately monitoring piglet behavior is crucial for health management and improving production efficiency. However, the complexity of existing models demands high computational resources, limiting the application of piglet behavior recognition in farming environments. …”
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Recent Efficient Iterative Algorithms on Cognitive Radio Cooperative Spectrum Sensing to Improve Reliability and Performance by Mohsen Akbari, Ahmed Wasif Reza, Kamarul Ariffin Noordin, Kaharudin Dimyati, Mohsen Riahi Manesh, Mohammad Nour Hindia

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Efficient Gearbox Fault Diagnosis Based on Improved Multi-Scale CNN with Lightweight Convolutional Attention by Bin Yuan, Yaoqi Li, Suifan Chen

“…The framework extracts the multi-band features of vibration signals through the improved multi-scale convolutional neural network, which significantly enhances adaptability to complex working conditions (variable rotational speed, strong noise); at the same time, the lightweight convolutional attention mechanism is used to replace the multi-attention of the traditional Transformer, which greatly reduces computational complexity while guaranteeing accuracy and realizes highly efficient, lightweight local–global feature modeling. …”
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Image-Based Object Identification for Efficient Event-Driven Sensing in Wireless Multimedia Sensor Networks by Mohsin S. Alhilal, Adel Soudani, Abdullah Al-Dhelaan

“…This paper presents a contribution to the design of low complexity scheme based on object identification for efficient sensing of multimedia information in wireless multimedia sensor networks. …”
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A spoonful of L‐fucose—an efficient therapy for GFUS‐CDG, a new glycosylation disorder by René G Feichtinger, Andreas Hüllen, Andreas Koller, Dieter Kotzot, Valerian Grote, Erdmann Rapp, Peter Hofbauer, Karin Brugger, Christian Thiel, Johannes A Mayr, Saskia B Wortmann

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Enhanced pulmonary nodule detection using a transformer framework with dual fusion and gated mechanism by Kelei Sun, Yihang Wang, Huaping Zhou

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Research and optimization of a multilevel fire detection framework based on deep learning and classical pattern recognition techniques by Qi Liu, Hong Chen, Da Lin

“…Despite advancements in both traditional methodologies and modern deep learning models, challenges such as suboptimal accuracy and elevated false alarm rates persist, particularly in complex environmental scenarios. This paper introduces the Fire Focused Detection Network (FFDNet), a state-of-the-art flame detection framework that seamlessly integrates classical approaches with deep learning strategies. …”
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A high-efficiency and stable transfection approach by using calcium phosphate in HEK293T cells by Kuang Ye, Fang Yourong, Liu Li, Wang Rong, Yan Ziqin, Li Hailong, Meng Fanguo, Sheng Qing, Ou Wenbin

“…Higher transfection efficiency was also detected when the vortex time was 10 s. …”
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Efficient Deep Learning-Based Device-Free Indoor Localization Using Passive Infrared Sensors by Sira Yongchareon, Jian Yu, Jing Ma

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Innovative Ghost Channel Spatial Attention Network with Adaptive Activation for Efficient Rice Disease Identification by Yang Zhou, Yang Yang, Dongze Wang, Yuting Zhai, Haoxu Li, Yanlei Xu

“…To address the computational complexity and deployment challenges of traditional convolutional neural networks in rice disease identification, this paper proposes an efficient and lightweight model: Ghost Channel Spatial Attention ShuffleNet with Mish-ReLU Adaptive Activation Function (GCA-MiRaNet). …”
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Efficient design of automated guided vehicle systems in operating theatres via discrete events simulation by Yao Ge, Marcus Eng Hock Ong, Sean Shao Wei Lam

“…This approach yielded a composite efficiency score that reflects multiple, often conflicting, design objectives. …”
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Creating and Validating Hybrid Large-Scale, Multi-Modal Traffic Simulations for Efficient Transport Planning by Fabian Schuhmann, Ngoc An Nguyen, Jörg Schweizer, Wei-Chieh Huang, Markus Lienkamp

“…Mobility digital twins (MDTs), which utilize multi-modal microscopic (micro) traffic simulations and an activity-based demand generation, are envisioned as flexible and reliable planning tools for addressing today’s increasingly complex and diverse transport scenarios. Hybrid models may become a resource-efficient solution for building MDTs by creating large-scale, mesoscopic (meso) traffic simulations, using simplified, queue-based network-link models, in combination with more detailed local micro-traffic simulations focused on areas of interest. …”
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