Effects of mental fatigue on isometric mid-thigh pull performance and muscle activities. by Hyung Suk Yang, Lee T Atkins, C Roger James

“…Sixteen healthy, resistance-trained males completed two sessions: a control condition and a mentally fatigued state induced by a 30-minute modified Stroop task. IMTP performance and muscle activation were assessed before and after the mental fatigue task. …”
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A Physics‐Informed Neural Network as a Digital Twin of Optically Turbid Media by Mohammadrahim Kazemzadeh, Liam Collard, Linda Piscopo, Massimo De Vittorio, Ferruccio Pisanello

“…Unlike previous approaches utilizing various deep neural network architectures that often function as black boxes, the method prioritizes interpretability, offering a clearer understanding of the underlying processes of light propagation through turbid media and estimating the transmission matrix. …”
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Bytecode-based approach for Ethereum smart contract classification by Dan LIN, Kaixin LIN, Jiajing WU, Zibin ZHENG

“…In recent years, blockchain technology has been widely used and concerned in many fields, including finance, medical care and government affairs.However, due to the immutability of smart contracts and the particularity of the operating environment, various security issues occur frequently.On the one hand, the code security problems of contract developers when writing contracts, on the other hand, there are many high-risk smart contracts in Ethereum, and ordinary users are easily attracted by the high returns provided by high-risk contracts, but they have no way to know the risks of the contracts.However, the research on smart contract security mainly focuses on code security, and there is relatively little research on the identification of contract functions.If the smart contract function can be accurately classified, it will help people better understand the behavior of smart contracts, while ensuring the ecological security of smart contracts and reducing or recovering user losses.Existing smart contract classification methods often rely on the analysis of the source code of smart contracts, but contracts released on Ethereum only mandate the deployment of bytecode, and only a very small number of contracts publish their source code.Therefore, an Ethereum smart contract classification method based on bytecode was proposed.Collect the Ethereum smart contract bytecode and the corresponding category label, and then extract the opcode frequency characteristics and control flow graph characteristics.The characteristic importance is analyzed experimentally to obtain the appropriate graph vector dimension and optimal classification model, and finally the multi-classification task of smart contract in five categories of exchange, finance, gambling, game and high risk is experimentally verified, and the F1 score of the XGBoost classifier reaches 0.913 8.Experimental results show that the algorithm can better complete the classification task of Ethereum smart contracts, and can be applied to the prediction of smart contract categories in reality.…”
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The classification and comparison of business ratios analysis methods by Sergejs Hilkevics, Valentina Semakina

“…Usually, special structural units are created in the company to perform most significant business functions, and the quantitative evaluation of business functions performance needs to consider business ratios, which describing appropriate units. …”
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Artificial Intelligent‐Enhanced Metabolite Profiling for Intraoperative IDH1 Genotyping in Glioma Using an Orthogonally Responsive SERS Probe by Hang Yin, Xin Zhang, Zheng Zhao, Chong Cao, Minhua Xu, Suhongrui Zhou, Tian Xuan, Ziyi Jin, Limei Han, Yang Fan, Cong Wang, Xiao Zhu, Ying Mao, Jinhua Yu, Cong Li

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The potential of large language models to advance precision oncology by Shufan Liang, Jiangjiang Zhang, Xingting Liu, Yinkui Huang, Jun Shao, Xiaohong Liu, Weimin Li, Guangyu Wang, Chengdi Wang

“…In this review, we provide a brief description of the development of LLMs, followed by model construction strategies and general medical functions. We then elaborate on the role of LLMs in cancer screening and diagnosis, metastasis identification, tumour staging, treatment recommendation, and documentation processing tasks by decoding various types of clinical data. …”
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Citizen scientists for MoveApps: Innovations and insights from volunteer coders in wildlife conservation by Andrea Kölzsch, Kamran Safi, Margaux M. Armfield, Chloe Beaupre, Emily Bennitt, Matthias Böck, William Brown, Christoph Eichler, Wolfgang Fielder, Clemens Hahn, Varalika Jain, Matthew W. Ketchin, Jed A. Long, Alexander V. Merdian‐Tarko, Lauren Minchin, Tomé Neves deMatos, Ambati Rakesh, Ockert Louis vanSchalkwyk, Martin Wikelski, Anne K. Scharf

“…These volunteers developed functioning applications capable of analysing animal tracking data to identify stationary behaviour, estimate ranges and movement corridors and assess human–wildlife conflicts using data sets from human infrastructure, such as OpenStreetMap. …”
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Sensor-Based Frailty Assessment Using Fitbit by Mohammad Hosseinalizadeh, Mehran Asghari, Nima Toosizadeh

“…This study evaluated the reliability of Fitbit in assessing frailty based on motor and heart rate (HR) parameters through a validated upper extremity function (UEF) test, which involves 20 s of rapid elbow flexion. …”
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Application of Multi-Objective Optimization for Path Planning and Scheduling: The Edible Oil Transportation System Framework by Chin S. Chen, Chia J. Lin, Yu J. Lin, Feng C. Lin

“…The method employs the A* and Dijkstra pathfinding algorithm to determine the shortest pipeline route for each task, and estimates pipeline resource usage to derive a node cost weight function. …”
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Current aspects of treatment and rehabilitation of patients with pharyngeal cancer by A. L. Kozhanov

“…The most important task after combined treatment is rehabilitation of the voice function, which may be restored by means of logopedic methods, voice-forming devices and reconstructive plastic operations.Hence novel approaches to treatment of pharyngeal cancer – both conservative and surgical have recently been worked out. …”
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Dynamic switching between brain networks predicts creative ability by Qunlin Chen, Yoed N. Kenett, Zaixu Cui, Hikaru Takeuchi, Andreas Fink, Mathias Benedek, Daniel C. Zeitlen, Kaixiang Zhuang, James Lloyd-Cox, Ryuta Kawashima, Jiang Qiu, Roger E. Beaty

“…Abstract Creativity is hypothesized to arise from a mental state which balances spontaneous thought and cognitive control, corresponding to functional connectivity between the brain’s Default Mode (DMN) and Executive Control (ECN) Networks. …”
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Research on Optimization of Large-Scale Heterogeneous Combat Network Based on Graph Embedding by Xianzheng Meng, Changrong Xie, Hui Li, Guangjun Zeng, Kebin Chen

“…Finally, we designed experiments to optimize the functional robustness of HCN, verifying the effectiveness of HCNOF. …”
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Navigating pressure and connection: goal orientation, coach-athlete relationships, and burnout among Chinese collegiate athletes—a mixed-methods study by Le Wang, Hongye Lian

“…Qualitative themes elaborated on these findings, highlighting how performance pressures were associated with goal orientation (e.g., ego orientation linked to cynicism via perceived system barriers) and the importance of specific functional CAR aspects (e.g., complementarity fostering competence, closeness associated with buffering life stress). …”
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An Unsupervised Moving Object Detection Network for UAV Videos by Xuxiang Fan, Gongjian Wen, Zhinan Gao, Junlong Chen, Haojun Jian

“…Domain-specific knowledge, including spatiotemporal consistency and foreground sparsity, is integrated into the loss function to mitigate false positives caused by motion parallax and platform movement. …”
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Insensitive Mechanism-Based Nonlinear Model Predictive Guidance for UAVs Intercepting Maneuvering Targets with Input Constraints by Danpeng Huang, Mingjie Zhang, Taideng Zhan, Jianjun Ma

“…Firstly, the basic principle of IM is proposed, and it is transformed into an additional cost function in NMPC. Secondly, in order to estimate the states of maneuvering target, a fixed-time sliding mode disturbance observer is developed. …”
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Prestimulus EEG Oscillations and Pink Noise Affect Go/No-Go ERPs by Robert J. Barry, Frances M. De Blasio, Alexander T. Duda, Beckett S. Munford

“…Scalp EEG contains not only oscillations but non-wave noise elements that may not relate to functional brain activity. These require identification and removal before the true impacts of brain oscillations can be assessed. …”
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The subthalamic microlesion story in Parkinson's disease: electrode insertion-related motor improvement with relative cortico-subcortical hypoactivation in fMRI. by Robert Jech, Karsten Mueller, Dušan Urgošík, Tomáš Sieger, Štefan Holiga, Filip Růžička, Petr Dušek, Petra Havránková, Josef Vymazal, Evžen Růžička

“…We studied this phenomenon by functional magnetic resonance imaging (fMRI) when considering the Unified Parkinson's Disease Rating Scale (UPDRS-III) and collateral oedema. …”
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AESware: Developing AES-enabled low-power multicore processors leveraging open RISC-V cores with a shared lightweight AES accelerator by Eunjin Choi, Jina Park, Kyuseung Han, Woojoo Lee

“…After FPGA (Field-Programmable Gate Array) prototyping for functional verification and power consumption analysis via 45 nm process technology synthesis, our findings revealed significant energy savings. …”
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Phase-lags in large scale brain synchronization: Methodological considerations and in-silico analysis. by Spase Petkoski, J Matias Palva, Viktor K Jirsa

“…Architecture of phase relationships among neural oscillations is central for their functional significance but has remained theoretically poorly understood. …”
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An Ensemble Fuzzy-Based Deep Learning Framework for Automatic Detection of Children with ADHD From EEG Signal by Jalil Manafian, Mehdi Fazli, Onur Ilhan, Subhiya Zeynalli, Sukaina Tuama Ghafel

“…The approach involves creating two images from EEGs, utilizing both functional connectivity and spectral attributes. These constructed images are then fed into an ensemble deep framework. …”
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