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Prolonged intermittent theta burst stimulation restores the balance between A_(2A)R-and A_(1)R-mediated adenosine signaling in the 6-hydroxidopamine model of Parkinson's disease

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Neural Regeneration Research 2025年第7期20卷 2053-2067页

作者： Milica Zeljkovic Jovanovic Jelena Stanojevic Ivana Stevanovic Milica Ninkovic Tihomir V.Ilic Nadezda Nedeljkovic Milorad Dragic Laboratory for Neurobiology Department of General Physiology and BiophysicsFaculty of BiologyUniversity of BelgradeBelgradeSerbia Medical faculty of military Medical Academy University of DefenceBelgradeSerbia

An imbalance in adenosine-mediated signaling,particularly the increased A_(2A)R-mediated signaling,plays a role in the pathogenesis of Parkinson's *** therapeutic approaches fail to alter disease progression,demonstrating the need for novel approaches in *** transcranial magnetic stimulation is a non-invasive approach that has been shown to improve motor and non-motor symptoms of Parkinson's ***,the underlying mechanisms of the beneficial effects of repetitive transcranial magnetic stimulation remain *** purpose of this study is to investigate the extent to which the beneficial effects of prolonged intermittent theta burst stimulation in the 6-hydroxydopamine model of experimental parkinsonism are based on modulation of adenosine-mediated *** with unilateral 6-hydroxydopamine lesions underwent intermittent theta burst stimulation for 3 weeks and were tested for motor skills using the Rotarod ***,quantitative reverse transcription polymerase chain reaction,immunohistochemistry,and biochemical analysis of components of adenosine-mediated signaling were performed on the synaptosomal fraction of the lesioned caudate *** intermittent theta burst stimulation improved motor symptoms in 6-hydroxydopamine-lesioned animals.A 6-hydroxydopamine lesion resulted in progressive loss of dopaminergic neurons in the caudate *** with intermittent theta burst stimulation began 7 days after the lesion,coinciding with the onset of motor *** treatment with prolonged intermittent theta burst stimulation,complete motor recovery was *** improvement was accompanied by downregulation of the e N/CD73-A_(2A)R pathway and a return to physiological levels of A_(1)R-adenosine deaminase 1 after 3 weeks of intermittent theta burst *** results demonstrated that 6-hydroxydopamine-induced degeneration reduced the expression of A_(1)R and elevated the expression of A_(2A)***

关键词： A_(1)R A_(2A)R adenosine receptors adenosine ecto-5′-nucleotidase intermittent theta burst stimulation non-invasive brain stimulation Parkinson's disease purinergic signalling

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Fatigue Strength Analysis of Dissimilar Aluminum Alloy TIG Welds

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Journal of Wuhan University of Technology(Materials science) 2025年第1期40卷 265-274页

作者： LIAO Xiangyun WANG Ruijie LIU Guoshou ZHAO Pinglin faculty of Mechanical and Electrical Engineering Kunming University of Science and TechnologyKunming 650500China faculty of Civil Engineering and Mechanics Kunming University of Science and TechnologyKunming 650500China

The constant amplitude loading fatigue tests were carried out on the 6061/7075 aluminum alloy TIG fillet welded lap specimens in this study,and the weld seam cross-section hardness was *** experimental results show that most specimens mainly failed at the 7075 side weld toes even though the base material tensile strength of 7075 is higher than that of *** maximum stress-strain concentration in the two finite element models is located at the 7075 side weld toe,which is basically consistent with the actual fracture *** weld zone on the 7075 side experiences severe material softening,with a large ***,the Vickers hardness value on the 6061 side negligibly changes and fluctuates around 70 *** obvious defects are found on the fatigue fracture,but a large number of secondary cracks *** germinate from the weld toe and propagate in the direction of the plate *** reinforcement has a serious impact on fatigue *** life will decrease exponentially as the weld reinforcement increases under low *** is found that the notch stress method can give a better fatigue life prediction for TIG weldments,and the errors of the predicted results are within the range of two factors,while the prediction accuracy decreases under low *** equivalent structural stress method can also be used for fatigue life prediction of TIG weldments,but the errors of prediction results are within the range of three factors,and the accuracy decreases under high stress.

关键词： TIG welding notch stress method equivalent structural stress method fatigue life finite element analysis

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Hourglass-GCN for 3D Human Pose Estimation Using Skeleton Structure and View Correlation

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Computers, Materials & Continua 2025年第1期82卷 173-191页

作者： Ange Chen Chengdong Wu Chuanjiang Leng faculty of Robot science and Engineering Northeastern UniversityShenyang110169China

Previous multi-view 3D human pose estimation methods neither correlate different human joints in each view nor model learnable correlations between the same joints in different views explicitly,meaning that skeleton structure information is not utilized and multi-view pose information is not completely ***,existing graph convolutional operations do not consider the specificity of different joints and different views of pose information when processing skeleton graphs,making the correlation weights between nodes in the graph and their neighborhood nodes *** Graph Convolutional Networks(GCNs)cannot extract global and deeplevel skeleton structure information and view correlations *** solve these problems,pre-estimated multiview 2D poses are designed as a multi-view skeleton graph to fuse skeleton priors and view correlations explicitly to process occlusion problem,with the skeleton-edge and symmetry-edge representing the structure correlations between adjacent joints in each viewof skeleton graph and the view-edge representing the view correlations between the same joints in different *** make graph convolution operation mine elaborate and sufficient skeleton structure information and view correlations,different correlation weights are assigned to different categories of neighborhood nodes and further assigned to each node in the *** on the graph convolution operation proposed above,a Residual Graph Convolution(RGC)module is designed as the basic module to be combined with the simplified Hourglass architecture to construct the Hourglass-GCN as our 3D pose estimation ***-GCNwith a symmetrical and concise architecture processes three scales ofmulti-viewskeleton graphs to extract local-to-global scale and shallow-to-deep level skeleton features *** results on common large 3D pose dataset Human3.6M and MPI-INF-3DHP show that Hourglass-GCN outperforms some excellent methods in 3D pose estimati

关键词： 3D human pose estimation multi-view skeleton graph elaborate graph convolution operation Hourglass-GCN

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When AI meets sustainable 6G

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science China(Information sciences) 2025年第1期68卷 4-22页

作者： Xiaohu YOU Yongming HUANG Cheng ZHANG Jiaheng WANG Hao YIN Hequan WU National Mobile Communications Research Laboratory Southeast University Purple Mountain Laboratories Chinese Academy of military science China Information Communication Technologies Group

Sixth-generation(6G) networks are anticipated to achieve transformative advancements, characterized by extreme connectivity, deep integration with artificial intelligence(AI) and sensing, and airground integration. The evolution of 6G exhibits two major trends: ubiquitous intelligence and sustainability. The former aims to embed state-of-the-art AI technology into the 6G network, from the physical layers to applications, while the latter emphasizes reducing energy consumption while enhancing network performance to address environmental concerns. Despite the amazing progress in recent years, AI advancements come with substantial increases in data and computational overhead, posing critical challenges for integrating AI into sustainable 6G networks. First, high energy consumption from large datasets and heavyweight AI models contradicts6G's green goals. Second, the precise collection of large datasets, message delivery latency, and inference delays in AI models pose challenges for real-time tasks in 6G. Third, the uninterpretability and unpredictability of AI models complicate meeting the stringent requirements for controllable transmission in dynamic wireless environments. Addressing these challenges and achieving sustainable 6G with ubiquitous intelligence calls for a revolutionary design of 6G architecture and AI frameworks. To this end, this paper introduces a novel and practical methodology for green, real-time, and controllable 6G native intelligence,starting with knowledge graph(KG) analysis to extract small but critical datasets, followed by the development of distributed lightweight AI models, and the use of digital twins(DTs) to create precise replicas of physical 6G networks. This leads to a pervasive multi-level(PML)-AI framework supported by a task-centric, three-layer 6G architecture. The AI framework operates through non-real-time and real-time cycles, leveraging three key technologies: wireless data KGs for efficient data management,lightweight AI mode

关键词： artificial intelligence sustainable 6G green real-time controllable

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Privacy-preserving explainable AI: a survey

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science China(Information sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN Phi Le NGUYEN Hongzhi YIN Quoc Viet Hung NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication sciences Ecole Polytechnique Federale de Lausanne faculty of Mathematics and Computer science University of Bremen faculty of Information Technology HUTECH University Department of Computer science Hanoi University of Science and Technology School of Electrical Engineering and Computer science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

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Synergistic effect of bonding heterogeneity and phonon localization in introducing excellent thermoelectric properties in layered heteroanionic NdZnSbO material

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Journal of Energy Chemistry 2025年第1期100卷 259-272页

作者： Da Wan Shuwei Tang Shulin Bai Xiaodong Li Peng Ai Wanrong Guo Tuo Zheng College of Materials science and Engineering Liaoning Technical University faculty of Chemistry Northeast Normal University

Layered rare-earth metal oxides, harnessing the dual properties of oxides and two-dimensional layered materials, exhibit remarkable thermal stability and quantum confinement effects. Therefore, this work adopts the first-principles calculation combined with the Boltzmann transport theory to predict the thermoelectric properties of NdZnSbO compound. The coexistence of weak interlayer van der Waals interactions, robust intralayer ionic bonding, and partial covalent bonding leads to remarkable bonding heterogeneity, which engenders pronounced phonon scattering and imposes constraints on thermal transport along the out-of-plane direction. The weakened chemical bonds induced by the antibonding states, together with the rattling-like behavior of the Zn atom, culminate in the profound anharmonicity in the layered NdZnSbO compound. The weakening bond and heavy element contribute to the softness of phonon modes, which significantly diminishes the phonon group velocity. The redistribution-dominated four-phonon scattering process spans a large optical gap, which effectively reduces the lattice thermal conductivity. The NdZnSbO compound exhibits direct semiconductor characteristic with a bandgap of 0.73 e V by adopting the Heyd-Scuseria-Ernzerhof(HSE06) functional in combination with spin–orbit coupling(SOC) effect. The multi-valley feature of NdZnSbO compound augur favorably for band degeneracy, thus amplifying the power factor. Consequently, an optimal figure-of-merit(ZT) of 3.40 at 900 K is achieved for the n-type NdZnSbO compound. The present study delves deeply insights into the origins for the low thermal conductivity of NdZnSbO compound and proposes an optimization scheme to enhance overall thermoelectric performance.

关键词： Bond strength (chemical)

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A Fully‑Printed Wearable Bandage‑Based Electrochemical Sensor with pH Correction for Wound Infection Monitoring

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Nano-Micro Letters 2025年第3期17卷 355-375页

作者： Kanyawee Kaewpradub Kornautchaya Veenuttranon Husanai Jantapaso Pimonsri Mittraparp‑arthorn Itthipon Jeerapan Center of Excellence for Trace Analysis and Biosensor Prince of Songkla UniversityHat Yai 90110SongkhlaThailand Division of Physical science Faculty of SciencePrince of Songkla UniversityHat Yai 90110SongkhlaThailand Center of Excellence for Innovation in Chemistry Faculty of SciencePrince of Songkla UniversityHat Yai 90110SongkhlaThailand The ijE Electrochemistry for All Laboratory Prince of Songkla UniversityHat Yai 90110SongkhlaThailand Division of Biological science Faculty of SciencePrince of Songkla UniversityHat Yai 90110SongkhlaThailand

Wearable sensing systems have been designed to monitor health conditions in real-time by detecting analytes in human *** diagnosis remains challenging,necessitating suitable materials for high-performance wearable sensors to offer prompt *** devices have limitations in measuring pH and the concentration of pH-dependent electroactive species simultaneously,which is crucial for obtaining a comprehensive understanding of wound status and optimizing ***,improving materials and analysis system accuracy is *** article introduces the first example of a flexible array capable of detecting pyocyanin,a bacterial virulence factor,while correcting dynamic pH *** demonstrate that this combined sensor enhances accuracy by mitigating the impact of pH variability on pyocyanin sensor *** screen-printable inks were developed to enhance analytical *** analytical performances of two sensitive sensor systems(i.e.,fully-printed porous graphene/multiwalled carbon nanotube(CNT)and polyaniline/CNT composites for pyocyanin and pH sensors)are *** least square regression is employed to analyze nonzero-order data arrays from square wave voltammetric and potentiometric measurements of pyocyanin and pH sensors to establish a predictive model for pyocyanin concentration in complex *** sensitive and effective strategy shows potential for personalized applications due to its affordability,ease of use,and ability to adjust for dynamic pH changes.

关键词： Pyocyanin Bandages Wound monitoring Biosensor Wearable device

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Optimizing Fine-Tuning in Quantized Language Models:An In-Depth Analysis of Key Variables

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Computers, Materials & Continua 2025年第1期82卷 307-325页

作者： Ao Shen Zhiquan Lai Dongsheng Li Xiaoyu Hu National Key Laboratory of Parallel and Distributed Computing National University of Defense TechnologyChangsha410073China Strategic Assessments and Consultation Institute Academy of Military ScienceBeijing100091China

Large-scale Language Models(LLMs)have achieved significant breakthroughs in Natural Language Processing(NLP),driven by the pre-training and fine-tuning *** this approach allows models to specialize in specific tasks with reduced training costs,the substantial memory requirements during fine-tuning present a barrier to broader ***-Efficient Fine-Tuning(PEFT)techniques,such as Low-Rank Adaptation(LoRA),and parameter quantization methods have emerged as solutions to address these challenges by optimizing memory usage and computational *** these,QLoRA,which combines PEFT and quantization,has demonstrated notable success in reducing memory footprints during fine-tuning,prompting the development of various QLoRA *** these advancements,the quantitative impact of key variables on the fine-tuning performance of quantized LLMs remains *** study presents a comprehensive analysis of these key variables,focusing on their influence across different layer types and depths within LLM *** investigation uncovers several critical findings:(1)Larger layers,such as MLP layers,can maintain performance despite reductions in adapter rank,while smaller layers,like self-attention layers,aremore sensitive to such changes;(2)The effectiveness of balancing factors depends more on specific values rather than layer type or depth;(3)In quantization-aware fine-tuning,larger layers can effectively utilize smaller adapters,whereas smaller layers struggle to do *** insights suggest that layer type is a more significant determinant of fine-tuning success than layer depth when optimizing quantized ***,for the same discount of trainable parameters,reducing the trainable parameters in a larger layer is more effective in preserving fine-tuning accuracy than in a smaller *** study provides valuable guidance for more efficient fine-tuning strategies and opens avenues for further research into optimizing LLM

关键词： Large-scale Language Model Parameter-Efficient Fine-Tuning parameter quantization key variable trainable parameters experimental analysis

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Dynamic Multi-Graph Spatio-Temporal Graph Traffic Flow Prediction in Bangkok:An Application of a Continuous Convolutional Neural Network

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Computer Modeling in Engineering & sciences 2025年第1期142卷 579-607页

作者： Pongsakon Promsawat Weerapan Sae-dan Marisa Kaewsuwan Weerawat Sudsutad Aphirak Aphithana Department of Civil Engineering Faculty of EngineeringRamkhamkaeng UniversityBangkok10240Thailand Department of Computer Engineering Faculty of EngineeringRamkhamkaeng UniversityBangkok10240Thailand Department of Statistics Faculty of ScienceRamkhamkaeng UniversityBangkok10240Thailand

The ability to accurately predict urban traffic flows is crucial for optimising city ***,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility *** learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal ***,these models often become overly complex due to the large number of hyper-parameters *** this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction *** comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest *** the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 ***,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer *** Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time *** numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.

关键词： Graph neural networks convolutional neural network deep learning dynamic multi-graph spatio-temporal

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Hybrid Deep Learning Approach for Automating App Review Classification:Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

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Computers, Materials & Continua 2025年第1期82卷 949-976页

作者： Nahed Alsaleh Reem Alnanih Nahed Alowidi Department of Computer science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer science College of Computer Science and EngineeringUniversity of HailHail81451Saudi Arabia

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.

关键词： Requirements Engineering(RE) app review analysis usabilitymetrics hybrid deep learning BERT-BiLSTM-CNN

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