Traumatic spinal cord injury(SCI)is a debilitating condition characterized by the impairment of neural circuits,leading to the loss of motor and sensory functions and accompanied by severe complications.Substantial re...Traumatic spinal cord injury(SCI)is a debilitating condition characterized by the impairment of neural circuits,leading to the loss of motor and sensory functions and accompanied by severe complications.Substantial research has reported the therapeutic potential of Omega-3 fatty acids for the central nervous system,particularly after traumatic SCI.Omega-3 fatty acids may contribute to improving SCI recovery through their anti-inflammatory,anti-oxidative,neurotrophic,and membrane integrity-preserving properties.These functions of Omega-3 fatty acids are primarily mediated via the activation of G protein-coupled receptor 120(GPR120),commonly known as the fish oil-specific receptor.Advancements in understanding of the molecular mechanisms of GPR120’s recognition of Omega-3 fatty acids and its downstream signaling mechanisms has significantly promoted research on the pharmacological potential of Omega-3 fatty acids and the development of highly selective and high-affinity alternatives.This review aims to provide in-depth analysis of the comprehensive therapeutic potential of Omega-3 fatty acids for SCI and its accompanying complications,and the prospects for developing novel drugs based on the recognition of Omega-3 fatty acids by GPR120.展开更多
The state-of-the-art optical atomic clocks and the time-frequency signal transmission open a fresh field for gravity potential(geopotential)determination.Various methods,including optical fiber frequency transfer,sate...The state-of-the-art optical atomic clocks and the time-frequency signal transmission open a fresh field for gravity potential(geopotential)determination.Various methods,including optical fiber frequency transfer,satellite two-way,satellite common-view,satellite carrier phase,VLBI,tri-frequency combination,and dual-frequency combination,were developed to determine the geopotential differences using optical atomic clocks and then determine the geopotential at station B based on the geopotential at station A.This review elaborates the principles,methods,scientific objectives,applications,and relevant research trends of geopotential determination based on time-frequency signals.展开更多
Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often stru...Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.展开更多
We discuss recent progress in using machine-learning(ML)-enabled inverse design techniques applied to photonic devices and components.Specifically,we highlight the design of optical sources,including fiber and semicon...We discuss recent progress in using machine-learning(ML)-enabled inverse design techniques applied to photonic devices and components.Specifically,we highlight the design of optical sources,including fiber and semiconductor lasers,as well as Raman and semiconductor optical amplifiers.Although inverse design approaches for optical detectors remain relatively underexplored,we examine optical layers,particularly metamaterial absorbers,as promising candidates for high-performance optical detection.In addition,we underscore advancements in inverse designing passive optical components,including beam splitters,gratings,and optical fibers.These optical blocks are fundamental in developing next-generation standalone optical communication systems and optical sensing networks,including integrated sensing and communication technologies.While categorizing various reported deep learning architectures across five paradigms,we offer a paradigm-based perspective that reveals how different ML techniques function within modern inverse design methods and enable fast,data-driven solutions that significantly reduce design time and computational demands compared with traditional optimization methods.展开更多
High-concentration photovoltaic(HCPV)systems present significant thermal management challenges due to the intense heat fluxes generated under concentrated solar irradiation,especially in arid environments.Effective he...High-concentration photovoltaic(HCPV)systems present significant thermal management challenges due to the intense heat fluxes generated under concentrated solar irradiation,especially in arid environments.Effective heat dissipation is critical to prevent performance degradation and structural failure.This study investigates the thermal performance and design optimization of an enhanced HCPV module,integrating numerical,analytical,and experimental methods.A coupled optical-thermal-electrical model was developed to simulate ray tracing,heat transfer,and temperature-dependent electrical behaviour,with predictions validated under real-world desert conditions.Compared to a baseline commercial module operating at 106℃,the optimized design achieved a peak temperature reduction of 16℃,lowering the cell temperature to 90℃under a concentration ratio of 961×and direct normal irradiance(DNI)of 950 W/m^(2).The total thermal resistance was reduced from 0.25 to 0.15 K/W(a 40%improvement),and the electrical efficiency increased from 37.5%to 38.6%,representing a relative gain of approximately 3.1%.The system consistently maintained a fill factor exceeding 78%,underscoring stable performance under high thermal load.These findings demonstrate that targeted thermal design,informed by integrated modeling,is essential for unlocking the reliability and efficiency of high-flux solar energy systems.展开更多
Digital twin technology,that creates virtual replicas of physical entities using real-time data and simulation models,has emerged as a transformative innovation across multiple healthcare domains.Its application in ph...Digital twin technology,that creates virtual replicas of physical entities using real-time data and simulation models,has emerged as a transformative innovation across multiple healthcare domains.Its application in physiotherapy and rehabilitation represents a paradigm shift from traditional therapeutic approaches to personalized data-driven interventions that optimize patient outcomes.This narrative review examines the current applications,benefits,challenges,and future prospects of digital twin technology in physiotherapy and rehabilitation,providing a comprehensive analysis of the manner in which this technology is reshaping clinical practice and patient care.A narrative review approach was employed,systematically searching PubMed,IEEE Xplore,Scopus,and Web of Science databases.Studies describing digital twin applications,development methodologies,clinical implementations,and theoretical frameworks in physiotherapy and rehabilitation contexts were included.Digital twin technology demonstrates significant potential in personalizing rehabilitation programs,enabling real-time monitoring of patient progress,predicting treatment outcomes,and facilitating remote therapeutic interventions.Current applications span musculoskeletal rehabilitation,neurological recovery,post surgical care,and sports injury management.Key benefits include enhanced treatment precision,improved patient engagement,reduced healthcare costs,and accelerated recovery times.However,implementation faces challenges including technological complexity,data privacy concerns,interoperability issues,and the need for substantial infrastructure investment.Digital twin technology represents a promising frontier in physiotherapy and rehabilitation,offering unprecedented opportunities for personalized,efficient,and effective patient care.Successful integration requires addressing the current limitations while fostering interdisciplinary collaboration between clinicians,engineers,and data scientists.展开更多
The healthcare field is fraught with challenges associated with severe class imbalance,wherein such critical conditions like sepsis,cardiac arrest,and drug adverse reactions are rare but have dire clinical consequence...The healthcare field is fraught with challenges associated with severe class imbalance,wherein such critical conditions like sepsis,cardiac arrest,and drug adverse reactions are rare but have dire clinical consequences.This paper presents a new framework,Deep Reinforcement Adaptive Gradient Optimization Network to Mining Rare Events(DRAGON-MINE),to demonstrate how deep reinforcement learning can be used synergistically with adaptive gradient optimization and address the inherent weaknesses of current methods in the prediction of rare health events.The suggested architecture uses a dual-pathway consisting of a reinforcement learning agent to dynamically reweigh samples and an adaptive gradient optimizer to follow novel learning rates.With extensive experiments on the MIMIC-IV and eICU-CRD datasets,DRAGON-MINE consistently outperforms recent state-of-the-art methods for sepsis,cardiac arrest,and adverse drug reaction prediction,achieving AUROC values of 92.3%and 91.6%for sepsis prediction on MIMIC-IV and eICU-CRD,respectively,while consistently outperforming Transformer-,CNN-RNN-,and Fed-Ensemble-based methods across all evaluated tasks and datasets,with particularly strong gains observed in precision-recall performance under severe class imbalance.With its high sensitivity(88.4%)and specificity(90.2%),DRAGON-MINE enables reliable early warning of rare clinical events in critical care settings while minimizing false alarms,supporting safer clinical decision support systems,and demonstrating strong potential for scalable deployment across multi-institutional intensive care environments through federated learning.展开更多
The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.Thi...The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.This paper proposes FE-ACS(Fog-Edge Adaptive Cybersecurity System),a novel hierarchical security framework that intelligently distributes AI-powered anomaly detection algorithms across edge,fog,and cloud layers to optimize security efficacy,latency,and privacy.Our comprehensive evaluation demonstrates that FE-ACS achieves superior detection performance with an AUC-ROC of 0.985 and an F1-score of 0.923,while maintaining significantly lower end-to-end latency(18.7 ms)compared to cloud-centric(152.3 ms)and fog-only(34.5 ms)architectures.The system exhibits exceptional scalability,supporting up to 38,000 devices with logarithmic performance degradation—a 67×improvement over conventional cloud-based approaches.By incorporating differential privacy mechanisms with balanced privacy-utility tradeoffs(ε=1.0–1.5),FE-ACS maintains 90%–93%detection accuracy while ensuring strong privacy guarantees for sensitive healthcare data.Computational efficiency analysis reveals that our architecture achieves a detection rate of 12,400 events per second with only 12.3 mJ energy consumption per inference.In healthcare risk assessment,FE-ACS demonstrates robust operational viability with low patient safety risk(14.7%)and high system reliability(94.0%).The proposed framework represents a significant advancement in distributed security architectures,offering a scalable,privacy-preserving,and real-time solution for protecting healthcare IoT ecosystems against evolving cyber threats.展开更多
Male breast cancer(MBC)is rare,representing 0.5%–1%of all breast cancers,but its incidence is increasing due to improved diagnostics and awareness.MBC typically presents in older men,is human epidermal growth factor ...Male breast cancer(MBC)is rare,representing 0.5%–1%of all breast cancers,but its incidence is increasing due to improved diagnostics and awareness.MBC typically presents in older men,is human epidermal growth factor receptor 2(HER2)-negative and estrogen receptor(ER)-positive,and lacks routine screening,leading to delayed diagnosis and advanced disease.Major risk factors include hormonal imbalance,radiation exposure,obesity,alcohol use,and Breast Cancer Gene 1 and 2(BRCA1/2)mutations.Clinically,it may resemble gynecomastia but usually appears as a unilateral,painless mass or nipple discharge.Advances in imaging and liquid biopsy have enhanced early detection.Molecular mechanisms involve hormonal signaling,HER2/epidermal growth factor receptor(EGFR)pathways,tumor suppressor gene alterations,and epigenetic changes.While standard treatments mirror those for female breast cancer,emerging options such as cyclin-dependent kinase 4 and 6(CDK4/6),and poly(ADP-ribose)polymerase(PARP)inhibitors,immunotherapy,and precision medicine are reshaping management.Incorporating artificial intelligence,molecular profiling,and male-specific clinical trials is essential to improve outcomes and bridge current diagnostic and therapeutic gaps.展开更多
Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that ...Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.展开更多
Micro/nanorobots represent a groundbreaking advancement in nanotechnology,with applications spanning medicine,envi-ronmental remediation,and industrial processes.A major challenge in their development is achieving eff...Micro/nanorobots represent a groundbreaking advancement in nanotechnology,with applications spanning medicine,envi-ronmental remediation,and industrial processes.A major challenge in their development is achieving efficient and bio-compatible propulsion.Enzyme-driven propulsion,particularly using catalase,offers a promising solution due to its ability to decompose hydrogen peroxide(H2O2)into water and oxygen,generating thrust for autonomous movement.Compared to metal-based catalysts,catalase-powered systems exhibit superior biocompatibility and lower toxicity,making them ideal for biomedical applications.This review explores the role of catalase in micro/nanorobot propulsion,highlighting self-propulsion mechanisms,different nanorobot types,and their applications in drug delivery,infection treatment,cancer therapy,and biosensing.Additionally,recent advancements in biodegradable enzyme-powered nanorobots and their poten-tial in overcoming biological barriers are discussed.With further research,catalase-driven nanorobots could revolutionize targeted therapy and diagnostic techniques,paving the way for innovative solutions in nanomedicine.展开更多
Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important a...Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.展开更多
Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relati...Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relationships among nodes.This paper proposes a novel graph coupling convolutional model that introduces an adaptive weighting mechanism to assign distinct importance to neighboring nodes based on their similarity to the central node.Unlike traditional methods,the proposed coupling strategy enhances the interpretability of node interactions while maintaining competitive classification performance.The model operates in the spatial domain,utilizing adjacency list structures for efficient convolution and addressing the limitations of weight sharing through a coupling-based similarity computation.Extensive experiments are conducted on five graph-structured datasets,including Cora,Citeseer,PubMed,Reddit,and BlogCatalog,as well as a custom topology dataset constructed from the Open University Learning Analytics Dataset(OULAD)educational platform.Results demonstrate that the proposed model achieves good classification accuracy,while significantly reducing training time through direct second-order neighbor fusion and data preprocessing.Moreover,analysis of neighborhood order reveals that considering third-order neighbors offers limited accuracy gains but introduces considerable computational overhead,confirming the efficiency of first-and second-order convolution in practical applications.Overall,the proposed graph coupling model offers a lightweight,interpretable,and effective framework for multi-label node classification in complex networks.展开更多
Recent advancements in Zn-halogen batteries have focused on enhancing the adsorptive or catalytic capability of host materials and stabilizing complex intermediates with electrolyte additives,while the halogen-ion ele...Recent advancements in Zn-halogen batteries have focused on enhancing the adsorptive or catalytic capability of host materials and stabilizing complex intermediates with electrolyte additives,while the halogen-ion electrolyte modifications exhibit strong potential for integrated interfacial regulation.Herein,we design an electrically insulating rigid electrolyte container to immobilize a liquid halogen-ion electrolyte for separator-free Zn-halogen batteries with customizable electron transfer.Robust hydrogen bonding of hydroxyl groups in SiO_(2)with fluorinated moieties in PVDF-hfp regulates Zn^(2+)solvation and suppresses H_(2)O activity,while multi-channels formed by microcracks and interparticle gaps not only enhance mass transfer but also buffer interfacial electric field,jointly enabling a durable Zn plating/stripping.Effective confinement of intermediates also ensures the high reversibility across single-(I^(-)/I0),double-(I^(-)/I0/I^(-)),and triple-(I^(-)/I0/I^(-),Cl-/Cl0)electron transfer mechanisms at cathode,as evidenced by the double-electron transfer systems exhibiting a low capacity decay rate of 0.02‰over 4500 cycles at 10 mA cm^(-2)and a high areal capacity of 11.9 mAh cm^(-2)at 2 mA cm^(-2).This work presents a novel“container engineering”approach to halogen-ion electrolyte design and provides fundamental insights into the relationships between redox reversibility and reaction kinetics.展开更多
Background:Hemifacial spasm(HFS)is a neurological disorder characterized by involuntary facial muscle contractions,significantly impacting quality of life.This study aims to provide a comprehensive bibliometric analys...Background:Hemifacial spasm(HFS)is a neurological disorder characterized by involuntary facial muscle contractions,significantly impacting quality of life.This study aims to provide a comprehensive bibliometric analysis of global research trends,collaborations,and scientific contributions in the field of HFS,addressing publication patterns,influential authors and institutions,and prominent research topics from 1999 to 2024.Methods:We conducted a bibliometric analysis based on 1,884 publications retrieved from the Web of Science Core Collection using the keyword"Hemifacial Spasm."Data analysis and visualization were performed using Microsoft Excel,R/Bibliometrix,Scimago Graphica,VOSviewer,Pajek,and CiteSpace.Parameters assessed included publication trends,author collaborations,institutional contributions,core journals,citation metrics,and keyword clusters.Results:Among the analyzed publications,1,646 were original research articles,and 238 were reviews,involving 6,063 researchers and citing 25,252 references.The United States,China,and Japan were identified as leading contributing countries,with prominent institutions including Shanghai Jiao Tong University,Sungkyunkwan University,and the University of Pittsburgh.Top authors by publication count were Li Shiting,Park Kwan,and Zhong Jun,whereas Peter J.Jannetta,Albert R.Møller,and Janko Jankovic were most frequently cited.Core journals,identified via Bradford’s Law,included Acta Neurochirurgica,World Neurosurgery,and Journal of Neurosurgery.Keyword analysis highlighted focal research areas:"hemifacial spasm","microvascular decompression",and"trigeminal neuralgia".Conclusion:This bibliometric study provides critical insights into the evolution of research on HFS,highlighting key contributors,institutional influence,and research hotspots.The findings underscore ongoing collaborative opportunities and essential areas for future research exploration.展开更多
The migration of tasks aided by machine learning(ML)predictions IN(DPM)is a system-level design technique that is used to reduce energy by enhancing the overall performance of the processor.In this paper,we address th...The migration of tasks aided by machine learning(ML)predictions IN(DPM)is a system-level design technique that is used to reduce energy by enhancing the overall performance of the processor.In this paper,we address the issue of system-level higher task dissipation during the execution of parallel workloads with common deadlines by introducing a machine learning-based framework that includes task migration using energy-efficient earliest deadline first scheduling(EA-EDF).ML-based EA-EDF enhances the overall throughput and optimizes the energy to avoid delay and performance degradation in a multiprocessor system.The proposed system model allocates processors to the ready task set in such a way that their deadlines are guaranteed.A full task migration policy is also integrated to ensure proper task mapping that ensures inter-process linkage among the arrived tasks with the same deadlines.The execution of a task can halt on one CPU and reschedule the execution on a different processor to avoid delay and ensure meeting the deadline.Our approach shows promising potential for machine-learning-based schedulability analysis enables a comparison between different ML models and shows a promising reduction in energy as compared with other ML-aware task migration techniques for SoC like Multi-Layer Feed-Forward Neural Networks(MLFNN)based on convolutional neural network(CNN),Random Forest(RF)and Deep learning(DL)algorithm.The Simulations are conducted using super pipelined microarchitecture of advanced micro devices(AMD)XScale PXA270 using instruction and data cache per core 32 Kbyte I-cache and 32 Kbyte D-cache on various utilization factors(u_(i))12%,31%and 50%.The proposed approach consumes 5.3%less energy when almost half of the CPU is running and on a lower workload consumes 1.04%less energy.The proposed design accumulatively gives significant improvements by reducing the energy dissipation on three clock rates by 4.41%,on 624 MHz by 5.4%and 5.9%on applications operating on 416 and 312 MHz standard operating frequencies.展开更多
Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosupp...Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosuppressive therapy,AI has the potential to improve both operational efficiency and patient outcomes.Despite these advancements,the perspectives of transplant professionals-those at the forefront of critical decision-making-remain insufficiently explored.To address this gap,this study utilizes a multi-round electronic Delphi approach to gather and analyses insights from global experts involved in organ transplantation.Participants are invited to complete structured surveys capturing demographic data,professional roles,institutional practices,and prior exposure to AI technologies.The survey also explores perceptions of AI’s potential benefits.Quantitative responses are analyzed using descriptive statistics,while open-ended qualitative responses undergo thematic analysis.Preliminary findings indicate a generally positive outlook on AI’s role in enhancing transplantation processes,particularly in areas such as donor matching and post-operative care.These mixed views reflect both optimism and caution among professionals tasked with integrating new technologies into high-stakes clinical workflows.By capturing a wide range of expert opinions,the findings will inform future policy development,regulatory considerations,and institutional readiness frameworks for the integration of AI into organ transplantation.展开更多
Particle Swarm Optimization(PSO)has been utilized as a useful tool for solving intricate optimization problems for various applications in different fields.This paper attempts to carry out an update on PSO and gives a...Particle Swarm Optimization(PSO)has been utilized as a useful tool for solving intricate optimization problems for various applications in different fields.This paper attempts to carry out an update on PSO and gives a review of its recent developments and applications,but also provides arguments for its efficacy in resolving optimization problems in comparison with other algorithms.Covering six strategic areas,which include Data Mining,Machine Learning,Engineering Design,Energy Systems,Healthcare,and Robotics,the study demonstrates the versatility and effectiveness of the PSO.Experimental results are,however,used to show the strong and weak parts of PSO,and performance results are included in tables for ease of comparison.The results stress PSO’s efficiency in providing optimal solutions but also show that there are aspects that need to be improved through combination with algorithms or tuning to the parameters of the method.The review of the advantages and limitations of PSO is intended to provide academics and practitioners with a well-rounded view of the methods of employing such a tool most effectively and to encourage optimized designs of PSO in solving theoretical and practical problems in the future.展开更多
基金supported by the National Key Research and Development Project of Stem Cell and Transformation Research(2019YFA0112100)Taishan Scholars Programof Shandong Province-Young Taishan Scholars(tsqn201909197)+1 种基金Cutting Edge Development Fund of Advanced Medical Research Institute(Shandong University)National Natural Science Foundation of China(82220108005)。
文摘Traumatic spinal cord injury(SCI)is a debilitating condition characterized by the impairment of neural circuits,leading to the loss of motor and sensory functions and accompanied by severe complications.Substantial research has reported the therapeutic potential of Omega-3 fatty acids for the central nervous system,particularly after traumatic SCI.Omega-3 fatty acids may contribute to improving SCI recovery through their anti-inflammatory,anti-oxidative,neurotrophic,and membrane integrity-preserving properties.These functions of Omega-3 fatty acids are primarily mediated via the activation of G protein-coupled receptor 120(GPR120),commonly known as the fish oil-specific receptor.Advancements in understanding of the molecular mechanisms of GPR120’s recognition of Omega-3 fatty acids and its downstream signaling mechanisms has significantly promoted research on the pharmacological potential of Omega-3 fatty acids and the development of highly selective and high-affinity alternatives.This review aims to provide in-depth analysis of the comprehensive therapeutic potential of Omega-3 fatty acids for SCI and its accompanying complications,and the prospects for developing novel drugs based on the recognition of Omega-3 fatty acids by GPR120.
基金National Natural Science Foundation of China(Grant Nos.42388102,42030105,42192535)the Open Fund of State Key Laboratory of Precision Geodesy,Innovation Academy for Precision Measurement Science and Technology,Chinese Academy of Sciences(Grant No.SKLPG2025-1-5)。
文摘The state-of-the-art optical atomic clocks and the time-frequency signal transmission open a fresh field for gravity potential(geopotential)determination.Various methods,including optical fiber frequency transfer,satellite two-way,satellite common-view,satellite carrier phase,VLBI,tri-frequency combination,and dual-frequency combination,were developed to determine the geopotential differences using optical atomic clocks and then determine the geopotential at station B based on the geopotential at station A.This review elaborates the principles,methods,scientific objectives,applications,and relevant research trends of geopotential determination based on time-frequency signals.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01295).
文摘Alzheimer’s Disease(AD)is a progressive neurodegenerative disorder that significantly affects cognitive function,making early and accurate diagnosis essential.Traditional Deep Learning(DL)-based approaches often struggle with low-contrast MRI images,class imbalance,and suboptimal feature extraction.This paper develops a Hybrid DL system that unites MobileNetV2 with adaptive classification methods to boost Alzheimer’s diagnosis by processing MRI scans.Image enhancement is done using Contrast-Limited Adaptive Histogram Equalization(CLAHE)and Enhanced Super-Resolution Generative Adversarial Networks(ESRGAN).A classification robustness enhancement system integrates class weighting techniques and a Matthews Correlation Coefficient(MCC)-based evaluation method into the design.The trained and validated model gives a 98.88%accuracy rate and 0.9614 MCC score.We also performed a 10-fold cross-validation experiment with an average accuracy of 96.52%(±1.51),a loss of 0.1671,and an MCC score of 0.9429 across folds.The proposed framework outperforms the state-of-the-art models with a 98%weighted F1-score while decreasing misdiagnosis results for every AD stage.The model demonstrates apparent separation abilities between AD progression stages according to the results of the confusion matrix analysis.These results validate the effectiveness of hybrid DL models with adaptive preprocessing for early and reliable Alzheimer’s diagnosis,contributing to improved computer-aided diagnosis(CAD)systems in clinical practice.
基金the School of Engineering and Built Environment at Anglia Ruskin University,UK,for the supportthe support of IRC-CSS and the Electrical Engineering Department,KFUPM,Saudi Arabia。
文摘We discuss recent progress in using machine-learning(ML)-enabled inverse design techniques applied to photonic devices and components.Specifically,we highlight the design of optical sources,including fiber and semiconductor lasers,as well as Raman and semiconductor optical amplifiers.Although inverse design approaches for optical detectors remain relatively underexplored,we examine optical layers,particularly metamaterial absorbers,as promising candidates for high-performance optical detection.In addition,we underscore advancements in inverse designing passive optical components,including beam splitters,gratings,and optical fibers.These optical blocks are fundamental in developing next-generation standalone optical communication systems and optical sensing networks,including integrated sensing and communication technologies.While categorizing various reported deep learning architectures across five paradigms,we offer a paradigm-based perspective that reveals how different ML techniques function within modern inverse design methods and enable fast,data-driven solutions that significantly reduce design time and computational demands compared with traditional optimization methods.
基金funded by King Abdullah City for Atomic and Renewable Energy(KACARE),grant number“PC-2020-1”.
文摘High-concentration photovoltaic(HCPV)systems present significant thermal management challenges due to the intense heat fluxes generated under concentrated solar irradiation,especially in arid environments.Effective heat dissipation is critical to prevent performance degradation and structural failure.This study investigates the thermal performance and design optimization of an enhanced HCPV module,integrating numerical,analytical,and experimental methods.A coupled optical-thermal-electrical model was developed to simulate ray tracing,heat transfer,and temperature-dependent electrical behaviour,with predictions validated under real-world desert conditions.Compared to a baseline commercial module operating at 106℃,the optimized design achieved a peak temperature reduction of 16℃,lowering the cell temperature to 90℃under a concentration ratio of 961×and direct normal irradiance(DNI)of 950 W/m^(2).The total thermal resistance was reduced from 0.25 to 0.15 K/W(a 40%improvement),and the electrical efficiency increased from 37.5%to 38.6%,representing a relative gain of approximately 3.1%.The system consistently maintained a fill factor exceeding 78%,underscoring stable performance under high thermal load.These findings demonstrate that targeted thermal design,informed by integrated modeling,is essential for unlocking the reliability and efficiency of high-flux solar energy systems.
文摘Digital twin technology,that creates virtual replicas of physical entities using real-time data and simulation models,has emerged as a transformative innovation across multiple healthcare domains.Its application in physiotherapy and rehabilitation represents a paradigm shift from traditional therapeutic approaches to personalized data-driven interventions that optimize patient outcomes.This narrative review examines the current applications,benefits,challenges,and future prospects of digital twin technology in physiotherapy and rehabilitation,providing a comprehensive analysis of the manner in which this technology is reshaping clinical practice and patient care.A narrative review approach was employed,systematically searching PubMed,IEEE Xplore,Scopus,and Web of Science databases.Studies describing digital twin applications,development methodologies,clinical implementations,and theoretical frameworks in physiotherapy and rehabilitation contexts were included.Digital twin technology demonstrates significant potential in personalizing rehabilitation programs,enabling real-time monitoring of patient progress,predicting treatment outcomes,and facilitating remote therapeutic interventions.Current applications span musculoskeletal rehabilitation,neurological recovery,post surgical care,and sports injury management.Key benefits include enhanced treatment precision,improved patient engagement,reduced healthcare costs,and accelerated recovery times.However,implementation faces challenges including technological complexity,data privacy concerns,interoperability issues,and the need for substantial infrastructure investment.Digital twin technology represents a promising frontier in physiotherapy and rehabilitation,offering unprecedented opportunities for personalized,efficient,and effective patient care.Successful integration requires addressing the current limitations while fostering interdisciplinary collaboration between clinicians,engineers,and data scientists.
文摘The healthcare field is fraught with challenges associated with severe class imbalance,wherein such critical conditions like sepsis,cardiac arrest,and drug adverse reactions are rare but have dire clinical consequences.This paper presents a new framework,Deep Reinforcement Adaptive Gradient Optimization Network to Mining Rare Events(DRAGON-MINE),to demonstrate how deep reinforcement learning can be used synergistically with adaptive gradient optimization and address the inherent weaknesses of current methods in the prediction of rare health events.The suggested architecture uses a dual-pathway consisting of a reinforcement learning agent to dynamically reweigh samples and an adaptive gradient optimizer to follow novel learning rates.With extensive experiments on the MIMIC-IV and eICU-CRD datasets,DRAGON-MINE consistently outperforms recent state-of-the-art methods for sepsis,cardiac arrest,and adverse drug reaction prediction,achieving AUROC values of 92.3%and 91.6%for sepsis prediction on MIMIC-IV and eICU-CRD,respectively,while consistently outperforming Transformer-,CNN-RNN-,and Fed-Ensemble-based methods across all evaluated tasks and datasets,with particularly strong gains observed in precision-recall performance under severe class imbalance.With its high sensitivity(88.4%)and specificity(90.2%),DRAGON-MINE enables reliable early warning of rare clinical events in critical care settings while minimizing false alarms,supporting safer clinical decision support systems,and demonstrating strong potential for scalable deployment across multi-institutional intensive care environments through federated learning.
基金supported by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01276).
文摘The rapid proliferation of Internet of Things(IoT)devices in critical healthcare infrastructure has introduced significant security and privacy challenges that demand innovative,distributed architectural solutions.This paper proposes FE-ACS(Fog-Edge Adaptive Cybersecurity System),a novel hierarchical security framework that intelligently distributes AI-powered anomaly detection algorithms across edge,fog,and cloud layers to optimize security efficacy,latency,and privacy.Our comprehensive evaluation demonstrates that FE-ACS achieves superior detection performance with an AUC-ROC of 0.985 and an F1-score of 0.923,while maintaining significantly lower end-to-end latency(18.7 ms)compared to cloud-centric(152.3 ms)and fog-only(34.5 ms)architectures.The system exhibits exceptional scalability,supporting up to 38,000 devices with logarithmic performance degradation—a 67×improvement over conventional cloud-based approaches.By incorporating differential privacy mechanisms with balanced privacy-utility tradeoffs(ε=1.0–1.5),FE-ACS maintains 90%–93%detection accuracy while ensuring strong privacy guarantees for sensitive healthcare data.Computational efficiency analysis reveals that our architecture achieves a detection rate of 12,400 events per second with only 12.3 mJ energy consumption per inference.In healthcare risk assessment,FE-ACS demonstrates robust operational viability with low patient safety risk(14.7%)and high system reliability(94.0%).The proposed framework represents a significant advancement in distributed security architectures,offering a scalable,privacy-preserving,and real-time solution for protecting healthcare IoT ecosystems against evolving cyber threats.
文摘Male breast cancer(MBC)is rare,representing 0.5%–1%of all breast cancers,but its incidence is increasing due to improved diagnostics and awareness.MBC typically presents in older men,is human epidermal growth factor receptor 2(HER2)-negative and estrogen receptor(ER)-positive,and lacks routine screening,leading to delayed diagnosis and advanced disease.Major risk factors include hormonal imbalance,radiation exposure,obesity,alcohol use,and Breast Cancer Gene 1 and 2(BRCA1/2)mutations.Clinically,it may resemble gynecomastia but usually appears as a unilateral,painless mass or nipple discharge.Advances in imaging and liquid biopsy have enhanced early detection.Molecular mechanisms involve hormonal signaling,HER2/epidermal growth factor receptor(EGFR)pathways,tumor suppressor gene alterations,and epigenetic changes.While standard treatments mirror those for female breast cancer,emerging options such as cyclin-dependent kinase 4 and 6(CDK4/6),and poly(ADP-ribose)polymerase(PARP)inhibitors,immunotherapy,and precision medicine are reshaping management.Incorporating artificial intelligence,molecular profiling,and male-specific clinical trials is essential to improve outcomes and bridge current diagnostic and therapeutic gaps.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia Grant No.KFU253765.
文摘Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.
基金The Large Research Group Project under grant number RGP.02/516/45.
文摘Micro/nanorobots represent a groundbreaking advancement in nanotechnology,with applications spanning medicine,envi-ronmental remediation,and industrial processes.A major challenge in their development is achieving efficient and bio-compatible propulsion.Enzyme-driven propulsion,particularly using catalase,offers a promising solution due to its ability to decompose hydrogen peroxide(H2O2)into water and oxygen,generating thrust for autonomous movement.Compared to metal-based catalysts,catalase-powered systems exhibit superior biocompatibility and lower toxicity,making them ideal for biomedical applications.This review explores the role of catalase in micro/nanorobot propulsion,highlighting self-propulsion mechanisms,different nanorobot types,and their applications in drug delivery,infection treatment,cancer therapy,and biosensing.Additionally,recent advancements in biodegradable enzyme-powered nanorobots and their poten-tial in overcoming biological barriers are discussed.With further research,catalase-driven nanorobots could revolutionize targeted therapy and diagnostic techniques,paving the way for innovative solutions in nanomedicine.
基金the Deanship of Graduate Studies and Scientific Research at Qassim University for financial support(QU-APC-2024-9/1).
文摘Control signaling is mandatory for the operation and management of all types of communication networks,including the Third Generation Partnership Project(3GPP)mobile broadband networks.However,they consume important and scarce network resources such as bandwidth and processing power.There have been several reports of these control signaling turning into signaling storms halting network operations and causing the respective Telecom companies big financial losses.This paper draws its motivation from such real network disaster incidents attributed to signaling storms.In this paper,we present a thorough survey of the causes,of the signaling storm problems in 3GPP-based mobile broadband networks and discuss in detail their possible solutions and countermeasures.We provide relevant analytical models to help quantify the effect of the potential causes and benefits of their corresponding solutions.Another important contribution of this paper is the comparison of the possible causes and solutions/countermeasures,concerning their effect on several important network aspects such as architecture,additional signaling,fidelity,etc.,in the form of a table.This paper presents an update and an extension of our earlier conference publication.To our knowledge,no similar survey study exists on the subject.
基金Support by Sichuan Science and Technology Program[2023YFSY0026,2023YFH0004]Guangzhou Huashang University[2024HSZD01,HS2023JYSZH01].
文摘Graph neural networks(GNN)have shown strong performance in node classification tasks,yet most existing models rely on uniform or shared weight aggregation,lacking flexibility in modeling the varying strength of relationships among nodes.This paper proposes a novel graph coupling convolutional model that introduces an adaptive weighting mechanism to assign distinct importance to neighboring nodes based on their similarity to the central node.Unlike traditional methods,the proposed coupling strategy enhances the interpretability of node interactions while maintaining competitive classification performance.The model operates in the spatial domain,utilizing adjacency list structures for efficient convolution and addressing the limitations of weight sharing through a coupling-based similarity computation.Extensive experiments are conducted on five graph-structured datasets,including Cora,Citeseer,PubMed,Reddit,and BlogCatalog,as well as a custom topology dataset constructed from the Open University Learning Analytics Dataset(OULAD)educational platform.Results demonstrate that the proposed model achieves good classification accuracy,while significantly reducing training time through direct second-order neighbor fusion and data preprocessing.Moreover,analysis of neighborhood order reveals that considering third-order neighbors offers limited accuracy gains but introduces considerable computational overhead,confirming the efficiency of first-and second-order convolution in practical applications.Overall,the proposed graph coupling model offers a lightweight,interpretable,and effective framework for multi-label node classification in complex networks.
基金supported by the Science Fund for Distinguished Young Scholars of Hunan Province(2023JJ10060)the National Natural Science Foundation of China(22575269)Young Elite Scientists Sponsorship Program by CAST(2023QNRC001)。
文摘Recent advancements in Zn-halogen batteries have focused on enhancing the adsorptive or catalytic capability of host materials and stabilizing complex intermediates with electrolyte additives,while the halogen-ion electrolyte modifications exhibit strong potential for integrated interfacial regulation.Herein,we design an electrically insulating rigid electrolyte container to immobilize a liquid halogen-ion electrolyte for separator-free Zn-halogen batteries with customizable electron transfer.Robust hydrogen bonding of hydroxyl groups in SiO_(2)with fluorinated moieties in PVDF-hfp regulates Zn^(2+)solvation and suppresses H_(2)O activity,while multi-channels formed by microcracks and interparticle gaps not only enhance mass transfer but also buffer interfacial electric field,jointly enabling a durable Zn plating/stripping.Effective confinement of intermediates also ensures the high reversibility across single-(I^(-)/I0),double-(I^(-)/I0/I^(-)),and triple-(I^(-)/I0/I^(-),Cl-/Cl0)electron transfer mechanisms at cathode,as evidenced by the double-electron transfer systems exhibiting a low capacity decay rate of 0.02‰over 4500 cycles at 10 mA cm^(-2)and a high areal capacity of 11.9 mAh cm^(-2)at 2 mA cm^(-2).This work presents a novel“container engineering”approach to halogen-ion electrolyte design and provides fundamental insights into the relationships between redox reversibility and reaction kinetics.
基金supported by grants from the National Natural Science Foundation of China(No.82271426).
文摘Background:Hemifacial spasm(HFS)is a neurological disorder characterized by involuntary facial muscle contractions,significantly impacting quality of life.This study aims to provide a comprehensive bibliometric analysis of global research trends,collaborations,and scientific contributions in the field of HFS,addressing publication patterns,influential authors and institutions,and prominent research topics from 1999 to 2024.Methods:We conducted a bibliometric analysis based on 1,884 publications retrieved from the Web of Science Core Collection using the keyword"Hemifacial Spasm."Data analysis and visualization were performed using Microsoft Excel,R/Bibliometrix,Scimago Graphica,VOSviewer,Pajek,and CiteSpace.Parameters assessed included publication trends,author collaborations,institutional contributions,core journals,citation metrics,and keyword clusters.Results:Among the analyzed publications,1,646 were original research articles,and 238 were reviews,involving 6,063 researchers and citing 25,252 references.The United States,China,and Japan were identified as leading contributing countries,with prominent institutions including Shanghai Jiao Tong University,Sungkyunkwan University,and the University of Pittsburgh.Top authors by publication count were Li Shiting,Park Kwan,and Zhong Jun,whereas Peter J.Jannetta,Albert R.Møller,and Janko Jankovic were most frequently cited.Core journals,identified via Bradford’s Law,included Acta Neurochirurgica,World Neurosurgery,and Journal of Neurosurgery.Keyword analysis highlighted focal research areas:"hemifacial spasm","microvascular decompression",and"trigeminal neuralgia".Conclusion:This bibliometric study provides critical insights into the evolution of research on HFS,highlighting key contributors,institutional influence,and research hotspots.The findings underscore ongoing collaborative opportunities and essential areas for future research exploration.
文摘The migration of tasks aided by machine learning(ML)predictions IN(DPM)is a system-level design technique that is used to reduce energy by enhancing the overall performance of the processor.In this paper,we address the issue of system-level higher task dissipation during the execution of parallel workloads with common deadlines by introducing a machine learning-based framework that includes task migration using energy-efficient earliest deadline first scheduling(EA-EDF).ML-based EA-EDF enhances the overall throughput and optimizes the energy to avoid delay and performance degradation in a multiprocessor system.The proposed system model allocates processors to the ready task set in such a way that their deadlines are guaranteed.A full task migration policy is also integrated to ensure proper task mapping that ensures inter-process linkage among the arrived tasks with the same deadlines.The execution of a task can halt on one CPU and reschedule the execution on a different processor to avoid delay and ensure meeting the deadline.Our approach shows promising potential for machine-learning-based schedulability analysis enables a comparison between different ML models and shows a promising reduction in energy as compared with other ML-aware task migration techniques for SoC like Multi-Layer Feed-Forward Neural Networks(MLFNN)based on convolutional neural network(CNN),Random Forest(RF)and Deep learning(DL)algorithm.The Simulations are conducted using super pipelined microarchitecture of advanced micro devices(AMD)XScale PXA270 using instruction and data cache per core 32 Kbyte I-cache and 32 Kbyte D-cache on various utilization factors(u_(i))12%,31%and 50%.The proposed approach consumes 5.3%less energy when almost half of the CPU is running and on a lower workload consumes 1.04%less energy.The proposed design accumulatively gives significant improvements by reducing the energy dissipation on three clock rates by 4.41%,on 624 MHz by 5.4%and 5.9%on applications operating on 416 and 312 MHz standard operating frequencies.
文摘Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosuppressive therapy,AI has the potential to improve both operational efficiency and patient outcomes.Despite these advancements,the perspectives of transplant professionals-those at the forefront of critical decision-making-remain insufficiently explored.To address this gap,this study utilizes a multi-round electronic Delphi approach to gather and analyses insights from global experts involved in organ transplantation.Participants are invited to complete structured surveys capturing demographic data,professional roles,institutional practices,and prior exposure to AI technologies.The survey also explores perceptions of AI’s potential benefits.Quantitative responses are analyzed using descriptive statistics,while open-ended qualitative responses undergo thematic analysis.Preliminary findings indicate a generally positive outlook on AI’s role in enhancing transplantation processes,particularly in areas such as donor matching and post-operative care.These mixed views reflect both optimism and caution among professionals tasked with integrating new technologies into high-stakes clinical workflows.By capturing a wide range of expert opinions,the findings will inform future policy development,regulatory considerations,and institutional readiness frameworks for the integration of AI into organ transplantation.
文摘Particle Swarm Optimization(PSO)has been utilized as a useful tool for solving intricate optimization problems for various applications in different fields.This paper attempts to carry out an update on PSO and gives a review of its recent developments and applications,but also provides arguments for its efficacy in resolving optimization problems in comparison with other algorithms.Covering six strategic areas,which include Data Mining,Machine Learning,Engineering Design,Energy Systems,Healthcare,and Robotics,the study demonstrates the versatility and effectiveness of the PSO.Experimental results are,however,used to show the strong and weak parts of PSO,and performance results are included in tables for ease of comparison.The results stress PSO’s efficiency in providing optimal solutions but also show that there are aspects that need to be improved through combination with algorithms or tuning to the parameters of the method.The review of the advantages and limitations of PSO is intended to provide academics and practitioners with a well-rounded view of the methods of employing such a tool most effectively and to encourage optimized designs of PSO in solving theoretical and practical problems in the future.
