Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combinin...Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.展开更多
The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-gener...The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.展开更多
The Routing Protocol for Low-power and Lossy Networks(RPL)is widely used in Internet of Things(IoT)systems,where devices usually have very limited resources.However,RPL still faces several problems,such as high energy...The Routing Protocol for Low-power and Lossy Networks(RPL)is widely used in Internet of Things(IoT)systems,where devices usually have very limited resources.However,RPL still faces several problems,such as high energy usage,unstable links,and inefficient routing decisions,which reduce the overall network performance and lifetime.In this work,we introduce TABURPL,an improved routing method that applies Tabu Search(TS)to optimize the parent selection process.The method uses a combined cost function that considers Residual Energy,Transmission Energy,Distance to the Sink,Hop Count,Expected Transmission Count(ETX),and Link Stability Rate(LSR).Simulation results show that TABURPL improves link stability,lowers energy consumption,and increases the packet delivery ratio compared with standard RPL and other existing approaches.These results indicate that Tabu Search can handle the complex trade-offs in IoT routing and can provide a more reliable solution for extending the network lifetime.展开更多
Inspections of power transmission lines(PTLs)conducted using unmanned aerial vehicles(UAVs)are complicated by the fine structure of the lines and complex backgrounds,making accurate and efficient segmentation challeng...Inspections of power transmission lines(PTLs)conducted using unmanned aerial vehicles(UAVs)are complicated by the fine structure of the lines and complex backgrounds,making accurate and efficient segmentation challenging.This study presents the Wavelet-Guided Transformer U-Net(WGT-UNet)model,a new hybrid net-work that combines Convolutional Neural Networks(CNNs),Discrete Wavelet Transform(DWT),and Transformer architectures.The model’s primary contribution is based on spatial and channel attention mechanisms derived from wavelet subbands to guide the Transformer’s self-attention structure.Thus,low and high frequency components are separated at each stage using DWT,suppressing structural noise and making linear objects more prominent.The developed design is supported by multi-component hybrid cost functions that simultaneously solve class imbalance,edge sharpness,structural integrity,and spatial regularity issues.Furthermore,high segmentation success has been achieved in producing sharp boundaries and continuous line structures with the DWT-guided attention mechanism.Experiments conducted on the TTPLA dataset reveal that the version using the ConvNeXt backbone outperforms the current state-of-the-art approaches with an F1-Score of 79.33%and an Intersection over Union(IoU)value of 68.38%.The models and visual outputs of the developed method and all compared models can be accessed at https://github.com/burhanbarakli/WGT-UNET.展开更多
Objective Elevated depressive symptoms are well-documented among geriatric adults with cardiovascular disease(CVD);however,few studies have accounted for long-term cumulative depressive symptom exposure.This study det...Objective Elevated depressive symptoms are well-documented among geriatric adults with cardiovascular disease(CVD);however,few studies have accounted for long-term cumulative depressive symptom exposure.This study determined the relationship between cumulative depressive symptoms and CVD.Methods Individual participant data were obtained from the China Health and Retirement Longitudinal Study(CHARLS)and Health and Retirement Study(HRS).Eligible participants had access to assessment information on depressive symptoms and had no history of CVD at baseline.Long-term cumulative depressive symptoms were estimated by calculating the area under the curve based on the Center for Epidemiological Studies Depression Scale.Results Herein,8,861 participants from CHARLS(mean age:58.58 years;male:48.6%)and 7,284 from HRS(60.94 years;35.0%)were enrolled.The median follow-up period was 5 years for the CHARLS and10 years for the HRS.Compared with the first quartile of cumulative depressive symptoms,the HRs(95%CI)in the fourth quartile were 1.73(1.48,2.02)for predicting CVD(P<0.001),1.83(1.52,2.19)for heart disease(P<0.001),1.53(95%CI:1.17,1.99)for stroke(P=0.002)in CHARLS.For HRS,the HRs(95%CI)were 1.41(95%CI:1.27,1.57;P<0.001),1.42(95%CI:1.26,1.59;P<0.001),and 1.30(95%CI:1.06,1.58;P=0.010)respectively.Strong dose-response relationships were observed,with similar results for the two cohorts.Conclusion Long-term cumulative depressive symptoms were significantly associated with incident CVD in middle-aged and older adults,providing insights into controlling long-term depressive symptoms to improve this cohort's health.展开更多
As a critical department ensuring the sterility of hospital instruments,the Sterile Supply Center(SSC)directly impacts the sterility status of clinical instruments through its sterilization qualification rate.Geriatri...As a critical department ensuring the sterility of hospital instruments,the Sterile Supply Center(SSC)directly impacts the sterility status of clinical instruments through its sterilization qualification rate.Geriatric patients,due to physiological decline and compromised immune function,constitute a high-risk group for hospital-acquired infections,with more stringent requirements for instrument sterility.This paper analyzes the current status and influencing factors of sterilization qualification rates in SSCs,explores the mechanistic association between sterilization qualification rates and infections in geriatric departments,and proposes targeted strategies to improve sterilization qualification rates.It highlights the pivotal role of SSC instrument sterilization in infection prevention and control for geriatric patients,providing theoretical basis and practical guidance for optimizing SSC management,reducing infection rates in geriatric departments,and ensuring the safety of elderly patients’medical care.These findings aim to enhance overall infection management standards in hospitals.展开更多
Objective:Early sepsis can be treated if recognised early,but progression to severe sepsis and septic shock and multiple organ dysfunction syndrome substantially increases mortality.The objectives of our study were to...Objective:Early sepsis can be treated if recognised early,but progression to severe sepsis and septic shock and multiple organ dysfunction syndrome substantially increases mortality.The objectives of our study were to assess morbidity and mortality of patients with sepsis and to compare the effectiveness of a simple bedside satisfiable Quick Sequential Organ Failure Assessment(qSOFA)score with National Early Warning Score(NEWS)in prognosticating sepsis.Methods:This prospective observational study was conducted among patients>18 years old presenting with sepsis at B.J.Medical College.The SOFA,qSOFA and NEWS scores were calculated.The effectiveness in predicting mortality was evaluated using receiver operating characteristic curve analysis.Results:A total of 200 patients were evaluated(56%male)with a mean age of 51.7 years.The mortality rate was 23%.Patients categorized under high risk according to SOFA score>8,qSOFA score of 2-3 and NEWS>7 had a mortality rate of 33.3%,27.5%and 28.4%,respectively.AUC for mortality prediction was 0.695 using SOFA score,0.665 using qSOFA and 0.725 using NEWS.At a cut off of 7.50,NEWS demonstrated a sensitivity of 97.8%with a specificity of 28.0%and outperformed both SOFA and qSOFA which yielded a sensitivity of 43.5%and 91.3%and a specificity of 77.9%and 27.9%,respectively.Conclusions:The NEWS score outperforms SOFA and qSOFA in predicting mortality among sepsis patients.However,qSOFA is more helpful in identifying high risk patients and performs better in intensive care setting.展开更多
The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT n...The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches.展开更多
Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may r...Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.展开更多
Objective:To compare the effectiveness,safety,acceptability,and confounding factors of the two-rod levonorgestrel implants between the Indoplant and Sinoplant implant brands.Methods:The study was a double-blind,random...Objective:To compare the effectiveness,safety,acceptability,and confounding factors of the two-rod levonorgestrel implants between the Indoplant and Sinoplant implant brands.Methods:The study was a double-blind,randomized controlled trial at three different centers in Indonesia.A total of 531 participants that met inclusion and exclusion criteria were randomized into two groups,with 264 participants in the Sinoplant group and 267 participants in the Indoplant group.At each center,participants were divided into two groups for Sinoplant and Indoplant.The participants were followed up for 36 months.Four parameters were evaluated:implant effectiveness,safety,acceptability,and confounding factors.Results:A total of 531 eligible participants were enrolled in this study.Both Sinoplant and Indoplant showed 100%efficacy in preventing pregnancy,with no significant differences in side effects.24.22%of the Sinoplant group and 22.18%of the Indoplant group reported weight changes.8.60%of the Sinoplant group and 9.73%of the Indoplant group reported menstrual changes,and 1.17%of the both groups experienced intermenstrual bleeding.Implant acceptability was 96.61%,with 3.39%dropout rates.Confounding factors such as age,parity,and contraceptive history did not significantly differ between the two groups.Conclusions:Sinoplant and Indoplant did not differ significantly in contraceptive effectiveness,safety,acceptability,and confounding factors.展开更多
In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic h...In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.展开更多
Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)t...Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.展开更多
Electron–hole(e–h)recombination is a fundamental process that governs energy dissipation and device efficiency in semiconductors.In two-dimensional(2D)materials,the formation of tightly bound excitons makes exciton-...Electron–hole(e–h)recombination is a fundamental process that governs energy dissipation and device efficiency in semiconductors.In two-dimensional(2D)materials,the formation of tightly bound excitons makes exciton-mediated e–h recombination the dominant decay pathway.In this work,nonradiative e–h recombination within excitons in monolayer MoS_(2) is investigated using first-principles simulations that combine nonadiabatic molecular dynamics with GW and real-time Bethe–Salpeter equation(BSE)propagation.A two-step process is identified:rapid intervalley redistribution induced by exchange interaction,followed by slower phonon-assisted recombination facilitated by exciton binding.By selectively removing the screened Coulomb and exchange terms from the BSE Hamiltonian,their respective contributions are disentangled—exchange interaction is found to increase the number of accessible recombination pathways,while binding reduces the excitation energy and enhances nonradiative decay.A reduction in recombination lifetime by over an order of magnitude is observed due to the excitonic many-body effects.These findings provide microscopic insights for understanding and tuning exciton lifetimes in 2D transition-metal dichalcogenides.展开更多
BACKGROUND:Maxillofacial trauma represents a significant challenge in emergency medicine,requiring both diagnostic accuracy and prompt intervention while balancing immediate life-saving interventions with preservation...BACKGROUND:Maxillofacial trauma represents a significant challenge in emergency medicine,requiring both diagnostic accuracy and prompt intervention while balancing immediate life-saving interventions with preservation of function and aesthetics.The complex anatomy of this region,with its proximity to critical structures,demands a thorough understanding of assessment and management principles.This narrative review aims to provide evidence-based guidelines for emergency physicians managing maxillofacial trauma,with particular emphasis on early recognition of critical injuries,airway management strategies,and special population considerations.METHODS:A narrative review was conducted via a comprehensive literature search of the PubMed and Scopus databases,which focused on maxillofacial trauma management in emergency settings.Articles were selected based on relevance to clinical practice,methodological quality,and current management guidelines.The review synthesized evidence from multiple study types,including original research,systematic reviews,and clinical practice guidelines,to provide practical guidance for emergency physicians.RESULTS:Initial assessment following Advanced Trauma Life Support(ATLS)principles is crucial,with airway management being a primary concern due to the risk of dynamic obstruction.Critical time-sensitive emergencies include orbital compartment syndrome,trapdoor fractures(in pediatric patients),and facial nerve injuries.Computed tomography(CT)imaging remains the gold standard for diagnosis.Special considerations are required for pediatric patients,who present unique anatomical challenges and injury patterns,and for elderly patients,who often have complex medical comorbidities and increased complication risks.Management strategies range from conservative treatment to urgent surgical intervention,with decisions based on the injury pattern and associated complications.CONCLUSION:Emergency physicians must maintain a structured yet fl exible approach to maxillofacial trauma,focusing on early recognition of critical injuries,appropriate airway management,and timely specialist consultation.Understanding injury patterns and their potential complications allows for eff ective risk stratifi cation and treatment planning,ultimately improving patient outcomes.展开更多
BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suita...BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suitable for rapid clinical application.METHODS:In this multi-center retrospective cohort study,AAS patient data from three hospitals were analyzed.The modeling cohort included data from the First Affiliated Hospital of Zhengzhou University and the People’s Hospital of Xinjiang Uygur Autonomous Region,with Peking University Third Hospital data serving as the external test set.Four machine learning algorithms—logistic regression(LR),multilayer perceptron(MLP),Gaussian naive Bayes(GNB),and random forest(RF)—were used to develop predictive models based on 34 early-accessible clinical variables.A simplifi ed model was then derived based on fi ve key variables(Stanford type,pericardial eff usion,asymmetric peripheral arterial pulsation,decreased bowel sounds,and dyspnea)via Least Absolute Shrinkage and Selection Operator(LASSO)regression to improve ED applicability.RESULTS:A total of 929 patients were included in the modeling cohort,and 210 were included in the external test set.Four machine learning models based on 34 clinical variables were developed,achieving internal and external validation AUCs of 0.85-0.90 and 0.73-0.85,respectively.The simplifi ed model incorporating fi ve key variables demonstrated internal and external validation AUCs of 0.71-0.86 and 0.75-0.78,respectively.Both models showed robust calibration and predictive stability across datasets.CONCLUSION:Both kinds of models were built based on machine learning tools,and proved to have certain prediction performance and extrapolation.展开更多
The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a n...The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.展开更多
Wi-Fi technology has evolved significantly since its introduction in 1997,advancing to Wi-Fi 6 as the latest standard,with Wi-Fi 7 currently under development.Despite these advancements,integrating machine learning in...Wi-Fi technology has evolved significantly since its introduction in 1997,advancing to Wi-Fi 6 as the latest standard,with Wi-Fi 7 currently under development.Despite these advancements,integrating machine learning into Wi-Fi networks remains challenging,especially in decentralized environments with multiple access points(mAPs).This paper is a short review that summarizes the potential applications of federated reinforcement learning(FRL)across eight key areas of Wi-Fi functionality,including channel access,link adaptation,beamforming,multi-user transmissions,channel bonding,multi-link operation,spatial reuse,and multi-basic servic set(multi-BSS)coordination.FRL is highlighted as a promising framework for enabling decentralized training and decision-making while preserving data privacy.To illustrate its role in practice,we present a case study on link activation in a multi-link operation(MLO)environment with multiple APs.Through theoretical discussion and simulation results,the study demonstrates how FRL can improve performance and reliability,paving the way for more adaptive and collaborative Wi-Fi networks in the era of Wi-Fi 7 and beyond.展开更多
There is a need for accurate prediction of heat and mass transfer in aerodynamically designed,non-Newtonian nanofluids across aerodynamically designed,high-flux biomedical micro-devices for thermal management and reac...There is a need for accurate prediction of heat and mass transfer in aerodynamically designed,non-Newtonian nanofluids across aerodynamically designed,high-flux biomedical micro-devices for thermal management and reactive coating processes,but existing work is not uncharacteristically remiss regarding viscoelasticity,radiative heating,viscous dissipation,and homogeneous–heterogeneous reactions within a single scheme that is calibrated.This research investigates the flow of Williamson nanofluid across a dynamically wedged surface under conditions that include viscous dissipation,thermal radiation,and homogeneous-heterogeneous reactions.The paper develops a detailed mathematical approach that utilizes boundary layers to transform partial differential equations into ordinary differential equations using similarity transformations.RK4 is the technique for gaining numerical solutions,but with the addition of ANNs,there is an improvement in prediction accuracy and computational efficiency.The study investigates the influence of wedge angle parameter,along with Weissenberg number,thermal radiation parameter and Brownian motion parameter,and Schmidt number,on velocity distribution,temperature distribution,and concentra-tion distribution.Enhanced Weissenberg numbers enhance viscoelastic responses that modify velocity patterns,but radiation parameters and thermophoresis have key impacts on thermal transfer phenomena.This research develops findings that are of enormous application in aerospace,biomedical(artificial hearts and drug delivery),and industrial cooling technology applications.New findings on non-Newtonian nanofluids under full flow systems are included in this work to enhance heat transfer methods in novel fluid-based systems.展开更多
Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted featur...Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems.In this study,we propose a hybrid model,BertGCN,that integrates BERT-based contextual embedding with Graph Convolutional Networks(GCNs)to identify anomalies in raw system logs,thereby eliminating the need for log parsing.TheBERT module captures semantic representations of log messages,while the GCN models the structural relationships among log entries through a text-based graph.This combination enables BertGCN to capture both the contextual and semantic characteristics of log data.BertGCN showed excellent performance on the HDFS and BGL datasets,demonstrating its effectiveness and resilience in detecting anomalies.Compared to multiple baselines,our proposed BertGCN showed improved precision,recall,and F1 scores.展开更多
BACKGROUND Non-suicidal self-injury(NSSI)is common among adolescents with depressive disorders and poses a major public health challenge.Rumination,a key cognitive feature of depression,includes different subtypes tha...BACKGROUND Non-suicidal self-injury(NSSI)is common among adolescents with depressive disorders and poses a major public health challenge.Rumination,a key cognitive feature of depression,includes different subtypes that may relate to NSSI through distinct psychological mechanisms.However,how these subtypes interact with specific NSSI behaviors remains unclear.AIM To examine associations between rumination subtypes and specific NSSI behaviors in adolescents.METHODS We conducted a cross-sectional study with 305 hospitalized adolescents diagnosed with depressive disorders.The subjects ranged from 12-18 years in age.Rumi-nation subtypes were assessed using the Ruminative Response Scale,and 12 NSSI behaviors were evaluated using a validated questionnaire.Network analysis was applied to explore symptom-level associations and identify central symptoms.RESULTS The network analysis revealed close connections between rumination subtypes and NSSI behaviors.Brooding was linked to behaviors such as hitting objects and burning.Scratching emerged as the most influential NSSI symptom.Symptomfocused rumination served as a key bridge connecting rumination and NSSI.CONCLUSION Symptom-focused rumination and scratching were identified as potential intervention targets.These findings highlight the psychological significance of specific cognitive-behavioral links in adolescent depression and suggest directions for tailored prevention and treatment.However,the cross-sectional,single-site design limits causal inference and generalizability.Future longitudinal and multi-center studies are needed to confirm causal pathways and verify the generalizability of the findings to broader adolescent populations.展开更多
文摘Optimizing routing and resource allocation in decentralized unmanned aerial vehicle(UAV)networks remains challenging due to interference and rapidly changing topologies.The authors introduce a novel framework combining double deep Q-networks(DDQNs)and graph neural networks(GNNs)for joint routing and resource allocation.The framework uses GNNs to model the network topology and DDQNs to adaptively control routing and resource allocation,addressing interference and improving network performance.Simulation results show that the proposed approach outperforms traditional methods such as Closest-to-Destination(c2Dst),Max-SINR(mSINR),and Multi-Layer Perceptron(MLP)-based models,achieving approximately 23.5% improvement in throughput,50% increase in connection probability,and 17.6% reduction in number of hops,demonstrating its effectiveness in dynamic UAV networks.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2025-00559546)supported by the IITP(Institute of Information&Coummunications Technology Planning&Evaluation)-ITRC(Information Technology Research Center)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2023-00259004).
文摘The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.
文摘The Routing Protocol for Low-power and Lossy Networks(RPL)is widely used in Internet of Things(IoT)systems,where devices usually have very limited resources.However,RPL still faces several problems,such as high energy usage,unstable links,and inefficient routing decisions,which reduce the overall network performance and lifetime.In this work,we introduce TABURPL,an improved routing method that applies Tabu Search(TS)to optimize the parent selection process.The method uses a combined cost function that considers Residual Energy,Transmission Energy,Distance to the Sink,Hop Count,Expected Transmission Count(ETX),and Link Stability Rate(LSR).Simulation results show that TABURPL improves link stability,lowers energy consumption,and increases the packet delivery ratio compared with standard RPL and other existing approaches.These results indicate that Tabu Search can handle the complex trade-offs in IoT routing and can provide a more reliable solution for extending the network lifetime.
文摘Inspections of power transmission lines(PTLs)conducted using unmanned aerial vehicles(UAVs)are complicated by the fine structure of the lines and complex backgrounds,making accurate and efficient segmentation challenging.This study presents the Wavelet-Guided Transformer U-Net(WGT-UNet)model,a new hybrid net-work that combines Convolutional Neural Networks(CNNs),Discrete Wavelet Transform(DWT),and Transformer architectures.The model’s primary contribution is based on spatial and channel attention mechanisms derived from wavelet subbands to guide the Transformer’s self-attention structure.Thus,low and high frequency components are separated at each stage using DWT,suppressing structural noise and making linear objects more prominent.The developed design is supported by multi-component hybrid cost functions that simultaneously solve class imbalance,edge sharpness,structural integrity,and spatial regularity issues.Furthermore,high segmentation success has been achieved in producing sharp boundaries and continuous line structures with the DWT-guided attention mechanism.Experiments conducted on the TTPLA dataset reveal that the version using the ConvNeXt backbone outperforms the current state-of-the-art approaches with an F1-Score of 79.33%and an Intersection over Union(IoU)value of 68.38%.The models and visual outputs of the developed method and all compared models can be accessed at https://github.com/burhanbarakli/WGT-UNET.
基金supported by the Guangdong Basic and Applied Basic Research Foundation(2021A1515011629)Construction of High-level University of Guangdong(G623330580and G621331128)Guangdong Province Universities and Colleges Pearl River Scholar Funded Scheme(2019)。
文摘Objective Elevated depressive symptoms are well-documented among geriatric adults with cardiovascular disease(CVD);however,few studies have accounted for long-term cumulative depressive symptom exposure.This study determined the relationship between cumulative depressive symptoms and CVD.Methods Individual participant data were obtained from the China Health and Retirement Longitudinal Study(CHARLS)and Health and Retirement Study(HRS).Eligible participants had access to assessment information on depressive symptoms and had no history of CVD at baseline.Long-term cumulative depressive symptoms were estimated by calculating the area under the curve based on the Center for Epidemiological Studies Depression Scale.Results Herein,8,861 participants from CHARLS(mean age:58.58 years;male:48.6%)and 7,284 from HRS(60.94 years;35.0%)were enrolled.The median follow-up period was 5 years for the CHARLS and10 years for the HRS.Compared with the first quartile of cumulative depressive symptoms,the HRs(95%CI)in the fourth quartile were 1.73(1.48,2.02)for predicting CVD(P<0.001),1.83(1.52,2.19)for heart disease(P<0.001),1.53(95%CI:1.17,1.99)for stroke(P=0.002)in CHARLS.For HRS,the HRs(95%CI)were 1.41(95%CI:1.27,1.57;P<0.001),1.42(95%CI:1.26,1.59;P<0.001),and 1.30(95%CI:1.06,1.58;P=0.010)respectively.Strong dose-response relationships were observed,with similar results for the two cohorts.Conclusion Long-term cumulative depressive symptoms were significantly associated with incident CVD in middle-aged and older adults,providing insights into controlling long-term depressive symptoms to improve this cohort's health.
文摘As a critical department ensuring the sterility of hospital instruments,the Sterile Supply Center(SSC)directly impacts the sterility status of clinical instruments through its sterilization qualification rate.Geriatric patients,due to physiological decline and compromised immune function,constitute a high-risk group for hospital-acquired infections,with more stringent requirements for instrument sterility.This paper analyzes the current status and influencing factors of sterilization qualification rates in SSCs,explores the mechanistic association between sterilization qualification rates and infections in geriatric departments,and proposes targeted strategies to improve sterilization qualification rates.It highlights the pivotal role of SSC instrument sterilization in infection prevention and control for geriatric patients,providing theoretical basis and practical guidance for optimizing SSC management,reducing infection rates in geriatric departments,and ensuring the safety of elderly patients’medical care.These findings aim to enhance overall infection management standards in hospitals.
文摘Objective:Early sepsis can be treated if recognised early,but progression to severe sepsis and septic shock and multiple organ dysfunction syndrome substantially increases mortality.The objectives of our study were to assess morbidity and mortality of patients with sepsis and to compare the effectiveness of a simple bedside satisfiable Quick Sequential Organ Failure Assessment(qSOFA)score with National Early Warning Score(NEWS)in prognosticating sepsis.Methods:This prospective observational study was conducted among patients>18 years old presenting with sepsis at B.J.Medical College.The SOFA,qSOFA and NEWS scores were calculated.The effectiveness in predicting mortality was evaluated using receiver operating characteristic curve analysis.Results:A total of 200 patients were evaluated(56%male)with a mean age of 51.7 years.The mortality rate was 23%.Patients categorized under high risk according to SOFA score>8,qSOFA score of 2-3 and NEWS>7 had a mortality rate of 33.3%,27.5%and 28.4%,respectively.AUC for mortality prediction was 0.695 using SOFA score,0.665 using qSOFA and 0.725 using NEWS.At a cut off of 7.50,NEWS demonstrated a sensitivity of 97.8%with a specificity of 28.0%and outperformed both SOFA and qSOFA which yielded a sensitivity of 43.5%and 91.3%and a specificity of 77.9%and 27.9%,respectively.Conclusions:The NEWS score outperforms SOFA and qSOFA in predicting mortality among sepsis patients.However,qSOFA is more helpful in identifying high risk patients and performs better in intensive care setting.
文摘The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches.
文摘Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.
基金supported by PT.Catur Dakwah Crane Pharmacy,an Indonesian pharmaceutical company.The funding was granted through a mutual agreement between the research team and the company,under the coordination of the National Population and Family Planning Board(BKKBN Indonesia).The funder provided financial support only and had no role in the study design,data collection,analysis,interpretation of data,or the decision to submit the manuscript for publication.
文摘Objective:To compare the effectiveness,safety,acceptability,and confounding factors of the two-rod levonorgestrel implants between the Indoplant and Sinoplant implant brands.Methods:The study was a double-blind,randomized controlled trial at three different centers in Indonesia.A total of 531 participants that met inclusion and exclusion criteria were randomized into two groups,with 264 participants in the Sinoplant group and 267 participants in the Indoplant group.At each center,participants were divided into two groups for Sinoplant and Indoplant.The participants were followed up for 36 months.Four parameters were evaluated:implant effectiveness,safety,acceptability,and confounding factors.Results:A total of 531 eligible participants were enrolled in this study.Both Sinoplant and Indoplant showed 100%efficacy in preventing pregnancy,with no significant differences in side effects.24.22%of the Sinoplant group and 22.18%of the Indoplant group reported weight changes.8.60%of the Sinoplant group and 9.73%of the Indoplant group reported menstrual changes,and 1.17%of the both groups experienced intermenstrual bleeding.Implant acceptability was 96.61%,with 3.39%dropout rates.Confounding factors such as age,parity,and contraceptive history did not significantly differ between the two groups.Conclusions:Sinoplant and Indoplant did not differ significantly in contraceptive effectiveness,safety,acceptability,and confounding factors.
基金funding from the European Commission by the Ruralities project(grant agreement no.101060876).
文摘In this paper,we propose a new privacy-aware transmission scheduling algorithm for 6G ad hoc networks.This system enables end nodes to select the optimum time and scheme to transmit private data safely.In 6G dynamic heterogeneous infrastructures,unstable links and non-uniform hardware capabilities create critical issues regarding security and privacy.Traditional protocols are often too computationally heavy to allow 6G services to achieve their expected Quality-of-Service(QoS).As the transport network is built of ad hoc nodes,there is no guarantee about their trustworthiness or behavior,and transversal functionalities are delegated to the extreme nodes.However,while security can be guaranteed in extreme-to-extreme solutions,privacy cannot,as all intermediate nodes still have to handle the data packets they are transporting.Besides,traditional schemes for private anonymous ad hoc communications are vulnerable against modern intelligent attacks based on learning models.The proposed scheme fulfills this gap.Findings show the probability of a successful intelligent attack reduces by up to 65%compared to ad hoc networks with no privacy protection strategy when used the proposed technology.While congestion probability can remain below 0.001%,as required in 6G services.
文摘Distributed Denial of Service(DDoS)attacks are one of the severe threats to network infrastructure,sometimes bypassing traditional diagnosis algorithms because of their evolving complexity.PresentMachine Learning(ML)techniques for DDoS attack diagnosis normally apply network traffic statistical features such as packet sizes and inter-arrival times.However,such techniques sometimes fail to capture complicated relations among various traffic flows.In this paper,we present a new multi-scale ensemble strategy given the Graph Neural Networks(GNNs)for improving DDoS detection.Our technique divides traffic into macro-and micro-level elements,letting various GNN models to get the two corase-scale anomalies and subtle,stealthy attack models.Through modeling network traffic as graph-structured data,GNNs efficiently learn intricate relations among network entities.The proposed ensemble learning algorithm combines the results of several GNNs to improve generalization,robustness,and scalability.Extensive experiments on three benchmark datasets—UNSW-NB15,CICIDS2017,and CICDDoS2019—show that our approach outperforms traditional machine learning and deep learning models in detecting both high-rate and low-rate(stealthy)DDoS attacks,with significant improvements in accuracy and recall.These findings demonstrate the suggested method’s applicability and robustness for real-world implementation in contexts where several DDoS patterns coexist.
基金supported by the National Key Research and Development Program of China (Grant Nos.2024YFA1409800 for J.Z.and2024YFA1408603 for Q.Z.)the National Natural Science Foundation of China (Grant Nos.12125408,12334004for J.Z.,and 12174363 for Q.Z.)+1 种基金the Innovation Program for Quantum Science and Technology (Grant No.2021ZD0303306 for J.Z.)the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB0450101 for J.Z.)。
文摘Electron–hole(e–h)recombination is a fundamental process that governs energy dissipation and device efficiency in semiconductors.In two-dimensional(2D)materials,the formation of tightly bound excitons makes exciton-mediated e–h recombination the dominant decay pathway.In this work,nonradiative e–h recombination within excitons in monolayer MoS_(2) is investigated using first-principles simulations that combine nonadiabatic molecular dynamics with GW and real-time Bethe–Salpeter equation(BSE)propagation.A two-step process is identified:rapid intervalley redistribution induced by exchange interaction,followed by slower phonon-assisted recombination facilitated by exciton binding.By selectively removing the screened Coulomb and exchange terms from the BSE Hamiltonian,their respective contributions are disentangled—exchange interaction is found to increase the number of accessible recombination pathways,while binding reduces the excitation energy and enhances nonradiative decay.A reduction in recombination lifetime by over an order of magnitude is observed due to the excitonic many-body effects.These findings provide microscopic insights for understanding and tuning exciton lifetimes in 2D transition-metal dichalcogenides.
文摘BACKGROUND:Maxillofacial trauma represents a significant challenge in emergency medicine,requiring both diagnostic accuracy and prompt intervention while balancing immediate life-saving interventions with preservation of function and aesthetics.The complex anatomy of this region,with its proximity to critical structures,demands a thorough understanding of assessment and management principles.This narrative review aims to provide evidence-based guidelines for emergency physicians managing maxillofacial trauma,with particular emphasis on early recognition of critical injuries,airway management strategies,and special population considerations.METHODS:A narrative review was conducted via a comprehensive literature search of the PubMed and Scopus databases,which focused on maxillofacial trauma management in emergency settings.Articles were selected based on relevance to clinical practice,methodological quality,and current management guidelines.The review synthesized evidence from multiple study types,including original research,systematic reviews,and clinical practice guidelines,to provide practical guidance for emergency physicians.RESULTS:Initial assessment following Advanced Trauma Life Support(ATLS)principles is crucial,with airway management being a primary concern due to the risk of dynamic obstruction.Critical time-sensitive emergencies include orbital compartment syndrome,trapdoor fractures(in pediatric patients),and facial nerve injuries.Computed tomography(CT)imaging remains the gold standard for diagnosis.Special considerations are required for pediatric patients,who present unique anatomical challenges and injury patterns,and for elderly patients,who often have complex medical comorbidities and increased complication risks.Management strategies range from conservative treatment to urgent surgical intervention,with decisions based on the injury pattern and associated complications.CONCLUSION:Emergency physicians must maintain a structured yet fl exible approach to maxillofacial trauma,focusing on early recognition of critical injuries,appropriate airway management,and timely specialist consultation.Understanding injury patterns and their potential complications allows for eff ective risk stratifi cation and treatment planning,ultimately improving patient outcomes.
基金supported by the special fund of the National Clinical Key Specialty Construction Program[(2022)301-2305].
文摘BACKGROUND:This study aims to develop and validate a machine learning-based in-hospital mortality predictive model for acute aortic syndrome(AAS)in the emergency department(ED)and to derive a simplifi ed version suitable for rapid clinical application.METHODS:In this multi-center retrospective cohort study,AAS patient data from three hospitals were analyzed.The modeling cohort included data from the First Affiliated Hospital of Zhengzhou University and the People’s Hospital of Xinjiang Uygur Autonomous Region,with Peking University Third Hospital data serving as the external test set.Four machine learning algorithms—logistic regression(LR),multilayer perceptron(MLP),Gaussian naive Bayes(GNB),and random forest(RF)—were used to develop predictive models based on 34 early-accessible clinical variables.A simplifi ed model was then derived based on fi ve key variables(Stanford type,pericardial eff usion,asymmetric peripheral arterial pulsation,decreased bowel sounds,and dyspnea)via Least Absolute Shrinkage and Selection Operator(LASSO)regression to improve ED applicability.RESULTS:A total of 929 patients were included in the modeling cohort,and 210 were included in the external test set.Four machine learning models based on 34 clinical variables were developed,achieving internal and external validation AUCs of 0.85-0.90 and 0.73-0.85,respectively.The simplifi ed model incorporating fi ve key variables demonstrated internal and external validation AUCs of 0.71-0.86 and 0.75-0.78,respectively.Both models showed robust calibration and predictive stability across datasets.CONCLUSION:Both kinds of models were built based on machine learning tools,and proved to have certain prediction performance and extrapolation.
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1A6A1A10044950).
文摘The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia,grant number RG-2-611-42(A.O.A.).
文摘Wi-Fi technology has evolved significantly since its introduction in 1997,advancing to Wi-Fi 6 as the latest standard,with Wi-Fi 7 currently under development.Despite these advancements,integrating machine learning into Wi-Fi networks remains challenging,especially in decentralized environments with multiple access points(mAPs).This paper is a short review that summarizes the potential applications of federated reinforcement learning(FRL)across eight key areas of Wi-Fi functionality,including channel access,link adaptation,beamforming,multi-user transmissions,channel bonding,multi-link operation,spatial reuse,and multi-basic servic set(multi-BSS)coordination.FRL is highlighted as a promising framework for enabling decentralized training and decision-making while preserving data privacy.To illustrate its role in practice,we present a case study on link activation in a multi-link operation(MLO)environment with multiple APs.Through theoretical discussion and simulation results,the study demonstrates how FRL can improve performance and reliability,paving the way for more adaptive and collaborative Wi-Fi networks in the era of Wi-Fi 7 and beyond.
基金supported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP)and the Ministry of Trade,Industry&Energy(MOTIE)of the Republic of Korea(No.RS-2025-02315209).
文摘There is a need for accurate prediction of heat and mass transfer in aerodynamically designed,non-Newtonian nanofluids across aerodynamically designed,high-flux biomedical micro-devices for thermal management and reactive coating processes,but existing work is not uncharacteristically remiss regarding viscoelasticity,radiative heating,viscous dissipation,and homogeneous–heterogeneous reactions within a single scheme that is calibrated.This research investigates the flow of Williamson nanofluid across a dynamically wedged surface under conditions that include viscous dissipation,thermal radiation,and homogeneous-heterogeneous reactions.The paper develops a detailed mathematical approach that utilizes boundary layers to transform partial differential equations into ordinary differential equations using similarity transformations.RK4 is the technique for gaining numerical solutions,but with the addition of ANNs,there is an improvement in prediction accuracy and computational efficiency.The study investigates the influence of wedge angle parameter,along with Weissenberg number,thermal radiation parameter and Brownian motion parameter,and Schmidt number,on velocity distribution,temperature distribution,and concentra-tion distribution.Enhanced Weissenberg numbers enhance viscoelastic responses that modify velocity patterns,but radiation parameters and thermophoresis have key impacts on thermal transfer phenomena.This research develops findings that are of enormous application in aerospace,biomedical(artificial hearts and drug delivery),and industrial cooling technology applications.New findings on non-Newtonian nanofluids under full flow systems are included in this work to enhance heat transfer methods in novel fluid-based systems.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under grant no.(GPIP:1074-612-2024).
文摘Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems.In this study,we propose a hybrid model,BertGCN,that integrates BERT-based contextual embedding with Graph Convolutional Networks(GCNs)to identify anomalies in raw system logs,thereby eliminating the need for log parsing.TheBERT module captures semantic representations of log messages,while the GCN models the structural relationships among log entries through a text-based graph.This combination enables BertGCN to capture both the contextual and semantic characteristics of log data.BertGCN showed excellent performance on the HDFS and BGL datasets,demonstrating its effectiveness and resilience in detecting anomalies.Compared to multiple baselines,our proposed BertGCN showed improved precision,recall,and F1 scores.
基金Supported by Key Research and Development Program of Shaanxi Province,China,No.2024SF-YBXM-078.
文摘BACKGROUND Non-suicidal self-injury(NSSI)is common among adolescents with depressive disorders and poses a major public health challenge.Rumination,a key cognitive feature of depression,includes different subtypes that may relate to NSSI through distinct psychological mechanisms.However,how these subtypes interact with specific NSSI behaviors remains unclear.AIM To examine associations between rumination subtypes and specific NSSI behaviors in adolescents.METHODS We conducted a cross-sectional study with 305 hospitalized adolescents diagnosed with depressive disorders.The subjects ranged from 12-18 years in age.Rumi-nation subtypes were assessed using the Ruminative Response Scale,and 12 NSSI behaviors were evaluated using a validated questionnaire.Network analysis was applied to explore symptom-level associations and identify central symptoms.RESULTS The network analysis revealed close connections between rumination subtypes and NSSI behaviors.Brooding was linked to behaviors such as hitting objects and burning.Scratching emerged as the most influential NSSI symptom.Symptomfocused rumination served as a key bridge connecting rumination and NSSI.CONCLUSION Symptom-focused rumination and scratching were identified as potential intervention targets.These findings highlight the psychological significance of specific cognitive-behavioral links in adolescent depression and suggest directions for tailored prevention and treatment.However,the cross-sectional,single-site design limits causal inference and generalizability.Future longitudinal and multi-center studies are needed to confirm causal pathways and verify the generalizability of the findings to broader adolescent populations.
