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
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
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
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 MoS2 is investigated using first-principles simulations that combine nonadiabatic molecular dynamics with𝐺𝑊and 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
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
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
Due to the growth of smart cities,many real-time systems have been developed to support smart cities using Internet of Things(IoT)and emerging technologies.They are formulated to collect the data for environment monit...Due to the growth of smart cities,many real-time systems have been developed to support smart cities using Internet of Things(IoT)and emerging technologies.They are formulated to collect the data for environment monitoring and automate the communication process.In recent decades,researchers have made many efforts to propose autonomous systems for manipulating network data and providing on-time responses in critical operations.However,the widespread use of IoT devices in resource-constrained applications and mobile sensor networks introduces significant research challenges for cybersecurity.These systems are vulnerable to a variety of cyberattacks,including unauthorized access,denial-of-service attacks,and data leakage,which compromise the network’s security.Additionally,uneven load balancing between mobile IoT devices,which frequently experience link interferences,compromises the trustworthiness of the system.This paper introduces a Multi-Agent secured framework using lightweight edge computing to enhance cybersecurity for sensor networks,aiming to leverage artificial intelligence for adaptive routing and multi-metric trust evaluation to achieve data privacy and mitigate potential threats.Moreover,it enhances the efficiency of distributed sensors for energy consumption through intelligent data analytics techniques,resulting in highly consistent and low-latency network communication.Using simulations,the proposed framework reveals its significant performance compared to state-of-the-art approaches for energy consumption by 43%,latency by 46%,network throughput by 51%,packet loss rate by 40%,and denial of service attacks by 42%.展开更多
Compared to the well-studied two-dimensional(2D)ferroelectricity,the appearance of 2D antiferroelectricity is much rarer,where local dipoles from the nonequivalent sublattices within 2D monolayers are oppositely orien...Compared to the well-studied two-dimensional(2D)ferroelectricity,the appearance of 2D antiferroelectricity is much rarer,where local dipoles from the nonequivalent sublattices within 2D monolayers are oppositely oriented.Using NbOCl_(2) monolayer with competing ferroelectric(FE)and antiferroelectric(AFE)phases as a 2D material platform,we demonstrate the emergence of intrinsic antiferroelectricity in NbOCl_(2) monolayer under experimentally accessible shear strain,along with new functionality associated with electric field-induced AFE-to-FE phase transition.Specifically,the complex configuration space accommodating FE and AFE phases,polarization switching kinetics,and finite temperature thermodynamic properties of 2D NbOCl_(2) are all accurately predicted by large-scale molecular dynamics simulations based on deep learning interatomic potential model.Moreover,room temperature stable antiferroelectricity with low polarization switching barrier and one-dimensional collinear polarization arrangement is predicted in shear-deformed NbOCl_(2) monolayer.The transition from AFE to FE phase in 2D NbOCl_(2) can be triggered by a low critical electric field,leading to a double polarization–electric(P–E)loop with small hysteresis.A new type of optoelectronic device composed of AFE-NbOCl_(2) is proposed,enabling electric“writing”and nonlinear optical“reading”logical operation with fast operation speed and low power consumption.展开更多
Modern power systems increasingly depend on interconnected microgrids to enhance reliability and renewable energy utilization.However,the high penetration of intermittent renewable sources often causes frequency devia...Modern power systems increasingly depend on interconnected microgrids to enhance reliability and renewable energy utilization.However,the high penetration of intermittent renewable sources often causes frequency deviations,voltage fluctuations,and poor reactive power coordination,posing serious challenges to grid stability.Conventional Interconnection FlowControllers(IFCs)primarily regulate active power flowand fail to effectively handle dynamic frequency variations or reactive power sharing in multi-microgrid networks.To overcome these limitations,this study proposes an enhanced Interconnection Flow Controller(e-IFC)that integrates frequency response balancing and an Interconnection Reactive Power Flow Controller(IRFC)within a unified adaptive control structure.The proposed e-IFC is implemented and analyzed in DIgSILENT PowerFactory to evaluate its performance under various grid disturbances,including frequency drops,load changes,and reactive power fluctuations.Simulation results reveal that the e-IFC achieves 27.4% higher active power sharing accuracy,19.6% lower reactive power deviation,and 18.2% improved frequency stability compared to the conventional IFC.The adaptive controller ensures seamless transitions between grid-connected and islanded modes and maintains stable operation even under communication delays and data noise.Overall,the proposed e-IFCsignificantly enhances active-reactive power coordination and dynamic stability in renewable-integrated multi-microgrid systems.Future research will focus on coupling the e-IFC with tertiary-level optimization frameworks and conducting hardware-in-the-loop validation to enable its application in large-scale smart microgrid environments.展开更多
Objective This study aims to investigate the joint associations of sarcopenia and social isolation with mortality risk.Methods Using data from the Chinese Longitudinal Healthy Longevity Survey(CLHLS)and the UK Biobank...Objective This study aims to investigate the joint associations of sarcopenia and social isolation with mortality risk.Methods Using data from the Chinese Longitudinal Healthy Longevity Survey(CLHLS)and the UK Biobank,sarcopenia was diagnosed according to European and Asian Working Groups for Sarcopenia criteria.Social isolation was assessed using standardized questionnaires,including questions on solitude,frequency of social activities,contact with others,and marital status(for the CLHLS only).Results During the follow-up period,8,249 deaths occurred in the CLHLS and 26,670 deaths in the UK Biobank groups.While no significant interaction was observed between sarcopenia and social isolation in predicting all-cause mortality in the CLHLS cohort,the association between social isolation and mortality was stronger among individuals with sarcopenia in the UK Biobank(P-interaction=0.03,relative risk due to interaction:0.23,95%confidence interval[CI]:0.06–0.41).Further joint analyses showed that participants with sarcopenia and high levels of social isolation had the highest mortality risk(hazard ration[HR]:1.99;95%CI:[1.74–2.28]in the CLHLS and 1.69[1.55–1.85]in the UK Biobank)compared to those without either condition.Conclusion The combination of social isolation and sarcopenia synergistically increases the risk of mortality in middle-aged and older adults across diverse populations.展开更多
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d...Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.展开更多
Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of...Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.展开更多
Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and ...Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance,and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks(CNNs).We frame skin lesion recognition as graph-based reasoning and,to ensure fair evaluation and avoid data leakage,adopt a strict lesion-level partitioning strategy.Each image is first over-segmented using SLIC(Simple Linear Iterative Clustering)to produce perceptually homogeneous superpixels.These superpixels form the nodes of a region-adjacency graph whose edges encode spatial continuity.Node attributes are 1280-dimensional embeddings extracted with a lightweight yet expressive EfficientNet-B0 backbone,providing strong representational power at modest computational cost.The resulting graphs are processed by a five-layer Graph Attention Network(GAT)that learns to weight inter-node relationships dynamically and aggregates multi-hop context before classifying lesions into seven classes with a log-softmax output.Extensive experiments on the DermaMNIST benchmark show the proposed pipeline achieves 88.35%accuracy and 98.04%AUC,outperforming contemporary CNNs,AutoML approaches,and alternative graph neural networks.An ablation study indicates EfficientNet-B0 produces superior node descriptors compared with ResNet-18 and DenseNet,and that roughly five GAT layers strike a good balance between being too shallow and over-deep while avoiding oversmoothing.The method requires no data augmentation or external metadata,making it a drop-in upgrade for clinical computer-aided diagnosis systems.展开更多
BACKGROUND Appropriate care for individuals who attempt suicide and are admitted to the emergency department(ED)can prevent future suicidal behavior.It is vital to understand their sociodemographic characteristics and...BACKGROUND Appropriate care for individuals who attempt suicide and are admitted to the emergency department(ED)can prevent future suicidal behavior.It is vital to understand their sociodemographic characteristics and the effects of targeted psychological care.AIM To analyze sociodemographic characteristics of suicide attempters treated in the ED and evaluate the efficacy of psychological care.METHODS Data from 239 suicide attempters treated in the ED of the Central Hospital of Enshi Tujia and Miao Autonomous Prefecture(Hubei Province,China)between January 2021 and February 2025 were divided into 2:Control(n=108)and psychological care(n=131).The demographic characteristics and effects of the psychological care were analyzed.RESULTS The mean(±SD)age of the 239 patients[114 male(47.7%),125 female(52.3%)]was 26.25±9.3 years,of whom 122(45.2%)were single,117(48.9%)were married,and 106(44.4%)had secondary education.Thirty-eight(15.9%)patients had suicidal intent,with a mean of 1.26±0.59 suicide attempts each.Twenty-two(9.21%)patients had a family history of suicide,while 8(3.34%)had a family history of suicide attempt(s).Before intervention,mean Suicidal Intent Scale scores in the psychological nursing and control groups were 21.57±5.28 and 19.86±5.92,respectively(P>0.05).After 1 month of nursing intervention,the respective scores were 10.09±1.11 and 16.48±0.87(P<0.001);and the re-suicide rates were 11.45%(15/131)and 24.07%(26/108)(P<0.001).CONCLUSION Psychological care significantly reduces suicide risk;EDs should provide comprehensive mental health care.展开更多
Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectra...Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.展开更多
This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for ...This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for identifying critical failure modes and their root causes,while BN introduces flexibility in probabilistic reasoning,enabling dynamic updates based on new evidence.This dual methodology overcomes the limitations of static FTA models,offering a comprehensive framework for system reliability analysis.Critical failures,including External Leakage(ELU),Failure to Start(FTS),and Overheating(OHE),were identified as key risks.By incorporating redundancy into high-risk components such as pumps and batteries,the likelihood of these failures was significantly reduced.For instance,redundant pumps reduced the probability of ELU by 31.88%,while additional batteries decreased the occurrence of FTS by 36.45%.The results underscore the practical benefits of combining FTA and BN for enhancing system reliability,particularly in maritime applications where operational safety and efficiency are critical.This research provides valuable insights for maintenance planning and highlights the importance of redundancy in critical systems,especially as the industry transitions toward more autonomous vessels.展开更多
Repolarizing tumor-associated macrophages(TAMs)toward the proinflammatory M1 phenotype represents a promising strategy to reverse the immunosuppressive tumor microenvironment(TME)and enhance antitumor immunotherapy.Re...Repolarizing tumor-associated macrophages(TAMs)toward the proinflammatory M1 phenotype represents a promising strategy to reverse the immunosuppressive tumor microenvironment(TME)and enhance antitumor immunotherapy.Recent studies have demonstrated that exogenous electrical stimulation can effectively repolarize TAMs toward the M1 phenotype.However,conventional electrical stimulation methods,relying on invasive implanted electrodes,are restricted to targeting localized tumor regions and pose inherent risks to patients.Notably,biological neural networks,distributed systems of interconnected neurons,can naturally permeate tissues and orchestrate cellular activities with high spatial efficiency.Inspired by this natural system,we developed a global in situ electric field network using piezoelectric BaTiO_(3)nanoparticles.Upon ultrasound stimulation,the nanoparticles generate a wireless electric field throughout the TME.In addtion,their nanoscale size enables them to function as synthetic“neurons”,allowing for uniform penetration throughout the tumor tissue and inducing significant repolarization of TAMs via the Ca^(2+)influx-activated nuclear factor-kappa B(NF-κB)signaling pathway.The repolarized M1 TAMs restore anti-tumor immunostimulatory functions and secrete key proinflammatory cytokines(e.g.,tumor necrosis factor-alpha(TNF-α)and interleukin-1 beta(IL-1β)),which enhance immunostimulation within the TME and directly contribute to tumor cell elimination.Remarkably,this strategy achieved robust in vivo tumor growth inhibition with excellent biosafety in a 4T1 breast tumor model.Overall,this work establishes a non-invasive,wireless electric field platform capable of globally repolarizing TAMs,offering a safe and efficient strategy to advance cancer immunotherapy and accelerate the clinical translation of bioelectronic therapies.展开更多
Clostridioides difficile(C.difficile)is one of the major causes of nosocomial infections.Pregnant women,who are generally considered at low risk for C.difficile infection(CDI),have attracted attention because of an in...Clostridioides difficile(C.difficile)is one of the major causes of nosocomial infections.Pregnant women,who are generally considered at low risk for C.difficile infection(CDI),have attracted attention because of an increasing number of reports.Oral vancomycin,the only first-line treatment for pregnant women infected with C.difficile,has been associated with increasing strain resistance,leading to decreased efficacy.Fecal microbiota transplantation(FMT)is recommended for severe,fulminant,and recurrent CDI;however,it is generally avoided in pregnant women because of safety concerns.We report a case of a pregnant woman with a primary ribotype 027 CDI who experienced a successful outcome with washed microbiota transplantation(WMT),an improved form of FMT,via enema.The specific strain of ribotype 027 is related to severe outcomes but has not previously been reported in pregnant women.The follow-up lasted for two years,during which the patient's diarrhea was fully alleviated without recurrence.The baby showed normal growth and development,and no adverse events were recorded for either.This case provides evidence for the efficacy and safety of WMT in pregnant women infected with C.difficile,indicating that WMT via enema may be a viable therapeutic strategy for this population for treating CDI.展开更多
Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association betw...Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association between body fat mass(FM)and OCD.Methods Summary statistics from genome-wide association studies of European ancestry were utilized to conduct two-sample Mendelian randomization analysis.Heterogeneity,horizontal pleiotropy,and sensitivity analyses were performed to assess the robustness.Results The inverse variance weighting method demonstrated that a genetically predicted decrease in FM was causally associated with an increased OCD risk[odds ratio(OR)=0.680,95%confidence interval(CI):0.528–0.875,P=0.003].Similar estimates were obtained using the weighted median approach(OR=0.633,95%CI:0.438–0.915,P=0.015).Each standard deviation increases in genetically predicted body fat percentage corresponded to a reduced OCD risk(OR=0.638,95%CI:0.455–0.896,P=0.009).The sensitivity analysis confirmed the robustness of these findings with no outlier instrument variables identified.Conclusion The negative causal association between FM and the risk of OCD suggests that the prevention or treatment of mental disorders should include not only the control of BMI but also fat distribution and body composition.展开更多
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.
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 MoS2 is investigated using first-principles simulations that combine nonadiabatic molecular dynamics with𝐺𝑊and 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.
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.
基金supported by the Deanship of Graduate Studies and Scientific Research at Jouf University.
文摘Due to the growth of smart cities,many real-time systems have been developed to support smart cities using Internet of Things(IoT)and emerging technologies.They are formulated to collect the data for environment monitoring and automate the communication process.In recent decades,researchers have made many efforts to propose autonomous systems for manipulating network data and providing on-time responses in critical operations.However,the widespread use of IoT devices in resource-constrained applications and mobile sensor networks introduces significant research challenges for cybersecurity.These systems are vulnerable to a variety of cyberattacks,including unauthorized access,denial-of-service attacks,and data leakage,which compromise the network’s security.Additionally,uneven load balancing between mobile IoT devices,which frequently experience link interferences,compromises the trustworthiness of the system.This paper introduces a Multi-Agent secured framework using lightweight edge computing to enhance cybersecurity for sensor networks,aiming to leverage artificial intelligence for adaptive routing and multi-metric trust evaluation to achieve data privacy and mitigate potential threats.Moreover,it enhances the efficiency of distributed sensors for energy consumption through intelligent data analytics techniques,resulting in highly consistent and low-latency network communication.Using simulations,the proposed framework reveals its significant performance compared to state-of-the-art approaches for energy consumption by 43%,latency by 46%,network throughput by 51%,packet loss rate by 40%,and denial of service attacks by 42%.
基金supported by the National Natural Science Foundation of China (Grant No.11574244 for G.Y.G.)the XJTU Research Fund for AI Science (Grant No.2025YXYC011 for G.Y.G.)the Hong Kong Global STEM Professorship Scheme (for X.C.Z.)。
文摘Compared to the well-studied two-dimensional(2D)ferroelectricity,the appearance of 2D antiferroelectricity is much rarer,where local dipoles from the nonequivalent sublattices within 2D monolayers are oppositely oriented.Using NbOCl_(2) monolayer with competing ferroelectric(FE)and antiferroelectric(AFE)phases as a 2D material platform,we demonstrate the emergence of intrinsic antiferroelectricity in NbOCl_(2) monolayer under experimentally accessible shear strain,along with new functionality associated with electric field-induced AFE-to-FE phase transition.Specifically,the complex configuration space accommodating FE and AFE phases,polarization switching kinetics,and finite temperature thermodynamic properties of 2D NbOCl_(2) are all accurately predicted by large-scale molecular dynamics simulations based on deep learning interatomic potential model.Moreover,room temperature stable antiferroelectricity with low polarization switching barrier and one-dimensional collinear polarization arrangement is predicted in shear-deformed NbOCl_(2) monolayer.The transition from AFE to FE phase in 2D NbOCl_(2) can be triggered by a low critical electric field,leading to a double polarization–electric(P–E)loop with small hysteresis.A new type of optoelectronic device composed of AFE-NbOCl_(2) is proposed,enabling electric“writing”and nonlinear optical“reading”logical operation with fast operation speed and low power consumption.
基金the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia,for funding this research work through the project number“NBU-FFR-2025-3623-11”.
文摘Modern power systems increasingly depend on interconnected microgrids to enhance reliability and renewable energy utilization.However,the high penetration of intermittent renewable sources often causes frequency deviations,voltage fluctuations,and poor reactive power coordination,posing serious challenges to grid stability.Conventional Interconnection FlowControllers(IFCs)primarily regulate active power flowand fail to effectively handle dynamic frequency variations or reactive power sharing in multi-microgrid networks.To overcome these limitations,this study proposes an enhanced Interconnection Flow Controller(e-IFC)that integrates frequency response balancing and an Interconnection Reactive Power Flow Controller(IRFC)within a unified adaptive control structure.The proposed e-IFC is implemented and analyzed in DIgSILENT PowerFactory to evaluate its performance under various grid disturbances,including frequency drops,load changes,and reactive power fluctuations.Simulation results reveal that the e-IFC achieves 27.4% higher active power sharing accuracy,19.6% lower reactive power deviation,and 18.2% improved frequency stability compared to the conventional IFC.The adaptive controller ensures seamless transitions between grid-connected and islanded modes and maintains stable operation even under communication delays and data noise.Overall,the proposed e-IFCsignificantly enhances active-reactive power coordination and dynamic stability in renewable-integrated multi-microgrid systems.Future research will focus on coupling the e-IFC with tertiary-level optimization frameworks and conducting hardware-in-the-loop validation to enable its application in large-scale smart microgrid environments.
基金supported by grants from the National Key Research and Development Program of China(No.2023YFC3606300,No.2022YFC3600300)the National Natural Science Foundation of China(No.82325043)the National Key Research and Development Program of Hubei Province(2022BCA036)。
文摘Objective This study aims to investigate the joint associations of sarcopenia and social isolation with mortality risk.Methods Using data from the Chinese Longitudinal Healthy Longevity Survey(CLHLS)and the UK Biobank,sarcopenia was diagnosed according to European and Asian Working Groups for Sarcopenia criteria.Social isolation was assessed using standardized questionnaires,including questions on solitude,frequency of social activities,contact with others,and marital status(for the CLHLS only).Results During the follow-up period,8,249 deaths occurred in the CLHLS and 26,670 deaths in the UK Biobank groups.While no significant interaction was observed between sarcopenia and social isolation in predicting all-cause mortality in the CLHLS cohort,the association between social isolation and mortality was stronger among individuals with sarcopenia in the UK Biobank(P-interaction=0.03,relative risk due to interaction:0.23,95%confidence interval[CI]:0.06–0.41).Further joint analyses showed that participants with sarcopenia and high levels of social isolation had the highest mortality risk(hazard ration[HR]:1.99;95%CI:[1.74–2.28]in the CLHLS and 1.69[1.55–1.85]in the UK Biobank)compared to those without either condition.Conclusion The combination of social isolation and sarcopenia synergistically increases the risk of mortality in middle-aged and older adults across diverse populations.
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.
基金supported by the Chung-Ang University Research Grants in 2023.Alsothe work is supported by the ELLIIT Excellence Center at Linköping–Lund in Information Technology in Sweden.
文摘Recommending personalized travel routes from sparse,implicit feedback poses a significant challenge,as conventional systems often struggle with information overload and fail to capture the complex,sequential nature of user preferences.To address this,we propose a Conditional Generative Adversarial Network(CGAN)that generates diverse and highly relevant itineraries.Our approach begins by constructing a conditional vector that encapsulates a user’s profile.This vector uniquely fuses embeddings from a Heterogeneous Information Network(HIN)to model complex user-place-route relationships,a Recurrent Neural Network(RNN)to capture sequential path dynamics,and Neural Collaborative Filtering(NCF)to incorporate collaborative signals from the wider user base.This comprehensive condition,further enhanced with features representing user interaction confidence and uncertainty,steers a CGAN stabilized by spectral normalization to generate high-fidelity latent route representations,effectively mitigating the data sparsity problem.Recommendations are then formulated using an Anchor-and-Expand algorithm,which selects relevant starting Points of Interest(POI)based on user history,then expands routes through latent similarity matching and geographic coherence optimization,culminating in Traveling Salesman Problem(TSP)-based route optimization for practical travel distances.Experiments on a real-world check-in dataset validate our model’s unique generative capability,achieving F1 scores ranging from 0.163 to 0.305,and near-zero pairs−F1 scores between 0.002 and 0.022.These results confirm the model’s success in generating novel travel routes by recommending new locations and sequences rather than replicating users’past itineraries.This work provides a robust solution for personalized travel planning,capable of generating novel and compelling routes for both new and existing users by learning from collective travel intelligence.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01296).
文摘Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance,and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks(CNNs).We frame skin lesion recognition as graph-based reasoning and,to ensure fair evaluation and avoid data leakage,adopt a strict lesion-level partitioning strategy.Each image is first over-segmented using SLIC(Simple Linear Iterative Clustering)to produce perceptually homogeneous superpixels.These superpixels form the nodes of a region-adjacency graph whose edges encode spatial continuity.Node attributes are 1280-dimensional embeddings extracted with a lightweight yet expressive EfficientNet-B0 backbone,providing strong representational power at modest computational cost.The resulting graphs are processed by a five-layer Graph Attention Network(GAT)that learns to weight inter-node relationships dynamically and aggregates multi-hop context before classifying lesions into seven classes with a log-softmax output.Extensive experiments on the DermaMNIST benchmark show the proposed pipeline achieves 88.35%accuracy and 98.04%AUC,outperforming contemporary CNNs,AutoML approaches,and alternative graph neural networks.An ablation study indicates EfficientNet-B0 produces superior node descriptors compared with ResNet-18 and DenseNet,and that roughly five GAT layers strike a good balance between being too shallow and over-deep while avoiding oversmoothing.The method requires no data augmentation or external metadata,making it a drop-in upgrade for clinical computer-aided diagnosis systems.
文摘BACKGROUND Appropriate care for individuals who attempt suicide and are admitted to the emergency department(ED)can prevent future suicidal behavior.It is vital to understand their sociodemographic characteristics and the effects of targeted psychological care.AIM To analyze sociodemographic characteristics of suicide attempters treated in the ED and evaluate the efficacy of psychological care.METHODS Data from 239 suicide attempters treated in the ED of the Central Hospital of Enshi Tujia and Miao Autonomous Prefecture(Hubei Province,China)between January 2021 and February 2025 were divided into 2:Control(n=108)and psychological care(n=131).The demographic characteristics and effects of the psychological care were analyzed.RESULTS The mean(±SD)age of the 239 patients[114 male(47.7%),125 female(52.3%)]was 26.25±9.3 years,of whom 122(45.2%)were single,117(48.9%)were married,and 106(44.4%)had secondary education.Thirty-eight(15.9%)patients had suicidal intent,with a mean of 1.26±0.59 suicide attempts each.Twenty-two(9.21%)patients had a family history of suicide,while 8(3.34%)had a family history of suicide attempt(s).Before intervention,mean Suicidal Intent Scale scores in the psychological nursing and control groups were 21.57±5.28 and 19.86±5.92,respectively(P>0.05).After 1 month of nursing intervention,the respective scores were 10.09±1.11 and 16.48±0.87(P<0.001);and the re-suicide rates were 11.45%(15/131)and 24.07%(26/108)(P<0.001).CONCLUSION Psychological care significantly reduces suicide risk;EDs should provide comprehensive mental health care.
基金supported by the Henan Province Key R&D Project under Grant 241111210400the Henan Provincial Science and Technology Research Project under Grants 252102211047,252102211062,252102211055 and 232102210069+2 种基金the Jiangsu Provincial Scheme Double Initiative Plan JSS-CBS20230474,the XJTLU RDF-21-02-008the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205the Higher Education Teaching Reform Research and Practice Project of Henan Province under Grant 2024SJGLX0126。
文摘Accurate and efficient detection of building changes in remote sensing imagery is crucial for urban planning,disaster emergency response,and resource management.However,existing methods face challenges such as spectral similarity between buildings and backgrounds,sensor variations,and insufficient computational efficiency.To address these challenges,this paper proposes a novel Multi-scale Efficient Wavelet-based Change Detection Network(MewCDNet),which integrates the advantages of Convolutional Neural Networks and Transformers,balances computational costs,and achieves high-performance building change detection.The network employs EfficientNet-B4 as the backbone for hierarchical feature extraction,integrates multi-level feature maps through a multi-scale fusion strategy,and incorporates two key modules:Cross-temporal Difference Detection(CTDD)and Cross-scale Wavelet Refinement(CSWR).CTDD adopts a dual-branch architecture that combines pixel-wise differencing with semanticaware Euclidean distance weighting to enhance the distinction between true changes and background noise.CSWR integrates Haar-based Discrete Wavelet Transform with multi-head cross-attention mechanisms,enabling cross-scale feature fusion while significantly improving edge localization and suppressing spurious changes.Extensive experiments on four benchmark datasets demonstrate MewCDNet’s superiority over comparison methods:achieving F1 scores of 91.54%on LEVIR,93.70%on WHUCD,and 64.96%on S2Looking for building change detection.Furthermore,MewCDNet exhibits optimal performance on the multi-class⋅SYSU dataset(F1:82.71%),highlighting its exceptional generalization capability.
基金supported by Istanbul Technical University(Project No.45698)supported through the“Young Researchers’Career Development Project-training of doctoral students”of the Croatian Science Foundation.
文摘This paper investigates the reliability of internal marine combustion engines using an integrated approach that combines Fault Tree Analysis(FTA)and Bayesian Networks(BN).FTA provides a structured,top-down method for identifying critical failure modes and their root causes,while BN introduces flexibility in probabilistic reasoning,enabling dynamic updates based on new evidence.This dual methodology overcomes the limitations of static FTA models,offering a comprehensive framework for system reliability analysis.Critical failures,including External Leakage(ELU),Failure to Start(FTS),and Overheating(OHE),were identified as key risks.By incorporating redundancy into high-risk components such as pumps and batteries,the likelihood of these failures was significantly reduced.For instance,redundant pumps reduced the probability of ELU by 31.88%,while additional batteries decreased the occurrence of FTS by 36.45%.The results underscore the practical benefits of combining FTA and BN for enhancing system reliability,particularly in maritime applications where operational safety and efficiency are critical.This research provides valuable insights for maintenance planning and highlights the importance of redundancy in critical systems,especially as the industry transitions toward more autonomous vessels.
基金supported by the National Natural Science Foundation of China(Nos.52373235 and 52573322)the National Natural Science Foundation of Hubei Province of China(No.2024AFB568).
文摘Repolarizing tumor-associated macrophages(TAMs)toward the proinflammatory M1 phenotype represents a promising strategy to reverse the immunosuppressive tumor microenvironment(TME)and enhance antitumor immunotherapy.Recent studies have demonstrated that exogenous electrical stimulation can effectively repolarize TAMs toward the M1 phenotype.However,conventional electrical stimulation methods,relying on invasive implanted electrodes,are restricted to targeting localized tumor regions and pose inherent risks to patients.Notably,biological neural networks,distributed systems of interconnected neurons,can naturally permeate tissues and orchestrate cellular activities with high spatial efficiency.Inspired by this natural system,we developed a global in situ electric field network using piezoelectric BaTiO_(3)nanoparticles.Upon ultrasound stimulation,the nanoparticles generate a wireless electric field throughout the TME.In addtion,their nanoscale size enables them to function as synthetic“neurons”,allowing for uniform penetration throughout the tumor tissue and inducing significant repolarization of TAMs via the Ca^(2+)influx-activated nuclear factor-kappa B(NF-κB)signaling pathway.The repolarized M1 TAMs restore anti-tumor immunostimulatory functions and secrete key proinflammatory cytokines(e.g.,tumor necrosis factor-alpha(TNF-α)and interleukin-1 beta(IL-1β)),which enhance immunostimulation within the TME and directly contribute to tumor cell elimination.Remarkably,this strategy achieved robust in vivo tumor growth inhibition with excellent biosafety in a 4T1 breast tumor model.Overall,this work establishes a non-invasive,wireless electric field platform capable of globally repolarizing TAMs,offering a safe and efficient strategy to advance cancer immunotherapy and accelerate the clinical translation of bioelectronic therapies.
基金supported by the Nanjing Medical University Fan Daiming Research Funds for Holistic Integrative Medicine(Grant No.2023-3HIM).
文摘Clostridioides difficile(C.difficile)is one of the major causes of nosocomial infections.Pregnant women,who are generally considered at low risk for C.difficile infection(CDI),have attracted attention because of an increasing number of reports.Oral vancomycin,the only first-line treatment for pregnant women infected with C.difficile,has been associated with increasing strain resistance,leading to decreased efficacy.Fecal microbiota transplantation(FMT)is recommended for severe,fulminant,and recurrent CDI;however,it is generally avoided in pregnant women because of safety concerns.We report a case of a pregnant woman with a primary ribotype 027 CDI who experienced a successful outcome with washed microbiota transplantation(WMT),an improved form of FMT,via enema.The specific strain of ribotype 027 is related to severe outcomes but has not previously been reported in pregnant women.The follow-up lasted for two years,during which the patient's diarrhea was fully alleviated without recurrence.The baby showed normal growth and development,and no adverse events were recorded for either.This case provides evidence for the efficacy and safety of WMT in pregnant women infected with C.difficile,indicating that WMT via enema may be a viable therapeutic strategy for this population for treating CDI.
基金supported by the Yanzhao Gold Talent Project of Hebei Province(NO.HJZD202506)。
文摘Objective Previous studies link lower body mass index(BMI)with increased obsessive-compulsive disorder(OCD)risk,yet other body mass indicators may be more etioloically relevant.We dissected the causal association between body fat mass(FM)and OCD.Methods Summary statistics from genome-wide association studies of European ancestry were utilized to conduct two-sample Mendelian randomization analysis.Heterogeneity,horizontal pleiotropy,and sensitivity analyses were performed to assess the robustness.Results The inverse variance weighting method demonstrated that a genetically predicted decrease in FM was causally associated with an increased OCD risk[odds ratio(OR)=0.680,95%confidence interval(CI):0.528–0.875,P=0.003].Similar estimates were obtained using the weighted median approach(OR=0.633,95%CI:0.438–0.915,P=0.015).Each standard deviation increases in genetically predicted body fat percentage corresponded to a reduced OCD risk(OR=0.638,95%CI:0.455–0.896,P=0.009).The sensitivity analysis confirmed the robustness of these findings with no outlier instrument variables identified.Conclusion The negative causal association between FM and the risk of OCD suggests that the prevention or treatment of mental disorders should include not only the control of BMI but also fat distribution and body composition.
