Floodplain wetlands are invaluable ecosystems providing numerous ecological benefits,yet they face a global crisis necessitating sustainable preservation efforts.This study examines the depletion of floodplain wetland...Floodplain wetlands are invaluable ecosystems providing numerous ecological benefits,yet they face a global crisis necessitating sustainable preservation efforts.This study examines the depletion of floodplain wetlands within the Hastinapur Wildlife Sanctuary(HWLS)in Uttar Pradesh.Encroachment activities such as grazing,agriculture,and human settlements have fragmented and degraded critical wetland ecosystems.Additionally,irrigation projects,dam construction,and water diversion have disrupted natural water flow and availability.To assess wetland inundation in 2023,five classification techniques were employed:Random Forest(RF),Support Vector Machine(SVM),artificial neural network(ANN),Spectral Information Divergence(SID),and Maximum Likelihood Classifier(MLC).SVM emerged as the most precise method,as determined by kappa coefficient and index-based validation.Consequently,the SVM classifier was used to model wetland inundation areas from 1983 to 2023 and analyze spatiotemporal changes and fragmentation patterns.The findings revealed that the SVM clas-sifier accurately mapped 2023 wetland areas.The modeled time-series data demonstrated a 62.55%and 38.12%reduction in inundated wetland areas over the past 40 years in the pre-and post-monsoon periods,respectively.Fragmentation analysis indicated an 86.27%decrease in large core wetland areas in the pre-monsoon period,signifying severe habitat degradation.This rapid decline in wetlands within protected areas raises concerns about their ecological impacts.By linking wetland loss to global sustainability objectives,this study underscores the global urgency for strengthened wetland protection measures and highlights the need for integrating wetland conservation into broader sustainable development goals.Effective policies and adaptive management strategies are crucial for preserving these ecosystems and their vital services,which are essential for biodiversity,climate regulation,and human well-being.展开更多
Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination syst...Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles(CAVs)at single-lane intersections,particularly in the context of left-hand side driving on roads.The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks.We consider that all approaching vehicles share relevant information through vehicular communications.The Intersection Coordination Unit(ICU)processes this information and communicates the optimal crossing or turning times to the vehicles.The primary objective of this coordination is to minimize overall traffic delays,which also helps improve the fuel consumption of vehicles.By considering information from upcoming vehicles at the intersection,the coordination system solves an optimization problem to determine the best timing for executing right turns,ultimately minimizing the total delay for all vehicles.The proposed coordination system is evaluated at a typical urban intersection,and its performance is compared to traditional traffic systems.Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.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
In this paper,we investigate the distributed Nash equilibrium(NE)seeking problem for aggregative games with multiple uncertain Euler–Lagrange(EL)systems over jointly connected and weight-balanced switching networks.T...In this paper,we investigate the distributed Nash equilibrium(NE)seeking problem for aggregative games with multiple uncertain Euler–Lagrange(EL)systems over jointly connected and weight-balanced switching networks.The designed distributed controller consists of two parts:a dynamic average consensus part that asymptotically reproduces the unknown NE,and an adaptive reference-tracking module responsible for steering EL systems’positions to track a desired trajectory.The generalized Barbalat’s Lemma is used to overcome the discontinuity of the closed-loop system caused by the switching networks.The proposed algorithm is illustrated by a sensor network deployment problem.展开更多
BACKGROUND Patients with connective tissue disease-associated interstitial lung disease(CTDILD)experience not only progressive respiratory impairment but also a significant psychological burden.The prevalence and impa...BACKGROUND Patients with connective tissue disease-associated interstitial lung disease(CTDILD)experience not only progressive respiratory impairment but also a significant psychological burden.The prevalence and impact of anxiety and depression and their intricate relationship with dyspnea severity and pulmonary function decline remain inadequately characterized in this population,hindering comprehensive care.AIM To explore the incidence of anxiety and depression in CTD-ILD and its relationship with dyspnea severity and pulmonary function index.METHODS Data of 100 patients with CTD-ILD(January 2022-June 2024)were retrospectively analyzed.Baseline demographic,pulmonary function[forced vital capacity(FVC%)and diffusing capacity of the lungs for carbon monoxide(DLCO%)],modified medical research council(mMRC)score,and psychological scale[generalized anxiety disorder-7(GAD-7)and patient health questionnaire-9(PHQ-9)]were collected.Pulmonary function was reviewed every 3 months,and highresolution computed tomography was performed every 6 months following standardized treatment(glucocorticoids+immunosuppressive+anti-fibrosis agents).Pearson/Spearman correlation analysis,linear mixed effect model,and Cox regression were used to analyze the correlation between anxiety and depression and physiological indicators as well as the prognosis.RESULTS Baseline prevalence of moderate-to-severe anxiety(GAD-7≥10)and depression(PHQ-9≥10)was 38%and 42%,respectively.Following 24 weeks of treatment,pulmonary function(FVC%:72.11±13.08 vs 67.89±12.73;DLCO%:60.67±13.76 vs 55.32±13.95,both P<0.05),psychological scores(GAD-7 and PHQ-9,P<0.05),and inflammatory markers[C-reactive protein(CRP)and erythrocyte sedimentation,P<0.05]significantly improved.The levels of inflammatory indicators were significantly decreased(P<0.05).The GAD-7/PHQ-9 scores negatively correlated with FVC%and DLCO%(P<0.05)and positively correlated with the mMRC scores and CRP(P<0.05).The mixed model showed that for each one-point increase in GAD-7/PHQ-9,FVC%decreased by 0.412%/0.426%(P<0.01).Cox regression analysis showed that for every liter of GAD-7 and PHQ-9,the risk of pulmonary function deterioration increased by 12.8%and 14.2%,respectively(hazard ratio=1.128 and 1.142,P<0.01).CONCLUSION Anxiety and depression in patients with CTD-ILD constituted a bidirectional negative feedback loop involving pulmonary function impairment,inflammatory activity,and dyspnea.Psychological disorders were identified as independent risk factors for deterioration of pulmonary function.Psychological evaluation and intervention should be integrated clinically to block brain–lung axis-mediated neuroendocrine–immune network imbalance and improve prognosis.展开更多
Connective tissue is a dynamic structure that reacts to environmental cues to maintain homeostasis,including mechanical properties.Mechanical load influences extracellular matrix(ECM)—cell interactions and modulates ...Connective tissue is a dynamic structure that reacts to environmental cues to maintain homeostasis,including mechanical properties.Mechanical load influences extracellular matrix(ECM)—cell interactions and modulates cellular behavior.Mechano-regulation processes involve matrix modification and cell activation to preserve tissue function.The ECM remodeling is crucial for force transmission.Cytoskeleton components are involved in force sensing and transmission,affecting cellular adhesion,motility,and gene expression.Proper mechanical loading helps to maintain tissue health,while imbalances may lead to pathological processes.Active and passive movement,including manual mobilization,improves connective tissue elasticity,promotes ECM-cell homeostasis,and reduces fibrosis.In rehabilitation,understanding mechanical-regulation processes is necessary for ameliorating and developing treatments aimed at preserving tissue elasticity and preventing fibrosis.In this commentary,we aim to globally describe the biological processes involved in mechanical force transmission in connective tissue as support for translational studies and clinical applications in the rehabilitation field.展开更多
BACKGROUND Mixed connective tissue disease(MCTD)is a rare autoimmune disorder charac-terized by overlapping features of systemic lupus erythematosus,systemic sc-lerosis,and polymyositis,and presence of anti-U1 ribonuc...BACKGROUND Mixed connective tissue disease(MCTD)is a rare autoimmune disorder charac-terized by overlapping features of systemic lupus erythematosus,systemic sc-lerosis,and polymyositis,and presence of anti-U1 ribonucleoprotein antibodies.Coexistence with tuberculosis(TB),a common infectious disease in endemic areas,poses a significant diagnostic challenge due to overlapping clinical and radio-logical features.CASE SUMMARY We report a 35-year-old Pakistani female presenting with oral ulcers,body rash,worsening dyspnea,and a history of joint pains initially treated as rheumatoid arthritis.She was on antituberculous therapy(ATT)for presumed pulmonary TB.Laboratory findings revealed anemia,leukopenia,raised erythrocyte sedimen-tation rate,positive anti-Sm/RNP,anti-dsDNA,and anti-SSA/Ro antibodies,confirming MCTD with clinical features of systemic lupus erythematosus,Sjogren syndrome,and systemic sclerosis.The patient was also positive for hepatitis C and active TB.Treatment involved corticosteroids alongside continuation of ATT,resulting in significant clinical improvement over 12 days,with resolution of symptoms and improved laboratory parameters.The patient remained stable on follow-up with hydroxychloroquine and prednisolone.CONCLUSION This case highlights the diagnostic complexity when autoimmune diseases coexist with TB,particularly in TB-endemic regions.Early recognition and integrated management of both conditions are crucial to improving outcomes.Clinicians should maintain a broad differential diagnosis and perform compre-hensive immunological workup in patients with overlapping symptoms.展开更多
Background:To investigate adverse event(AE)signals associated with six proton pump inhibitors(PPIs),enhance drug labeling information,and provide guidance for their safe clinical use.Methods:Adverse reaction data for ...Background:To investigate adverse event(AE)signals associated with six proton pump inhibitors(PPIs),enhance drug labeling information,and provide guidance for their safe clinical use.Methods:Adverse reaction data for musculoskeletal and connective tissue disorders related to six PPI formulations—omeprazole,pantoprazole,lansoprazole,esomeprazole,rabeprazole,and dexlansoprazole—from Q12004 to Q42023 were collected from the FDA Adverse Event Reporting System(FAERS).Signal detection was performed using the Reporting Odds Ratio(ROR),Proportional Reporting Ratio(PRR),Bayesian Confidence Propagation Neural Network(BCPNN),and Empirical Bayesian Geometric Mean(EBGM).Data processing and statistical analysis were conducted using R Studio 4.40.Results:A total of 6,635,3,853,1,792,15,731,483,and 534 adverse events were identified for the six PPIs,respectively.The four algorithms(ROR,PRR,BCPNN,and EBGM)generated 17,19,8,27,5,and 2 positive signals.Notably,signals for renal osteodystrophy and osteoporosis were more frequent,with stronger signals for lumbar flexion syndrome and renal osteodystrophy.Conclusion:Patients with chronic kidney disease,a high risk of osteoporosis and fractures,or those using statins should select PPIs with a lower risk of adverse musculoskeletal and connective tissue reactions to minimize these adverse effects and ensure standardized clinical use of PPIs.展开更多
With the proliferation of online services and applications,adopting Single Sign-On(SSO)mechanisms has become increasingly prevalent.SSO enables users to authenticate once and gain access to multiple services,eliminati...With the proliferation of online services and applications,adopting Single Sign-On(SSO)mechanisms has become increasingly prevalent.SSO enables users to authenticate once and gain access to multiple services,eliminating the need to provide their credentials repeatedly.However,this convenience raises concerns about user security and privacy.The increasing reliance on SSO and its potential risks make it imperative to comprehensively review the various SSO security and privacy threats,identify gaps in existing systems,and explore effective mitigation solutions.This need motivated the first systematic literature review(SLR)of SSO security and privacy,conducted in this paper.The SLR is performed based on rigorous structured research methodology with specific inclusion/exclusion criteria and focuses specifically on the Web environment.Furthermore,it encompasses a meticulous examination and thematic synthesis of 88 relevant publications selected out of 2315 journal articles and conference/proceeding papers published between 2017 and 2024 from reputable academic databases.The SLR highlights critical security and privacy threats relating to SSO systems,reveals significant gaps in existing countermeasures,and emphasizes the need for more comprehensive protection mechanisms.The findings of this SLR will serve as an invaluable resource for scientists and developers interested in enhancing the security and privacy preservation of SSO and designing more efficient and robust SSO systems,thus contributing to the development of the authentication technologies field.展开更多
Reliable electricity infrastructure is critical for modern society,highlighting the importance of securing the stability of fundamental power electronic systems.However,as such systems frequently involve high-current ...Reliable electricity infrastructure is critical for modern society,highlighting the importance of securing the stability of fundamental power electronic systems.However,as such systems frequently involve high-current and high-voltage conditions,there is a greater likelihood of failures.Consequently,anomaly detection of power electronic systems holds great significance,which is a task that properly-designed neural networks can well undertake,as proven in various scenarios.Transformer-like networks are promising for such application,yet with its structure initially designed for different tasks,features extracted by beginning layers are often lost,decreasing detection performance.Also,such data-driven methods typically require sufficient anomalous data for training,which could be difficult to obtain in practice.Therefore,to improve feature utilization while achieving efficient unsupervised learning,a novel model,Densely-connected Decoder Transformer(DDformer),is proposed for unsupervised anomaly detection of power electronic systems in this paper.First,efficient labelfree training is achieved based on the concept of autoencoder with recursive-free output.An encoder-decoder structure with densely-connected decoder is then adopted,merging features from all encoder layers to avoid possible loss of mined features while reducing training difficulty.Both simulation and real-world experiments are conducted to validate the capabilities of DDformer,and the average FDR has surpassed baseline models,reaching 89.39%,93.91%,95.98%in different experiment setups respectively.展开更多
Neural organoids and confocal microscopy have the potential to play an important role in microconnectome research to understand neural patterns.We present PLayer,a plug-and-play embedded neural system,which demonstrat...Neural organoids and confocal microscopy have the potential to play an important role in microconnectome research to understand neural patterns.We present PLayer,a plug-and-play embedded neural system,which demonstrates the utilization of sparse confocal microscopy layers to interpolate continuous axial resolution.With an embedded system focused on neural network pruning,image scaling,and post-processing,PLayer achieves high-performance metrics with an average structural similarity index of 0.9217 and a peak signal-to-noise ratio of 27.75 dB,all within 20 s.This represents a significant time saving of 85.71%with simplified image processing.By harnessing statistical map estimation in interpolation and incorporating the Vision Transformer–based Restorer,PLayer ensures 2D layer consistency while mitigating heavy computational dependence.As such,PLayer can reconstruct 3D neural organoid confocal data continuously under limited computational power for the wide acceptance of fundamental connectomics and pattern-related research with embedded devices.展开更多
This paper proposes a reliability evaluation model for a multi-dimensional network system,which has potential to be applied to the internet of things or other practical networks.A multi-dimensional network system with...This paper proposes a reliability evaluation model for a multi-dimensional network system,which has potential to be applied to the internet of things or other practical networks.A multi-dimensional network system with one source element and multiple sink elements is considered first.Each element can con-nect with other elements within a stochastic connection ranges.The system is regarded as successful as long as the source ele-ment remains connected with all sink elements.An importance measure is proposed to evaluate the performance of non-source elements.Furthermore,to calculate the system reliability and the element importance measure,a multi-valued decision diagram based approach is structured and its complexity is analyzed.Finally,a numerical example about the signal transfer station system is illustrated to analyze the system reliability and the ele-ment importance measure.展开更多
The Arctic plays a pivotal role in the Earth’s climate system,with its rapid transformation exerting profound impacts on global climate dynamics,ecosystems,and human societies.In recent decades,Arctic warming has sig...The Arctic plays a pivotal role in the Earth’s climate system,with its rapid transformation exerting profound impacts on global climate dynamics,ecosystems,and human societies.In recent decades,Arctic warming has significantly outpaced the global mean temperature increase,driving the enhanced sea ice decline,the accelerated mass loss of the Greenland Ice Sheet,permafrost degradation,and glacier retreat.These changes modulate atmospheric and oceanic circulation patterns,establishing teleconnections with mid-and low-latitude climate systems.Investigating the historical evolution,current state,and projected future trends of the Arctic climate system,as well as its global impacts,is crucial for elucidating the mechanisms underlying Arctic amplification,refining climate change projections,attributing extreme weather and climate events,and informing sustainable development strategies.展开更多
On November 3,the Philippine Embassy in China and the Philippine Department of Tourism jointly launched the Philippine e-visa system in Beijing,aiming to make travel more convenient for Chinese visitors and promote pe...On November 3,the Philippine Embassy in China and the Philippine Department of Tourism jointly launched the Philippine e-visa system in Beijing,aiming to make travel more convenient for Chinese visitors and promote people-to-people exchange between the two countries.Philippine Ambassador to China Jaime FlorCruz said the government launched the program to ensure a smoother visa experience for Chinese applicants.展开更多
The impact of the adaptive cruise control（ ACC）system on improving fuel efficiency is evaluated based on the vehicle-specific power. The intelligent driver model was first modified to simulate the ACC system and it ...The impact of the adaptive cruise control（ ACC）system on improving fuel efficiency is evaluated based on the vehicle-specific power. The intelligent driver model was first modified to simulate the ACC system and it was calibrated by using empirical traffic data. Then, a five-step procedure based on the vehicle-specific power was introduced to calculate fuel efficiency. Five scenarios with different ACC ratios were tested in simulation experiments, and sensitivity analyses of two key ACC factors affecting the perception-reaction time and time headway were also conducted. The simulation results indicate that all the scenarios with ACC vehicles have positive impacts on reducing fuel consumption. Furthermore, from the perspective of fuel efficiency, the extremely small value of the perception-reaction time of the ACC system is not necessary due to the fact that the value of 0.5 and 0.1 s can almost lead to the same reduction in fuel consumption. Finally, the designed time headway of the ACC system is also proposed to be large enough for fuel efficiency, although its small value can increase capacity. The findings of this study provide useful information for connected vehicles and autonomous vehicle manufacturers to improve fuel efficiency on roadways.展开更多
Aim To present a quantitative method for structural complexity analysis and evaluation of information systems. Methods Based on Petri net modeling and analysis techniques and with the aid of mathematical tools in ge...Aim To present a quantitative method for structural complexity analysis and evaluation of information systems. Methods Based on Petri net modeling and analysis techniques and with the aid of mathematical tools in general net theory(GNT), a quantitative method for structure description and analysis of information systems was introduced. Results The structural complexity index and two related factors, i.e. element complexity factor and connection complexity factor were defined, and the relations between them and the parameters of the Petri net based model of the system were derived. Application example was presented. Conclusion The proposed method provides a theoretical basis for quantitative analysis and evaluation of the structural complexity and can be applied in the general planning and design processes of the information systems.展开更多
The effects of random long-range connections （shortcuts） on the transitions of neural firing patterns in coupled Hindmarsh-Rose neurons are investigated, where each neuron is subjected to an external current. It is ...The effects of random long-range connections （shortcuts） on the transitions of neural firing patterns in coupled Hindmarsh-Rose neurons are investigated, where each neuron is subjected to an external current. It is found that, on one hand, the system can achieve the transition of neural firing patterns from the fewer-period state to the multi-period one, when the number of the added shortcuts in the neural network is greater than a threshold value, indicating the occurrence of in-transition of neural firing patterns. On the other hand, for a stronger coupling strength, we can also find the similar but reverse results by adding some proper random connections. In addition, the influences of system size and coupling strength on such transition behavior, as well as the internality between the transition degree of firing patterns and its critical characteristics for different external stimulation current, are also discussed.展开更多
In the Kigongo area of Mwanza Region,northwest Tanzania,fishmonger Neema Aisha remembers how the morning’s fresh catch would sour while she queued for the ferry,putting her business at risk.
BACKGROUND Suicide constitutes the second leading cause of death among adolescents globally and represents a critical public health concern.The neural mechanisms underlying suicidal behavior in adolescents with major ...BACKGROUND Suicide constitutes the second leading cause of death among adolescents globally and represents a critical public health concern.The neural mechanisms underlying suicidal behavior in adolescents with major depressive disorder(MDD)remain poorly understood.Aberrant resting-state functional connectivity(rsFC)in the amygdala,a key region implicated in emotional regulation and threat detection,is strongly implicated in depression and suicidal behavior.AIM To investigate rsFC alterations between amygdala subregions and whole-brain networks in adolescent patients with depression and suicide attempts.METHODS Resting-state functional magnetic resonance imaging data were acquired from 32 adolescents with MDD and suicide attempts(sMDD)group,33 adolescents with MDD but without suicide attempts(nsMDD)group,and 34 demographically matched healthy control(HC)group,with the lateral and medial amygdala(MeA)defined as regions of interest.The rsFC patterns of amygdala subregions were compared across the three groups,and associations between aberrant rsFC values and clinical symptom severity scores were examined.RESULTS Compared with the nsMDD group,the sMDD group exhibited reduced rsFC between the right lateral amygdala(LA)and the right inferior occipital gyrus as well as the left middle occipital gyrus.Compared with the HC group,the abnormal brain regions of rsFC in the sMDD group and nsMDD group involve the parahippocampal gyrus(PHG)and fusiform gyrus.In the sMDD group,right MeA and right temporal pole:Superior temporal gyrus rsFC value negatively correlated with the Rosenberg Self-Esteem Scale scores(r=-0.409,P=0.025),while left LA and right PHG rsFC value positively correlated with the Adolescent Self-Rating Life Events Checklist interpersonal relationship scores(r=0.372,P=0.043).CONCLUSION Aberrant rsFC changes between amygdala subregions and these brain regions provide novel insights into the underlying neural mechanisms of suicide attempts in adolescents with MDD.展开更多
The concept of the brain cognitive reserve is derived from the well-acknowledged notion that the degree of brain damage does not always match the severity of clinical symptoms and neurological/cognitive outcomes.It ha...The concept of the brain cognitive reserve is derived from the well-acknowledged notion that the degree of brain damage does not always match the severity of clinical symptoms and neurological/cognitive outcomes.It has been suggested that the size of the brain(brain reserve) and the extent of neural connections acquired through life(neural reserve) set a threshold beyond which noticeable impairments occur.In contrast,cognitive reserve refers to the brain's ability to adapt and reo rganize stru cturally and functionally to resist damage and maintain function,including neural reserve and brain maintenance,resilience,and compensation(Verkhratsky and Zorec,2024).展开更多
基金support through the“Trans-Disciplinary Research”Grant(No.R/Dev/IoE/TDRProjects/2023-24/61658),which played a crucial role in enabling this research endeavor.
文摘Floodplain wetlands are invaluable ecosystems providing numerous ecological benefits,yet they face a global crisis necessitating sustainable preservation efforts.This study examines the depletion of floodplain wetlands within the Hastinapur Wildlife Sanctuary(HWLS)in Uttar Pradesh.Encroachment activities such as grazing,agriculture,and human settlements have fragmented and degraded critical wetland ecosystems.Additionally,irrigation projects,dam construction,and water diversion have disrupted natural water flow and availability.To assess wetland inundation in 2023,five classification techniques were employed:Random Forest(RF),Support Vector Machine(SVM),artificial neural network(ANN),Spectral Information Divergence(SID),and Maximum Likelihood Classifier(MLC).SVM emerged as the most precise method,as determined by kappa coefficient and index-based validation.Consequently,the SVM classifier was used to model wetland inundation areas from 1983 to 2023 and analyze spatiotemporal changes and fragmentation patterns.The findings revealed that the SVM clas-sifier accurately mapped 2023 wetland areas.The modeled time-series data demonstrated a 62.55%and 38.12%reduction in inundated wetland areas over the past 40 years in the pre-and post-monsoon periods,respectively.Fragmentation analysis indicated an 86.27%decrease in large core wetland areas in the pre-monsoon period,signifying severe habitat degradation.This rapid decline in wetlands within protected areas raises concerns about their ecological impacts.By linking wetland loss to global sustainability objectives,this study underscores the global urgency for strengthened wetland protection measures and highlights the need for integrating wetland conservation into broader sustainable development goals.Effective policies and adaptive management strategies are crucial for preserving these ecosystems and their vital services,which are essential for biodiversity,climate regulation,and human well-being.
基金supported by the Japan Society for the Promotion of Science(JSPS)Grants-in-Aid for Scientific Research(C)23K03898.
文摘Traffic at urban intersections frequently encounters unexpected obstructions,resulting in congestion due to uncooperative and priority-based driving behavior.This paper presents an optimal right-turn coordination system for Connected and Automated Vehicles(CAVs)at single-lane intersections,particularly in the context of left-hand side driving on roads.The goal is to facilitate smooth right turns for certain vehicles without creating bottlenecks.We consider that all approaching vehicles share relevant information through vehicular communications.The Intersection Coordination Unit(ICU)processes this information and communicates the optimal crossing or turning times to the vehicles.The primary objective of this coordination is to minimize overall traffic delays,which also helps improve the fuel consumption of vehicles.By considering information from upcoming vehicles at the intersection,the coordination system solves an optimization problem to determine the best timing for executing right turns,ultimately minimizing the total delay for all vehicles.The proposed coordination system is evaluated at a typical urban intersection,and its performance is compared to traditional traffic systems.Numerical simulation results indicate that the proposed coordination system significantly enhances the average traffic speed and fuel consumption compared to the traditional traffic system in various scenarios.
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 Research Grants Council of the Hong Kong Special Administration Region under the Grant No.14201621。
文摘In this paper,we investigate the distributed Nash equilibrium(NE)seeking problem for aggregative games with multiple uncertain Euler–Lagrange(EL)systems over jointly connected and weight-balanced switching networks.The designed distributed controller consists of two parts:a dynamic average consensus part that asymptotically reproduces the unknown NE,and an adaptive reference-tracking module responsible for steering EL systems’positions to track a desired trajectory.The generalized Barbalat’s Lemma is used to overcome the discontinuity of the closed-loop system caused by the switching networks.The proposed algorithm is illustrated by a sensor network deployment problem.
基金Supported by Construction of a High-level Research-oriented Traditional Chinese Medicine Hospital,No.YC-2023-0901.
文摘BACKGROUND Patients with connective tissue disease-associated interstitial lung disease(CTDILD)experience not only progressive respiratory impairment but also a significant psychological burden.The prevalence and impact of anxiety and depression and their intricate relationship with dyspnea severity and pulmonary function decline remain inadequately characterized in this population,hindering comprehensive care.AIM To explore the incidence of anxiety and depression in CTD-ILD and its relationship with dyspnea severity and pulmonary function index.METHODS Data of 100 patients with CTD-ILD(January 2022-June 2024)were retrospectively analyzed.Baseline demographic,pulmonary function[forced vital capacity(FVC%)and diffusing capacity of the lungs for carbon monoxide(DLCO%)],modified medical research council(mMRC)score,and psychological scale[generalized anxiety disorder-7(GAD-7)and patient health questionnaire-9(PHQ-9)]were collected.Pulmonary function was reviewed every 3 months,and highresolution computed tomography was performed every 6 months following standardized treatment(glucocorticoids+immunosuppressive+anti-fibrosis agents).Pearson/Spearman correlation analysis,linear mixed effect model,and Cox regression were used to analyze the correlation between anxiety and depression and physiological indicators as well as the prognosis.RESULTS Baseline prevalence of moderate-to-severe anxiety(GAD-7≥10)and depression(PHQ-9≥10)was 38%and 42%,respectively.Following 24 weeks of treatment,pulmonary function(FVC%:72.11±13.08 vs 67.89±12.73;DLCO%:60.67±13.76 vs 55.32±13.95,both P<0.05),psychological scores(GAD-7 and PHQ-9,P<0.05),and inflammatory markers[C-reactive protein(CRP)and erythrocyte sedimentation,P<0.05]significantly improved.The levels of inflammatory indicators were significantly decreased(P<0.05).The GAD-7/PHQ-9 scores negatively correlated with FVC%and DLCO%(P<0.05)and positively correlated with the mMRC scores and CRP(P<0.05).The mixed model showed that for each one-point increase in GAD-7/PHQ-9,FVC%decreased by 0.412%/0.426%(P<0.01).Cox regression analysis showed that for every liter of GAD-7 and PHQ-9,the risk of pulmonary function deterioration increased by 12.8%and 14.2%,respectively(hazard ratio=1.128 and 1.142,P<0.01).CONCLUSION Anxiety and depression in patients with CTD-ILD constituted a bidirectional negative feedback loop involving pulmonary function impairment,inflammatory activity,and dyspnea.Psychological disorders were identified as independent risk factors for deterioration of pulmonary function.Psychological evaluation and intervention should be integrated clinically to block brain–lung axis-mediated neuroendocrine–immune network imbalance and improve prognosis.
文摘Connective tissue is a dynamic structure that reacts to environmental cues to maintain homeostasis,including mechanical properties.Mechanical load influences extracellular matrix(ECM)—cell interactions and modulates cellular behavior.Mechano-regulation processes involve matrix modification and cell activation to preserve tissue function.The ECM remodeling is crucial for force transmission.Cytoskeleton components are involved in force sensing and transmission,affecting cellular adhesion,motility,and gene expression.Proper mechanical loading helps to maintain tissue health,while imbalances may lead to pathological processes.Active and passive movement,including manual mobilization,improves connective tissue elasticity,promotes ECM-cell homeostasis,and reduces fibrosis.In rehabilitation,understanding mechanical-regulation processes is necessary for ameliorating and developing treatments aimed at preserving tissue elasticity and preventing fibrosis.In this commentary,we aim to globally describe the biological processes involved in mechanical force transmission in connective tissue as support for translational studies and clinical applications in the rehabilitation field.
文摘BACKGROUND Mixed connective tissue disease(MCTD)is a rare autoimmune disorder charac-terized by overlapping features of systemic lupus erythematosus,systemic sc-lerosis,and polymyositis,and presence of anti-U1 ribonucleoprotein antibodies.Coexistence with tuberculosis(TB),a common infectious disease in endemic areas,poses a significant diagnostic challenge due to overlapping clinical and radio-logical features.CASE SUMMARY We report a 35-year-old Pakistani female presenting with oral ulcers,body rash,worsening dyspnea,and a history of joint pains initially treated as rheumatoid arthritis.She was on antituberculous therapy(ATT)for presumed pulmonary TB.Laboratory findings revealed anemia,leukopenia,raised erythrocyte sedimen-tation rate,positive anti-Sm/RNP,anti-dsDNA,and anti-SSA/Ro antibodies,confirming MCTD with clinical features of systemic lupus erythematosus,Sjogren syndrome,and systemic sclerosis.The patient was also positive for hepatitis C and active TB.Treatment involved corticosteroids alongside continuation of ATT,resulting in significant clinical improvement over 12 days,with resolution of symptoms and improved laboratory parameters.The patient remained stable on follow-up with hydroxychloroquine and prednisolone.CONCLUSION This case highlights the diagnostic complexity when autoimmune diseases coexist with TB,particularly in TB-endemic regions.Early recognition and integrated management of both conditions are crucial to improving outcomes.Clinicians should maintain a broad differential diagnosis and perform compre-hensive immunological workup in patients with overlapping symptoms.
文摘Background:To investigate adverse event(AE)signals associated with six proton pump inhibitors(PPIs),enhance drug labeling information,and provide guidance for their safe clinical use.Methods:Adverse reaction data for musculoskeletal and connective tissue disorders related to six PPI formulations—omeprazole,pantoprazole,lansoprazole,esomeprazole,rabeprazole,and dexlansoprazole—from Q12004 to Q42023 were collected from the FDA Adverse Event Reporting System(FAERS).Signal detection was performed using the Reporting Odds Ratio(ROR),Proportional Reporting Ratio(PRR),Bayesian Confidence Propagation Neural Network(BCPNN),and Empirical Bayesian Geometric Mean(EBGM).Data processing and statistical analysis were conducted using R Studio 4.40.Results:A total of 6,635,3,853,1,792,15,731,483,and 534 adverse events were identified for the six PPIs,respectively.The four algorithms(ROR,PRR,BCPNN,and EBGM)generated 17,19,8,27,5,and 2 positive signals.Notably,signals for renal osteodystrophy and osteoporosis were more frequent,with stronger signals for lumbar flexion syndrome and renal osteodystrophy.Conclusion:Patients with chronic kidney disease,a high risk of osteoporosis and fractures,or those using statins should select PPIs with a lower risk of adverse musculoskeletal and connective tissue reactions to minimize these adverse effects and ensure standardized clinical use of PPIs.
文摘With the proliferation of online services and applications,adopting Single Sign-On(SSO)mechanisms has become increasingly prevalent.SSO enables users to authenticate once and gain access to multiple services,eliminating the need to provide their credentials repeatedly.However,this convenience raises concerns about user security and privacy.The increasing reliance on SSO and its potential risks make it imperative to comprehensively review the various SSO security and privacy threats,identify gaps in existing systems,and explore effective mitigation solutions.This need motivated the first systematic literature review(SLR)of SSO security and privacy,conducted in this paper.The SLR is performed based on rigorous structured research methodology with specific inclusion/exclusion criteria and focuses specifically on the Web environment.Furthermore,it encompasses a meticulous examination and thematic synthesis of 88 relevant publications selected out of 2315 journal articles and conference/proceeding papers published between 2017 and 2024 from reputable academic databases.The SLR highlights critical security and privacy threats relating to SSO systems,reveals significant gaps in existing countermeasures,and emphasizes the need for more comprehensive protection mechanisms.The findings of this SLR will serve as an invaluable resource for scientists and developers interested in enhancing the security and privacy preservation of SSO and designing more efficient and robust SSO systems,thus contributing to the development of the authentication technologies field.
基金supported in part by the National Natural Science Foundation of China under Grant 62303090,U2330206in part by the Postdoctoral Science Foundation of China under Grant 2023M740516+1 种基金in part by the Natural Science Foundation of Sichuan Province under Grant 2024NSFSC1480in part by the New Cornerstone Science Foundation through the XPLORER PRIZE.
文摘Reliable electricity infrastructure is critical for modern society,highlighting the importance of securing the stability of fundamental power electronic systems.However,as such systems frequently involve high-current and high-voltage conditions,there is a greater likelihood of failures.Consequently,anomaly detection of power electronic systems holds great significance,which is a task that properly-designed neural networks can well undertake,as proven in various scenarios.Transformer-like networks are promising for such application,yet with its structure initially designed for different tasks,features extracted by beginning layers are often lost,decreasing detection performance.Also,such data-driven methods typically require sufficient anomalous data for training,which could be difficult to obtain in practice.Therefore,to improve feature utilization while achieving efficient unsupervised learning,a novel model,Densely-connected Decoder Transformer(DDformer),is proposed for unsupervised anomaly detection of power electronic systems in this paper.First,efficient labelfree training is achieved based on the concept of autoencoder with recursive-free output.An encoder-decoder structure with densely-connected decoder is then adopted,merging features from all encoder layers to avoid possible loss of mined features while reducing training difficulty.Both simulation and real-world experiments are conducted to validate the capabilities of DDformer,and the average FDR has surpassed baseline models,reaching 89.39%,93.91%,95.98%in different experiment setups respectively.
基金supported by the National Key R&D Program of China(Grant No.2021YFA1001000)the National Natural Science Foundation of China(Grant Nos.82111530212,U23A20282,and 61971255)+2 种基金the Natural Science Founda-tion of Guangdong Province(Grant No.2021B1515020092)the Shenzhen Bay Laboratory Fund(Grant No.SZBL2020090501014)the Shenzhen Science,Technology and Innovation Commission(Grant Nos.KJZD20231023094659002,JCYJ20220530142809022,and WDZC20220811170401001).
文摘Neural organoids and confocal microscopy have the potential to play an important role in microconnectome research to understand neural patterns.We present PLayer,a plug-and-play embedded neural system,which demonstrates the utilization of sparse confocal microscopy layers to interpolate continuous axial resolution.With an embedded system focused on neural network pruning,image scaling,and post-processing,PLayer achieves high-performance metrics with an average structural similarity index of 0.9217 and a peak signal-to-noise ratio of 27.75 dB,all within 20 s.This represents a significant time saving of 85.71%with simplified image processing.By harnessing statistical map estimation in interpolation and incorporating the Vision Transformer–based Restorer,PLayer ensures 2D layer consistency while mitigating heavy computational dependence.As such,PLayer can reconstruct 3D neural organoid confocal data continuously under limited computational power for the wide acceptance of fundamental connectomics and pattern-related research with embedded devices.
基金supported by the National Natural Science Foundation of China(72101025,72271049),the Interdisciplinary Research Project for Young Teachers of USTB(Fundamental Research Funds for the Central Universities,FRF-IDRY-24-024)the Hebei Natural Science Foundation(F2023501011)+1 种基金the Fundamental Research Funds for the Central Universities(FRF-TP-20-073A1)the R&D Program of Beijing Municipal Education Commission(KM202411232015).
文摘This paper proposes a reliability evaluation model for a multi-dimensional network system,which has potential to be applied to the internet of things or other practical networks.A multi-dimensional network system with one source element and multiple sink elements is considered first.Each element can con-nect with other elements within a stochastic connection ranges.The system is regarded as successful as long as the source ele-ment remains connected with all sink elements.An importance measure is proposed to evaluate the performance of non-source elements.Furthermore,to calculate the system reliability and the element importance measure,a multi-valued decision diagram based approach is structured and its complexity is analyzed.Finally,a numerical example about the signal transfer station system is illustrated to analyze the system reliability and the ele-ment importance measure.
文摘The Arctic plays a pivotal role in the Earth’s climate system,with its rapid transformation exerting profound impacts on global climate dynamics,ecosystems,and human societies.In recent decades,Arctic warming has significantly outpaced the global mean temperature increase,driving the enhanced sea ice decline,the accelerated mass loss of the Greenland Ice Sheet,permafrost degradation,and glacier retreat.These changes modulate atmospheric and oceanic circulation patterns,establishing teleconnections with mid-and low-latitude climate systems.Investigating the historical evolution,current state,and projected future trends of the Arctic climate system,as well as its global impacts,is crucial for elucidating the mechanisms underlying Arctic amplification,refining climate change projections,attributing extreme weather and climate events,and informing sustainable development strategies.
文摘On November 3,the Philippine Embassy in China and the Philippine Department of Tourism jointly launched the Philippine e-visa system in Beijing,aiming to make travel more convenient for Chinese visitors and promote people-to-people exchange between the two countries.Philippine Ambassador to China Jaime FlorCruz said the government launched the program to ensure a smoother visa experience for Chinese applicants.
基金The National Natural Science Foundation of China(No.51338003,51478113,51378120)
文摘The impact of the adaptive cruise control（ ACC）system on improving fuel efficiency is evaluated based on the vehicle-specific power. The intelligent driver model was first modified to simulate the ACC system and it was calibrated by using empirical traffic data. Then, a five-step procedure based on the vehicle-specific power was introduced to calculate fuel efficiency. Five scenarios with different ACC ratios were tested in simulation experiments, and sensitivity analyses of two key ACC factors affecting the perception-reaction time and time headway were also conducted. The simulation results indicate that all the scenarios with ACC vehicles have positive impacts on reducing fuel consumption. Furthermore, from the perspective of fuel efficiency, the extremely small value of the perception-reaction time of the ACC system is not necessary due to the fact that the value of 0.5 and 0.1 s can almost lead to the same reduction in fuel consumption. Finally, the designed time headway of the ACC system is also proposed to be large enough for fuel efficiency, although its small value can increase capacity. The findings of this study provide useful information for connected vehicles and autonomous vehicle manufacturers to improve fuel efficiency on roadways.
文摘Aim To present a quantitative method for structural complexity analysis and evaluation of information systems. Methods Based on Petri net modeling and analysis techniques and with the aid of mathematical tools in general net theory(GNT), a quantitative method for structure description and analysis of information systems was introduced. Results The structural complexity index and two related factors, i.e. element complexity factor and connection complexity factor were defined, and the relations between them and the parameters of the Petri net based model of the system were derived. Application example was presented. Conclusion The proposed method provides a theoretical basis for quantitative analysis and evaluation of the structural complexity and can be applied in the general planning and design processes of the information systems.
文摘The effects of random long-range connections （shortcuts） on the transitions of neural firing patterns in coupled Hindmarsh-Rose neurons are investigated, where each neuron is subjected to an external current. It is found that, on one hand, the system can achieve the transition of neural firing patterns from the fewer-period state to the multi-period one, when the number of the added shortcuts in the neural network is greater than a threshold value, indicating the occurrence of in-transition of neural firing patterns. On the other hand, for a stronger coupling strength, we can also find the similar but reverse results by adding some proper random connections. In addition, the influences of system size and coupling strength on such transition behavior, as well as the internality between the transition degree of firing patterns and its critical characteristics for different external stimulation current, are also discussed.
文摘In the Kigongo area of Mwanza Region,northwest Tanzania,fishmonger Neema Aisha remembers how the morning’s fresh catch would sour while she queued for the ferry,putting her business at risk.
基金Supported by Suzhou Clinical Medical Center for Mood Disorders,No.Szlcyxzx202109Suzhou Key Laboratory,No.SZS2024016Multicenter Clinical Research on Major Diseases in Suzhou,No.DZXYJ202413.
文摘BACKGROUND Suicide constitutes the second leading cause of death among adolescents globally and represents a critical public health concern.The neural mechanisms underlying suicidal behavior in adolescents with major depressive disorder(MDD)remain poorly understood.Aberrant resting-state functional connectivity(rsFC)in the amygdala,a key region implicated in emotional regulation and threat detection,is strongly implicated in depression and suicidal behavior.AIM To investigate rsFC alterations between amygdala subregions and whole-brain networks in adolescent patients with depression and suicide attempts.METHODS Resting-state functional magnetic resonance imaging data were acquired from 32 adolescents with MDD and suicide attempts(sMDD)group,33 adolescents with MDD but without suicide attempts(nsMDD)group,and 34 demographically matched healthy control(HC)group,with the lateral and medial amygdala(MeA)defined as regions of interest.The rsFC patterns of amygdala subregions were compared across the three groups,and associations between aberrant rsFC values and clinical symptom severity scores were examined.RESULTS Compared with the nsMDD group,the sMDD group exhibited reduced rsFC between the right lateral amygdala(LA)and the right inferior occipital gyrus as well as the left middle occipital gyrus.Compared with the HC group,the abnormal brain regions of rsFC in the sMDD group and nsMDD group involve the parahippocampal gyrus(PHG)and fusiform gyrus.In the sMDD group,right MeA and right temporal pole:Superior temporal gyrus rsFC value negatively correlated with the Rosenberg Self-Esteem Scale scores(r=-0.409,P=0.025),while left LA and right PHG rsFC value positively correlated with the Adolescent Self-Rating Life Events Checklist interpersonal relationship scores(r=0.372,P=0.043).CONCLUSION Aberrant rsFC changes between amygdala subregions and these brain regions provide novel insights into the underlying neural mechanisms of suicide attempts in adolescents with MDD.
文摘The concept of the brain cognitive reserve is derived from the well-acknowledged notion that the degree of brain damage does not always match the severity of clinical symptoms and neurological/cognitive outcomes.It has been suggested that the size of the brain(brain reserve) and the extent of neural connections acquired through life(neural reserve) set a threshold beyond which noticeable impairments occur.In contrast,cognitive reserve refers to the brain's ability to adapt and reo rganize stru cturally and functionally to resist damage and maintain function,including neural reserve and brain maintenance,resilience,and compensation(Verkhratsky and Zorec,2024).
