Let G=(V,E)be a connected graph.For an integer h≥0,a subset F■V(G)(resp.F■E(G))of G,if any,is called an h-restricted vertex cut(resp.h-restricted edge cut)of G,if G-F is disconnected and every vertex in G-F has at ...Let G=(V,E)be a connected graph.For an integer h≥0,a subset F■V(G)(resp.F■E(G))of G,if any,is called an h-restricted vertex cut(resp.h-restricted edge cut)of G,if G-F is disconnected and every vertex in G-F has at least h neighbors.The cardinality of a minimum h-restricted vertex-cut(resp.h-restricted edge cut)of G is the h-restricted connectivity(resp.h-restricted edge connectivity)of G,and denoted by κ^(h)(G)(resp.λ^(h)(G)).The enhanced hypercube Q_(n,κ)(1≤k≤n)is a variant of the hypercube Q_(n).In this paper,we consider the h-restricted connectivity of Q_(n,κ) for 2≤k≤n-1.Our main results are as follows:(1)κ^(h)(Q_(n,κ))=2^(h)(n-h+1)for 4≤k≤n-1 and 0≤h≤n-3,λ^(h)(Q_(n,κ))=2^(h)(n-h+1)for 2≤k≤n-1 and 0≤h≤n-2.(2)κ^(h)(Q_(n,3))=2^(h-1)(n-h+1)for n≥5 and 4≤h≤n-1,κ^(h)(Q_(n,2))=2^(h-1)(n-h+1)for n≥4 and 3≤h≤n-1.(3)κ^(3)(Q_(n,3))=6n-16 for n≥5,κ^(2)(Q_(n,3))=4n-8 for n≥4 and κ^(2)(Q_(n,2))=3n-5 for n≥3,κ^(1)(Q_(n,3))=2n and κ^(3)(Q_(n,2))=2n-2 for n≥3.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.
Blockchain offers a promising solution to the security challenges faced by the Internet of Vehicles(IoV).However,due to the dynamic connectivity of IoV,blockchain based on a single-chain structure or Directed Acyclic ...Blockchain offers a promising solution to the security challenges faced by the Internet of Vehicles(IoV).However,due to the dynamic connectivity of IoV,blockchain based on a single-chain structure or Directed Acyclic Graph(DAG)structure often suffer from performance limitations.The DAG lattice structure is a novel blockchain model in which each node maintains its own account chain,and only the node itself is allowed to update it.This feature makes the DAG lattice structure particularly suitable for addressing the challenges in dynamically connected IoV environment.In this paper,we propose a blockchain architecture based on the DAG lattice structure,specifically designed for dynamically connected IoV.In the proposed system,nodes must obtain authorization from a trusted authority before joining,forming a permissioned blockchain.Each node is assigned an individual account chain,allowing vehicles with limited storage capacity to participate in the blockchain by storing transactions only from nearby vehicles’account chains.Every transmitted message is treated as a transaction and added to the blockchain,enablingmore efficient data transmission in a dynamic network environment.Areputation-based incentivemechanism is introduced to encourage nodes to behave normally.Experimental results demonstrate that the proposed architecture achieves better performance compared with traditional single-chain and DAG-based approaches in terms of average transmission delay and storage cost.展开更多
Advances in Alzheimer's disease(AD)research have deepened our understanding,yet the mechanisms driving its progression remain unclear.Although a range of in vivo biomarkers is now available(e.g.,measurements of am...Advances in Alzheimer's disease(AD)research have deepened our understanding,yet the mechanisms driving its progression remain unclear.Although a range of in vivo biomarkers is now available(e.g.,measurements of amyloidbeta(Aβ)and ta u accumulation-the molecular hallmarks of AD-structural magnetic resonance imaging(MRI),assessments of brain metabolism,and,more recently,blood-based markers),a definitive diagnosis of AD continues to be challenging.For example,Frisoni et al.展开更多
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 advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-gener...The advent of sixth-generation(6G)networks introduces unprecedented challenges in achieving seamless connectivity,ultra-low latency,and efficient resource management in highly dynamic environments.Although fifth-generation(5G)networks transformed mobile broadband and machine-type communications at massive scales,their properties of scaling,interference management,and latency remain a limitation in dense high mobility settings.To overcome these limitations,artificial intelligence(AI)and unmanned aerial vehicles(UAVs)have emerged as potential solutions to develop versatile,dynamic,and energy-efficient communication systems.The study proposes an AI-based UAV architecture that utilizes cooperative reinforcement learning(CoRL)to manage an autonomous network.The UAVs collaborate by sharing local observations and real-time state exchanges to optimize user connectivity,movement directions,allocate power,and resource distribution.Unlike conventional centralized or autonomous methods,CoRL involves joint state sharing and conflict-sensitive reward shaping,which ensures fair coverage,less interference,and enhanced adaptability in a dynamic urban environment.Simulations conducted in smart city scenarios with 10 UAVs and 50 ground users demonstrate that the proposed CoRL-based UAV system increases user coverage by up to 10%,achieves convergence 40%faster,and reduces latency and energy consumption by 30%compared with centralized and decentralized baselines.Furthermore,the distributed nature of the algorithm ensures scalability and flexibility,making it well-suited for future large-scale 6G deployments.The results highlighted that AI-enabled UAV systems enhance connectivity,support ultra-reliable low-latency communications(URLLC),and improve 6G network efficiency.Future work will extend the framework with adaptive modulation,beamforming-aware positioning,and real-world testbed deployment.展开更多
The Qaidam Basin,a typical alpine arid inland basin on the northern Qinghai-Xizang Plateau,China,hosts wetland ecosystems that are strongly constrained by topography and extreme climate.These ecosystems exhibit pronou...The Qaidam Basin,a typical alpine arid inland basin on the northern Qinghai-Xizang Plateau,China,hosts wetland ecosystems that are strongly constrained by topography and extreme climate.These ecosystems exhibit pronounced spatiotemporal heterogeneity and fragmented distribution patterns,rendering them highly sensitive to environmental change.This study integrated Sentinel-2 remote sensing imagery with the SedInConnect model to delineate wetland patch distributions and calculate the Index of Connectivity(IC)values across the basin.Based on IC values,we stratified field sampling sites into high-,moderate-,and lowconnectivity gradient groups to analyze the relationships among plant community characteristics,vegetation spatial patterns,and wetland connectivity in the Qaidam Basin.Partial Least Squares Path Modeling(PLSPM)was further employed to quantify the driving mechanisms underlying wetland vegetation characteristics.The results revealed that wetland connectivity across the basin was generally low,with IC values up to 1.32 and displaying a west-to-east decreasing gradient.The west and northwest were characterized by relatively continuous high-connectivity wetland networks,while fragmented and low-connectivity wetlands predominated in the east and southeast.Connectivity regulated wetland vegetation patterns primarily by affecting patch size,fragmentation,and internal adjacency.High-connectivity areas had higher class area(CA),largest patch index(LPI),and area-weighted mean patch size(AREA_AM)than low-connectivity areas.Connectivity had the strongest effect on vegetation coverage,which declined sharply from 87.577%in highconnectivity areas to 12.152%in low-connectivity areas.Meanwhile,species diversity showed a moderately negative response to connectivity changes,whereas species evenness remained relatively unaffected.PLS-PM explained 78.300%and 67.500%of the variance in vegetation community and vegetation pattern,respectively.Climate played a dominant role in shaping vegetation characteristics,with significant negative effects on both vegetation community and pattern.Topography influenced vegetation indirectly through climate,and connectivity was influenced by both drivers and exerted positive effects on vegetation community and pattern.This study reveals the multi-pathway driving mechanisms underlying vegetation pattern formation in alpine wetlands,providing a theoretical foundation and decision-support framework for the scientific conservation and adaptive management of wetlands in the Qaidam Basin.展开更多
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
The development of brain-computer interfaces(BCI)based on motor imagery(MI)has greatly improved patients’quality of life with movement disorders.The classification of upper limb MI has been widely studied and applied...The development of brain-computer interfaces(BCI)based on motor imagery(MI)has greatly improved patients’quality of life with movement disorders.The classification of upper limb MI has been widely studied and applied in many fields,including rehabilitation.However,the physiological representations of left and right lower limb movements are too close and activated deep in the cerebral cortex,making it difficult to distinguish their features.Therefore,classifying lower limbs motor imagery is more challenging.In this study,we propose a feature extraction method based on functional connectivity,which utilizes phase-locked values to construct a functional connectivity matrix as the features of the left and right legs,which can effectively avoid the problem of physiological representations of the left and right lower limbs being too close to each other during movement.In addition,considering the topology and the temporal characteristics of the electroencephalogram(EEG),we designed a temporal-spatial convolutional network(TSGCN)to capture the spatiotemporal information for classification.Experimental results show that the accuracy of the proposed method is higher than that of existing methods,achieving an average classification accuracy of 73.58%on the internal dataset.Finally,this study explains the network mechanism of left and right foot MI from the perspective of graph theoretic features and demonstrates the feasibility of decoding lower limb MI.展开更多
This study aims to explore the impact of fatigue induced by different limb exercises on cerebral cortical oxygenation levels and functional connectivity strength using functional near-infrared spectroscopy(fNIRS).Fati...This study aims to explore the impact of fatigue induced by different limb exercises on cerebral cortical oxygenation levels and functional connectivity strength using functional near-infrared spectroscopy(fNIRS).Fatigue was induced using an upper limb ergometer or a lower limb ergometer,with the load increasing gradually each minute.fNIRS covering the prefrontal cortex and motor cortex were used to collect data during the resting state,both before and after fatigue induction.A two-way ANOVA was conducted to examine differences in oxyhemoglobin(HbO_(2))and functional connectivity before and after fatigue induction in both groups,with the significance level set at 0.05.Exercise-induced fatigue in both the upper and lower limbs leads to a significant decrease in cerebral cortical oxygenation levels.Upper limb fatigue leads to a significant reduction in functional connectivity,there were significant decreases in connectivity within the motor cortex,between the motor cortex and frontal regions,and between the right ventrolateral prefrontal cortex and other frontal regions.Conversely,no significant changes were observed before and after lower limb fatigue.Future studies should focus on examining the extent to which how changes in the cerebral cortex,induced by exercise fatigue,are linked to exercise-and/or performance-related outcomes.展开更多
The sustainability of the Internet of Things(IoT)involves various issues,such as poor connectivity,scalability problems,interoperability issues,and energy inefficiency.Although the Sixth Generation of mobile networks(...The sustainability of the Internet of Things(IoT)involves various issues,such as poor connectivity,scalability problems,interoperability issues,and energy inefficiency.Although the Sixth Generation of mobile networks(6G)allows for Ultra-Reliable Low-Latency Communication(URLLC),enhanced Mobile Broadband(eMBB),and massive Machine-Type Communications(mMTC)services,it faces deployment challenges such as the short range of sub-THz and THz frequency bands,low capability to penetrate obstacles,and very high path loss.This paper presents a network architecture to enhance the connectivity of wireless IoT mesh networks that employ both 6G and Wi-Fi technologies.In this architecture,local communications are carried through the mesh network,which uses a virtual backbone to relay packets to local nodes,while remote communications are carried through the 6G network.The virtual backbone is created using a heuristic distributed ConnectedDominating Set(CDS)algorithm.In this algorithm,each node uses information collected from its one-and two-hop neighbors to determine its role and find the set of expansion nodes that are used to select the next CDS nodes.The proposed algorithm has O(n)message and O(K)time complexities,where n is the number of nodes in the network,and K is the depth of the cluster.The study proved that the approximation ratio of the algorithmhas an upper bound of 2.06748(3.4306MCDS+4.8185).Performance evaluations compared the size of the CDS against the theoretical limit and recent CDS clustering algorithms.Results indicate that the proposed algorithm has the smallest average slope for the size of the CDS as the number of nodes increases.展开更多
Against a backdrop of rising global trade protectionism and accelerated restructuring of international industrial and supply chains,regional institutional cooperation is becoming a key force in stabilizing cross-borde...Against a backdrop of rising global trade protectionism and accelerated restructuring of international industrial and supply chains,regional institutional cooperation is becoming a key force in stabilizing cross-border trade and investment.The ASEAN region enjoys strong economic growth momentum with a distinct demographic advantage while China boasts a complete industrial system and manufacturing capacity.展开更多
Northwest China serves as a critical ecological barrier region for maintaining national water,energy,and food security,as well as transboundary ecological governance.However,under the dual pressures of climate change ...Northwest China serves as a critical ecological barrier region for maintaining national water,energy,and food security,as well as transboundary ecological governance.However,under the dual pressures of climate change and human activities,ecosystem services(ESs)are facing severe challenges in this region.Based on multi-source remote sensing and statistical data during 2000–2020,this study investigated the spatiotemporal evolution characteristics of four key ESs(water yield,habitat quality,carbon storage,and food provisioning)in Northwest China using the Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST)model.Integrating morphological spatial pattern analysis(MSPA)and circuit theory,we identified ecological sources,corridors,pinch points,and barriers,and further designed three optimization scenarios(bottleneck optimization,high-resistance corridor buffering,and barrier removal optimization)to enhance landscape connectivity.The results revealed that ES supply and demand exhibited marked spatial heterogeneity,with high-supply areas concentrated in the southeastern sectors.Ecological sources primarily distributed in the southeastern and northern sectors,and ecological resistance surfaces continuously intensified.Water yield and habitat quality demands were increasing,food provisioning demand was decreasing,and carbon storage demand was surging.A total of 61 ecological sources(8%of the study area),142 ecological corridors(24,957 km in total length),237 ecological pinch points,and 89 barrier zones were identified.Among the three optimization scenarios,barrier removal achieved optimal connectivity improvement across all distance thresholds,with the probability of connectivity index improvement reaching up to 4%.This study provides scientific foundations and spatial decision support for ecological network optimization and sustainable governance in arid and semi-arid areas.展开更多
Carbonate gas reservoirs are often characterized by strong heterogeneity,complex inter-well connectivity,extensive edge or bottom water,and unbalanced production,challenges that are also common in many heterogeneous g...Carbonate gas reservoirs are often characterized by strong heterogeneity,complex inter-well connectivity,extensive edge or bottom water,and unbalanced production,challenges that are also common in many heterogeneous gas reservoirs with intricate storage and flow behavior.To address these issues within a unified,data-driven framework,this study develops a multi-block material balance model that accounts for inter-block flow and aquifer influx,and is applicable to a wide range of reservoir types.The model incorporates inter-well and well-group conductive connectivity together with pseudo–steady-state aquifer support.The governing equations are solved using a Newton–Raphson scheme,while particle swarm optimization is employed to estimate formation pressures,inter-well connectivity,and effective aquifer volumes.An unbalanced exploitation factor,UEF,is introduced to quantify production imbalance and to guide development optimization.Validation using a synthetic reservoir model demonstrates that the approach accurately reproduces pressure evolution,crossflow behavior,and water influx.Application to a representative case(the Longwangmiao)field further confirms its robustness under highly heterogeneous conditions,achieving a 12.9%reduction in UEF through optimized production allocation.展开更多
Spartina alterniflora invasions seriously threaten the structure and functions of coastal wetlands in China.In this study,the Suaeda salsa community in the Yellow River Estuary wetland was monitored using long-term La...Spartina alterniflora invasions seriously threaten the structure and functions of coastal wetlands in China.In this study,the Suaeda salsa community in the Yellow River Estuary wetland was monitored using long-term Landsat satellite images acquired from 1997 to 2020 to quantify the impact of changes in hydrological connectivity induced by S.alterniflora on neighboring vegetation com-munities.The results showed that S.alterniflora rapidly expanded in the estuary area at a rate of 4.91 km^(2)/yr from 2010 to 2020.At the same time,the hydrological connectivity of the area and the distribution of S.salsa changed significantly.Small tidal creeks dominated the S.alterniflora landscape.The number of tidal creeks increased significantly,but their average length decreased and they tended to develop in a horizontal tree-like pattern.Affected by the changes in hydrological connectivity due to the S.alterniflora invasion,the area of S.salsa decreased by 41.1%,and the degree of landscape fragmentation increased from 1997 to 2020.Variations in the Largest Patch Index(LPI)indicated that the S.alterniflora landscape had become the dominant landscape type in the Yellow River Estuary.The res-ults of standard deviation ellipse(SDE)and Pearson’s correlation analyses indicated that a well-developed hydrological connectivity could promote the maintenance of the S.salsa landscape.The degradation of most S.salsa communities is caused by the influence of S.alterniflora on the morphological characteristics of the hydrological connectivity of tidal creek systems.展开更多
Background:Alterations of brain connectivity within resting-state networks(RSNs)have been widely reported in observational studies on epilepsy.However,the causal relationship between epilepsy and structural connectivi...Background:Alterations of brain connectivity within resting-state networks(RSNs)have been widely reported in observational studies on epilepsy.However,the causal relationship between epilepsy and structural connectivity(SC)/functional connectivity(FC)within RSNs remain unclear.We conducted a bidirectional two-sample Mendelian randomization(MR)to explore the causal relationship between epilepsy subtypes and brain connectivity properties within RSNs.Methods:Genetic instruments were obtained from the latest genome-wide association studies(GWAS)of 69,995 individuals(N_(cases)=27,559,N_(controls)=42,436)issued by the International League Against Epilepsy.The GWAS summary SC/FC data within RSNs(N_(SC)=23,985,N_(FC)=24,336)were sourced from the Center for Neurogenomics and Cognitive Research.We investigate the causal relationship between epilepsy subtypes and brain connectivity within RSNs through a bidirectional two-sample MR analysis.Results:We found that the increased risk of generalized genetic epilepsy is consistent with a causal effect on dorsal attention and somatomotor FC.In the reverse MR analysis,there was no suggestive causal effect of FC/SC connectivity on epilepsy subtypes.Conclusions:This study shed light on the associations of FC/SC levels within the RSNs and epilepsy along with its subtypes.This insight could yield crucial intervention strategies to different subtypes of epilepsy at the level of brain structure and functional networks.展开更多
Background The heterogeneity of depression limits the treatment outcomes of intermittent theta burst stimulation(iTBS)and hinders the identification of predictive factors.This study investigated functional network con...Background The heterogeneity of depression limits the treatment outcomes of intermittent theta burst stimulation(iTBS)and hinders the identification of predictive factors.This study investigated functional network connectivity and predictors of iTBS treatment outcomes in adolescents and young adults with depression.Aim This study aimed to identify default mode network(DMN)-based connectivity patterns associated with varying iTBS treatment outcomes in depression.Methods Data from a randomised controlled trial of iTBS in depression(n=82)were analysed using a data-driven approach to classify homogeneous subgroups based on the DMN.Connectivity subgroups were compared on depressive symptoms and cognitive function at pretreatment and post-treatment.Furthermore,the predictive significance of baseline inflammatory cytokines on post-treatment outcomes was evaluated.Results Two distinct subgroups were identified.Subgroup 1 exhibited high heterogeneity and greater centrality in the posterior cingulate cortex and retrosplenial cortex,while subgroup 2 showed more homogeneous connectivity patterns and greater centrality in the temporoparietal junction and posterior inferior parietal lobule.No main effect for subgroup,treatment or subgroup×treatment interaction was revealed in the improvement of depressive symptoms.A significant subgroup×treatment interaction related to symbol coding improvement was detected(F=5.22,p=0.026).Within subgroup 1,the active group showed significantly greater improvement in symbol coding compared with the sham group(t=2.30,p=0.028),while baseline levels of interleukin-6 and C-reactive protein emerged as significant indicators for predicting improvements in symbolic coding(R2=0.35,RMSE(root-mean-square error)=5.72,p=0.013).Subgroup 2 showed no significant findings in terms of cognitive improvement or inflammatory cytokines predictions.展开更多
During oilfield development,a comprehensive model for assessing inter-well connectivity and connected volume within reservoirs is crucial.Traditional capacitance(TC)models,widely used in inter-well data analysis,face ...During oilfield development,a comprehensive model for assessing inter-well connectivity and connected volume within reservoirs is crucial.Traditional capacitance(TC)models,widely used in inter-well data analysis,face challenges when dealing with rapidly changing reservoir conditions over time.Additionally,TC models struggle with complex,random noise primarily caused by measurement errors in production and injection rates.To address these challenges,this study introduces a dynamic capacitance(SV-DC)model based on state variables.By integrating the extended Kalman filter(EKF)algorithm,the SV-DC model provides more flexible predictions of inter-well connectivity and time-lag efficiency compared to the TC model.The robustness of the SV-DC model is verified by comparing relative errors between preset and calculated values through Monte Carlo simulations.Sensitivity analysis was performed to compare the model performance with the benchmark,using the Qinhuangdao Oilfield as a case study.The results show that the SV-DC model accurately predicts water breakthrough times.Increases in the liquid production index and water cut in two typical wells indicate the development time of ineffective circulation channels,further confirming the accuracy and reliability of the model.The SV-DC model offers significant advantages in addressing complex,dynamic oilfield production scenarios and serves as a valuable tool for the efficient and precise planning and management of future oilfield developments.展开更多
文摘Let G=(V,E)be a connected graph.For an integer h≥0,a subset F■V(G)(resp.F■E(G))of G,if any,is called an h-restricted vertex cut(resp.h-restricted edge cut)of G,if G-F is disconnected and every vertex in G-F has at least h neighbors.The cardinality of a minimum h-restricted vertex-cut(resp.h-restricted edge cut)of G is the h-restricted connectivity(resp.h-restricted edge connectivity)of G,and denoted by κ^(h)(G)(resp.λ^(h)(G)).The enhanced hypercube Q_(n,κ)(1≤k≤n)is a variant of the hypercube Q_(n).In this paper,we consider the h-restricted connectivity of Q_(n,κ) for 2≤k≤n-1.Our main results are as follows:(1)κ^(h)(Q_(n,κ))=2^(h)(n-h+1)for 4≤k≤n-1 and 0≤h≤n-3,λ^(h)(Q_(n,κ))=2^(h)(n-h+1)for 2≤k≤n-1 and 0≤h≤n-2.(2)κ^(h)(Q_(n,3))=2^(h-1)(n-h+1)for n≥5 and 4≤h≤n-1,κ^(h)(Q_(n,2))=2^(h-1)(n-h+1)for n≥4 and 3≤h≤n-1.(3)κ^(3)(Q_(n,3))=6n-16 for n≥5,κ^(2)(Q_(n,3))=4n-8 for n≥4 and κ^(2)(Q_(n,2))=3n-5 for n≥3,κ^(1)(Q_(n,3))=2n and κ^(3)(Q_(n,2))=2n-2 for n≥3.
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funded in part by the Supported by Natural Science Foundation of Inner Mongolia Autonomous Region of China under Grants 2024QN06022 and 2023QN06008in part by the First-Class Discipline Research Special Project under Grant YLXKZX-NGD-015in part by the Inner Mongolia University of Technology Scientific Research Start-Up Project under Grant BS2024067.
文摘Blockchain offers a promising solution to the security challenges faced by the Internet of Vehicles(IoV).However,due to the dynamic connectivity of IoV,blockchain based on a single-chain structure or Directed Acyclic Graph(DAG)structure often suffer from performance limitations.The DAG lattice structure is a novel blockchain model in which each node maintains its own account chain,and only the node itself is allowed to update it.This feature makes the DAG lattice structure particularly suitable for addressing the challenges in dynamically connected IoV environment.In this paper,we propose a blockchain architecture based on the DAG lattice structure,specifically designed for dynamically connected IoV.In the proposed system,nodes must obtain authorization from a trusted authority before joining,forming a permissioned blockchain.Each node is assigned an individual account chain,allowing vehicles with limited storage capacity to participate in the blockchain by storing transactions only from nearby vehicles’account chains.Every transmitted message is treated as a transaction and added to the blockchain,enablingmore efficient data transmission in a dynamic network environment.Areputation-based incentivemechanism is introduced to encourage nodes to behave normally.Experimental results demonstrate that the proposed architecture achieves better performance compared with traditional single-chain and DAG-based approaches in terms of average transmission delay and storage cost.
文摘Advances in Alzheimer's disease(AD)research have deepened our understanding,yet the mechanisms driving its progression remain unclear.Although a range of in vivo biomarkers is now available(e.g.,measurements of amyloidbeta(Aβ)and ta u accumulation-the molecular hallmarks of AD-structural magnetic resonance imaging(MRI),assessments of brain metabolism,and,more recently,blood-based markers),a definitive diagnosis of AD continues to be challenging.For example,Frisoni et al.
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.
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funded by the National Natural Science Foundation of China(42230720)the 2023 Annual Qinghai Province"Kunlun Talents-High-end Innovation and Entrepreneurship Talent"Program Project.
文摘The Qaidam Basin,a typical alpine arid inland basin on the northern Qinghai-Xizang Plateau,China,hosts wetland ecosystems that are strongly constrained by topography and extreme climate.These ecosystems exhibit pronounced spatiotemporal heterogeneity and fragmented distribution patterns,rendering them highly sensitive to environmental change.This study integrated Sentinel-2 remote sensing imagery with the SedInConnect model to delineate wetland patch distributions and calculate the Index of Connectivity(IC)values across the basin.Based on IC values,we stratified field sampling sites into high-,moderate-,and lowconnectivity gradient groups to analyze the relationships among plant community characteristics,vegetation spatial patterns,and wetland connectivity in the Qaidam Basin.Partial Least Squares Path Modeling(PLSPM)was further employed to quantify the driving mechanisms underlying wetland vegetation characteristics.The results revealed that wetland connectivity across the basin was generally low,with IC values up to 1.32 and displaying a west-to-east decreasing gradient.The west and northwest were characterized by relatively continuous high-connectivity wetland networks,while fragmented and low-connectivity wetlands predominated in the east and southeast.Connectivity regulated wetland vegetation patterns primarily by affecting patch size,fragmentation,and internal adjacency.High-connectivity areas had higher class area(CA),largest patch index(LPI),and area-weighted mean patch size(AREA_AM)than low-connectivity areas.Connectivity had the strongest effect on vegetation coverage,which declined sharply from 87.577%in highconnectivity areas to 12.152%in low-connectivity areas.Meanwhile,species diversity showed a moderately negative response to connectivity changes,whereas species evenness remained relatively unaffected.PLS-PM explained 78.300%and 67.500%of the variance in vegetation community and vegetation pattern,respectively.Climate played a dominant role in shaping vegetation characteristics,with significant negative effects on both vegetation community and pattern.Topography influenced vegetation indirectly through climate,and connectivity was influenced by both drivers and exerted positive effects on vegetation community and pattern.This study reveals the multi-pathway driving mechanisms underlying vegetation pattern formation in alpine wetlands,providing a theoretical foundation and decision-support framework for the scientific conservation and adaptive management of wetlands in the Qaidam Basin.
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 in part by the National Natural Science Foundation of China under Grant 62172368the Natural Science Foundation of Zhejiang Province under Grant LR22F020003.
文摘The development of brain-computer interfaces(BCI)based on motor imagery(MI)has greatly improved patients’quality of life with movement disorders.The classification of upper limb MI has been widely studied and applied in many fields,including rehabilitation.However,the physiological representations of left and right lower limb movements are too close and activated deep in the cerebral cortex,making it difficult to distinguish their features.Therefore,classifying lower limbs motor imagery is more challenging.In this study,we propose a feature extraction method based on functional connectivity,which utilizes phase-locked values to construct a functional connectivity matrix as the features of the left and right legs,which can effectively avoid the problem of physiological representations of the left and right lower limbs being too close to each other during movement.In addition,considering the topology and the temporal characteristics of the electroencephalogram(EEG),we designed a temporal-spatial convolutional network(TSGCN)to capture the spatiotemporal information for classification.Experimental results show that the accuracy of the proposed method is higher than that of existing methods,achieving an average classification accuracy of 73.58%on the internal dataset.Finally,this study explains the network mechanism of left and right foot MI from the perspective of graph theoretic features and demonstrates the feasibility of decoding lower limb MI.
基金supported by National Natural Science Foundation of China[NO.11932013].
文摘This study aims to explore the impact of fatigue induced by different limb exercises on cerebral cortical oxygenation levels and functional connectivity strength using functional near-infrared spectroscopy(fNIRS).Fatigue was induced using an upper limb ergometer or a lower limb ergometer,with the load increasing gradually each minute.fNIRS covering the prefrontal cortex and motor cortex were used to collect data during the resting state,both before and after fatigue induction.A two-way ANOVA was conducted to examine differences in oxyhemoglobin(HbO_(2))and functional connectivity before and after fatigue induction in both groups,with the significance level set at 0.05.Exercise-induced fatigue in both the upper and lower limbs leads to a significant decrease in cerebral cortical oxygenation levels.Upper limb fatigue leads to a significant reduction in functional connectivity,there were significant decreases in connectivity within the motor cortex,between the motor cortex and frontal regions,and between the right ventrolateral prefrontal cortex and other frontal regions.Conversely,no significant changes were observed before and after lower limb fatigue.Future studies should focus on examining the extent to which how changes in the cerebral cortex,induced by exercise fatigue,are linked to exercise-and/or performance-related outcomes.
基金Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through the project number RI-44-0028.
文摘The sustainability of the Internet of Things(IoT)involves various issues,such as poor connectivity,scalability problems,interoperability issues,and energy inefficiency.Although the Sixth Generation of mobile networks(6G)allows for Ultra-Reliable Low-Latency Communication(URLLC),enhanced Mobile Broadband(eMBB),and massive Machine-Type Communications(mMTC)services,it faces deployment challenges such as the short range of sub-THz and THz frequency bands,low capability to penetrate obstacles,and very high path loss.This paper presents a network architecture to enhance the connectivity of wireless IoT mesh networks that employ both 6G and Wi-Fi technologies.In this architecture,local communications are carried through the mesh network,which uses a virtual backbone to relay packets to local nodes,while remote communications are carried through the 6G network.The virtual backbone is created using a heuristic distributed ConnectedDominating Set(CDS)algorithm.In this algorithm,each node uses information collected from its one-and two-hop neighbors to determine its role and find the set of expansion nodes that are used to select the next CDS nodes.The proposed algorithm has O(n)message and O(K)time complexities,where n is the number of nodes in the network,and K is the depth of the cluster.The study proved that the approximation ratio of the algorithmhas an upper bound of 2.06748(3.4306MCDS+4.8185).Performance evaluations compared the size of the CDS against the theoretical limit and recent CDS clustering algorithms.Results indicate that the proposed algorithm has the smallest average slope for the size of the CDS as the number of nodes increases.
文摘Against a backdrop of rising global trade protectionism and accelerated restructuring of international industrial and supply chains,regional institutional cooperation is becoming a key force in stabilizing cross-border trade and investment.The ASEAN region enjoys strong economic growth momentum with a distinct demographic advantage while China boasts a complete industrial system and manufacturing capacity.
基金supported by the Tianchi Talent Introduction Program of Xinjiang Uygur Autonomous Region(2024000104)the National Key Research and Development Program of China(2023YFF0805603).
文摘Northwest China serves as a critical ecological barrier region for maintaining national water,energy,and food security,as well as transboundary ecological governance.However,under the dual pressures of climate change and human activities,ecosystem services(ESs)are facing severe challenges in this region.Based on multi-source remote sensing and statistical data during 2000–2020,this study investigated the spatiotemporal evolution characteristics of four key ESs(water yield,habitat quality,carbon storage,and food provisioning)in Northwest China using the Integrated Valuation of Ecosystem Services and Tradeoffs(InVEST)model.Integrating morphological spatial pattern analysis(MSPA)and circuit theory,we identified ecological sources,corridors,pinch points,and barriers,and further designed three optimization scenarios(bottleneck optimization,high-resistance corridor buffering,and barrier removal optimization)to enhance landscape connectivity.The results revealed that ES supply and demand exhibited marked spatial heterogeneity,with high-supply areas concentrated in the southeastern sectors.Ecological sources primarily distributed in the southeastern and northern sectors,and ecological resistance surfaces continuously intensified.Water yield and habitat quality demands were increasing,food provisioning demand was decreasing,and carbon storage demand was surging.A total of 61 ecological sources(8%of the study area),142 ecological corridors(24,957 km in total length),237 ecological pinch points,and 89 barrier zones were identified.Among the three optimization scenarios,barrier removal achieved optimal connectivity improvement across all distance thresholds,with the probability of connectivity index improvement reaching up to 4%.This study provides scientific foundations and spatial decision support for ecological network optimization and sustainable governance in arid and semi-arid areas.
基金supported by the National Natural Science Foundation of China(No.52104018,52274030)China National Petroleum Corporation(CNPC)Innovation Foundation(No.2024DQ02-0303)China National Petroleum Corporation(CNPC)14th Five-Year Plan Major Strategic Scientific and Technological Project for Prospective and Fundamental Research(2024DJ86).
文摘Carbonate gas reservoirs are often characterized by strong heterogeneity,complex inter-well connectivity,extensive edge or bottom water,and unbalanced production,challenges that are also common in many heterogeneous gas reservoirs with intricate storage and flow behavior.To address these issues within a unified,data-driven framework,this study develops a multi-block material balance model that accounts for inter-block flow and aquifer influx,and is applicable to a wide range of reservoir types.The model incorporates inter-well and well-group conductive connectivity together with pseudo–steady-state aquifer support.The governing equations are solved using a Newton–Raphson scheme,while particle swarm optimization is employed to estimate formation pressures,inter-well connectivity,and effective aquifer volumes.An unbalanced exploitation factor,UEF,is introduced to quantify production imbalance and to guide development optimization.Validation using a synthetic reservoir model demonstrates that the approach accurately reproduces pressure evolution,crossflow behavior,and water influx.Application to a representative case(the Longwangmiao)field further confirms its robustness under highly heterogeneous conditions,achieving a 12.9%reduction in UEF through optimized production allocation.
基金Under the auspices of Key Program of the National Natural Science Foundation of China(No.U2006215,U1806218)the National Key R&D Program of China(No.2017YFC0505902)。
文摘Spartina alterniflora invasions seriously threaten the structure and functions of coastal wetlands in China.In this study,the Suaeda salsa community in the Yellow River Estuary wetland was monitored using long-term Landsat satellite images acquired from 1997 to 2020 to quantify the impact of changes in hydrological connectivity induced by S.alterniflora on neighboring vegetation com-munities.The results showed that S.alterniflora rapidly expanded in the estuary area at a rate of 4.91 km^(2)/yr from 2010 to 2020.At the same time,the hydrological connectivity of the area and the distribution of S.salsa changed significantly.Small tidal creeks dominated the S.alterniflora landscape.The number of tidal creeks increased significantly,but their average length decreased and they tended to develop in a horizontal tree-like pattern.Affected by the changes in hydrological connectivity due to the S.alterniflora invasion,the area of S.salsa decreased by 41.1%,and the degree of landscape fragmentation increased from 1997 to 2020.Variations in the Largest Patch Index(LPI)indicated that the S.alterniflora landscape had become the dominant landscape type in the Yellow River Estuary.The res-ults of standard deviation ellipse(SDE)and Pearson’s correlation analyses indicated that a well-developed hydrological connectivity could promote the maintenance of the S.salsa landscape.The degradation of most S.salsa communities is caused by the influence of S.alterniflora on the morphological characteristics of the hydrological connectivity of tidal creek systems.
基金partly funded by the Key Research and Development Program of China(grant 2022YFC3601600)the National Natural Science Foundation of China(NSFC)(grant 61876194)+1 种基金the Province Natural Science Foundation of Guangdong,China(grant 2024A1515011989)the Key Technologies Research and Development Program of Guangzhou Municipality(grant 202206010028).
文摘Background:Alterations of brain connectivity within resting-state networks(RSNs)have been widely reported in observational studies on epilepsy.However,the causal relationship between epilepsy and structural connectivity(SC)/functional connectivity(FC)within RSNs remain unclear.We conducted a bidirectional two-sample Mendelian randomization(MR)to explore the causal relationship between epilepsy subtypes and brain connectivity properties within RSNs.Methods:Genetic instruments were obtained from the latest genome-wide association studies(GWAS)of 69,995 individuals(N_(cases)=27,559,N_(controls)=42,436)issued by the International League Against Epilepsy.The GWAS summary SC/FC data within RSNs(N_(SC)=23,985,N_(FC)=24,336)were sourced from the Center for Neurogenomics and Cognitive Research.We investigate the causal relationship between epilepsy subtypes and brain connectivity within RSNs through a bidirectional two-sample MR analysis.Results:We found that the increased risk of generalized genetic epilepsy is consistent with a causal effect on dorsal attention and somatomotor FC.In the reverse MR analysis,there was no suggestive causal effect of FC/SC connectivity on epilepsy subtypes.Conclusions:This study shed light on the associations of FC/SC levels within the RSNs and epilepsy along with its subtypes.This insight could yield crucial intervention strategies to different subtypes of epilepsy at the level of brain structure and functional networks.
基金supported by the Guangzhou Municipal Key Discipline in Medicine(2021-2023)the Guangzhou High-level Clinical Key Specialty,the Guangzhou Research-oriented Hospital,the Innovative Clinical Technique of Guangzhou(2024-2026)+6 种基金the Guangdong Basic and Applied Basic Research Foundation(grant number 2022A1515011567,2020A1515110565)the Guangzhou Science,Technology Planning Project(grant number 202201010714,202103000032)the National Natural Science Foundation of China(grant number 82471546)the Guangdong College Students Innovation and Entrepreneurship Training Project(grant number S202310570038)the Guangzhou Health Science and Technology Project(grant number 20231A010038)the Guangzhou Traditional Chinese Medicine and Integrated Traditional Chinese and Western Medicine Technology Project(grant number:20232A010013)the Science and Technology Plan Project of Guangzhou(2023A03J0842).
文摘Background The heterogeneity of depression limits the treatment outcomes of intermittent theta burst stimulation(iTBS)and hinders the identification of predictive factors.This study investigated functional network connectivity and predictors of iTBS treatment outcomes in adolescents and young adults with depression.Aim This study aimed to identify default mode network(DMN)-based connectivity patterns associated with varying iTBS treatment outcomes in depression.Methods Data from a randomised controlled trial of iTBS in depression(n=82)were analysed using a data-driven approach to classify homogeneous subgroups based on the DMN.Connectivity subgroups were compared on depressive symptoms and cognitive function at pretreatment and post-treatment.Furthermore,the predictive significance of baseline inflammatory cytokines on post-treatment outcomes was evaluated.Results Two distinct subgroups were identified.Subgroup 1 exhibited high heterogeneity and greater centrality in the posterior cingulate cortex and retrosplenial cortex,while subgroup 2 showed more homogeneous connectivity patterns and greater centrality in the temporoparietal junction and posterior inferior parietal lobule.No main effect for subgroup,treatment or subgroup×treatment interaction was revealed in the improvement of depressive symptoms.A significant subgroup×treatment interaction related to symbol coding improvement was detected(F=5.22,p=0.026).Within subgroup 1,the active group showed significantly greater improvement in symbol coding compared with the sham group(t=2.30,p=0.028),while baseline levels of interleukin-6 and C-reactive protein emerged as significant indicators for predicting improvements in symbolic coding(R2=0.35,RMSE(root-mean-square error)=5.72,p=0.013).Subgroup 2 showed no significant findings in terms of cognitive improvement or inflammatory cytokines predictions.
基金the National Natural Science Foundation of China(Grant No.52374051)the Joint Fund for Enterprise Innovation and Development of NSFC(Grant No.U24B2037).
文摘During oilfield development,a comprehensive model for assessing inter-well connectivity and connected volume within reservoirs is crucial.Traditional capacitance(TC)models,widely used in inter-well data analysis,face challenges when dealing with rapidly changing reservoir conditions over time.Additionally,TC models struggle with complex,random noise primarily caused by measurement errors in production and injection rates.To address these challenges,this study introduces a dynamic capacitance(SV-DC)model based on state variables.By integrating the extended Kalman filter(EKF)algorithm,the SV-DC model provides more flexible predictions of inter-well connectivity and time-lag efficiency compared to the TC model.The robustness of the SV-DC model is verified by comparing relative errors between preset and calculated values through Monte Carlo simulations.Sensitivity analysis was performed to compare the model performance with the benchmark,using the Qinhuangdao Oilfield as a case study.The results show that the SV-DC model accurately predicts water breakthrough times.Increases in the liquid production index and water cut in two typical wells indicate the development time of ineffective circulation channels,further confirming the accuracy and reliability of the model.The SV-DC model offers significant advantages in addressing complex,dynamic oilfield production scenarios and serves as a valuable tool for the efficient and precise planning and management of future oilfield developments.
