We investigate numerically the effects of long-range temporal and spatial correlations based on the rescaled distributions of the squared interface width W^(2)(L, t) and the interface height h(x, t)in the(1+1)-dimensi...We investigate numerically the effects of long-range temporal and spatial correlations based on the rescaled distributions of the squared interface width W^(2)(L, t) and the interface height h(x, t)in the(1+1)-dimensional Kardar-Parisi-Zhang(KPZ) growth system within the early growth regime. Through extensive numerical simulations, we find that long-range temporally correlated noise does not significantly impact the distribution form of the interface width. Generally,W^(2)(L, t) approximately obeys a lognormal distribution when the temporal correlation exponentθ ≥0. On the other hand, the effects of long-range spatially correlated noise are evidently different from the temporally correlated case. Our results show that, when the spatial correlation exponent ρ ≤ 0.20, the distribution forms of W^(2)(L, t) approach the lognormal distribution, and when ρ > 0.20, the distribution becomes more asymmetric, steep, and fat-tailed, and tends to an unknown distribution form. As a comparison, probability distributions of the interface height are also provided in the temporally and spatially correlated KPZ system, exhibiting quite different characteristics from each other within the whole correlated strengths. For the temporal correlation, the height distributions follow Tracy-Widom Gaussian orthogonal ensemble(TW-GOE) when θ → 0, and with increasing θ, the height distributions crossover continuously to an unknown distribution. However, for the spatial correlation, the height distributions gradually transition from the TW-GOE distribution to the standard Gaussian form.展开更多
In this paper,the joint design of transmit and receive beamformers for transmit subaperturing multiple-input-multiple-output(TS-MIMO)radar is investigated,aiming to enhance its low probability of intercept(LPI)capabil...In this paper,the joint design of transmit and receive beamformers for transmit subaperturing multiple-input-multiple-output(TS-MIMO)radar is investigated,aiming to enhance its low probability of intercept(LPI)capability.The main objective is to simultaneously minimize the transmission power,suppress the transmit sidelobe levels,and minimize the probability of intercept,thus bolstering the LPI performance of the radar system while maintaining the desired target detection performance.An alternative optimization method is proposed to jointly optimize the transmit and receive beamformers,yielding an unified LPI optimization framework.Particularly,the proposed iterative algorithm based on the Lagrange duality theory for transmit beamforming is more efficient than the conventional convex optimization method.Numerical experiments highlight the effectiveness of the proposed approach in sidelobe suppression and computational efficiency.展开更多
Hepatocellular carcinoma(HCC)is a pressing global health problem and is the sixth most common cancer and the third leading cause of cancer mortality worldwide.Despite continuous advances in treatment modalities,the 5-...Hepatocellular carcinoma(HCC)is a pressing global health problem and is the sixth most common cancer and the third leading cause of cancer mortality worldwide.Despite continuous advances in treatment modalities,the 5-year survival rate is low with a high propensity for recurrence and metastasis1.This clinical challenge in treating HCC is largely attributed to the heterogeneity and intrinsic therapy resistance of cancer stem cells(CSCs),which are a subpopulation of cells with self-renewal capability and multidirectional differentiation potential to induce tumorigenicity2.The behavior and maintenance of CSCs are not autonomous but critically dependent on the complex bidirectional crosstalk between CSCs and the tumor immune microenvironment(TIME)1.In this review we first summarize the recent progress in characterizing CSCs and the interactions between CSCs and the TIME in HCC.Next,we discuss the emerging therapeutic strategies targeting CSC populations with the ongoing challenges.Finally,we give our perspectives on the future directions in HCC CSC research.展开更多
The western Los Angeles(LA)wildfires of early January 2025 caused catastrophic social and environmental impacts,drawing widespread attention.This study investigates the characteristics of these wildfires and quantifie...The western Los Angeles(LA)wildfires of early January 2025 caused catastrophic social and environmental impacts,drawing widespread attention.This study investigates the characteristics of these wildfires and quantifies the influence of heat and drought on their likelihood using a copula-based Bayesian probability framework.The wildfires were characterized by burned area(BA)and intensity(fire radiative power,FRP).The criteria establishing the presence of“hot drought”conditions were identified using the 5-day Standardized Temperature Index(STI)and 75-day Standardized Precipitation Index(SPI),respectively.The wildfire outbreak began on 7 January 2025 and burned for more than six days,with the total burned area exceeding 245 km^(2) and the cumulative FRP exceeding 41060 MW.Based on satellite-derived active fire observations from 2001 to 2025,we estimate that such large and intense wildfires during LA’s rainy season represent a once-in-a-67-year event.The wildfires were largely driven by the combination of hot and dry conditions,which dried out soils and vegetation that had proliferated due to above-average precipitation in previous winter seasons,thereby providing abundant fuel.Our seasonal analysis reveals that extreme drought increased the probability of wildfires matching the 2025 intensity and BA by 54%and 75%,respectively.Hot drought further amplified these probabilities by 149%(intensity)and 210%(BA).These findings suggest an elevated risk of large wildfires under hot drought conditions,contributing to their expansion into the non-traditional fire season.展开更多
In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper prese...In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic probability.Firstly,a parallel thread employs the YOLOX object detectionmodel to gather 2D semantic information and compensate for missed detections.Next,an improved K-means++clustering algorithm clusters bounding box regions,adaptively determining the threshold for extracting dynamic object contours as dynamic points change.This process divides the image into low dynamic,suspicious dynamic,and high dynamic regions.In the tracking thread,the dynamic point removal module assigns dynamic probability weights to the feature points in these regions.Combined with geometric methods,it detects and removes the dynamic points.The final evaluation on the public TUM RGB-D dataset shows that the proposed dynamic SLAM algorithm surpasses most existing SLAM algorithms,providing better pose estimation accuracy and robustness in dynamic environments.展开更多
To address prediction errors and limited information extraction in machine learning(ML)-based interval prediction,a hybrid model was proposed for interval estimation and failure assessment of step-like landslides unde...To address prediction errors and limited information extraction in machine learning(ML)-based interval prediction,a hybrid model was proposed for interval estimation and failure assessment of step-like landslides under uncertainty.The model decomposed displacements into trend and periodic components via Variational Mode Decomposition(VMD)and K-shape clustering.The Residual and Moving Block Bootstrap methods were used to generate pseudo datasets.Polynomial regressionwas adopted for trend forecasting,whereas the Dense Convolutional Network(DenseNet)and Long Short-Term Memory(LSTM)networks were employed for periodic displacement prediction.An Extreme Learning Machine(ELM)was used to estimate the noise variance,enabling the construction of Prediction Intervals(PIs)and quantificationof displacement uncertainty.Failure probabilities(Pf)were derived from PIs using an improved tangential angle criterion and reliability analysis.The model was validated on three step-like landslides in the Three Gorges Reservoir Area,achieving stability assessment accuracies of 99.88%(XD01),99.93%(ZG93),99.89%(ZG118),and 100%for ZG110 and ZG111 across the Baishuihe and Bazimen landslides.For the Shuping landslide,the predictions aligned with fieldobservations before and after the 2014–2015 remediation,with P_(f)remaining near zero post-2015 except for occasional peaks.The model outperformed conventional ML approaches by yielding narrower PIs.At XD01 with 90%PI nominal confidencelevel(PINC),the coverage width-based criterion(CWC)and PI average width(PIAW)were 3.38 mm.The mean values of the PIs exhibited high accuracy,with a Mean Absolute Error(MAE)of 0.28 mm and Root Mean Square Error(RMSE)of 0.39 mm.These results demonstrate the robustness of the proposed model in improving landslide risk assessment and decision-making under uncertainty.展开更多
Fatigue analysis of engine turbine blade is an essential issue.Due to various uncertainties during the manufacture and operation,the fatigue damage and life of turbine blade present randomness.In this study,the random...Fatigue analysis of engine turbine blade is an essential issue.Due to various uncertainties during the manufacture and operation,the fatigue damage and life of turbine blade present randomness.In this study,the randomness of structural parameters,working condition and vibration environment are considered for fatigue life predication and reliability assessment.First,the lowcycle fatigue problem is modelled as stochastic static system with random parameters,while the high-cycle fatigue problem is considered as stochastic dynamic system under random excitations.Then,to deal with the two failure modes,the novel Direct Probability Integral Method(DPIM)is proposed,which is efficient and accurate for solving stochastic static and dynamic systems.The probability density functions of accumulated damage and fatigue life of turbine blade for low-cycle and high-cycle fatigue problems are achieved,respectively.Furthermore,the time–frequency hybrid method is advanced to enhance the computational efficiency for governing equation of system.Finally,the results of typical examples demonstrate high accuracy and efficiency of the proposed method by comparison with Monte Carlo simulation and other methods.It is indicated that the DPIM is a unified method for predication of random fatigue life for low-cycle and highcycle fatigue problems.The rotational speed,density,fatigue strength coefficient,and fatigue plasticity index have a high sensitivity to fatigue reliability of engine turbine blade.展开更多
To ensure the structural integrity of life-limiting component of aeroengines,Probabilistic Damage Tolerance(PDT)assessment is applied to evaluate the failure risk as required by airworthiness regulations and military ...To ensure the structural integrity of life-limiting component of aeroengines,Probabilistic Damage Tolerance(PDT)assessment is applied to evaluate the failure risk as required by airworthiness regulations and military standards.The PDT method holds the view that there exist defects such as machining scratches and service cracks in the tenon-groove structures of aeroengine disks.However,it is challenging to conduct PDT assessment due to the scarcity of effective Probability of Detection(POD)model and anomaly distribution model.Through a series of Nondestructive Testing(NDT)experiments,the POD model of real cracks in tenon-groove structures is constructed for the first time by employing the Transfer Function Method(TFM).A novel anomaly distribution model is derived through the utilization of the POD model,instead of using the infeasible field data accumulation method.Subsequently,a framework for calculating the Probability of Failure(POF)of the tenon-groove structures is established,and the aforementioned two models exert a significant influence on the results of POF.展开更多
Sodium-sulfur(Na-S)batteries are considered as a promising successor to the next-generation of high-capacity,low-cost and environmentally friendly sulfur-based battery systems.However,Na-S batteries still suffer from ...Sodium-sulfur(Na-S)batteries are considered as a promising successor to the next-generation of high-capacity,low-cost and environmentally friendly sulfur-based battery systems.However,Na-S batteries still suffer from the“shuttle effect”and sluggish ion transport kinetics due to the dissolution of sodium polysulfides and poor conductivity of sulfur.MXenes,as 2D transition metal carbides/nitrides,have exhibited excellent conductivity,diverse structure and tunable surface groups,particularly playing a crucial role in inhibiting polysulfide shuttle and sodium dendrite growth.In this review,achievements and advancements of MXene-based Na-S batteries are discussed,including applications of a sulfur cathode,separator,interlayer between separator and cathode,and sodium anode.In the end,perspectives and challenges on the future development of MXene-based materials in Na-S batteries are proposed.展开更多
Objective:This study aims to explore the experiences of social alienation among adolescents with depression,providing practical This study aims to explore the experiences of social alienation among adolescents with de...Objective:This study aims to explore the experiences of social alienation among adolescents with depression,providing practical This study aims to explore the experiences of social alienation among adolescents with depression,providing practical guidance for improving their interpersonal relationships and facilitating their reintegration into society.Methods:This qualitative research was conducted following the conventional content analysis method.20 adolescents with depression were employed to select from June to August 2024 for face-to-face semi-structured interviews.The collected data were analyzed using Colaizzi's seven-step method.Results:Three themes and eight sub-themes were analyzed and identified:individual level(feelings of helplessness and powerlessness,cognitive distortion,avoidance and withdrawal),family level(lack of family awareness,family conflict),social level(limitations of academic stress and social circle,lack and degradation of skills,generalization of virtual reality,social“stigma”).Conclusion:Adolescents with depression experience complex social alienation.Healthcare providers should enhance their self-awareness and social adaptation skills,improve family dynamics,and provide a comprehensive range of services and services to help them to cope with the challenges of depression.Healthcare providers should enhance their self-awareness and social adaptation skills,improve family dynamics,strengthen communication,bolster family support systems,and collaborate to develop comprehensive social networks and psychological services.This will create a supportive social atmosphere to help adolescents gradually alleviate their feelings of social alienation.展开更多
Driven by the goal of carbon neutrality,prefabricated buildings,as an important form of green construction,have become a key focus in the study of lifecycle carbon footprint management.Based on this,this paper starts ...Driven by the goal of carbon neutrality,prefabricated buildings,as an important form of green construction,have become a key focus in the study of lifecycle carbon footprint management.Based on this,this paper starts from the perspective of carbon footprint and combines the digital and visual advantages of BIM technology to construct a green evaluation system for prefabricated buildings.It explores the carbon emissions in each stage of the building and proposes corresponding improvement measures,aiming to provide necessary references for the low-carbon transformation of prefabricated buildings.展开更多
Parental educational anxiety has become a social symptom in China,and rural primary school students’mothers exhibit unique educational anxieties due to their special living environment.Based on interviews with 10 rur...Parental educational anxiety has become a social symptom in China,and rural primary school students’mothers exhibit unique educational anxieties due to their special living environment.Based on interviews with 10 rural primary school students’mothers,five typical educational anxiety experiences were selected for analysis,and themes such as rural life burden,children’s learning habits,mothers’educational expectations,mothers’educational methods,mothers’emotional state,deviation between reality and expectations,homework guidance ability,mothers’educational level,and attitudes towards children’s future development were refined.The root causes of educational anxiety among rural primary school students’mothers include the deviation between children’s actual performance and mothers’educational expectations,the sense of disparity under social comparison,physical and mental exhaustion caused by role overload,anxiety triggered by excessive economic burden,and a sense of powerlessness towards children’s educational outcomes.To alleviate the educational anxiety of rural primary school students’mothers,mothers should actively adjust themselves,fathers should actively participate in their children’s education,society should create a healthy atmosphere,and schools should strengthen family education guidance.展开更多
We study the conditional entropy of topological dynamical systems using a family of metrics induced by probability bi-sequences.We present a Brin-Katok formula by replacing the mean metric by a family of metrics induc...We study the conditional entropy of topological dynamical systems using a family of metrics induced by probability bi-sequences.We present a Brin-Katok formula by replacing the mean metric by a family of metrics induced by a probability bi-sequence.We also establish the Katok’s entropy formula for conditional entropy for ergodic measures in the case of the new family of metrics.展开更多
The red cultural resources in rural areas bear the heavy historical and spiritual strength,and are the key rich ore and spiritual pillar in the field of education.This study discusses the connotation of red culture re...The red cultural resources in rural areas bear the heavy historical and spiritual strength,and are the key rich ore and spiritual pillar in the field of education.This study discusses the connotation of red culture resources and the current situation of educating people,and then analyzes how to integrate interdisciplinary learning theory into red culture to enhance the value of educating people.On this basis,it proposes to explore the educational path of optimizing rural red cultural resources from an interdisciplinary perspective by integrating multi-disciplinary knowledge and red cultural resources.展开更多
The natural visibility graph method has been widely used in physiological signal analysis,but it fails to accurately handle signals with data points below the baseline.Such signals are common across various physiologi...The natural visibility graph method has been widely used in physiological signal analysis,but it fails to accurately handle signals with data points below the baseline.Such signals are common across various physiological measurements,including electroencephalograph(EEG)and functional magnetic resonance imaging(fMRI),and are crucial for insights into physiological phenomena.This study introduces a novel method,the baseline perspective visibility graph(BPVG),which can analyze time series by accurately capturing connectivity across data points both above and below the baseline.We present the BPVG construction process and validate its performance using simulated signals.Results demonstrate that BPVG accurately translates periodic,random,and fractal signals into regular,random,and scale-free networks respectively,exhibiting diverse degree distribution traits.Furthermore,we apply BPVG to classify Alzheimer’s disease(AD)patients from healthy controls using EEG data and identify non-demented adults at varying dementia risk using resting-state fMRI(rs-fMRI)data.Utilizing degree distribution entropy derived from BPVG networks,our results exceed the best accuracy benchmark(77.01%)in EEG analysis,especially at channels F4(78.46%)and O1(81.54%).Additionally,our rs-fMRI analysis achieves a statistically significant classification accuracy of 76.74%.These findings highlight the effectiveness of BPVG in distinguishing various time series types and its practical utility in EEG and rs-fMRI analysis for early AD detection and dementia risk assessment.In conclusion,BPVG’s validation across both simulated and real data confirms its capability to capture comprehensive information from time series,irrespective of baseline constraints,providing a novel method for studying neural physiological signals.展开更多
Ecological network(EN)identification and optimization is an essential research tool for safeguarding regional ecological security patterns and planning territorial space.Especially for the ecologically fragile inland ...Ecological network(EN)identification and optimization is an essential research tool for safeguarding regional ecological security patterns and planning territorial space.Especially for the ecologically fragile inland river basins,EN optimization is of significance in ensuring regional ecological security and virtuous cycle of ecosystems.In addition,EN is a dynamically changing structural system that is more applicable to the regional development by optimizing it from comprehensive future development perspective.EN of Shiyang River basin was constructed on account of the circuit theory,and land use/cover changes(LUCC)of the basin in 2035 was predicted by PLUS model,so as to explore the ecological conservation priorities and formulate optimization strategies.54 ecological sources(ESs)were identified,covering an area of 12,198 km^(2),mainly in the southern basin.133 ecological corridors(ECs)with an area of 3,176.92 km^(2)were extracted.38 ecological pinchpoints(EPs)and 22 ecological barriers(EBs)were identified respectively,which were mainly distributed in the lower basin.To effectively enhance the connectivity of EN in Minqin County,which has the worst ecological environment,we added five stepping stones based on the Ant Forest project.In addition,the optimal EPS is selected according to the development and limitation needs of inland river basins and the threat degree of warning points(WPs)under different scenarios.Scientific and reasonable optimization of future urban layout to prevent WPs can effectively alleviate the contradiction between ecological protection and economic development.The study is intended to provide basis for ecological sustainable development and rational planning territorial space in Shiyang River basin,as well as opinion for EN optimization in inland river basin.展开更多
The Argo program measures temperature and salinity in the upper ocean(0–2000 m).These observations are critical for weather/climate studies,ocean circulation analysis,and sea-level monitoring.To address the limitatio...The Argo program measures temperature and salinity in the upper ocean(0–2000 m).These observations are critical for weather/climate studies,ocean circulation analysis,and sea-level monitoring.To address the limitations of traditional thresholds in Argo data quality control(QC),this study proposes a novel probability distribution-based inference method(PDIM)for temperature-salinity threshold inference.By integrating historical observations with climatological data,the method utilizes historical data corresponding to latitude and longitude grids,calculates temperature/salinity frequency distributions for each depth,and determines“zero probability”boundaries through combined frequency distribution and climatology data.Then a probability distribution model is established to detect outliers automatically based on the features in the probability density function,which eliminates the traditional dependence on the normal distribution hypothesis.When applied to global Argo datasets from China Argo Real-time Data Center(CARDC),PDIM successfully identifies suspicious profiles and sensor drifts with high reliability,achieving a low false positive rate(0.55%for temperature,0.18%for salinity)while maintaining competitive true positive rate(28.29%for temperature,55.15%for salinity).This method is expected to improve the reliability of Argo data QC and has important significance for Argo QC.展开更多
Estimating probability density functions(PDFs)is critical in data analysis,particularly for complex multimodal distributions.traditional kernel density estimator(KDE)methods often face challenges in accurately capturi...Estimating probability density functions(PDFs)is critical in data analysis,particularly for complex multimodal distributions.traditional kernel density estimator(KDE)methods often face challenges in accurately capturing multimodal structures due to their uniform weighting scheme,leading to mode loss and degraded estimation accuracy.This paper presents the flexible kernel density estimator(F-KDE),a novel nonparametric approach designed to address these limitations.F-KDE introduces the concept of kernel unit inequivalence,assigning adaptive weights to each kernel unit,which better models local density variations in multimodal data.The method optimises an objective function that integrates estimation error and log-likelihood,using a particle swarm optimisation(PSO)algorithm that automatically determines optimal weights and bandwidths.Through extensive experiments on synthetic and real-world datasets,we demonstrated that(1)the weights and bandwidths in F-KDE stabilise as the optimisation algorithm iterates,(2)F-KDE effectively captures the multimodal characteristics and(3)F-KDE outperforms state-of-the-art density estimation methods regarding accuracy and robustness.The results confirm that F-KDE provides a valuable solution for accurately estimating multimodal PDFs.展开更多
Vaccination is critical for controlling infectious diseases,but negative vaccination information can lead to vaccine hesitancy.To study how the interplay between information diffusion and disease transmission impacts ...Vaccination is critical for controlling infectious diseases,but negative vaccination information can lead to vaccine hesitancy.To study how the interplay between information diffusion and disease transmission impacts vaccination and epidemic spread,we propose a novel two-layer multiplex network model that integrates an unaware-acceptant-negative-unaware(UANU)information diffusion model with a susceptible-vaccinated-exposed-infected-susceptible(SVEIS)epidemiological framework.This model includes individual exposure and vaccination statuses,time-varying forgetting probabilities,and information conversion thresholds.Through the microscopic Markov chain approach(MMCA),we derive dynamic transition equations and the epidemic threshold expression,validated by Monte Carlo simulations.Using MMCA equations,we predict vaccination densities and analyze parameter effects on vaccination,disease transmission,and the epidemic threshold.Our findings suggest that promoting positive information,curbing the spread of negative information,enhancing vaccine effectiveness,and promptly identifying asymptomatic carriers can significantly increase vaccination rates,reduce epidemic spread,and raise the epidemic threshold.展开更多
The study aims to develop an empirical model to predict the rainfall intensity in Al-Diwaniyah City,Iraq,according to a statistical analysis based on probability and the specific rainfall return period.Rainfall data w...The study aims to develop an empirical model to predict the rainfall intensity in Al-Diwaniyah City,Iraq,according to a statistical analysis based on probability and the specific rainfall return period.Rainfall data were collected daily for 25 years starting in 2000.Daily rainfall data were converted to rainfall intensity for five duration periods ranging from one to five hours.The extreme values were checked,and data that deviated from the group trend were removed for each period,and then arranged in descending order using the Weibull formula to calculate the probability.Statistically,the model performance with a return period of two years is considered good when compared with observed results and other methods such as Talbot and Sherman with a coefficient of determination(R2)>0.97 and Nash-Sutcliffe efficiency(NSE)>0.80.The results showed that a mathematical equation was obtained that describes the relationship between rainfall intensity,probability,and rainfall duration,which can be used for a confined return period with a 50% probability.Therefore,decision-makers can rely on the model to improve the performance of the city’s current drainage system during flood periods in the future.展开更多
文摘We investigate numerically the effects of long-range temporal and spatial correlations based on the rescaled distributions of the squared interface width W^(2)(L, t) and the interface height h(x, t)in the(1+1)-dimensional Kardar-Parisi-Zhang(KPZ) growth system within the early growth regime. Through extensive numerical simulations, we find that long-range temporally correlated noise does not significantly impact the distribution form of the interface width. Generally,W^(2)(L, t) approximately obeys a lognormal distribution when the temporal correlation exponentθ ≥0. On the other hand, the effects of long-range spatially correlated noise are evidently different from the temporally correlated case. Our results show that, when the spatial correlation exponent ρ ≤ 0.20, the distribution forms of W^(2)(L, t) approach the lognormal distribution, and when ρ > 0.20, the distribution becomes more asymmetric, steep, and fat-tailed, and tends to an unknown distribution form. As a comparison, probability distributions of the interface height are also provided in the temporally and spatially correlated KPZ system, exhibiting quite different characteristics from each other within the whole correlated strengths. For the temporal correlation, the height distributions follow Tracy-Widom Gaussian orthogonal ensemble(TW-GOE) when θ → 0, and with increasing θ, the height distributions crossover continuously to an unknown distribution. However, for the spatial correlation, the height distributions gradually transition from the TW-GOE distribution to the standard Gaussian form.
基金supported by the National Natural Science Foundation of China(62271247)the Natural Science Foundation of Jiangsu Province(BK20240181)+4 种基金the Dreams Foundation of Jianghuai Advance Technology Center(2023-ZM01D001)the National Aerospace Science Foundation of China(20220055052001)the Qing Lan Project of Jiangsu Provincethe Fund of Prospective Layout of Scientific Research for Nanjing University of Aeronautics and Astronauticsthe Key Laboratory of Radar Imaging and Microwave Photonics(Nanjing University of Aeronautics and Astronautics),Ministry of Education。
文摘In this paper,the joint design of transmit and receive beamformers for transmit subaperturing multiple-input-multiple-output(TS-MIMO)radar is investigated,aiming to enhance its low probability of intercept(LPI)capability.The main objective is to simultaneously minimize the transmission power,suppress the transmit sidelobe levels,and minimize the probability of intercept,thus bolstering the LPI performance of the radar system while maintaining the desired target detection performance.An alternative optimization method is proposed to jointly optimize the transmit and receive beamformers,yielding an unified LPI optimization framework.Particularly,the proposed iterative algorithm based on the Lagrange duality theory for transmit beamforming is more efficient than the conventional convex optimization method.Numerical experiments highlight the effectiveness of the proposed approach in sidelobe suppression and computational efficiency.
基金supported by the Hong Kong Research Grants Council Theme-based Research Scheme(Grant No.T12-716/22-R)Innovation and Technology Commission grant for State Key Laboratory of Liver Research(Grant No.ITC PD/17-9)University Development Fund of The University of Hong Kong,and Loke Yew Endowed Professorship award.I.O.L.Ng is Loke Yew Professor in Pathology.
文摘Hepatocellular carcinoma(HCC)is a pressing global health problem and is the sixth most common cancer and the third leading cause of cancer mortality worldwide.Despite continuous advances in treatment modalities,the 5-year survival rate is low with a high propensity for recurrence and metastasis1.This clinical challenge in treating HCC is largely attributed to the heterogeneity and intrinsic therapy resistance of cancer stem cells(CSCs),which are a subpopulation of cells with self-renewal capability and multidirectional differentiation potential to induce tumorigenicity2.The behavior and maintenance of CSCs are not autonomous but critically dependent on the complex bidirectional crosstalk between CSCs and the tumor immune microenvironment(TIME)1.In this review we first summarize the recent progress in characterizing CSCs and the interactions between CSCs and the TIME in HCC.Next,we discuss the emerging therapeutic strategies targeting CSC populations with the ongoing challenges.Finally,we give our perspectives on the future directions in HCC CSC research.
基金supported by the National Natural Science Foundation of China(Grant Nos.42471034,42330604)the Qing Lan Projectsupport from the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(EarthLab).
文摘The western Los Angeles(LA)wildfires of early January 2025 caused catastrophic social and environmental impacts,drawing widespread attention.This study investigates the characteristics of these wildfires and quantifies the influence of heat and drought on their likelihood using a copula-based Bayesian probability framework.The wildfires were characterized by burned area(BA)and intensity(fire radiative power,FRP).The criteria establishing the presence of“hot drought”conditions were identified using the 5-day Standardized Temperature Index(STI)and 75-day Standardized Precipitation Index(SPI),respectively.The wildfire outbreak began on 7 January 2025 and burned for more than six days,with the total burned area exceeding 245 km^(2) and the cumulative FRP exceeding 41060 MW.Based on satellite-derived active fire observations from 2001 to 2025,we estimate that such large and intense wildfires during LA’s rainy season represent a once-in-a-67-year event.The wildfires were largely driven by the combination of hot and dry conditions,which dried out soils and vegetation that had proliferated due to above-average precipitation in previous winter seasons,thereby providing abundant fuel.Our seasonal analysis reveals that extreme drought increased the probability of wildfires matching the 2025 intensity and BA by 54%and 75%,respectively.Hot drought further amplified these probabilities by 149%(intensity)and 210%(BA).These findings suggest an elevated risk of large wildfires under hot drought conditions,contributing to their expansion into the non-traditional fire season.
基金the National Natural Science Foundation of China(No.62063006)to the Guangxi Natural Science Foundation under Grant(Nos.2023GXNSFAA026025,AA24010001)+3 种基金to the Innovation Fund of Chinese Universities Industry-University-Research(ID:2023RY018)to the Special Guangxi Industry and Information Technology Department,Textile and Pharmaceutical Division(ID:2021 No.231)to the Special Research Project of Hechi University(ID:2021GCC028)to the Key Laboratory of AI and Information Processing,Education Department of Guangxi Zhuang Autonomous Region(Hechi University),No.2024GXZDSY009。
文摘In dynamic scenarios,visual simultaneous localization and mapping(SLAM)algorithms often incorrectly incorporate dynamic points during camera pose computation,leading to reduced accuracy and robustness.This paper presents a dynamic SLAM algorithm that leverages object detection and regional dynamic probability.Firstly,a parallel thread employs the YOLOX object detectionmodel to gather 2D semantic information and compensate for missed detections.Next,an improved K-means++clustering algorithm clusters bounding box regions,adaptively determining the threshold for extracting dynamic object contours as dynamic points change.This process divides the image into low dynamic,suspicious dynamic,and high dynamic regions.In the tracking thread,the dynamic point removal module assigns dynamic probability weights to the feature points in these regions.Combined with geometric methods,it detects and removes the dynamic points.The final evaluation on the public TUM RGB-D dataset shows that the proposed dynamic SLAM algorithm surpasses most existing SLAM algorithms,providing better pose estimation accuracy and robustness in dynamic environments.
基金funding support from the National Science Fund for Distinguished Young Scholars(Grant No.52125904)the National Key R&D Plan(Grant No.2022YFC3004403)the National Natural Science Foundation of China(Grant No.52039008).
文摘To address prediction errors and limited information extraction in machine learning(ML)-based interval prediction,a hybrid model was proposed for interval estimation and failure assessment of step-like landslides under uncertainty.The model decomposed displacements into trend and periodic components via Variational Mode Decomposition(VMD)and K-shape clustering.The Residual and Moving Block Bootstrap methods were used to generate pseudo datasets.Polynomial regressionwas adopted for trend forecasting,whereas the Dense Convolutional Network(DenseNet)and Long Short-Term Memory(LSTM)networks were employed for periodic displacement prediction.An Extreme Learning Machine(ELM)was used to estimate the noise variance,enabling the construction of Prediction Intervals(PIs)and quantificationof displacement uncertainty.Failure probabilities(Pf)were derived from PIs using an improved tangential angle criterion and reliability analysis.The model was validated on three step-like landslides in the Three Gorges Reservoir Area,achieving stability assessment accuracies of 99.88%(XD01),99.93%(ZG93),99.89%(ZG118),and 100%for ZG110 and ZG111 across the Baishuihe and Bazimen landslides.For the Shuping landslide,the predictions aligned with fieldobservations before and after the 2014–2015 remediation,with P_(f)remaining near zero post-2015 except for occasional peaks.The model outperformed conventional ML approaches by yielding narrower PIs.At XD01 with 90%PI nominal confidencelevel(PINC),the coverage width-based criterion(CWC)and PI average width(PIAW)were 3.38 mm.The mean values of the PIs exhibited high accuracy,with a Mean Absolute Error(MAE)of 0.28 mm and Root Mean Square Error(RMSE)of 0.39 mm.These results demonstrate the robustness of the proposed model in improving landslide risk assessment and decision-making under uncertainty.
基金supports of the National Natural Science Foundation of China(Nos.12032008,12102080)the Fundamental Research Funds for the Central Universities,China(No.DUT23RC(3)038)are much appreciated。
文摘Fatigue analysis of engine turbine blade is an essential issue.Due to various uncertainties during the manufacture and operation,the fatigue damage and life of turbine blade present randomness.In this study,the randomness of structural parameters,working condition and vibration environment are considered for fatigue life predication and reliability assessment.First,the lowcycle fatigue problem is modelled as stochastic static system with random parameters,while the high-cycle fatigue problem is considered as stochastic dynamic system under random excitations.Then,to deal with the two failure modes,the novel Direct Probability Integral Method(DPIM)is proposed,which is efficient and accurate for solving stochastic static and dynamic systems.The probability density functions of accumulated damage and fatigue life of turbine blade for low-cycle and high-cycle fatigue problems are achieved,respectively.Furthermore,the time–frequency hybrid method is advanced to enhance the computational efficiency for governing equation of system.Finally,the results of typical examples demonstrate high accuracy and efficiency of the proposed method by comparison with Monte Carlo simulation and other methods.It is indicated that the DPIM is a unified method for predication of random fatigue life for low-cycle and highcycle fatigue problems.The rotational speed,density,fatigue strength coefficient,and fatigue plasticity index have a high sensitivity to fatigue reliability of engine turbine blade.
基金supported by the National Major Science and Technology Project,China(No.J2019-Ⅳ-0007-0075)the Fundamental Research Funds for the Central Universities,China(No.JKF-20240036)。
文摘To ensure the structural integrity of life-limiting component of aeroengines,Probabilistic Damage Tolerance(PDT)assessment is applied to evaluate the failure risk as required by airworthiness regulations and military standards.The PDT method holds the view that there exist defects such as machining scratches and service cracks in the tenon-groove structures of aeroengine disks.However,it is challenging to conduct PDT assessment due to the scarcity of effective Probability of Detection(POD)model and anomaly distribution model.Through a series of Nondestructive Testing(NDT)experiments,the POD model of real cracks in tenon-groove structures is constructed for the first time by employing the Transfer Function Method(TFM).A novel anomaly distribution model is derived through the utilization of the POD model,instead of using the infeasible field data accumulation method.Subsequently,a framework for calculating the Probability of Failure(POF)of the tenon-groove structures is established,and the aforementioned two models exert a significant influence on the results of POF.
基金supported by the National Natural Science Foundation of China(Nos.51972198 and 61633015)the Natural Science Foundation of Shandong Province(No.ZR2020JQ19)+1 种基金Taishan Scholars Program of Shandong Province(No.ts20190908)Shenzhen Fundamental Research Program(No.JCYJ20190807093405503).
文摘Sodium-sulfur(Na-S)batteries are considered as a promising successor to the next-generation of high-capacity,low-cost and environmentally friendly sulfur-based battery systems.However,Na-S batteries still suffer from the“shuttle effect”and sluggish ion transport kinetics due to the dissolution of sodium polysulfides and poor conductivity of sulfur.MXenes,as 2D transition metal carbides/nitrides,have exhibited excellent conductivity,diverse structure and tunable surface groups,particularly playing a crucial role in inhibiting polysulfide shuttle and sodium dendrite growth.In this review,achievements and advancements of MXene-based Na-S batteries are discussed,including applications of a sulfur cathode,separator,interlayer between separator and cathode,and sodium anode.In the end,perspectives and challenges on the future development of MXene-based materials in Na-S batteries are proposed.
基金2024 Annual project of National Social Science Foundation“Research on Problem Identification and Governance Countermeasures of Minor Mental Health Network Support”(Project No.:24BXW044).
文摘Objective:This study aims to explore the experiences of social alienation among adolescents with depression,providing practical This study aims to explore the experiences of social alienation among adolescents with depression,providing practical guidance for improving their interpersonal relationships and facilitating their reintegration into society.Methods:This qualitative research was conducted following the conventional content analysis method.20 adolescents with depression were employed to select from June to August 2024 for face-to-face semi-structured interviews.The collected data were analyzed using Colaizzi's seven-step method.Results:Three themes and eight sub-themes were analyzed and identified:individual level(feelings of helplessness and powerlessness,cognitive distortion,avoidance and withdrawal),family level(lack of family awareness,family conflict),social level(limitations of academic stress and social circle,lack and degradation of skills,generalization of virtual reality,social“stigma”).Conclusion:Adolescents with depression experience complex social alienation.Healthcare providers should enhance their self-awareness and social adaptation skills,improve family dynamics,and provide a comprehensive range of services and services to help them to cope with the challenges of depression.Healthcare providers should enhance their self-awareness and social adaptation skills,improve family dynamics,strengthen communication,bolster family support systems,and collaborate to develop comprehensive social networks and psychological services.This will create a supportive social atmosphere to help adolescents gradually alleviate their feelings of social alienation.
文摘Driven by the goal of carbon neutrality,prefabricated buildings,as an important form of green construction,have become a key focus in the study of lifecycle carbon footprint management.Based on this,this paper starts from the perspective of carbon footprint and combines the digital and visual advantages of BIM technology to construct a green evaluation system for prefabricated buildings.It explores the carbon emissions in each stage of the building and proposes corresponding improvement measures,aiming to provide necessary references for the low-carbon transformation of prefabricated buildings.
基金Hunan Provincial Social Science Foundation“A Phenomenological Study on the Educational Life Experiences of Rural Young Teachers”(20YBA017)。
文摘Parental educational anxiety has become a social symptom in China,and rural primary school students’mothers exhibit unique educational anxieties due to their special living environment.Based on interviews with 10 rural primary school students’mothers,five typical educational anxiety experiences were selected for analysis,and themes such as rural life burden,children’s learning habits,mothers’educational expectations,mothers’educational methods,mothers’emotional state,deviation between reality and expectations,homework guidance ability,mothers’educational level,and attitudes towards children’s future development were refined.The root causes of educational anxiety among rural primary school students’mothers include the deviation between children’s actual performance and mothers’educational expectations,the sense of disparity under social comparison,physical and mental exhaustion caused by role overload,anxiety triggered by excessive economic burden,and a sense of powerlessness towards children’s educational outcomes.To alleviate the educational anxiety of rural primary school students’mothers,mothers should actively adjust themselves,fathers should actively participate in their children’s education,society should create a healthy atmosphere,and schools should strengthen family education guidance.
文摘We study the conditional entropy of topological dynamical systems using a family of metrics induced by probability bi-sequences.We present a Brin-Katok formula by replacing the mean metric by a family of metrics induced by a probability bi-sequence.We also establish the Katok’s entropy formula for conditional entropy for ergodic measures in the case of the new family of metrics.
基金Supported by the Research Project of Jiangsu Second Normal University"Research on the Construction and Application of Economics MOOC(Micro Course)from the Perspective of Ideological and Political Education JSSNUJXGG 2023YB08".
文摘The red cultural resources in rural areas bear the heavy historical and spiritual strength,and are the key rich ore and spiritual pillar in the field of education.This study discusses the connotation of red culture resources and the current situation of educating people,and then analyzes how to integrate interdisciplinary learning theory into red culture to enhance the value of educating people.On this basis,it proposes to explore the educational path of optimizing rural red cultural resources from an interdisciplinary perspective by integrating multi-disciplinary knowledge and red cultural resources.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFF1204803)the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20190736)+1 种基金the Fundamental Research Funds for the Central Universities(Grant No.NJ2024029)the National Natural Science Foundation of China(Grant Nos.81701346 and 62201265).
文摘The natural visibility graph method has been widely used in physiological signal analysis,but it fails to accurately handle signals with data points below the baseline.Such signals are common across various physiological measurements,including electroencephalograph(EEG)and functional magnetic resonance imaging(fMRI),and are crucial for insights into physiological phenomena.This study introduces a novel method,the baseline perspective visibility graph(BPVG),which can analyze time series by accurately capturing connectivity across data points both above and below the baseline.We present the BPVG construction process and validate its performance using simulated signals.Results demonstrate that BPVG accurately translates periodic,random,and fractal signals into regular,random,and scale-free networks respectively,exhibiting diverse degree distribution traits.Furthermore,we apply BPVG to classify Alzheimer’s disease(AD)patients from healthy controls using EEG data and identify non-demented adults at varying dementia risk using resting-state fMRI(rs-fMRI)data.Utilizing degree distribution entropy derived from BPVG networks,our results exceed the best accuracy benchmark(77.01%)in EEG analysis,especially at channels F4(78.46%)and O1(81.54%).Additionally,our rs-fMRI analysis achieves a statistically significant classification accuracy of 76.74%.These findings highlight the effectiveness of BPVG in distinguishing various time series types and its practical utility in EEG and rs-fMRI analysis for early AD detection and dementia risk assessment.In conclusion,BPVG’s validation across both simulated and real data confirms its capability to capture comprehensive information from time series,irrespective of baseline constraints,providing a novel method for studying neural physiological signals.
基金funded by the National Natural Science Foundation of China(Grant No.42101276)。
文摘Ecological network(EN)identification and optimization is an essential research tool for safeguarding regional ecological security patterns and planning territorial space.Especially for the ecologically fragile inland river basins,EN optimization is of significance in ensuring regional ecological security and virtuous cycle of ecosystems.In addition,EN is a dynamically changing structural system that is more applicable to the regional development by optimizing it from comprehensive future development perspective.EN of Shiyang River basin was constructed on account of the circuit theory,and land use/cover changes(LUCC)of the basin in 2035 was predicted by PLUS model,so as to explore the ecological conservation priorities and formulate optimization strategies.54 ecological sources(ESs)were identified,covering an area of 12,198 km^(2),mainly in the southern basin.133 ecological corridors(ECs)with an area of 3,176.92 km^(2)were extracted.38 ecological pinchpoints(EPs)and 22 ecological barriers(EBs)were identified respectively,which were mainly distributed in the lower basin.To effectively enhance the connectivity of EN in Minqin County,which has the worst ecological environment,we added five stepping stones based on the Ant Forest project.In addition,the optimal EPS is selected according to the development and limitation needs of inland river basins and the threat degree of warning points(WPs)under different scenarios.Scientific and reasonable optimization of future urban layout to prevent WPs can effectively alleviate the contradiction between ecological protection and economic development.The study is intended to provide basis for ecological sustainable development and rational planning territorial space in Shiyang River basin,as well as opinion for EN optimization in inland river basin.
基金The National Key Research and Development Program of China under contract No.2021YFC3101503the Hunan Provincial Natural Science Foundation of China under contract No.2023JJ10053+1 种基金the National Natural Science Foundation of China under contract Nos 42276205 and 42406195the Youth Independent Innovation Science Foundation under contract No.ZK24-54.
文摘The Argo program measures temperature and salinity in the upper ocean(0–2000 m).These observations are critical for weather/climate studies,ocean circulation analysis,and sea-level monitoring.To address the limitations of traditional thresholds in Argo data quality control(QC),this study proposes a novel probability distribution-based inference method(PDIM)for temperature-salinity threshold inference.By integrating historical observations with climatological data,the method utilizes historical data corresponding to latitude and longitude grids,calculates temperature/salinity frequency distributions for each depth,and determines“zero probability”boundaries through combined frequency distribution and climatology data.Then a probability distribution model is established to detect outliers automatically based on the features in the probability density function,which eliminates the traditional dependence on the normal distribution hypothesis.When applied to global Argo datasets from China Argo Real-time Data Center(CARDC),PDIM successfully identifies suspicious profiles and sensor drifts with high reliability,achieving a low false positive rate(0.55%for temperature,0.18%for salinity)while maintaining competitive true positive rate(28.29%for temperature,55.15%for salinity).This method is expected to improve the reliability of Argo data QC and has important significance for Argo QC.
基金supported by the Natural Science Foundation of Guangdong Province(Grant 2023A1515011667)Science and Technology Major Project of Shenzhen(Grant KJZD20230923114809020)Key Basic Research Foundation of Shenzhen(Grant JCYJ20220818100205012).
文摘Estimating probability density functions(PDFs)is critical in data analysis,particularly for complex multimodal distributions.traditional kernel density estimator(KDE)methods often face challenges in accurately capturing multimodal structures due to their uniform weighting scheme,leading to mode loss and degraded estimation accuracy.This paper presents the flexible kernel density estimator(F-KDE),a novel nonparametric approach designed to address these limitations.F-KDE introduces the concept of kernel unit inequivalence,assigning adaptive weights to each kernel unit,which better models local density variations in multimodal data.The method optimises an objective function that integrates estimation error and log-likelihood,using a particle swarm optimisation(PSO)algorithm that automatically determines optimal weights and bandwidths.Through extensive experiments on synthetic and real-world datasets,we demonstrated that(1)the weights and bandwidths in F-KDE stabilise as the optimisation algorithm iterates,(2)F-KDE effectively captures the multimodal characteristics and(3)F-KDE outperforms state-of-the-art density estimation methods regarding accuracy and robustness.The results confirm that F-KDE provides a valuable solution for accurately estimating multimodal PDFs.
基金supported by the National Social Science Foundation of China(Grant Nos.21BGL217 and 22CGL050)the Philosophy and Social Science Fund of Education Department of Jiangsu Province(Grant No.2020SJA2346).
文摘Vaccination is critical for controlling infectious diseases,but negative vaccination information can lead to vaccine hesitancy.To study how the interplay between information diffusion and disease transmission impacts vaccination and epidemic spread,we propose a novel two-layer multiplex network model that integrates an unaware-acceptant-negative-unaware(UANU)information diffusion model with a susceptible-vaccinated-exposed-infected-susceptible(SVEIS)epidemiological framework.This model includes individual exposure and vaccination statuses,time-varying forgetting probabilities,and information conversion thresholds.Through the microscopic Markov chain approach(MMCA),we derive dynamic transition equations and the epidemic threshold expression,validated by Monte Carlo simulations.Using MMCA equations,we predict vaccination densities and analyze parameter effects on vaccination,disease transmission,and the epidemic threshold.Our findings suggest that promoting positive information,curbing the spread of negative information,enhancing vaccine effectiveness,and promptly identifying asymptomatic carriers can significantly increase vaccination rates,reduce epidemic spread,and raise the epidemic threshold.
文摘The study aims to develop an empirical model to predict the rainfall intensity in Al-Diwaniyah City,Iraq,according to a statistical analysis based on probability and the specific rainfall return period.Rainfall data were collected daily for 25 years starting in 2000.Daily rainfall data were converted to rainfall intensity for five duration periods ranging from one to five hours.The extreme values were checked,and data that deviated from the group trend were removed for each period,and then arranged in descending order using the Weibull formula to calculate the probability.Statistically,the model performance with a return period of two years is considered good when compared with observed results and other methods such as Talbot and Sherman with a coefficient of determination(R2)>0.97 and Nash-Sutcliffe efficiency(NSE)>0.80.The results showed that a mathematical equation was obtained that describes the relationship between rainfall intensity,probability,and rainfall duration,which can be used for a confined return period with a 50% probability.Therefore,decision-makers can rely on the model to improve the performance of the city’s current drainage system during flood periods in the future.
