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 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
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 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
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
Decision-makers usually have an aspiration level,a target,or a benchmark they aim to achieve.This behavior can be rationalized within the expected utility framework,which incorporates the probability of success(achiev...Decision-makers usually have an aspiration level,a target,or a benchmark they aim to achieve.This behavior can be rationalized within the expected utility framework,which incorporates the probability of success(achieving the aspiration level)as an important aspect of decision-making.Motivated by these theories,this study defines the probability of success as the number of days a firm’s return outperformed its benchmark in the portfolio formation month.This study uses portfolio-level and firm-level analyses,revealing an economically substantial and statistically significant relationship between the probability of success and expected stock returns,even after controlling for common risk factors and various characteristics.Additional analyses support the behavioral theory of the firm,which posits that firms act to achieve short-term aspiration levels.展开更多
Atomically precise metal nanoclusters are an emerging type of nanomaterial which has diverse interfacial metal-ligand coordination motifs that can significantly affect their physicochemical properties and functionalit...Atomically precise metal nanoclusters are an emerging type of nanomaterial which has diverse interfacial metal-ligand coordination motifs that can significantly affect their physicochemical properties and functionalities.Among that,Cu nanoclusters have been gaining continuous increasing research attentions,thanks to the low cost,diversified structures,and superior catalytic performance for various reactions.In this review,we first summarize the recent progress regarding the synthetic methods of atomically precise Cu nanoclusters and the coordination modes between Cu and several typical ligands and then discuss the catalytic applications of these Cu nanoclusters with some explicit examples to explain the atomical-level structure-performance relationship.Finally,the current challenges and future research perspectives with some critical thoughts are elaborated.We hope this review can not only provide a whole picture of the current advances regarding the synthesis and catalytic applications of atomically precise Cu nanoclusters,but also points out some future research visions in this rapidly booming field.展开更多
The Chinese perspective is an open and evolving theoretical system.From a spatiotemporal viewpoint,it can be theoretically distilled into such four dimensions as the world dimension,the historical dimension,the practi...The Chinese perspective is an open and evolving theoretical system.From a spatiotemporal viewpoint,it can be theoretically distilled into such four dimensions as the world dimension,the historical dimension,the practical dimension,and the theoretical dimension,which collectively form a“unified framework of four”of logical construction.The“world”dimension represents the synchronic extension of human rights practices,outwardly touching on the shared values of all humanity guided by relational rationality and the vision of a community with a shared future for humanity.The“historical”dimension reflects the diachronic extension of China’s path of human rights development,encompassing the cultural subjectivity of Chinese civilization and the complex context of modern human rights endeavors.The“practical”dimension serves as the“meta-perspective”of contemporary Chinese perspective on human rights,where the leadership of the Communist Party of China and the people-centered approach constitute the fundamental stance for developing the perspectives of human rights and human rights governance.The“theoretical”dimension focuses on the deconstruction and reconstruction of indigenous human rights notions,emphasizing a set of values that are confident,inclusive,equitable,shared,and forward-looking.The“world”dimension of“taking the world as a method”,provides a reference perspective for“taking China as a method”narrative centered on the“historical-practical-theoretical”framework,while the latter injects a human rights methodology grounded in Chinese wisdom into the former.By integrating these four dimensions,a more profound and comprehensive understanding of the value core and normative paradigm of contemporary Chinese perspective on human rights can be achieved.展开更多
The transverse incision with longitudinal ligation(TILL)procedure is a new method for treating circumferential prolapsed hemorrhoids.A study by Song et al found TILL to be better than the traditional Milligan-Morgan h...The transverse incision with longitudinal ligation(TILL)procedure is a new method for treating circumferential prolapsed hemorrhoids.A study by Song et al found TILL to be better than the traditional Milligan-Morgan hemorrhoidectomy for short-term results,showing less pain,quicker healing,and lower risk of anal stenosis.TILL reduces tissue tension and controls blood supply,allowing effective removal of diseased tissue while maintaining anal function and structure.However,the study's limitations,including its retrospective,single-center design,small sample size,and short follow-up,restrict the findings'generalizability and ability to assess long-term outcomes like recurrence.Larger,multicenter trials are needed for a thorough evaluation and wider clinical adoption of TILL.展开更多
Risk prediction has long been a cornerstone of surgical oncology,enabling surgeons to anticipate complications,tailor perioperative care,and improve outcomes.With the rise of artificial intelligence,machine learning(M...Risk prediction has long been a cornerstone of surgical oncology,enabling surgeons to anticipate complications,tailor perioperative care,and improve outcomes.With the rise of artificial intelligence,machine learning(ML)models are increasingly being applied to predict outcomes,highlighting the growing significance of data-driven methods for clinical decision-making.Currently,frequentist approaches dominate prediction models,including most ML algorithms;these rely exclusively on observed datasets and risk overlooking the cumulative value of prior clinical knowledge.In contrast,Bayesian reasoning formally integrates existing evidence with new data.In this letter,we examine the strengths of frequentist-based prediction models,discuss how Bayesian methods may improve predictive accuracy,and argue that combining both approaches offers a promising path toward more robust,interpretable,and clinically useful prediction tools in surgery.This integration can yield robust,interpretable,and clinically relevant tools that advance personalized surgical care.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
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.
文摘Decision-makers usually have an aspiration level,a target,or a benchmark they aim to achieve.This behavior can be rationalized within the expected utility framework,which incorporates the probability of success(achieving the aspiration level)as an important aspect of decision-making.Motivated by these theories,this study defines the probability of success as the number of days a firm’s return outperformed its benchmark in the portfolio formation month.This study uses portfolio-level and firm-level analyses,revealing an economically substantial and statistically significant relationship between the probability of success and expected stock returns,even after controlling for common risk factors and various characteristics.Additional analyses support the behavioral theory of the firm,which posits that firms act to achieve short-term aspiration levels.
基金supported by the open funds of Key Laboratory of Functional Inorganic Material Chemistry (Heilongjiang University), Ministry of Education, Chinathe funding from Guangdong Natural Science Funds (No. 2023A0505050107)。
文摘Atomically precise metal nanoclusters are an emerging type of nanomaterial which has diverse interfacial metal-ligand coordination motifs that can significantly affect their physicochemical properties and functionalities.Among that,Cu nanoclusters have been gaining continuous increasing research attentions,thanks to the low cost,diversified structures,and superior catalytic performance for various reactions.In this review,we first summarize the recent progress regarding the synthetic methods of atomically precise Cu nanoclusters and the coordination modes between Cu and several typical ligands and then discuss the catalytic applications of these Cu nanoclusters with some explicit examples to explain the atomical-level structure-performance relationship.Finally,the current challenges and future research perspectives with some critical thoughts are elaborated.We hope this review can not only provide a whole picture of the current advances regarding the synthesis and catalytic applications of atomically precise Cu nanoclusters,but also points out some future research visions in this rapidly booming field.
基金This paper is a phased achievement of the Major Project of the Key Research Base for Humanities and Social Sciences of the Ministry of Education in 2024,titled“Theoretical Legal Studies in the Field of Human Rights”(Project Approval Number 24JJD820002).
文摘The Chinese perspective is an open and evolving theoretical system.From a spatiotemporal viewpoint,it can be theoretically distilled into such four dimensions as the world dimension,the historical dimension,the practical dimension,and the theoretical dimension,which collectively form a“unified framework of four”of logical construction.The“world”dimension represents the synchronic extension of human rights practices,outwardly touching on the shared values of all humanity guided by relational rationality and the vision of a community with a shared future for humanity.The“historical”dimension reflects the diachronic extension of China’s path of human rights development,encompassing the cultural subjectivity of Chinese civilization and the complex context of modern human rights endeavors.The“practical”dimension serves as the“meta-perspective”of contemporary Chinese perspective on human rights,where the leadership of the Communist Party of China and the people-centered approach constitute the fundamental stance for developing the perspectives of human rights and human rights governance.The“theoretical”dimension focuses on the deconstruction and reconstruction of indigenous human rights notions,emphasizing a set of values that are confident,inclusive,equitable,shared,and forward-looking.The“world”dimension of“taking the world as a method”,provides a reference perspective for“taking China as a method”narrative centered on the“historical-practical-theoretical”framework,while the latter injects a human rights methodology grounded in Chinese wisdom into the former.By integrating these four dimensions,a more profound and comprehensive understanding of the value core and normative paradigm of contemporary Chinese perspective on human rights can be achieved.
文摘The transverse incision with longitudinal ligation(TILL)procedure is a new method for treating circumferential prolapsed hemorrhoids.A study by Song et al found TILL to be better than the traditional Milligan-Morgan hemorrhoidectomy for short-term results,showing less pain,quicker healing,and lower risk of anal stenosis.TILL reduces tissue tension and controls blood supply,allowing effective removal of diseased tissue while maintaining anal function and structure.However,the study's limitations,including its retrospective,single-center design,small sample size,and short follow-up,restrict the findings'generalizability and ability to assess long-term outcomes like recurrence.Larger,multicenter trials are needed for a thorough evaluation and wider clinical adoption of TILL.
文摘Risk prediction has long been a cornerstone of surgical oncology,enabling surgeons to anticipate complications,tailor perioperative care,and improve outcomes.With the rise of artificial intelligence,machine learning(ML)models are increasingly being applied to predict outcomes,highlighting the growing significance of data-driven methods for clinical decision-making.Currently,frequentist approaches dominate prediction models,including most ML algorithms;these rely exclusively on observed datasets and risk overlooking the cumulative value of prior clinical knowledge.In contrast,Bayesian reasoning formally integrates existing evidence with new data.In this letter,we examine the strengths of frequentist-based prediction models,discuss how Bayesian methods may improve predictive accuracy,and argue that combining both approaches offers a promising path toward more robust,interpretable,and clinically useful prediction tools in surgery.This integration can yield robust,interpretable,and clinically relevant tools that advance personalized surgical care.
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.
