An effective energy management strategy(EMS)is essential to optimize the energy efficiency of electric vehicles(EVs).With the advent of advanced machine learning techniques,the focus on developing sophisticated EMS fo...An effective energy management strategy(EMS)is essential to optimize the energy efficiency of electric vehicles(EVs).With the advent of advanced machine learning techniques,the focus on developing sophisticated EMS for EVs is increasing.Here,we introduce LearningEMS:a unified framework and open-source benchmark designed to facilitate rapid development and assessment of EMS.LearningEMS is distinguished by its ability to support a variety of EV configurations,including hybrid EVs,fuel cell EVs,and plug-in EVs,offering a general platform for the development of EMS.The framework enables detailed comparisons of several EMS algorithms,encompassing imitation learning,deep reinforcement learning(RL),offline RL,model predictive control,and dynamic programming.We rigorously evaluated these algorithms across multiple perspectives:energy efficiency,consistency,adaptability,and practicability.Furthermore,we discuss state,reward,and action settings for RL in EV energy management,introduce a policy extraction and reconstruction method for learning-based EMS deployment,and conduct hardware-in-the-loop experiments.In summary,we offer a unified and comprehensive framework that comes with three distinct EV platforms,over 10000 km of EMS policy data set,ten state-of-the-art algorithms,and over 160 benchmark tasks,along with three learning libraries.Its flexible design allows easy expansion for additional tasks and applications.The open-source algorithms,models,data sets,and deployment processes foster additional research and innovation in EV and broader engineering domains.展开更多
With the continuous advancement of unmanned technology in various application domains,the development and deployment of blind-spot-free panoramic video systems have gained increasing importance.Such systems are partic...With the continuous advancement of unmanned technology in various application domains,the development and deployment of blind-spot-free panoramic video systems have gained increasing importance.Such systems are particularly critical in battlefield environments,where advanced panoramic video processing and wireless communication technologies are essential to enable remote control and autonomous operation of unmanned ground vehicles(UGVs).However,conventional video surveillance systems suffer from several limitations,including limited field of view,high processing latency,low reliability,excessive resource consumption,and significant transmission delays.These shortcomings impede the widespread adoption of UGVs in battlefield settings.To overcome these challenges,this paper proposes a novel multi-channel video capture and stitching system designed for real-time video processing.The system integrates the Speeded-Up Robust Features(SURF)algorithm and the Fast Library for Approximate Nearest Neighbors(FLANN)algorithm to execute essential operations such as feature detection,descriptor computation,image matching,homography estimation,and seamless image fusion.The fused panoramic video is then encoded and assembled to produce a seamless output devoid of stitching artifacts and shadows.Furthermore,H.264 video compression is employed to reduce the data size of the video stream without sacrificing visual quality.Using the Real-Time Streaming Protocol(RTSP),the compressed stream is transmitted efficiently,supporting real-time remote monitoring and control of UGVs in dynamic battlefield environments.Experimental results indicate that the proposed system achieves high stability,flexibility,and low latency.With a wireless link latency of 30 ms,the end-to-end video transmission latency remains around 140 ms,enabling smooth video communication.The system can tolerate packet loss rates(PLR)of up to 20%while maintaining usable video quality(with latency around 200 ms).These properties make it well-suited for mobile communication scenarios demanding high real-time video performance.展开更多
Scalable simulation leveraging real-world data plays an essential role in advancing autonomous driving,owing to its efficiency and applicability in both training and evaluating algorithms.Consequently,there has been i...Scalable simulation leveraging real-world data plays an essential role in advancing autonomous driving,owing to its efficiency and applicability in both training and evaluating algorithms.Consequently,there has been increasing attention on generating highly realistic and consistent driving videos,particularly those involving viewpoint changes guided by the control commands or trajectories of ego vehicles.However,current reconstruction approaches,such as Neural Radiance Fields and 3D Gaussian Splatting,frequently suffer from limited generalization and depend on substantial input data.Meanwhile,2D generative models,though capable of producing unknown scenes,still have room for improvement in terms of coherence and visual realism.To overcome these challenges,we introduce GenScene,a world model that synthesizes front-view driving videos conditioned on trajectories.A new temporal module is presented to improve video consistency by extracting the global context of each frame,calculating relationships of frames using these global representations,and fusing frame contexts accordingly.Moreover,we propose an innovative attention mechanism that computes relations of pixels within each frame and pixels in the corresponding window range of the initial frame.Extensive experiments show that our approach surpasses various state-of-the-art models in driving video generation,and the introduced modules contribute significantly to model performance.This work establishes a new paradigm for goal-oriented video synthesis in autonomous driving,which facilitates on-demand simulation to expedite algorithm development.展开更多
Background:This study aims to investigate the underlying mechanisms between parental marital conflict and adolescent short video dependence by constructing a chain mediation model,focusing on the mediating roles of ex...Background:This study aims to investigate the underlying mechanisms between parental marital conflict and adolescent short video dependence by constructing a chain mediation model,focusing on the mediating roles of experiential avoidance and emotional disturbance(anxiety,depression,and stress).Methods:Conducted in January 2025,the research recruited 4125 adolescents from multiple Chinese provinces through convenience sampling;after data cleaning,3957 valid participants(1959 males,1998 females)were included.Using a cross-sectional design,measures included parental marital conflict,experiential avoidance,anxiety,depression,stress,and short video dependence.Results:Pearson correlation analysis revealed significant positive correlations among all variables.Mediation analysis using the SPSS PROCESS macro showed that parental marital conflict directly predicted short video dependence(β=0.269,p<0.001),and also significantly predicted experiential avoidance(β=0.519,p<0.001),anxiety(β=0.072,p<0.001),depression(β=0.067,p<0.001),and stress(β=0.048,p<0.05).Experiential avoidance further predicted anxiety(β=0.521,p<0.001),depression(β=0.489,p<0.001),stress(β=0.408,p<0.001),and short video dependence(β=0.244,p<0.001).While both anxiety(β=0.050,p<0.05)and depression(β=0.116,p<0.001)positively predicted short video dependence,stress did not(β=0.019,p=0.257).Overall,experiential avoidance,anxiety,depression,and stress significantly mediated the relationship between parental marital conflict and short video dependence.Conclusion:These findings confirm that parental marital conflict not only directly influences adolescent short video dependence but also operates through a chain mediation pathway involving experiential avoidance and emotional disturbance,highlighting central psychological mechanisms and providing theoretical support for integrated mental health and behavioral interventions.展开更多
Background:In the Chinese context,the impact of short video applications on the psychological well-being of older adults is contested.While often examined through a pathological lens of addiction,this perspective may ...Background:In the Chinese context,the impact of short video applications on the psychological well-being of older adults is contested.While often examined through a pathological lens of addiction,this perspective may overlook paradoxical,context-dependent positive outcomes.Therefore,the main objective of this study is to challenge the traditional Compensatory Internet Use Theory by proposing and testing a chained mediation model that explores a paradoxical pathway from social support to life satisfaction via problematic social media use.Methods:Data were collected between July and August 2025 via the Credamo online survey platform,yielding 384 valid responses from Chinese older adults aged 60 and above.Key constructs were assessed using the Social Support Rating Scale(SSRS),Bergen Social Media Addiction Scale(BSMAS),Simplified UCLA Loneliness Scale,and Satisfaction with Life Scale(SWLS).A chained mediation model was tested using stepwise regression and non-parametric bootstrapping(5000 resamples),controlling for age,gender,household income,and health status.Results:The analysis revealed a paradoxical pathway,which was clarified by a key statistical suppression effect.Social support significantly and positively predicted problematic usage(β=0.157,p=0.002).After controlling for the suppressor effect of social support,problematic usage in turn negatively predicted social connectedness(β=−0.177,p<0.001).Finally,reduced social connectedness—reflecting a state of solitude—positively predicted life satisfaction(β=−0.227,p<0.001).Conclusion:The findings suggest that for older adults with sufficient offline social support,these resources may serve a“social empowerment”function.This empowerment allows behaviors measured as“problematic usage”to be theoretically reframed as a form of“deep immersive entertainment”.This immersion appears to occur alongside a state of“high-quality solitude”,which ultimately is associated with higher life satisfaction.This study provides a novel,non-pathological theoretical perspective on the consequences of high engagement with emerging social media,offering empirical grounds for non-abstinence-based intervention strategies.展开更多
We present a high performance modularly-built open-source software-OpenIFEM.OpenIFEM is a C++implementation of the modified immersed finite element method(mIFEM)to solve fluid-structure interaction(FSI)problems.This s...We present a high performance modularly-built open-source software-OpenIFEM.OpenIFEM is a C++implementation of the modified immersed finite element method(mIFEM)to solve fluid-structure interaction(FSI)problems.This software is modularly built to perform multiple tasks including fluid dynamics(incompressible and slightly compressible fluid models),linear and nonlinear solid mechanics,and fully coupled fluid-structure interactions.Most of open-source software packages are restricted to certain discretization methods;some are under-tested,under-documented,and lack modularity as well as extensibility.OpenIFEM is designed and built to include a set of generic classes for users to adapt so that any fluid and solid solvers can be coupled through the FSI algorithm.In addition,the package utilizes well-developed and tested libraries.It also comes with standard test cases that serve as software and algorithm validation.The software can be built on cross-platform,i.e.,Linux,Windows,and Mac OS,using CMake.Efficient parallelization is also implemented for high-performance computing for large-sized problems.OpenIFEM is documented using Doxygen and publicly available to download on GitHub.It is expected to benefit the future development of FSI algorithms and be applied to a variety of FSI applications.展开更多
Topology optimization(TO),a numerical technique to find the optimalmaterial layoutwith a given design domain,has attracted interest from researchers in the field of structural optimization in recent years.For beginner...Topology optimization(TO),a numerical technique to find the optimalmaterial layoutwith a given design domain,has attracted interest from researchers in the field of structural optimization in recent years.For beginners,opensource codes are undoubtedly the best alternative to learning TO,which can elaborate the implementation of a method in detail and easily engage more people to employ and extend the method.In this paper,we present a summary of various open-source codes and related literature on TO methods,including solid isotropic material with penalization(SIMP),evolutionary method,level set method(LSM),moving morphable components/voids(MMC/MMV)methods,multiscale topology optimization method,etc.Simultaneously,we classify the codes into five levels,fromeasy to difficult,depending on their difficulty,so that beginners can get started and understand the form of code implementation more quickly.展开更多
With the rapid development of Open-Source(OS),more and more software projects are maintained and developed in the form of OS.These Open-Source projects depend on and influence each other,gradually forming a huge OS pr...With the rapid development of Open-Source(OS),more and more software projects are maintained and developed in the form of OS.These Open-Source projects depend on and influence each other,gradually forming a huge OS project network,namely an Open-Source Software ECOsystem(OSSECO).Unfortunately,not all OS projects in the open-source ecosystem can be healthy and stable in the long term,and more projects will go from active to inactive and gradually die.In a tightly connected ecosystem,the death of one project can potentially cause the collapse of the entire ecosystem network.How can we effectively prevent such situations from happening?In this paper,we first identify the basic project characteristics that affect the survival of OS projects at both project and ecosystem levels through the proportional hazards model.Then,we utilize graph convolutional networks based on the ecosystem network to extract the ecosystem environment characteristics of OS projects.Finally,we fuse basic project characteristics and environmental project characteristics and construct a Hybrid Structured Prediction Model(HSPM)to predict the OS project survival state.The experimental results show that HSPM significantly improved compared to the traditional prediction model.Our work can substantially assist OS project managers in maintaining their projects’health.It can also provide an essential reference for developers when choosing the right open-source project for their production activities.展开更多
Scientific research requires the collection of data in order to study, monitor, analyze, describe, or understand a particular process or event. Data collection efforts are often a compromise: manual measurements can b...Scientific research requires the collection of data in order to study, monitor, analyze, describe, or understand a particular process or event. Data collection efforts are often a compromise: manual measurements can be time-consuming and labor-intensive, resulting in data being collected at a low frequency, while automating the data-collection process can reduce labor requirements and increase the frequency of measurements, but at the cost of added expense of electronic data-collecting instrumentation. Rapid advances in electronic technologies have resulted in a variety of new and inexpensive sensing, monitoring, and control capabilities which offer opportunities for implementation in agricultural and natural-resource research applications. An Open Source Hardware project called Arduino consists of a programmable microcontroller development platform, expansion capability through add-on boards, and a programming development environment for creating custom microcontroller software. All circuit-board and electronic component specifications, as well as the programming software, are open-source and freely available for anyone to use or modify. Inexpensive sensors and the Arduino development platform were used to develop several inexpensive, automated sensing and datalogging systems for use in agricultural and natural-resources related research projects. Systems were developed and implemented to monitor soil-moisture status of field crops for irrigation scheduling and crop-water use studies, to measure daily evaporation-pan water levels for quantifying evaporative demand, and to monitor environmental parameters under forested conditions. These studies demonstrate the usefulness of automated measurements, and offer guidance for other researchers in developing inexpensive sensing and monitoring systems to further their research.展开更多
Resource-scarce regions with serious COVID-19 outbreaks do not have enough ventilators to support critically ill patients,and these shortages are especially devastating in developing countries.To help alleviate this s...Resource-scarce regions with serious COVID-19 outbreaks do not have enough ventilators to support critically ill patients,and these shortages are especially devastating in developing countries.To help alleviate this strain,we have designed and tested the accessible low-barrier in vivo-validated economical ventilator(ALIVE Vent),a COVID-19-inspired,cost-effective,open-source,in vivo-validated solution made from commercially available components.The ALIVE Vent operates using compressed oxygen and air to drive inspiration,while two solenoid valves ensure one-way flow and precise cycle timing.The device was functionally tested and profiled using a variable resistance and compliance artificial lung and validated in anesthetized large animals.Our functional test results revealed its effective operation under a wide variety of ventilation conditions defined by the American Association of Respiratory Care guidelines for ventilator stockpiling.The large animal test showed that our ventilator performed similarly if not better than a standard ventilator in maintaining optimal ventilation status.The FiO2,respiratory rate,inspiratory to expiratory time ratio,positive-end expiratory pressure,and peak inspiratory pressure were successfully maintained within normal,clinically validated ranges,and the animals were recovered without any complications.In regions with limited access to ventilators,the ALIVE Vent can help alleviate shortages,and we have ensured that all used materials are publicly available.While this pandemic has elucidated enormous global inequalities in healthcare,innovative,cost-effective solutions aimed at reducing socio-economic barriers,such as the ALIVE Vent,can help enable access to prompt healthcare and life saving technology on a global scale and beyond COVID-19.展开更多
In today’s society with advanced Internet,the amount of information increases dramatically with each passing day,which leads to increasingly complex processes of open-source intelligence.Therefore,it is more importan...In today’s society with advanced Internet,the amount of information increases dramatically with each passing day,which leads to increasingly complex processes of open-source intelligence.Therefore,it is more important to rationalize the operation mode and improve the operation efficiency of open-source intelligence under the premise of satisfying users’needs.This paper focuses on the simulation study of the process system of opensource intelligence from the user’s perspective.First,the basic concept and development status of open-source intelligence are introduced in details.Second,six existing intelligence operation process models are summarized and their advantages and disadvantages are compared in focus.Based on users’preference,the open-source intelligence system simulation theory model is constructed from four aspects:intelligence collection,intelligence processing,intelligence analysis,and intelligence delivery.Meanwhile,the dynamics model of the open-source intelligence process system is constructed based on the open-source intelligence system simulation theoretical model,which specifically includes five parts:determination of system boundary,construction of causal loop diagram,construction of stock flow diagram,writing ofmathematical equations,and system sensitivity test.Finally,the system simulation results were analyzed.It was found that improving the system of intelligence agencies,opening up government affairs,improving the professional level of intelligence personnel,strengthening the communication and cooperation among personnel of various intelligence departments,and expressing intelligence products through diverse forms can effectively improve the operational efficiency of the open-source intelligence process system.展开更多
Surface albedo is defined as the ratio of incident and reflected solar irradiance and describes the ability of a surface to reflect, rather than absorb incident solar shortwave radiation. It is thus a crucial paramete...Surface albedo is defined as the ratio of incident and reflected solar irradiance and describes the ability of a surface to reflect, rather than absorb incident solar shortwave radiation. It is thus a crucial parameter in the climate system, particularly in the polar oceans. Sea ice albedo is a main driver for light transmission into the polar oceans and thus has a high impact on ocean warming, ice melting and marine primary production. During spring and summer, sea ice albedo can exhibit a significant spatial variability caused by meltwater accumulations on the ice. While complex and expensive solutions for albedo measurements are already available, we want to present a simple open-source design that allows for affordable mapping of spatially varying surface albedo on sea ice and beyond. Our solution is based on off-the-shelf components, such as an Arduino microcontroller integrating affordable light sensors, a GPS unit, data recording on memory card and data display into a simple field strengthened unit. We provide example data from two Arctic field deployments showing the capabilities and limitations of this system.展开更多
Apnoea,a major sleep disorder,affects many adults and causes several issues,such as fatigue,high blood pressure,liver conditions,increased risk of type II diabetes,and heart problems.Therefore,advanced monitoring and ...Apnoea,a major sleep disorder,affects many adults and causes several issues,such as fatigue,high blood pressure,liver conditions,increased risk of type II diabetes,and heart problems.Therefore,advanced monitoring and diagnosing tools of apnoea disorders are needed to facilitate better treatment,with advantages such as accuracy,comfort of use,cost effectiveness,and embedded computation capabilities to recognise,store,process,and transmit time series data.In this work we present an adaptation of our apnoea-Pi open-source surface acoustic wave(SAW)platform(Apnoea-Pi)to monitor and recognise apnoea in patients.The platform is based on a thin-film SAW device using bimorph ZnO and Al structures,including those fabricated as Al foils or plates,to achieve breath tracking based on humidity and temperature changes.We applied open-source electronics and provided embedded computing characteristics for signal processing,data recognition,storage,and transmission of breath signals.We show that the thin-film SAW device out-performed standard and off-the-shelf capacitive electronic sensors in terms of their response and accuracy for human breath-tracking purposes.This in combination with embedded electronics makes a suitable platform for human breath monitoring and sleep disorder recognition.展开更多
There is a growing need for web-based geographic information systems for easy and fast dissemination, sharing, displaying and processing of spatial information. The tremendous growth in the use of web and open-source ...There is a growing need for web-based geographic information systems for easy and fast dissemination, sharing, displaying and processing of spatial information. The tremendous growth in the use of web and open-source geospatial resources has sparked development of web-based spatial applications to address multidisciplinary issues with spatial dimensions. This paper presents the integration of open-source geospatial tools and web technology to visualize and interact with spatial data using web browser. The goal of this paper is to implement a prototype system for web-based mapping by providing step-by-step instructions in order to encourage the eager developers and interested readers to publish their maps on the web with no prior technical experience in map servers. The implementation of mapping prototype shows the utilization of open-source geospatial tools which results in a rapid implementation with minimal or no software input cost.展开更多
With the rise of open-source software,the social development paradigm occupies an indispensable position in the current software development process.This paper puts forward a variant of the PageRank algorithm to build...With the rise of open-source software,the social development paradigm occupies an indispensable position in the current software development process.This paper puts forward a variant of the PageRank algorithm to build the importance assessment model,which provides quantifiable importance assessment metrics for new Java projects based on Java open-source projects or components.The critical point of the model is to use crawlers to obtain relevant information about Java open-source projects in the GitHub open-source community to build a domain knowledge graph.According to the three dimensions of the Java open-source project’s project influence,project activity and project popularity,the project is measured.A modified PageRank algorithm is proposed to construct the importance evaluation model.Thereby providing quantifiable importance evaluation indicators for new Java projects based on or components of Java open-source projects.This article evaluates the importance of 4512 Java open-source projects obtained on GitHub and has a good effect.展开更多
The use of open-source data and tools in disaster exposure mapping is presented in this paper. Disaster exposure is a collection of the element at risk to potential loss. Gampaha divisional secretariat (DS) is a study...The use of open-source data and tools in disaster exposure mapping is presented in this paper. Disaster exposure is a collection of the element at risk to potential loss. Gampaha divisional secretariat (DS) is a study area laid on the lower part of the Attanagalu Oya river basin. As the geospatial tools, OpenStreetMap (OSM), Java OpenStreetMap (JOSM), QGIS, GPS Essentials, and Open Map Kit (OMK) are used. The elements of disaster exposure, including the number of people or types of assets, are surveyed and inventoried using the OSM platforms. Local, national, and international agencies produce and evaluate the data. The study developed spatial data for building footprints of 165,000 households, street lengths of 2300 km, hospital units of 16, and utility units of 2300. This could overcome the main challenges of exposure mapping in the area. The procedure developed in the exposure mapping can be used in a data-sparse environment. Exposure mapping is generally used to estimate the impact of hazards or disasters, which are essential in effective disaster management. How are there still remaining challenges in disaster exposure mapping such as less awareness about the mapping procedure, lack of government support, internet access, hardware, and inability to understand the value of exposure mapping?展开更多
Open-wheeled race car aerodynamics is unquestionably challenging insofar as it involves many physical phenomena,such as slender and blunt body aerodynamics,ground effect,vortex management and interaction between diffe...Open-wheeled race car aerodynamics is unquestionably challenging insofar as it involves many physical phenomena,such as slender and blunt body aerodynamics,ground effect,vortex management and interaction between different sophisticated aero devices.In the current work,a 2017 F1 car aerodynamics has been investigated from a numerical point of view by using an open-source code.The vehicle project was developed by PERRINN(Copyright.2011—Present PERRINN),an engineering community founded by Nicolas Perrin in 2011.The racing car performance is quantitatively evaluated in terms of drag,downforce,efficiency and front balance.The goals of the present CFD(computational fluid dynamics)-based research are the following:analyzing the capabilities of the open-source software OpenFOAM in dealing with complex meshes and external aerodynamics calculation,and developing a reliable workflow from CAD(computer aided design)model to the post-processing of the results,in order to meet production demands.展开更多
The rapid development of short video platforms poses new challenges for traditional recommendation systems.Recommender systems typically depend on two types of user behavior feedback to construct user interest profile...The rapid development of short video platforms poses new challenges for traditional recommendation systems.Recommender systems typically depend on two types of user behavior feedback to construct user interest profiles:explicit feedback(interactive behavior),which significantly influences users’short-term interests,and implicit feedback(viewing time),which substantially affects their long-term interests.However,the previous model fails to distinguish between these two feedback methods,leading it to predict only the overall preferences of users based on extensive historical behavior sequences.Consequently,it cannot differentiate between users’long-term and shortterm interests,resulting in low accuracy in describing users’interest states and predicting the evolution of their interests.This paper introduces a video recommendationmodel calledCAT-MFRec(CrossAttention Transformer-Mixed Feedback Recommendation)designed to differentiate between explicit and implicit user feedback within the DIEN(Deep Interest Evolution Network)framework.This study emphasizes the separate learning of the two types of behavioral feedback,effectively integrating them through the cross-attention mechanism.Additionally,it leverages the long sequence dependence capabilities of Transformer technology to accurately construct user interest profiles and predict the evolution of user interests.Experimental results indicate that CAT-MF Rec significantly outperforms existing recommendation methods across various performance indicators.This advancement offers new theoretical and practical insights for the development of video recommendations,particularly in addressing complex and dynamic user behavior patterns.展开更多
Internal learning-based video inpainting methods have shown promising results by exploiting the intrinsic properties of the video to fill in the missing region without external dataset supervision.However,existing int...Internal learning-based video inpainting methods have shown promising results by exploiting the intrinsic properties of the video to fill in the missing region without external dataset supervision.However,existing internal learning-based video inpainting methods would produce inconsistent structures or blurry textures due to the insufficient utilisation of motion priors within the video sequence.In this paper,the authors propose a new internal learning-based video inpainting model called appearance consistency and motion coherence network(ACMC-Net),which can not only learn the recurrence of appearance prior but can also capture motion coherence prior to improve the quality of the inpainting results.In ACMC-Net,a transformer-based appearance network is developed to capture global context information within the video frame for representing appearance consistency accurately.Additionally,a novel motion coherence learning scheme is proposed to learn the motion prior in a video sequence effectively.Finally,the learnt internal appearance consistency and motion coherence are implicitly propagated to the missing regions to achieve inpainting well.Extensive experiments conducted on the DAVIS dataset show that the proposed model obtains the superior performance in terms of quantitative measurements and produces more visually plausible results compared with the state-of-the-art methods.展开更多
基金supported in part by the National Natural Science Foundation of China(52172377).
文摘An effective energy management strategy(EMS)is essential to optimize the energy efficiency of electric vehicles(EVs).With the advent of advanced machine learning techniques,the focus on developing sophisticated EMS for EVs is increasing.Here,we introduce LearningEMS:a unified framework and open-source benchmark designed to facilitate rapid development and assessment of EMS.LearningEMS is distinguished by its ability to support a variety of EV configurations,including hybrid EVs,fuel cell EVs,and plug-in EVs,offering a general platform for the development of EMS.The framework enables detailed comparisons of several EMS algorithms,encompassing imitation learning,deep reinforcement learning(RL),offline RL,model predictive control,and dynamic programming.We rigorously evaluated these algorithms across multiple perspectives:energy efficiency,consistency,adaptability,and practicability.Furthermore,we discuss state,reward,and action settings for RL in EV energy management,introduce a policy extraction and reconstruction method for learning-based EMS deployment,and conduct hardware-in-the-loop experiments.In summary,we offer a unified and comprehensive framework that comes with three distinct EV platforms,over 10000 km of EMS policy data set,ten state-of-the-art algorithms,and over 160 benchmark tasks,along with three learning libraries.Its flexible design allows easy expansion for additional tasks and applications.The open-source algorithms,models,data sets,and deployment processes foster additional research and innovation in EV and broader engineering domains.
基金supported by the National Natural Science Foundation of China(Grant No.72334003)the National Key Research and Development Program of China(Grant No.2022YFB2702804)+1 种基金the Shandong Key Research and Development Program(Grant No.2020ZLYS09)the Jinan Program(Grant No.2021GXRC084-2).
文摘With the continuous advancement of unmanned technology in various application domains,the development and deployment of blind-spot-free panoramic video systems have gained increasing importance.Such systems are particularly critical in battlefield environments,where advanced panoramic video processing and wireless communication technologies are essential to enable remote control and autonomous operation of unmanned ground vehicles(UGVs).However,conventional video surveillance systems suffer from several limitations,including limited field of view,high processing latency,low reliability,excessive resource consumption,and significant transmission delays.These shortcomings impede the widespread adoption of UGVs in battlefield settings.To overcome these challenges,this paper proposes a novel multi-channel video capture and stitching system designed for real-time video processing.The system integrates the Speeded-Up Robust Features(SURF)algorithm and the Fast Library for Approximate Nearest Neighbors(FLANN)algorithm to execute essential operations such as feature detection,descriptor computation,image matching,homography estimation,and seamless image fusion.The fused panoramic video is then encoded and assembled to produce a seamless output devoid of stitching artifacts and shadows.Furthermore,H.264 video compression is employed to reduce the data size of the video stream without sacrificing visual quality.Using the Real-Time Streaming Protocol(RTSP),the compressed stream is transmitted efficiently,supporting real-time remote monitoring and control of UGVs in dynamic battlefield environments.Experimental results indicate that the proposed system achieves high stability,flexibility,and low latency.With a wireless link latency of 30 ms,the end-to-end video transmission latency remains around 140 ms,enabling smooth video communication.The system can tolerate packet loss rates(PLR)of up to 20%while maintaining usable video quality(with latency around 200 ms).These properties make it well-suited for mobile communication scenarios demanding high real-time video performance.
基金supported by the Cultivation Program for Major Scientific Research Projects of Harbin Institute of Technology(ZDXMPY20180109).
文摘Scalable simulation leveraging real-world data plays an essential role in advancing autonomous driving,owing to its efficiency and applicability in both training and evaluating algorithms.Consequently,there has been increasing attention on generating highly realistic and consistent driving videos,particularly those involving viewpoint changes guided by the control commands or trajectories of ego vehicles.However,current reconstruction approaches,such as Neural Radiance Fields and 3D Gaussian Splatting,frequently suffer from limited generalization and depend on substantial input data.Meanwhile,2D generative models,though capable of producing unknown scenes,still have room for improvement in terms of coherence and visual realism.To overcome these challenges,we introduce GenScene,a world model that synthesizes front-view driving videos conditioned on trajectories.A new temporal module is presented to improve video consistency by extracting the global context of each frame,calculating relationships of frames using these global representations,and fusing frame contexts accordingly.Moreover,we propose an innovative attention mechanism that computes relations of pixels within each frame and pixels in the corresponding window range of the initial frame.Extensive experiments show that our approach surpasses various state-of-the-art models in driving video generation,and the introduced modules contribute significantly to model performance.This work establishes a new paradigm for goal-oriented video synthesis in autonomous driving,which facilitates on-demand simulation to expedite algorithm development.
文摘Background:This study aims to investigate the underlying mechanisms between parental marital conflict and adolescent short video dependence by constructing a chain mediation model,focusing on the mediating roles of experiential avoidance and emotional disturbance(anxiety,depression,and stress).Methods:Conducted in January 2025,the research recruited 4125 adolescents from multiple Chinese provinces through convenience sampling;after data cleaning,3957 valid participants(1959 males,1998 females)were included.Using a cross-sectional design,measures included parental marital conflict,experiential avoidance,anxiety,depression,stress,and short video dependence.Results:Pearson correlation analysis revealed significant positive correlations among all variables.Mediation analysis using the SPSS PROCESS macro showed that parental marital conflict directly predicted short video dependence(β=0.269,p<0.001),and also significantly predicted experiential avoidance(β=0.519,p<0.001),anxiety(β=0.072,p<0.001),depression(β=0.067,p<0.001),and stress(β=0.048,p<0.05).Experiential avoidance further predicted anxiety(β=0.521,p<0.001),depression(β=0.489,p<0.001),stress(β=0.408,p<0.001),and short video dependence(β=0.244,p<0.001).While both anxiety(β=0.050,p<0.05)and depression(β=0.116,p<0.001)positively predicted short video dependence,stress did not(β=0.019,p=0.257).Overall,experiential avoidance,anxiety,depression,and stress significantly mediated the relationship between parental marital conflict and short video dependence.Conclusion:These findings confirm that parental marital conflict not only directly influences adolescent short video dependence but also operates through a chain mediation pathway involving experiential avoidance and emotional disturbance,highlighting central psychological mechanisms and providing theoretical support for integrated mental health and behavioral interventions.
基金funded by the Guangxi Philosophy and Social Science Research Project,grant number 24XWC002.
文摘Background:In the Chinese context,the impact of short video applications on the psychological well-being of older adults is contested.While often examined through a pathological lens of addiction,this perspective may overlook paradoxical,context-dependent positive outcomes.Therefore,the main objective of this study is to challenge the traditional Compensatory Internet Use Theory by proposing and testing a chained mediation model that explores a paradoxical pathway from social support to life satisfaction via problematic social media use.Methods:Data were collected between July and August 2025 via the Credamo online survey platform,yielding 384 valid responses from Chinese older adults aged 60 and above.Key constructs were assessed using the Social Support Rating Scale(SSRS),Bergen Social Media Addiction Scale(BSMAS),Simplified UCLA Loneliness Scale,and Satisfaction with Life Scale(SWLS).A chained mediation model was tested using stepwise regression and non-parametric bootstrapping(5000 resamples),controlling for age,gender,household income,and health status.Results:The analysis revealed a paradoxical pathway,which was clarified by a key statistical suppression effect.Social support significantly and positively predicted problematic usage(β=0.157,p=0.002).After controlling for the suppressor effect of social support,problematic usage in turn negatively predicted social connectedness(β=−0.177,p<0.001).Finally,reduced social connectedness—reflecting a state of solitude—positively predicted life satisfaction(β=−0.227,p<0.001).Conclusion:The findings suggest that for older adults with sufficient offline social support,these resources may serve a“social empowerment”function.This empowerment allows behaviors measured as“problematic usage”to be theoretically reframed as a form of“deep immersive entertainment”.This immersion appears to occur alongside a state of“high-quality solitude”,which ultimately is associated with higher life satisfaction.This study provides a novel,non-pathological theoretical perspective on the consequences of high engagement with emerging social media,offering empirical grounds for non-abstinence-based intervention strategies.
文摘We present a high performance modularly-built open-source software-OpenIFEM.OpenIFEM is a C++implementation of the modified immersed finite element method(mIFEM)to solve fluid-structure interaction(FSI)problems.This software is modularly built to perform multiple tasks including fluid dynamics(incompressible and slightly compressible fluid models),linear and nonlinear solid mechanics,and fully coupled fluid-structure interactions.Most of open-source software packages are restricted to certain discretization methods;some are under-tested,under-documented,and lack modularity as well as extensibility.OpenIFEM is designed and built to include a set of generic classes for users to adapt so that any fluid and solid solvers can be coupled through the FSI algorithm.In addition,the package utilizes well-developed and tested libraries.It also comes with standard test cases that serve as software and algorithm validation.The software can be built on cross-platform,i.e.,Linux,Windows,and Mac OS,using CMake.Efficient parallelization is also implemented for high-performance computing for large-sized problems.OpenIFEM is documented using Doxygen and publicly available to download on GitHub.It is expected to benefit the future development of FSI algorithms and be applied to a variety of FSI applications.
基金supported by the National Key R&D Program of China[Grant Number 2020YFB1708300]the National Natural Science Foundation of China[Grant Number 52075184].
文摘Topology optimization(TO),a numerical technique to find the optimalmaterial layoutwith a given design domain,has attracted interest from researchers in the field of structural optimization in recent years.For beginners,opensource codes are undoubtedly the best alternative to learning TO,which can elaborate the implementation of a method in detail and easily engage more people to employ and extend the method.In this paper,we present a summary of various open-source codes and related literature on TO methods,including solid isotropic material with penalization(SIMP),evolutionary method,level set method(LSM),moving morphable components/voids(MMC/MMV)methods,multiscale topology optimization method,etc.Simultaneously,we classify the codes into five levels,fromeasy to difficult,depending on their difficulty,so that beginners can get started and understand the form of code implementation more quickly.
基金This work was supported by the National Social Science Foundation(NSSF)Research on intelligent recommendation of multi-modal resources for children’s graded reading in smart library(22BTQ033)the Science and Technology Research and Development Program Project of China railway group limited(Project No.2021-Special-08).
文摘With the rapid development of Open-Source(OS),more and more software projects are maintained and developed in the form of OS.These Open-Source projects depend on and influence each other,gradually forming a huge OS project network,namely an Open-Source Software ECOsystem(OSSECO).Unfortunately,not all OS projects in the open-source ecosystem can be healthy and stable in the long term,and more projects will go from active to inactive and gradually die.In a tightly connected ecosystem,the death of one project can potentially cause the collapse of the entire ecosystem network.How can we effectively prevent such situations from happening?In this paper,we first identify the basic project characteristics that affect the survival of OS projects at both project and ecosystem levels through the proportional hazards model.Then,we utilize graph convolutional networks based on the ecosystem network to extract the ecosystem environment characteristics of OS projects.Finally,we fuse basic project characteristics and environmental project characteristics and construct a Hybrid Structured Prediction Model(HSPM)to predict the OS project survival state.The experimental results show that HSPM significantly improved compared to the traditional prediction model.Our work can substantially assist OS project managers in maintaining their projects’health.It can also provide an essential reference for developers when choosing the right open-source project for their production activities.
文摘Scientific research requires the collection of data in order to study, monitor, analyze, describe, or understand a particular process or event. Data collection efforts are often a compromise: manual measurements can be time-consuming and labor-intensive, resulting in data being collected at a low frequency, while automating the data-collection process can reduce labor requirements and increase the frequency of measurements, but at the cost of added expense of electronic data-collecting instrumentation. Rapid advances in electronic technologies have resulted in a variety of new and inexpensive sensing, monitoring, and control capabilities which offer opportunities for implementation in agricultural and natural-resource research applications. An Open Source Hardware project called Arduino consists of a programmable microcontroller development platform, expansion capability through add-on boards, and a programming development environment for creating custom microcontroller software. All circuit-board and electronic component specifications, as well as the programming software, are open-source and freely available for anyone to use or modify. Inexpensive sensors and the Arduino development platform were used to develop several inexpensive, automated sensing and datalogging systems for use in agricultural and natural-resources related research projects. Systems were developed and implemented to monitor soil-moisture status of field crops for irrigation scheduling and crop-water use studies, to measure daily evaporation-pan water levels for quantifying evaporative demand, and to monitor environmental parameters under forested conditions. These studies demonstrate the usefulness of automated measurements, and offer guidance for other researchers in developing inexpensive sensing and monitoring systems to further their research.
基金the National Institutes of Health(NIH R01 HL089315-01 and NIH R01 HL152155,YJW)the Thoracic Surgery Foundation Resident Research Fellowship(YZ)the National Science Foundation Graduate Research Fellowship Program(AMI).
文摘Resource-scarce regions with serious COVID-19 outbreaks do not have enough ventilators to support critically ill patients,and these shortages are especially devastating in developing countries.To help alleviate this strain,we have designed and tested the accessible low-barrier in vivo-validated economical ventilator(ALIVE Vent),a COVID-19-inspired,cost-effective,open-source,in vivo-validated solution made from commercially available components.The ALIVE Vent operates using compressed oxygen and air to drive inspiration,while two solenoid valves ensure one-way flow and precise cycle timing.The device was functionally tested and profiled using a variable resistance and compliance artificial lung and validated in anesthetized large animals.Our functional test results revealed its effective operation under a wide variety of ventilation conditions defined by the American Association of Respiratory Care guidelines for ventilator stockpiling.The large animal test showed that our ventilator performed similarly if not better than a standard ventilator in maintaining optimal ventilation status.The FiO2,respiratory rate,inspiratory to expiratory time ratio,positive-end expiratory pressure,and peak inspiratory pressure were successfully maintained within normal,clinically validated ranges,and the animals were recovered without any complications.In regions with limited access to ventilators,the ALIVE Vent can help alleviate shortages,and we have ensured that all used materials are publicly available.While this pandemic has elucidated enormous global inequalities in healthcare,innovative,cost-effective solutions aimed at reducing socio-economic barriers,such as the ALIVE Vent,can help enable access to prompt healthcare and life saving technology on a global scale and beyond COVID-19.
基金supported by the National Social Science Foundation of China under the project“Research on the mechanism of developing and utilizing domestic and foreign open-source intelligence under product-oriented thinking(20BTQ049)”.
文摘In today’s society with advanced Internet,the amount of information increases dramatically with each passing day,which leads to increasingly complex processes of open-source intelligence.Therefore,it is more important to rationalize the operation mode and improve the operation efficiency of open-source intelligence under the premise of satisfying users’needs.This paper focuses on the simulation study of the process system of opensource intelligence from the user’s perspective.First,the basic concept and development status of open-source intelligence are introduced in details.Second,six existing intelligence operation process models are summarized and their advantages and disadvantages are compared in focus.Based on users’preference,the open-source intelligence system simulation theory model is constructed from four aspects:intelligence collection,intelligence processing,intelligence analysis,and intelligence delivery.Meanwhile,the dynamics model of the open-source intelligence process system is constructed based on the open-source intelligence system simulation theoretical model,which specifically includes five parts:determination of system boundary,construction of causal loop diagram,construction of stock flow diagram,writing ofmathematical equations,and system sensitivity test.Finally,the system simulation results were analyzed.It was found that improving the system of intelligence agencies,opening up government affairs,improving the professional level of intelligence personnel,strengthening the communication and cooperation among personnel of various intelligence departments,and expressing intelligence products through diverse forms can effectively improve the operational efficiency of the open-source intelligence process system.
基金the Alfred-Wegener-Institut Helmholtz-Zentrum für Polar und Meeresforschungthe Helmholtz infrastructure initiative “Frontiers in Arctic marine Monitoring” (FRAM) for funding this projectfunded by a Sentinel North Postdoctoral Research Fellowship at Université Laval, Canada
文摘Surface albedo is defined as the ratio of incident and reflected solar irradiance and describes the ability of a surface to reflect, rather than absorb incident solar shortwave radiation. It is thus a crucial parameter in the climate system, particularly in the polar oceans. Sea ice albedo is a main driver for light transmission into the polar oceans and thus has a high impact on ocean warming, ice melting and marine primary production. During spring and summer, sea ice albedo can exhibit a significant spatial variability caused by meltwater accumulations on the ice. While complex and expensive solutions for albedo measurements are already available, we want to present a simple open-source design that allows for affordable mapping of spatially varying surface albedo on sea ice and beyond. Our solution is based on off-the-shelf components, such as an Arduino microcontroller integrating affordable light sensors, a GPS unit, data recording on memory card and data display into a simple field strengthened unit. We provide example data from two Arctic field deployments showing the capabilities and limitations of this system.
基金financially supported by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant EP/P018998/1the UK Fluidic Network Special Interest Group of Acoustofluidics (EP/N032861/1).
文摘Apnoea,a major sleep disorder,affects many adults and causes several issues,such as fatigue,high blood pressure,liver conditions,increased risk of type II diabetes,and heart problems.Therefore,advanced monitoring and diagnosing tools of apnoea disorders are needed to facilitate better treatment,with advantages such as accuracy,comfort of use,cost effectiveness,and embedded computation capabilities to recognise,store,process,and transmit time series data.In this work we present an adaptation of our apnoea-Pi open-source surface acoustic wave(SAW)platform(Apnoea-Pi)to monitor and recognise apnoea in patients.The platform is based on a thin-film SAW device using bimorph ZnO and Al structures,including those fabricated as Al foils or plates,to achieve breath tracking based on humidity and temperature changes.We applied open-source electronics and provided embedded computing characteristics for signal processing,data recognition,storage,and transmission of breath signals.We show that the thin-film SAW device out-performed standard and off-the-shelf capacitive electronic sensors in terms of their response and accuracy for human breath-tracking purposes.This in combination with embedded electronics makes a suitable platform for human breath monitoring and sleep disorder recognition.
文摘There is a growing need for web-based geographic information systems for easy and fast dissemination, sharing, displaying and processing of spatial information. The tremendous growth in the use of web and open-source geospatial resources has sparked development of web-based spatial applications to address multidisciplinary issues with spatial dimensions. This paper presents the integration of open-source geospatial tools and web technology to visualize and interact with spatial data using web browser. The goal of this paper is to implement a prototype system for web-based mapping by providing step-by-step instructions in order to encourage the eager developers and interested readers to publish their maps on the web with no prior technical experience in map servers. The implementation of mapping prototype shows the utilization of open-source geospatial tools which results in a rapid implementation with minimal or no software input cost.
基金This work has been supported by the National Science Foundation of China Grant No.61762092“Dynamic multi-objective requirement optimization based on transfer learning,”and the Open Foundation of the Key Laboratory in Software Engineering of Yunnan Province,Grant No.2017SE204+1 种基金“Research on extracting software feature models using transfer learning,”and the National Science Foundation of China Grant No.61762089“The key research of high order tensor decomposition in a distributed environment”.
文摘With the rise of open-source software,the social development paradigm occupies an indispensable position in the current software development process.This paper puts forward a variant of the PageRank algorithm to build the importance assessment model,which provides quantifiable importance assessment metrics for new Java projects based on Java open-source projects or components.The critical point of the model is to use crawlers to obtain relevant information about Java open-source projects in the GitHub open-source community to build a domain knowledge graph.According to the three dimensions of the Java open-source project’s project influence,project activity and project popularity,the project is measured.A modified PageRank algorithm is proposed to construct the importance evaluation model.Thereby providing quantifiable importance evaluation indicators for new Java projects based on or components of Java open-source projects.This article evaluates the importance of 4512 Java open-source projects obtained on GitHub and has a good effect.
文摘The use of open-source data and tools in disaster exposure mapping is presented in this paper. Disaster exposure is a collection of the element at risk to potential loss. Gampaha divisional secretariat (DS) is a study area laid on the lower part of the Attanagalu Oya river basin. As the geospatial tools, OpenStreetMap (OSM), Java OpenStreetMap (JOSM), QGIS, GPS Essentials, and Open Map Kit (OMK) are used. The elements of disaster exposure, including the number of people or types of assets, are surveyed and inventoried using the OSM platforms. Local, national, and international agencies produce and evaluate the data. The study developed spatial data for building footprints of 165,000 households, street lengths of 2300 km, hospital units of 16, and utility units of 2300. This could overcome the main challenges of exposure mapping in the area. The procedure developed in the exposure mapping can be used in a data-sparse environment. Exposure mapping is generally used to estimate the impact of hazards or disasters, which are essential in effective disaster management. How are there still remaining challenges in disaster exposure mapping such as less awareness about the mapping procedure, lack of government support, internet access, hardware, and inability to understand the value of exposure mapping?
文摘Open-wheeled race car aerodynamics is unquestionably challenging insofar as it involves many physical phenomena,such as slender and blunt body aerodynamics,ground effect,vortex management and interaction between different sophisticated aero devices.In the current work,a 2017 F1 car aerodynamics has been investigated from a numerical point of view by using an open-source code.The vehicle project was developed by PERRINN(Copyright.2011—Present PERRINN),an engineering community founded by Nicolas Perrin in 2011.The racing car performance is quantitatively evaluated in terms of drag,downforce,efficiency and front balance.The goals of the present CFD(computational fluid dynamics)-based research are the following:analyzing the capabilities of the open-source software OpenFOAM in dealing with complex meshes and external aerodynamics calculation,and developing a reliable workflow from CAD(computer aided design)model to the post-processing of the results,in order to meet production demands.
基金supported by National Natural Science Foundation of China(62072416)Key Research and Development Special Project of Henan Province(221111210500)Key TechnologiesR&DProgram of Henan rovince(232102211053,242102211071).
文摘The rapid development of short video platforms poses new challenges for traditional recommendation systems.Recommender systems typically depend on two types of user behavior feedback to construct user interest profiles:explicit feedback(interactive behavior),which significantly influences users’short-term interests,and implicit feedback(viewing time),which substantially affects their long-term interests.However,the previous model fails to distinguish between these two feedback methods,leading it to predict only the overall preferences of users based on extensive historical behavior sequences.Consequently,it cannot differentiate between users’long-term and shortterm interests,resulting in low accuracy in describing users’interest states and predicting the evolution of their interests.This paper introduces a video recommendationmodel calledCAT-MFRec(CrossAttention Transformer-Mixed Feedback Recommendation)designed to differentiate between explicit and implicit user feedback within the DIEN(Deep Interest Evolution Network)framework.This study emphasizes the separate learning of the two types of behavioral feedback,effectively integrating them through the cross-attention mechanism.Additionally,it leverages the long sequence dependence capabilities of Transformer technology to accurately construct user interest profiles and predict the evolution of user interests.Experimental results indicate that CAT-MF Rec significantly outperforms existing recommendation methods across various performance indicators.This advancement offers new theoretical and practical insights for the development of video recommendations,particularly in addressing complex and dynamic user behavior patterns.
基金Shenzhen Science and Technology Programme,Grant/Award Number:JCYJ202308071208000012023 Shenzhen sustainable supporting funds for colleges and universities,Grant/Award Number:20231121165240001Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology,Grant/Award Number:2024B1212010006。
文摘Internal learning-based video inpainting methods have shown promising results by exploiting the intrinsic properties of the video to fill in the missing region without external dataset supervision.However,existing internal learning-based video inpainting methods would produce inconsistent structures or blurry textures due to the insufficient utilisation of motion priors within the video sequence.In this paper,the authors propose a new internal learning-based video inpainting model called appearance consistency and motion coherence network(ACMC-Net),which can not only learn the recurrence of appearance prior but can also capture motion coherence prior to improve the quality of the inpainting results.In ACMC-Net,a transformer-based appearance network is developed to capture global context information within the video frame for representing appearance consistency accurately.Additionally,a novel motion coherence learning scheme is proposed to learn the motion prior in a video sequence effectively.Finally,the learnt internal appearance consistency and motion coherence are implicitly propagated to the missing regions to achieve inpainting well.Extensive experiments conducted on the DAVIS dataset show that the proposed model obtains the superior performance in terms of quantitative measurements and produces more visually plausible results compared with the state-of-the-art methods.
