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
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
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
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
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
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
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
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
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
Over the past decade, open-source software use has grown. Today, many companies including Google, Microsoft, Meta, RedHat, MongoDB, and Apache are major participants of open-source contributions. With the increased us...Over the past decade, open-source software use has grown. Today, many companies including Google, Microsoft, Meta, RedHat, MongoDB, and Apache are major participants of open-source contributions. With the increased use of open-source software or integration of open-source software into custom-developed software, the quality of this software component increases in importance. This study examined a sample of open-source applications from GitHub. Static software analytics were conducted, and each application was classified for its risk level. In the analyzed applications, it was found that 90% of the applications were classified as low risk or moderate low risk indicating a high level of quality for open-source applications.展开更多
The accelerated global adoption of electric vehicles(EVs)is driving significant expansion and increasing complexity within the EV charging infrastructure,consequently presenting novel and pressing cybersecurity challe...The accelerated global adoption of electric vehicles(EVs)is driving significant expansion and increasing complexity within the EV charging infrastructure,consequently presenting novel and pressing cybersecurity challenges.While considerable effort has focused on preventative cybersecurity measures,a critical deficiency persists in structured methodologies for digital forensic analysis following security incidents,a gap exacerbated by system heterogeneity,distributed digital evidence,and inconsistent logging practices which hinder effective incident reconstruction and attribution.This paper addresses this critical need by proposing a novel,data-driven forensic framework tailored to the EV charging infrastructure,focusing on the systematic identification,classification,and correlation of diverse digital evidence across its physical,network,and application layers.Our methodology integrates open-source intelligence(OSINT)with advanced system modeling based on a three-layer cyber-physical system architecture to comprehensively map potential evidentiary sources.Key contributions include a comprehensive taxonomy of cybersecurity threats pertinent to EV charging ecosystems,detailed mappings between these threats and the resultant digital evidence to guide targeted investigations,the formulation of adaptable forensic investigation workflows for various incident scenarios,and a critical analysis of significant gaps in digital evidence availability within current EV charging systems,highlighting limitations in forensic readiness.The practical application and utility of this method are demonstrated through illustrative case studies involving both empirically-derived and virtual incident scenarios.The proposed datadriven approach is designed to significantly enhance digital forensic capabilities,support more effective incident response,strengthen compliance with emerging cybersecurity regulations,and ultimately contribute to bolstering the overall security,resilience,and trustworthiness of this increasingly vital critical infrastructure.展开更多
Plagiarism in software code and hardware design threatens the open-source movement and the software and hardware industries.It is essential to differentiate between the unethical act of plagiarism and the legitimate u...Plagiarism in software code and hardware design threatens the open-source movement and the software and hardware industries.It is essential to differentiate between the unethical act of plagiarism and the legitimate use of open-source resources.Existing copyright protection measures,such as license design,inadequately address copyright ownership and protection issues.Furthermore,they fail to detect plagiarism methods for open-source hardware projects,such as circuit location modification.To address these challenges,this paper proposes a blockchain-based copyright management scheme,which introduces a general originality detection model based on community detection,extracting adjustable granularity digests from code and design files.These digests are stored on a peer-to-peer blockchain,enabling nodes to verify the originality via smart contracts.Additionally,the scheme improves the storage structure,protecting the rights of authors and contributors.Experimental results demonstrate the effectiveness and runtime efficiency of the proposed model in extracting digests for blockchain storage while maintaining verification accuracy.The scheme offers enhanced generality,practical performance,and suitability for distributed development and maintenance,with considerable implications for evidence gathering,fostering innovation and integrity.展开更多
REST(Rust-based electronic structure toolkit)is a modern open-source electronic structure code entirely written in Rust,combining high performance,memory safety,and expressive concurrency.As a community-driven project...REST(Rust-based electronic structure toolkit)is a modern open-source electronic structure code entirely written in Rust,combining high performance,memory safety,and expressive concurrency.As a community-driven project,its source code is freely available at https://gitee.com/restgroup,fostering open collaboration and transparent development.It supports a wide range of density functional methods-from local density approximation(LDA),generalized gradient approximation(GGA),meta-GGA,and hybrids to doubly hybrids,as well as machine learning-augmented functionals-enabling high-accuracy simulations with low computational overhead.Its“disk-free”RI-based(RI:resolution-of-the-identity)implementation and efficient shared-memory parallelism(via Rayon)ensure rapid calculations even for challenging systems.REST also of-fers unique user support through large language model-assisted input generation and develop-erfriendly tensor libraries for rapid algorithm prototyping.展开更多
Seismic phase pickers based on deep neural networks have been extensively used recently,demonstrating their advantages on both performance and efficiency.However,these pickers are trained with and applied to different...Seismic phase pickers based on deep neural networks have been extensively used recently,demonstrating their advantages on both performance and efficiency.However,these pickers are trained with and applied to different data.A comprehensive benchmark based on a single dataset is therefore lacking.Here,using the recently released DiTing dataset,we analyzed performances of seven phase pickers with different network structures,the efficiencies are also evaluated using both CPU and GPU devices.Evaluations based on F1-scores reveal that the recurrent neural network(RNN)and EQTransformer exhibit the best performance,likely owing to their large receptive fields.Similar performances are observed among PhaseNet(UNet),UNet++,and the lightweight phase picking network(LPPN).However,the LPPN models are the most efficient.The RNN and EQTransformer have similar speeds,which are slower than those of the LPPN and PhaseNet.UNet++requires the most computational effort among the pickers.As all of the pickers perform well after being trained with a large-scale dataset,users may choose the one suitable for their applications.For beginners,we provide a tutorial on training and validating the pickers using the DiTing dataset.We also provide two sets of models trained using datasets with both 50 Hz and 100 Hz sampling rates for direct application by end-users.All of our models are open-source and publicly accessible.展开更多
With the deep integration of software collaborative development and social networking, social coding represents a new style of software production and creation paradigm. Because of their good flexibility and openness,...With the deep integration of software collaborative development and social networking, social coding represents a new style of software production and creation paradigm. Because of their good flexibility and openness,a large number of external contributors have been attracted to the open-source communities. They are playing a significant role in open-source development. However, the open-source development online is a globalized and distributed cooperative work. If left unsupervised, the contribution process may result in inefficiency. It takes contributors a lot of time to find suitable projects or tasks from thousands of open-source projects in the communities to work on. In this paper, we propose a new approach called "RepoLike," to recommend repositories for developers based on linear combination and learning to rank. It uses the project popularity, technical dependencies among projects, and social connections among developers to measure the correlations between a developer and the given projects. Experimental results show that our approach can achieve over 25% of hit ratio when recommending 20 candidates, meaning that it can recommend closely correlated repositories to social developers.展开更多
In recent years,the widespread applications of open-source software(OSS)have brought great convenience for software developers.However,it is always facing unavoidable security risks,such as open-source code defects an...In recent years,the widespread applications of open-source software(OSS)have brought great convenience for software developers.However,it is always facing unavoidable security risks,such as open-source code defects and security vulnerabilities.To find out the OSS risks in time,we carry out an empirical study to identify the indicators for evaluating the OSS.To achieve a comprehensive understanding of the OSS assessment,we collect 56 papers from prestigious academic venues(such as IEEE Xplore,ACM Digital Library,DBLP,and Google Scholar)in the past 21 years.During the process of the investigation,we first identify the main concerns for selecting OSS and distill five types of commonly used indicators to assess OSS.We then conduct a comparative analysis to discuss how these indicators are used in each surveyed study and their differences.Moreover,we further undertake a correlation analysis between these indicators and uncover 13 confirmed conclusions and four cases with controversy occurring in these studies.Finally,we discuss several possible applications of these conclusions,which are insightful for the research on OSS and software supply chain.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.
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.
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.
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.
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Over the past decade, open-source software use has grown. Today, many companies including Google, Microsoft, Meta, RedHat, MongoDB, and Apache are major participants of open-source contributions. With the increased use of open-source software or integration of open-source software into custom-developed software, the quality of this software component increases in importance. This study examined a sample of open-source applications from GitHub. Static software analytics were conducted, and each application was classified for its risk level. In the analyzed applications, it was found that 90% of the applications were classified as low risk or moderate low risk indicating a high level of quality for open-source applications.
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(RS-2023-00242528,50%)supported by a grant from the Korea Electric Power Corporation(R24XO01-4,50%)for basic research and development projects starting in 2024.
文摘The accelerated global adoption of electric vehicles(EVs)is driving significant expansion and increasing complexity within the EV charging infrastructure,consequently presenting novel and pressing cybersecurity challenges.While considerable effort has focused on preventative cybersecurity measures,a critical deficiency persists in structured methodologies for digital forensic analysis following security incidents,a gap exacerbated by system heterogeneity,distributed digital evidence,and inconsistent logging practices which hinder effective incident reconstruction and attribution.This paper addresses this critical need by proposing a novel,data-driven forensic framework tailored to the EV charging infrastructure,focusing on the systematic identification,classification,and correlation of diverse digital evidence across its physical,network,and application layers.Our methodology integrates open-source intelligence(OSINT)with advanced system modeling based on a three-layer cyber-physical system architecture to comprehensively map potential evidentiary sources.Key contributions include a comprehensive taxonomy of cybersecurity threats pertinent to EV charging ecosystems,detailed mappings between these threats and the resultant digital evidence to guide targeted investigations,the formulation of adaptable forensic investigation workflows for various incident scenarios,and a critical analysis of significant gaps in digital evidence availability within current EV charging systems,highlighting limitations in forensic readiness.The practical application and utility of this method are demonstrated through illustrative case studies involving both empirically-derived and virtual incident scenarios.The proposed datadriven approach is designed to significantly enhance digital forensic capabilities,support more effective incident response,strengthen compliance with emerging cybersecurity regulations,and ultimately contribute to bolstering the overall security,resilience,and trustworthiness of this increasingly vital critical infrastructure.
文摘Plagiarism in software code and hardware design threatens the open-source movement and the software and hardware industries.It is essential to differentiate between the unethical act of plagiarism and the legitimate use of open-source resources.Existing copyright protection measures,such as license design,inadequately address copyright ownership and protection issues.Furthermore,they fail to detect plagiarism methods for open-source hardware projects,such as circuit location modification.To address these challenges,this paper proposes a blockchain-based copyright management scheme,which introduces a general originality detection model based on community detection,extracting adjustable granularity digests from code and design files.These digests are stored on a peer-to-peer blockchain,enabling nodes to verify the originality via smart contracts.Additionally,the scheme improves the storage structure,protecting the rights of authors and contributors.Experimental results demonstrate the effectiveness and runtime efficiency of the proposed model in extracting digests for blockchain storage while maintaining verification accuracy.The scheme offers enhanced generality,practical performance,and suitability for distributed development and maintenance,with considerable implications for evidence gathering,fostering innovation and integrity.
基金supported by the National Natural Science Foundation of China (Nos. 22125301, 22393911, 22393912, 22321003, 22233002)the Innovation Program for Quantum Science and Technology (2021ZD0303305)the robotic AI-Scientist platform of the Chinese Academy of Science。
文摘REST(Rust-based electronic structure toolkit)is a modern open-source electronic structure code entirely written in Rust,combining high performance,memory safety,and expressive concurrency.As a community-driven project,its source code is freely available at https://gitee.com/restgroup,fostering open collaboration and transparent development.It supports a wide range of density functional methods-from local density approximation(LDA),generalized gradient approximation(GGA),meta-GGA,and hybrids to doubly hybrids,as well as machine learning-augmented functionals-enabling high-accuracy simulations with low computational overhead.Its“disk-free”RI-based(RI:resolution-of-the-identity)implementation and efficient shared-memory parallelism(via Rayon)ensure rapid calculations even for challenging systems.REST also of-fers unique user support through large language model-assisted input generation and develop-erfriendly tensor libraries for rapid algorithm prototyping.
基金jointly supported by the National Natural Science Foundation of China (No. 42074060)the Special Fund, Institute of Geophysics, China Earthquake Administration (CEA-IGP) (Nos. DQJB19B29, DQJB20B15, and DQJB22Z01)supported by XingHuo Project, CEA (No. XH211103)
文摘Seismic phase pickers based on deep neural networks have been extensively used recently,demonstrating their advantages on both performance and efficiency.However,these pickers are trained with and applied to different data.A comprehensive benchmark based on a single dataset is therefore lacking.Here,using the recently released DiTing dataset,we analyzed performances of seven phase pickers with different network structures,the efficiencies are also evaluated using both CPU and GPU devices.Evaluations based on F1-scores reveal that the recurrent neural network(RNN)and EQTransformer exhibit the best performance,likely owing to their large receptive fields.Similar performances are observed among PhaseNet(UNet),UNet++,and the lightweight phase picking network(LPPN).However,the LPPN models are the most efficient.The RNN and EQTransformer have similar speeds,which are slower than those of the LPPN and PhaseNet.UNet++requires the most computational effort among the pickers.As all of the pickers perform well after being trained with a large-scale dataset,users may choose the one suitable for their applications.For beginners,we provide a tutorial on training and validating the pickers using the DiTing dataset.We also provide two sets of models trained using datasets with both 50 Hz and 100 Hz sampling rates for direct application by end-users.All of our models are open-source and publicly accessible.
基金Project supported by the National Natural Science Foundation of China(Nos.61432020,61472430,and 61502512)the National Key R&D Program of China(No.2016YFB1000805)
文摘With the deep integration of software collaborative development and social networking, social coding represents a new style of software production and creation paradigm. Because of their good flexibility and openness,a large number of external contributors have been attracted to the open-source communities. They are playing a significant role in open-source development. However, the open-source development online is a globalized and distributed cooperative work. If left unsupervised, the contribution process may result in inefficiency. It takes contributors a lot of time to find suitable projects or tasks from thousands of open-source projects in the communities to work on. In this paper, we propose a new approach called "RepoLike," to recommend repositories for developers based on linear combination and learning to rank. It uses the project popularity, technical dependencies among projects, and social connections among developers to measure the correlations between a developer and the given projects. Experimental results show that our approach can achieve over 25% of hit ratio when recommending 20 candidates, meaning that it can recommend closely correlated repositories to social developers.
文摘In recent years,the widespread applications of open-source software(OSS)have brought great convenience for software developers.However,it is always facing unavoidable security risks,such as open-source code defects and security vulnerabilities.To find out the OSS risks in time,we carry out an empirical study to identify the indicators for evaluating the OSS.To achieve a comprehensive understanding of the OSS assessment,we collect 56 papers from prestigious academic venues(such as IEEE Xplore,ACM Digital Library,DBLP,and Google Scholar)in the past 21 years.During the process of the investigation,we first identify the main concerns for selecting OSS and distill five types of commonly used indicators to assess OSS.We then conduct a comparative analysis to discuss how these indicators are used in each surveyed study and their differences.Moreover,we further undertake a correlation analysis between these indicators and uncover 13 confirmed conclusions and four cases with controversy occurring in these studies.Finally,we discuss several possible applications of these conclusions,which are insightful for the research on OSS and software supply chain.
