Open-Source Codes of Topology Optimization: A Summary for Beginners to Start Their Research

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.

Analysis and Simulations of Open-Source Intelligence Process System Dynamics from User’s Perspective

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.

HSPM:A Better Model to Effectively Preventing Open-Source Projects from Dying

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.

Software Metric Analysis of Open-Source Business Software

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.

A Novel Agricultural Data Sharing Mode Based on Rice Disease Identification

In this paper,a variety of classical convolutional neural networks are trained on two different datasets using transfer learning method.We demonstrated that the training dataset has a significant impact on the training results,in addition to the optimization achieved through the model structure.However,the lack of open-source agricultural data,combined with the absence of a comprehensive open-source data sharing platform,remains a substantial obstacle.This issue is closely related to the difficulty and high cost of obtaining high-quality agricultural data,the low level of education of most employees,underdeveloped distributed training systems and unsecured data security.To address these challenges,this paper proposes a novel idea of constructing an agricultural data sharing platform based on a federated learning(FL)framework,aiming to overcome the deficiency of high-quality data in agricultural field training.

OpenIFEM:A High Performance Modular Open-Source Software of the Immersed Finite Element Method for Fluid-Structure Interactions

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.

Open-Source Hardware Is a Low-Cost Alternative for Scientific Instrumentation and Research

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.

From hardware store to hospital:a COVID-19-inspired,cost-effective,open-source,in vivo-validated ventilator for use in resource-scarce regions

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.

An open-source albedo-wand for the measurement of sea ice albedo

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.

Breath monitoring,sleep disorder detection,and tracking using thin-film acoustic waves and open-source electronics

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.

Mapping Spatial Data on the Web Using Free and Open-Source Tools: A Prototype Implementation

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.

Aerodynamic Simulation of a 2017 F1 Car with Open-Source CFD Code

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.

An Importance Assessment Model of Open-Source Community Java Projects Based on Domain Knowledge Graph

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.

Mapping the Flood Risk Exposure Using Open-Source Geospatial Tools and Techniques: A Case of Gampaha Divisional Secretariat Division, Sri Lanka

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?

Benchmark on the accuracy and efficiency of several neural network based phase pickers using datasets from China Seismic Network

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.

Graph Convolutional Network-Based Repository Recommendation System

GitHub repository recommendation is a research hotspot in the field of open-source software. The current problemswith the repository recommendation systemare the insufficient utilization of open-source community informationand the fact that the scoring metrics used to calculate the matching degree between developers and repositoriesare developed manually and rely too much on human experience, leading to poor recommendation results. Toaddress these problems, we design a questionnaire to investigate which repository information developers focus onand propose a graph convolutional network-based repository recommendation system (GCNRec). First, to solveinsufficient information utilization in open-source communities, we construct a Developer-Repository networkusing four types of behavioral data that best reflect developers’ programming preferences and extract features ofdevelopers and repositories from the repository content that developers focus on. Then, we design a repositoryrecommendation model based on a multi-layer graph convolutional network to avoid the manual formulation ofscoringmetrics. Thismodel takes the Developer-Repository network, developer features and repository features asinputs, and recommends the top-k repositories that developers are most likely to be interested in by learning theirpreferences. We have verified the proposed GCNRec on the dataset, and by comparing it with other open-sourcerepository recommendation methods, GCNRec achieves higher precision and hit rate.

An Effective Threat Detection Framework for Advanced Persistent Cyberattacks

Recently,with the normalization of non-face-to-face online environments in response to the COVID-19 pandemic,the possibility of cyberattacks through endpoints has increased.Numerous endpoint devices are managed meticulously to prevent cyberattacks and ensure timely responses to potential security threats.In particular,because telecommuting,telemedicine,and teleeducation are implemented in uncontrolled environments,attackers typically target vulnerable endpoints to acquire administrator rights or steal authentication information,and reports of endpoint attacks have been increasing considerably.Advanced persistent threats(APTs)using various novel variant malicious codes are a form of a sophisticated attack.However,conventional commercial antivirus and anti-malware systems that use signature-based attack detectionmethods cannot satisfactorily respond to such attacks.In this paper,we propose a method that expands the detection coverage inAPT attack environments.In this model,an open-source threat detector and log collector are used synergistically to improve threat detection performance.Extending the scope of attack log collection through interworking between highly accessible open-source tools can efficiently increase the detection coverage of tactics and techniques used to deal with APT attacks,as defined by MITRE Adversarial Tactics,Techniques,and Common Knowledge(ATT&CK).We implemented an attack environment using an APT attack scenario emulator called Carbanak and analyzed the detection coverage of Google Rapid Response(GRR),an open-source threat detection tool,and Graylog,an open-source log collector.The proposed method expanded the detection coverage against MITRE ATT&CK by approximately 11%compared with that conventional methods.

CBE Clima Tool:A free and open-source web application for climate analysis tailored to sustainable building design

Climate-responsive building design holds immense potential for enhancing comfort,energy efficiency,and environmental sustainability.However,many social,cultural,and economic obstacles might prevent the wide adoption of designing climate-adapted buildings.One of these obstacles can be removed by enabling practitioners to easily access,visualize and analyze local climate data.The CBE Clima Tool(Clima)is a free and open-source web application that offers easy access to publicly available weather files and has been created for building energy simulation and design.It provides a series of interactive visualizations of the variables contained in the EnergyPlus Weather Files and several derived ones like the UTCI or the adaptive comfort indices.It is aimed at students,educators,and practitioners in the architecture and engineering fields.Since its inception,Clima’s user base has exhibited robust growth,attracting over 25,000 unique users annually from across 70 countries.Our tool is poised to revolutionize climate-adaptive building design,transcending geographical boundaries and fostering innovation in the architecture and engineering fields.

“stppSim”: A Novel Analytical Tool for Creating Synthetic Spatio-Temporal Point Data

In crime science, understanding the dynamics and interactions between crime events is crucial for comprehending the underlying factors that drive their occurrences. Nonetheless, gaining access to detailed spatiotemporal crime records from law enforcement faces significant challenges due to confidentiality concerns. In response to these challenges, this paper introduces an innovative analytical tool named “stppSim,” designed to synthesize fine-grained spatiotemporal point records while safeguarding the privacy of individual locations. By utilizing the open-source R platform, this tool ensures easy accessibility for researchers, facilitating download, re-use, and potential advancements in various research domains beyond crime science.

Computational Reproducibility Within Prognostics and Health Management

Scientific research frequently involves the use of computational tools and methods.Providing thorough documentation,open-source code,and data–the creation of reproducible computational research(RCR)–helps others understand a researcher’s work.In this study,we investigate the state of reproducible computational research,broadly,and from within the field of prognostics and health management(PHM).In a text mining survey of more than 300 articles,we show that fewer than 1%of PHM researchers make their code and data available to others.To promote the RCR further,our work also highlights several personal benefits for those engaged in the practice.Finally,we introduce an open-source software tool,called PyPHM,to assist PHM researchers in accessing and preprocessing common industrial datasets.