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The Journal of Chemical Physics has released a special issue focused on software for atomistic machine learning. This issue aims to address the lack of journals dedicated to publishing scientific software papers. The collection of papers in this issue provides insight into the tools and goals of software implementations in the field of atomistic machine learning. The articles cover a range of topics, including machine-learning interatomic potentials, sampling, dataset repositories, workflows, and auxiliary tooling and analysis. The article concludes by emphasizing the importance of software implementations in the field and encourages further submissions on relevant topics. [Extracted from the article]

We present the Python-based Molecule Builder for ESPResSo (pyMBE), an open source software application to design custom coarse-grained (CG) models, as well as pre-defined models of polyelectrolytes, peptides, and globular proteins in the Extensible Simulation Package for Research on Soft Matter (ESPResSo). The Python interface of ESPResSo offers a flexible framework, capable of building custom CG models from scratch. As a downside, building CG models from scratch is prone to mistakes, especially for newcomers in the field of CG modeling, or for molecules with complex architectures. The pyMBE module builds CG models in ESPResSo using a hierarchical bottom-up approach, providing a robust tool to automate the setup of CG models and helping new users prevent common mistakes. ESPResSo features the constant pH (cpH) and grand-reaction (G-RxMC) methods, which have been designed to study chemical reaction equilibria in macromolecular systems with many reactive species. However, setting up these methods for systems, which contain several types of reactive groups, is an error-prone task, especially for beginners. The pyMBE module enables the automatic setup of cpH and G-RxMC simulations in ESPResSo, lowering the barrier for newcomers and opening the door to investigate complex systems not studied with these methods yet. To demonstrate some of the applications of pyMBE, we showcase several case studies where we successfully reproduce previously published simulations of charge-regulating peptides and globular proteins in bulk solution and weak polyelectrolytes in dialysis. The pyMBE module is publicly available as a GitHub repository (https://github.com/pyMBE-dev/pyMBE), which includes its source code and various sample and test scripts, including the ones that we used to generate the data presented in this article. [ABSTRACT FROM AUTHOR]

In this study we identify a process of "entrainment" around open source software (OSS) development release cycles to capture patterns of self-organized task allocation among developers. We conducted an abductive, computationally intensive study of eight OSS projects, using relational event modeling to analyze 1,169,489 actions covering 93 major software releases. The process of entrainment that we identify involves three task allocation mechanisms: (1) developer-issue inertia, (2) developer contribution frequency, and (3) issue popularity. Our analysis demonstrates that these mechanisms and the phases of the release cycle entrain each other. Before a major release, developers engage in a concentrated mobilization phase, whereby they democratize development activity and increasingly allocate community contributions to the set of issues related to the release. After a major release, the extended cleanup phase garners a greater share of development work from recently highly active developers and dilutes the activity of these developers across a wider range of issues. Our theorizing suggests that major releases constitute important events around which OSS communities self-organize and we characterize how this occurs. Our research contributes to theorizing on organizing in OSS communities by explaining how self-organizing task allocation interacts with release cycles through the mechanism of entrainment. We also contribute to the literature on entrainment by showing how it may unfold in the context of online peer production communities such as OSS. [ABSTRACT FROM AUTHOR]

High-quality data from surveillance of vaccine-preventable disease (VPD) are critical for timely detection of and response to outbreaks. In 2019, the World Health Organization (WHO) Regional Office for Africa (AFRO) began a change from Epi Info, a statistical software package developed by the US Centers for Disease Control and Prevention (CDC), with limited capability to integrate with other information systems, affecting reporting timeliness and data use, to the District Health Information Software 2 (DHIS2), a free, open-source software platform for electronic aggregate Integrated Disease Surveillance and Response (IDSR) and case-based surveillance reporting. A national reporting system was introduced to allow countries to adopt this system. Regionally, the Epi Info database will be replaced by a DHIS2 regional data platform. This report describes phased implementation from 2019 to the present. Phase 1 (2019-2021) involved developing IDSR aggregate and case-based surveillance packages and pilot studies in Mali, Rwanda and Togo. Phase 2 (2022) included extending national implementation to 27 countries and establishing the WHO AFRO DHIS2 regional data platform. Phase 3 (from 2023 onwards) comprised building local capacity and support for national reporting to the regional platform. By February 2024, 8 of 47 AFRO countries had adopted both the aggregate IDSR and case-based surveillance packages, and 2 had successfully transferred VPD surveillance data to the AFRO regional platform. The challenges included limited human and financial resources, establishment of data-sharing and governance agreements, technical support for data transfer, and building local capacity to report to the regional platform. Transition to DHIS2 will ensure efficient data transmission to strengthen detection and response of VPD and public health emergencies through greater system integration and interoperability. Il est essentiel de disposer de donnees de surveillance de qualite sur les maladies a prevention vaccinale (MPV) pour detecter rapidement les flambees epidemiques et y riposter. En 2019, le Bureau regional de l'Organisation mondiale de la Sante (OMS) pour l'Afrique a commence a remplacer Epi Info, un progiciel statistique developpe par les Centers for Disease Control and Prevention (CDC) des Etats-Unis, s'adaptant difficilement a d'autres systemes d'information et ne permettant pas de bien tenir les delais ou de partager les donnees de maniere optimale, par le logiciel d'information sanitaire de district DHIS2 (District Health Information Software 2), une plateforme logicielle libre et gratuite pour les donnees de surveillance integree des maladies et la riposte et les donnees de surveillance basee sur l'identification des cas. Un systeme de notification au niveau national a ete mis en place, offrant aux pays la possibilite d'adopter ce nouveau systeme. Au niveau regional, la base de donnees Epi Info sera remplacee par une plateforme de donnees DHIS2 regionale. Le present rapport decrit la mise en oeuvre progressive de cette plateforme de 2019 a aujourd'hui. La phase 1 (2019-2021) a consiste a elaborer des modules pour les donnees agregees de surveillance integree des maladies et la riposte et de surveillance basee sur les cas; des essais pilotes ont ete menes au Mali, au Rwanda et au Togo. La phase 2 (2022) a vu l'extension de la mise en oeuvre au niveau national a 27 pays et la mise en place de la plateforme de donnees regionale DHIS2 du Bureau regional de l'OMS pour l'Afrique. Les activites de la phase 3 (a partir de 2023) ont porte sur le renforcement des capacites locales et l'appui aux pays pour la transmission des donnees nationales a la plateforme regionale. Les difficultes recensees concernaient le manque de ressources humaines et financieres, la necessite d'etablir des accords de partage et de gouvernance des donnees, l'appui technique pour le transfert des donnees et le renforcement des capacites locales pour la transmission des donnees a la plateforme regionale. En fevrier 2024, 8 pays parmi les 47 pays de la Region africaine de l'OMS avaient adopte les modules pour la surveillance integree des maladies et la riposte et pour la surveillance basee sur les cas, et 2 avaient transfere avec succes leurs donnees de surveillance sur les MPV vers la plateforme regionale. La transition vers la plateforme DHIS2 favorisera une transmission efficace des donnees pour renforcer la detection des MPV et des urgences de sante publique et la riposte a ces evenements grace a une meilleure integration et interoperabilite des systemes., Introduction VPD surveillance is critical to public health, as it provides data for timely detection of and response to VPD cases and outbreaks. High-quality, timely data are necessary to guide [...]

Although the prices of cryptocurrencies remained volatile for the past decade, the factors that impact the price dynamics of the new type of investment instrument have not been fully identified yet. In this study, we recognize the dual nature of cryptocurrencies, that is, being a software program and a financial instrument, and examine the impact of software advancement on the price dynamics of cryptocurrencies. The open-source software (OSS) platform functionality enables social behaviors that we use as signals. Using data from the largest OSS platform, we establish the connection between open-source activities and the price movement of cryptocurrency. In particular, as project popularity (forks and watches) and users' feedback (issues) increase, the market price increases by 4.3 percent, 2.4 percent, and 4.4 percent per annum, respectively. On the contrary, the number of code corrections (pull requests) is negatively related to prices leading to a 5 percent annual price decrease. Our results suggest that OSS contributions create a perfect selection mechanism, where higher quality projects receive more developers' attention and user feedback, whereas lower quality projects do not, thus creating the separating equilibrium. [ABSTRACT FROM AUTHOR]

Polymer-based materials are of high potential for application as active materials in contemporary electronic gadgets. The functionality of such materials is highly dependent on their oxidation state and requires fine control during their synthesis. Owing to that, we have designed a cost-effective, simple, portable, system for calibration and the detection of the optical and electrical properties of polyaniline loaded on a paper substrate using a smartphone-based colorimetric sensor. The vapor phase polymerization technique was adopted for preparing polyaniline. Colorimetric measurements of polyaniline-loaded papers were achieved by estimating the hue, saturation and value (HSV) color parameter values using an open-source smartphone application called 'Color Grab (Color Detection)' during synthesis at different time intervals. The proposed sensor relies on the principle of Beer-Lambert law. The rear camera of the smartphone acts as a sensor and captures the light signal from the samples and provides information to the colorimetric app. The colorimetric results showed a promising correlation between the color parameter values with their electrical properties. The optical absorbance results of all polyaniline-loaded papers are also in accordance with the H parameter values. The proposed sensor can be used as a portable smartphone-based point-of-care testing kit to bring in-situ control during synthesis. Highlights * Conducting polymer on flexible cellulose substrate. * Smartphone-based visual colorimetric sensor. * Biodegradable and environment-friendly electronic material. * Variation of electrical properties in terms of hue values. * Cheap, portable sensor for point-of-care testing. KEYWORDS charge transport, colorimetry, conducting polymer, point-of-care testing, portable, smartphone application, 1 | INTRODUCTION The evolution of polymers is now an emerging topic in the field of materials and nanoscience. Different polymer materials are evolving for application in electronic and optoelectronic [...]

The increased computational and experimental interest in perovskite systems comprising novel phases and reduced dimensionality has greatly expanded the search space for this class of materials. In similar fields, unified frameworks exist for the procedural generation and subsequent analysis of these complex condensed matter systems. Given the relatively recent rise in popularity of these novel perovskite phases, such a framework is yet to be created. In this work, we introduce Pyrovskite, an open source software package, to aid in both the high-throughput and fine-grained generation, simulation, and subsequent analysis of this expanded family of perovskite systems. Additionally, we introduce a new descriptor for octahedral distortions in systems, including, but not limited to, perovskites. This descriptor quantifies diagonal displacements of the B-site cation in a BX6 octahedral coordination environment, which has been shown to contribute to increased Rashba–Dresselhaus splitting in perovskite systems. [ABSTRACT FROM AUTHOR]

With the development of open source software (OSS), software reliability has become an important issue. Due to the complexity of OSS development and testing environment, the fault detection rate of OSS will show a variety of changes. The existing OSS reliability models cannot fully adapt to the complex changes of OSS fault detection. In this paper, we proposed a multi-release OSS reliability model based on three-parameter lifetime distribution. The proposed model can effectively adapt to the complicated changes of fault detection in the processes of development and testing of OSS. Experimental results indicate that the proposed model has the better fitting and predictive performance compared with other multi-release OSS reliability models. Moreover, the proposed model can better adapt to the variety of OSS fault detection environment, and assist developers to evaluate the reliability of OSS. [ABSTRACT FROM AUTHOR]

This article presents a Python‐based program, DFT2FEFFIT, to regress theoretical extended X‐ray absorption fine structure (EXAFS) spectra calculated from density functional theory structure models against experimental EXAFS spectra. To showcase its application, Ce‐doped fluorapatite [Ca10(PO4)6F2] is revisited as a representative of a material difficult to analyze by conventional multi‐shell least‐squares fitting of EXAFS spectra. The software is open source and publicly available. [ABSTRACT FROM AUTHOR]

Software reliability growth model (SRGM) is used as one of the reliability assessment methods to assess the software reliability. In SRGM, the degree of reliability growth may fluctuate greatly according to change in the internal state of the software. It is called the change point (CP). Several researchers proposed the SRGM considering CP. In the open source software (OSS), there are many projects that continue development even after the software is released. Therefore, major updates with breaking changes may occur in it. The major updates can be a factor that causes a CP because it greatly changes the internal state of the OSS. This paper focuses on the relationship between CP and software updates. We collect OSS fault data from a bug tracking system. Moreover, we examine the behavior of SRGM before and after software updates. Furthermore, we discuss the applicability of SRGM for CP in OSS. Also, we compare the proposed model based on CP with the model without CP. As a result, we have confirmed that the SRGM can evaluate the reliability in the environment with major updates. Moreover, the proposed method performs better than without considering CP model. Especially, the exponential model's mean value function is the suitable method to assess the OSS reliability for the proposed method. [ABSTRACT FROM AUTHOR]

Summary: To ensure the quality of software systems, software engineers can make use of a variety of quality assurance approaches, for example, software testing, modern code review, automated static analysis, and build automation. Each of these quality assurance practices have been studied in depth in isolation, but there is a clear knowledge gap when it comes to our understanding of how these approaches are being used in conjunction, or not. In our study, we broadly investigate whether and how these quality assurance approaches are being used in conjunction in the development of 1454 popular open source software projects on GitHub. Our study indicates that typically projects do not follow all quality assurance practices together with high intensity. In fact, we only observe weak correlation among some quality assurance practices. In general, our study provides a deeper understanding of how existing quality assurance approaches are currently being used in Java‐based open source software development. Besides, we specifically zoom in on the more mature projects in our dataset, and generally we observe that more mature projects are more intense in their application of the quality assurance practices, with more focus on their ASAT usage, and code reviewing, but no strong change in their CI usage. [ABSTRACT FROM AUTHOR]

Introduction: Evidence indicates that postpartum and post-abortion women accept family planning at a higher rate when offered timely at appropriate sites. Therefore, this study explored barriers and enablers of postpartum and post-abortion family planning utilization in primary health care units of Wolaita Zone, Southern Ethiopia, from June 20 to July 25, 2022. Methods: We used a case study strategy of qualitative research using both the Consolidated Framework for Implementation Research (CFIR) and Gender, Youth, and Social Inclusion (GYSI) frameworks was conducted from June to July 2022. We conducted 41 in-depth and key informant interviews and six focus group discussions. We also used Open Code software version 4.02 for coding and further analysis and applied a framework analysis. Results: The analysis of this study identified barriers and enablers of postpartum and post-abortion family planning service uptake in five CFIR domains and four GYSI components. The barriers included misconceptions and sole decision-making by husbands, cultural and religious barriers, and healthcare providers paying less attention to adolescents and husbands, which prevented them from using immediate postpartum and postabortion family planning services. The health facilities were not adequately staffed; there was a shortage and delay of supplies and infrastructure, trained staff turnover, and poor accountability among service providers. The existence of community structure, equal access and legal rights to the service, and having waivered services were enablers for postpartum and post-abortion family planning service uptake. Conclusion and recommendation: The current study identified various barriers and enablers to the uptake of postpartum and post-abortion family planning. Therefore, there is a need for high-impact interventions such as targeting male partners and girls, ensuring infrastructure, supplies, and equipment, building staff capacity, and making decisions jointly. [ABSTRACT FROM AUTHOR]

Whole genome analysis for microbial genomics is critical to studying and monitoring antimicrobial resistance strains. The exponential growth of microbial sequencing data necessitates a fast and scalable computational pipeline to generate the desired outputs in a timely and cost-effective manner. Recent methods have been implemented to integrate individual genomes into large collections of specific bacterial populations and are widely employed for systematic genomic surveillance. However, they do not scale well when the population expands and turnaround time remains the main issue for this type of analysis. Here, we introduce AMRomics, an optimized microbial genomics pipeline that can work efficiently with big datasets. We use different bacterial data collections to compare AMRomics against competitive tools and show that our pipeline can generate similar results of interest but with better performance. The software is open source and is publicly available at https://github.com/amromics/amromics under an MIT license. [ABSTRACT FROM AUTHOR]

Background: A major stream of research on symptom checkers aims at evaluating the technology's predictive accuracy, but apart from general trends, the results are marked by high variability. Several authors suggest that this variability might in part be due to different assessment methods and a lack of standardization. To improve the reliability of symptom checker evaluation studies, several approaches have been suggested, including standardizing input procedures, the generation of test vignettes, and the assignment of gold standard solutions for these vignettes. Recently, we suggested a third approach––test-theoretic metrics for standardized performance reporting–– to allow systematic and comprehensive comparisons of symptom checker performance. However, calculating these metrics is time-consuming and error prone, which could hamper the use and effectiveness of these metrics. Results: We developed the R package symptomcheckR as an open-source software to assist researchers in calculating standard metrics to evaluate symptom checker performance individually and comparatively and produce publication-ready figures. These metrics include accuracy (by triage level), safety of advice (i.e., rate of correctly or overtriaged cases), comprehensiveness (i.e., how many cases could be entered or were assessed), inclination to overtriage (i.e., how risk-averse a symptom checker is) and a capability comparison score (i.e., a score correcting for case difficulty and comprehensiveness that enables a fair and reliable comparison of different symptom checkers). Each metric can be obtained using a single command and visualized with another command. For the analysis of individual or the comparison of multiple symptom checkers, single commands can be used to produce a comprehensive performance profile that complements the standard focus on accuracy with additional metrics that reveal strengths and weaknesses of symptom checkers. Conclusions: Our package supports ongoing efforts to improve the quality of vignette-based symptom checker evaluation studies by means of standardized methods. Specifically, with our package, adhering to reporting standards and metrics becomes easier, simple, and time efficient. Ultimately, this may help users gain a more systematic understanding of the strengths and limitations of symptom checkers for different use cases (e.g., all-purpose symptom checkers for general medicine versus symptom checkers that aim at improving triage in emergency departments), which can improve patient safety and resource allocation. [ABSTRACT FROM AUTHOR]

Electrical stimulation of peripheral nerves has been used in various pathological contexts for rehabilitation purposes or to alleviate the symptoms of neuropathologies, thus improving the overall quality of life of patients. However, the development of novel therapeutic strategies is still a challenging issue requiring extensive in vivo experimental campaigns and technical development. To facilitate the design of new stimulation strategies, we provide a fully open source and self-contained software framework for the in silico evaluation of peripheral nerve electrical stimulation. Our modeling approach, developed in the popular and well-established Python language, uses an object-oriented paradigm to map the physiological and electrical context. The framework is designed to facilitate multi-scale analysis, from single fiber stimulation to whole multifascicular nerves. It also allows the simulation of complex strategies such as multiple electrode combinations and waveforms ranging from conventional biphasic pulses to more complex modulated kHz stimuli. In addition, we provide automated support for stimulation strategy optimization and handle the computational backend transparently to the user. Our framework has been extensively tested and validated with several existing results in the literature. Author summary: Electrical stimulation of the peripheral nervous system is a powerful therapeutic approach for treating and alleviating patients suffering from a large variety of disorders, including loss of motor control or loss of sensation. Electrical stimulation works by connecting the neural target to a neurostimulator through an electrode that delivers a stimulus to modulate the electrical activity of the targeted nerve fiber population. Therapeutic efficacy is directly influenced by electrode design, placement, and stimulus parameters. Computational modeling approaches have proven to be an effective way to select the appropriate stimulation parameters. Such an approach is, however, poorly accessible to inexperienced users as it typically requires the use of multiple commercial software and/or development in different programming languages. Here, we describe a Python-based framework that aims to provide an open-source turnkey solution to any end user. The framework we developed is based on open-source packages that are fully encapsulated, thus transparent to the end-user. The framework is also being developed to enable simulation of granular complexity, from rapid first-order simulation to the evaluation of complex stimulation scenarios requiring a deeper understanding of the ins and outs of the framework. [ABSTRACT FROM AUTHOR]

Motivation The data independent acquisition (DIA) mass spectrometry (MS) method is increasingly popular in the field of proteomics. But the loss of the correspondence between peptide ions and their spectra in DIA makes the identification challenging. One effective approach to reduce false positive identification is to calculate the deviation between the peptide's estimated retention time (RT) and measured RT. During this process, scaling the spectral library RT into the estimated RT, known as the RT calibration, is a prerequisite for calculating the deviation. Currently, within the DIA algorithm ecosystem, there is a lack of engine-independent and readily usable RT calibration toolkits. Results In this work, we introduce Calib-RT, a RT calibration method tailored to the characteristics of RT data. This method can achieve the nonlinear calibration across various data scales and tolerate a certain level of noise interference. Calib-RT is expected to enrich the open source DIA algorithm toolchain and assist in the development of DIA identification algorithms. Availability and implementation Calib-RT is released as an open source software under the MIT license and can be installed from PyPi as a python module. The source code is available on GitHub at https://github.com/chenghui03/Calib_RT. [ABSTRACT FROM AUTHOR]

The rise of water‐based electromagnetic devices opens up new possibilities for controlling electromagnetic performance due to the liquid state of crucial parts of the device. This letter proposes the siphon mechanism as a novel way to achieve reconfigurability in these types of electromagnetic devices. To demonstrate this, a simple polarization‐reconfigurable device based on a siphon is introduced. The device is designed to scatter horizontal and vertical polarizations differently depending on the amount of water added to it. The siphon mechanism offers a sharp transition between two states after water is added above a threshold. The device is simulated, manufactured using 3D printing, and measured in different states filled with both distilled and tap water. The results demonstrate that polarization reconfigurability is achieved with the different states scattering the two polarizations as expected. Siphons similar to this could be used to offer new types of control in other electromagnetic devices based on liquids. [ABSTRACT FROM AUTHOR]

In this study, we explore the future of work by examining differences in productivity when teams are composed of only humans or both humans and machine agents. Our objective was to characterize the similarities and differences between human and human–machine teams as they work to coordinate across their specialized roles. This form of research is increasingly important given that machine agents are becoming commonplace in sociotechnical systems and playing a more active role in collaborative work. One particular class of machine agents, bots, is being introduced to these systems to facilitate both taskwork and teamwork. We investigated the association between bots and productivity outcomes in open source software development through an analysis of hundreds of project teams. The presence of bots in teams was associated with higher levels of productivity and higher work centralization in addition to greater amounts of observed communication. The adoption of bots in software teams may have tradeoffs, in that doing so may increase productivity, but could also increase workload. We discuss the theoretical and practical implications of these findings for advancing human–machine teaming research. This research characterizes the changing landscape of collaborative work brought on by the increasing presence of autonomous machine agents in teams. We report on a multidimensional analysis of work in software development projects examining differences between human‐only and human‐bot teams. [ABSTRACT FROM AUTHOR]

Open source software (OSS) follows a software development paradigm whereby self‐motivated volunteers scattered all around the globe contribute to the development in the form of code, documentation, feedback, feature recommendations, bug reporting, and bug resolution. These volunteers, commonly referred to as OSS project community, serve as the foundation of the OSS project, fostering its creation and sustenance and providing long‐term support. The quality and sustainability of the OSS project is reliant upon the development and structure of the self‐governing community. When a business organization plans to acquire an OSS solution, it not only takes into consideration the factors such as reliability, security, and scalability but also attaches significant importance to the likelihood of the OSS project being maintained and supported in the future so that it can rely on it as a stable and secure technology solution. Modern cloud‐based software hosting platforms, such as GitHub, offer a range of options for automatically and freely accessing the complete development history of millions of OSS projects. This easy availability of detailed development history has enabled researchers to analyze and draw quantitative and scientific inferences about the quality of an OSS project which generally involves assessing three aspects, namely, software product, development process, and project community. With focus on project community part, a Framework for Assessment and Ranking of OSS Community is being presented in the current research work, following a detailed examination of the largest source code hosting and project collaboration platform, GitHub. Technique for Order of Preference by Similarity to Ideal Solution from Multi‐Criteria Decision‐Making toolkit has been utilized for assessing the quality of the project community. The framework has been validated by applying it on nine OSS projects and comparing the results with the outcomes obtained through an existing OSS evaluation methodology. The comparative analysis demonstrated that the proposed framework aligns with the aforementioned evaluation methodology while offering an opportunity for in‐depth analysis on the dynamics of volunteer communities, which is lacking in previous evaluation methods. These insights can prove valuable for both potential adopters and project maintainers, aiding them in making informed strategic decisions. [ABSTRACT FROM AUTHOR]

The article discusses the issue of whether software codes generated from publicly funded research should be freely accessible and open source. Brendan Whelan argues in favor of this proposition, stating that open source software accelerates scientific advancement, promotes robust science, and increases impact and translation. On the other hand, Kristy Brock argues against the proposition, highlighting the challenges and limitations associated with mandating the release of software from publicly funded research. She emphasizes the importance of commercialization and the need for a carefully constructed infrastructure for software dissemination. Whelan responds by qualifying his argument and addressing the concerns raised by Brock. He asserts that open source code can accelerate research progress without hindering commercial development. [Extracted from the article]

Software Defined Networking (SDN) is programmable by separation of forwarding control through the centralization of the controller. The controller plays the role of the 'brain' that dictates the intelligent part of SDN technology. Various versions of SDN controllers exist as a response to the diverse demands and functions expected of them. There are several SDN controllers available in the open market besides a large number of commercial controllers; some are developed to meet carrier-grade service levels and one of the recent trends in open-source SDN controllers is the Open Network Operating System (ONOS). This paper presents a comparative study between open source SDN controllers, which are known as Network Controller Platform (NOX), Python-based Network Controller (POX), component-based SDN framework (Ryu), Java-based OpenFlow controller (Floodlight), OpenDayLight (ODL) and ONOS. The discussion is further extended into ONOS architecture, as well as, the evolution of ONOS controllers. This article will review use cases based on ONOS controllers in several application deployments. Moreover, the opportunities and challenges of open source SDN controllers will be discussed, exploring carrier-grade ONOS for future real-world deployments, ONOS unique features and identifying the suitable choice of SDN controller for service providers. In addition, we attempt to provide answers to several critical questions relating to the implications of the open-source nature of SDN controllers regarding vendor lock-in, interoperability, and standards compliance, Similarly, real-world use cases of organizations using open-source SDN are highlighted and how the open-source community contributes to the development of SDN controllers. Furthermore, challenges faced by open-source projects, and considerations when choosing an open-source SDN controller are underscored. Then the role of Artificial Intelligence (AI) and Machine Learning (ML) in the evolution of open-source SDN controllers in light of recent research is indicated. In addition, the challenges and limitations associated with deploying open-source SDN controllers in production networks, how can they be mitigated, and finally how open-source SDN controllers handle network security and ensure that network configurations and policies are robust and resilient are presented. Potential opportunities and challenges for future Open SDN deployment are outlined to conclude the article. [ABSTRACT FROM AUTHOR]

Unoccupied aerial vehicles (UAVs; drones) offer mobile platforms for ecological investigation, but can be impractical in some environments and the resulting noise can disturb wildlife.We developed a mobile alternative using a bird‐borne platform to record the behaviour of other animals in the field. This unit consists of a lightweight audio and video sensor that is carried by a trained Harris's hawk Parabuteo unicinctus.We tested the hypothesis that our bird‐borne platform is a viable option for collecting behavioural data from mobile animals. We recorded acoustic and video data as the hawk flew through a dense group of Brazilian free‐tailed bats Tadarida brasiliensis emerging from a cave, with a test case of investigating how echolocation calls change depending on spatial position in the bat group.The HawkEar platform is an alternative for collecting behavioural data when a mobile platform that is less noisy and restrictive than traditional UAVs is needed. The design and software are open source and can be modified to accommodate additional sensor needs. [ABSTRACT FROM AUTHOR]

The poor application effect of traditional sports training methods and the difficulty of recording data due to the time and space constraints of sports make it difficult for trainers to improve their learning outcomes. Based on this, the study proposes to apply human posture recognition in sports teaching design, and use VGGNet-19 as a feature extractor and OpenCV open source software to capture posture movements, and introduce the concepts of joint angle and movement similarity to design a sports assessment system for physical education based on the geometric spatial feature variability analysis of posture based on limb angle information. The testing outcomes demonstrate that the study's improved gesture recognition algorithm has a recognition rate of more than 90% on gesture movements, and the maximum recognition error value (0.010) is smaller than that of the dynamic time-regularised gesture algorithm (0.014) and the convolutional neural network algorithm (0.017). The assessment system is also better able to improve students' professional performance and satisfaction, with its average professional score and satisfaction reaching 86 and 92%, which is significantly better than other comparative algorithms. The method is effective in providing trainers with data-based training scenarios and helping them to improve their learning in sport. [ABSTRACT FROM AUTHOR]

Conformal prediction has seen many applications in pharmaceutical science, being able to calibrate outputs of machine learning models and producing valid prediction intervals. We here present the open source software CPSign that is a complete implementation of conformal prediction for cheminformatics modeling. CPSign implements inductive and transductive conformal prediction for classification and regression, and probabilistic prediction with the Venn-ABERS methodology. The main chemical representation is signatures but other types of descriptors are also supported. The main modeling methodology is support vector machines (SVMs), but additional modeling methods are supported via an extension mechanism, e.g. DeepLearning4J models. We also describe features for visualizing results from conformal models including calibration and efficiency plots, as well as features to publish predictive models as REST services. We compare CPSign against other common cheminformatics modeling approaches including random forest, and a directed message-passing neural network. The results show that CPSign produces robust predictive performance with comparative predictive efficiency, with superior runtime and lower hardware requirements compared to neural network based models. CPSign has been used in several studies and is in production-use in multiple organizations. The ability to work directly with chemical input files, perform descriptor calculation and modeling with SVM in the conformal prediction framework, with a single software package having a low footprint and fast execution time makes CPSign a convenient and yet flexible package for training, deploying, and predicting on chemical data. CPSign can be downloaded from GitHub at https://github.com/arosbio/cpsign. Scientific contribution CPSign provides a single software that allows users to perform data preprocessing, modeling and make predictions directly on chemical structures, using conformal and probabilistic prediction. Building and evaluating new models can be achieved at a high abstraction level, without sacrificing flexibility and predictive performance—showcased with a method evaluation against contemporary modeling approaches, where CPSign performs on par with a state-of-the-art deep learning based model. [ABSTRACT FROM AUTHOR]

To address our climate emergency, "we must rapidly, radically reshape society"—Johnson & Wilkinson, All We Can Save. In science, reshaping requires formidable technical (cloud, coding, reproducibility) and cultural shifts (mindsets, hybrid collaboration, inclusion). We are a group of cross‐government and academic scientists that are exploring better ways of working and not being too entrenched in our bureaucracies to do better science, support colleagues, and change the culture at our organizations. We share much‐needed success stories and action for what we can all do to reshape science as part of the Open Science movement and 2023 Year of Open Science. [ABSTRACT FROM AUTHOR]

Background: Multiple sclerosis (MS) is a clinically heterogeneous disease underpinned by inflammatory, demyelinating and neurodegenerative processes, the extent of which varies between individuals and over the course of the disease. Recognising the clinicopathological features that most strongly associate with disease outcomes will inform future efforts at patient phenotyping. Aims: We used a digital pathology workflow, involving high-resolution image acquisition of immunostained slides and opensource software for quantification, to investigate the relationship between clinical and neuropathological features in an autopsy cohort of progressive MS. Methods: Sequential sections of frontal, cingulate and occipital cortex, thalamus, brain stem (pons) and cerebellum including dentate nucleus (n = 35 progressive MS, females = 28, males = 7; age died = 53.5 years; range 38–98 years) were immunostained for myelin (anti-MOG), neurons (anti-HuC/D) and microglia/macrophages (anti-HLA). The extent of demyelination, neurodegeneration, the presence of active and/or chronic active lesions and quantification of brain and leptomeningeal inflammation was captured by digital pathology. Results: Digital analysis of tissue sections revealed the variable extent of pathology that characterises progressive MS. Microglia/macrophage activation, if found at a higher level in a single block, was typically elevated across all sampled blocks. Compartmentalised (perivascular/leptomeningeal) inflammation was associated with age-related measures of disease severity and an earlier death. Conclusion: Digital pathology identified prognostically important clinicopathological correlations in MS. This methodology can be used to prioritise the principal pathological processes that need to be captured by future MS biomarkers. [ABSTRACT FROM AUTHOR]

The Robotic Observations of Microlensing Events/Reactive Event Assessment Survey was a Key Project at Las Cumbres Observatory (hereafter LCO) which continuously monitored 20 selected fields (3.76 sq.deg) in the Galactic Bulge throughout their seasonal visibility window over a three-year period, between 2017 March and 2020 March. Observations were made in three optical passbands (SDSS − g ′ , − r ′ , − i ′ ), and LCO's multi-site telescope network enabled the survey to achieve a typical cadence of ∼10 hr in i ′ and ∼15 hr in g ′ and r ′ . In addition, intervals of higher cadence (<1 hr) data were obtained during monitoring of key microlensing events within the fields. This paper describes the Difference Image Analysis data reduction pipeline developed to process these data, and the process for combining the photometry from LCO's three observing sites in the Southern Hemisphere. The full timeseries photometry for all ∼8 million stars, down to a limiting magnitude of i ∼ 18 mag is provided in the data release accompanying this paper, and samples of the data are presented for exemplar microlensing events, illustrating how the tri-band data are used to derive constraints on the microlensing source star parameters, a necessary step in determining the physical properties of the lensing object. The timeseries data also enables a wealth of additional science, for example in characterizing long-timescale stellar variability, and a few examples of the data for known variables are presented. [ABSTRACT FROM AUTHOR]

The adoption and development of high technology situations such as additive manufacturing is complicated because the technology system characteristics used by manufacturers are different than the technology-performance characteristics evaluated by customers. This study explores the relationship between the desktop 3D printers' underlying technology characteristics, its performance, and its price. Data were collected for 107 Fused Deposition Modelling desktop 3D printers from 2014 to 2019. Twelve technology characteristics were included such as the build volume, the material of the build platform and whether it is heated, the number of extruders, type of movements and whether it uses open source software, hardware or filament. It was found that 8% of the technology characteristics only matter for performance, 33% only matter for price, 33% for neither and 25% of the characteristics matter for both performance and price. [ABSTRACT FROM AUTHOR]

This study presents an aerodynamic design optimization of a micro micro‐horizontal‐axis wind turbine (HAWT). To obtain an optimal design, it is essential to understand the design parameters and select the responsible factors that affect the blade efficiency. The aim of this work is to redesign a 3D micro‐HAWT to improve aerodynamic performance, through improving the distribution of chord length and twist angle along the blade. Performance analysis and flow visualization of the initial design and the optimal design were carried out using CFD analyzer. In the blade optimization design, eight significant input parameters were selected, five to characterize the chord length distribution and three to represent the twist angle along the blade. To maximize the efficiency, design points that are created by Design of Experiment (DoE) are evaluated through (Multi‐Objective‐Genetic Algorithm) MOGA method. The results showed a reduction on the separation effect area on the optimal blade surface compared to the initial one. The use of response surface optimization (RSO), when combined with CFD simulation, has proven beneficial in selecting the optimal HAWT design. Finally, the open source software (Qblade) was used to investigate and to compare the performance of the initial and optimum design. An efficiency enhancement of approximately 3.6% is achieved at tip speed ration TSR = 3.4. [ABSTRACT FROM AUTHOR]

Electrical impedance tomography (EIT) is an imaging modality based on electrical currents injected through the imaging domain and voltages measured on the boundary. It has a wide variety of applications ranging e.g. from medical imaging to industrial process monitoring. Computing the EIT reconstruction from the measurements is a severely ill-posed inverse problem, and hence it is not a straightforward task. Therefore, the EIT reconstruction algorithms remain an active area of research. To facilitate this research, we present in this article object-oriented EIT (OOEIT), an open source software package for MATLAB. It is designed for ease of use for those with no deep understanding of EIT, while also featuring a modular structure that allows for straightforward customization for those interested in developing their own algorithms. OOEIT includes implementations for both 2D and 3D computations, for both static and dynamic reconstructions, and supports user defined meshes. As examples, we present reconstructions computed from the Kuopio Tomography Challenge (KTC2023) data set. [ABSTRACT FROM AUTHOR]

Summary: JSON is a popular standard for data interchange on the Internet. Ingesting JSON documents can be a performance bottleneck. A popular parsing strategy consists in converting the input text into a tree‐based data structure—sometimes called a Document Object Model or DOM. We designed and implemented a novel JSON parsing interface—called On‐Demand—that appears to the programmer like a conventional DOM‐based approach. However, the underlying implementation is a pointer iterating through the content, only materializing the results (objects, arrays, strings, numbers) lazily. On recent commodity processors, an implementation of our approach provides superior performance in multiple benchmarks. To ensure reproducibility, our work is freely available as open source software. Several systems use On Demand: for example, Apache Doris, the Node.js JavaScript runtime, Milvus, and Velox. [ABSTRACT FROM AUTHOR]

The lack of comprehensive sources of accurate vulnerability data represents a critical obstacle to studying and understanding software vulnerabilities (and their corrections). In this article, we present an approach that combines heuristics stemming from practical experience and machine-learning (ML)—specifically, natural language processing (NLP)—to address this problem. Our method consists of three phases. First, we construct an advisory record object containing key information about a vulnerability that is extracted from an advisory, such as those found in the National Vulnerability Database (NVD). These advisories are expressed in natural language. Second, using heuristics, a subset of candidate fix commits is obtained from the source code repository of the affected project, by filtering out commits that can be identified as unrelated to the vulnerability at hand. Finally, for each of the remaining candidate commits, our method builds a numerical feature vector reflecting the characteristics of the commit that are relevant to predicting its match with the advisory at hand. Based on the values of these feature vectors, our method produces a ranked list of candidate fixing commits. The score attributed by the ML model to each feature is kept visible to the users, allowing them to easily interpret the predictions. We implemented our approach and we evaluated it on an open data set, built by manual curation, that comprises 2,391 known fix commits corresponding to 1,248 public vulnerability advisories. When considering the top-10 commits in the ranked results, our implementation could successfully identify at least one fix commit for up to 84.03% of the vulnerabilities (with a fix commit on the first position for 65.06% of the vulnerabilities). Our evaluation shows that our method can reduce considerably the manual effort needed to search open-source software (OSS) repositories for the commits that fix known vulnerabilities. [ABSTRACT FROM AUTHOR]

Abstract With the development of open source software (OSS), software reliability has become an important issue. Due to the complexity of OSS development and testing environment, the fault detection rate of OSS will show a variety of changes. The existing OSS reliability models cannot fully adapt to the complex changes of OSS fault detection. In this paper, we proposed a multi-release OSS reliability model based on three-parameter lifetime distribution. The proposed model can effectively adapt to the complicated changes of fault detection in the processes of development and testing of OSS. Experimental results indicate that the proposed model has the better fitting and predictive performance compared with other multi-release OSS reliability models. Moreover, the proposed model can better adapt to the variety of OSS fault detection environment, and assist developers to evaluate the reliability of OSS.