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Non-Markovian dynamics arising from the strong coupling of a system to a structured environment is essential in many applications of quantum mechanics and emerging technologies. Deriving an accurate description of general quantum dynamics including memory effects is, however, a demanding task, prohibitive to standard analytical or direct numerical approaches. We present a major release of our open source software package, OQuPy (Open Quantum System in Python), which provides several recently developed numerical methods that address this challenging task. It utilizes the process tensor approach to open quantum systems (OQS) in which a single map, the process tensor, captures all possible effects of an environment on the system. The representation of the process tensor in a tensor network form allows for an exact yet highly efficient description of non-Markovian OQS (NM-OQS). The OQuPy package provides methods to (1) compute the dynamics and multi-time correlations of quantum systems coupled to single and multiple environments, (2) optimize control protocols for NM-OQS, (3) simulate interacting chains of NM-OQS, and (4) compute the mean-field dynamics of an ensemble of NM-OQS coupled to a common central system. Our aim is to provide an easily accessible and extensible tool for researchers of OQS in fields such as quantum chemistry, quantum sensing, and quantum information. [ABSTRACT FROM AUTHOR]

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]

Actionable Warning Identification (AWI) plays a significant role in improving the usability of static code analyzers. Recent studies show that actionable warning identification using machine learning is effective and works well within projects as long as there is sufficient amount of data available to train the models. Currently, the AWI classifier meets the issue of constrained performance on a small number of labeled warnings. We study the Cross Project Actionable Warning Identification (CP-AWI) for cases in which few or no data are available. CP-AWI uses the labeled data of a source project as training to construct a model for the target project. However, the difference in data distribution between the source project and the target project reduces the performance of warning identification. To solve this problem, this paper proposes a CP-AWI based on hybrid PCA-MI with machine learning. Firstly, Principal Component Analysis (PCA) is used to transform the features and adapt the feature dimensions between the source and target project datasets. Subsequently, Mutual Information (MI) is applied to measure the dependency between the input transformed features obtained from PCA and the target variable. Finally, machine learning models, Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Decision Tree (DT), and Naïve Bayes (NB), are used to evaluate the proposed combination of the PCA-MI hybrid method for CP-AWI. The proposed CP-AWI system in our research, PCA-MI with RF, achieves the highest accuracy rate. This research is implemented on the eight open source java software project datasets (Ant, Cassandra, Commons.lang, Derby, Jmeter, Luence-solr, Maven, and Tomcat) and evaluated with standard evaluation metrics such as accuracy, Mean Squared Error (MAE) and Root Mean Squared Error (RMSE) and F-measure. The PCA-MI achieves an average 85% and 74% F-measure in compare to state-of-the-art methods indicates the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Although research on open collaboration for innovation has generally focused on the voluntary participation of individuals who are not strictly governed by formal authority concentrated at higher levels of organizational hierarchy, more recent studies highlight the importance of exploring how their tasks should be organized to enhance performance outcomes. This study extends this line of work by conceptualizing polyarchy as a decentralized decision-making structure that directs how core members of a project team screen a large collection of novel ideas generated by team members, and empirically examining whether and how polyarchy affects task completion time. In the context of online projects for open-source software development at GitHub, we find that a polyarchical decision-making structure reduces task completion time. Closer examination also shows that this positive effect on decision-making speed becomes stronger when tasks are explorative rather than exploitative in nature. Implications for open, distributed models of innovation and nontraditional forms of decision-making structures are discussed. [ABSTRACT FROM AUTHOR]

Adaptive optics ophthalmoscopy has enabled visualization of the in vivo human photoreceptor mosaic in health, disease and its treatment. Despite this, the clinical utility of the imaging technology has been limited by a lack of automated analysis techniques capable of accurately quantifying photoreceptor structure and a lack of an available normative image database. Here, we present a fully automated algorithm for estimating cone spacing and density over a complete adaptive optics montage along with a database of normative images and cone densities. We imaged the cone mosaics surrounding the fovea and along the horizontal and vertical meridians of fifty normal-sighted controls with a custom-built, multimodal adaptive optics scanning light ophthalmoscope. Cone spacing was automatically measured in the frequency domain and spacing measurements were converted to estimates of cone density at all locations across the montage. Consistent with previous reports, cone density measurements were highest near fovea (152,906 ± 53,209 cones/mm2) and decreased exponentially with eccentricity. A 2.5-fold variation was found in cone density estimates at 0.1 mm, this variation decreased to 1.75-fold at 1 mm. We provide all images, mosaic quantifications, and automated software open source. This database will aid investigators in translating adaptive optics ophthalmoscopy to clinical applications. [ABSTRACT FROM AUTHOR]

Physics-based computational modeling of subsurface CO2 migration constitutes the primary tool to assess geologic carbon storage. Such models are often required to plan injection operations and assess hazards such as CO2 migration into units above the storage formation. Here, we present three tools developed to increase fidelity of black-oil type geologic carbon storage models in the open-source MATLAB Reservoir Simulation Toolbox. These tools, which are published in the co2lab-mit module, include functionality to: (1) Calculate and output PVT properties of miscible brine and CO2 as a function of pressure, temperature, and salinity; (2) account for relative permeability hysteresis, necessary to model residual trapping; and (3) model CO2 transport due to concentration gradients (molecular diffusion). We validate our implementation with published results including experimental observations, present MRST examples, and conclude with some remarks on applicability, limitations, and potential extensions. Source code and examples are provided. [ABSTRACT FROM AUTHOR]

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Accurate and efficient forest inventories are essential for effective forest management and conservation. The advent of ground-based remote sensing has revolutionized the data acquisition process, enabling detailed and precise 3D measurements of forested areas. Several algorithms and methods have been developed in the last years to automatically derive tree metrics from such terrestrial/ground-based point clouds. However, few attempts have been made to make these automatic tree metrics algorithms accessible to wider audiences by producing software solutions that implement these methods. To fill this major gap, we have developed 3DFin , a novel free software program designed for user-friendly, automatic forest inventories using ground-based point clouds. 3DFin empowers users to automatically compute key forest inventory parameters, including tree Total Height, Diameter at Breast Height (DBH), and tree location. To enhance its user-friendliness, the program is open-access, cross-platform, and available as a plugin in CloudCompare and QGIS as well as a standalone in Windows. 3DFin capabilities have been tested with Terrestrial Laser Scanning, Mobile Laser Scanning, and terrestrial photogrammetric point clouds from public repositories across different forest conditions, achieving nearly full completeness and correctness in tree mapping and highly accurate DBH estimations (root mean squared error <2 cm, bias <1 cm) in most scenarios. In these tests, 3DFin demonstrated remarkable efficiency, with processing times ranging from 2 to 7 min per plot. The software is freely available at: https://github.com/3DFin/3DFin. [ABSTRACT FROM AUTHOR]

Introduction: Colorectal cancer (CRC) constitutes around 10% of global cancer diagnoses and death due to cancer. Treatment involves the surgical resection of the tumor and regional lymph nodes. Assessment of multiple lymph node demands meticulous examination by skilled pathologists, which can be arduous, prompting consideration for an artificial intelligence (AI)-supported workflow due to the growing number of slides to be examined, demanding heightened precision and the global shortage of pathologists. Method: This was a retrospective cross-sectional study including digital images of glass slides containing sections of positive and negative lymph nodes obtained from radical resection of primary CRC. Lymph nodes from 165 previously diagnosed cases were selected from Agha Khan University Hospital, from Jan 2021 to Jan 2022. The images were prepared at 10X and uploaded into an open source software, Q path and deep learning model Ensemble was applied for the identification of tumor deposits in lymph node. Results: Out of the 87 positive lymph nodes detected by AI, 73(84%) were true positive and 14(16%) were false positive. The total number of negative lymph nodes detected by AI was 78. Out of these, 69(88.5%) were true negative and 9 (11.5%) were false negative. The sensitivity was 89% and specificity 83.1%. The odds ratio was 40 with a confidence interval of 16.26–98.3. P-value was < 0.05 (< 0.0001). Conclusion: Though it was a small study but its results were really appreciating and we encourage more such studies with big sample data in future. [ABSTRACT FROM AUTHOR]

[Display omitted] The Gibbs free energies of the formation of several polyynes (C 6 H 2 , C 10 H 2 and C 16 H 2) and aromatic species (C 6 H 6 , C 10 H 8 and C 16 H 10) from methane and acetylene at temperatures of 1000–2600 K and atmospheric pressure were obtained by quantum chemical calculations using the RI-MP2 method in the ORCA open source software. At lower temperatures, aromatic species form more readily than polyynes, while at temperatures >2200 K the trend reverses and polyyne formation becomes predominant. [ABSTRACT FROM AUTHOR]

The study focuses on actor participation and power dynamics in the REDD+ project within the Bale eco-region, a vital facet of global climate policy for sustainable forest resource management. The Forest Carbon Partnership Facility (FCPF) of the World Bank supports developing countries in preparing for international REDD+ policy implementation, emphasizing the necessity of effective actor participation. The study uses text documents, Socio metric survey, and interviews with project actors and participants. This study employs both qualitative framework and social network analysis (SNA). While SNA looks at power dynamics and interactions between actors, the qualitative framework allows for a comprehensive evaluation of actor participation. The social network data was analyzed using an open source software called NodeXL. Results show that Bale eco-region REDD+ involves actors from different groups. Actors contribute to national strategies, funding, capacity building, and knowledge sharing. However, local communities have limited involvement in decision-making, lack knowledge sharing and capacity building. This hinders the implementation process and contributes to unequal power dynamics and lack the comprehensive input needed for effective and tailored strategies in the region. Calculated centrality values highlight a strong alliance among Ethiopia's national government entity overseeing REDD+ Environment, Forest and Climate Change Commission (EFCCC), and the regional entities Oromiya Forest and Wildlife Enterprise (OFWI), as well as Farm Africa, indicating significant institutional power across national and local levels. EFCCC holds influence in decision-making and strategy development, while OFWI wields substantial regional institutional power in information control and collaboration. Thus, climate policies must prioritize involvement of local communities in decision-making, capacity development, and resource accessibility. This helps to tackle power imbalances through collaborative governance structures. It also enables stakeholders to actively manage forests through imparting skills, knowledge and share benefits equally. Key Policy Insights: Implementing an inclusive, collaborative approach to governance of any REDD+ initiative creates the conditions for success through legitimate decision making and equitable. Decentralizing decision-making helps to balance power dynamics between powerful stakeholders and weaker stakeholders. Involving the private sector is recommended to enhance investment coordination for REDD+ development and mitigate potential negative impacts. Successfully implementing climate policy depends on leveraging current structures, adjusting engagement to local contexts, and fostering ongoing dialogue. [ABSTRACT FROM AUTHOR]

Software firms are increasingly adopting an open source strategy, allowing them to leverage the effort exerted by the open source community toward improving software quality. In addition to embracing a proprietary or fully open source strategy, several firms choose a partial openness strategy wherein only certain parts of the code are open source while the rest is proprietary. Specifically, when adopting a partial openness strategy, a firm may choose to make the core software code open source while keeping the extension software code proprietary or keep the core proprietary and make the extension open source. When making decisions related to different openness strategies, firms need to take into account the level of effort they are exerting toward the improvement of the quality of software, the level of engagement of the open source community, and pricing. Hence, the decisions related to a firm's openness strategy are not straightforward. While this is an important question for many firms, it has not been analyzed in the literature. In this research, we attempt to fill this important gap by analyzing different openness strategies in the context of resource allocation for fixing defects. Specifically, using a game-theoretic model, we explore when a firm should make its software fully open source or partially open source and when it should keep it proprietary. Our results show that when the baseline demand for the firm increases with the extent of openness, the firm should either make its software fully open or keep it proprietary and, importantly, should not rely on partial openness. Next, in scenarios where customers are highly sensitive to security risks, the demand loss to efficiency gain ratio has an important role in determining a firm's optimal openness strategies. These findings provide important insights to firms on how to effectively plan their openness strategies and also establish a basis for future research on the topic of partial openness. [ABSTRACT FROM AUTHOR]

Open Source Software (OSS) is one of the most trusted technologies for implementing industry 4.0 solutions. The study aims to assist a community of OSS developers in quantifying the product's reliability. This research proposes reliability growth models for OSS by incorporating dynamicity in the debugging process. For this, stochastic differential equation-based analytical models are developed to represent the instantaneous rate of error generation. The fault introduction rate is modeled using exponential and Erlang distribution functions. The empirical applications of the proposed methodology are verified using the real-life failure data of the Open Source Software projects, GNU Network Object Model Environment, and Eclipse. A soft computing technique, Genetic Algorithm, is applied to estimate model parameters. Cross-validation is also performed to examine the forecasting efficacy of the model. The predictive power of the developed models is compared with various benchmark studies. The data analysis is conducted using the R statistical computing software. The results demonstrate the proposed models' efficacy in parameter estimation and predictive performance. In addition, the optimal release time policy based on the proposed mathematical models is presented by formulating the optimization model that intends to minimize the total cost of software development under reliability constraints. The numerical illustration and sensitivity analysis exhibit the proposed problem's practical significance. The findings of the numerical analysis exemplify the proposed study's capability of decision-making under uncertainty. [ABSTRACT FROM AUTHOR]

Purpose: In order to estimate the value of semi-automated subject indexing in operative library catalogues, the study aimed to investigate five different automated implementations of an open source software package on a large set of Swedish union catalogue metadata records, with Dewey Decimal Classification (DDC) as the target classification system. It also aimed to contribute to the body of research on aboutness and related challenges in automated subject indexing and evaluation. Design/methodology/approach: On a sample of over 230,000 records with close to 12,000 distinct DDC classes, an open source tool Annif, developed by the National Library of Finland, was applied in the following implementations: lexical algorithm, support vector classifier, fastText, Omikuji Bonsai and an ensemble approach combing the former four. A qualitative study involving two senior catalogue librarians and three students of library and information studies was also conducted to investigate the value and inter-rater agreement of automatically assigned classes, on a sample of 60 records. Findings: The best results were achieved using the ensemble approach that achieved 66.82% accuracy on the three-digit DDC classification task. The qualitative study confirmed earlier studies reporting low inter-rater agreement but also pointed to the potential value of automatically assigned classes as additional access points in information retrieval. Originality/value: The paper presents an extensive study of automated classification in an operative library catalogue, accompanied by a qualitative study of automated classes. It demonstrates the value of applying semi-automated indexing in operative information retrieval systems. [ABSTRACT FROM AUTHOR]

Colorblindness is a genetic condition that affects a person's ability to accurately perceive colors. Several papers still exist making use of rainbow colors palette to show output. In such cases, for colorblind people such graphs are meaningless. In this paper, we propose good practices and coding solutions developed in the R Free and Open Source Software to (i) simulate colorblindness, (ii) develop colorblind friendly color palettes and (iii) provide the tools for converting a noncolorblind friendly graph into a new image with improved colors. [ABSTRACT FROM AUTHOR]

Some of the most deployed infrastructures nowadays are Overlay Networks (ONs). They consist of hardware and software components designed to establish private and secure communication channels, typically over the Internet. ONs are among the most reliable technologies for achieving this objective and represent the next-generation solution for secure communication. In this paper, we analyze important network performance metrics (RTT, bandwidth) while varying the type of Overlay Network used for interconnecting traffic between two or more hosts (within the same data center, in different data centers in the same building, or over the Internet). These networks establish connections between KVM (Kernel-based Virtual Machine) instances rather than the typical Docker/LXC/Podman containers. The first analysis will assess network performance as it is, without any overlay channels. The second will establish various types of channels without encryption, and the final one will encapsulate overlay traffic via IPsec (Transport mode), where encrypted channels like VTI are not already available for use. The obtained performance is demonstrated through a comprehensive set of traffic-simulation campaigns. [ABSTRACT FROM AUTHOR]

Research Summary: Recognizing the role of complementors in creating value in interdependent platform ecosystems, strategy research has recently started to examine performance heterogeneity across complementors. However, research has thus far focused on the performance implications of dynamics unfolding within a particular ecosystem. We take a step toward exploring influences that arise beyond the focal ecosystem by focusing conceptually on multihoming. We argue that multihoming to another platform produces learning benefits that enhance a complementor's performance on the home platform, especially when dealing with a high level of interdependencies and having greater similarity to other complements. We find supportive evidence in our analysis of open‐source software platforms between 2012 and 2018 and discuss implications for research on platform ecosystems, multihoming, and open‐source software. Managerial Summary: Prior studies viewed multihoming as an important strategy for complementors in platform ecosystems. However, little is known about the extent to which such expansion affects the performance of complementors on their home platforms. This study investigates this issue using data on software package complementors in a variety of platforms housed in GitHub, the world's largest repository of open‐source software. The findings show that following multihoming, a complementor experiences a performance improvement in the home platform even when compared to the performance change observed during the same period for another complementor with similar attributes but that remains in single‐homing. These findings underscore the strategic implications of multihoming as a significant driver of performance heterogeneity across complementors in platform ecosystems. [ABSTRACT FROM AUTHOR]

Background. Technological innovations have become an important agent of social change, creating new socio-psychological meaning. In this context, G.M. Andreeva's idea that social psychology should help people adapt to changing environments remains relevant. Objectives. This paper aims to highlight the socio-psychological perspective on the role of innovative technologies in social change. We present two empirical studies that demonstrate emerging socio-psychological patterns in the context of open-source software development (study 1) and the use of virtual assistants (VA) for economic decision-making (study 2). Study Participants. Study (1): 900 posts from a collaborative development platform. Study (2): 200 users of the VA popular in Russia, residents of Russian cities with a population of more than 500,000 people. Methods. Study (1): Content analysis using large language models. Study (2): Quantitative online survey assessed users' perception of their VA and previously unknown VA across 15 characteristics, subjective trust levels, and economic trust through scenarios involving monetary transactions. Results. Study (1): A pre-configured GPT-4 turbo model with an instruction consisting of two examples achieved 56% accuracy in classification. Using a fine-tuned model of the previous generation, DeBERTa-v3-Small, increased the efficiency of classifying developer messages by up to 70%. This allowed the model to be used to encode developer interactions in order to identify the factors that contribute to effective collaboration. Study (2): Both forms of trust (declared and economic) are significantly related to components of social representations (SR) of VA. Individual components of these perceptions correlate with both declared and economic trust at the level of r > 0.3, p < 0.05. However, there is a difference in the strength of these relationships for familiar and unfamiliar VAs. An assumption can be made about the possible influence of the duration of use of VA on the strength of connections between SR and trust in VA. Thus, SR formed through interaction with VA contributes to a trusting attitude towards it and, accordingly, to decision-making processes involving its use. Conclusions. Socio-psychological knowledge about the changing socio-psychological meaning can form the foundation for the development of human-centered technologies. As a part of professional work, a developer can improve current tools for the creation, management, and optimization of collaborative activities. In everyday life, it is also important to establish a level of trust that is appropriate for the situation in which a Virtual Assistant is used.

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]

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 [...]

Contributions by individual open source software (OSS) community members are the lifeblood of the OSS projects that power today’s digital economy and are important for the very survival of such communities. Individual contributions by OSS community members to others’ projects and their own determine whether OSS communities are resilient in the face of major shocks. Arguably, if crises such as the COVID-19 pandemic prompt users to reduce their contributions to others’ projects relative to the contributions to their own projects, such behavior can have implications for the overall resilience of the OSS community. Therefore, whether and how individuals change their contributions in the face of a crisis is an important question. We examine whether members in an OSS community increased or decreased their contributions to others’ projects relative to their own in the face of the COVID-19 pandemic, a sudden and unexpected global health-related shock that has affected almost everyone. We also compare and contrast this behavior when the OSS community faced increasing unemployment, an economic cyclic shock that is arguably and relatively more personal. Drawing on the concept of prosocial behavior and conservation of resources (COR) theory, we hypothesize that the pandemic increased OSS community members’ contributions to others’ projects relative to their own; on the other hand, the threat of rising unemployment decreased OSS community members’ contributions to others’ projects relative to their own. Our empirical analyses of a longitudinal dataset of over 18,000 OSS community members on GitHub, with more than 1.4 million member-day observations, support our hypotheses. This study contributes by uncovering the differential effects of exogenous health-related and economic shocks on the resilience of the OSS community. We conclude with a discussion of our findings’ implications for OSS community resilience. [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.

Most cybersecurity professionals are accustomed by now to the widespread adoption of open-source software (OSS) and the https://www.csoonline.com/article/574615/top-10-open-source-software-risks.html that target the OSS ecosystem. What we don’t seem to be getting [...]

Open source application packages, including those in Python and JavaScript, have a vulnerability in their entry points that could be used by threat actors to execute malicious code to steal [...]

Malware is infiltrating the open-source software development ecosystem at an alarming rate, according to a new report from software supply chain management firm Sonatype. The company has tracked over 500,000 [...]

In the rapidly evolving landscape of Industry 4.0, the demand for skilled professionals well-versed in Internet of Things (IoT) technologies is escalating. However, a significant challenge faced in educational settings is the lack of comprehensive and effective methodologies for imparting practical knowledge and skills in IoT. This paper presents an approach for designing and implementing an Internet of Things (IoT) laboratory in which students may practice and comprehend many components of an IoT environment, such as analysis of sensor data, IoT platform development, and setup of messaging protocols. The teaching methodology adopts a Cyber-Physical System (CPS) framework, which integrates teachers, classrooms, and resources to create a comprehensive learning environment. Bloom's taxonomy is employed to assess the efficacy of the suggested technique in terms of cognitive skills and knowledge acquisition. The evaluation procedure demonstrated the advantages of a hybrid learning environment, which integrated both face-to-face and remote instruction. The case study is conducted in an engineering higher education course on first year students. The findings of this paper lay the groundwork for a remotely deliverable IoT training course, contributing to the advancement of IoT education and equipping students with the necessary skills for the evolving landscape of Industry 4.0. [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]

Biologists increasingly rely on computer code to collect and analyze their data, reinforcing the importance of published code for transparency, reproducibility, training, and a basis for further work. Here, we conduct a literature review estimating temporal trends in code sharing in ecology and evolution publications since 2010, and test for an influence of code sharing on citation rate. We find that code is rarely published (only 6% of papers), with little improvement over time. We also found there may be incentives to publish code: Publications that share code have tended to be low‐impact initially, but accumulate citations faster, compensating for this deficit. Studies that additionally meet other Open Science criteria, open‐access publication, or data sharing, have still higher citation rates, with publications meeting all three criteria (code sharing, data sharing, and open access publication) tending to have the most citations and highest rate of citation accumulation. [ABSTRACT FROM AUTHOR]