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Recently, software application techniques have evolved from the initial manual coding to efficient parallel computing and have been widely used in cross-domain disciplines. Scene matching and recognition model are one of the typical applications of software technology in cross-domain subjects. However, the model cannot e fficiently handle large scene datasets. To address this problem, an efficient scene matching and recognition algorithm, ESMR-FFTCS, based on FFT convolution and Spark is proposed. First, a weight initialization strategy, Wl-TDEK, based on image two-dimensional entropy and K-Means is presented to use the feature values extracted by clustering as the initial weights of the convolution kernel, solving the problem of slour model training convergence. Next, a parallel convolution operation strategy, PC-FFTS, is designed to improve the performance of convolution by parallel processing of denoised image data through Spark and FFT method. Finally, a load balancing strategy, LB-AWO, based on adaptive whale optimization algorithm, is proposed to obtain the node load value at parameters merging by iterative merit seeking, solving the problem of low efficiency in parameters parallel merging. The experimental results show that the recognition accuracy of EMST algorithm is suitable for fast recognition of large-scale scene datasets and has high training efficiency. [ABSTRACT FROM AUTHOR]

Developing empathy skills is crucial for software developers to create user-centric solutions and design exceptional user experiences addressing the diverse needs of customers. This paper presents the findings of a quasi-experimental study that aimed to enhance empathy among computer science students through the exposure of two interventions utilizing teaching accessibility design. The study included 15 participants from a computer science course. Qualitative data analysis of participants' reflections highlighted the transformative impact of the interventions, as participants expressed changes in their views towards people with impairments and reported the development of technical and soft skills, as well as enhanced empathy. The interventions also motivated participants to make changes to their team website designs, prioritize accessibility, and apply their learnings in their professional lives. A second measure assessed in this study was an expert website review which provided valuable feedback for improvement and yielded a high average score for screen reader accessibility. By equipping future computer scientists with these skills, we can ensure that technology meets the diverse needs of all users, promoting inclusivity and enhancing user experiences. [ABSTRACT FROM AUTHOR]

During the early phases of software system development, error detection can be challenging due to the complexity of both the requirements and the operating environments. This paper advocates for the utilization of formal modelling and verification throughout the first phases of systems development to promptly detect and correct errors. The formalism employed throughout is Event‐B, which is backed by the Rodin toolset. To conquer requirements complexity, the frameworks of set theory and first‐order logic are employed, which provide the necessary tools for formalizing and analysing the properties and behaviours associated with Event‐B. Also, we detail the way in which modelling may be used to achieve abstraction, as well as the way in which refinement can be used to manage complexity through layering. Furthermore, we emphasize the significance of model validation and verification in improving the precision of formal models and requirements in IoT communication systems. The model is exemplified using a Content‐Based Publish Subscribe System (CBPS), with a special emphasis on a fire alarm system as a motivating example. [ABSTRACT FROM AUTHOR]

ABSTRACT Recent studies have shown that many software developers do not have sufficient knowledge and understanding of how to develop a privacy‐friendly system. This may become a challenge in developing systems complying with data protection laws. To address this issue, we investigated the factors that influence developers' decision‐making when developing privacy‐sensitive systems. We conducted an empirical study by means of a survey with 109 practitioners. Our data analysis is based on the principles of social cognitive theory, which includes personal, behavioral, and external environmental factors. We identified six personal, five behavioral, and five external environment factors that affect how developers make decisions regarding privacy, including confusion between privacy and security and reliance on informal practices and organizational support gaps. These findings contribute to understanding how practitioners and companies consider privacy, showing improvements in formal training and structured support over previous studies yet highlighting persistent challenges in consistent privacy integration. [ABSTRACT FROM AUTHOR]

Background: Visual impairment can significantly impact an individual's daily activities. Patients require regular monitoring, typically occurring within hospital eye services. Capacity constraints have necessitated innovative solutions to improve patient care. Existing digital solutions rely on task-based digital home monitoring such as visual acuity testing. These require active involvement from patients and do not typically offer an indication of quality of life. Digital phenotyping refers to the use of personal digital devices to quantify passive behaviour for detecting clinically significant changes in vision and act as biomarkers for disease. Its uniqueness lies in the ability to detect changes passively. The objective was to co-design an accessible smartphone app (OverSight) for the purposes of digital phenotyping in people with sight impairment. Methods: Development of OverSight included stakeholder consultations following principles of user-centred design. Apple iOS software frameworks (HealthKit, ResearchKit, and SensorKit) and a SwiftUI developer toolkit were used to enable the collection of active and passive data streams. Accessibility and usability were assessed using the System Usability Scale (SUS) and feedback following a 3-month pilot study. Consultations with patients informed the design of OverSight, including preferred survey scheduling and the relevancy of patient support resources. Results: Twenty visually impaired participants (mean age 42 ± 19 years) were recruited to the pilot study. The average score on the SUS was 76.8 (±8.9), indicating good usability. There was a statistically significant moderate negative correlation between SUS scores and visual acuity in both the better (r = −0.494; p ≤ 0.001) and worse eye (r = −0.421; p ≤ 0.001). Conclusions: OverSight offers promising potential for collecting patient-generated health data for the purposes of digital phenotyping in patients with eye disease. Through further testing and validation, this novel approach to patient care may ultimately provide opportunities for remote monitoring in ophthalmology. [ABSTRACT FROM AUTHOR]

The advancement of technologies and the development of more efficient artificial intelligence (AI) enable the processing of large amounts of data in a very short time. Concurrently, the increase in information within biological databases, such as 3D molecular structures or networks of functional macromolecule associations, will facilitate the creation of new methods for risk assessment that can serve as alternatives to animal testing. Specifically, the predictive capabilities of AI as new approach methodologies (NAMs) are poised to revolutionise risk assessment approaches. Our previous studies on molecular docking predictions, using the software Autodock Vina, indicated high‐affinity binding of certain toxic chemicals to the 3D structures of human proteins associated with nervous and reproductive functions. Similar approaches revealed potential sublethal interactions of neonicotinoids with proteins linked to the bees' immune system. Building on these findings, we plan to develop an AI‐based decision tool that exploits the data available on the toxicity of the most know chemical, such as LD50, and the data obtainable by their interaction with the human proteins to support risk assessment studies for multiple stressors still not characterised. Our focus will be on utilising these new bioinformatics methodologies to develop specific experimental designs that allow for confident and predictable study of the toxic and sublethal effects of pesticides on humans. We will also validate the developed NAMs by integrating existing in vivo information from scientific literature and technical reports. These approaches will significantly impact toxicity studies, guiding researchers' experiments and greatly reducing the need for animal testing. [ABSTRACT FROM AUTHOR]

The transition of cities towards a smarter approach significantly benefits from citizen participation in the development and implementation of innovative information and communication technology (ICT) products and services. Despite the emergence of various initiatives in recent years aimed at guiding the development of smart cities, there is still a lack of effective strategies to actively engage citizens, businesses, and educational institutions during the creation of these products and services. This study describes a set of practices that includes four co-creation techniques to facilitate the effort of software system development in collaboration with citizens and other stakeholders. The SEMAT standard is used to create and represent a method in which these practices are distributed across four stages: focus, definition, development, and validation. In each stage, a practice is proposed that incorporates a co-creation technique and complementary activities from various software engineering disciplines to promote active citizen participation; stimulate idea generation; and facilitate the creation of necessary documents and components for the development of the desired software system, including design systems, code files, conceptual representations, and technical diagrams, among others. Finally, the applicability and completeness of the method are validated through expert consultation in the fields of software engineering and smart cities. Recognized procedures are followed to obtain qualitative and quantitative results, such as improvement actions (addition or removal of elements), levels of consensus or acceptance, and opportunities for future work. [ABSTRACT FROM AUTHOR]

Because of their high spatial resolution over extended lengths, distributed fiber optic sensors (DFOS) enable us to monitor a wide range of structural effects and offer great potential for diverse structural health monitoring (SHM) applications. However, even under controlled conditions, the useful signal in distributed strain sensing (DSS) data can be concealed by different types of measurement principle-related disturbances: strain reading anomalies (SRAs), dropouts, and noise. These disturbances can render the extraction of information for SHM difficult or even impossible. Hence, cleaning the raw measurement data in a pre-processing stage is key for successful subsequent data evaluation and damage detection on engineering structures. To improve the capabilities of pre-processing procedures tailored to DSS data, characteristics and common remediation approaches for SRAs, dropouts, and noise are discussed. Four advanced pre-processing algorithms (geometric threshold method (GTM), outlier-specific correction procedure (OSCP), sliding modified z-score (SMZS), and the cluster filter) are presented. An artificial but realistic benchmark data set simulating different measurement scenarios is used to discuss the features of these algorithms. A flexible and modular pre-processing workflow is implemented and made available with the algorithms. Dedicated algorithms should be used to detect and remove SRAs. GTM, OSCP, and SMZS show promising results, and the sliding average is inappropriate for this purpose. The preservation of crack-induced strain peaks' tips is imperative for reliable crack monitoring. [ABSTRACT FROM AUTHOR]

The goal of this research study was to identify and prioritize the significant cybersecurity challenges that vendor firms encounter during software development. Using Systematic Literature Reviews (SLRs), 13 significant challenges were found, including "Security issues/Access of Cyberattacks", "Lack of Right Knowledge", "Cost Security Issues", and "Lack of Confidentiality and Trust" among others. To address these concerns, a multifaceted strategy that prioritizes continuing education, training, and investment in cybersecurity measures, as well as cross‐industry cooperation and coordination with government entities, is required. These challenges were ranked using the Fuzzy Analytic Hierarchy Process (F‐AHP). We obtained the following results after applying the Fuzzy Analytic Hierarchy Process: CSC1 (Cyber Security Challenge‐1) "Security Issues/Access of Cyber Attacks", CSC2 "Lack of Right Knowledge", and CSC3 "Framework" are the top most critical cyber security challenges, with weightages of 0.1687, 0.1672, and 0.1194, respectively. This study lays the groundwork for future research and assists vendor organizations in addressing the cybersecurity concerns they face during software development. The study also emphasizes the significance of addressing cybersecurity during the software development process in order to avoid the financial and reputational losses associated with cyber intrusions. [ABSTRACT FROM AUTHOR]

The paper presents an analysis of tasks performed by the software for automated control systems of autonomous electric power plants, leading to the identification of functional requirements. This analysis establishes operational modes, such as scheme designer mode and autonomous electric power plant monitoring and control mode, defines the component library, and outlines requirements for each component. The use of discretedeterministic models in the form of digital automata, the research formalizes the problem of analyzing and synthesizing control algorithms. The novelty of this methodology lies in integrating digital automata with UML diagrams to develop adaptive software. By linking UML state diagrams directly with digital automata models, the system ensures consistency between conceptual design and code implementation. The research contributes to best practices in software engineering for complex, distributed control systems. The approach, proposed in the paper, allows developers to conceptualize the user interface of automated operator workstations as interconnected systems with defined relationships and communication types. [ABSTRACT FROM AUTHOR]

The growing and unmet demand for coding skills is becoming critical in a world that is ever-more driven by digital technologies, embedded algorithms and artificial intelligence systems. However, sustainability of the profession is threatened because of the failure to attract and retain women developers, which has been an ongoing and corrosive problem for decades and remains unresolved. While many previous studies attribute 'toxic' workplace cultures in the software development industry, as a major contributing factor, few examine their root causes and almost none offer practical solutions. To address this lack of both knowledge and effective response, we propose a novel approach building on psychoanalytical Transactional Analysis (TA) theory that is little used in the field of management and organisation studies. TA theory provides a framework using common and simplified language to better understand why communications in the workplace fail, and how occupying incompatible ego states might lead to ' negative ', ' problematic' and, in the worst cases, ' toxic' behaviours and workplaces. We propose a TA-based model (OCTAPos) that helps explain how crossed communications at work can result in a lack of acceptance of women in the workplace and attrition among female software developers and the resultant dearth of diverse coders. We further propose a theoretically informed HRM Structured TA Response (STAR) to help increase self-awareness, emotional intelligence, empathy and mutual understanding, with the ultimate aim of positively impacting prevailing attitudes, behaviours and organisational culture to achieve more inclusive and sustainable recruitment and retention in the longer term. [ABSTRACT FROM AUTHOR]

Food waste is a pressing global issue, particularly in urban settings, where substantial amounts of surplus food go unused. In corporate environments, this challenge is compounded by the lack of dedicated platforms to facilitate food sharing and reduce waste effectively. This paper examines the current landscape of food waste, existing solutions, and the need for a specialised platform aimed at corporate employees. The proposed solution is the creation of a user-friendly application that enables the sharing of untouched homemade meals. Suppliers can post their meal boxes with details such as location, type of food, and availability status, while consumers can search for and select meal boxes based on their preferences. This paper addresses the gap in solutions for reducing food waste within corporate environments. The meal-box-sharing app provides a practical and sustainable method for minimising food waste and promoting productivity, health, and safety in the workplace. [ABSTRACT FROM AUTHOR]

This article contributes to recent scholarship on platform, software and media studies by critically engaging with the 'social coding' platform GitHub, one of the most prominent actors in the online proprietary and F/OSS (free and/or open-source software) code hosting space. It examines the platformisation of software and project development on GitHub by combining institutional and cultural analysis. The institutional analysis focuses on critically examining the platform from a material-economic perspective to understand how it configures contemporary software and project development work. It proposes the concept of 'connective coding' to characterise how software intermediaries such as GitHub configure, valorise and capitalise on public repositories, developer and organisation profiles. This institutional perspective is complemented by a case study analysing cultural practices mediated by the platform. The case study examines the platform vernaculars of news media and journalism initiatives highlighted by Source, a key publication in the newsroom software development space, and how GitHub modulates visibility in this space. It finds that the high-visibility platform vernacular of this news media and journalism space is dominated by a mix of established actors such as the New York Times, the Guardian and Bloomberg, as well as more recent actors and initiatives such as ProPublica and Document Cloud. This high-visibility news media and journalism platform vernacular is characterised by multiple F/OSS and F/OSS-inspired practices and styles. Finally, by contrast, low-visibility public repositories in this space may be seen as indicative of GitHub's role in facilitating various kinds of 'post-F/OSS' software development cultures. [ABSTRACT FROM AUTHOR]

ABSTRACT Context Purpose Method Results Conclusion With the fast advancement of techniques in artificial intelligence (AI) and of the target infrastructures in the last decades, AI software is becoming an undeniable part of software system projects. As in most cases in history, however, development methods and guides follow the advancements in technology with phase differences.With an aim to elicit and integrate available evidence from AI software development practices into a process model, this study synthesizes the contributions of the validation studies reported in scientific literature.We applied a systematic literature review to retrieve, select, and analyze the primary studies. After a comprehensive and rigorous search and scoping review, we identified 82 studies that make various contributions in relation to AI software development practices. To increase the effectiveness of the synthesis and the usefulness of the outcome, for detailed analysis, we selected 14 primary studies (out of 82) that empirically validated their contributions.We carefully reviewed the selected studies that validate proposals on approaches/models, methods/techniques, tasks/phases, lessons learned/best practices, or workflows. We mapped the steps/activities in these proposals with the knowledge areas in SWEBOK, and using the evidence in this mapping and the primary studies, we synthesized a process model that integrates activities, artifacts, and roles for AI‐enabled software system development.To the best of our knowledge, this is the first study that proposes such a process model by eliciting and gathering the contributions of the validation studies in a bottom‐up manner. We expect that the output of this synthesis will be input for further research to validate or improve the process model. [ABSTRACT FROM AUTHOR]

The increasing demands on software development are putting serious pressure on its pace. To assist software developers, an increasing number of tools powered by generative artificial intelligence are being introduced. This paper aims to investigate how the use and integration of generative AI have evolved among professionals in the software industry, based on a study involving 104 individuals working in Bulgarian software companies. Data was collected in April 2024 through an online questionnaire with four separate groups of questions related to the use of generative AI at work. The study found that 2/3 of the respondents use generative AI actively in their daily work. They highly value the practical benefits of this type of technology, which most often consist of automating routine activities, accessing information quickly, generating initial code, and writing documentation. As a result of these benefits, developers are increasingly moving towards using generative AI at the expense of professional support platforms. The main benefits they cite include faster solutions, more specific and relevant answers, and significantly shorter time to reach the desired outcome. [ABSTRACT FROM AUTHOR]

In predictive microbiology, both primary and secondary models are widely used to estimate microbial growth, often applied through two-step or one-step modelling approaches. This study focused on developing a tool to predict the growth of Pseudomonas spp., a prominent bacterial genus in food spoilage, by applying machine learning regression models, including Support Vector Regression (SVR), Random Forest Regression (RFR) and Gaussian Process Regression (GPR). The key environmental factors—temperature, water activity, and pH—served as predictor variables to model the growth of Pseudomonas spp. in culture media. To assess model performance, these machine learning approaches were compared with traditional models, namely the Gompertz, Logistic, Baranyi, and Huang models, using statistical indicators such as the adjusted coefficient of determination (R2adj) and root mean square error (RMSE). Machine learning models provided superior accuracy over traditional approaches, with R2adj values from 0.834 to 0.959 and RMSE values between 0.005 and 0.010, showcasing their ability to handle complex growth patterns more effectively. GPR emerged as the most accurate model for both training and testing datasets. In external validation, additional statistical indices (bias factor, Bf: 0.998 to 1.047; accuracy factor, Af: 1.100 to 1.167) further supported GPR as a reliable alternative for microbial growth prediction. This machine learning-driven approach bypasses the need for the secondary modelling step required in traditional methods, highlighting its potential as a robust tool in predictive microbiology. [ABSTRACT FROM AUTHOR]

This study addresses the problem of how to automatically generate source code that is not only functional, but also well-structured, readable, and maintainable. Existing generative models for source code often produce functional code, but they lack consistency in structure and adherence to coding standards, essential for integration into existing application development projects and long-term software maintenance. By training the model on specific code structures, including a dataset with Italian annotations, the proposed methodology ensures that the generated code is compliant with both the functional requirements and the pre-defined coding standards. The methodology proposed in this study applies transfer learning techniques on the DeepSeek Coder model, to refine pre-trained models to generate code that integrates additional structuring constraints. By training the model on specific code structures, including a dataset with Italian comments, the proposed methodology ensures that the generated code meets both functional requirements and coding structure. Experimental results, evaluated using the perplexity metric, demonstrate the effectiveness of the proposed approach, which impacts the goals of reducing errors, and ultimately improves software development quality. [ABSTRACT FROM AUTHOR]

Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of WISTA Wirtschaft und Statistik is the property of Statistisches Bundesamt and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The activity of game modding has often been viewed as a fringe form of informal, hobbyist and amateurish software development despite its considerable growth in popularity in the past two decades. However, it is hard to see how supposedly free-form and unorganized groups of volunteers are able to successfully carry out projects of such magnitude as total conversion mods. Considering the small number of studies dedicated to understanding the mechanical processes of mod development, this research is focused on analyzing the project development practices of nine total conversion game mod teams. Relying on semi-structured interviews guided by the 10 knowledge areas of the Project Management Body of Knowledge, we aimed to develop an initial and clearer picture of the activities related to project organization, management, and software development that take place among these groups. Our findings point towards total conversion modding being an activity that, although highly informal, does tend to present recurring patterns that can be seen as preferred practices, thereby positioning it as more than just a collective and voluntary form of 'lawless' software development. [ABSTRACT FROM AUTHOR]

Abstract-- In the domain of software development, the evaluation of developer expertise has gained prominence, particularly with the rise of serverless functions. These functions, which simplify the development process by delegating infrastructure management to cloud providers, are becoming more common. As developers may utilize functions created by their peers, understanding the expertise of the original developer is crucial since it can serve as an indicator of the functions' quality. While there are existing methods for expertise evaluation, certain gaps remain, especially concerning serverless functions. To address this, our research aims to enhance the assessment of developer expertise in this area by extracting activity-based features from both GitHub and Stack Overflow. After processing the extracted data, we applied various machine learning algorithms. Our findings suggest a potential improvement in evaluating developer expertise when incorporating features from Stack Overflow compared to using only GitHub data. The extent of this improvement was observed to differ among programming languages, with variations in accuracy improvement percentages ranging from 2% to 19%. This study contributes to the ongoing discourse on developer expertise evaluation, highlighting the potential benefits of drawing from multiple data sources.

The manual room booking process at Paramadina University faces several challenges, including inefficiencies, human errors, and communication breakdowns between applicants and facility managers. These issues often result in room booking conflicts and delays, hindering effective facility usage. The need for a more streamlined, user-friendly solution led to the development of the Roomhub Paramadina application, a web-based system designed to facilitate room bookings within the university. The objective of this research is to design and implement a digital room booking system that simplifies the current manual process. The application was developed using the Software Development Life Cycle (SDLC) with the Waterfall framework. This approach ensures structured and systematic development, enabling the team to follow sequential stages from requirements gathering to testing. The Roomhub application allows users to view available rooms, submit booking requests, and receive confirmations directly via an online platform. The system integrates with existing university infrastructure and ensures real-time room availability, reducing errors such as double bookings. Black-box testing was employed to verify the application’s functionality, and the results confirmed that the system meets user needs while significantly improving booking efficiency. By automating room bookings and enhancing communication between the parties involved, this research offers a practical solution that improves operational efficiency, reduces paperwork, and minimizes scheduling conflicts, thereby benefiting the entire academic community at Paramadina University.

Human capital has encountered many challenges and changes during the COVID-19 Pandemic. This adaptability that we needed to embrace, to face the many challenges of the Pandemic context, have also increased the human capital capabilities in the labour market. Many companies have significantly transformed their business operations. Both workers and companies had to adopt new practices and ways of working while facing various challenges. One of the most notable changes is the shift towards remote work, which has led to a surge in the use of Agile Methodology, particularly in software development. Agile Methodology is a dynamic and flexible approach that fosters an empowering work environment, encouraging creativity, autonomy, continuous feedback, effective communication, and iterative progress. This paper aims to explore the positive correlation between the increased adoption of Agile Methodology during the pandemic, driven by social distancing measures, and its impact on Human Capital, especially within the IT development workforce.

This article explores the transformative potential of gamification in software development education, aiming to address the challenges of sustaining student engagement. Traditional teaching methods, foundational yet at times lacking in dynamism, are juxtaposed against the immersive experience offered by gamification. The case study unfolds in a renewable energy degree programming class, where Genially games inject vitality into the learning environment. Students, organized into groups, embark on a virtual board game journey using coding challenges. Game formats such as hangman, 3 in a row, snakes and ladders, and Jumanji introduce basic programming concepts, strategically progressing in complexity. Genially serves as a versatile digital tool, crafting interactive board games that not only teach coding but transform learning into a thrilling adventure. Research questions delve into the impact of gamification on student accomplishment, challenge perception, competence, guidance, immersion, playfulness, and social involvement. By examining the experiential journey of a gamified programming class, the article contributes to the growing knowledge on gamification in education. This transformative approach not only teaches programming concepts but also immerses students in a dynamic and engaging adventure, addressing the evolving landscape of education with a focus on motivation and personalized learning experiences. [ABSTRACT FROM AUTHOR]

Multi-core shared memory architectures have become ubiquitous in computing hardware nowadays. As a result, there is a growing need to fully utilize these architectures by introducing appropriate parallelization schemes, such as OpenMP worksharing-loop constructs, to applications. However, most developers find introducing OpenMP directives to their code hard due to pervasive pitfalls in managing parallel shared memory. To assist developers in this process, many compilers, as well as source-to-source (S2S) translation tools, have been developed over the years, tasked with inserting OpenMP directives into code automatically. In addition to having limited robustness to their input format, these compilers still do not achieve satisfactory coverage and precision in locating parallelizable code and generating appropriate directives. Recently, many data-driven AI-based code completion (CC) tools, such as GitHub CoPilot, have been developed to ease and improve programming productivity. Leveraging the insights from existing AI-based programming-assistance tools, this work presents a novel AI model that can serve as a parallel-programming assistant. Specifically, our model, named PragFormer, is tasked with identifying for loops that can benefit from conversion to parallel worksharing-loop construct (OpenMP directive) and even predict the need for specific data-sharing attributes clauses on the fly. We created a unique database, named Open-OMP, specifically for this goal. Open-OMP contains over 32,000 unique code snippets from different domains, half of which contain OpenMP directives, while the other half do not. We experimented with different model design parameters for these tasks and showed that our best-performing model outperforms a statistically-trained baseline as well as a state-of-the-art S2S compiler. In fact, it even outperforms the popular generative AI model of ChatGPT. In the spirit of advancing research on this topic, we have already released source code for PragFormer as well as Open-OMP dataset to public. Moreover, an interactive demo of our tool, as well as a Hugging Face webpage to experiment with our tool, are already available. [ABSTRACT FROM AUTHOR]

In order to study the characteristics of the wake magnetic anomaly when multiple ships are sailing at the same time, based on the basic theoretical knowledge of ship wake, the waveform, velocity, and distribution of the induced magnetic field of the ship wake were studied, and the variation characteristics and distribution patterns of the induced magnetic field of the multi-ship wake were obtained. By taking the formation of three ships as an example, the distribution of the induced magnetic field and the distribution of the magnetic field in the directions of x and y during the movement of the ships were analyzed. The magnetic field distribution characteristics of the wake formed when the three ships sail at the same time were solved, and the abnormal induced magnetic field generated during various ship combination operations was analyzed. The experimental results obtained from the simulation were consistent with the existing measurement results. APP Design tools were used for magnetic anomaly simulation system software development of multi-ship wake. The system not only could accurately, intuitively, and quickly simulate and set the magnetic induction intensity and variation patterns of the wake of three ships in any direction but also had the advantages of simple operation and cost saving, which provided a theoretical reference and a more effective simulation platform for ship wake experiment.

【Objective】 Improving efficiency and water flow rate is a criterion in designing pumping stations. This paper presents a software we developed to optimize the combination of running pumps and blade angles for drainage pumping stations that are equipped with fully adjustable axial (mixed) flow pumps. 【Method】 The software was developed using Java in the integrated environment IntelliJ IDE, with a universal visualization interface. It calculated water flow rate and flow resistance parameters of each pump, based on measured operating parameters, which include head and motor input powers, and performance curve of each pump. The software generated curves illustrating how the device efficiency changes with the blade angle. A dynamic programming model was established to optimize the operation and scheduling of the station, with the optimization objective to maximize the average device efficiency or total flow rate. This enables us to obtain an optimal combination of running pumps and blade angle for each running pump. 【Result】 The software was applied to some drainage pumping stations to graphically analyze the variation in device efficiency with blade angle (or flow rate). It provides optimized operation plans under different required flow rates when Hsy=5.3 m. When the required flow rate was 32 m3/s, the optimal scheme provided by the software improves the efficiency of the device by 0.76%, compared to the simple decision scheme for each running pump with a blade angle of +1.0°. 【Conclusion】 The software we developed is accurate and user - friendly. Practical applications showed that its performance is stable and can improve the operation and management of drainage pumping stations.

Changes in flow rates in pressurized pipeline systems, due to power loss in a pump, opening or closing of a valve, load rejection by a turbine, etc., may lead to unsteady conditions called fluid transients or water hammer in the pipeline, which, in turn, can lead to dangerous consequences. This potential phenomenon must be checked during the design phase of pipeline systems in order to prevent such dangerous situations. For this purpose, many software programs have been developed worldwide to analyze fluid transients. In this study, a computer software was developed that analyzes this type of flow in pipeline systems using C# programming language. In addition to simulating and solving problems in transient flows in pipeline systems, this will help the purpose of having a free domestic transient flow software for education and research purposes, avoiding expensive alternatives. The method of characteristics is used to solve the nonlinear, hyperbolic partial differential equations of unsteady pipe flow. The developed software was tested using various well-known benchmark cases for validation purposes. It was shown that the computed results of the new software are very similar to those obtained in the literature, proving the accuracy and reliability of the new program. In future studies, it is hoped that the program will be more comprehensive with more boundary conditions added to the program. In this paper, the developed program and some of its important features will be introduced, and validation of the program with certain benchmark studies will be provided. [ABSTRACT FROM AUTHOR]

DevOps is an approach that describes a set of practices and cultural values whose main objective is to integrate different teams within an organization at the development and operations level. This approach allows for improving the effectiveness of communication, collaboration, value delivery in software development, and organizational culture. Currently, organizations from different economic sectors find themselves in need of developing software and implementing DevOps practices to improve delivery frequency without sacrificing the quality and stability of the solution. Among the main issues when implementing DevOps is how to perform traceability of added value, henceforth, Value Stream. The Value Stream is presented as a sequence of activities responsible for producing and delivering value to customers and users through a product or service. Given its importance for organizations, this work addresses the most common challenges in organizations to perform Value Stream traceability, the proposals presented in the current literature for this process, and the unexplored research challenges found in the literature on the Value Stream. To do this, a systematic literature mapping is developed, resulting in the identification of various practices. From these, a set of challenges and practices that various organizations could implement to perform DevOps process traceability with a focus on the Value Stream are consolidated and presented. [ABSTRACT FROM AUTHOR]

Copyright of Informatics Control Measurement in Economy & Environment Protection / Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska is the property of Lublin University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Most organizations have begun to adopt agile methods to pursue successful software development. However, the adoption and implementation of agile approaches are facing various challenges. The success of agile software development depends on Critical Success Factors (CSFs), which this study aims to identify and classify based on their relative importance. Through an extensive literature review, these factors are summarized, screened, and categorized into six dimensions. Their evolution is also outlined and analyzed. Then, the factors are illustrated through a bubble chart. Furthermore, this study determines the relevant CFSs that have a significant impact on how effectively can agile software development be implemented in China. The findings suggest certain recommendations to ensure that agile software projects are efficiently implemented in China, maximizing the chances of project success, providing valuable insights and practical guidance. [ABSTRACT FROM AUTHOR]