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Matching the supply of medical equipment with soaring demand caused by an epidemic event is a daunting challenge. Focusing on medical equipment manufacturers, this study empirically examines how supply chain management impacts manufacturers' responses to epidemic-driven demand. This study employs novel supply chain data from South Korea collected before and after the MERS (Middle East Respiratory Syndrome) epidemic event. In the results, we show that such an epidemic positively influences the sales performance of medical equipment manufacturers (i.e. a 10.2 percentage-point increase). To identify the mechanism behind this result, we provide evidence that increasing the supply base (i.e. the number of supply channels) enables medical equipment manufacturers to better respond to epidemic-driven demand and ultimately improves their sales performance. More interestingly, we find that medical equipment manufacturers with lower market status (i.e. lower market shares and lower supplier shares) expand their supply bases more extensively than other medical equipment manufacturers with higher market status do. Interpreting these results, we offer managerial insights into how medical equipment manufacturers can effectively manage their supply chains and improve their performance when responding to epidemic-driven demand. [ABSTRACT FROM AUTHOR]

The interface of L3 automated driving systems needs to take both automated and manual driving into consideration. This calls for a comprehension of drivers' differed risk perception under manual and automated driving conditions, thus supporting description of contextual information and appropriate risk warnings. Existing studies have reported drivers' impaired ability during automated driving, however, a quantified measurement is still lacking. This study tried to measure the difference in drivers' ability to perceive risks during automated and manual driving. Specifically, a simulated driving experiment in car-following scenarios was conducted to collect drivers' perceived risk under multiple manual and automated driving conditions, including varied motion directions, speed, and distance among vehicles. Then, the influences of driving mode, motion directions, speed, and distance on drivers' risk perceptions were described using a linear mixed model. The result demonstrated a complicated interaction effect. Automated driving impaired drivers' risk perception, and this effect was less severe in highly risky events. In both manual and automated driving, drivers were less sensitive to risk only when risky events happened backwards. These results indicated drivers' varied ability under multiple conditions, and supported warning design and interface refinement under automated and manual driving conditions. [ABSTRACT FROM AUTHOR]

This article examines university students' competence regarding plagiarism in their final theses, including bachelor's, master's, diploma theses, and dissertations. We used a structured survey to evaluate students' accuracy in answering twelve plagiarism-related statements. Despite extensive efforts, only 131 individuals participated. In addition to the knowledge about plagiarism, the survey also assessed perceptions, experiences, and awareness of the issue. The article begins by briefly defining plagiarism relevant to this study, focusing primarily on textual plagiarism, and outlines the existing legal frameworks in Austria and Germany. The survey results indicate that students possess satisfactory knowledge of plagiarism: over 80 % correctly assessed the truthfulness of at least nine statements, and ten students achieved a maximum score. This high level of knowledge is likely due to 76 % of respondents having previously engaged with plagiarism and 95 % claiming to understand what plagiarism is. However, there is room for improvement in the information university teachers provide. Only around a third of the respondents reported being (very) well informed about plagiarism by their instructors during their studies. [ABSTRACT FROM AUTHOR]

Objectives: This study investigates the changing perceptions of Korean care workers regarding a robotic bed designed to assist with repositioning and prevent pressure ulcers. With the primary aim of assessing the technology acceptance among care workers using the robotic bed to solve the problem of a shortage of care workers in Korea, we sought to examine the possibility of applying care robots in the field. Methods: A total of 20 long-term care workers participated in the experiment, and their attitudes were measured before and after using the robot. Frequency analysis and paired t-tests were conducted using Stata 17 to analyze the data. Results: The results show significant changes in the perceived ease of use (PEOU), facilitating conditions (FCs), and gerontology anxiety (GA), with the PEOU increasing by 19.87%, FC increasing by 20.63%, and GA decreasing by 17.2%. However, there was no significant change in the perceived usefulness (PU) and intention to use (IU). Conclusions: The results showing that the perception of technology acceptance changed significantly mean that the use of the care robot means that there is a high possibility of positive perceptions in Korean care settings when care robots are distributed in the field in the future, considering that the experimental environment was limited due to the early stage of development of care robots. This study highlights the need for practical demonstrations and thorough training to improve technology acceptance among care workers before the application of care technology in the long-term care environment in South Korea. [ABSTRACT FROM AUTHOR]

Finding pathways to enable values-for-sustainability transformation is crucial. Despite the emergence of strategic insights and theoretical frameworks like the Four Perspectives, there remains a scarcity of empirical work to directly engage with values for pragmatic learning. We address this gap by presenting an empirical case study of an intervention designed to move two groups from non-collaborative to collaborative on the basis of newly found shared values. By conceptualizing values as tacit knowledge and employing the Knowledge Creation Theory, our intervention facilitated the development of cross-group shared values through Externalization and Combination modes. Our results demonstrate empirical evidence of values-engagement processes including Surfacing and Negotiation from Four Perspectives (i.e. Surfacing values, Negotiating values, Eliciting values, Transforming through values), and informs their sequence of appearance. We highlight the importance of a pool of shared experiences as basis for Surfacing, and demonstrate the utility of the SECI (Socialization, Externalization, Combination, Internalization) model to plan the utilization and sequencing of values-engagement processes towards achieving sustainability transformations. The findings suggest that starting with participants having shared experiences facilitates Surfacing and Negotiation of values, and enables developments of cross-group collaboration valuable for providing preparedness for a diversity of sustainability transformation contexts. [ABSTRACT FROM AUTHOR]

This systematic review synthesizes the literature (involving 62 empirical studies) regarding the effectiveness of cognitive linguistics‐inspired language pedagogies (CL‐ILPs) on second language (L2) learning. It begins with an overview of the main theoretical tenets of cognitive linguistics followed by a description of the data selection, coding, and analysis. Then, besides noting a sharp increase of research on CL‐ILPs in the past 20 years, the review presents four main findings: (a) While various language features have been taught in CL‐ILPs, the teaching targets of CL‐ILPs have been mainly low‐schematic constructions, such as phrasal verbs and prepositions, but a few recent studies have explored the teaching of clause‐ or sentence‐level structures including conditional clauses, (b) conceptual metaphor, cognitive grammar, construction grammar, and cognitive semantics have been the main guiding cognitive linguistics theories applied in language teaching, and the hallmark teaching practices of CL‐ILPs include the use of technology‐supported or technology‐delivered visuals, schemas, and diagrams; embodied activities; and group and/or pair work, (c) methodologies used in CL‐ILP research have become increasingly sophisticated, and (d) CL‐ILPs have been found to be effective in 93.5% (i.e., 58) of the 62 reviewed studies covering learners of different age groups, first languages, and learning contexts. These findings are discussed to uncover insights concerning CL‐ILP research. Pedagogical implications and future research directions are also discussed. [ABSTRACT FROM AUTHOR]

This study is based on the theory of emotional events and explores the relationship between emotional labor and the job performance of university teachers. Relevant data was obtained through a questionnaire, and a structural equation model was constructed for path analysis. The study found that the surface performance of university teachers has a significant negative impact on relationship performance and task performance; deep performance and proactive authenticity of university teachers have a significant positive impact on relationship performance, task performance, and adaptability performance, respectively; the passive reality of university teachers has a significant negative impact on relationship performance, task performance, and adaptability performance. Based on research findings, this study proposes suggestions for the performance evaluation mechanism of university teachers, including improving the recruitment methods, increasing the emotional labor assessment and incentive mechanism, focusing on alleviating negative emotions of teachers, strengthening the awareness guidance and education of school management and teachers on emotional labor, establishing training courses on emotional labor for teachers, arranging various activities reasonably, and creating a good working atmosphere. Ultimately, these suggestions aim to help university teachers recognize the significance of emotional labor, promote their educational and teaching work, and provide new methods and paths for improving the work performance of university teachers. [ABSTRACT FROM AUTHOR]

In the context of global carbon peak and carbon neutrality goals, researching the driving forces and influencing factors behind the growth in sales of new energy vehicles (NEVs) is particularly urgent and crucial. Although the academic community has extensively explored various factors affecting NEV sales, technological innovation, as the core engine driving industry progress, is yet to receive sufficient and in-depth exploration regarding its potential profound impact on sales. Therefore, this study thoroughly analyzes the mechanism by which technological innovation influences sales, supplementing the existing literature, exploring sustainable industry development, and guiding the optimization of business strategies and precise government policies. This research employs the quality of NEV technology patents as a proxy variable for technological innovation, and conducts an empirical analysis using China's NEV sales data from 2015 to 2022. The results of the study show the following: (1) the quality of technological innovation significantly contributes to the growth of new energy vehicle sales. This conclusion still holds when the administrative protection of intellectual property rights and the government's public innovation environment indicators are used as instrumental variables of technological innovation quality. (2) Technological innovations in different links of the new energy vehicle industry chain have a positive impact on sales, especially those in the midstream battery, motor, and downstream charging services. This shows that in the critical period of new energy vehicle development, not only is the breakthrough of key technologies crucial, but also, non-critical technologies can promote sales growth by improving user experience and product performance. (3) Compared with plug-in hybrid electric vehicles (PHEVs), the overall technological innovation of new energy vehicles (NEVs) has a more significant effect on the sales of battery electric vehicles (BEVs). (4) In addition, the study finds that there is no significant difference between high-quality and low-quality technological innovations on the promotion of new energy vehicle sales, which indicates that the market values the overall effect of technological innovations more than the mere level of quality. The study of the impact of technological innovation on sales in different branches of the new energy industry chain can help to clarify the direction of technological innovation, optimize resource allocation, promote the synergistic development of the industry chain, enhance market competitiveness, and provide a scientific basis for policy formulation. [ABSTRACT FROM AUTHOR]

AbstractFace Recognition Payment (FRP) technology and devices in the offline market continue to mature and popularize, and how to further promote the use of FRP deserves our attention. Previous studies have focused more on the antecedent elements that influence users’ intention to use FRP. To date, we still know little about what motivates the gap between FRP use intention and behavior. We conduct a two-stage study using a mixed-methods approach. We conduct 142 interviews in Study 1 to identify barriers to this study. Based on this, the appropriate research model is formulated. In study 2, we collect a valid sample of 275 data points and employ regression analysis to examine the model. To answer this question, this study adopts innovation resistance theory (IRT) to develop a research model. The results suggest that the degree of influence of FRP use intention on use behavior depends on users’ habits, complexity, embarrassment, perceived privacy risk, and expected switching costs. All of these factors negatively influence the extent to which intention acts on behavior. This study helps bridge the FRP use intention–behavior gap and provides practical suggestions for offline retailers and design units in designing and marketing FRP technology in a rational way. [ABSTRACT FROM AUTHOR]

Raising the user's self-confidence is a promising strategy to reduce product-related user stigma. In the context of product usage, the commonly used term self-confidence refers to the psychological construct of self-efficacy. To strengthen a user's self-efficacy through product design, providing both good usability and emotionality in a product seems to be a reasonable starting point. However, their suitability and validity for this purpose has not yet been sufficiently assessed. This paper examines whether self-efficacy would be associated with perceptions of a product's usability and emotionality. By conducting an online survey (n = 105; stigma-sensitive product demonstrator: walker), it was confirmed that the perception of good usability and emotionality of walkers were positively associated with the user's perceived self-efficacy. Moreover, a negative interaction effect was identified showing that the association between emotionality and self-efficacy increased with lower levels of perceived usability and vice versa. This may indicate that emotions can compensate the importance of usability at least to some extent. [ABSTRACT FROM AUTHOR]

The port sector encompasses businesses and institutions that manage the transport of cargo through maritime means. This sector is responsible for the transportation of vehicles, medical products, food, and other goods managed by various stakeholders. To appropriately manage ports, it becomes necessary to comprehend how management approaches are employed in the decision-making process to fulfil the demands of potential consumers. This study aims to assess how marketing, entrepreneurship and innovation are employed in the management of the port sector. This systematic literature review started with a bibliometric analysis using Bibliometrix tool in R to offer a broader view of scientific production in the theme. The data employed in the bibliometric analysis were extracted from Web of Science and Scopus. Next, a content analysis was performed in order to identify barriers, trends, and an agenda for future research. The results are believed to offer new insights on the role of marketing, entrepreneurship, and innovation in ports both to academics and practitioners. Among the outcomes achieved, we highlight the fundamental role of boosting port management in the three variables of interest, as well as the encouragement of port funding. [ABSTRACT FROM AUTHOR]

Natural hazards are the extreme events that significantly distress life on Earth. To mitigate the detrimental impacts of these extreme events, it is essential to examine and model them using a probabilistic approach. Probability distributions are competent enough to analyze the exponential behavior and estimate the pattern of randomness in these real-life phenomena. We use the generalized Pareto-exponential distribution (GPED) and find it to be an appropriate model for extreme events that involve exponentially decaying variables. Interestingly, the GPED also comprises the features of both the well-known exponential and Pareto distributions and approaches several other well-known distributions after certain transformations. We derive its various probabilistic characteristics and provide an empirical study for different parametric values to observe their behavior. We follow the maximum likelihood method to estimate the unknown model parameters and conduct a simulation study for different sample sizes and different combinations of the model parameters to examine their stability. We also demonstrate the applicability of our model by using a data set from the field of seismology and establish its better performance by comparing it with some extant distributions. [ABSTRACT FROM AUTHOR]

A large body of research effort has been dedicated to automated issue classification for Issue Tracking Systems (ITSs). Although the existing approaches have shown promising performance, the different design choices, including the different textual fields, feature representation methods and machine learning algorithms adopted by existing approaches, have not been comprehensively compared and analyzed. To fill this gap, we perform the first extensive study of automated issue classification on 9 state-of-the-art issue classification approaches. Our experimental results on the widely studied dataset reveal multiple practical guidelines for automated issue classification, including: (1) Training separate models for the issue titles and descriptions and then combining these two models tend to achieve better performance for issue classification; (2) Word embedding with Long Short-Term Memory (LSTM) can better extract features from the textual fields in the issues, and hence, lead to better issue classification models; (3) There exist certain terms in the textual fields that are helpful for building more discriminating classifiers between bug and non-bug issues; (4) The performance of the issue classification model is not sensitive to the choices of ML algorithms. Based on our study outcomes, we further propose an advanced issue classification approach, DeepLabel, which can achieve better performance compared with the existing issue classification approaches. [ABSTRACT FROM AUTHOR]

AbstractAdvancements in technology have made autonomous delivery vehicles (ADVs) a feasible option for “last-mile” delivery, improving logistic efficiency in the global digital age. The promotion and popularization of ADVs must be based on widespread user acceptance. This study combines the technology readiness theory and innovation resistance theory to construct a research model to explore the key factors influencing users’ use and recommendation of ADVs. We collected 349 valid samples through a questionnaire survey and used partial least squares-based structural equation modeling (PLS-SEM) to validate the model. The results showed that (1) positive technology readiness reduces users’ perceptions of value barrier, tradition barrier, and image barrier, while negative technology readiness increases users’ perceptions of the five categories of innovation barriers: usage barrier, value barrier, risk barrier, tradition barrier, and image barrier; (2) users’ intention to use and recommend ADVs is negatively affected by the usage, value, and tradition barriers; (3) users of different genders have varying perceptions of the relationship between technology readiness, barrier factors, and the decision to use ADVs. This study reveals the influencing factors that hinder users from using and recommending ADVs and their functioning mechanisms from the perspective of barriers and examines the impact of gender differences between males and females. The study enriches the empirical research in ADVs, providing theoretical support and practical guidance for logistics companies and the government to develop autonomous delivery technology and publicize and promote ADVs. [ABSTRACT FROM AUTHOR]

Recent years have witnessed an unprecedented rise in attacks on women's rights and freedom in sub‐Saharan Africa. No historical quantitative research exists that analyzes how sexual, physical, and emotional violence against women is influenced by the rise of radical Islam in Nigeria. Consecutive survey data for the years 2008, 2013, and 2018 were analyzed to determine associations between religion and other risk factors for violence against women. Multiple logistic regression was used to control for covariates. Sexual violence against Muslim women increased by 216.7% compared to a 25.4% increase among Catholics. Muslim women with no education had a lower estimate of the sexual violence they experienced (β = −0.15, CI: −0.137 to −0.016) than Christian women with no education (β = −0.05, CI: −0.138 to −0.004). The risk of violence against women has increased in recent years due to the activities of Boko Haram in the Muslim‐dominant north. We show that the effects of religion and education on violence against women are interconnected. [ABSTRACT FROM AUTHOR]

Organisations are becoming concerned with effectively dealing with privacy-related requirements. Existing Requirements Engineering methods based on structured natural language suffer from several limitations both in eliciting and specifying privacy requirements. In our previous study, we proposed a structured natural-language approach called the "Privacy Criteria Method" (PCM), which demonstrates potential advantages over user stories. Our goal is to present a PCM evaluation that focused on the opinions of software practitioners from different companies on PCM's ability to support the specification of privacy requirements and the quality of the privacy requirements specifications produced by these software practitioners. We conducted a multiple case study to evaluate PCM in four different industrial contexts. We gathered and analysed the opinions of 21 practitioners on PCM usage regarding Coverage, Applicability, Usefulness, and Scalability. Moreover, we assessed the syntactic and semantic quality of the PCM artifacts produced by these practitioners. PCM can aid developers in elaborating requirements specifications focused on privacy with good quality. The practitioners found PCM to be useful for their companies' development processes. PCM is considered a promising method for specifying privacy requirements. Some slight extensions of PCM may be required to tailor the method to the characteristics of the company. [ABSTRACT FROM AUTHOR]

Database-backed applications rely on the database access code to interact with the underlying database management systems (DBMSs). Although many prior studies aim at database access issues like SQL anti-patterns or SQL code smells, there is a lack of study of database access bugs during the maintenance of database-backed applications. In this paper, we empirically investigate 423 database access bugs collected from seven large-scale Java open-source applications that use relational DBMSs (e.g., MySQL or PostgreSQL). We study the characteristics (e.g., occurrence and root causes) of the bugs by manually examining the bug reports and commit histories. We find that the number of reported database and non-database access bugs share a similar trend but their modified files in bug fixing commits are different. Additionally, we generalize categories of the root causes of database access bugs, containing five main categories (SQL queries, Schema, API, Configuration, and SQL query result) and 25 unique root causes. We find that the bugs pertaining to SQL queries, Schema, and API cover 84.2% of database access bugs across all studied applications. In particular, SQL queries bug (54%) and API bug (38.7%) are the most frequent issues when using JDBC and Hibernate, respectively. Finally, we provide a discussion on the implications of our findings for developers and researchers. [ABSTRACT FROM AUTHOR]

This study introduces a novel teaching model called Presentation-Assimilation-Discussion (PAD), which incorporates multidimensional smart teaching tools aligned with Outcome-Based Education principles. The model is designed to address the limitations of traditional teaching methods by integrating various content and activities suitable for both online and offline environments. A total of 58 students, divided into experimental (n=29) and control (n=29) groups, participated in this study. The effectiveness of the PAD model was rigorously evaluated through comparative experiments involving test scores and a specialized Likert scale questionnaire, which examined changes in students' self-directed learning, practical application skills, and value cultivation before and after the intervention. Results indicated that students engaged with the PAD model not only achieved higher test scores but also demonstrated enhanced self-directed learning, improved practical application skills, and developed more substantial value qualities. These findings underscore the significance of the PAD Model in enhancing educational outcomes, highlighting its potential for broader application in teaching various disciplines. [ABSTRACT FROM AUTHOR]

Maritime taught postgraduate education plays a vital role in cultivating expertise within the ever-evolving global logistics industry. Notably, the diverse backgrounds of students are of great importance in this context, given the scarcity of undergraduate maritime programs and the industry's demand for professionals from various disciplines. However, this diversity also poses a challenge to educators in terms of effectively training and evaluating these students. This study focuses on a maritime taught postgraduate professional program and investigates how student background characteristics affect their learning outcomes and academic performance. By utilizing both stated preference data (SP) and revealed preference data (RP) collected from a postgraduate maritime professional program in Hong Kong, the study aims to mitigate hypothetical bias and gain a comprehensive understanding of the factors influencing student perceptions and experiences in maritime industry. The study measures five dimensions of student learning outcomes, including professionalism, strategic thinking, lifelong learning capability, career development, and academic performance. The findings suggest that student background characteristics have minimal impact on maritime learning outcomes, with only gender and undergraduate programs showing some influence. The study highlights the inclusive nature of maritime professional education, emphasizing that training quality, rather than personal background, is the key determinant of success. • We highlight the inclusive nature of maritime professional education. • Both stated preference data and revealed preference data are applied. • Student background have minimal impact on maritime learning outcomes. • Findings emphasize that training quality is the key determinant of success. [ABSTRACT FROM AUTHOR]

Feature model integration is pivotal in software development, particularly in evolving software product lines through new feature accommodations. Despite its significance, the influence of developers’ experience on integration efforts and correctness still needs to be more adequately understood. This study conducted a controlled experiment with 25 participants (18 students and seven professionals) following well-known guidelines to run empirical studies. Each participant addressed ten experimental tasks, encompassing 250 integration scenarios, to explore two research questions. The effort and correctness rate in integrating feature models were quantified, revealing that students exerted higher effort (29.23%) and achieved a higher number of correct integrations (39.53%) than professionals. Notably, this superiority lacked statistical significance. Additionally, this article highlights practical implications and noteworthy challenges for the scientific community, providing valuable insights for software development practices. The findings lay a foundation for future studies, delving into software development tasks where students and professionals may achieve comparable results. Finally, this study marks an initial step towards an ambitious agenda, empirically advancing the feature model integration field.

The vulnerability of modern neural networks to random noise and deliberate attacks has raised concerns about their robustness, particularly as they are increasingly utilized in safety- and security-critical applications. Although recent research efforts were made to enhance robustness through retraining with adversarial examples or employing data augmentation techniques, a comprehensive investigation into the effects of training data perturbations on model robustness remains lacking. This paper presents the first extensive empirical study investigating the influence of data perturbations during model retraining. The experimental analysis focuses on both random and adversarial robustness, following established practices in the field of robustness analysis. Various types of perturbations in different aspects of the dataset are explored, including input, label, and sampling distribution. Single-factor and multi-factor experiments are conducted to assess individual perturbations and their combinations. The findings provide insights into constructing high-quality training datasets for optimizing robustness and recommend the appropriate degree of training set perturbations that balance robustness and correctness, and contribute to understanding model robustness in deep learning and offer practical guidance for enhancing model performance through perturbed retraining, promoting the development of more reliable and trustworthy deep learning systems for safety-critical applications. [ABSTRACT FROM AUTHOR]

Cyclones are pivotal in mechanical process engineering and crucial in the complex field of separation technology. Their robustness and compact spatial requirements render them universally applicable and versatile across various industrial domains. Depending on the utilized fluid and field of application, both gas-based cyclones and hydrocyclones (HCs) are well established. Regarding HC design, enduring elongated flat cones have seen minimal alterations in shape and structure since their introduction over more than a hundred years ago. Experimental investigations regarding unconventional cone designs within scientific studies remain the exception. Therefore, this study focuses on alternative geometric configurations of the separation chambers and highlights their impact on separation and energy efficiency. To achieve this objective, different geometric shapes are investigated and retrofitted into HCs. The geometric foundation is derived from upcycled glass bottles. The repurposed bottles with a volume of 750 mL are used in conjunction with an inlet part, following the established Rietema design. Experimental tests are conducted with dilute phase separation, using 0.1–200 µm test particles in water. Comparisons between a bottle-based HC and a conventional Rietema design were conducted, establishing a benchmark against the standard. The findings revealed a noticeable correlation between separation efficiency and cone geometry. Conical designs demonstrated enhanced separation, particularly at lower volume flows. At the highest volume flow of 75 L min−1, the best performing bottle cyclones showed separation efficiencies of 78.5%, 78.4% and 77.9% and therefore are in a competitive range with 78.0% efficiency, achieved using the commercial Rietema design. Minimal disparities in cut sizes were observed in terms of separation grade efficiency among the models tested. Variations in separation efficiency and fractional efficiency curves indicated nuanced differences in classification efficiency. [ABSTRACT FROM AUTHOR]

Data protection is the most prior and valuable concern as stealing personal/organizational data by cyber criminals may arise issues ranging from privacy disclosure to system hacking. Blockchain is considered a top leading technology with the influence to reshape future industries, but like other revolutionary technologies, blockchain has its merits and demerits as well. However, due to its nascent stage, blockchain technology poses numerous challenges that hinder the effective development of blockchain. To uncover this potential hindrance and group the relevant barriers, a total of 12 challenges that may hinder the progress of blockchain application development have been identified from a sample of 52 primary studies. Based on the results of our research, the six challenges are considered critical an account for more than 30% occurrence. The critical challenges are "Lack of Proper Development Tools and Technology," "Security Precaution Measures," "Lack of Governance and Standards," "Lack of Professional Expertise with Essential Skills and Knowledge," "Lack of Organizational Support," and "Interoperability Integration." Using a comprehensive systematic literature review (SLR) and a questionnaire survey, a list of 65 solutions/practices has been identified to address the challenges that were identified. These solutions/practices will help blockchain developers to address the identified challenges and develop benign blockchain application in future. The results of our questionnaire survey largely align with the findings of the SLR. However, there are variations in the ranking of the challenges between the two datasets. The finding of this paper is to provide insights that can assist in streamlining and optimizing the development process of blockchain applications with greater ease and efficiency. [ABSTRACT FROM AUTHOR]

The software development life cycle relies heavily on the software release note, a crucial document. Various practitioners, including project managers and clients, benefit from release notes as they provide an overview of the latest software release. However, the manual generation of release notes is a time‐consuming and stressful task. Researchers have recently proposed automated techniques to generate release notes, saving developers' time and enhancing their understanding of software projects. Unfortunately, the adoption of these tools in practice remains limited. To address this gap, we have taken steps to understand the expectations and requirements of practitioners regarding release note generation techniques before implementing new automated approaches. Consequently, our approach involves two main stages: First, we conduct a comprehensive review of the relevant literature and analyze existing release notes from GitHub repositories to gain insights into the current practices. Second, we conduct an online survey study to gather input from practitioners and understand their expectations regarding release notes. We have reviewed 16 papers related to release notes and explored 3347 release notes from 21 GitHub repositories. Our analysis revealed key artifacts present in release note contents, including issues (29%), pull requests (32%), commits (19%), and common vulnerabilities and exposures (CVE) issues (6%). Additionally, we conducted a survey study involving 32 professionals to understand the essential information that should be included in release notes based on users' roles. For instance, project managers were more interested in learning about new features rather than less critical bug fixes. Furthermore, we identified gaps in existing systems and essential factors to consider when implementing release notes techniques in software engineering. The insights gained from our study can guide future research directions and assist practitioners in generating release notes with relevant content, thus improving the overall quality of documentation in software development. [ABSTRACT FROM AUTHOR]

Purpose: This study intends to examine the factors that impact the intention and actual usage of Electronic Knowledge Repositories (EKR) by knowledge seekers in the Indian software sector. The study examines how perceived utility, perceived output quality, resource availability, and perceived organisational structure support influence the adoption of EKR. Design/methodology/approach: The data were gathered from 505 employees in 27 software engineering companies in the National Capital Region (NCR) of India using a self-administered survey. The study employed structural equation modelling (SEM) to examine the connections among the variables. Findings: The findings suggest that perceived usefulness, perceived output quality, resource availability, and reported organisational structure support have a favourable impact on the desire to adopt EKR. Using EKR favourably influences its utilisation by knowledge seekers in the Indian software sector. Research limitations/implications: The study focussed exclusively on the software sector in the NCR area of India, perhaps restricting the applicability of the results. Future studies should examine more variables that impact EKR adoption and study various organisational contexts. Practical implications: The research indicates that organisations should prioritise improving the perceived utility of EKR, assuring high output quality, allocating sufficient resources for EKR access, and maintaining supporting organisational structures to encourage EKR adoption among knowledge seekers. Originality/value: This study experimentally investigates the characteristics that influence EKR adoption in the Indian software sector, contributing to existing literature. The results offer useful insights for firms looking to enhance their knowledge management strategies by effectively utilising EKR. [ABSTRACT FROM AUTHOR]

This emphirical study aims to identify the learning innovation improvement strategy through the MBKM program at STIP Jakarta. The literature research technique was used to conduct this study, and information was gathered through literature references, pertinent observations and documentation, interviews with members of the Academic Division, as well as other studies on the MBKM policy. Merdeka Belajar Kampus Merdeka (MBKM), a self-study independent campus project of the Government of Indonesia through the Ministry of Education, Culture, Research and Technology, enables students to explore their talents and interests outside of academic learning. This program is a part of a larger endeavor that has two parts: Merdeka Belajar, which encourages talent and autonomous study and Kampus Merdeka, which offers contextual field experiences to enhance skills and prepare students for the future. According to the study's findings, the MBKM program's implementation at STIP had not been as effective as it could have been because only three out of eight types of off-campus learning--research, internships/work experiences, and humanitarian projects--had been completed, while five more were still being worked on through the management policy process. In order for students to attain the best learning outcomes and to constantly be relevant in terms of attitude, knowledge, and skills, STIP must develop policies and execute innovative learning methods. The Kampus Merdeka is an independent and adaptable organization that promotes a creative learning environment that doesn't restrict and fits the demands of cadets, which in turn has implications for improving the quality of STIP education. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the relationship between organizational culture (OC) profiles and the adoption of Industry 4.0 (I4.0) design principles. We surveyed 153 middle and senior managers from Brazil and India, whose manufacturing companies have been implementing I4.0. Participants provided answers regarding the perceived OC characteristics that prevail in their companies, which were assessed based on the Competing Values Framework. We also collected data on the adoption levels of I4.0 design principles, whose analysis was conducted through the utilization of multivariate data analysis techniques. Our results indicated that, depending on the I4.0 design principle, OC profiles (Clan, Adhocracy, Market and Hierarchy) may either corroborate or impair its adoption, allowing the verification of the hypothesized relationships. The understanding of the association between OC profiles and I4.0 design principles provides practitioners with arguments to identify possible problems during their digital transformation. As changes in OC require significant efforts and are usually time-consuming, companies that can anticipate those issues might face a smoother transition towards the Fourth Industrial Revolution, hence, obtaining competitive advantages. [ABSTRACT FROM AUTHOR]

Purpose: Though the quantity of time adolescents spend using digital devices has raised widespread concerns that this time could be detrimental to mental health, these possible harmful effects have not been well investigated. Intersectionality is a theoretical approach that has been used more often in empirical studies of health disparities to examine how social variables interact to produce health inequalities. Digital technologies have emerged as a primary means of delivering health-related services in recent times, a tendency that has been further strengthened by the ongoing COVID-19 pandemic. This paper investigates the connection between the general well-being of adolescents and suggests that, by using our country's "Adolescent Physical Health Statistics Analysis in 2020" as a benchmark for regression evaluation, the results of health training on interventions within the framework of the empirical research on the establishment of adolescent health data management and services systems can effectively reinforce the attachment the hypothesis of the physical in nature health of adolescents. Method: The findings of the study demonstrate that by preventing students from engaging in physical activity, the notion of adolescence physical health management can enhance the physical health of teenagers. When processing data, GBDT works well with a sizable training collection. As sample size grows, the precision rate can approach 97.47%. The RF a position outperforms the GBDT method in terms of precise classification for male gender sitting forward bending development. With regard to the boys' 1000-meter managing promotional classifying operation, the RF approach yielded the greatest promotion identification rate, at 79.45%. When 70% of the training set is used in the male pulling ups to enhance the classification effect, the RF technique achieves the greatest precision rate, 97.07%. Results: The classification success rate for females who stand for long jumping advancement ranges from 41% to 78%, according to the results of the effect of classification study. The RF method works best when the learning set is less than 50%; its maximum is 53.93%; the GBDT method performs best whenever the training set is lower than 50%; both the RF and GBDT indications have advantages in Macro-F1. Conclusion: When practice rate arrives at 80%, the GBDT technique achieves an average reliability rate of 87.77%; on the Macro-F1 the index, the GBDT method is clearly the best. In the advertising of assessing the effect on the final health level of girls, the accuracy rates of the RF and GBDT methods vary between 80% to 97%, and the precision rates of the NN technique range from 70% to 97%. The article aims to educate the reader on the subtle distinctions between pertinent terms used at the interface of physical activity and digital wellness, as well as provide state-of-the-art descriptions for them. This is because unclear terminologies is a major source of difficulty in communicating science and can impede the advancement of empirical as well as theoretical studies. [ABSTRACT FROM AUTHOR]

The impact of contextual bias has been demonstrated repeatedly across forensic domains; however, research on this topic in forensic toxicology is very limited. In our previous study, experimental data from only one context version were compared with the actual forensic biasing casework. As a follow‐up, this controlled experiment with 159 forensic toxicology practitioners was conducted, to test whether knowledge of different contextual information influenced their forensic decision‐making. Participants in different context groups were tasked to identify testing strategies for carbon monoxide and opiate drugs. The results of chi‐squared tests for their selections and two context groups exhibited statistically significant differences (p < 0.05 or p < 0.01). These findings show contextual information can bias forensic toxicology decisions about testing strategies, despite it is a relatively objective domain in forensic science. [ABSTRACT FROM AUTHOR]

• A controlled experiment revealed contextual bias in handwriting examination of China. • Participants disclosed their thoughts and reasoning as they analyzed handwriting samples. • Context management with complementary strategies can be used to combat bias. The impact of contextual bias has been repeatedly demonstrated across forensic domains; however, research on this topic in China is scarce. To examine the prevalence of contextual bias in pattern feature-comparison disciplines, we conducted an experiment involving 24 forensic document examination students. The aim was to determine whether knowledge of different contextual information influenced their forensic decision-making. Participants were divided into different context groups and tasked with examining whether questioned signatures with ambiguous features matched reference signatures. The results of independent-samples t -tests for their decision score data in the two context groups exhibited a statistically significant difference (p < 0.05, Cohen's d > 0.8). Moreover, the submitted forensic reports by participants disclosed a biased evaluation of handwriting features. These findings show how contextual information can bias forensic decision-making in handwriting examination. Context management with complementary strategies such as case triage, cognitive training and decision-making transparency must be implemented to minimize bias in handwriting examination. [ABSTRACT FROM AUTHOR]

The study of neutrosophy offers a fresh approach for handling uncertain data with adaptability. This article explores the application of neutrosophic probability distribution in constructing a transmuted neutrosophic framework. Specifically, it introduces a generalized transmuted neutrosophic distribution. Building upon this generalization, quadratic and cubic transmuted distributions are developed and examined alongside certain lifetime distributions serving as foundational neutrosophic models. Additionally, an empirical investigation is conducted to assess the practicality and versatility of these distributions in real-world contexts. [ABSTRACT FROM AUTHOR]

Value Added Tax (VAT) has been widely adopted globally as a consumption tax, but its implementation can vary significantly across countries. This study aims to develop a suggested accounting framework for applying VAT in the State of Kuwait, considering its unique economic and legal environment. Through an empirical analysis of current VAT practices in Kuwait, the study identifies challenges and gaps in the existing framework. The proposed framework outlines clear guidelines for VAT registration, invoicing, tax calculations, and reporting, ensuring compliance with both domestic regulations and international VAT standards. By addressing the identified issues and providing a comprehensive accounting framework, this research seeks to enhance VAT administration, improve tax revenue collection, and facilitate business operations in Kuwait. [ABSTRACT FROM AUTHOR]

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The increase of software supply chain threats has underscored the necessity for robust security mechanisms, among which the Software Bill of Materials (SBOM) stands out as a promising solution. SBOMs, by providing a machine-readable inventory of software composition details, play a crucial role in enhancing transparency and traceability within software supply chains. This empirical study delves into the practical challenges and solutions associated with the adoption of SBOMs through an analysis of 4,786 GitHub discussions across 510 SBOM-related projects. Through repository mining and analysis, this research delineates key topics, challenges, and solutions intrinsic to the effective utilization of SBOMs. Furthermore, we shed light on commonly used tools and frameworks for SBOM generation, exploring their respective strengths and limitations. This study underscores a set of findings, for example, there are four phases of the SBOM life cycle, and each phase has a set of SBOM development activities and issues; in addition, this study emphasizes the role SBOM play in ensuring resilient software development practices and the imperative of their widespread adoption and integration to bolster supply chain security. The insights of our study provide vital input for future work and practical advancements in this topic. [ABSTRACT FROM AUTHOR]

With the fast development of large software projects, automatic code summarization techniques, which summarize the main functionalities of a piece of code using natural languages as comments, play essential roles in helping developers understand and maintain large software projects. Many research efforts have been devoted to building automatic code summarization approaches. Typical code summarization approaches are based on deep learning models. They transform the task into a sequence-to-sequence task, which inputs source code and outputs summarizations in natural languages. All code summarization models impose different input size limits, such as 50 to 10,000, for the input source code. However, how the input size limit affects the performance of code summarization models still remains under-explored. In this article, we first conduct an empirical study to investigate the impacts of different input size limits on the quality of generated code comments. To our surprise, experiments on multiple models and datasets reveal that setting a low input size limit, such as 20, does not necessarily reduce the quality of generated comments. Based on this finding, we further propose to use function signatures instead of full source code to summarize the main functionalities first and then input the function signatures into code summarization models. Experiments and statistical results show that inputs with signatures are, on average, more than 2 percentage points better than inputs without signatures and thus demonstrate the effectiveness of involving function signatures in code summarization. We also invite programmers to do a questionnaire to evaluate the quality of code summaries generated by two inputs with different truncation levels. The results show that function signatures generate, on average, 9.2% more high-quality comments than full code. [ABSTRACT FROM AUTHOR]

Python's dynamic typing nature provides developers with powerful programming abstractions. However, many type-related bugs are accumulated in code bases of Python due to the misuse of dynamic typing. The goal of this article is to aid in the understanding of developers' high-risk practices toward dynamic typing and the early detection of type-related bugs. We first formulate the rules of six types of risky dynamic typing-related practices (type smells for short) in Python. We then develop a rule-based tool named RUPOR, which builds an accurate type base to detect type smells. Our evaluation shows that RUPOR outperforms the existing type smell detection techniques (including the Large Language Models–based approaches, Mypy, and PYDYPE) on a benchmark of 900 Python methods. Based on RUPOR, we conduct an empirical study on 25 real-world projects. We find that type smells are significantly related to the occurrence of post-release faults. The fault-proneness prediction model built with type smell features slightly outperforms the model built without them. We also summarize the common patterns, including inserting type check to fix type smell bugs. These findings provide valuable insights for preventing and fixing type-related bugs in the programs written in dynamic-typed languages. [ABSTRACT FROM AUTHOR]

AbstractAutonomous vehicles (AVs) are rapidly evolving as a novel way of transportation. Nevertheless, there is a consensus that AVs cannot address all traffic scenarios independently. Consequently, there arises a need for remote human intervention. To pave the way for large-scale deployment of AVs onto public roadways, innovative models of remote operation must evolve. Such a paradigm is Tele-assistance, which posits that the low-level control of AVs should be delegated through high-level commands. Our work explores how such a command language should be constructed as a first step in designing a Tele-assistance user interface. Through interviews with 17 experienced teleoperators, we elicit a set of discrete commands that a remote operator can use to resolve various road scenarios. Subsequently, we create a scenario-command mapping and a thematic classification of the defined commands. Finally, we present an initial Tele-assistance interface design based on these commands. [ABSTRACT FROM AUTHOR]

This paper presents an eye image segmentation-based computer-aided system for automatic diagnosis of ocular myasthenia gravis (OMG), called OMGMed. It provides great potential to effectively liberate the diagnostic efficiency of expert doctors (the scarce resources) and reduces the cost of healthcare treatment for diagnosed patients, making it possible to disseminate high-quality myasthenia gravis healthcare to under-developed areas. The system is composed of data pre-processing, indicator calculation, and automatic OMG scoring. Building upon this framework, an empirical study on the eye segmentation algorithm is conducted. It further optimizes the algorithm from the perspectives of "network structure" and "loss function", and experimentally verifies the effectiveness of the hybrid loss function. The results show that the combination of "nnUNet" network structure and "Cross-Entropy + Iou + Boundary" hybrid loss function can achieve the best segmentation performance, and its MIOU on the public and private myasthenia gravis datasets reaches 82.1% and 83.7%, respectively. The research has been used in expert centers. The pilot study demonstrates that our research on eye image segmentation for OMG diagnosis is very helpful in improving the healthcare quality of expert doctors. We believe that this work can serve as an important reference for the development of a similar auxiliary diagnosis system and contribute to the healthy development of proactive healthcare services. [ABSTRACT FROM AUTHOR]

Requirements elicitation is critical phase of software development that involve direct communication of clients and vendors. In global software development (GSD), importance of requirements elicitation becomes more critical due to geographical distance. The aim of this research is to design and implement requirement elicitation model (REM) where GSD organization can evaluate their organizations and can classify their level of implementation where organization lies. Systematic literature review (SLR) was conducted for the identification of critical success factors (CSFs) from a sample of 70 research articles. Furthermore, the identified CSFs are organized into five levels based on Capability Maturity Model Integration (CMMI) and Software Outsourcing Vendors' Readiness Model (SOVRM). A second SLR was conducted for identifying all possible practices for the implementation of CSFs. To validate the findings of SLR and to find more practices in the view of industrial practitioners for the identified CSFs, a questionnaire survey was conducted in the GSD organizations in which 35 experts, from different countries, contributed. For REM evaluation, we have conducted two case studies. The outcome analysis after conducting case studies confirms that our assessment framework is easier to understand, apply, and successfully critic the strengths and weaknesses of organizations during requirements elicitation in GSD. [ABSTRACT FROM AUTHOR]

This paper aims to promote technology usage among older Saudi people by addressing the common problems they encounter around adhering to medication schedules and using mobile health applications. An interactive mobile user interface has been designed based on requirements determined using three inputs: (1) relevant existing research work, (2) interviews with medicine specialists, and (3) a survey of 602 older Saudis. The output from the first input is a set of updated guidelines adapted to the older Saudi population and reconstructed based on cognitive process attributes. The interview output identifies medication adherence issues and proposed solutions. The survey output produced a mobile technology model for older Saudis. The guidelines have been applied to a prototype and then tested in two phases: (1) pilot testing and (2) usability testing. In the testing phase, 50 older Saudi users provided insights into the effectiveness, error safety, and productivity factors of the solution developed, with the results confirming that designing mobile applications based on the model of older users and cognitive attributes improved effectiveness and reduced errors when performing mobile tasks, specifically around adherence to medications. For the productivity factor, the results align with the physical characteristics of the targeted older individuals, who typically require more time than younger people to perform certain tasks. [ABSTRACT FROM AUTHOR]

Technical debt presents sub-optimal choices made in development, which are beneficial in the short term but not in the long run. Consciously admitted debt, which is marked with a keyword, e.g., TODO, is called keyword-labeled self-admitted technical debt (KL-SATD). KL-SATD can lead to adverse effects in software development, e.g., to a rise in complexity within the developed software. We investigated the relationship between KL-SATD from source code comments and reports from the highly popular industrial program analysis tool SonarQube. The goal was to find which SonarQube metrics and issues are related to KL-SATD introduction and removal and how many KL-SATD in the context of an issue addresses that issue. We performed a study with 33 software repositories. We analyzed the changes in SonarQube reports (sqale index, reliability and security remediation metrics, and SonarQube issues) and the relationship to KL-SATD addition and removal with mixed model analysis. We manually annotated a sample to investigate how many KL-SATD comments are in the context of SonarQube issues and how many address them directly. KL-SATD is associated with a reduction in code maintainability measured with SonarQube's sqale index. KL-SATD removal is associated with an increase in code maintainability (sqale index) and reliability measured with SonarQube's reliability remediation effort. The introduction and removal of KL-SATD have a predominantly relationship with code smells, and not with vulnerabilities and bugs. Manual annotation revealed that 36% of KL-SATD comments are in the context of a SonarQube issue, but only 15% of the comment address an issue. This means that despite of statistical relationship between KL-SATD comments and SonarQube reports there is a large set of KL-SATD comments that are in areas that Sonarqube reports as clean or free of maintainability issues. KL-SATD introduction and removal are connected mainly to code smells, connecting them to maintainability rather than reliability or security. This is reinforced by the relationship with the sqale index, as well as the dominance of code smells in SonarQube issues. Many KL-SATD issues have characteristics going beyond static analysis tools and require future studies extending the capabilities of the current tools. As KL-SATD comments and SonarQube reports appear to have limited overlap, it suggests that they are complementary and both are needed for getting a comprehensive view coverage of code maintainability. The study also presents rules violations developers should be aware of regarding KL-SATD introduction and removal. [ABSTRACT FROM AUTHOR]

In current increasingly virtual business environment driven by information and digital technology, knowledge management (KM) capability is important for firm's innovative development. However, the research on the role of KM capability in business model innovation (BMI) is scarce. The study therefore explores the role from the perspective of dynamic capability theory (DCT). We empirically analyzed a sample of 115 Chinese top-ranking innovative enterprises by applying hierarchical regression analysis (HRA) and fuzzy-set qualitative comparative analysis (fsQCA). HRA results indicate that both internal and external KM capabilities positively affect BMI, but the effect of KM capability on different types of BMI is heterogeneous. In detail, KM capability promotes novelty-centered BMI, but inhibits efficiency-centered BMI. Furthermore, fsQCA results substantiate and expand the above findings by recognizing the antecedent conditions for high-level BMI. The study enriches the literature on KM and innovation as well as extends the explanatory power of DCT, providing new implications for firm managers to implement BMI. [ABSTRACT FROM AUTHOR]

This study investigates ad hoc knowledge in interpreting – knowledge specifically relevant to an interpreting task, usually acquired by interpreters at pre-service and in-service stages. A quasi-experiment asked eight English–Chinese professional interpreters to prepare for and interpret two speeches with different degrees of specialisation. Five datasets were collected: (1) screen and video recordings of their preparation processes; (2) their responses to a follow-up questionnaire; (3) their scores and answers on a domain knowledge test; (4) their interpreting performance, rated by both professional interpreters and domain experts; (5) the speaker's answers to a few open questions on domain knowledge. Parts of the first and second datasets were used to examine what ad hoc knowledge the interpreters needed; the remaining three datasets, together with recording data from the first, were used to investigate the extent of their need for this knowledge. The findings show that ad hoc knowledge is domain-relevant, yet its scope is highly interpreting-specific, being neither deep nor wide; and it is by nature different from domain knowledge, mainly including speech-relevant expressive knowledge about certain domain(s) and the contextual knowledge interpreters use in their preparation. The implications of these findings for interpreting practice and training are also discussed. [ABSTRACT FROM AUTHOR]

In recent years, dynamic languages such as Python have become popular due to their flexibility and productivity. The lack of static typing makes programs face the challenges of fixing type errors, early bug detection, and code understanding. To alleviate these issues, PEP 484 introduced optional type annotations for Python in 2014, but unfortunately, a large number of programs are still not annotated by developers. Annotation generation tools can utilize type inference techniques. However, several important aspects of type annotation generation are overlooked by existing works, such as in-depth effectiveness analysis, potential improvement exploration, and practicality evaluation. And it is unclear how far we have been and how far we can go. In this paper, we set out to comprehensively investigate the effectiveness of type inference tools for generating type annotations, applying three categories of state-of-the-art tools on a carefully-cleaned dataset. First, we use a comprehensive set of metrics and categories, finding that existing tools have different effectiveness and cannot achieve both high accuracy and high coverage. Then, we summarize six patterns to present the limitations in type annotation generation. Next, we implement a simple but effective tool to demonstrate that existing tools can be improved in practice. Finally, we conduct a controlled experiment showing that existing tools can reduce the time spent annotating types and determine more precise types, but cannot reduce subjective difficulty. Our findings point out the limitations and improvement directions in type annotation generation, which can inspire future work. [ABSTRACT FROM AUTHOR]

With the rapid development of deep learning (DL), the recent trend of log-based anomaly detection focuses on extracting semantic information from log events (i.e., templates of log messages) and designing more advanced DL models for anomaly detection. Indeed, the effectiveness of log-based anomaly detection can be improved, but these DL-based techniques further suffer from the limitations of more heavy dependency on training data (such as data quality or data labels) and higher costs in time and resources due to the complexity and scale of DL models, which hinder their practical use. On the contrary, the techniques based on traditional machine learning or data mining algorithms are less dependent on training data and more efficient but produce worse effectiveness than DL-based techniques, which is mainly caused by the problem of unseen log events (some log events in incoming log messages are unseen in training data) confirmed by our motivating study. Intuitively, if we can improve the effectiveness of traditional techniques to be comparable with advanced DL-based techniques, then log-based anomaly detection can be more practical. Indeed, an existing study in the other area (i.e., linking questions posted on Stack Overflow) has pointed out that traditional techniques with some optimizations can indeed achieve comparable effectiveness with the state-of-the-art DL-based technique, indicating the feasibility of enhancing traditional log-based anomaly detection techniques to some degree. Inspired by the idea of "try-with-simpler," we conducted the first empirical study to explore the potential of improving traditional techniques for more practical log-based anomaly detection. In this work, we optimized the traditional unsupervised PCA (Principal Component Analysis) technique by incorporating a lightweight semantic-based log representation in it, called SemPCA, and conducted an extensive study to investigate the potential of SemPCA for more practical log-based anomaly detection. By comparing seven log-based anomaly detection techniques (including four DL-based techniques, two traditional techniques, and SemPCA) on both public and industrial datasets, our results show that SemPCA achieves comparable effectiveness as advanced supervised/semi-supervised DL-based techniques while being much more stable under insufficient training data and more efficient, demonstrating that the traditional technique can still excel after small but useful adaptation. [ABSTRACT FROM AUTHOR]

GitHub Issues is a widely used issue tracking tool in open-source software projects. Originally designed with broad flexibility, its lack of standardization led to incomplete issue reports, impeding software development and maintenance efficiency. To counteract this, GitHub introduced issue templates in 2016, which rapidly became popular. Our study assesses the current use and evolution of these templates in large-scale open-source projects and their impact on issue tracking metrics, including resolution time, number of reopens, and number of issue comments. Employing a comprehensive analysis of 350 templates from 100 projects, we also evaluated over 1.9 million issues for template conformity and impact. Additionally, we solicited insights from open-source software maintainers through a survey. Our findings highlight issue templates' extensive usage in 99 of the 100 surveyed projects, with a growing preference for YAML-based templates, a more structured template variant. Projects with a template exhibited markedly reduced resolution time (381.02 days to 103.18 days) and reduced issue comment count (4.95 to 4.32) compared to those without. The use of YAML-based templates further significantly decreased resolution time, the number of reopenings, and the discussion extent. Thus, our research underscores issue templates' positive impact on large-scale open-source projects, offering recommendations for improved effectiveness. [ABSTRACT FROM AUTHOR]