Hydrogen production via water electrolysis faces significant commercialization challenges due to the high cost and scarcity of iridium (Ir). Reducing Ir loading while maintaining high catalytic performance is critical for advancing proton exchange membrane (PEM) water electrolyzers. To address these challenges, this study introduces Ni@Ir core-shell nanowires (CSNW) as a cost-effective catalyst for enhancing Ir utilization in oxygen evolution reaction (OER). Ni@Ir-CSNW features an amorphous Ir shell coating crystalline Ni nanowires, achieving a high BET surface area of 417 m2 g−1 and an electrochemical surface area (ECSA) of 324 m2 g−1. With a reduced Ir loading of 22 wt.%, Ni@Ir-CSNW delivers remarkable performance, including a mass activity of 9.4 A mg−1 which is a 35-fold increase compared to conventional Ir nanoparticle (Ir-NP) catalysts. Ni@Ir-CSNW achieves an overpotential of 260 mV at 10 mA cm−2 and a favorable Tafel slope of 54 mV dec−1. Stability tests further demonstrate 68% retention of mass activity after 100 cycles. This work presents an effective strategy to enhance catalyst performance while reducing Ir usage, contributing to more sustainable and economically viable PEM water electrolysis systems.

Advocates of AI in Education (AIEd) assert that the current generation of technologies, collectively dubbed artificial intelligence, including generative artificial intelligence (GenAI), promise results that can transform our conceptions of what education looks like. Therefore, it is imperative to investigate how educators perceive GenAI and its potential use and future impact on education. Adopting the methodology of collective writing as an inquiry, this study reports on the participating educators’ perceived grey areas (i.e. issues that are unclear and/or controversial) and recommendations on future research. The grey areas reported cover decision-making on the use of GenAI, AI ethics, appropriate levels of use of GenAI in education, impact on learning and teaching, policy, data, GenAI outputs, humans in the loop and public–private partnerships. Recommended directions for future research include learning and teaching, ethical and legal implications, ownership/authorship, funding, technology, research support, AI metaphor and types of research. Each theme or subtheme is presented in the form of a statement, followed by a justification. These findings serve as a call to action to encourage a continuing debate around GenAI and to engage more educators in research. The paper concludes that unless we can ask the right questions now, we may find that, in the pursuit of greater efficiency, we have lost the very essence of what it means to educate and learn.

The quick-service restaurant (QSR) industry has grown significantly over the last 10 years, accounting for nearly one-third of all restaurant revenues, making it a fiercely competitive sector. In order to remain competitive in the marketplace and subsequently expand, businesses need to recognize the importance of service quality and customer satisfaction. Using the SERVQUAL model as the base framework, this research aims to determine the factors that influence customer satisfaction. Using the quantitative descriptive correlational method, the study examined the data from 300 Millennial customers of QSR. A mediation analysis was conducted using Partial Least Square – Structural Equation Modelling (PLS-SEM) to test the mediating effect of corporate image and customer trust. The results found that service quality, customer trust, and corporate image influence customer satisfaction. Moreover, corporate image and customer trust mediate the relationship between service quality and customer satisfaction. Furthermore, customer trust is a mediator between corporate image and customer satisfaction. The findings suggest that Millennial customers are no longer dependent on service quality but are also putting a premium on corporate image and trust. With these findings, QSR owners, business strategists, marketing practitioners, and store personnel must offer the highest level of quality service, must ensure that the image as seen by customers is not tarnished, and must exhibit authenticity, honesty, and transparency.

The global shortage of skilled programmers remains a persistent challenge. High dropout rates in introductory programming courses pose a significant obstacle to graduation. Previous studies highlighted learning difficulties in programming students, but their specific weaknesses remained unclear. This gap exists due to the predominant focus on the overall academic performance evaluation. To address this gap, this study employed cognitive diagnostic modeling (CDM) to profile the skill mastery of programming students. An empirical analysis was conducted to select the most appropriate model for the data, and the linear logistic model (LLM) was determined to be the best fit. Final examination results from 308 information technology (IT) and 279 computer science (CS) students were analyzed using the LLM. Unfortunately, findings revealed that programming students exhibited proficiency primarily in code tracing and language proficiency but displayed deficits in theoretical understanding, logical reasoning, and algorithmic thinking. From a practical standpoint, this deficiency in fundamental skills sheds light on the factors contributing to academic failures and potentially eventual dropout in programming education. When comparing the student population by academic program, CS students demonstrated superior mastery compared to their IT counterparts, although both groups exhibited a lack of mastery in code tracing. These deviations underscore the pressing need for tailored educational strategies that address the unique strengths and weaknesses of each student group. Overall, this study offers valuable insights into programming education literature and contributes to the expanding application of CDM in educational research.

The growing adoption of renewable energy, particularly photovoltaic (PV) solar systems, has led to the development of numerous simulation software tools to simplify system design, analysis, and optimization. This study evaluates five widely used PV simulation software packages—SAM, PVsyst, HOMER, PV∗SOL, and RETScreen—by analyzing their features and performance across ten critical criteria, including cost, solar database accessibility, modeling capabilities, and ease of use. Using the Analytic Hierarchy Process (AHP), the criteria are ranked by importance, with the working platform identified as the most influential factor in software selection, followed by economic modeling capabilities and software cost. Additionally, the flexibility of simulation data requirements, reporting and analysis options, and user friendliness and ease of use are identified as important criteria, albeit ranking lower in importance. Performance analysis using simulation and real data further validates the evaluation process, providing insights into the accuracy and reliability of the simulation results generated by each software. Our findings indicate that SAM delivers superior accuracy when compared to real-world data, making it the most reliable tool for PV system analysis. PV∗SOL also ranks highly for its robust reporting and modeling capabilities. These results provide valuable insights for professionals and researchers in selecting the most suitable software for PV system design and optimization, emphasizing the balance between functionality, cost-effectiveness, and user-friendliness.

Teaching in the metaverse presents a dynamic frontier for educational innovation. Avatars, serving as digital representations of teachers, play a pivotal role in shaping virtual learning experiences. This study explores the impact of avatar design and behavioral realism on student perceptions of credibility and teaching effectiveness in avatar-mediated environments. True experimental research with a 2 × 2 factorial design was conducted involving students from three campuses. Across all experimental conditions, students consistently favored realistic avatars over cartoonish ones. A crisscross pattern emerged in relation to behavioral realism. Cartoonish avatars exhibiting realistic behaviors received higher ratings for instructor credibility but not for teaching effectiveness, whereas realistic avatars with the same gestures received higher ratings for teaching effectiveness but not for instructor credibility. From an educational standpoint, leveraging realistic avatars with authentic behaviors holds great promise for enhancing the teaching and learning experiences in the metaverse. Overall, this study contributes to the growing body of literature on educational metaverse and avatar-mediated teaching and learning by shedding light on the importance of avatar design and behavioral realism in shaping student perceptions and experiences.

The emergence of generative AI tools like ChatGPT has sparked investigations into their applications in teaching and learning. In computer programming education, efforts are underway to explore how this tool can enhance instructional practices. Despite the growing literature, there is a lack of synthesis on its use in this field. This rapid review addresses this gap by examining the current literature to outline research trends, assess how it supports teaching and learning processes, and discern the issues that emerge from its application in programming instruction. A total of 107 documents disseminated across 81 distinct sources and authored by 394 contributors were identified. The review adopted a broad and inclusive approach, selecting literature based on relevance to ChatGPT's application in programming education and encompassing studies from diverse settings and methodologies. Results highlight applications such as personalized tutoring, knowledge reinforcement, instructional material creation, source code generation, immediate feedback, and assessment support. However, its use also introduces challenges such as academic dishonesty, ethical dilemmas, diminished critical thinking, overdependence on ChatGPT, and various technical limitations. Considering these findings, a balanced approach to the utilization of ChatGPT in programming education is essential. Implications and recommendations have been provided to guide policymakers, curriculum designers, teachers, and students in harnessing the benefits of this technology while mitigating potential challenges.

The integration of AI technologies like ChatGPT has transformed academic research, yet substantial gaps exist in understanding the implications of AI-generated non-existent references in literature searches. While prior studies have predominantly focused on medical and geography fields using descriptive statistics, a systematic investigation into ChatGPT 4.0’s effectiveness in generating accurate references within the realm of science and technology education remains unexplored, highlighting a significant dearth of research in this critical area. This study, therefore, investigates the reliability of AI-generated references in academic writing utilizing ChatGPT 4.0. Employing a non-experimental correlational design, the research examines the impact of prompt specificity on citation accuracy across various types of prompts, including general, specific, methodological, review, and interdisciplinary prompts. The findings indicate that specific, review, and interdisciplinary prompts correlate positively with accurate references, while general prompts frequently result in non-existent references. Visualizations, including a confusion matrix and precision-recall curve, illustrate the model’s performance. Ultimately, the study underscores the necessity of well-structured prompts to enhance reference quality and cautions against AI-induced hallucinations that produce non-existent references, which can significantly undermine research credibility.

This study examined factors driving the adoption of generative artificial intelligence tools like ChatGPT for research writing through an integrated framework combining the Technology Acceptance Model, Task Technology Fit, and Trust in Specific Technology. Responses from 564 researchers in 12 countries were analyzed using a structural equation modeling approach. Intriguingly, perceived usefulness and ease of use were insignificant despite being considered the strongest predictors of behavioral intention in countless studies. Instead, researchers prioritize trusting beliefs and the compatibility between a technology and a task when considering its use. It was also found that trust in the technology has greater explanatory power than task-technology compatibility, and this trust is influenced by beliefs that ChatGPT is a socially and academically accepted tool for manuscript writing. Overall, this study contributes new insights for researchers, funding bodies, publishers, policymakers, and the academic community as they navigate the evolving role of AI in scholarly writing.

With the increasing popularity of Raspberry Pi (RPi) comes the increase in available use cases and projects that utilize this single-board computer. One of these uses cases is the RPi Cluster, where multiple nodes are connected to a local network to form one logical resource pool. Implementing this build, however, can be a tedious and needs manual intervention such that each node needs to be configured to the cluster, one-by-one. While easily implementable if the scale is relatively small, the task becomes complex if several nodes need to be configured all at once. To save time, as well as to automate the whole process, developers and hobbyists would use Docker Swarm as an alternative. This paper explores the possibility of going beyond the popular Docker Swarm by proposing the use of OpenStack, a Cloud Computing Platform, as an alternative. Document analysis and review of existing research were used to prove the build’s technical feasibility. After comparing five relevant use cases, it can be concluded that deploying OpenStack to the RPi Cluster, or OpenStack-on-RPi, is feasible, though further research and testing that will optimize this use case is recommended.