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.

This study introduces a centralized Business Management System (BMS) tailored for small and mediumsized enterprises (SMEs), with an innovative approach due to the integration of a foot traffic detection system through video processing. The system allows businesses to access common business management features such as point of sales, staff scheduling, inventory management, and reports. With the integration of YOLOv7, foot traffic detection for customer count is made possible through LACAD. By automating data collection and providing foot traffic counts, alongside graphical reports, the system empowers SMEs to make better decisions for their businesses. This research highlights the strategic advantage of leveraging foot traffic insights to drive performance and competitiveness in the modern business landscape. As a guide for the study the researchers used Scrum methodology. The study was then evaluated through a quantitative survey using FURPS with 12 IT professionals and 7 beneficiaries as the respondents where the calculated total weighted mean for both the respondent types resulted in 4.60 which means that the users “Strongly Agree” with the system's overall components.

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.

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.

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.

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 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 chapter provides an overview of the current state of medical imaging methods in body composition analysis. It advocates a holistic approach that combines the strengths of different approaches and addresses their limitations. We discuss the importance of using standardized protocols to improve the accuracy of body composition studies across populations and settings. By examining the capabilities and limitations of imaging modalities such as DEXA, MRI, CT, and ultrasound, we emphasize the need for a multidimensional approach to obtain body composition emphasis on complete understanding.

Insulation coordination and surge protection devices (SPDs) are fundamental to the safety and reliability of modern power systems, especially in renewable energyintegrated grids. These systems protect critical electrical infrastructure from transient overvoltages, ensuring stable and sustainable operation. However, traditional methods, such as simulation-based analyses and manual fault detection, face challenges in scalability, adaptability, and efficiency, particularly in dynamic energy environments. Advancements in artificial intelligence (AI) and Internet of Things (IoT) technologies have introduced transformative capabilities for insulation coordination and SPDs. AI techniques, such as machine learning and neural networks, enable precise fault prediction, real-time monitoring, and adaptive control, significantly enhancing grid reliability. IoT-enabled SPDs further improve operational efficiency through predictive maintenance and continuous performance monitoring, aligning with sustainable energy goals. These innovations also address the needs of resilient infrastructure development, smart grid implementation, and urban sustainability. This paper explores the evolution of these systems, emphasizing the shift from traditional to AI-driven and hybrid approaches. By integrating advanced technologies, power systems can achieve enhanced reliability, efficiency, and resilience.