In an era of intensified market competition, internal brand management (IBM) has emerged as a critical strategy for aligning employee behavior with brand values. This study investigates how IBM influences brand citizenship behavior (BCB) among front-line restaurant employees in Macao, emphasizing the mediating role of brand identification (BI) and simultaneously testing the moderating effect of leader-member exchange (LMX). Drawing from Social Identity Theory and Social Exchange Theory, the structural equation modeling (SEM) was used to test the model using data from 315 employees across 11 Macao restaurant companies. Analyzing via software package Smart-Pls 4.1, we found that IBM significantly enhances BI, which in turn strongly predicts BCB. While IBM directly impacts BCB, the effect is mediated by BI. Furthermore, LMX moderates the IBM-BI relationships, underscoring the role of leadership in internal branding effectiveness. These findings contribute to the internal branding literature by validating BI as a key psychological mechanism and LMX as a boundary condition. Practically, the study provides insights for restaurant industry seeking to foster brand-aligned behaviors through internal brand management.

This study aims to determine the extent to which talent identification is implemented in talent management. A Systematic Literature Review (SLR) was conducted to summarize the application of talent identification in the last six years. Researchers use Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) to process scientific articles. The literature reveals that while topics related to talent management garner significant attention, research on talent identification within talent management remains relatively scarce despite a gradual increase each year. We compared documents indexed by Scopus Q1 and Q2. The results show that the United States accounted for a significant portion of research on talent identification, representing 16% of the total existing research. Researchers have conducted extensive studies on the medical and pharmaceutical sectors, public services, tourism, and hospitality. The number of citations varied greatly from 1 to 93, with a median value of 20. These studies have also used various research methods with different theoretical bases and produced different analyses. This finding enriches the perspective of talent identification.

The power of Artificial Intelligence (AI) combined with the surgeons’ expertise leads to breakthroughs in surgical care, bringing new hope to patients. Utilizing deep learning-based computer vision techniques in surgical procedures will enhance the healthcare industry. Laparoscopic surgery holds excellent potential for computer vision due to the abundance of real-time laparoscopic recordings captured by digital cameras containing significant unexplored information. Furthermore, with computing power resources becoming increasingly accessible and Machine Learning methods expanding across various industries, the potential for AI in healthcare is vast. There are several objectives of AI’s contribution to laparoscopic surgery; one is an image guidance system to identify anatomical structures in real-time. However, few studies are concerned with intraoperative anatomy recognition in laparoscopic surgery. This study provides a comprehensive review of the current state-of-the-art semantic segmentation techniques, which can guide surgeons during laparoscopic procedures by identifying specific anatomical structures for dissection or avoiding hazardous areas. This review aims to enhance research in AI for surgery to guide innovations towards more successful experiments that can be applied in real-world clinical settings. This AI contribution could revolutionize the field of laparoscopic surgery and improve patient outcomes.

COVID was initially detected in Wuhan City, Hubei Province, People's Republic of China, in late 2019, as reported by researchers. Subsequently, it rapidly disseminated to numerous nations at the beginning of 2020, ultimately manifested as a pandemic with worldwide prevalence. Regarded as one of the most severe pandemics in documented human history, this outbreak resulted in deaths and infection over a quite millions of individuals globally. Due to its airborne nature, the coronavirus can be transmitted through actions such as coughing, sneezing, talking, and similar activities. Enclosed spaces lacking sufficient airflow are more likely to facilitate the spread of air borne diseases. Wearing a face mask that can provide protection against airborne pollutants, considered as Standard Operation Procedures (SOPS) for COVID-19. It is crucial to monitor the implementation of preventive measures both within and outside the building or workplace in order to prevent the transmission of COVID-19. The main objective of this project is to develop a face mask and social distance detector. You Only Learn One Representation (YOLOR) was implemented as a most advanced end-to-end target identification approach to develop the proposed system. An online available facemask dataset was utilized. The developed system can track individuals wearing masks in real time and can also identify and highlight persons with a rectangular box if their social distance is violated. This proposed interactive framework enables constant monitoring both internally and externally, thereby enhancing the capacity to identify offenders and ensure the safety of all individuals involved.

The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant interest in modern agriculture. The appeal of AI arises from its ability to rapidly and precisely analyze extensive and complex information, allowing farmers and agricultural experts to quickly identify plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has gained significant attention in the world of agriculture and agronomy. By harnessing the power of AI to identify and diagnose plant diseases, it is expected that farmers and agricultural experts will have improved capabilities to tackle the challenges posed by these diseases. This will lead to increased effectiveness and efficiency, ultimately resulting in higher agricultural productivity and reduced losses caused by plant diseases. The use of artificial intelligence (AI) in the detection and diagnosis of plant diseases has resulted in significant benefits in the field of agriculture. By using AI technology, farmers and agricultural professionals can quickly and accurately identify illnesses affecting their crops. This allows for the prompt adoption of appropriate preventative and corrective actions, therefore reducing losses caused by plant diseases.

The research aimed to: 1) analyze components and indicators of digital transformation leadership among school administrators, 2) assess their leadership needs, and 3) develop mechanism models to promote this leadership. A mixed-method approach was applied, involving three sample groups: 8 experts, 406 administrators, and 7 experts. Data collection tools included semi-structured interviews, leadership scales, needs assessments, and focus group discussions, with analysis performed through construct validity testing, needs assessment, and content analysis. The findings revealed: 1) The components and indicators of digital transformation leadership showed structural validity, as confirmed by the model's alignment with empirical data (Chi-Square = 82.3, df = 65, p = 0.072, CFI = 0.998, TLI = 0.997, RMR = 0.00965, RMSEA = 0.0256). 2) Among the leadership components, "innovative knowledge" ranked highest in need (PNImodified = 0.075), followed by "ideological influence" (0.066), "consideration of individuality" (0.055), "intellectual stimulation" (0.052), and "inspiration" (0.053). 3) Mechanism models for promoting leadership emphasized enhancing these five components to strengthen administrators' skills in applying technology, managing teaching and development plans, and fostering innovation. Administrators were encouraged to tailor strategies to individual needs, inspire personnel, and create a commitment to organizational change and development. These mechanisms aim to equip administrators to effectively lead transformations, motivate staff, and drive educational institutions to adapt and thrive in evolving environments.