The purpose of this study is to investigate the relationship between the use of business intelligence applications in accounting, particularly in invoice handling, and the resultant disruption and technical challenges. Traditionally a manual process, accounting has fundamentally changed with the incorporation of BI technology that automates processes and allows for sophisticated data analysis. This study addresses the lack of understanding about the strategic implications and nuances of implementation. Data was collected from 467 accounting stakeholder surveys and analyzed quantitatively using correlational analysis. Multiple regression was utilized to investigate the effect of BI adoption, technical sophistication on operational and organizational performance enhancements. The results show a weak association between the use of BI tools and operational enhancements, indicating that the time for processing invoices has decreased. Challenges due to information privacy and bias were significant and negative on both operational and organizational performance. This study suggests that a successful implementation of a BI technology requires an integrated plan that focuses on strategic management, organizational learning, and sound policies This paper informs practitioners of how accounting is being transformed in the digital age, motivating accountants and policy makers to better understand accounting as it evolves with technology and for businesses to invest in concomitant advances.

In this study, we are interested in WCM (working capital management) strategies and profitability in the UK furniture manufacturing sector. Observing the period from 2007 to 2023 of public companies panel data has found that extreme (aggressive and conservative) and moderate (moderate) WCM approaches are associated with firm performance. The results indicate that a conservative WCM investment policy augments liquidity and profitability and thereby confirms that maintaining liquidity is conducive to operational efficiency. Novel to the literature and considering economic externalities and technological progress, the analysis carries important implications for academics and working capitalists concerning profitability enhancement via better WCM.

This research investigates the effects of drying on some selected vegetables, which are Telfaria occidentalis, Amaranthu scruentus, Talinum triangulare, and Crussocephalum biafrae. These vegetables were collected fresh, sliced into smaller sizes of 0.5 cm, and dried in a convective dryer at varying temperatures of 60.0 °C, 70.0 °C and 80.0 °C respectively, for a regulated fan speed of 1.50 ms‒1, 3.00 ms‒1 and 6.00 ms‒1, and for a drying period of 6 hours. It was discovered that the drying rate for fresh samples was 4.560 gmin‒1 for Talinum triangulare, 4.390 gmin‒1for Amaranthu scruentus, 4.580 gmin‒1 for Talinum triangulare, and 4.640 gmin‒1 for Crussocephalum biafrae at different controlled fan speeds and regulated temperatures when the mass of the vegetable samples at each drying time was compared to the mass of the final samples dried for 6 hours. The samples are considered completely dried when the drying time reaches a certain point, as indicated by the drying rate and moisture contents tending to zero. According to drying kinetics, the rate of moisture loss was extremely high during the first two hours of drying and then steadily decreased during the remaining drying duration. The rate at which moisture was removed from the vegetable samples after the drying process at varying regulated temperatures was noted to be in this trend: 80.0 °C > 70.0 °C > 60.0 °C and 6.0 ms‒1 > 3.0 ms‒1 > 1.5 ms‒1 for regulated fan speed. It can be stated here that the moisture contents has significant effects on the drying rate of the samples of vegetables investigated because the drying rate decreases as the regulated temperatures increase and the moisture contents decrease. The present investigation is useful in the agricultural engineering and food engineering industries.

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.

The process of digitalization within the realm of tourism is not merely a trend but rather a significant catalyst that is rapidly propelling the comprehensive transformation of the tourism industry into a new era of technological advancement. This intricate process fundamentally involves the seamless integration and application of cutting-edge digital technologies across various tourism-related activities and services. The advent of innovative solutions that harness the immense capabilities of artificial intelligence, the analytical power of big data, the security features of blockchain, and the interconnectedness provided by the Internet of Things primarily serves to enhance the overall quality of services offered, optimize pricing strategies to align with market demands, and improve risk management protocols within the industry. This paper methods uses 100 Scopus indexed papers about Smart Tourism Development in Kazakhstan. It is imperative to underscore the fact that the ongoing digitalization process, while offering numerous advantages, simultaneously imposes rigorous new requirements concerning the qualifications and competencies of staff members, as well as the paramount importance of data security measures and the protection of consumer rights in the digital environment. The effective management of this digital transformation necessitates a holistic and integrated approach that encompasses not only the development of robust infrastructure but also the enhancement of digital literacy among employees and the establishment of a dynamic and innovative ecosystem that encourages creativity and adaptability.

Purpose: This review mainly aims to identify the lean practice conducted in hospitals, determining what problems lean practice can be helpful to solve in the hospitals. Data sources: Four electronic databases (Scopus, Web of science, Medline, and PubMed) were conducted for searching related literature in this review. Study selection: These studies in the hospitals that related lean healthcare practice and contained outcome variables were included. Data extraction: Related information such as research design, countries, lean tools, outcome variables, results were extracted. Results of data synthesis: 20 eligible articles were identified in this review. There was 20% lean practice being conducted in emergency department of hospitals in this review. Six cases have implemented lean in Brazilian hospitals. There were 12 cases implemented lean practice through Value Stream Mapping. Conclusion: Lean practices were highly valued in Brazilian hospitals, and it was frequently implemented in hospital emergency department. Value Stream Mapping and process mapping were the most commonly used lean tool. Waiting time, lead time and Length of Hospital Stay (LOS) were the primary indicators reflecting improvements in this review.