Optimizing Storage Location Assignment (SLA) is essential for improving warehouse operations, reducing operational costs, travel distances and picking times. The effectiveness of the optimization process should be evaluated. This study introduces a novel, generalized objective function tailored to optimize SLA through integration with a Genetic Algorithm. The method incorporates key parameters such as item order frequency, storage grouping, and proximity of items frequently ordered together. Using simulation tools, this research models a picker-to-part system in a warehouse environment characterized by complex storage constraints, varying item demands and family-grouping criteria. The study explores four scenarios with distinct parameter weightings to analyze their impact on SLA. Contrary to other research that focuses on frequency-based assignment, this article presents a novel framework for designing SLA using key parameters. The study proves that it is advantageous to deviate from a frequency-based assignment, as considering other key parameters to determine the layout can lead to more favorable operations. The findings reveal that adjusting the parameter weightings enables effective SLA customization based on warehouse operational characteristics. Scenario-based analyses demonstrated significant reductions in travel distances during order picking tasks, particularly in scenarios prioritizing ordered-together proximity and group storage. Visual layouts and picking route evaluations highlighted the benefits of balancing frequency-based arrangements with grouping strategies. The study validates the utility of a tailored generalized objective function for SLA optimization. Scenario-based evaluations underscore the importance of fine-tuning SLA strategies to align with specific operational demands, paving the way for more efficient order picking and overall warehouse management.

Accounting can be regulated using either a principle-based or rule-based approach; however, profit determined for taxes purposes is invariably subject to rigorous regulation, permitting minimal flexibility. Entities are strongly motivated to utilize same or highly similar tax figures for financial accounting and tax purposes, as it reduces costs and effort. Nevertheless, this form of tax-book conformity frequently results in decreased financial reporting quality, as proven by prior studies. In numerous jurisdictions, governments are developing simplified accounting systems that utilize figures established by accounting regulations, as this facilitates accurate tax calculations and enables entities to optimize efforts and expenses in preparing financial statements. However, these systems result in lower-quality financial statements, which consequently reduce transparency and makes decision-making. more complicated and less accurate. This study examines a specific example from Hungary where a simplified accounting system was introduced in conformity with tax regulations; nonetheless, the principle of true and fair view was replaced by standardization and uniformity. The research investigates if this tradeoff is acceptable as organizations utilizing this legislation (qualifying entities) are those whose scale suggests that such simplification will not significantly compromise public interest. The study reveals that in Hungary, smaller entities typically do not make significant changes to determine their taxable earnings. The introduction of this system is justifiable given the regulations available for smaller organizations. 

Management and efficiency have a fundamental impact on the performance of public hospitals, as well as on their philanthropic mission. Various studies have shown that the financial weaknesses of these entities affect the planning, setting of goals and objectives, monitoring, evaluation and feedback necessary to improve health systems and guarantee accessibility as an inalienable right. This study aims to analyze the management and efficiency of third-level and/or high-complexity hospitals in Colombia, through a statistical model that uses financial analysis and key performance indicators (KPIs) such as ROA, ROE and EBITDA. A non-experimental cross-sectional design is used, with an analytical-synthetic, documentary, exploratory and descriptive approach. The results show financial deficiencies in the hospitals evaluated; hence it is recommended to make adjustments in the operating cycle to increase efficiency rates. In addition, the use of the KPIs ROA and ROE under adjusted models is suggested for a more precise analysis of the financial ratios, since these adequately explain the variability of each indicator and are appropriate to evaluate hospital management and efficiency, but not in EBITDA ratio, hence the latter is not recommended to evaluate hospital efficiency reliably. This study provides relevant information for public health policy makers, hospital managers and researchers, in order to promote the efficiency and improvement of health services.

This study conducts a systematic review to explore the applications of Artificial Intelligence (AI) in mobile learning to support indigenous communities in Malaysia. It also examines the AI techniques used more broadly in education. The main objectives of this research are to investigate the role of Artificial Intelligence (AI) in support the mobile learning and education and provide a taxonomy that shows the stages of process that used in this research and presents the main AI applications that used in mobile learning and education. To identify relevant studies, four reputable databases—ScienceDirect, Web of Science, IEEE Xplore, and Scopus—were systematically searched using predetermined inclusion/exclusion criteria. This screening process resulted in 50 studies which were further classified into groups: AI Technologies (19 studies), Machine Learning (11), Deep Learning (8), Chatbots/ChatGPT/WeChat (4), and Other (8). The results were analyzed taxonomically to provide a structured framework for understanding the diverse applications of AI in mobile learning and education. This review summarizes current research and organizes it into a taxonomy that reveals trends and techniques in using AI to support mobile learning, particularly for indigenous groups in Malaysia.

Support through the corporate tax system is a very specific form of funding to promote the functioning of team sports. The basic idea of the mechanism is that profit-oriented companies can donate a larger part of their corporate tax to sports organisations. The scheme has been in operation in Hungary since 2011. Its introduction and fine-tuning required several legislative changes and EU approval. Its importance is reflected in the increase in the number of sports organisations in the respective sports. While funding is available to many sports organisations, in some cases it is quite concentrated. In our empirical research we sought to find out how the degree of concentration has changed over time. The degree of concentration has an impact on how balanced the competition is. One of the key values for sports services is the requirement of an uncertain output. The data reveal that over time the distribution has become more evenly balanced across all sport operators. The amount of funding for sports organisations has started to converge. According to these figures, there are several sports organisations with equivalent subsidies participating in the competition system. However, the majority of clubs with the highest subsidies tend to be the same from year to year. The allocation of grants is determined by the sports federation of the given sport according to the submitted applications. Decision-makers should pay particular attention to maintaining the balance of competition over a long period of time. To this end, the list of sporting organisations with the highest subsidies should be continuously assessed and revised.

With the advent of the big data era, the amount of various types of data is growing exponentially. Technologies such as big data, cloud computing, and artificial intelligence have achieved unprecedented development speed, and countries, regions, and multiple fields have included big data technology in their key development strategies. Big data technology has been widely applied in various aspects of society and has achieved significant results. Using data to speak, analyze, manage, make decisions, and innovate has become the development direction of various fields in society. Taxation is the main form of China’s fiscal revenue, playing an important role in improving the national economic structure and regulating income distribution, and is the fundamental guarantee for promoting social development. Re examining the tax administration of tax authorities in the context of big data can achieve efficient and reasonable application of big data technology in tax administration, and better serve tax administration. Big data technology has the characteristics of scale, diversity, and speed. The effect of tax big data on tax collection and management is becoming increasingly prominent, gradually forming a new tax collection and management system driven by tax big data. The key research content of this article is how to organically combine big data technology with tax management, how to fully leverage the advantages of big data, and how to solve the problems of insufficient application of big data technology, lack of data security guarantee, and shortage of big data application talents in tax authorities when applying big data to tax management.