This study conducts a systematic literature review to analyze the integration of artificial intelligence (AI) within business excellence frameworks. An analysis of the findings in the reviewed articles yielded five major themes: AI technologies and intelligent systems; impact of AI on business operations, strategies, and models; AI-driven decision-making in infrastructure and policy contexts; new forms of innovation and competitiveness; and the impact of AI on organizational performance and value creation in infrastructure projects. The findings provide a comprehensive understanding of how AI can be integrated into organizational excellence emerged frameworks to address challenges in infrastructure governance, and sustainable development. Key questions addressed include: how AI affects consumer behavior and marketing strategies. What AI’s capabilities for businesses, especially marketing and digital strategies? How can organizations address the drivers and barriers to help make better use of AI in these business operations? Should organizations even do anything with these insights? These questions and more will be tackled throughout this discussion. This paper attempts to derive a comprehensive conceptual framework from several fields of human resources, operational excellence, and digital transformation, that can help guide organizations and policymakers in embedding AI into infrastructure and development initiatives. This framework will help practitioners navigate the complexities of AI integration, ensuring profitability and sustainable growth in a highly competitive landscape. By bridging the gap between AI technologies and development-related policy initiatives, this research contributes to the advancement of infrastructure governance, public management, and sustainable development.

The United Arab Emirates is the most involved country in the world in terms of developing community awareness of the value and importance of tolerance, and high-level human solidarity, enhancing it as a community culture, and informing it of a strong institutional framework, legal and legislative frameworks. The research aims to highlight the United Arabian Emirates government’s contribution to promote tolerance in society. The research fellow is descriptive analytic. The research concluded that the UAE government has succeeded to a large extent in establishing the concept of tolerance through its global role in developing the concept of tolerance. The research recommends the need to expand the application of the culture of tolerance in Arab and international societies and benefit from the experience of the United Arab Emirates in promoting the culture of tolerance.

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.

Photovoltaic systems have shown significant attention in energy systems due to the recent machine learning approach to addressing photovoltaic technical failures and energy crises. A precise power production analysis is utilized for failure identification and detection. Therefore, detecting faults in photovoltaic systems produces a considerable challenge, as it needs to determine the fault type and location rapidly and economically while ensuring continuous system operation. Thus, applying an effective fault detection system becomes necessary to moderate damages caused by faulty photovoltaic devices and protect the system against possible losses. The contribution of this study is in two folds: firstly, the paper presents several categories of photovoltaic systems faults in literature, including line-to-line, degradation, partial shading effect, open/close circuits and bypass diode faults and explores fault discovery approaches with specific importance on detecting intricate faults earlier unexplored to address this issue; secondly, VOSviewer software is presented to assess and review the utilization of machine learning within the solar photovoltaic system sector. To achieve the aims, 2258 articles retrieved from Scopus, Google Scholar, and ScienceDirect were examined across different machine learning and energy-related keywords from 1990 to the most recent research papers on 14 January 2025. The results emphasise the efficiency of the established methods in attaining fault detection with a high accuracy of over 98%. It is also observed that considering their effortlessness and performance accuracy, artificial neural networks are the most promising technique in finding a central photovoltaic system fault detection. In this regard, an extensive application of machine learning to solar photovoltaic systems could thus clinch a quicker route through sustainable energy production.

Rapid population growth and inadequate adherence to scientific and managerial principles in urban planning have intensified numerous challenges, pushing major Iranian cities toward instability. Tehran, as the capital and one of the most urbanized regions in the country, faces significant sustainability threats that require immediate attention. These challenges are not unique to Tehran but represent a broader issue faced by rapidly urbanizing cities worldwide, particularly in developing countries. Addressing such challenges is critical to fostering sustainable development on a global scale. While urban sustainability has been extensively studied, limited research has focused on the indicators of urban instability and their tangible impacts on sustainable urban planning. This study aims to bridge this gap by identifying and analyzing key factors contributing to urban instability across economic, environmental, and social dimensions, with Tehran serving as a representative case. The findings reveal that economic instability is driven by uncertainty in economic policies, fluctuating housing prices, non-standard housing conditions, income disparity, unemployment, and cost of living pressures. Environmental instability is exacerbated by climate change, urban heat islands, floods, transportation mismanagement, energy insecurity, pollution, and insufficient green infrastructure. Social instability arises from limited social interaction, unequal access to services, weak community participation, social harms, and diminished urban safety and welfare. By framing these local challenges within a global context, the study underscores the interconnectedness of these dimensions and highlights the necessity for integrated, evidence-based approaches that combine local insights with global best practices. The findings aim to contribute to the broader discourse on sustainable urban development by offering actionable insights and strategies that can be adapted and implemented in other rapidly urbanizing cities. This research serves as a guide for policymakers, urban planners, and stakeholders worldwide, emphasizing the importance of holistic and resilient urban strategies to address the multifaceted challenges of sustainability and instability.

The concept of sustainable urban mobility has gained increasing attention in recent years due to the challenges posed by rapid urbanization and environmental degradation. The objective of this study is to explore the role of on-demand transportation in promoting sustainable urban mobility, incorporating insights from customer interests and demands through survey analysis. To fulfill this objective, a mixed-methods approach was employed, combining a systematic literature review with survey analysis of customer interests and demands regarding on-demand transportation services. This study combines a systematic literature review and a targeted survey to provide a comprehensive analysis of sustainable urban mobility, addressing gaps in understanding customer preferences alongside technological and financial considerations. The literature review encompassed various aspects including technological advancements, regulatory frameworks, user preferences, and environmental impacts. The survey analysis involved collecting data on customer preferences, satisfaction levels, and suggestions for improving on-demand transportation services. The findings of the study revealed significant insights into customer interests and demands regarding on-demand transportation services. Analysis of survey data indicated that factors such as convenience, affordability, reliability, and environmental sustainability were key considerations for customers when choosing on-demand transportation options. Additionally, the survey identified specific areas for improvement, including service coverage, accessibility, and integration with existing transportation networks. By providing flexible, efficient, and environmentally friendly transportation options, on-demand services have the potential to reduce congestions, improve air quality, and enhance overall urban livability.

The study aims to investigate and analyse the social media, precisely the Instagram activity of several hotels in the city of Yogyakarta, Indonesia. Having been the second most popular destination besides Bali, it is mainly dominated by domestic tourism. Although several governmental institutions exist, the study focuses on the hotel’s activity only. The main purpose was to find, that after the classification of the posts, whether there is a more positive effect of one as opposed to the other type of posts. In addition, it was also important to see if with the time advancing positive effect of likes and comments appear and the relation of hashtags, likes and comments. Data was collected between 1st of January 2023. and 15th of July 2024. The first step was to collect posts done by the suppliers and then the posts were classified. Also, the number of hashtags used were collected. Second step was to collect the response from the demand side by gathering their likes and comments. Data then was analysed with SPSS 24 and JASP program. Results show that while there is no significance on increasing likes and comments with the months advancing, but in terms of the type of the posts there is. Promotional posts with other suppliers tend to bring a lot more comments and likes than self-promotional posts. This study’s main purpose to analyse through social media posts to enhance online networking by local suppliers promoting each other’s products.

The study examines the relationship between EPS and the gearing ratios and return on equity (ROE) ratio of 9 public listed firms on the Malaysian Stock Exchange from 2014 to 2022 financial years. The firms are selected at random. From this study it was established that there is a negative relation between EPS and gearing and a positive relation between EPS and ROE. Companies that want to attract more investors need to keep their gearing ratio low and increase the return on equity ratio high. To obtain the benefits of gearing or external funding, there need to be a balance between equity and debts. There is no one optimal balance between debt and equity. This balance is difference for each company and the sector they operate in. It is important for managers of companies to find the optimal balance between debt and equity, unique to their company.