Cyber-physical Systems (CPS) have revolutionized urban transportation worldwide, but their implementation in developing countries faces significant challenges, including infrastructure modernization, resource constraints, and varying internet accessibility. This paper proposes a methodological framework for optimizing the implementation of Cyber-Physical Urban Mobility Systems (CPUMS) tailored to improve the quality of life in developing countries. Central to this framework is the Dependency Structure Matrix (DSM) approach, augmented with advanced artificial intelligence techniques. The DSM facilitates the visualization and integration of CPUMS components, while statistical and multivariate analysis tool such as Principal Component Analysis (PCA) and artificial intelligence methods such as K-means clustering enhance complex system the analysis and optimization of complex system decisions. These techniques enable engineers and urban planners to design modular and integrated CPUMS components that are crucial for efficient, and sustainable urban mobility solutions. The interdisciplinary approach addresses local challenges and streamlines the design process, fostering economic development and technological innovation. Using DSM and advanced artificial intelligence, this research aims to optimize CPS-based urban mobility solutions, by identifying critical outliers for targeted management and system optimization.

This study aims to analyse the current state of library and information science (LIS) education in South Korea and identify educational challenges in building a sustainable library infrastructure in the digital age. As libraries’ role expands in a rapidly changing information environment, LIS education must evolve. Using topic modelling techniques, this study analysed course descriptions from 37 universities and identified 10 key topics. The analysis revealed that, while the current curricula cover both traditional library science and digital technology topics, focus on the latest technology trends and practical, hands-on education is lacking. Based on these findings, this study suggests strengthening digital technology education by incorporating project-based learning; integrating emerging technologies, such as data science and artificial intelligence; and emphasising community engagement and soft skills development. This study provides insights into improving LIS education to better align with the digital era’s evolving demands.

Technological advancements are transforming agriculture, yet adoption rates among agricultural extension officers, especially in regions like West Java, remain modest due to several challenges. This study applies the Technology Acceptance Model (TAM) to investigate factors influencing the adoption of agricultural technologies by agricultural extension officers in West Java. Specifically, we explore the role of socialization, training, access to technology, cost, perceived ease of use, and perceived usefulness in shaping behavioral intention and actual adoption. Data were collected from 295 agricultural extension officers via structured surveys and analyzed using SmartPLS 4 software. The findings indicate that socialization and training collectively enhance both perceived ease of use and perceived usefulness, while Technology Investment Worth specifically enhances perceived usefulness by emphasizing the value of the investment. Access to technology also plays a critical role in increasing ease of use perceptions. Both perceived ease of use and usefulness positively influence behavioral intention, which in turn is a strong predictor of actual adoption. The results provide valuable insights for policymakers aiming to increase technology uptake among agricultural extension officers, promoting sustainable agricultural practices through improved access, support, and cost reduction initiatives.

This study examines the impact of state highway construction contracts on state spending efficiency controlling for production structure, service demands, and situational factors. The theoretical argument is that because highway construction projects are relatively large in scale, complex, and can be monitored through objective performance measurement, state highway construction programs may save government production costs through contracts. Contracting helps highway producers achieve efficiency by optimizing production size based on workload and task complexity. The unit of analysis is 48 state governments’ highway construction contracts from 1998 to 2008. Through a two-stage analysis method including a Total Function Productivity (TFP) index and system dynamic panel data analysis, the results suggest that highway construction contracts enhance state highway spending efficiency, especially for large-scale construction projects.

The objective is to determine the impact of economic growth on the externalities of infrastructure investments for the Peruvian case for the periods from 2000 to 2022. The methodologies used are descriptive, explanatory and correlational, analyzing qualitative and mainly quantitative methods. Econometric software was used, and correlations of variables were created for each proposed hypothesis. The estimated model shows that all the independent variables have a significant t-statistic greater than 2 and a probability of less than 5%, which indicates that they are significant and explains the model. The R² is 98.02% which indicates that there is a high level of explanation by the independent variables to the LOG(RGDP). The results of the estimated models demonstrate the existence of a positive and significant relationship of investments in infrastructure and externalities on the growth of the non-deterministic component of real GDP, therefore, in a practical way, private and public investment has a positive effect on the non-deterministic growth of real GDP.

Nowadays investors are measuring the performances of a business organization not only based on their operating efficiency but also fulfilling their social responsibility. At least the investors need to know whether the activities of the business have any adverse impact on the society and environment. This study explores the accountability of the business from the social and environmental context. This empirical study tends to investigate the nature of the ownership structure that influences the environmental disclosure of a business entity. Based on the sample of fifty-five DSE-listed textile companies, this study used multiple regression to assess the causal relationship between the ownership structure and corporate environmental disclosure. Moreover, this cross-sectional study also considers the agency theory and stakeholder theory to explain the relationship between the ownership structure and environmental disclosure. The findings indicate that corporate environmental disclosure is positively influenced by foreign ownership and institutional ownership whereas director ownership and public ownership have no significant association with the environmental disclosure. These insightful results challenge conventional assumptions and highlight the need for a nuanced understanding of the factors that drive environmental reporting practices in the context of an emerging economy. The main contribution of this article lies in its provision of empirical evidence from an emerging economy, Bangladesh, which helps in understanding sustainable practices in a global context. Additionally, it aids in developing effective corporate governance policies and strategies tailored to similar emerging economies by recognizing the role of ownership structures in influencing environmental accountability. These findings further assist policymakers, managers, and other sustainability advocates in understanding how different ownership structures affect corporate environmental disclosure.