The paper proposes a methodology for the analysis and evaluation of the traffic scheme of Bulgarian cities. The authors combine spatial, network, and socio-economic analyses of cities with transport operators’ financial-economic evaluation, sociological studies of transport habits, and the possibilities of new information technologies for transport modeling (such as geographic information systems). The model proposes several approaches to optimize the municipality’s transport scheme. It results from a new need to improve urban traffic, the quality of transport services, and the integration of urban transport into the regional economy of Stara Zagora municipality. It presents a description, analysis, and outline of the opportunities for developing urban transport connectivity and mobility in Stara Zagora municipality. The research results show a deficit of transport connectivity between the different parts of the city, reflecting on the regional economy’s development and the efficiency of the environment and the population.

This study investigates the public’s perceptions of digital innovations in pharmacy, with a focus on health informatics and medication management. Despite the rapid development of these technologies, a comprehensive understanding of how various demographics perceive and interact with them is lacking hence, this research aims to bridge this gap by offering insights into public attitudes and the factors influencing the adoption of digital tools in pharmacy practice, as KSA population and healthcare professionals after Covid-19 has observed the significant potential of digital health. A cross-sectional survey involving 1132 participants was conducted, employing SPSS for data analysis to ensure precise and reliable results. The findings indicate general optimism about the potential of digital innovations to enhance healthcare outcomes but concerns about data privacy and usability significantly affect user acceptance. The researchers recommended tailored educational programs and user-centered design to facilitate the adoption of digital pharmacy innovations. Key contributions include the identification of ‘Ease of Use’ and ‘Data Security and Privacy’ as predominant factors in the adoption of digital health tools.

In recent years, information technology and social media has developed very rapidly and has had an impact on government services to the public. Social media technology is used hugely by several developing countries to provide services, information and promote information disclosure in its government to improve its performance. This study aims to build role of social media technology concept as a public service delivery facilitator to the public. Furthermore, it discusses the potential impact of social media use on government culture. To achieve the goal, this study combines two theories, namely government public value theory and green smart city with four variables, namely quality of public services, user orientation, openness, and greenness. These variables are used as the foundation for data collection through in-depth interviews and group discussion forums. In-depth interviews are utilized as data search and direct observation. The informants consist of several government elements, including heads of regional apparatus organizations, heads of public service malls and Palembang city government employees. The study revealed that the Palembang government has several social media-based public services that have quality of services, user-orientation, openness, and environmental friendliness.

Sustainability is a top priority for municipal administrations, particularly in large urban centers where citizens rely on transportation for work, study, and daily errands. Public transportation faces a significant challenge beyond availability, performance, safety, and comfort: balancing the cost for the city with fare attractiveness for passengers. Meanwhile, bicycles, supported by public incentives due to their clean and healthy appeal, compete with public transit. In Curitiba, the integrated transport system has been consistently losing passengers, exacerbated by the pandemic and the rise in private vehicle usage. To address this, the city is expanding bicycle infrastructure and electric bike rental services, impacting public transit revenue, and prompting the need for financial compensation to maintain affordable fares for those reliant on public transport. Therefore, this study’s objective is to analyze the bicycle’s impact on public transportation, considering the impact of public policies on economic and social efficiency, not just ecological and environmental factors. Data from six main bus lines were collected and analyzed in two separate linear regression models to verify the effects of new bicycles in circulation, bus tariffs, and weather conditions on public transportation demand. Research results revealed a significant impact of bus tariffs and fuel prices on the number of new bicycles that are diverting passengers from public transportation. The discussion may offer a different perspective on public transport policies and improve city infrastructure investments to strategically change the urban form to address social and economic issues.

This study aims to identify the causes of delays in public construction projects in Thailand, a developing country. Increasing construction durations lead to higher costs, making it essential to pinpoint the causes of these delays. The research analyzed 30 public construction projects that encountered delays. Delay causes were categorized into four groups: contractor-related, client-related, supervisor-related, and external factors. A questionnaire was used to survey these causes, and the Relative Importance Index (RII) method was employed to prioritize them. The findings revealed that the primary cause of delays was contractor-related financial issues, such as cash flow problems, with an RII of 0.777 and a weighted value of 84.44%. The second most significant cause was labor issues, such as a shortage of workers during the harvest season or festivals, with an RII of 0.773. Additionally, various algorithms were used to compare the Relative Importance Index (RII) and four machine learning methods: Decision Tree (DT), Deep Learning, Neural Network, and Naïve Bayes. The Deep Learning model proved to be the most effective baseline model, achieving a 90.79% accuracy rate in identifying contractor-related financial issues as a cause of construction delays. This was followed by the Neural Network model, which had an accuracy rate of 90.26%. The Decision Tree model had an accuracy rate of 85.26%. The RII values ranged from 68.68% for the Naïve Bayes model to 77.70% for the highest RII model. The research results indicate that contractor financial liquidity and costs significantly impact construction operations, which public agencies must consider. Additionally, the availability of contractor labor is crucial for the continuity of projects. The accuracy and reliability of the data obtained using advanced data mining techniques demonstrate the effectiveness of these results. This can be efficiently utilized by stakeholders involved in construction projects in Thailand to enhance construction project management.