Noise pollution in construction sites is a significant concern, impacting worker health, safety, communication, and productivity. The current study aims to assess the paramount consequences of ambient noise pollution on construction activities and workers’ productivity in Peshawar, Pakistan. Noise measurements have been recorded at four different construction sites in Peshawar at different times of the day. Statistical analysis and Relative Importance Index (RII) are employed to evaluate the data Risk variables, such as equipment maintenance, noise control, increased workload, material handling challenges, quality control issues, and client satisfaction. The results indicated that noise levels often exceeded permissible limits, particularly in the afternoon, posing significant worker risks. In addition, RII analysis identified communication difficulties, safety hazards, and decreased productivity as significant issues. The results show that noise pollution is directly linked with safety risks, decreased performance, and client dissatisfaction and needs immediate attention by authorities. This paper proposes a strategic policy framework, recommending uniform hand signals and visual communication methods without noise for workers, worker training about safety, and using wearable devices in noisy settings. Communication training for teams and crane operators, proactive quality control, and customer-oriented project schedules are also proposed. These recommendations aim to mitigate the adverse effects of noise pollution, enhance construction industry resilience, and improve overall operational efficiency, worker safety, and client satisfaction in the construction sector of Peshawar, aligning with policy and sustainable development objectives.

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.

Desert environments face the challenge of promoting sustainable tourism while balancing economic growth with cultural and environmental preservation. In the context of rapid global tourism expansion, effective destination management becomes crucial for positive economic impact and long-term preservation. This study aims to identify key factors influencing the sustainability of tourism. It explores the interactions between local stakeholders, the supply of tourism products and services, and tourism governance. Utilizing structural equation modeling through the PLS-SEM method, data was collected from 150 stakeholders in desert environments. The findings reveal that the involvement of local tourism stakeholders and the supply of tourism products and services significantly impact sustainable tourism in the desert environment. However, we observe a lack of influence between tourism governance and sustainable desert tourism. The novelty of the study lies in the identification of promotional factors for sustainable desert tourism. The originality of this study lies in its in-depth exploration of the mechanisms for promoting sustainable tourism.

The objective of this article is to examine the provision of temporary exhibitions and events by Slovak museums and galleries, and to highlight their significance in the context of selected performances of these cultural attractions in the tourism sector. The article employs a secondary data analysis of the Ministry of Culture of the Slovak Republic and annual reports from 105 museums and 10 galleries in 2017, as well as 99 museums and 15 galleries in 2022. Correlation and regression analyses were employed to assess the dependence of variables. The results of the analysis confirm a direct, moderate dependence between the number of temporary exhibitions and events and the total number of visitors in museums and galleries. Additionally, the examination demonstrated that the exhibited activity has not had a positive effect on the revenues of museums and galleries. However, with an increasing number of events, their revenues from their own activities grew. The average revenues from one event were found to be higher in museums than in galleries.

In order to assess the effects of e-learning integration on university performance and competitiveness, this study uses Oman as a model for the Gulf. Analyzing how e-learning impacts technology integration, diversity, community engagement, infrastructure, financial strength, institutional reputation, student outcomes, research and innovation, and academic quality can reveal whether universities are effectively incorporating digital tools to enhance teaching and learning. By offering a framework for comparable institutions in the Gulf area, this study provides insights into optimizing e-learning techniques to improve university performance and competitiveness. This study uses the Structural Equation Modeling (SEM) with a dataset comprising 424 participants and 55 indicators, analyzed using both measurement and structural models. The results of the hypothesis testing, which indicate that e-learning has a positive effect on factors like student outcomes (B = 0.080, t = 2.859, P = 0.004) and institutional reputation (B = 0.058, t = 2.770, P = 0.005), lend credence to these beliefs. Omani universities need culturally sensitive e-learning, stronger institutional support, and training to enhance diversity (B = 0.002, t = 0.456, P = 0.647) and technology integration (B = −0.009, t = 0.864, P = 0.387). These improvements increase the visibility of Gulf institutions abroad, attracting the best students from all around the world and fostering an inclusive learning atmosphere. Financially speaking, e-learning offers reasonably priced solutions such as digital libraries and virtual laboratories, which are especially beneficial in a region where education plays a major role in socioeconomic development.

The Heating, Ventilation, Air Conditioning, and Refrigeration (HVAC&R) industry is pivotal to Europe’s goals for energy efficiency, sustainability, and technological advancement. As demand for skilled HVAC&R professionals rises, the effectiveness of educational programs in this field has become a focal point. This article explores the Portuguese case to analyze how pedagogical strategies and student motivation contribute to the quality of HVAC&R training across Europe. The study highlights innovative teaching methodologies such as active and competency-based learning, as well as the use of laboratory training and digital simulations to provide hands-on experience. Additionally, it emphasizes Bloom’s Taxonomy as a framework for curriculum development, ensuring that students advance from foundational knowledge to complex problem-solving abilities. Motivation is also identified as a critical factor for student engagement and long-term career commitment. The article concludes that a balanced integration of theoretical knowledge, practical skills, and motivational support is essential for producing highly qualified HVAC&R professionals. This approach not only meets current industry needs but also aligns with Europe’s broader environmental and technological objectives, offering valuable insights for educators, policymakers, and industry stakeholders.