The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R² from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R² is compared to the experiments. No changes in Test R²’s were observed on GBM and XGBoost, which retained same Test R² across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R² of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R²; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

The problem of stunting is not only related to children’s short height, but also has an impact on high morbidity rates, due to long-term nutritional deficiencies. which hinders motor and mental development in children. The objectives of this research are: 1) to understand household food security, 2) to understand the eating habits of pregnant women and toddlers regarding existing belief systems and traditions, and 3) to understand resilience mechanisms in overcoming food emergencies to prevent stunting. The data collection process uses a mixed methods approach by combining qualitative and quantitative research. The research results show that the determining factor for the incidence of stunting in coastal areas of Indonesia is the lack of household food availability due to subsistence economic life which then has an impact on eating behavior in the household, namely the lack of quality and quantity of the types of food consumed. daily. Apart from that, there is still a lack of understanding by pregnant women regarding the importance of providing complementary breast milk food to toddlers, low literacy of food diversity among toddlers, and low public trust in the importance of immunization. Furthermore, the high rate of early marriage in society and the limited awareness of using clean water is caused by a philosophy that still considers rivers as a source of life, so the water is used for consumption. Apart from that, socio-cultural mechanisms as a strategy to resolve the problem of food shortages have not yet been implemented.

Evaluating tourist destinations is extremely important as it is the basis for helping local authorities and the leadership of tourist destinations implement reasonable solutions to strengthen the state management of tourism, encourage investment and upgrade service quality at destinations, better exploit the tourist market, position the tourist destination brand in the international tourism market, increase the length of stay, and increase tourist spending when coming to the tourist destination. The current state of investment and development of tourist destinations means that tourist areas across the country need to be evaluated and classified to have a basis for encouraging investment and strengthening effective management, upgrading service quality at destinations, and gradually positioning the Vietnamese tourism destination brand in the international tourism market. This study evaluates the Ba Na tourist area (Da Nang city, Vietnam) based on the “Set of criteria for evaluating tourist destinations” issued by the Ministry of Culture, Sports and Tourism of Vietnam (2016). issued under Decision No. 4640/QĐ-BVHTTDL on 28 December 2016. Evaluation results show that criteria for tourism resources, landscape, facilities, participation of local communities, and the management of the tourist area are evaluated very well. On the contrary, services for entertainment, shopping, entertainment, and prices of services in the tourist area are limited problems in the Ba Na tourist area.

This study investigates the impacts of converting agricultural land into agrotourism areas on environmental, socio-cultural, and economic perspectives within Batukliang District, Central Lombok Regency, Indonesia. With a case study approach, this qualitative descriptive research employed interviews with three target groups: local farmers, residents, and tourism actors. The findings revealed seven key points identified as influences affecting the socio-cultural aspects of land change, including community impact, cultural preservation, cultural identity loss, community dynamics change, local cultural commercialization, cultural heritage loss, and traditional livelihoods. The results also unveiled nine financial impacts, 8 of which were associated with economic implications such as economic challenges, risk management, brand building, costs and investments, market access, increased revenue, and income diversity, which contribute positively to local economic development. The study concluded that integrating community involvement empowerment strategies, income diversification, sustainable farming promotion, and land-use regulation is crucial for developing a successful sustainable agrotourism destination.

This research explores the factors influencing consumers’ intentions and behaviors toward purchasing green products in two culturally and economically distinct countries, Saudi Arabia and Pakistan. Drawing on Ajzen’s Theory of Planned Behavior (TPB), the study examines the roles of altruistic and egoistic motivations, alongside environmental knowledge, in shaping green consumer behavior. Altruistic motivation, driven by concern for societal well-being and environmental sustainability, is found to have a stronger impact on green purchase intention and behavior in both countries, particularly in Pakistan. Egoistic motivation, which focuses on personal benefits like health and cost savings, also contributes but with a lesser influence. The research employs a cross-sectional survey design, collecting data from 1000 respondents (500 from each country) using a stratified random sampling technique. The collected data were analyzed using structural equation modeling (SEM) to examine the relationships between variables and test the moderating effects of environmental knowledge. The results reveal that environmental knowledge significantly moderates the effect of both altruistic and egoistic motivations on green purchase intention, enhancing the likelihood of eco-friendly consumption. These findings underscore the importance of environmental education in promoting sustainable consumer behavior. The originality of this study lies in its comparative analysis of green consumerism in two distinct contexts and its exploration of motivational factors through the TPB framework. Practical implications suggest that policymakers and marketers can develop strategies that appeal to both altruistic and egoistic drivers while enhancing consumer knowledge of environmental issues. The study contributes to the literature by expanding TPB to include the moderating role of environmental knowledge in understanding green consumption behavior across diverse cultures.

This study investigates the influence of service quality, destination facilities, destination image, and tourist satisfaction on tourist loyalty in the Pasar Lama Chinatown area of Tangerang City. Utilizing data from 400 respondents, the study employed structured questionnaires analyzed through descriptive statistics, reliability analysis, exploratory and confirmatory factor analysis, and structural equation modeling (SEM). The results reveal that service quality (β = 0.47, p < 0.001), destination facilities (β = 0.33, p < 0.001), and destination image (β = 0.4, p < 0.001) all significantly enhance tourist satisfaction, which in turn has a strong positive effect on loyalty (β = 0.58, p < 0.001). Direct paths also show that service quality, destination facilities, and destination image independently contribute to tourist loyalty. Bootstrapping confirms satisfaction’s mediating role between these factors and loyalty. Practical recommendations suggest prioritizing service quality improvements, facility enhancements, and a positive destination image to foster loyalty and promote tourism sustainability in Pasar Lama, China. These insights assist tourism managers in developing strategies to enhance long-term visitor retention and engagement in the area.