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 fear of ghosts is a common thing that can be managed as a social condition that turns out to have an impact on the continuity of forest maintenance. Applying a qualitative approach supported by in-depth interview methods, observation, and literature study. This research does not attempt to prove the existence of ghosts or discuss the psychological conditions of people who fear ghosts. The main finding of this research is the reality of the reproduction of stories and experiences of fear of ghosts, as well as the implementation of traditions or rituals related to community activities in the forest. Stories of fear of ghosts with various forms and versions of naming not only enrich the cultural life of the community but also encourage social conditioning in the form of togetherness to agree on the fear of ghosts as a means of creating a social system in order to carry out activities in the forest. The social system is identified in the form of pamali traditions or things that should not be done in the forest, balian rituals to eliminate or treat ghost disturbances, and besoyong rituals to utilize forest products, which then have an impact on the awareness to respect the continuity of these rituals and tradition. So, even though the fear of ghosts can be overcome psychologically and disappear quickly, the reality of respect for the social system related to the forest can still survive. In addition, ghost stories’ reproduction continues to be rolled out and adapted to the times. In turn, ghosts and forest rituals continue to be conditioned into a social system that has implications for forest conservation.

Rural tourism plays a crucial role in rural development in Indonesia by providing employment opportunities, livelihood, infrastructure, cultural preservation, and environmental preservation. However, it is prone to external shocks such as natural disasters, public health events, and volatility in the national and global economy. This study measures the resilience of rural tourism to external shocks caused by the COVID-19 pandemic in 24 rural tourism destinations in Indonesia covering four years from 2019 to 2022. A synthetic composite index of the Adjusted Mazziotta-Pareto index (AMPI) is used to measure rural tourism resilience followed by clustering analysis to determine the typology of the resilience. The AMPI measure is also compared with the conventional Mazziotta-Pareto index (MPI) method. The resilience index is composed of capacity and performance components related to resilience. The results show that in the first year of COVID-19, most tourism villages in Indonesia were severely affected by the pandemic, yet they were able to recover afterward, as indicated by positive differences in the AMPI index before and after COVID-19. Thus, rural tourism villages in Indonesia have a strong capacity and performance to recover from pandemic shock. Lessons learned from this analysis can be applied to policies related to rural tourism resilience in developing countries.

The convergence of multifaceted global challenges encompassing the rise of populism, Brexit, the climate crisis, the COVID-19 pandemic, and the Russian invasion of Ukraine has catalyzed a profound reassessment of international trade policies. This article critically examines the intricate linkages between these challenges and their profound implications for the contemporary international trading system. Traditionally, globalization debates in the 1990s underscored the social and environmental dimensions of trade, yet the current landscape reveals an undeniable entwining of societal implications with trade policies. This article delves into the interconnectedness of these global challenges with trade, evaluating how each phenomenon influences and reshapes policy discourse. In particular, the rise of populism and its attendant protectionist sentiments have engendered a reevaluation of trade relationships and multilateral agreements. The seismic geopolitical event of Brexit has disrupted regional trade dynamics, signaling a paradigm shift in established trade blocs. Simultaneously, the imperatives of addressing the escalating climate crisis have spotlighted the necessity for trade policies to align with environmental sustainability goals. The COVID-19 pandemic, acting as a disruptor on a global scale, has accentuated vulnerabilities within supply chains, emphasizing the need for resilience and adaptability in trade frameworks. Additionally, the Russian invasion of Ukraine has introduced geopolitical tensions that further complicate the trade-policy landscape. By critically evaluating these intersecting challenges, this article delineates the evolving nature of trade policies and their inextricable relationship with societal and geopolitical realities. It underscores the imperative for a holistic approach in policy formulation that integrates social, environmental, and geopolitical considerations, acknowledging the integral role of trade policies in addressing contemporary global challenges.

Smart electric meters play a pivotal role in making energy systems decarbonized and automating the energy system. Smart electric meters denote huge business opportunities for both public and private companies. Utility players can manage the electricity demand more efficiently whereas customers can monitor and control the electricity bill through the adoption of smart electric meters. The study examines the factors affecting the adoption intention of smart electric meters in Indian households. This study draws a roadmap that how utility providers and customers can improve the smart electric meters adoption. The study has five independent variables (performance expectancy, effort expectancy, social influence, environmentalism, and hedonic motivation) and one dependent variable (adoption intention). The sample size for the study is four hundred and sixty-two respondents from Delhi and the National Capital Region (NCR). The data was analysed using structural equation modelling (SEM). The results of this study have confirmed that performance expectancy, environmentalism, and social influence have a significant impact on the intention of adopting smart electric meters. Therefore, utility providers can improve their strategies to attract more customers to adopt smart electric meters by focusing more on the performance of smart electric meters and by making them environmentally friendly. This research offers meaningful insights to both customers and utility providers to make energy systems decarbonized and control energy consumption.