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.

The journey towards better healthcare sustainability in Asian nations demands a comprehensive investigation into the impact of urban governance, poverty, and female literacy on infant mortality rates. This study undertakes a rigorous exploration of these key factors to pave the way for evidence-based policy interventions, utilizing data from a panel of six selected Asian countries: Pakistan, China, India, Indonesia, Malaysia, and the Philippines, spanning the years 2001 to 2020. The findings reveal that adequate sanitation facilities, higher female literacy rates, and sustained economic growth contribute to a reduction in infant mortality. Conversely, increased poverty levels and limited women’s autonomy exacerbate the infant mortality rates observed in these countries. The Granger causality analysis validates the reciprocal relationship between urban sanitation (and poverty) and infant mortality rates. Furthermore, the study establishes a causal relationship where female literacy rates Granger-cause infant mortality rates, and conversely, infant mortality rates Granger-cause women’s autonomy in these countries. The variance decomposition analysis indicates that sustained economic growth, improved female literacy rates, and enhanced women’s empowerment will likely impact infant mortality rates in the coming decade. Consequently, in low-income regions where numerous children face potentially hazardous circumstances, it is imperative to allocate resources towards establishing and maintaining accessible fundamental knowledge regarding sanitation services, as this will aid in reducing infant mortality rates.

This study analyzes the studies on project finance (PF) and renewable energy (RE) arena, employing a comprehensive scientometric analysis to illuminate the current research landscape, identify prominent scholars, and uncover emerging trends. Encompassing several analyses, we have charted the evolution of this domain from 1993 to March 2024 and showed the way for further research. We analyzed 80 studies selected from several databases by means scientometric tools. Despite decent citation rates, research in this relatively young field is surprisingly scarce. While geographically diverse, research leadership stems from the UK, USA, Australia, and Germany. Interestingly, a significant portion of the studies originates from broad energy and sustainability areas, highlighting a potential knowledge gap in finance and economics areas. Additionally, the prevalence of case studies points to a strong connection between theory and practice. The research also revealed prominent topics like the interplay between PF and RE, various renewable resources, infrastructure development, financial considerations, risk management, among others. While many themes exist, areas like technological advancements, diverse cost approaches, valuation methodologies, and policy considerations remain underexplored. Other results unveiled an unexpected finding: limited evidence of large-scale collaborations, with individual or small-group research efforts currently dominating the field. However, existing collaborative networks promise future advancements through the emergence of more formalized research groups, which can perform future research endeavors with a wide spectrum of unexplored topics.

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.

Hazards are the primary cause of occupational accidents, as well as occupational safety and health issues. Therefore, identifying potential hazards is critical to reducing the consequences of accidents. Risk assessment is a widely employed hazard analysis method that mitigates and monitors potential hazards in our everyday lives and occupational environments. Risk assessment and hazard analysis are observing, collecting data, and generating a written report. During this process, safety engineers manually and periodically control, identify, and assess potential hazards and risks. Utilizing a mobile application as a tool might significantly decrease the time and paperwork involved in this process. This paper explains the sequential processes involved in developing a mobile application designed for hazard analysis for safety engineers. This study comprehensively discusses creating and integrating mobile application features for hazard analysis, adhering to the Unified Modeling Language (UML) approach. The mobile application was developed by implementing a 10-step approach. Safety engineers from the region were interviewed to extract the knowledge and opinions of experts regarding the application’s effectiveness, requirements, and features. These interview results are used during the requirement gathering phase of the mobile application design and development. Data collection was facilitated by utilizing voice notes, photos, and videos, enabling users to engage in a more convenient alternative to manual note-taking with this mobile application. The mobile application will automatically generate a report once the safety engineer completes the risk assessment.