Objectives. The digital revolution has brought rapid developments to the health sector. People were taking advantage of telemedicine technology during the COVID-19 pandemic. Telemedicine is highly recommended during a pandemic because it will reduce the transmission rate of viruses, and it is considered adequate and low-cost. However, a fundamental challenge still occurs; most people need to be used to telemedicine technology. Presumably, inadequate education and lack of experience regarding the use of telemedicine are obstacles for society in utilizing telemedicine. Methods. This study is aimed at determining the factors that influence the use of telemedicine. It focused on variables such as data confidentiality, administration, and knowledge to measure potential factors that pushed people to utilize telemedicine. We used a quantitative approach, using multivariate analysis, namely, simple linear regression. Most of our respondents are people aged 18-30 years young. Results. All respondents stated that administration factors in the implementation of telemedicine were good. Through the Chi-square test, the data safety factor has no effect ( value =0.090 or >0.05) on telemedicine implementation, while the knowledge factor has a significant effect on telemedicine implementation with a value =0.043 (<0.005). The multivariate analysis explained that the knowledge variable influenced telemedicine use with a value =0.033 (<0.05), meaning it contributed 1.624 times to telemedicine. Conclusion. This study discusses the factors that influence the use of telemedicine. The study’s results explain that the knowledge variable is the most significant factor influencing telemedicine use. Knowledge is an intellectual property that everyone must have to capitalize on with telemedicine. A lack of knowledge will become an information gap and a barrier for someone to reach new tools/technologies.

Bioconversion of black soldier fly (BSF) larvae is a method of processing organic waste that is environmentally friendly and has no impact on public health. This research aims to analyze the influence of the type of waste as a feed formulation on the effectiveness of organic waste processing with bioconversion of black soldier fly larvae. The research was conducted in Tubunan Village, Jepara Regency, the district with the second lowest waste processing in Central Java on 108 families. The initial procedure for this experimental research was carried out by sorting the types of waste (community waste, fish slaughter waste, and a mixture of both). Each type of waste is then given 1.5 grams of larvae/kg of waste. The larvae will be harvested after 20 days and weighed to record data on the increase in biomass and waste left behind. The collected data was then analyzed using SPSS through ANOVA and Kruskal Wallis tests. The results showed that the nutritional formulation of the feed had a significant effect on total larval biomass (p=0.0005) but had no significant effect on the waste reduction index (p=0.651) and feed conversion efficiency (p=0.180). In this study, processing organic waste with bioconversion of black soldier fly larvae reduced waste piles significantly (p=0.008). Thus, bioconversion waste processing using BSF can solve the waste problem at the research location. These findings can be considered in planning effective and efficient waste processing.

This study analyzes the determinants of self-reported behaviours and perceptions associated with pesticide toxicity risk in children using the Social Practice Theory framework, linking individual factors and agricultural practices to understand vulnerability and prevention opportunities. This research was conducted in Pattapang Village, Tinggimoncong District, Gowa Regency, South Sulawesi Province, Indonesia. To examine the relationship between pesticide use patterns, social norms, competence, material, and individual aspects and the risk of sensitive toxicity in children, data were analyzed using structural equation modeling-partial least squares (SEM-PLS) with bootstrapping resampling. Pesticide use patterns had a significant negative effect on toxicity risk. Competence was the strongest predictor of pesticide use patterns, followed by materials and short-term goals. Personal values dominate personal norms and long-term goals, while social norms only influence personal norms. Self-efficacy, personal norms, and long-term goals showed no significant effects. The novelty of this research lies in the integration of a socio-ecological approach with individual psychological factors in a comprehensive structural model that explains the complex mechanisms of children’s protective behavior formation from pesticide toxicity, identifying that personal values—not personal norms or self-efficacy—are the most effective leverage points for farmer behavior change interventions.

Flooding is one of the most frequent and damaging natural disasters, accounting for nearly half of global disasters and posing a major challenge in Indonesia, where floods represent approximately 77% of all nationally recorded disaster events. Rapid urbanisation, land-use change, and climate-induced extreme rainfall have intensified flood risks nationwide. However, existing vulnerability assessments remain fragmented and localised, limiting their relevance for national-scale adaptation planning. This study develops a measurable and explainable framework for assessing urban flood vulnerability across Indonesia using cloud-based geospatial data and interpretable machine learning. The approach integrates CEMS-GLOFAS (flood hazard), WorldPop (population exposure), SRTM (topography), and ESA WorldCover (land cover) datasets within Google Earth Engine (GEE). Flood vulnerability is quantified through a modified Flood Vulnerability Index (FVI) combining hazard, exposure, and physical vulnerability components. The Extreme Gradient Boosting (XGBoost) model predicts FVI values, while SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs) enhance model transparency and identify the influence of key variables such as flood depth, population density, and elevation. The model achieved high predictive accuracy (R² = 0.89; RMSE = 0.04728 FVI units, dimensionless) and revealed substantial spatial heterogeneity across 514 districts, with the highest FVI (0.75–0.85) in Banda Aceh, Mojokerto, Pasuruan, Samarinda, and Merauke. The integration of GEE and explainable AI offers a transparent, scalable framework to support data-driven flood risk mitigation and urban climate resilience in Indonesia.

The increasing frequency of climate change and extreme weather makes the issue of urban flood vulnerability an important one. Flood disaster mitigation efforts have progressed rapidly with the utilization of technology in flood risk assessment and community preparedness. However, there is still a gap between technology and community engagement in preparedness. This study was conducted to analyze the trend of technology-based urban flood vulnerability and community preparedness integration. The study used bibliometric methods to see research trends and a systematic review of Scopus data with Biblioshiny, VOSviewer, and Convidence to identify relevant research articles. Trends in urban flood vulnerability show an increasing use of advanced technologies such as multivariate LSTM (Long Short-Term Memory) artificial neural networks, LiDAR (Light Detection and Ranging), GIS (Geographic Information System), and blockchain for flood risk assessment. There are gaps in community engagement and preparedness, highlighting the need for a comprehensive approach that combines technology with community-based strategies. A balance between technology implementation and community engagement is needed to improve community preparedness and safeguard water security in flood-prone urban areas. The development of an urban flood vulnerability index can bridge the technology gap with standardized community engagement. All the articles in the study contribute greatly to the changes and improvements in flood disaster mitigation, as well as to the role of technology and preparedness. The gap between technology and community engagement in preparedness requires an urban flood vulnerability index with comprehensive strategies.