This research examines the preparedness of individuals in Indonesia’s green building sector to utilise digital construction technologies, including Internet of Things (IoT), building information modelling (BIM), and artificial intelligence (AI). The objective is to enhance energy conservation and efficiency. The research integrates Unified Theory of Acceptance and Use of Technology (UTAUT2) and Task-Technology Fit (TTF) to develop a model that assesses the readiness of green construction teams to implement digital tools to enhance energy performance. The Partial Least Squares Structural Equation Modelling (PLS-SEM) approach is employed to determine reliability, validity, and structural correlations. The final model accounts for 93.0% of the variance in behavioural intention (BI), 34.4% in use behaviour (UB), and 44.2% in performance expectancy (PE). BI is a robust predictor of actual usage ($\beta$ = 0.586, $p$ $<$ 0.001). Social influence (SI) ($\beta$ = 1.037, $p$ $<$ 0.001), perceived value (PV) ($\beta$ = 1.300, $p$ $<$ 0.001), PE ($\beta$ = 0.181, $p$ = 0.0049), and habit (HB) ($\beta$ = 0.283, $p$ = 0.047) all positively affect BI. Conversely, facilitating situations exert a significant negative impact ($\beta$ = -1.584, $p$ $<$ 0.001). When individuals excessively rely on organisational assistance, they diminish their intrinsic motivation. TTF is a significant predictor of PE ($\beta$ = 0.665, $p$ $<$ 0.001); however, it does not directly influence BI. The integration of technology into tasks is primarily driven by individuals’ perceptions of its performance advantages rather than by direct adoption. This study focuses on the unique requirements of green-construction processes, in which digital technologies contribute to reducing energy consumption, an approach notably different from prior UTAUT2 + TTF studies. The research presents a model illustrating how task alignment, performance perceptions, and the evaluation of costs against benefits influence individuals’ readiness to adopt digital technology in green building project initiatives.

The plastic waste is the promising environmental pitfall faced across the globe, no matter what India is not exempted. Today we are having digital nativity among the Gen z or iGeneration leads to diverse environmental behavioral pattern. The study was focused area of Bangalore the reason which it is filled with the multi-cultural and diverse community. The study collects the structured questionnaire considering 942 samples. The novelty of the article through a light on identification of major information sources influencing behavioral change, gender-based differences in environmental concern, and limited awareness of health impacts. The methodology incorporated Cronbach Alpha, factor analysis, correlation, and analysis of variance (ANOVA) to ensure empirical rigor and interpret complex relationships in behavior and awareness. The study also limelight to the policy makers to leveraging the educational institutions to mandate to conduct the sustainable drive practices among the growing iGeneration.

The development of research, innovation, and entrepreneurship (RIE) competencies has been positioned as a strategic priority within Saudi Arabia’s Vision 2030; however, a persistent discrepancy between awareness and active engagement remains insufficiently characterised. In this study, the levels of RIE awareness, perceptions, and experiential participation among university students in Saudi Arabia, with particular reference to the Eastern region, were systematically examined, and their statistical associations with competency development were evaluated. A cross-sectional survey design was employed, in which data were collected from 301 students during April–May 2025 using a validated 24-item, five-point Likert-scale instrument encompassing five constructs: RIE awareness, influencing factors, perceptions and attitudes, educational experiences, and sustainability orientation. High internal consistency was demonstrated (Cronbach’s α = 0.89–0.93), and construct validity was assessed through exploratory factor analysis (EFA). Descriptive statistics indicated that RIE awareness was moderately high (M = 3.54, SD = 1.00), whereas a pronounced participation gap was observed: although 56.6% of respondents reported involvement in research activities, substantially lower engagement was recorded in innovation and entrepreneurship initiatives (24.9%) and start-up activities (19.2%). Perceived importance of RIE for future career development was high (M = 4.13), yet awareness of entrepreneurial mindset constructs remained comparatively limited (M = 3.15). Significant positive correlations were identified among the principal constructs (Spearman’s ρ = 0.666–0.902, p < 0.001), although potential inflation effects attributable to shared measurement items were noted and critically considered. Ordinal logistic regression analysis revealed that participation in research projects and exposure to structured educational experiences constituted the most robust predictors of RIE competency development, surpassing attitudinal variables in explanatory power. These findings suggest that favourable perceptions alone are insufficient to foster competency acquisition in the absence of sustained experiential engagement. It is therefore implied that higher education institutions should prioritise the integration of practice-oriented RIE programmes, strengthen mentorship quality, and enhance transparency in resource accessibility, with policy interventions oriented towards capability development rather than motivational reinforcement. The study provides an empirically grounded baseline for assessing RIE competencies in emerging higher education contexts and offers a transferable measurement framework applicable to Gulf and comparable innovation-driven economies.

Urban waste management requires a data-driven approach to understand community characteristics as a basis for designing public service information systems. This study aims to analyze the social dimensions of household waste management in Gorontalo City as a basis for the pre-design stage of the waste management information system. The research design used a cross-sectional survey of 400 households selected through stratified random sampling in nine subdistricts. Data were collected through a structured questionnaire covering sociodemographic characteristics, types of waste produced, and waste disposal behavior. The analysis was conducted descriptively and inferentially using the Chi-square and Cramer's V tests. The results show that household waste is dominated by organic waste (73%) and plastic (24%). The most common disposal behavior is through government transportation services (46.5%) and public trash bins (31.8%), while burning waste (15.0%) and disposal into rivers/open spaces (6.3%) are still found. Although there were minor variations in the contingency table between socio-demographic groups, the Chi-square test results showed that gender, age, and education were not significantly related to waste type and disposal behavior ($p$ $>$ 0.05). This indicates that waste management behavior is relatively homogeneous across all social groups. These findings reinforce the need for a universal service approach to waste management and provide an empirical basis for the development of the Gorontalo City SIMS, which focuses on improving service access, reporting disposal points, public education, and the integration of waste banks and city waste facilities.

This study aimed to develop a predictive model of water availability using artificial neural networks (ANN) in the Chalcas River basin, located in the district of San Pedro de Palco, Ayacucho, Peru. A quantitative, predictive, and non-experimental longitudinal design was applied. Hydrological data were used, including monthly average precipitation (ranging from 3.44 mm in June to 123.49 mm in February), weighted crop coefficients (Kc), monthly evapotranspiration (ETo), and a drainage density of 5.81 km/km$^2$. A multilayer ANN was structured and trained over 2000 epochs, achieving an average accuracy of 90.62% and a normalized mean absolute error (MAE) of 0.0528. The model determined the flow rate for the period 2003–2030 period, identifying critical seasonal patterns: a peak of 317.45 l/s in January 2028 and a minimum of 28.55 l/s in July 2026. These findings highlight the need to implement water storage strategies during wet seasons and optimize water use during dry periods. Ultimately, the ANN-based model enhances water resource management, reduces scarcity-related risks, and promotes the sustainability of the irrigation system. This methodology demonstrates broad applicability and can be replicated in other basins facing similar hydrological challenges, using the ANN model.

Natural radioactive nuclides $^{238}$U, $^{232}$Th, and $^{40}$K present in brick manufacturing facilities pose potential environmental, health, and economic concerns. This study employed gamma-ray spectroscopy using a NaI(Tl) detector to accurately determine radionuclide activity concentrations in ten samples collected from brick factories, Iraq. The investigation evaluated several critical health risk parameters, including radium equivalent activity, excess lifetime cancer risk, absorbed dose rate, and gamma representative index. The measured specific activities for $^{238}$U, $^{232}$Th, and $^{40}$K ranged from 32.67 ± 1.22 to 34.87 ± 1.26 Bq/kg, 24.65 ± 0.92 to 38.84 ± 1.16 Bq/kg, and 405.76 ± 4.91 to 419.92 ± 5.01 Bq/kg, respectively. All calculated radiation hazard indices were found to be within the permissible limits established by international regulatory organizations as recommended by Organisation for Economic Co-operation and Development (OECD), United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR), and International Commission on Radiological Protection (ICRP). The findings indicate that natural radioactivity levels in these facilities pose no significant health risks. Specifically, both occupational workers and the surrounding population remain protected under current operational conditions. These results provide important baseline data for radiation safety assessment in the brick manufacturing industry and demonstrate compliance with international safety standards.

Landfill leachate poses a major challenge to urban waste management, particularly in tropical regions with high rainfall and heterogeneous waste composition. This study developed an artificial neural network (ANN) based on a multilayer perceptron (MLP) architecture to predict leachate volume at the Supit Urang landfill in Malang City, Indonesia. The dataset combined primary measurements of leachate discharge with secondary meteorological and environmental data, including rainfall, temperature, humidity, wind, and waste volume. Data preprocessing involved cleaning, imputation, transformation, and normalization to improve data quality and model readiness. The ANN model used two hidden layers with 64 neurons each and was optimized with the Adam algorithm, early stopping, and L2 regularization to balance predictive accuracy and generalization. The model achieved an R$^2$ of 0.61 and correlation coefficients above 0.82, indicating a good ability to capture nonlinear relationships and overall leachate trends. However, the relatively high root mean square error (RMSE) values showed that individual predictions still deviated substantially from observed values. Overall, the findings indicate that ANN models are promising decision-support tools for sustainable landfill management, although further improvements in data quality and model optimization are still required. The study also offers practical insight for estimating leachate generation and planning treatment strategies in urban landfills.