This study explores the fluctuations in temperature and precipitation in Chandrapur, Maharashtra, over the last 30 years from 1991 to 2024. The recorded data suggest an increase in temperature, particularly in the summer months from March to May. In addition, winter nights are gradually warmer. Furthermore, the quantity of rainfall is also changing; less rain is observed in June and August, yet an increase is seen in July and September. Not only are these fluctuations evident, but they also showcase the true and escalating impacts of climate change in the area. The Chandrapur district is an industrial and agrarian hub. Therefore, there is an urgent need to devise and prioritize climate adaptation policies.
The study examined riverine urban areas and spaces as a strategic factor in the sustainable economic development of cities situated along the major rivers of Central Russia—the Oka and the Volga. The study focuses on empirical data from three Russian cities—Nizhny Novgorod, Ryazan, and Samara. The study’s purpose was to identify the most pressing problems of riverine urban areas and determine key criteria for their sustainable transformation. Through a comprehensive approach, combining literature review and an expert survey ($n$ = 44), the study identified six critical problems hindering the development of riverine areas and determined the priority criteria for sustainable restoration. The greatest significance was attributed to developing and improving the quality of life and attractiveness of the urban environment, green infrastructure, eco-friendly construction, and transport infrastructure. The findings suggest that a focus on these criteria will contribute to the revival of degraded embankment zones and catalyze socioeconomic development. The results demonstrate a high level of expert consistency ($W$ $>$ 0.6, $p$ $<$ 0.01) and can be used to develop sustainable development strategies for riverine urban areas in Russia and beyond.
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.
The rapid expansion of e-commerce has intensified the complexity of last-mile delivery, where increasing parcel volumes and urban constraints continue to challenge traditional distribution models. Among emerging solutions, parcel lockers have gained attention for their potential to improve delivery efficiency while reducing operational and environmental pressures. However, their effectiveness largely depends on appropriate location planning, which requires the simultaneous consideration of multiple and often conflicting criteria. This study develops a multi-criteria decision framework for parcel locker location selection by integrating the Opinion Weight Criteria Method (OWCM) and the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method. The proposed framework enables the systematic evaluation of alternative locations by combining structured expert judgment with compromise-based ranking. Criteria weights are derived through OWCM to ensure consistency in preference representation, while MARCOS is employed to assess alternatives based on their relative distance from ideal and anti-ideal solutions. The model is applied within a last-mile delivery context to examine its practical applicability. The results identify the most suitable location among a set of feasible alternatives and demonstrate stable performance under varying weighting scenarios. Sensitivity and comparative analyses confirm that the ranking outcomes remain consistent across different conditions and methodological configurations. The findings provide a structured approach to location planning in urban logistics and offer practical support for decision-makers seeking to deploy parcel locker systems under complex operational environments. The proposed framework can be extended to similar decision problems involving infrastructure placement and multi-criteria evaluation.
Ensuring the safety of public transport systems has become increasingly challenging with the growing complexity of traffic environments and vehicle–road–driver interactions. Conventional approaches that rely on single-source information are often insufficient to support comprehensive monitoring and coordinated response. This study proposes a bio-inspired multi-modal state evaluation approach for active safety in intelligent public transport systems. Drawing on principles of biological multi-sensory integration, the proposed method integrates driver physiological signals with heterogeneous road perception data through a multi-sensor fusion framework, enabling real-time assessment of traffic safety states. On this basis, a game-theoretic coordination strategy is developed to support collaborative prevention and response among vehicle, driver, and road-side elements under dynamic traffic conditions. The approach is evaluated across urban roads, expressways, and intersection scenarios. Experimental results show that the proposed method achieves improved accuracy, recall, and real-time performance compared with baseline methods, while maintaining stable performance under noisy and incomplete data conditions. This work provides a system-oriented approach for integrating multi-source sensing and coordinated decision-making in intelligent public transport safety management.
West Java Province is being exposed to a high risk of natural disasters, especially hydrometeorological disasters such as floods and landslides, hence hindering potential economic growth. The increasing frequency of disasters has shed light on the issue of regional resilience, an important concern for public authorities. Therefore, efforts to assess and strengthen regional resilience are crucial to reducing disaster risks and supporting the achievement of sustainable development. However, up till recently there has been no practical and applicable methodology for resilience assessment, which has become more complicated at the regional level, taking into account the economic, social, ecological, infrastructural, and institutional dimensions. The present paper proposed a composite indicator-based approach to evaluate the level of regional resilience to disasters in West Java Province. To describe the current conditions of resilience in each regency/city in the province, this study adopted 17 indicators that were adjusted for measurement in the actual context. The composite index combined by the macro-regional indicators in five main dimensions were calculated using arithmetic, geometric, harmonic, entropy, and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods. The integrated Regional Disaster Resilience Composite Index (RDRCI) scores across the 27 regencies/cities ranged from 6.33 to 33.98, with 9 regions recording values above the provincial mean of 15.26. The results of this analysis could be employed by policy-makers to evaluate the resilience of a region to natural disasters. Furthermore, the findings highlight the necessity of incorporating all dimensions into policy formulation to strengthen regional resilience to disasters.
Small object detection in aerial imagery remains challenging due to limited spatial resolution, background clutter, and severe scale variation. Existing deep learning–based detectors often suffer from weakened shallow representations and insufficient cross-scale feature interaction, leading to missed detections and unstable localization in dense scenes. This work presents Dynamic Reconstruction and Fusion Network (DRF-Net), a frequency-guided feature reconstruction framework for small object detection. Built upon a one-stage detection paradigm, the proposed method introduces three key components: a frequency-guided channel–spatial augmentation (FCSA) module to enhance fine-grained representations, a multi-frequency reconstruction block (MFRB) to restore cross-scale structural information, and a Dynamic Reconstruction Fusion Neck (DRF-Neck) to adaptively regulate multi-scale feature aggregation. By jointly modeling high- and low-frequency components and integrating saliency-aware fusion mechanisms, the framework improves the preservation of small-object contours while suppressing redundant background responses. Extensive experiments conducted on the VisDrone2019 benchmark demonstrate that DRF-Net consistently outperforms the baseline detector in terms of detection accuracy, particularly for small and densely distributed objects, while maintaining real-time inference efficiency. Ablation studies further verify the complementary contributions of the proposed modules to feature representation and fusion stability. The results indicate that frequency-guided reconstruction and dynamic fusion provide an effective learning strategy for enhancing small-object detection performance in complex visual scenes.
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