To determine the suitability of the soils of the Fallujah and Karma regions for agricultural purposes, a field study was conducted. Soil samples were taken to a depth of 30 cm from a number of pedunclear soils in the study area. They were characterized morphologically, physically, and chemically, and were then classified accordingly. Based on the 2015 Food and Agriculture Organization (FAO) classification system, spatial distribution maps of selected soil characteristics were generated using ArcGIS 10. The analysis relied on the SYS 1980 coordinate system to determine and visualize the spatial extent of soil suitability across the study area. The results showed that the soils of the study area are distributed between the group of advanced desert soils and desert sedimentary soils. The soil textures of the study area are distributed between medium to coarse textures within the alluvial, sandy, and sandy loam types. The study also showed that the salinity in the area is distributed into four class, and is divided into three class in the distribution of gypsum and lime ratios. The suitability results for the soils of the study area showed the presence of four types: (N) and (N1), which are unsuitable, type (S4) which is slightly suitable, and (S3) which is moderately suitable. The main determinants in the study area are soil salinity and the proportions of gypsum and lime.
The transition from fossil fuels to renewable energy is vital for addressing climate change and ensuring energy security. Hybrid renewable energy systems (HRES), particularly those integrating solar photovoltaic (PV) and wind power, have emerged as a promising solution to overcome the intermittency and variability of individual sources. This study develops a comprehensive simulation and optimization framework for hybrid PV–wind systems, incorporating advanced energy storage options such as lithium-ion batteries and ultracapacitors. Using high-resolution meteorological and load data, both grid-connected and off-grid configurations are analyzed to evaluate system reliability, cost-effectiveness, and adaptability across different climates. A special focus is given to Kuwait, where high solar irradiance and moderate wind resources align with national energy diversification goals under Kuwait Vision 2035. The results highlight the technical and economic feasibility of hybrid systems, showing significant improvements in energy yield, load matching, and levelized cost of energy (LCOE) compared to standalone technologies. Furthermore, the study underscores the importance of intelligent control strategies, advanced component technologies, region-specific optimization, and explicit planning and performance evaluation insights in ensuring sustainable and resilient deployment of hybrid renewable systems.
Effective maintenance planning in high-performance mechanical systems requires a structured approach to identifying and prioritizing potential failure modes under multiple, often conflicting criteria. Conventional Failure Mode and Effects Analysis (FMEA) relies heavily on subjective judgment, which can limit consistency and transparency in decision-making. To address this limitation, this study develops a decision-oriented framework that integrates Shannon entropy-based weighting with three Multi-Criteria Decision-Making (MCDM) methods, namely SAW, TOPSIS, and VIKOR. The framework is applied to a representative high-performance mechanical system, in which maintenance-related factors, including failure probability, detection capability, economic impact, repair time, and resource availability, are evaluated in a unified structure. Entropy weighting is employed to derive criterion importance directly from data, reducing reliance on expert bias. The combined use of multiple MCDM techniques enables cross-validation of ranking outcomes and improves the robustness of the prioritization process. The results show a high degree of consistency among the three methods (Spearman’s $\rho>0.80$), indicating stable identification of critical failure modes. The proposed framework provides a transparent basis for risk-informed maintenance planning and supports more effective allocation of inspection and repair resources. From an engineering management perspective, the approach facilitates the transition from experience-driven decisions to data-supported strategies, contributing to improved system reliability and operational efficiency. Although demonstrated in a specific application context, the framework can be extended to other engineering systems where structured failure prioritization is required.
Optical Character Recognition (OCR) plays a crucial role in the digitization and preservation of textual information; however, for low-resource languages such as Kashmiri, reliable OCR solutions remain largely unavailable. Kashmiri, primarily written in the Perso-Arabic (Nastaliq) script, poses significant challenges due to its cursive structure, extensive use of ligatures, complex diacritical marks, and limited availability of annotated datasets. This research aims to address these challenges by developing a functional OCR system specifically tailored for Kashmiri text. The proposed system is built using the open-source Kraken OCR engine and leverages deep learning techniques with transfer learning from a pre-trained Arabic OCR model. A synthetic dataset was generated using Unicode Kashmiri text, enriched with Kashmiri-specific diacritics and exclusive characters, and rendered into images through automated text-to-image pipelines. Extensive preprocessing, augmentation, and iterative fine-tuning were performed to improve recognition accuracy. Model performance was evaluated using standard metrics such as Character Error Rate (CER) and Word Error Rate (WER) on both seen and unseen data. Experimental results demonstrate a substantial improvement over the initial model, with character accuracy increasing from 54.91% to 79.91% and word accuracy improving from 4.65% to 44.19%. The final model shows strong recognition capability for common and Arabic script characters, while Kashmiri-specific inherited diacritics remain a challenging area. In addition, a cross-platform user interface developed using Flutter enables users to upload or capture images and obtain digitized Kashmiri text through a simple and accessible workflow. Rather than proposing a new recognition architecture, this work contributes empirical insights, reproducible methodology, and error characterization for OCR in a previously unsupported low-resource Nastaliq language. This work is positioned as a baseline OCR system for printed Kashmiri Nastaliq text at the line level and does not claim state-of-the-art performance.
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 investigates how investors’ perceptions of environmental, social, and governance (ESG) factors influence equity investment decisions in Vietnam, emphasising the mediating role of corporate image and the moderating effect of investment horizon. Using survey data from 549 individual investors and analysing the model through partial least squares structural equation modelling (PLS-SEM), the findings indicate that environmental and governance dimensions significantly and positively affect investment decisions, while the social dimension exhibits a weaker direct effect. Corporate image plays a key mediating role, transmitting the impact of ESG practices to investor behaviour. However, investment horizon does not significantly moderate the relationship between ESG factors and investment decisions. The study contributes empirical evidence from an emerging market and highlights the importance of integrating ESG performance with corporate image management to attract sustainable equity investment.
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