India annually produces about 62 million tons of municipal solid waste, comprising 50–60% of organic matter. Accelerating urbanization and population growth in this country have intensified the challenges confronted by managing food, agricultural, and biodegradable waste, as the waste if handled improperly, would lead to groundwater contamination, soil degradation, and methane emission from landfills. This review provided a comprehensive assessment of the organic waste management (OWM) landscape in India, ranging from conventional methods like composting and vermicomposting to advanced approaches such as anaerobic digestion and biogas generation. It also evaluated the influence of policy frameworks and community-led initiatives on promoting sustainable practices. The focus of this study on the emerging role of artificial intelligence (AI) in the OWM highlighted its potential for improving waste segregation, process optimization, and real-time monitoring. While the application of AI in waste management has demonstrated over 90% of segregation accuracy in the pilot and global studies, its adoption remains minimal in India. By systematically comparing national practices with global benchmarks, this review identified critical gaps in technology adoption, scalability, and integration between policy and infrastructure; to fill a noticeable void in the existing literature, AI-driven innovations were adopted to deal with the unique challenge of waste management in India. The findings underscored the need for targeted support, capacity building, and technological deployment to transform organic waste from an environmental liability into a renewable and value-generating resource. Practical recommendations were offered to align technology, governance, and community participation with sustainable and resource-efficient OWM.
Real-time traffic sign recognition (TSR) plays a crucial role in intelligent transportation systems (ITS) and autonomous driving technologies. It enhances road safety, ensures efficient traffic rule enforcement, and supports the seamless operation of both autonomous and driver-assist systems. This paper proposes a hybrid TSR model that integrates mathematical morphology, edge detection, and fuzzy logic to accurately identify and classify traffic signs across diverse environmental conditions. The preprocessing stage applies contrast enhancement and Gaussian filtering to improve the visibility of key features. Next, shape- and color-based segmentation using mathematical morphology extracts regions of interest that are likely to contain traffic signs. These regions are then analyzed using a fuzzy inference system (FIS) that evaluates features such as color intensity, geometric shape ratios, and edge sharpness. The fuzzy system handles the inherent ambiguity in visual patterns, enabling robust decision-making. The entire model is developed in MATLAB R2015a, ensuring both computational efficiency and real-time performance. The integration of classical mathematical techniques with fuzzy reasoning allows the system to maintain high accuracy and reliability across a wide variety of traffic scenes. The proposed approach demonstrates significant potential for practical deployment in ITS applications, including smart vehicles and automated road safety systems.
Operations managers and engineers in the automotive industry confront the key challenge in ensuring the reliability of the manufacturing process. To accurately classify failure modes, this study proposed a novel Multi-Criteria Decision-Making (MCDM) model integrated with Single-Valued Neutrosophic Sets (SVNSs) for operations management to prioritize actions in eliminating failure modes that had the greatest impact on the concerned reliability. The identification and evaluation of failure modes were grounded in the conventional Failure Mode and Effect Analysis (FMEA), while the relative importance of risk factors (RFs) was expressed through predefined linguistic terms modelled with the SVNSs. The assessment of these risk factors was formulated as a fuzzy group decision-making problem and the fuzzy weight vector was derived from the Order Weighted Averaging (OWA) operator. Failure rankings were conducted through a modified version of the Elimination and Choice Translating Reality (ELECTRE) method; being tested and validated with real-world data from an automotive company, the proposed FMEA-ELECTRE model could inspire stakeholders in various industries to explore this scientific contribution further.
The mechanisms governing underground pipeline rupture in erodible soils remain a critical focus in geotechnical engineering, particularly under full pipe flow conditions. In this study, the impact of geogrid reinforcement on the fracture behavior of buried pipelines was systematically investigated using transparent soil modelling techniques, which enabled real-time visualization of subsurface erosion dynamics. Geogrid reinforcement was applied across varying spatial extents to identify the optimal reinforcement zone for mitigating collapse-induced failure. Soil-particle migration and cavity formation were monitored under different hydraulic scenarios, facilitating a detailed characterization of erosion pit evolution and subgrade instability. Test results demonstrated that appropriately positioned geogrid reinforcement significantly delayed the initiation and progression of subsidence, reduced the depth and volume of collapse zones, and enhanced the structural integrity of the surrounding subgrade. Under pressure-free conditions, geogrid installation was found to slow the erosion rate, whereas under full pipe flow, the reinforcement effectively suppressed sudden cavity collapse and curtailed the expansion of erosion-prone areas. These findings highlight the critical role of geogrid placement in maintaining pipeline stability by moderating soil loss and controlling void development. The use of transparent soil provided unique insights into the spatial and temporal characteristics of internal erosion, allowing for a more precise delineation of geogrid influence zones. This research contributes to a deeper understanding of subsurface failure mechanisms in reinforced systems and offers practical guidance for infrastructure resilience against hydraulic-induced ground deformation.
The existing literature focused primarily on practical applications of the BIM in project management, sustainable development, and facility management (FM), while the theoretical foundations of the model remained largely underdeveloped. This article provides a systematic literature review on the basic mechanisms of the BIM, including information representation, data exchange mechanisms, decision support, and new network models integrating semantic, topological, and spatial aspects. Despite the widespread adoption of standards such as Industry Foundation Classes (IFC), Construction Operations Building Information Exchange (COBie), and BIM Collaboration Format (BCF), there is a lack of consistent ontologies integrating the function, structure, and behavior of objects. As data exchange mechanisms remain limited by interoperability issues, the impact of the BIM on decision-making processes has not been captured in universal theoretical models. The latest approaches, based on networked data representation, offer promising prospects but require further empirical validation. The results of the review imply the development of integrated ontological frameworks, formalization of information exchange processes, and creation of theoretical models to support decision-making.
The environmental and health-related challenges associated with intensive and conventional farming practices have underscored the urgency of transitioning towards more sustainable agricultural systems, such as organic farming. Degradation of soil quality, nutrient depletion, biodiversity loss, chemical exposure, and erosion have been widely attributed to prolonged conventional agricultural methods. The adoption of organic farming practices is therefore considered pivotal in addressing these ecological and public health concerns. However, the effectiveness of this transition is largely contingent on farmers’ willingness to adopt and sustain organic cultivation methods. In this context, A thorough examination of peer-reviewed literature was conducted to examine the behavioral drivers and systemic barriers influencing decisions by farmers to adopt organic farming. Special attention was given to the level knowledge and perception regarding organic practices, as well as theoretical models of technology adoption in agricultural contexts by the farmers. The findings indicate that perceived health benefits, environmental sustainability, and long-term economic viability are primary motivators of adoption. Conversely, constraints such as reduced yields, labor intensiveness, and certification complexity were identified as significant deterrents. Furthermore, a lack of awareness and limited technical knowledge regarding organic methods were shown to hinder adoption of organic farming practices. These insights highlight the need for coordinated interventions by policymakers, agricultural agencies, and industry stakeholders to facilitate the adoption process. Emphasis is recommended on expanding awareness campaigns concerning the environmental and health benefits of organic farming, enhancing access to training programmes, simplifying certification procedures, and reinforcing institutional support through well-structured extension services. Greater alignment between farmers’ perceived risks and the long-term benefits of organic agriculture is essential to achieving widespread and sustainable adoption.
This study presents an all-inclusive benchmarking framework as a strategic tool for Saudi Arabian higher education institutions (HEIs) aiming to enhance their performance in the UI GreenMetric World University Ranking (UIGWUR), with extended applications for HEIs in other countries. The proposed framework progresses beyond statistical reporting to offer a transferable data-driven tool that could support HEIs worldwide in diagnosing gaps, prioritizing actions and strategically advancing sustainability outcomes. The number and trends of ranking by Saudi Arabian HEIs participated in the UIGWUR between year 2014 and 2024 are quantitatively analyzed to reveal insights into their sustainability performance and areas for improvement. Results from the analysis indicated steady growth in their participation, beginning from one HEI in year 2014 to 14 out of 67 HEIs in year 2024. Four institutions, in particular, could serve as benchmark models for others aspiring to improve their global standing: King Abdulaziz University (KAU) and Princess Nourah bint Abdulrahman University (PNU) have ranked among the top 100 consistently whereas Qassim University (QU) and Imam Abdulrahman Bin Faisal University (IABFU) have also secured top 100 positions in the recent years. To help other HEIs obtain comparable achievement, this study, with a detailed benchmarking analysis from year 2020 onward, identified the minimum performance scores for attaining a top 100 position in year 2025. The study categorized the required levels of effort into Aligned, Low, Medium, and High across different UIGWUR criteria, hence offering a structured roadmap for improvement. It was recommended that approximately 79% of the participated HEIs in year 2024 should invest Medium to High levels of effort to be qualified for top 100 in year 2025. Though the current analysis focused on Saudi Arabian HEIs, the proposed framework could offer a scalable tool applicable to global HEIs to boost their sustainability performance.
The assessment of urban sustainability and the development of performance-based practical tools for achieving Sustainable Development Goals (SDGs) are key items for discussion on the public agenda. Despite the urgency of the issues, there is a noticeable lack of studies related to a comprehensive model that could holistically assess sustainability performance at the city level. To address this research gap, SIMURG_CITIES conceptual model, the sub-project of “A Performance-based and Sustainability-oriented Integration Model Using Relational database architecture to increase Global competitiveness of the construction industry” (SIMURG), introduces a system-based methodology to evaluate urban sustainability of different cities. SIMURG_CITIES adopts multiple city layers and their associated key performance indicator (KPI) sets within the built environment dimension of 3D Cartesian system architecture to offer new insights. The purpose of this paper is to develop conceptual models at paradigmatic/philosophical, organizational process, interoperability/integrational, and computational/assessment components, paving the way for practical applications with a relational database model. The model and its relationship with interrelated components are explored by an iterative systems approach using “input–process–output–outcome–impact” (IPO) model and the “people-process-technology” (PPT) methodology. This structure steers the integration of humane, procedural, and technological factors into urban sustainability assessment. In addition, the model could help individuals select ideal urban environments to align with their expectations and to enhance accountability, transparency, and legitimacy in the decision-making processes of public authorities. Through this study, a technology-based approach is found to be effective in assessing urban sustainability and a conceptual framework is established in the contexts of Society 5.0 and urban governance.
In an increasingly dynamic and complex industrial landscape, the continuous enhancement of organizational performance has emerged as a critical imperative. To this end, structured quality assessment frameworks, such as the European Foundation for Quality Management (EFQM) Excellence Model, have been widely adopted as integrative tools for diagnosing, monitoring, and improving business performance. Despite its comprehensive nature, the EFQM model often requires the incorporation of additional quantitative methods to refine the evaluation of the relative significance of its criteria. In this study, the Analytic Hierarchy Process (AHP) method, extended with triangular fuzzy numbers, has been employed to determine the weighted importance of the EFQM model's criteria under conditions of uncertainty and expert subjectivity. This fuzzy extension of AHP allows for a more nuanced capture of linguistic judgments, thereby enhancing the robustness of decision-making in ambiguous environments. Expert assessments were elicited through structured interviews with quality managers from three manufacturing companies, enabling the construction of pairwise comparison matrices for each criterion. These matrices were then aggregated and analyzed to derive consensus-based priority weights. The findings reveal significant variations in the perceived importance of enabler and result criteria, underscoring the context-dependent applicability of the EFQM model. Furthermore, the results offer a more granular understanding of the internal structure of the model, providing a foundation for its adaptive use in quality management systems across the manufacturing sector. The integration of fuzzy logic into the hierarchical decision-making process is demonstrated to yield improved precision and flexibility, making it a valuable methodological enhancement for organizations pursuing excellence under uncertainty. The proposed approach also contributes to the broader discourse on multi-criteria decision analysis in quality management by addressing limitations in conventional crisp AHP applications.
Accurate rainfall forecasting remains critical for climate-sensitive agricultural planning, particularly in monsoon-driven regions where rice production is highly vulnerable to hydrometeorological variability. In this study, rainfall in Central Lombok Regency was forecasted using an enhanced version of the Facebook Prophet model incorporating a multiplicative seasonal component. Univariate monthly rainfall data (measured in millimeters) from January 1991 to July 2024 were utilized to train and evaluate the model. A configuration yielding optimal performance produced a Mean Absolute Percentage Error (MAPE) of 18.08% on the testing set, with 80.19% of the forecasted values exhibiting an Absolute Percentage Error (APE) below 20%, indicating a high level of predictive reliability. Forecasting was conducted over a short-term horizon of nine 10-day periods (approximately three months). Analysis of the forecast outputs identified the transition period from the dry to the rainy season—early August to late October—as the most favorable window for initiating rice planting. By aligning planting schedules with anticipated rainfall patterns, the likelihood of crop failure can be mitigated, thereby enhancing productivity and supporting local food security. The findings underscore the practical utility of interpretable time series models in developing data-driven agricultural calendars and advancing climate-resilient farming practices. This approach is particularly relevant for tropical monsoon regions facing increasingly erratic rainfall due to climate change. Furthermore, the demonstrated integration of seasonality effects within the Prophet framework contributes methodologically to the broader field of agro-meteorological forecasting.
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