Open Access
gatut bintoro 
, agus tumulyadi , tri djoko lelono 
, arief setyanto , daduk setyohadi , fuad fuad , ledhyane ika harlyan , mihrobi khalwatu rihmi , lisa nur hidayah , almira syawli , gilang ardyanto pamungkas , dian aliviyanti , Andik Isdianto , Aulia Lanudia Fathah , Berlania Mahardika Putri |
Available online: 08-30-2025
Artificial reefs have been widely used to enhance marine biodiversity and support sustainable fisheries management. This study evaluates the ecological performance of artificial reefs in Damas Beach, Indonesia, assessing their impact on fish populations and environmental conditions from 2020 to 2024. Water quality was measured in situ, and a stationary visual census was used to analyze changes in species composition and habitat conditions. Results indicate increased turbidity and shifts in fish community structure including declines, stability, and the emergence of previously unrecorded type. These changes highlight the influence of water quality and habitat modifications on fish assemblages. The study also underscores the importance of integrating technology into fisheries management, such as real-time monitoring, sediment control strategies, and adaptive reef maintenance. Community involvement also plays a key role in ensuring long-term sustainability. Future efforts should focus on optimizing reef design to enhance structural complexity, reduce sedimentation, and strengthen ecosystem resilience. A strategic approach combining artificial reefs, advanced monitoring technologies, and stakeholder participation offers a viable solution for improving fisheries productivity while maintaining ecological stability in sediment-rich coastal environments.
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.
Osteoarthritis (OA) affects approximately 240 million individuals globally. Knee osteoarthritis, a crippling ailment marked by joint stiffness, discomfort, and functional impairment, is particularly the most widespread kind of arthritis among the elderly. To assess the severity of this disease, physical symptoms, medical history, and further joint screening examinations including radiography, Magnetic Resonance Imaging (MRI), and Computed Tomography (CT) scans have frequently been considered. It is difficult to identify early development of this disease as conventional diagnostic methods could be subjective. Therefore, doctors utilize the Kellgren and Lawrence (KL) scale to evaluate the severity of knee OA with visual images obtained from X-ray or MRI. The detection and prediction of the severity of knee OA indeed requires a novel model that uses deep learning models, including Inception and Xception. Utilizing the KL grading scale, the model, including Xception, ResNet-50, and Inception-ResNet-v2 could determine the degree of knee OA suffered by patients. The experimental results revealed that the Xception network achieved the highest classification accuracy of 67%, surpassing ResNet-50 and Inception-ResNet-v2, demonstrating its superior ability to automatically grade OA severity from radiographic images.
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.
Imperfect production and rework in contemporary manufacturing systems, are inevitable realities hampering overall performance and cost efficiency. To address this challenge, this study developed an Economic Production Quantity (EPQ) model which integrated defective items, rework, disposal, and penalties for lost sales within a fuzzy decision-making framework. The convexity of the model implied the possible existence of an optimal solution. Compared to conventional crisp models, the proposed approach provided a more robust and realistic evaluation of inventory and cost structures by representing indeterminate parameters such as production cost, backordering cost, and penalty cost through Hexagonal Fuzzy Numbers (HFNs) and Graded Mean Deviation Method (GMDM) for defuzzification. The numerical illustration demonstrated superiority of the fuzzy model in minimizing the total cost, balancing inventory levels, and enhancing service quality. Sensitivity analysis further highlighted the adaptability of the model to combat unpredictable changes in the parameters. The study concluded with valuable insights for decision-makers to optimize imperfect production processes, strengthen resource allocation, and tackle uncertainty in real-world manufacturing environment.
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.
Modern high-input, intensive agricultural systems predominantly emphasize productivity and profitability at the expense of ecological balance. The Green Revolution, though instrumental in enhancing food security, relied heavily on mechanization, intensive cultivation, and high-yielding varieties, often compromising long-term sustainability. These practices have accelerated land use change and deforestation, leading to a substantial decline in soil organic matter (SOM), a reduction in terrestrial carbon sinks, and a rise in atmospheric carbon dioxide (CO₂) emissions. Under the increasing pressures of climate change—manifested in the form of drought, flooding, and pest outbreaks—the vulnerability of conventional farming systems has been exacerbated. In response to these challenges, regenerative organic agriculture (ROA) has been recognized as a holistic framework capable of restoring ecosystem functions, enhancing soil health, and supporting sustainable food production. This review synthesizes current research on ROA, with particular emphasis on practices that contribute to soil building and ecological regeneration. A meta-analysis of cover cropping practices across diverse soil types has demonstrated the potential to sequester soil organic carbon (SOC) between 0.32 and 16.70 Mg·ha⁻¹·yr⁻¹. Globally, an estimated SOC sequestration of 0.03 Pg·C·yr⁻¹ via cover crops could offset approximately 8% of anthropogenic greenhouse gas emissions. The physical, chemical, and biological improvements to soil properties facilitated by ROA have been systematically examined. Traditional Vedic agricultural practices in India have also been revisited for their ecological relevance and compatibility with regenerative principles. Integrated farming systems combining leguminous crops, agroforestry, horticulture, pasture, and animal husbandry have been reviewed for their synergistic effects on biodiversity enhancement, nutrient cycling, and climate mitigation. Additionally, the transition to renewable energy sources, reliance on self-saved seeds, and minimization of external inputs have been underscored as key strategies for achieving farm-level self-sufficiency and ecological sustainability. This review synthesizes scientific findings and traditional knowledge to highlight ROA as a holistic solution for restoring soil function, conserving natural resources, and advancing sustainable agricultural development.
