Search Results (38)

Smart city decision systems must balance conflicting objectives including efficiency, sustainability, equity, safety, and public accountability. Existing AI and reinforcement learning approaches often optimize isolated objectives and rarely provide integrated mechanisms for sustainability alignment, transparency, and pre-deployment validation. This paper introduces MORL-SGF, a governance-aware framework that integrates ESG/SDG-aligned multi-objective reinforcement learning, Digital Twin (DT)-based policy validation, and Pareto-based policy auditing within a single learning pipeline. The framework preserves vector-valued rewards to avoid hidden scalarization bias and supports auditable policy selection from a portfolio of Pareto-optimal candidates. MORL-SGF is validated analytically and conceptually through formal modeling and structured evidence synthesis rather than empirical deployment, providing a blueprint for subsequent simulation-based and real-world implementation studies. Future work will focus on large-scale Digital Twin benchmarking, stakeholder preference modeling, and deployment-oriented evaluation. Full article

Trypanosoma vivax is a hemoprotozoan parasite of major veterinary importance affecting domestic ungulates in Africa and the Americas. While traditionally addressed within cattle-centered paradigms, accumulating evidence indicates that water buffaloes (Bubalus bubalis) are both clinically susceptible and epidemiologically significant hosts. This structured narrative review provides a host-centered synthesis of global evidence on T. vivax infection in buffaloes, integrating pathogenesis, transmission biology, epidemiology, diagnostics, chemotherapy, and integrated control. The analysis encompasses literature from 2000 to 2025 and incorporates seminal experimental studies published prior to 2000 that established buffalo susceptibility and reservoir competence. Evidence from cyclical (tsetse-mediated) and mechanical transmission systems is comparatively interpreted to clarify host–parasite dynamics. The Amazon biome is discussed as a model system for high-density buffalo production under mechanical vector pressure, offering case-based contextualization without geographic restriction. Particular attention is given to immunopathological mechanisms, chronic low-parasitemia carriage, diagnostic sensitivity in subclinical infections, emerging trypanocide resistance, and ecological constraints on vector control. Controversies and buffalo-specific knowledge gaps are highlighted throughout. By adopting a buffalo-centered analytical framework, this review supports translational diagnostics, targeted surveillance, and sustainable control strategies for trypanosomiasis in tropical livestock systems. Full article

The integration of renewable energy sources into modern power systems requires advanced control strategies to maintain stability, reliability, and efficiency. This paper presents a comprehensive review of the application of artificial intelligence techniques, including machine learning, deep learning, and reinforcement learning, in improving droop control for renewable energy integration. These artificial intelligence-based methods address key challenges such as frequency and voltage regulation, power sharing, and grid compliance under conditions of high renewable penetration. Machine learning approaches, such as support vector machines, are used to optimize droop parameters for dynamic grid conditions, while deep learning models, including recurrent neural networks, capture complex system dynamics to enhance the stability of distributed energy systems. Reinforcement learning algorithms enable adaptive, autonomous control, improving multi-objective optimization within microgrids. In addition, emerging directions such as transfer learning and real-time data analytics are explored for their potential to enhance scalability and resilience. Overall, this review synthesizes recent advances to demonstrate the growing impact of artificial intelligence in droop control and outlines future pathways toward more intelligent and sustainable power systems. Full article

Social media has the potential to serve beneficial purposes. The abundance of uploaded content and responses from the public generates various opinions, allowing them to be identified as positive or negative regarding the portrayal of Bandung Regency. This research aims to analyse the classification and frequency of words for each sentiment expressed by X (Twitter) users regarding Bandung Regency. The research employs the Support Vector Machine (SVM) method. We expect the results to aid in formulating governmental programmes for Bandung Regency. The research revealed that the SVM model, which uses the Sigmoid kernel function with parameters $C$ = 10 and gamma ($\gamma$) = 1, is the most optimal sentiment classification model for handling an imbalanced dataset. This model achieved an 83.01% negative recall value. Furthermore, frequent words appearing in both classes indicate that several positive opinions about Bandung Regency exhibit similar dominance, except for football dominance in negative opinions. This research pertains to the United Nations Sustainable Development Goals (SDGs), particularly SDG 11 (Sustainable Cities and Communities) and SDG 16 (Peace, Justice, and Strong Institutions). The suggested technique facilitates evidence-based policy reviews, transparent governance, and enhanced responsive public services by analysing public sentiment regarding local government performance. The results illustrate how social media analytics can aid local governments in assessing popular sentiment and pinpointing areas for policy response. Full article

This study develops a value co-creation-oriented analytical framework to evaluate the performance and evolutionary dynamics of China’s national-level quality policies from 1979 to 2023. A comprehensive categorization and scoring system is established to measure policy intensity, coordination, and comprehensiveness. Policy texts are systematically coded through content analysis, and indicator weights are determined using the Analytic Hierarchy Process (AHP). The resulting composite effect values are further analyzed through punctuated-equilibrium testing, breakpoint analysis, and a Vector Autoregression (VAR) model to estimate the temporal lag of policy implementation. Based on 10,962 policy documents retrieved from the Peking University Law Database, the results reveal clear evolutionary stages and cyclical upward trends in policy performance since the reform and opening-up, while the insufficient supply of demand-side policies remains a long-term structural weakness. The overall evolution path shows a transition from unilateral government provision centered on public value to dual government–market regulation driven by mixed commercial value, and finally toward pluralistic quality governance under value co-creation. Empirical evidence also indicates that quality policies act as short-term stimulus instruments that generate positive but sectorally differentiated effects across the three major industries. These findings highlight the need to expand policy coverage, enhance coordination and comprehensiveness, and rebalance the supply structure. Strengthening short-term stimulus effects while promoting inclusive, co-governed, and sustainable quality policy systems can further improve long-term effectiveness and provide useful insights for international discussions on value co-creation-based governance. Full article

Landslides rank among the most frequent and devastating natural hazards globally, causing significant loss of life and property. As a result, landslide susceptibility assessment has become a central focus in geohazard research, which is devoted to preventing and alleviating the frequent occurrence of landslides. Numerous analytical models have been applied to evaluate landslide susceptibility, including Frequency Ratio (FR), Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and various hybrid and neural network-based approaches. This review synthesizes current progress in integrating Nature-based Solutions (NBS) with modeling and policy frameworks, highlighting their potential to provide cost-effective, sustainable, and adaptive alternatives to conventional landslide mitigation strategies. Based on a systematic review of 127 peer-reviewed publications published between 2023 and 2025, selected from Web of Science, ScienceDirect, MDPI, Springer, and Google Scholar using predefined keywords and screening criteria, this study reveals that the most frequently used conditioning factors in landslide susceptibility modeling are slope (96 times), aspect (77 times), elevation (77 times), and lithology (77 times). Among modeling approaches, Random Forest (RF), Support Vector Machine (SVM), hybrid models, and neural network models consistently demonstrate high predictive performance. Despite the expanding body of literature on NBS, only 2.3% of all NBS-related studies specifically address landslide mitigation. The existing literature primarily concentrates on assessing the biophysical effectiveness of interventions such as vegetation cover, root reinforcement, and forest-based stabilization using a range of predictive modeling techniques. However, significant gaps remain in the integration of economic valuation frameworks, particularly cost–benefit analysis (CBA), to quantify the monetary value of NBS interventions in landslide risk reduction. This highlights a critical area for future research to support evidence-based decision-making and sustainable risk governance. Full article

Population expansion, urban development, climate change, and precipitation patterns are complicating sustainable natural resource management. Subbasin prioritization enhances the efficiency and cost-effectiveness of resource management. Artificial intelligence and data analytics eradicate the constraints of traditional methodologies, facilitating more precise evaluations of soil erosion, water management, and environmental risks. This research has created a comprehensive decision support system for the multidimensional assessment of sub-basins. The Erosion and Flood Risk-Based Soil Protection (EFR), Socio-Economic Integrated Basin Management (SEW), and Prioritization Based on Basin Water Yield (PBW) functions were utilized to prioritize sustainability objectives. EFR addresses erosion and flood risks, PBW evaluates water yield potential, and SEW integrates socio-economic drivers that directly influence water use and management feasibility. Our approach integrates principal component analysis–technique for order preference by similarity to ideal solution (PCA–TOPSIS) with machine learning (ML) and provides a scalable, data-driven alternative to conventional methods. The combination of machine learning algorithms with PCA and TOPSIS not only improves analytical capabilities but also offers a scalable alternative for prioritization under changing data scenarios. Among the models, support vector machine (SVM) achieved the highest performance for PBW (R² = 0.87) and artificial neural networks (ANNs) performed best for EFR (R² = 0.71), while random forest (RF) and gradient boosting machine (GBM) models exhibited stable accuracy for SEW (R² ~ 0.65–0.69). These quantitative results confirm the robustness and consistency of the proposed hybrid framework. The findings show that some sub-basins are prioritized for sustainable land and water resources management; these areas are generally of high priority according to different risk and management criteria. For these basins, it is suggested that comprehensive local-scale studies be carried out, making sure that preventive and remedial measures are given top priority for execution. The SVM model worked best for the PBW function, the ANN model worked best for the EFR function, and the RF and GBM models worked best for the SEW function. This framework not only finds sub-basins that are most important, but it also gives useful information for managing watersheds in a way that is sustainable even when the climate and economy change. Full article

As hydrogen emerges as a key vector in the shift toward cleaner energy systems, the evaluation of storage technologies becomes essential to support its integration across diverse applications. This work provides a comparative analysis of four hydrogen storage methods, compressed gas, metal hydrides, metal–organic frameworks (MOFs), and carbon-based materials, using a structured multi-criteria decision-making (MCDM) approach, specifically the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The evaluation is based on a comprehensive set of technical, economic, and environmental criteria, including safety, storage capacity, efficiency, cycle durability, technological maturity, environmental impact, cost, and scalability. The analysis adopts a technology-oriented perspective, focusing on the intrinsic performance and feasibility of hydrogen storage systems rather than on a detailed techno-economic optimization. The results show that metal hydrides offer the most balanced performance, driven by high volumetric capacity and solid-phase stability, followed closely by compressed hydrogen, which stands out for its technological maturity and well-established infrastructure, despite facing significant challenges related to safety and space efficiency due to high-pressure storage requirements. Carbon-based materials and MOFs, although promising in specific aspects such as safety, storage density, or material sustainability, are hindered by technological immaturity and operational limitations. Full article

The construction industry remains among the most hazardous sectors globally, facing persistent safety challenges despite advancements in occupational health and safety OHS) measures. The objective of this study is to systematically analyze the use of Artificial Intelligence (AI) in construction safety management and to identify the most effective techniques, data modalities, and validation practices. The method involved a systematic review of 122 peer-reviewed studies published between 2016 and 2025 and retrieved from major academic databases. The selected studies were classified by AI technologies including Machine Learning (ML), Deep Learning (DL), Computer Vision (CV), Natural Language Processing (NLP), and the Internet of Things (IoT), and by their applications in real-time hazard detection, predictive analytics, and automated compliance monitoring. The results show that DL and CV models, particularly Convolutional Neural Network (CNN) and You Only Look Once (YOLO)-based frameworks, are the most frequently implemented for personal protective equipment recognition and proximity monitoring, while ML approaches such as Support Vector Machines (SVM) and ensemble algorithms perform effectively on structured and sensor-based data. Major challenges identified include data quality, generalizability, interpretability, privacy, and integration with existing workflows. The paper concludes that explainable, scalable, and user-centric AI integrated with Building Information Modeling (BIM), Augmented Reality (AR) or Virtual Reality (VR), and wearable technologies is essential to enhance safety performance and achieve sustainable digital transformation in construction environments. Full article

Small and medium-sized enterprises (SMEs) often face severe resource constraints and operational fragility during crises. However, little is known about how managerial resilience (MR) translates into performance through time-related psychological resources and innovation—two capabilities that are both scarce and critical under such conditions. Drawing on Temporal Motivation Theory (TMT), this study develops and tests a dual-mediation model in which employee temporal psychological capital (TPC) and employee innovative behavior (EIB) transmit the effects of MR on performance. As a core methodological innovation, we adopt a multi-method analytical strategy to provide robust and complementary evidence rather than a hierarchy of results: Partial Least Squares Structural Equation Modeling (PLS-SEM) is used to examine sufficiency-based causal pathways and quantify the mediating mechanisms; Support Vector Machine (SVM) classification offers a non-parametric predictive validation of how MR and its mediators distinguish high- and low-performance cases; and Necessary Condition Analysis (NCA) identifies non-compensatory conditions that must be present for high performance to occur. These three methods address different research questions—sufficiency, classification robustness, and necessity—therefore serving as parallel, equally important components of the analysis. A total of 455 SME managers and employees were surveyed, and results show that MR significantly enhances all three dimensions of TPC (temporal control, temporal fit, time pressure resilience) and EIB (idea generation, idea promotion, idea realization), which in turn improve employee performance. SVM classification confirms that high MR, strong TPC, and active innovation align with high performance, while NCA reveals temporal control, idea generation, and idea realization as necessary bottleneck conditions. By integrating sufficiency–necessity logic with predictive classification, our findings suggest that SMEs should prioritize leadership resilience training to strengthen managers’ adaptive capacity, while simultaneously implementing time management interventions—such as temporal control workshops, workload balancing, and innovation pipeline support—to enhance employees’ ability to align tasks with organizational timelines, execute ideas effectively, and sustain performance during crises. Full article

Poor rural households still face vulnerability of the sustainable livelihood capacity caused by multiple risk disturbances even after they are lifted out of poverty, and become vulnerable poverty-eradicated households. However, quantifying the spatiotemporal heterogeneity of the impact of rural household livelihood vulnerability on resilience from a multi-risk perspective remains a challenge. This study integrates the theoretical connotations of livelihood vulnerability and resilience to develop a systematic analysis framework of sustainable livelihood-vulnerability-resilience for rural households from the perspective of multi-risk disturbance, and reveals the dynamic interaction process and mechanism of the three. On this basis, the VEP model for forward-looking and multi-risk perspectives, which embeds multiple risk factors as feature vectors, and the cloud-based fuzzy integrated evaluation method are employed to measure rural households’ livelihood vulnerability and resilience, respectively. Subsequently, based on clarifying the correlation between the two, we use the quantile regression method and factor contribution model to reveal the spatiotemporal impact mechanism of multi-level and multi-risk dominated vulnerability of rural households on resilience. These methods collectively enable us to quantify the spatiotemporal heterogeneity of vulnerability and resilience impacts from a risk perspective, taking a step forward and broadening the analytical perspective in the field of sustainable livelihoods research. The case study in Fugong County of China shows that, both rural households’ livelihood vulnerability and resilience exhibit spatiotemporal heterogeneity, and the negative correlation between the two gradually increases over time; as the level of livelihood vulnerability increases, the internal main contributing factors of livelihood resilience and their degree of contribution change accordingly; as the types of risks that dominate vulnerability change, the impact of vulnerability on the overall livelihood resilience and its internal dimensions also varies, where the change in resilience is greatest when the vulnerability is dominated by social risks, while the least change occurred when vulnerability is dominated by labor and income risks. This study provides a feasible methodological reference and a technical foundation for decision-making aimed at guiding rural households out of poverty sustainably and achieving sustainable livelihood. It can effectively enhance the predictive and post-event coping capacity of vulnerable rural households when subjected to multi-risk disturbances. Full article

Unmanned maritime vehicles (UMVs) have become essential tools in marine research and monitoring, significantly enhancing operational efficiency and reducing risks and costs. Fiber-reinforced composites have been widely used in marine applications due to their excellent characteristics. However, environmental concerns and the pursuit of sustainable development goals have driven the development of environmentally friendly materials. The development of eco-friendly biocomposites for UMV construction can effectively reduce the environmental impact of marine equipment. This study investigates the effects of seawater aging on kenaf/flax/glass-fiber-reinforced composites under artificial seawater conditions and determines their ranking for UMVs using the Analytic Hierarchy Process (AHP). These hybrid composites, fabricated with various stacking sequences, were prepared using a combination of hand lay-up and vacuum bagging techniques. All plant fibers underwent sodium hydroxide treatment to eliminate impurities and enhance interfacial bonding, while nano-silica was incorporated into the epoxy matrix to improve overall performance. After 50 days of immersion in artificial seawater, mechanical tests were conducted to evaluate the extent of changes in mechanical properties. Subsequently, the AHP analysis was performed based on three main criteria and thirteen sub-criteria to determine the most suitable configuration for marine applications. The results demonstrate that the stacking sequence plays a critical role in resisting seawater-induced degradation and maintaining mechanical performance. GKFKG exhibited the highest retention rates for both tensile strength (86.77%) and flexural strength (88.36%). Furthermore, the global priority vector derived from the AHP analysis indicates that hybrid composites consisting of kenaf, flax, and glass fibers consistently ranked highest. The optimum configuration among these hybrid composites was determined to be GKFKG, followed by GFKFG, GKKKG, and GKGKG. Full article

Healthcare cost acceleration and resource allocation issues have worsened across European health systems, where a small group of patients drives excessive healthcare spending. The prediction of high-cost utilization patterns is important for the sustainable management of healthcare and focused intervention measures. The aim of our study was to derive and validate machine learning algorithms for high-cost healthcare utilization prediction based on detailed administrative data and by comparing three algorithmic methods for the best risk stratification performance. The research analyzed extensive insurance beneficiary records which compile data from health group collective funds operated by non-life insurers across EU countries, across multiple service classes. The definition of high utilization was equivalent to the upper quintile of overall health expenditure using a moderate cost threshold. The research applied three machine learning algorithms, namely logistic regression using elastic net regularization, the random forest, and support vector machines. The models used a comprehensive set of predictor variables including demographics, policy profiles, and patterns of service utilization across multiple domains of healthcare. The performance of the models was evaluated using the standard train–test methodology and rigorous cross-validation procedures. All three models demonstrated outstanding discriminative ability by achieving area under the curve values at near-perfect levels. The random forest achieved the best test performance with exceptional metrics, closely followed by logistic regression with comparable exceptional performance. Service diversity proved to be the strongest predictor across all models, while dentistry services produced an extraordinarily high odds ratio with robust confidence intervals. The group of high utilizers comprised approximately one-fifth of the sample but demonstrated significantly higher utilization across all service classes. Machine learning algorithms are capable of classifying patients eligible for the high utilization of healthcare services with nearly perfect discriminative ability. The findings justify the application of predictive analytics for proactive case management, resource planning, and focused intervention measures across private group health insurance providers in EU countries. Full article

Overextraction of groundwater, as well as rapidly changing land use patterns, climatic change, and anthropogenic activities, in the densely populated Melur of Tamil Nadu state in India, has led to aquifer degradation. This study maps the groundwater potential (GWPZ) by evaluating 678 km² of this region in the Analytical Hierarchy Processes (AHP) and by using remote sensing and GIS tools as part of SDG 6 for the sustainable management of drinking, irrigation, and industrial uses for future generations. Data information layers, such as aquifer (a), topography (t), lineaments (l), land-use/land-cover (LuLc), soil (s), rainfall (r), and drainage (d) characteristics, separated the study area between poor and excellent groundwater potential zones with 361 km² or 53% of the study area remaining as low GWP and the prospective excellent groundwater potential zone covering only 9 km² (1.3% of total area). The integrated approach of the GWPZ and Water Quality Index (WQI) can effectively identify different zones based on their suitability for extraction and consumption for better understanding. This study also evaluates the performance of three machine learning models, such as Random Forest (RF), Gradient Boosting, and Support Vector Machine (SVM), based on a classification method using the same layers that govern the groundwater potential. The results indicate that both the RF model and Gradient Boosting achieved 100% accuracy, while SVM had a lower accuracy of 50%. Performance metrics such as precision, recall, and F1-score were analyzed to assess classification effectiveness. The findings highlight the importance of model selection, dataset size, and feature importance in achieving optimal classification performance. Results of this study highlight that the aquifer system of Melur has a low groundwater reserve, and it requires adequate water resource management strategies such as artificial recharge, pumping restriction, and implementation of groundwater tariffs for sustainability. Full article

Addressing the insufficient identification of key consumer requirements in refrigerator design and the current limitations in understanding the impacts and underlying mechanisms of product design on sustainability, this study develops an interdisciplinary methodological framework that synergizes industrial design principles with advanced computer-aided design techniques and deep neural network approaches. Initially, consumer decision preferences concerning essential product attributes and sustainability indicators are systematically elucidated through semi-structured interviews and multi-source data fusion, with a particular emphasis on user sensitivity to energy efficiency ratings, based on a high-quality sample of 303 respondents. Subsequently, a latent diffusion model alongside a ControlNet architecture is employed to intelligently generate design solutions, followed by comprehensive multi-attribute optimization screening using an integrated decision-making model. The empirical evidence reveals that the synergistic interplay between functional rationality and design coordination plays a critical role in determining the overall competitiveness of the design solutions. Furthermore, by incorporating established industrial design practices, prototypes of mini desktop and vehicle-mounted multifunctional refrigerators—derived from neural network-generated design features—are developed and assessed. Finally, a nonlinear predictive mapping model is constructed to delineate the relationship between industrial design characteristics and consumer appeal. The experimental results show that the proposed support vector regression model achieves a root mean square error of 0.0719 and a coefficient of determination of 0.8480, significantly outperforming the Bayesian regularization backpropagation neural network baseline. These findings validate the model’s predictive accuracy and its applicability in small-sample, high-dimensional, and nonlinear industrial design scenarios. This research provides a data-driven, intelligent analytical approach that bridges industrial design with computer-aided design, thereby optimizing product market competitiveness and sustainable consumer value while promoting both theoretical innovation and practical advancements in sustainable design practices. Full article