Search Results (13,670)

Background: Managing complex infrastructure increasingly requires predictive, adaptive, and human-centered systems. Traditional approaches often struggle with operational complexity, fragmented data, and high technical barriers. Methods: This study presents a TRL4 proof of concept integrating a conversational AI agent with a user-adaptive digital twin for occupancy forecasting. Users can upload their own datasets, and dynamically configure prediction models (ARIMA, SARIMA, Random Forest, XGBoost) based on input variables such as occupancy or demand drivers. The AI agent, powered by Gemini 2.5 Flash Lite, functions as an orchestration layer, translating natural language instructions into data ingestion, model execution, and query actions. While the digital twin supports additional variables (energy, water, waste), these are envisioned for future work and were not part of the current validation. Results: Functional validation confirmed the system’s capability to interpret user intentions accurately, adapt model training to the characteristics of user-provided data, and present results through convenient and comprehensible visualization methods. The integrated architecture demonstrated stable performance across multiple validation scenarios, achieving satisfactory prediction accuracy (within expected ranges for TRL 4). Conclusions: This work validates the technical and functional viability of integrating conversational AI agents with digital twins as an emergent system of systems, extending beyond conventional predictive pipelines by enabling context-specific modeling. The systems engineering approach reveals how such integration transforms reactive infrastructure management into proactive, data-driven, and human-centered decision-making processes, establishing a foundation for future developments toward higher technology readiness levels. Full article

Shuttle services are a fundamental service for faculty and students in universities. Aiming at the core challenges of “uncertain demand, limited resources, and supply–demand mismatching” in university shuttle services, this study proposes a shuttle demand prediction approach based on multi-algorithm ensemble learning and multi-dimensional evaluation, enhancing both prediction accuracy and generalization ability. Furthermore, a multi-objective evaluation system and optimization model for shuttle supply–demand matching were constructed. A fast and simple solution method was provided and formally proven to achieve the complete pareto optimal set for shuttle resources allocation. Finally, a three-layer decision-making framework of “prediction-optimization-evaluation” was established. Experimental results demonstrate that, in terms of four regression metrics and three hit rate metrics, the Bagging ensemble algorithm can significantly improve model performance. In terms of resource utilization rate and demand satisfaction rate, the supply–demand matching multi-objective optimization model and solution fast and simply yields a complete Pareto optimal set. This study drives the transformation of shuttle resource allocation from experience-based decision-making to quantitative decision-making, and provides a reusable solution for resource supply–demand matching optimization in campus scenarios, bridging the application gap between forecasting and optimization technologies and university resource management practices. Full article

Supply chains are increasingly exposed to compounding disruptions, volatile demand, and sustainability constraints, which challenge optimization approaches designed for stable operating conditions. This review synthesizes recent advances in supply chain optimization with a focus on the integration of artificial intelligence and operations research in decision-making. The paper examines three major capability layers: prescriptive optimization for planning and resource allocation, predictive modeling for demand and risk anticipation, and digitalized execution through simulation and digital twin environments. Across these layers, the analysis shows that hybrid AI-OR architectures tend to outperform isolated methods in settings characterized by high demand volatility, multi-echelon complexity, and disruption exposure, by combining predictive adaptability with constraint-aware decision quality. The review also highlights a strategic shift from single-objective efficiency toward multi-objective performance that jointly manages cost, service, resilience, and environmental impact. From an implementation perspective, the evidence indicates that measurable industrial gains depend less on algorithm novelty alone and more on system-level integration, data governance, and cross-functional deployment. Key research gaps remain in benchmark standardization, explainability, uncertainty-aware optimization, and long-horizon validation under disruption. The paper concludes that the next generation of supply chain optimization will be defined by continuously learning, human-supervised decision ecosystems that remain robust under uncertainty while delivering operational and sustainability outcomes. Full article

Natural resources play a fundamental role in ensuring global food security, while agricultural production itself strongly influences their demand, extraction, and availability. This article discusses natural strategies for increasing crop productivity within the framework of sustainable intensification, focusing on the integrated role of plant biostimulants and micronutrients. Both groups of substances are analyzed from a resource-oriented perspective, highlighting their potential to be derived from renewable sources, particularly agro-industrial by-products and plant biomass. Plant extracts obtained from fruit, vegetable, and cereal processing residues contain numerous bioactive compounds, including phenolics, amino acids, peptides, and organic acids, which can stimulate plant growth, improve nutrient uptake, and enhance tolerance to abiotic stress. Micronutrients such as Fe, Zn, Mn, Cu, and B are also strategic resources in crop production because they regulate key metabolic processes and influence the efficiency of macronutrient utilization. Their effectiveness, however, depends strongly on chemical form and bioavailability in soil–plant systems. The novelty of this work lies in integrating perspectives from plant physiology, coordination chemistry, and resource management to propose a conceptual framework in which plant-derived extracts and micronutrient complexes act as complementary tools supporting circular and resource-efficient agricultural systems. Full article

Growing municipal solid wastes, environmental deterioration, and the world’s increasing energy demand highlight the urgent need for effective, sustainable energy recovery solutions. Uncontrolled municipal solid wastes contribute explicitly to the global crises of climate change, pollution, and biodiversity loss. Food and organic waste are converted into value-added products using biochemical and thermochemical techniques. Anaerobic digestion (AD) is a versatile, multi-phase waste-to-energy technology that transforms organic waste into renewable energy in an oxygen-free environment. AD uses microorganisms to break down waste, yielding biogas (mostly methane and carbon dioxide) and digestate, a nutrient-fortified by-product. Compared with traditional Single-Stage Anaerobic Digesters (SSAD), Two-Stage Anaerobic Digesters (TSAD) offer notable benefits by separating hydrolysis–acidogenesis from acetogenesis–methanogenesis. These include increased methane yield, improved process control, increased microbial stability, and resistance to inhibitory substances. According to the literature, TSAD systems have been shown to increase methane yield by about 10–30% compared to SSAD. This article covers the dynamics of the microbial population at various stages, the impact of operational factors (HRT, OLR, pH, and temperature), and novel reactor designs with modular and multi-state functions. In line with Oman’s Vision 2040, this study discusses the continuous operation of a two-phase AD co-digestion process and the in-depth techno-economic feasibility of decentralized waste management through optimized biogas production. Optimizing the carbon-to-nitrogen (C/N) ratio within the range of 20–30 in co-digestion systems significantly enhances microbial activity and methane production. The potential of recent developments, such as microbial immobilization, biogas generation techniques, and hybrid integration with photobioreactors or electrochemical systems, to enhance the scalability and efficiency of bioconversion is addressed in a TSAD system. In addition to encouraging circular economy principles through efficient organic waste valorization, this review identifies TSAD as a promising approach to achieving the SDGs related to sustainable cities, clean energy, and responsible consumption. Full article

Mobile thermal energy storage (M-TES) demonstrates significant commercialization potential in industrial waste heat recovery, distributed heating, and clean heating applications, which is primarily based on three technical pathways: sensible heat storage, latent heat storage using phase change materials (PCMs), and thermochemical heat storage. The updated status of M-TES, mainly on PCMs and thermochemical ones, and the challenges facing application were reviewed, and potential development trends were discussed in the present study. Sensible heat storage is relatively mature and cost-effective; however, it suffers from low energy density and comparatively high heat loss during storage and transport. Latent heat storage utilizes the phase transition enthalpy of PCMs to store thermal energy, offering higher energy density and near-isothermal heat release, making it a focal point of current academic and industrial research. Nevertheless, latent heat storage still faces technical bottlenecks, including low thermal conductivity, phase separation, and supercooling of PCMs. Thermochemical heat storage relies on reversible chemical reactions to convert and store thermal energy as chemical energy, theoretically achieving the highest energy density and minimal heat loss. However, due to its technical complexity and high system cost, thermochemical storage remains largely in the early stages of research and demonstration. Overall, as a bridge between heat supply and demand, the development trend emphasizes the design of high-performance composite PCMs, enhanced system integration, and intelligent operational management. However, its large-scale deployment is still constrained by challenges related to energy density, heat transfer enhancement, long-term material stability, and techno-economic feasibility. Full article

Despite the widespread perception of Chilean Patagonia as water-abundant, the region exhibits marked climatic and landscape heterogeneity. This study evaluates relative water availability across Coyhaique Province (12,712 km²), where projections indicate a trend toward warmer and drier conditions. The province has a marked west–east gradient: humid valleys in the west contrast with much drier areas to the east, where most of the population and development are concentrated. To identify water stress hotspots, we combine spatially explicit water yield estimates derived from the InVEST Seasonal Water Yield model with an anthropization index used as a proxy for water demand, constructing a relative Water Stress Index. The results indicate that water stress increases toward the east, driven by the combined influence of climate variables and anthropogenic pressure. These results indicate that the characterization of Patagonia as uniformly water-rich does not hold at the provincial scale, and highlight the limitations of coarse regional assessments in capturing intra-regional hydrological heterogeneity. The spatial pattern of water stress revealed here exposes a mismatch between the resolution at which hydrological heterogeneity operates and the scale at which prevailing water governance frameworks are formulated, underscoring the need for bottom-up, fine-resolution diagnostics that incorporate local hydrological variability into water planning and governance. The province-scale analysis presented here provides a representative case study for Aysén and illustrates the broader relevance of spatially explicit diagnostics in contexts where regional indicators mask local water stress. Strengthening monitoring networks, protecting headwater catchments, and promoting a decentralized approach to water management remain essential to reduce the risk of human-driven water scarcity. Full article

Offshore industries depend solely on diesel-based power generation systems or mainland grids, which are expensive and carbon-intensive. The demand for renewable energy-based offshore DC microgrids (MGs) has significantly increased due to rising fuel prices, high costs of fuel transportation and storage, extreme operation and maintenance expenses, and associated carbon emissions. This research study optimises the size of an offshore DC MG that integrates wave, solar, energy storage, and diesel, utilising real-world data from a specific geographical location (latitude −33.525587 and longitude 114.772211), thereby accurately representing the availability of renewable energy sources. An algorithm is designed to optimise the utilisation of highly variable renewable sources via battery-based energy management, resulting in optimal energy dispatch. Utilising economic performance metrics, such as levelised cost of energy (LCoE) and net present value (NPV), this research aims to minimise the energy, operating, and greenhouse gas emission costs while maximising the economic feasibility of the system. A sensitivity analysis is performed to determine the impact of fuel prices, discount rates, and system lifespans on the feasibility of the system. The findings demonstrate that the proposed renewable-based offshore DC MG can substantially reduce fuel consumption (93%), operational expenses (77.56%), and carbon emissions (89.50%) compared with a diesel-only system for offshore platforms, while improving the sustainability and reliability of power supply for aquaculture and marine activities. In addition, the proposed renewable-energy-based offshore DC MG achieves a lower LCoE (0.5649 $/kWh) and a higher NPV (2.987 × ${10}^4$ $) than a conventional diesel-based power generation system for offshore industries. The results provide a decision-making framework for the design and implementation of renewable energy-based offshore DC MGs. Full article

Tourism development in the Yucatán Peninsula has long been dominated by coastal mass tourism, resulting in environmental pressure and pronounced spatial imbalances. In response, increasing attention has been directed toward diversification strategies based on inland and nature-based attractions. Among these, cenotes—karst sinkholes connected to regional groundwater systems—have emerged as a distinctive tourism resource. This paper introduces the concept of cenotourism as a form of nature-based and geoculturally embedded tourism centred on cenotes and their associated karst environments. The analysis combines conceptual development with empirical evidence from a large-scale tourism survey conducted in Yucatán (n ≈ 2800). The findings suggest that cenotes constitute a meaningful component of tourists’ activity portfolios, with 24.6% of respondents declaring an intention to visit them. Cenotourism contributes to diversification and appears to support the redistribution of tourist flows toward inland areas, while simultaneously increasing exposure to highly sensitive groundwater systems. These results point to a clear sustainability trade-off, although its magnitude may vary depending on local governance conditions. While cenotourism may strengthen local economies and reduce pressure on coastal destinations, it also introduces risks related to groundwater contamination, cultural commodification, and uneven benefit distribution. Such outcomes depend strongly on governance conditions, including visitor management, environmental monitoring, and community participation. By conceptualizing cenotourism as an integrative framework linking tourism demand, environmental vulnerability, and governance processes, the study contributes to understanding tourism development in groundwater-dependent systems. The findings emphasize the need for context-specific management approaches and situate cenotourism within broader water-sensitive tourism planning. Full article

This study evaluates simple exhaust, relative humidity-controlled and heat recovery ventilation systems in northern Spain (SEV, RHCV, HRV systems) through simulations of indoor air quality (IAQ), energy, and exergy performance. The IAQ analysis reveals poor performance of the RHCV system for indoor source pollutants such as formaldehyde (HCHO) and total volatile organic compounds (TVOC). The HRV system demonstrates superior energy efficiency, with 30% lower primary energy consumption than the SEV system, though it is necessary to evaluate whether the heat recovered compensates for the increased fan energy consumption. This condition is evaluated by defining an outdoor air temperature limit value. The exergy analysis shows the HRV system requires 30% less primary exergy than the SEV system despite higher system demand. While HRV emerges as the optimal solution for balancing IAQ and energy performance, the findings highlight that source control remains necessary to effectively manage HCHO and TVOC concentrations. The research provides guidance for selecting ventilation systems that minimize pollutant exposure while optimizing energy resources. Full article

Inventory policy design in modern supply chains must balance cost efficiency, service reliability, and responsible resource utilization under significant demand and supply uncertainty. In many real-world supply chains, both customer demand and replenishment lead time exhibit substantial variability, making the design of continuous-review (s, S) inventory policies challenging. Although stochastic inventory models have been widely studied, many existing approaches rely on simplified assumptions or single-objective formulations, which may limit their applicability under simultaneous demand and lead-time uncertainty. This study proposes a data-driven multi-objective simulation–optimization framework for designing sustainable (s, S) inventory policies under dual uncertainty. The framework integrates empirical stochastic modeling, Monte Carlo simulation, and evolutionary multi-objective optimization to evaluate trade-offs between expected inventory cost and service performance. To enhance methodological rigor, statistical reliability control is incorporated into the simulation-based evaluation process to ensure that Pareto dominance relationships are not distorted by simulation noise. Historical operational data are used to estimate probability distributions for demand and lead time, which are incorporated into a stochastic simulation model representing inventory system dynamics. A multi-objective evolutionary algorithm (NSGA-II) is employed to identify Pareto-efficient policy parameters. An empirical case study from a health supplement supply chain demonstrates how the framework identifies efficient replenishment policies under realistic uncertainty conditions. The results reveal structural trade-offs between inventory cost and service level and show that data-driven policy design can improve decision transparency compared with heuristic replenishment rules. The proposed approach provides a structured decision-support tool for selecting replenishment policies that balance service continuity and inventory sustainability in shelf-life-constrained supply chains. Full article

The Spanish tourism sector is experiencing an unprecedented boom. However, this macroeconomic success coexists with a growing crisis of burnout and job insecurity. While the macroeconomic effects of minimum wage policies are widely debated, the micro-level psychosocial reality of employees operating within these cost-pressured environments remains largely unexplored. This research uses the Job Demands–Resources (JD-R) framework to descriptively explore the current state of employee well-being in the Spanish hotel sector, operating within the macroeconomic context of recent minimum wage increases. Specifically, the study evaluates how environments characterized by high cost-containment pressures are associated with exacerbated labour demands and depleted resources, a pattern consistent with burnout, thus analysing the implications for social sustainability. Our data come from a survey of 384 hotel employees in Spain and were analysed using the Labour Demands–Resources (JD-R) framework and bootstrap methods. The results reveal that employees report very low agreement that their workloads are reasonable and manageable (mean = 1.8/5) and perceive limited development opportunities (mean = 1.9/5), despite acknowledging the importance of well-being for sustainability (mean = 4.8/5). Work intensification is particularly acute in regions with high seasonality and among cleaning staff. Furthermore, sustainability awareness moderates the negative impact of workload on employee engagement. The study concludes that within high-pressure hospitality environments, macroeconomic wage improvements can be offset by a decline in job quality, threatening the long-term social sustainability of the sector. We advocate for more nuanced policies and a shift in human resource management strategy toward genuine investment in human capital. Full article

The rapid increase in the number of cars in large cities has made efficient parking management one of the major challenges of urban transportation systems. The present study aims to develop a smart framework for sustainable allocation of parking spaces in urban environments, and presents an integrated approach based on digital twin and multi-objective optimization. In this framework, a digital model of the urban parking system is created that is able to analyze real and simulated data related to parking demand, space occupancy status, and traffic flow and support optimal allocation decisions. The results of the analysis show that using the proposed framework can reduce parking search time by an average of 28%, make the distribution of parking use more balanced, and consequently reduce the amount of pollutant emissions from vehicle movement by about 17%. Also, sensitivity and scalability analyses show that the proposed model also has stable and reliable performance in large urban networks. These results indicate that the proposed framework can be used as an effective tool for developing sustainable parking management systems in smart cities. Full article

Ensuring the quality, reliability, and efficiency of cold chain logistics for thermolabile pharmaceutical products, particularly vaccines, remains a critical challenge in global health supply chains. These biologics require stringent temperature control throughout storage, transport, and distribution to preserve their efficacy. Persistent issues such as maintaining product integrity, accurately forecasting vaccine demand, and fostering trust among stakeholders often result in inefficiencies, waste, and public mistrust. This study proposes an intelligent digital management framework specifically designed for vaccine cold chains, integrating blockchain, the Internet of Things (IoT), and machine learning (ML) to address these challenges in a holistic and sustainable manner. The main innovation of the study lies in combining secure traceability, real-time cold chain monitoring, and predictive decision support within a unified vaccine cold chain management framework rather than treating these functions as isolated technological solutions. Using WHO immunization coverage data and vaccine-related review data, the framework supports vaccine demand forecasting through the Informer model and stakeholder trust assessment through BERT-based sentiment analysis. In the sentiment analysis task, the BERT model achieved ~80% accuracy on dominant sentiment classes, with a weighted F1-score of 0.6974, demonstrating strong performance on imbalanced datasets. By minimizing vaccine spoilage and enabling more accurate demand planning, the system reduces excess production and distribution, thus lowering resource consumption, carbon emissions, and financial waste. Moreover, trust-informed analytics support better alignment of supply with actual community needs, fostering equity and resilience in vaccine distribution. While this framework has been validated through simulations and experimental evaluation, further real-world testing is needed to assess long-term stability and stakeholder adoption. Nonetheless, it provides a scalable and adaptive foundation for advancing sustainability and transparency in pharmaceutical cold chains. Full article

This article assesses the potential for intra-day redistribution of the electrical load of water intake systems under different electricity tariff models, using water supply systems in Belarus and China as case studies. It demonstrates how tariff policy influences the electrical load profile of a water intake system and quantitatively evaluates the economic effect of optimizing the operating modes of pumping equipment. The analysis is based on daily profiles of electric power and water supply. For the Belarusian water supply system, data for 2019 were considered, corresponding to the baseline operating mode without targeted load management, and data for 2023 were considered after the transition to dispatch-based control of well activation with account taken of tariff constraints (without automation tools). For the Chinese water intake system, hourly data for 2025 were used. The load redistribution potential was assessed on the basis of lagged correlation between power and water supply profiles. In addition, the F-index was applied as an aggregated diagnostic indicator intended for the comparative assessment of potential load transferability across technological stages, taking into account their share in total energy consumption. For the Chinese case, it was shown that the maximum correlation between water supply and electricity consumption across all technological stages is achieved near zero lag, which indicates a high adaptation of system operating modes to current demand; at the same time, the R values were 0.19 for reservoir intake, 0.86 for water treatment, and 0.51 for the pumping station. In the Belarusian case, for the first-lift stage, the maximum correlation is shifted by −6 h relative to zero lag, indicating a less rigid linkage of pump operation to current demand and a more inertial response of the system. A comparison of 2019 and 2023 for the Belarusian facility showed that targeted regulation of well activation and load redistribution across tariff zones reduced the total electricity cost by 1.58%, confirming the potential for further optimization of electricity consumption regimes. Full article