Search Results (5,242)

Cerebrospinal fluid (CSF) oligoclonal band (OCB) analysis remains central to the diagnostic evaluation of neuroinflammatory diseases of the central nervous system (CNS), as it reflects intrathecal immunoglobulin synthesis. However, its reliance on lumbar puncture limits its applicability for screening and repeated longitudinal assessment. Serum metabolomics offers a minimally invasive strategy to explore peripheral biochemical correlates of central immune activity. Building on previous binary OCB comparisons, the present study extends serum metabolomic analysis to encompass all five classical OCB patterns, thereby capturing a broader immunological spectrum. A total of 92 adults undergoing diagnostic evaluation for suspected CNS inflammatory disorders were retrospectively stratified according to OCB type (Types 1–5). Serum samples were analysed using targeted ¹H-NMR spectroscopy on a Bruker Avance Neo 600 MHz platform and processed using Bruker’s IVDr pipeline. Group-wise differences were assessed using non-parametric statistical testing with false discovery rate (FDR) correction, complemented by effect size estimation, exploratory multivariate analyses, and Receiver Operating Characteristic (ROC) modelling. Distributional characteristics were further examined using boxplots and violin plots. Across analytical approaches, several metabolites—most prominently leucine, 2-oxoglutaric acid, histidine, threonine, and glycerol—exhibited nominal variation and moderate effect sizes across OCB patterns. Rather than discrete metabolic separation, these metabolites demonstrated graded shifts in central tendency accompanied by substantial overlap between groups. Unsupervised principal component analysis did not reveal robust clustering, while supervised multivariate models highlighted amino acid- and tricarboxylic acid cycle-related metabolites as contributors to partial differentiation. Post hoc power analysis indicated limited sensitivity to detect small-to-moderate effects under multiple-testing correction, supporting an exploratory interpretation of the findings. Taken together, this first targeted serum ¹H-NMR metabolomic evaluation spanning all classical OCB patterns suggests that peripheral metabolic profiles may reflect graded immunometabolic variation associated with intrathecal immune activity. While not intended for diagnostic classification, these findings provide a spectrum-based framework for integrating serum metabolomics with OCB phenotyping and identify candidate metabolites for future prospectively powered and clinically characterised studies. Full article

Armenia is located within the central segment of the Arabia–Eurasia continental collision zone and is exposed to significant seismic hazard. This study presents an updated probabilistic seismic hazard assessment (PSHA) for Armenia based on an integrated seismotectonic framework incorporating active fault data, paleoseismological evidence, and historical and instrumental seismicity. A hybrid seismic source model was developed by combining fault-based characteristic earthquake sources with distributed background seismicity. Hazard calculations were performed using the OpenQuake engine within a logic-tree framework to account for epistemic uncertainties in earthquake occurrence and ground-motion prediction. Ground motion was estimated using a weighted set of ground motion prediction equations (GMPEs). Peak ground acceleration (PGA) hazard maps were computed for several return periods, with emphasis on the 475-year return period (10% probability of exceedance in 50 years). The results indicate PGA values across Armenia ranging from approximately 0.2 g to 0.5 g, with the highest hazard levels in northwestern Armenia along the Pambak–Sevan–Syunik Fault System. Hazard deaggregation shows that seismic hazard in major Armenian cities is primarily controlled by shallow earthquakes with magnitudes Mw 6.8–7.4 occurring within ~30 km of urban centers. The results provide a scientific basis for seismic hazard assessment, zonation, and earthquake risk mitigation in Armenia. Full article

Multi-baseline phase unwrapping is a critical procedure in interferometric synthetic aperture radar (InSAR) data processing, and cluster analysis (CA)-based algorithms have become an important research direction in this field. However, traditional CA algorithms suffer from cluster group loss, cluster centerline offset under high noise, and time-consuming search, leading to limited unwrapping performance. To address these issues, this article proposes a multi-baseline phase unwrapping algorithm based on the integrated processing of intercept pre-filtering and ambiguity number vector determination, achieving significant performance improvements through four core technical optimisations. First, the linear relationship model of ambiguity numbers is extended to be compatible not only with the traditional one-transmitter, multi-receiver architecture but also with distributed multi-baseline InSAR systems with independent transmit–receive links for each baseline. Second, through verification from both forward and reverse uniqueness perspectives, a strict one-to-one mapping relationship between reference intercepts and ambiguity number combinations is established and validated. Third, a double constraints screening strategy for ambiguity number combinations combining the single-baseline elevation range intersection constraint and the multi-baseline elevation space common intersection constraint is designed. Integrating the effective elevation range of the observation area, this strategy accurately filters out valid ambiguity number combinations with physical rationality, ensuring the reliability of the reference intercept vector. Fourth, an intercept pre-filtering method based on the reference intercept vector is proposed, which unifies actual intercept pre-filtering and ambiguity number vector determination. To verify the performance of the proposed algorithm, a simulation data experiment under varying noise levels and real data experiments are conducted. Results demonstrate that the algorithm can maintain intact cluster structures under complex noise conditions. It achieves a synergistic improvement in unwrapping accuracy and computational efficiency, and thus significantly outperforms comparative algorithms. The proposed algorithm achieves high precision and efficiency for multi-baseline InSAR processing in complex scenarios, with important application value in practical engineering. Full article

In transport and logistics systems, decision-making is increasingly influenced by uncertainty stemming from demand variability, technological disruptions, and systemic risks present in supply chains. In these contexts, organizations need approaches that are rooted in data and analysis to assess key elements affecting system resilience and performance. Although current studies widely utilize stochastic and fuzzy models for operational decision-making, there has been insufficient focus on the systematic assessment of human-centric system elements—especially competencies—as decision variables in intricate logistics systems. This research proposes an analytical framework for multi-criteria decision-making that is driven by data and aimed at evaluating the significance of various competencies that affect labor market competitiveness and the adaptability of supply chains. The approach combines expert assessment with statistical and information-theoretic metrics, utilizing Kendall’s coefficient of concordance for evaluating consistency, Shannon entropy for analyzing distributional uncertainty, and the Gini coefficient for measuring concentration. This integrated method allows for the measurement of both variability and inequality within decision frameworks in the face of uncertainty. The findings indicate that hands-on experience and professional skills play a crucial role in decision-making structures, whereas the ability to adapt to technological advancements and a commitment to ongoing learning greatly enhance system resilience. The entropy results reveal a significant degree of structural balance in the decision criteria, while the low Gini values affirm a lack of concentration, indicating a distributed and multi-dimensional decision-making environment. The study provides analytical insights into the structure and relative importance of competencies in decision-making contexts related to supply chain resilience. Full article

This paper proposes a collaborative optimal scheduling strategy based on asymmetric Nash bargaining for the integrated electricity–heat–hydrogen multi-microgrid system, which can minimize the overall system operation cost while guaranteeing the dynamic fairness of multi-microgrids energy transactions with full consideration of wind–solar uncertainty. First, a scenario generation method based on temporally correlated Latin hypercube sampling and Wasserstein probability distance-based scenario reduction is adopted to construct representative wind–solar uncertainty scenarios, which effectively mitigates the operational risks arising from wind and solar power output fluctuations in the coordinated dispatch of multi-microgrids. Then, an asymmetric Nash bargaining-based cooperative game model for energy trading is established, with each microgrid’s optimal independent operation cost as the negotiation breakdown point. The alternating direction method of multipliers is used for a distributed solution to obtain the optimal scheme that balances total system cost and trading fairness. Simulation results verify that the proposed strategy can effectively suppress operation risks from renewable uncertainty, significantly cut total system cost by 36.85%, and fully ensure trading fairness among multi-microgrid entities, with favorable engineering application value. Full article

Inherent defects in engineering rock masses inevitably lead to randomness in mechanical parameters and uncertainty in tunnel deformation and failure. To address these challenges, this study proposes a novel coupled analysis method that integrates complex function theory, physical model testing, and Monte Carlo simulation (MCS) for the deformation estimation and failure probability analysis of non-circular tunnels. Theoretically, this method provides a high-speed, high-accuracy analytical framework that overcomes the limitations of purely numerical approaches, particularly in handling continuous–discontinuous failure processes. Practically, it enables a more reliable and efficient stability assessment of tunnel systems under uncertain geological conditions. The proposed method is applied to a traffic tunnel at the Baihetan Hydropower Station. A series of uniaxial compression tests on 40 rock specimens are conducted to obtain statistical distributions of rock deformation parameters. An analytical solution for tunnel displacement is derived using plane elastic complex function theory, and the random displacement field is estimated via MCS. Physical model tests reveal that the elastic stage accounts for 83% of the overload failure process, based on which an elastic limit displacement function is established for tunnel arch settlement and surrounding rock convergence. The failure probability of the tunnel is then calculated, and the effects of the mean, coefficient of variation, and cross-correlation coefficient of rock deformation parameters on failure probability are discussed. The entire computational process is characterized by high speed and precision, offering a new and practical tool for tunnel stability evaluation and reliability-based design. 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

Increasing load demand and localized constraints are driving the need for cost-effective alternatives to traditional network reinforcement. However, existing Non-Wires Alternative (NWA) planning approaches often rely on simplified assumptions or computationally intensive full-year optimization, limiting their practical applicability. This study proposes a planning-oriented method integrating 8760-h Direct Load Flow (DLF)-based assessment, worst-case screening, and Mixed-Integer Linear Programming (MILP)-based resource sizing for the coordinated deployment of Energy Storage Systems (ESSs), Demand Response (DR), and Photovoltaic (PV) resources, along with building-scale microgrid candidates. The proposed microgrid candidates are modeled as grid-connected, building-scale configurations in which PV, ESSs, and DR are co-located at a single node, representing integrated resource units within the distribution system. The results show that voltage constraints are the dominant limiting factor and that NWAs primarily function as an investment deferral strategy rather than a full replacement for traditional reinforcement, delaying constraint violations by approximately 2 to 14 years. An ESS provides the most direct contribution to constraint mitigation, while DR and PV offer complementary support. The results also highlight the importance of locational deployment. In particular, a co-located microgrid configuration (MG_111) is selected as the optimal portfolio under moderate load growth conditions (Case B, 2%), demonstrating the practical feasibility of integrated DER deployment at a single node. Economic feasibility is found to be highly sensitive to incentive design, with profitability achieved only under favorable compensation conditions. These results demonstrate that coordinated DER portfolios can effectively extend deferral periods and provide practical insights into cost-effective NWA planning under realistic operating conditions. Full article

Transboundary river basins (TRBs) sustain billions of livelihoods, yet they face enduring systemic challenges of cooperative water governance. Although collaborative governance models consistently yield acceptable outcomes, adversarial dynamics and zero-sum approaches continue to dominate transboundary water management. This systematic review synthesizes the peer-reviewed literature (2000–2026) to evaluate how four major governance dimensions—and the cross-cutting integration of the water–energy–food (WEF) nexus—shape the effectiveness of water diplomacy in international basins. Socio-economic analysis reveals that benefit-sharing arrangements grounded in joint investment outperform zero-sum volumetric allocation, though implementation remains constrained by institutional fragmentation and governance lock-in. Power relations analysis demonstrates that material, institutional, knowledge-based, and narrative-framing asymmetries systematically define the range of achievable agreements and the reliability of cooperative commitments, with case analysis from the Nile, Mekong, Tigris–Euphrates, and Central Asian basins showing that comparable hydrological conditions yield divergent diplomatic outcomes depending on how power is distributed. Stakeholder engagement findings indicate that formal participatory mechanisms frequently produce symbolic rather than substantive inclusion, particularly where structural imbalances limit procedural access. Gender analysis provides that women’s inclusion improves agricultural productivity, water-use efficiency, and adaptive capacity—functioning as a governance variable with measurable system-performance effects rather than solely an equity objective. The WEF nexus operates as the integrative mechanism binding these dimensions, reframing diplomacy from volumetric allocation toward adaptive benefit arrangements that coordinate interdependent services across sectors. This review concludes that effective transboundary governance emerges from the concurrent integration of socio-economic benefit-sharing, power-responsive institutions, meaningful stakeholder participation, gender equity, and nexus-based coordination in global TRBs. Full article

As modern energy systems become increasingly complex and multi-source integrated, efficient coordination between diverse energy carriers and dynamic demand is essential. This study identifies a structural parallel between integrated energy system (IES) scheduling and the weapon-target assignment (WTA) problem in joint command and control, and proposes a quantity–quality–spatiotemporal (QQST) framework to model multi-dimensional supply–demand matching. The QQST framework formulates scheduling as a coupled optimization problem integrating quantity balance, energy quality (exergy), spatial distribution, and temporal dynamics. A real-world industrial IES case, involving 60 textile enterprises and a 62 km steam network, is used for validation. The proposed model is benchmarked against a conventional mixed-integer linear programming-based scheduling approach under identical system configurations. Results show that QQST improves overall exergy efficiency by 8.4% and reduces energy quality mismatch by 18.2%, as measured by an exergy-based index. Sensitivity analysis under varying load conditions further confirms the robustness of the approach. These findings demonstrate that the QQST framework provides a structured and effective methodology for enhancing multi-dimensional coordination in complex energy systems. 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

The retrofitting of existing stadiums with retractable roof systems presents a complex interdisciplinary challenge, requiring the reconciliation of aged structural capacity with modern performance demands. This paper investigates the engineering design and analysis of a new retractable roof system for the Ningbo (Yinzhou) Tennis Center, a facility originally completed in 2007 and now requiring an upgrade to host higher-tier WTA 500 events. The retrofit is further complicated by increased seismic design requirements and the need to preserve the existing structure. To address these constraints, this study proposes a novel, structurally independent roof system comprising 12 radially deployable units supported by an external single-layer spatial grid and lambda-shaped columns. A multidisciplinary approach integrates structural engineering, mechanical systems, and architectural technology. Key innovations include (1) the selection and detailed modeling of a rack-and-pinion drive mechanism, with a floating engagement design to accommodate dynamic load transfer; (2) a two-stage analytical framework employing both sub-assembly and integrated assembly finite element models to capture the unique mechanical behavior and coupling effects between the new and existing structures; (3) the strategic implementation of circumferential hoop cables to counteract uplift forces and redirect the internal force distribution in the supporting bifurcated columns; and (4) the validation of structural integrity through comprehensive static, stability, and seismic gap analyses, informed by wind tunnel testing. The results demonstrate that the proposed system satisfies all strength, stiffness, and stability criteria under multiple operational states (open, closed, and transitional) and meets the enhanced seismic fortification standards. This research provides a validated theoretical foundation and practical implementation guidelines for this specific stadium retrofit, demonstrating a viable pathway for extending the service life of aging sports infrastructure, with insights that may inform similar urban renewal projects under comparable conditions. Full article

Compressed air systems (CASs) represent a significant portion of energy consumption in large-scale built environments and manufacturing facilities, suffering from both micro-level physical pipeline leaks and macro-level operational inefficiencies. This paper proposes a unified, dual-action artificial intelligence framework aimed at advancing building decarbonization by systematically integrating acoustic-vision leak quantification with schedule-aware machine learning. Specifically, the framework targets pneumatic pipe connection leaks, fitting leaks, and joint degradation faults within compressed air distribution networks, which are the primary sources of micro-level volumetric energy losses in industrial building systems. First, a probabilistic multimodal fusion algorithm (MPSF) using an ultrasonic camera is developed to detect and geometrically quantify physical leaks, successfully translating pixel areas into physical facility energy loss metrics (estimating 11.0 kW of wasted power from detected severe leaks). Second, to optimize the compressor’s supply matching the actual facility demand without risking data leakage from internal flow sensors, an eXtreme Gradient Boosting (XGBoost) model is proposed. By utilizing only external building environmental conditions and the real-time operational schedules of 13 distinct zones, the model achieves highly accurate dynamic power prediction (R² = 0.9698). Finally, comprehensive simulations based on real-world digital monitoring data from a facility-scale built environment demonstrate that only the concurrent application of both modules ensures stable end-point pressure. The integrated framework achieves a substantial system-wide building energy reduction of over 20% to 40% compared to baseline constant-pressure operations, yielding an estimated annual reduction of 116 tons of CO₂ emissions, thereby providing a direct pathway toward carbon-neutral building operations. Full article

Uncertainties in generation and dynamic load behavior provide new problems for radial distribution systems (RDS) caused by the growing integration of renewable distributed generators (RDGs), including solar photovoltaic (PV) systems and wind turbines (WTs), as well as electric vehicle charging stations (EVCS). This article offers a thorough techno-economic evaluation of how to best distribute RDG resources (solar PV, wind, and EVCS) inside a 28-bus distribution test system in India, taking into account generation volatility due to the seasons. Optimization of installation and operating costs, enhancing voltage stability, and decreasing active power loss are done all at once using a new Catch Fish Optimization Algorithm (CFOA). Integrating beta and Weibull distributions, respectively, into the probabilistic modeling of solar irradiance and wind speed allows for economic analysis to adhere to recognized approaches from contemporary multi-objective optimization frameworks. The simulation findings confirm that the proposed CFOA-based placement method improves economic efficiency, decreases energy loss, and increases system performance. Full article