Search Results (3,668)

The industrial metaverse is a digital twin space that integrates the real world with virtual environments through bidirectional synchronization. It supports critical services, such as time-sensitive machine control and large-scale collaboration, which require Time-Sensitive Networking and scalable Data Distribution Services. DDS, developed by the Object Management Group, provides excellent scalability and diverse QoS policies but struggles to guarantee transmission delay and jitter for time-critical applications. TSN, based on IEEE 802.1 standards, addresses these challenges by ensuring time-criticality. However, current research lacks comprehensive integration mechanisms for DDS and TSN, particularly from the viewpoints of semantics and system framework. Additionally, there is no adaptive QoS mapping converting the abstract DDS QoS policies to the sophisticated TSN QoS parameters. This paper presents a novel DDS-over-TSN framework that incorporates three key functions to address these challenges. First, Cross-layer QoS Mapping automates correspondences between DDS and TSN parameters, deriving technical constraints from standard documentation through retrieval-augmented generation. Second, Semantic Priority Estimation extracts substantial priority levels by utilizing language model embedding vectors as high-dimensional feature extractors. Third, Adaptive Resource Allocation performs dynamic bandwidth distribution for each priority level through reinforcement learning. Simulation results reveal over 99% mapping accuracy and 97% consistency in priority extraction. The applied Deep Reinforcement Learning paradigm allocated 99% of required resources to high-priority classes and reduced resource wastage by 15% compared to conventional methods. This methodology meets industrial requirements by ensuring both deterministic real-time performance and efficient resource isolation. Full article

Hybrid renewable energy systems (HRES) combining photovoltaic, wind, fuel cell, and energy storage technologies are becoming established as viable options for reliable, environmentally friendly distributed electricity generation. In this review, we examine the key architectures, monitoring and forecast approaches, and control systems that improve the efficiency of HRES and facilitate the just-energy transition to low-carbon power generation systems. The main optimization and decision-aware approaches, particularly the evolutionary generation algorithms and machine learning-based prediction models, are addressed with a focus on improving energy allocation, cost minimization, and increased use of clean renewable energy sources. Technical, economic, and environmental performance indicators, such as the levelized cost of energy (LCOE), net present cost (NPC), renewable fraction (RF), and CO₂ emissions reduction, have been compared to demonstrate the feasibility of various system scenarios. This paper evaluates and summarizes recent case studies from around the world and presents the best practices and the challenges they encounter, including resource availability, governance, and economic drivers. The balance of the paper demonstrates that smart forecasting with advanced energy management approaches is crucial for developing sustainable and resilient hybrid distributed power systems for the future. Full article

In response to global climate change, virtual power plants (VPPs) have emerged as critical entities for integrating distributed energy resources and enabling demand response. However, the design of multi-energy collaborative pricing mechanisms for VPPs remains a significant challenge, particularly under carbon trading regulation. This paper addresses this gap by proposing a bi-level optimization model that captures the real-time interactions between users and energy suppliers. The model is designed to simultaneously maximize user utility and minimize supplier costs, explicitly accounting for energy costs, equipment operation and maintenance (O&M) costs, carbon emission costs, and power generation structure constraints. A particle swarm optimization (PSO) algorithm is employed to solve the formulated problem. The results of a case study demonstrate that the proposed mechanism effectively guides users toward peak shaving and valley filling, achieving a real-time balance between supply and demand. Furthermore, the simulation results indicate that the model significantly enhances power system operational efficiency and economic benefits while reducing carbon emissions. This work offers a practical approach for improving renewable energy integration and overall system performance within a carbon-constrained environment. Full article

Elucidating the trade-offs and synergistic relationships between different ecosystem services is essential to optimize the benefits of ecosystem services and ensure their proper management for human well-being and ecosystem health. However, previous studies have focused only on quantitative analysis based on statistical relationships to explore ecosystem service trade-offs and synergistic relationships as a whole; additionally, some of them lack scientific expression of spatial and temporal differences within regions. Therefore, here, we explored the trade-offs and synergies among ecosystem services in the Taihang Mountains region and conducted ecological service zoning based on the findings to support ecological conservation and high-quality development in the Taihang Mountains and North China Plain. We employed yield spatialization, the InVEST model, and ArcGIS kernel density analysis to assess the interactions among ecosystem services: provisioning (food supply), regulating (water yield and carbon density), supporting (soil retention and habitat quality), and cultural services (leisure and recreation) in the study area. Linear Pearson correlation coefficients and non-linear bagplots were utilized to analyze the interrelationships among these services. Based on the bagplot results, the geographic patterns of ecosystem service trade-offs/synergies and the distribution of dominant services were identified. The results revealed considerable trade-offs between food supply and both regulating and supporting services, with most of the latter exhibiting synergistic relationships with one another. In contrast, leisure and recreation services showed a neutral relationship with other services. Among ecosystem services, carbon density services demonstrated the highest synergistic effects, whereas food supply services exhibited the most conflicts. The various ecosystem trade-off/synergy zones and dominant service distributions generated through bagplot mappings may optimize management methods for multiple ecosystem services. Overall, these findings provide significant insights for improving ecological service zoning and natural resource management. Full article

To advance the Sustainable Development Goals, it is essential to correct imbalances in how the benefits of urban trees are distributed across different demographic and socioeconomic groups. Environmental justice studies have frequently overlooked assumptions regarding the data-generating process and have not considered spatial confounding. This oversight potentially misestimates patterns of inequity. This study evaluates the sensitivity of inequity to model assumptions using urban tree inventories from Málaga and Sevilla and Bayesian hierarchical models. City-level differences dominated the inequity patterns, and model specification influenced the magnitude, precision, and credibility of estimated effects, though directionality remained consistent. Patterns were highly consistent across the four ecosystem services, indicating that model assumptions affected all services equivalently. Málaga and Seville exhibited divergent inequity patterns, indicating that local urban context mediates these relationships. In Seville, inequity patterns were inconsistent with the luxury hypothesis and occurred primarily across age-based demographic strata, whereas in Málaga they manifested predominantly along ethnicity, with weaker evidence of income inequities. We advocate for explicitly modeling spatial data-generating processes and comparing conventional versus confounding-mitigated approaches. This city-specific rigor is essential for urban planners to prevent resource misallocation, ensuring that tree-planting strategies address genuine inequities rather than methodological biases. Full article

Against the backdrop of dual-carbon goals and resource constraints, the high-value utilization of recycled fine aggregates (RFAs) remains limited, leading to inconsistent engineering performance and insufficient durability. Enzyme-induced carbonate precipitation (EICP) represents a promising low-carbon cementation method, yet its deposition uniformity and cementation efficiency are influenced by the pore structure of granular media and associated mass transfer pathways. This study employs a two-stage experimental design to investigate the synergistic effects of particle size distribution characteristics, represented primarily by d₅₀, and fiber addition on EICP-cemented RFA. Phase I (fiber-free; d₅₀ = 0.67–1.14 mm) results indicate that, across the tested gradation schemes, the CaCO₃ content generally decreased from 9.49% to 7.72% as the representative d₅₀ increased, while the dry density changed only slightly (1.637–1.617 g/cm³). However, the unconfined compressive strength (UCS) decreased from 1000 kPa to 541 kPa (45.9% reduction), indicating that strength is primarily governed by the connectivity of the cementation network rather than solely by the degree of densification. In Phase II, glass fiber (GF), polypropylene fiber (PPF), and jute fiber (JF) were incorporated into the ERFA4 gradation scheme selected for fiber modification. All three systems exhibited a unimodal optimum pattern: the peak CaCO₃ contents reached 10.71% (GF 0.5%), 10.11% (PPF 0.7%), and 11.46% (JF 0.7%), corresponding to peak UCS values of 1917, 1874, and 2450 kPa, respectively. Microscopic analysis suggested that fiber bridging coupled with CaCO₃ deposition may contribute to the formation of a “fiber-CaCO₃-particle” stress-transfer network, which is consistent with the observed enhancements in load-bearing capacity, ductility, and post-peak stability. Full article

The rapid growth of distributed energy resources (DERs) is transforming distribution networks and increasing the need for coordinated flexibility management to maintain secure and economically efficient operation. In this work, we examine how uncertainty in load demand and photovoltaic (PV) generation affects incentive-based flexibility coordination within a hierarchical three-level framework. The proposed architecture integrates household energy management systems (HEMSs), an aggregator responsible for incentive allocation, and a distribution system operator (DSO) model based on AC optimal power flow. To account for demand and PV variability, a $\mathrm{\Gamma }$-budget-robust optimization approach is adopted. Also, an incentive–penalty mechanism is introduced to allocate compensation according to each prosumer’s actual flexibility contribution while promoting economic fairness. The entire framework is implemented in PYOMO and tested on the IEEE 33-bus distribution system. A comparative evaluation between deterministic and uncertainty-aware cases is conducted to quantify the cost of robustness and to analyze its influence on flexibility participation, incentive distribution, household net cost, and voltage regulation performance. The results indicate that uncertainty can lead to deviations from initially scheduled flexibility commitments, thereby triggering penalty signals during re-optimization and strengthening contractual compliance. Although the robust formulation results in a moderate increase in operational cost, it substantially improves voltage compliance and overall system reliability. Overall, the findings highlight the importance of explicitly incorporating uncertainty in multi-level flexibility coordination to ensure both technical consistency and practical enforceability in modern distribution networks. Full article

As the scale and complexity of Internet of Things (IoT) deployments increase, maintaining resilience in resource-constrained mesh networks becomes a significant challenge. Frequent node failures due to battery depletion, environmental interference, or hardware degradation can disrupt data flows and lead to operational downtime. To address this, we propose EdgeRescue, a novel lightweight AI-driven framework for self-healing in energy-constrained IoT mesh environments. EdgeRescue enables each node to perform local anomaly detection using compact 1D Convolutional Neural Networks (1D-CNNs) and initiates distributed, energy-aware routing reconfiguration when faults are detected. Unlike cloud-dependent methods, EdgeRescue operates entirely at the edge, requiring minimal computation, memory, and communication overhead. Extensive simulations on a 100-node testbed demonstrate that EdgeRescue improves packet delivery by 13.2%, reduces recovery latency by 57%, and lowers average node energy consumption by 18.8% compared to state-of-the-art baselines. These results establish EdgeRescue as a scalable and practical solution for achieving real-time resilience in next-generation IoT mesh networks. Full article

Sustainable groundwater management in hyper-arid regions requires accurate water quality assessments, yet remote desert environments present major challenges due to data scarcity, high sampling costs, and limited laboratory infrastructure. This study proposes a framework integrating the Water Quality Index (WQI) with Inverse Distance Weighting (IDW)-based spatial data augmentation and machine learning classification for groundwater quality assessment in the Tabelbala region, southwestern Algeria. Three classifiers were evaluated, Random Forest (RF), Support Vector Machines (SVMs), and Artificial Neural Networks (ANNs), and trained on an augmented dataset generated from 178 original groundwater samples using IDW interpolation with a sensitivity-optimized 150 m radius, producing 2779 augmented training points. RF achieved the highest predictive accuracy (85.9%), followed by ANNs (84.7%) and SVMs (83.1%), with all models demonstrating excellent discriminative performances (area under the receiver operating characteristic curve > 0.96). Permutation Feature Importance analysis identified total dissolved solids (TDS), sulfates (SO₄²⁻), total hardness (TH), and chlorides (Cl⁻) as the most influential parameters, consistent with World Health Organization (WHO) guidelines. Spatial distribution maps revealed that the majority of groundwater sources exhibited poor to very poor quality, highlighting the urgent need for local water management interventions. The proposed framework offers a replicable decision-support tool for water resource managers in data-scarce arid environments, supporting SDG 6 (Clean Water and Sanitation) and SDG 13 (Climate Action). Full article

Deploying lightweight convolutional neural networks (CNNs) to provide vision services on resource-constrained Internet of Things (IoT) devices has become the mainstream approach to addressing computing and energy consumption constraints. However, these IoT devices often operate in complex outdoor environments (e.g., fog, rain, and snow), and the quality of the data they collect is easily degraded, causing standard lightweight CNNs to experience a significant performance drop under such corrupted data. To this end, this paper proposes a Generative Nonlinear Transformation Filter (GNTF) method to improve the generalization performance of lightweight CNNs on corrupted data. The core of the GNTF is that only a portion of the filters are used as learnable parameters (named seed filters), while the remaining filters are generated by applying the nonlinear transformation to the seed filters, which is randomly initialized and fixed during training. This design makes the model parameters less dependent on the training data distribution, thereby regularizing the model, mitigating overfitting, and enhancing its robustness to data degradation. The GNTF further analyzes the structural characteristics of lightweight CNNs, showing that significant performance improvements can be achieved simply by replacing the depthwise convolutional modules. Furthermore, this paper examines the properties of various nonlinear transformation functions and finds that model robustness can be improved by applying simple translations. To verify the effectiveness of the GNTF, we conducted extensive experiments on the CIFAR-10/-100, CIFAR-10-C/-100-C, and ICONS-50 datasets, using the MobileNetV2, ShuffleNetV2, EfficientNet, and GhostNet models. The results show that the proposed GNTF can improve the model’s accuracy on corrupted data while reducing the number of trainable parameters in most cases. For example, on the CIFAR-10-C dataset, ShuffleNetV2 with the GNTF improves accuracy by about 3.3% over the original model while slightly reducing the number of trainable parameters. Full article

This paper investigates the interaction between fiscal policy and non-performing loans (NPLs), a nexus often overlooked in banking stability literature. By proposing a generalized theoretical framework that augments the industrial organization (IO) theory of banking with liquidity preference theory, this study explains why a fiscal contraction (an improvement in the primary balance from deficit toward surplus) can decrease NPLs in a bank’s portfolio. Using bank-level quarterly data from Guyana (2009: Q4 to 2024: Q4) and a Panel Autoregressive Distributed Lag Pooled Mean Group (ARDL-PMG) model, we find that a fiscal contraction reduces NPLs in the long run. Specifically, a one-percentage-point improvement in the seasonally adjusted primary balance (as a % of GDP) is associated with a 0.473 percentage point decrease in NPLs in the long run. This finding contrasts with the existing literature, which often suggests that fiscal consolidations increase credit risk. In the short run, however, the results indicate a divergent effect where fiscal contractions lead to a temporary increase in NPLs, with a coefficient of 0.103, likely because of immediate pressure on borrower debt-service capacity. This study contributes to the literature by extending the IO theory of banking to the fiscal policy–NPL relationship in a developing, resource-rich economy. Notably, while higher oil prices and bank efficiency significantly lower NPLs, traditional macroeconomic drivers such as GDP growth, inflation, and the real effective exchange rate—as well as the COVID-19 pandemic—are found to be statistically insignificant in this framework. Full article

Geomagnetic navigation is a promising alternative for positioning and localization of UAV swarm systems in GNSS-denied environments. However, strong and heterogeneous electromagnetic interference generated by onboard power, propulsion, and electronic subsystems severely degrades magnetic measurement fidelity, limiting the achievable accuracy of cooperative UAV swarm navigation. To address this challenge, this paper proposes PG-TLNet, a physics-guided aeromagnetic interference compensation framework based on the extended Tolles–Lawson (T–L) model. By integrating onboard state information (current, voltage, and attitude) with magnetic measurements through physics-consistency constraints and a lightweight multi-branch convolutional neural network, the framework enables robust real-time compensation under strong and time-varying interference while remaining suitable for resource-constrained UAV nodes. Experimental validation using multiple scalar magnetometers under heterogeneous interference conditions, with amplitudes up to 1000 nT, shows that PG-TLNet consistently outperforms the conventional T–L model across all sensing nodes, maintaining residual magnetic interference at approximately 0–30 nT under long-duration and highly dynamic operations. The proposed method achieves an improvement ratio (IR) of up to 15 with an end-to-end inference latency below 94 μs. These results indicate that PG-TLNet meets the practical measurement fidelity requirements for geomagnetic navigation in GNSS-denied environments. By ensuring reliable and consistent magnetic measurements at the individual UAV node level, the proposed framework establishes a practical sensing foundation for geomagnetic navigation and distributed magnetic sensing in UAV swarm systems operating in GNSS-denied environments. Full article

The transition from linear production and consumption models toward circular economy (CE) systems represents a key pathway for improving the sustainability and resilience of energy supply chains. This review provides a structured synthesis of circular economy approaches applied across the full lifecycle of energy systems, encompassing resource sourcing, energy generation and conversion, processing, distribution, and end-of-life recovery. The analysis integrates conceptual frameworks with system-based and analytical modelling approaches, as well as environmental, economic, and operational performance assessment methods. The results reveal that current research remains largely fragmented across material, energy, and residual flow perspectives, with limited system-level integration and persistent inconsistencies in modelling and evaluation approaches. While circular strategies such as resource recovery, energy recirculation, and industrial symbiosis demonstrate significant potential for improving resource efficiency and reducing environmental impacts, their implementation continues to be constrained by data limitations, technological maturity, and coordination complexity across stakeholders. By consolidating the dispersed literature into a coherent analytical structure, this review clarifies the critical interdependencies between circularity strategies, modelling approaches, and performance metrics, and identifies the methodological gaps that currently limit progress toward integrated circular energy supply chains. The findings offer a structured foundation for researchers and practitioners working to develop more robust evaluation frameworks and governance mechanisms in this field, and point toward the convergence of digital technologies, multi-stakeholder governance, and lifecycle thinking as a productive direction for advancing the field. Full article

Peer-to-peer (P2P) energy trading offers a decentralised framework for integrating distributed renewable resources. When local renewable energy generation exceeds demand, surplus electricity can be converted into hydrogen for long-duration storage, providing flexibility beyond the electricity vector. However, most existing P2P markets are focused only on electricity, do not account for network losses and are not designed to coordinate multi-vector trading with inter-temporal couplings. To address these gaps, we propose a distance-aware periodic double auction (DA-PDA) market-clearing mechanism that extends the conventional PDA by incorporating loss-aware pricing and enabling trades between peers with the lowest loss cost. The DA-PDA provides a distributed, market-based coordination mechanism for joint electricity–hydrogen trading, improving efficiency through dynamic price signals. The framework enhances system-level performance by reducing renewable curtailment, increasing utilisation of surplus electricity and enabling hydrogen-supported flexibility. Using a real-world case study, we demonstrate that sector-coupled P2P markets can improve local social welfare and act as an effective energy-conservation mechanism in highly renewable, electrified systems. Full article

Despite the growing number of publications using the value network approach to analyze agro-industrial competitiveness, knowledge gaps persist in other food production sectors. The objective of this study is to analyze, through bibliometric techniques, the scientific articles that have studied the competitiveness of food products using the value network framework. The study will determine the spatial and temporal distribution of the identified food products and detect opportunities for generating new research. Articles from major publishing databases (Elsevier, Scopus, Frontiers, MDPI, and Springer) were considered. The keywords used were “red de valor” and “value network”, combined with “sustainable agricultural production” and “food security”. This information formed the basis of a spatial–temporal analysis and bibliometric indicators using descriptive statistics, as well as keyword and author networks generated with VOSviewer software. A total of 147 scientific articles were documented. The highest growth in publications occurred from 2017 to 2024 and was concentrated in Latin America, Europe, and Asia. Studies in these regions analyzed basic food products such as maize, mango, rice, and coffee in Latin America; wine and bovine milk in Europe; and rice and sugar in Asia. Research in aquaculture, apiculture, and non-timber forest sectors was limited, positioning these areas as opportunities for generating new knowledge, particularly through the analysis of local resources to enhance their market positioning while incorporating sustainability aspects. Full article