Search Results (4,236)

The emergence of citizen energy communities under the European Clean Energy Package is creating new opportunities for neighboring households to collectively reduce electricity costs through local energy sharing. This paper presents a distributed multi-agent energy management system for a two-household residential energy community in which each household is equipped with photovoltaic generation and a battery energy storage system operating under realistic hourly-varying electricity prices. Each household is managed by an independent Deep Q-Learning agent that learns a cost-optimal charging and discharging policy using only local observations. In parallel, a coordination agent, implemented on the SPADE platform with XMPP-based messaging, oversees real-time peer-to-peer energy transfers between households, enabling energy exchange whenever one household has surplus generation and another faces a deficit. The two households are deliberately configured with complementary profiles: one has higher PV generation capacity while the other has higher energy consumption. This setup creates natural opportunities for local energy sharing between them. Performance is assessed through a three-level evaluation framework: (i) individual household economics (cost reduction, battery management, grid exchanges), (ii) coordination efficiency (transfer frequency, direction, and volume), and (iii) aggregate community performance, which isolates the added value of peer-to-peer sharing beyond what each household achieves through individual BESS optimization. Numerical experiments using GEFCom2014 solar generation data, synthetic residential load profiles calibrated following documented consumption patterns, and day-ahead price signals representative of the Spanish electricity market demonstrate that both Deep Q-Learning agents independently learn effective charge/discharge strategies aligned with price signals and PV availability. They also show that the coordination layer further reduces community grid dependence by routing surplus energy locally rather than exchanging it with the main grid at less favorable rates. The results confirm that a well-engineered integration of decentralized reinforcement learning with a lightweight coordination protocol can deliver measurable economic benefits in realistic residential energy communities without requiring centralized training, shared data, or complex multi-agent reinforcement learning architectures. Full article

Wastewater treatment plants (WWTPs) are important interfaces for the persistence and dissemination of antimicrobial resistance genes (ARGs) in the environment. This study investigated colistin resistance and the presence of mobile colistin resistance (mcr) genes in Enterobacterales isolated from a poultry slaughterhouse WWTP in Brazil. Samples were collected from raw sewage, an equalization tank, and treated effluent. A total of 27 Enterobacter spp. isolates were identified, of which 70.4% showed resistance to colistin (MIC range: 2 to ≥512 mg/L). PCR screening detected mcr-1 in two isolates and mcr-10 in three isolates distributed across all treatment stages, including the final effluent. Whole-genome sequencing of a representative isolate from treated effluent identified Enterobacter vonholyi ST3343, carrying a plasmid-borne mcr-10 gene on an ~107 kb IncFII(Yp) plasmid, along with additional resistance determinants. Phylogenetic analysis supported the classification of this gene as a novel allele, mcr-10.6. The persistence of a clonal lineage harboring mcr-10.6 throughout the treatment system indicates that conventional wastewater treatment may not effectively eliminate clinically relevant ARGs. These findings highlight treated effluent as a potential route for environmental dissemination of colistin resistance and reinforce the need for improved monitoring and mitigation strategies within a One Health framework. Full article

Reinforced concrete (RC) buildings are the most common structural system in urbanising regions. In many cases, architectural constraints and uneven distribution of structural elements often create eccentricity between the centre of mass (CM) and the centre of rigidity (CR). This eccentricity may induce torsional effects during earthquakes that can significantly influence structural response and increase seismic vulnerability. This study investigates the impact of in-plan irregularity on the seismic performance of RC buildings using nonlinear numerical analyses. Three-dimensional models of four- and six-storey RC buildings with moment resisting frames were developed in OpenSees, where different levels of irregularity were introduced by artificially shifting the lumped mass to generate controlled eccentricities without modifying the structural configuration. Seismic performance was evaluated using nonlinear incremental dynamic analysis (IDA) based on forty ground motion records under bidirectional excitation. The results indicate that increasing CM–CR eccentricity amplifies inter-storey drift demands and elevates the probability of damage due to intensified torsional stresses. The adverse effect is most pronounced when eccentricity aligns with the direction of lower stiffness, whereas eccentricity in the stiffer direction has a limited impact on severe damage states, particularly for taller buildings. These findings provide valuable insights for risk-informed assessment, retrofitting, and prioritisation of existing plan-irregular RC buildings. Full article

The increasing complexity of residential energy systems, driven by rising electricity demand, renewable energy integration, and dynamic pricing mechanisms, has intensified the need for intelligent load scheduling within Home Energy Management Systems (HEMSs). This paper presents a comprehensive literature review of optimization algorithms applied to residential load scheduling, based on an analysis of 78 peer-reviewed studies published between 2020 and 2025. The analysis reveals a clear shift from conventional deterministic optimization toward adaptive and data-driven approaches capable of operating in uncertain and dynamic environments. Metaheuristic methods are widely used for solving complex scheduling problems, while Machine Learning and Deep Learning (DL) techniques primarily support forecasting tasks related to energy demand and renewable generation. Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) approaches enable autonomous real-time decision-making, although challenges related to scalability, computational cost, and practical deployment remain unresolved. The review identifies hybrid architectures that combine forecasting, optimization, and control mechanisms as the most promising direction for future HEMS development. Finally, the paper highlights key research gaps, including limited real-world validation, insufficient consideration of physical infrastructure constraints, and the need for scalable distributed control frameworks for future smart grids and energy communities. Full article

The reinforcement and retrofit of damaged steel buildings has emerged as a primary focus in civil engineering. It should be noted that completing the reasonable strengthening design for avoiding the sudden collapse of a structure in extreme engineering conditions was an urgent task, while the existing method required a long time which significantly influenced the reinforcing practice. In the present study, an improved explainable machine learning (ML) framework was developed for the rapid assessment of the bending property of repaired laminated channel beams. Firstly, a comprehensive database of 192 samples combining experimental and finite element data was established. The Mahalanobis distance analysis and Pearson correlation analysis were sequentially performed to evaluate the singularity of the samples and the dependencies between the variables. Secondly, the adversarial tests were conducted on the randomly selected 10 pairs of training and testing sets to determine the database with the best distribution consistency. Then, three machine-learning models of artificial neural networks (ANN), random forest (RF), and extreme gradient boosting tree (XGBoost) were respectively trained and validated. Finally, the explainability analysis of the XGBoost model was carried out in the global and local perspectives based on the SHAP method. The prediction accuracy (R²) of all ML models exceeded 90%, demonstrating good accuracy and providing a useful reference within the current database for the reinforcement design of damaged steel beams in emergency situations. In addition, the XGBoost model achieved superior prediction accuracy (R² = 97.98%) and stability (CoV = 0.82%) compared to ANN and RF. The explainability analysis revealed that boundary conditions and load type had the most significant influence on bending capacity. The proposed ML approach enabled efficient and reliable bending capacity estimation, supporting rapid decision-making in emergency reinforcement scenarios for damaged steel structures. Full article

Punching shear failure in reinforced concrete RC slabs is one of the most significant and detrimental failure modes due to its sudden nature and its dependence on a complex interaction between concrete strength, the reinforcement, and the loading conditions. In recent years, there has been increasing interest in utilizing sustainable cementitious materials and steel fibers as a way of enhancing structural performance and improving the durability of concrete. The study aims to assess the structural behavior of RC slabs utilizing a partial cement substitution with limestone powder (LP) and granulated blast-furnace slag (GBFS), with the addition of steel fibers. Twelve RC slabs were examined under uniform concentric loading to analyze cracking behavior, load–deflection relationship, stiffness variation, and ultimate punching shear strength. The results demonstrated that using limestone powder (LP) had a significant impact on the crack distribution pattern and resulted in a slight reduction in initial stiffness, with the load-bearing capacity decreasing to approximately 55.8% of the control mixture at high replacement ratios. Due to a slower hydraulic reaction than with other mixtures, increasing additional granulated blast-furnace slag resulted in a decrease in crack resistance and relative deformation. With a load-bearing capacity of approximately 92.9% of the control mixture, a tertiary mixture of limestone powder and granulated blast-furnace slag (GBFS) demonstrated a better balance in structural behavior, leading to improved crack control while maintaining a sufficient level of load-bearing capacity. The steel fibers also significantly contributed to enhanced post-cracking behavior by decreasing crack width and improving the stress redistribution mechanism within the RC slab. This led to increased punching shear resistance and enhanced energy absorption, with the ultimate load increased to 119 kN compared to the control mixture. Overall, the findings show that combining sustainable cementitious materials with steel fibers can effectively improve punching shear performance and enhance the efficiency and durability of reinforced concrete. Full article

Cracking in reinforced concrete beam bridges severely compromises their durability and structural integrity. Although external prestressed CFRP plate reinforcement technology has emerged as an effective repair solution, current design codes primarily rely on idealized crack-free or simplified single-crack assumptions, leading to inadequate precision in prestressing application for real-world structures with complex crack networks. This study investigated the reinforcement effectiveness of externally prestressed CFRP plates on three pre-cracked reinforced concrete T-beams with varying reinforcement ratios (1.20%, 2.41%, and 3.61%). A comprehensive experimental program was conducted to monitor crack closure behavior, strain distributions, and deflection changes during tensioning and loading phases. A three-dimensional finite element model was developed using Midas FEA NX 2022, and theoretical formulas for crack closure prestressing were derived under the plane-section assumption, supplemented by engineering correction factors. Results demonstrated that calculation errors for both crack closure prestressing and secondary cracking loads were below 5%, while correlation coefficients between finite element simulations and experimental data ranged from 0.93 to 0.99. External prestressing significantly enhanced the stiffness of cracked beams, with stiffness recovery rates reaching up to 156.2%, and exhibited excellent synergistic performance among CFRP plates, steel reinforcement, and concrete. These findings provide a theoretical foundation and technical support for the precision design of external prestressing reinforcement in cracked reinforced concrete beams. Full article

This paper investigates the output consensus problem in heterogeneous multi-agent systems. To address the challenges of traditional analytical methods in handling unknown dynamics and disturbances, a control framework is proposed that combines known model structures with a data-driven adaptive mechanism. The framework uses a distributed internal model to compensate for system heterogeneity and incorporates an event-triggered mechanism to reduce communication burden. To improve transient tracking performance, a reinforcement learning strategy based on centralized training and decentralized execution is introduced to adaptively optimize local feedback gains. Simulation results show that the proposed method effectively bounds closed-loop signals, achieves relatively fast convergence, and demonstrates some robustness and communication efficiency under process noise. Full article

The rapid expansion of solar photovoltaics is intensifying competition for land and highlighting the need for scalable energy solutions that can be integrated into existing power systems without displacing agricultural activity. Once the technical and agronomic viability of agrivoltaic configurations has been demonstrated at field scale, a critical next step toward their market consolidation is the assessment of their deployment potential at regional scales from an energy systems and grid integration perspective. This study presents a GIS-based framework to evaluate the large-scale implementation of vertically integrated agrivoltaic systems, using vineyard landscapes in the Region of Murcia (southeastern Spain) as a representative case study. The analysis combines high-resolution land-use data, crop distribution, regulatory constraints on grid connection distances, and existing electrical infrastructure to quantify installable capacity, energy production, self-consumption potential, and grid accessibility. Results indicate that vertically mounted bifacial PV systems could reach up to 7.06 GWp, generating approximately 11.84 TWh/year, while revealing a pronounced spatial mismatch between optimal agrivoltaic production sites and current grid connection points. This distance-dependent distribution highlights the need for differentiated deployment strategies, balancing local self-consumption, grid reinforcement, and centralized injection. Beyond the specific case examined, the proposed approach provides a transferable framework for energy system planning, supporting grid-aware agrivoltaic deployment in diverse regions and regulatory contexts. Full article

This study investigates the influence of temperature on the bending properties, fatigue life, and breakdown voltage of glass fiber/epoxy composites (EPGF). The three-point bending tests were conducted at room temperature (RT) and 60 °C, and the bending fatigue tests were carried out under three displacement amplitudes (0.80, 0.75, 0.70). At the same time, fatigue life prediction was conducted using the Weibull distribution fitting, microscopic structure analysis by scanning electron microscopy (SEM), and breakdown voltage tests in accordance with the GB/T1408-2006 standard. The results show that at 60 °C, the ultimate bending strength and flexural modulus of EPGF decreased by 52.67% and 65.45%, respectively. At high displacement amplitudes (S = 0.80, 0.75), 60 °C leads to a sharp rise in data dispersion with the coefficient of variation (CV) surging by 1.56 and 2.32 times separately. S and temperature exert a significant synergistic degradation effect on fatigue life, and the two-parameter Weibull distribution (R² > 0.85) can well characterize the fatigue life of EPGF. In terms of dielectric properties, 60 °C reduces the initial breakdown voltage of EPGF by 4.23% (p < 0.05). Fatigue damage causes a continuous drop in breakdown voltage. At RT with 80% damage, the reduction rate increases from 16.28% to 26.95% as S rises, showing a synergistic characteristic between amplitude and fatigue damage. Moreover, 60 °C only affects the initial breakdown voltage and has no significant effect on the fatigue-induced decrease in breakdown voltage. SEM observations indicate that 60 °C induces matrix cracking, fiber curling and interfacial debonding in EPGF. This study provides key experimental data and theoretical support for the fatigue life prediction and insulation performance evaluation of EPGF under different temperature fatigue conditions. Full article

The study proposes and investigates a seismic retrofitting strategy based on an external reinforced concrete exoskeleton, grounded in the analysis of the actual force transfer mechanisms between the existing structure and the added system. The three-dimensional numerical model was developed in ETABS, employing linear response spectrum analysis in accordance with EN 1998-1 and P100-1/2013. The internal forces transmitted at the structural interface were determined using the Section Cut method, enabling the identification of integrated resultants and the prioritization of critical connections. Three types of connections are examined—slab-to-slab, column-to-wall, and beam-to-joint—while the distribution of stresses within the anchor groups is assessed based on an elastic model under combined axial force and bending action. The results indicate that the global structural response is governed by diaphragm coupling, whereas the vertical interfaces ensure kinematic compatibility and the redistribution of axial and bending effects. The proposed methodology provides a coherent framework for the rational design of interface connections in retrofit interventions carried out without interrupting building operation. Full article

Stabilizing weak soils is a well-known pavement and geotechnical engineering technique. This technique involves introducing minimal cementitious materials to improve the soil’s geotechnical characteristics. This paper investigates the use of recycled industrial polymer waste (IPW) as a reinforcement material in the presence of cementitious binders to stabilize weak silty sand soil (SM), supporting sustainable engineering practices. The randomly distributed IPW were added as percentages of 0%, 5%, and 10% to a mixture of lime soil and cement soil, with varying amounts of 0% to 6% of lime (L) and 0% to 6% of ordinary Portland cement (OPC), respectively. The laboratory experiments were conducted on natural and stabilized samples in wet (unsoaked) and submerged (soaked) conditions. The experimental program included Proctor compaction, California bearing ratio (CBR), unconfined compressive strength (UCS), durability tests, scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), and X-ray diffraction analyses. The resilient modulus (Mr) was estimated using an empirical equation. The outcomes of this experimental study show that adding a combination of IPW shreds with a small amount of L and/or OPC to the SM soil provides a significant increase in the UCS, CBR, durability and Mr values compared with case of SM with only L, which allows for superior characteristics and increases strength and stiffness parameters throughout any phase of earthwork construction design, resulting in stronger and stiffer subgrades. These results were reinforced by microstructural observations from SEM, EDS, and DRX, confirming the formation of cementitious gels and chemical compounds, consistent with the macro-scale mechanical improvements. The expected practical outcomes include potential reductions in pavement thickness, which can help lower pavement stabilization costs and extend its service life. Additionally, the use of waste materials to replace raw materials contributes to decreased energy consumption and emissions, although detailed assessments are needed to quantify these effects. Full article

To address the control coupling challenges arising from task heterogeneity of unmanned surface vehicle (USV) formation, a distributed hybrid deep reinforcement learning (HDRL) framework is proposed. The framework decomposes the formation task into two subtasks: leader path planning using the single-agent deep reinforcement learning (SADRL) algorithm and follower formation tracking using the multi-agent deep reinforcement learning (MADRL) algorithm. By embedding the physical constraints of the real Otter USV into the training loop, the policy network outputs are mapped to propeller revolutions that conform to its dynamic characteristics. To optimize control performance, a dynamic gating mechanism triggered by formation position error is developed to mitigate multi-objective interference through temporal task scheduling. Concurrently, a mirror mapping mechanism leveraging the physical symmetry of the formation is designed to achieve policy sharing and data augmentation. Furthermore, the desired velocity calculated based on rigid-body kinematics is used to achieve kinematic-compensated formation tracking. The simulation results indicate that, compared to the SADRL algorithm, the planning success rate of HDRL is improved by 44.59%. Furthermore, compared to the MADRL algorithm, the integrated tracking performance is enhanced by 21.79–39.64%. Full article

Makerspaces can serve as distributed urban infrastructures for repair, reuse, tool sharing, and peer learning, yet their contributions to circular economy (CE) goals often occur without being explicitly recognized or framed as CE practices. Inspired by practice theory and the literature on quiet sustainability, this study introduces implicit circularity as circular practices enacted without an explicit sustainability/CE framing by participants, and examines how such practices shape bottom-up circular transitions. Using reflexive thematic analysis informed by constructivist grounded theory procedures, we examined three linked questions: which circular practices occur in makerspaces and how they cluster into domains, how these practices vary across makerspace types, and which barriers and governance arrangements shape makerspaces’ consolidation as circular urban infrastructure. A qualitative multi-method design was employed in Czechia, combining field mapping with in-depth qualitative inquiry. Data included 40 semi-structured interviews with makerspace founders and operators, documentary analysis based on websites, social media, event listings, rules, and other documents, and 21 observations. Using reflexive thematic analysis informed by constructivist grounded theory procedures, we analyzed how circular practices cluster into domains, how implicit versus explicit circularity varies across makerspace types, which barriers constrain makerspaces’ consolidation as circular urban infrastructure, and what governance arrangements could mitigate them. Circularity was dominated by implicit, routine practices rather than formal, CE-branded programs. Three practice domains were identified: repair and maintenance, material flows, and learning/education. Explicit programming was comparatively less common and context-dependent. Barriers formed a reinforcing system spanning institutional fragmentation and coordination deficits, capability gaps, infrastructural constraints, and tensions around autonomy and legitimacy, which together kept many circular contributions low-visibility. Makerspaces constitute an under-recognized form of circular micro-infrastructure that couples technical capacity with social learning and can translate CE ambitions into everyday practice. To mobilize these latent capacities, cities need hybrid governance, especially light-touch coordination platforms, long-horizon operational support, and integration of makerspaces into municipal material-flow systems and repair/reuse strategies. The study offers a practice-based framework and a cross-case typology to support comparative research and grounded urban CE policy design. Full article

Low-latency content delivery is essential in the Internet of Vehicles (IoV) to support autonomous driving, cooperative perception, and infotainment services. However, rapidly changing vehicular mobility and demand patterns limit the effectiveness of existing content prediction and caching strategies, which often capture either short-term temporal trends or long-range dependencies, but not both. This paper proposes a hybrid deep learning architecture that integrates Long Short-Term Memory (LSTM) networks with Transformer encoders to jointly model fine-grained temporal dynamics and global correlations in content requests. The resulting popularity predictions are incorporated into a reinforcement learning (RL)-based caching policy, enabling proactive and adaptive cache placement at roadside units (RSUs) within an end-to-end optimization framework. Simulation results across representative IoV scenarios show that the proposed approach consistently improves cache hit ratio, retrieval latency, and prediction accuracy compared with LSTM-only, Transformer-only, Least Frequently Used (LFU), and Least Recently Used (LRU) baselines. Ablation studies further demonstrate the complementary strengths of the hybrid components, highlighting improved convergence behavior and robustness under varying demand distributions. Full article