Search Results (1,359)

To address the low-voltage violations that may occur in distribution networks with high penetration of distributed photovoltaic (PV) during sunset and evening peak periods under extreme high-temperature conditions, this paper establishes a source–grid–load electro-thermal coupling model that accounts for load thermal accumulation, transient conductor thermal inertia, temperature-dependent line impedance, and PV thermal derating. Based on a soft safety lower bound and a risk-preference utility function, the probability of voltage violation, violation depth, and expected violation duration are introduced to construct node-level and system-level comprehensive risk factors. The cumulant method combined with the Cornish–Fisher expansion is used to reconstruct the probability distribution of nodal voltages, enabling analytical risk calculation. Simulation results on the IEEE 33-bus system at 45 °C show that the proposed method can quantitatively reflect the temporal variations of nodal voltage distributions, physical violation depth, dimensionless severity utility, and expected violation duration, and identify weak nodes in the later part of the evening peak, providing a reference for risk early warning in distribution networks under extreme heat. Full article

The increasing integration of renewable generation introduces highly stochastic operating conditions, substantially enlarging the operating space and posing severe computational challenges for traditional iterative power flow solvers. To address this, we propose a Physics-Informed Graph Attention Network (PI-GAT) for fast and physically consistent power flow assessment in power systems with high renewable penetration. PI-GAT represents buses and branches as graph-structured inputs and employs edge-aware multi-head attention to adaptively capture electrical interactions between connected nodes. By embedding AC power flow equations as residuals in the training loss, PI-GAT promotes physical consistency, improving nodal power balance consistency even under high renewable variability and N−1 contingency scenarios. Experimental results on IEEE 30-bus and 118-bus systems demonstrate that PI-GAT reduces active and reactive power mismatches by up to approximately 62% across the two benchmark systems relative to the edge-aware GAT baseline. This improvement in physical consistency is accompanied by a modest increase in point-wise voltage and phase-angle errors. Moreover, PI-GAT achieves substantial inference-time speedups over conventional numerical solvers, especially under batched multi-scenario inference. These findings indicate that PI-GAT provides a reliable and efficient surrogate model for real-time security assessment and contingency screening in power systems with high penetration of renewable generation. Full article

This data descriptor presents CINES-REC-CITY, an open synthetic dataset providing high-resolution load profiles for energy community research. The dataset represents a typical German urban district with 70 apartments across eight multi-family buildings, including diverse socioeconomic characteristics. Three main components are provided at 15 min resolution for a full year: non-controllable residential electricity consumption for all apartments, charging profiles for 17 battery electric vehicles with trip information, and heat pump operation data for both variable-speed and hysteresis-controlled ground-source systems. All profiles were generated using validated bottom-up stochastic simulation models accounting for realistic user behavior, mobility patterns, and thermal building physics. The modular structure allows for selective combination of components, enabling investigation of different technology penetration scenarios. The dataset serves as a reference benchmark for reproducible research, allowing for direct comparison of optimization approaches, business models, and control strategies using identical underlying consumption patterns. It is suitable for techno-economic analysis, algorithm development for flexible load control, and grid impact assessment. All data is provided in CSV format with weather data for consistent extensions. Full article

Electric Vehicle Charging Stations (EVCSs) have become increasingly important due to the growing penetration of electric vehicles (EVs) and renewable-based power generation. However, challenges such as fluctuating renewable energy availability, increasing charging demand, power losses, operational cost, and charging delays continue to affect overall grid performance and stability. To address these issues, this study proposes a hybrid Goat Optimization Algorithm–Hierarchical Multi-Granularity Interaction Graph Convolutional Network (GOA–HMGIGCN) framework for intelligent smart grid energy management and EV charging coordination. The proposed framework combines the Goat Optimization Algorithm (GOA) for optimal EVCS placement and charging scheduling with the Hierarchical Multi-Granularity Interaction Graph Convolutional Network (HMGIGCN) for forecasting renewable generation, charging demand, and load variations. The framework was implemented and evaluated in MATLAB/Simulink R2024a using the IEEE 14-bus smart grid test system under varying operating conditions. Simulation results demonstrated that the proposed framework achieved superior performance compared with the Coot Optimization Algorithm–Fractional Backpropagation Physics-Informed Neural Network (COA-FBPINN), Dingo Optimization Algorithm–Convolutional Hypergraph Graph Neural Network (DOA-CHGNN), Self-Feedback Feedforward Artificial Neural Network (SFFANN), Deep Neural Network (DNN), and Golden Jackal Optimization–Attention-Based Probabilistic Convolutional Neural Network (GJO-APCNN) techniques by attaining the lowest operational cost of USD 1561, the highest efficiency of 99.2%, the minimum power loss of 10.6 kW, and the shortest charging time of 32 min. In addition, the proposed framework and overall grid reliability, confirming its effectiveness for intelligent renewable-integrated smart grid applications. Full article

Accurate day-ahead wind speed forecasting is essential for wind power forecasting and electricity market participation under increasing renewable energy penetration. This study proposes a hybrid forecasting framework that combines raw global forecasts from GFS and IFS, the KMA KIM-RDAPS regional forecast, and dynamically downscaled GFS/IFS forecasts generated with alternative boundary-layer physics. Seven forecast members were synthesized using arithmetic averaging, performance-weighted averaging, and LightGBM-based machine learning (ML) regression. The framework was evaluated over Jeju Island, Republic of Korea, using 10 m Automatic Weather Station observations from 2023 to 2024 and 80 m meteorological mast observations from 2023. For the AWS evaluation, 2023 was used for training and validation, and 2024 was reserved for independent testing. The site-specific LightGBM synthesis achieved the most consistent improvement, reducing the median site-wise MAE across 31 AWS sites to 0.90 m s⁻¹, corresponding to a 39.2% improvement relative to the best non-downscaled member and 47.2% relative to the unweighted multi-model mean. In the 80 m mast-based diagnostic assessment, the same approach reduced derived normalized power MAE to 11.4%. These results indicate that ML synthesis of multi-source NWP forecasts can improve day-ahead wind speed and power-oriented forecast information over complex island terrain. Full article

Sponge spicules have emerged as promising biomaterial scaffolds due to their biocompatibility and unique structural properties; however, achieving stable and bioactive functionalization remains a key challenge. The tripeptide GHK is known to promote collagen synthesis and wound repair, yet its therapeutic efficacy is often limited by rapid diffusion and instability. Here, we report ALTUM, a thiol-functionalized sponge spicule composite in which GHK is covalently conjugated via disulfide linkage to enable controlled and redox-responsive peptide delivery. ALTUM exhibited sustained GHK retention under physiological and storage conditions, while exposure to reduced glutathione (GSH) selectively accelerated peptide release through disulfide bond cleavage. This dual release behavior—long-term stability combined with reduction-triggered activation—distinguishes ALTUM from conventional delivery systems. The composite also demonstrated structural stability under thermal, cyclic, and photostability conditions. In an artificial human skin model, ALTUM enhanced dermal penetration of GHK and significantly increased collagen deposition in the dermal layer, demonstrating its capacity to promote collagen production within deeper skin tissue, compared to simple spicule–peptide mixtures. ALTUM was fabricated at an optimized spicule-to-peptide ratio of 3% (w/w), preserving the needle-shaped spicule morphology after surface modification. In vitro, ALTUM exhibited a sustained release profile, with GHK release markedly accelerated in the presence of 10 mM glutathione (GSH) compared with non-reductive conditions, reaching approximately 60% cumulative release over 35 days. In the bioprinted artificial human skin model, ALTUM delivered 9.72 ng/cm² of GHK, more than five-fold higher than the physical mixture of spicules and free GHK (1.9 ng/cm²), and significantly increased type I collagen expression in human dermal fibroblasts. Mechanistically, ALTUM-mediated delivery was associated with increased TGF-β expression and engagement of the SMAD signaling pathway, as indicated by increased phosphorylation of SMAD2/3, consistent with involvement of the TGF-β–SMAD axis in the observed collagen induction. Collectively, these findings establish ALTUM as a structurally stable, redox-responsive dermal delivery platform that enhances collagen synthesis and skin regeneration. Full article

Bridge decks are continuously subjected to high environmental exposure, traffic loading, and material aging, leading to progressive delamination which can negatively affect structural integrity and public safety. More specifically, subsurface delamination of concrete and corroded steel reinforcement must be repaired to keep the decks operational. Among non-destructive evaluation techniques, Ground-Penetrating Radar (GPR) and Infrared Thermography (IRT) offer complementary capabilities for detecting subsurface and near-surface defects; however, effective GPR-IRT data fusion remains challenging due to fundamental differences in sensing principles, spatial resolution and sensitivity. This study introduces a Physics-Enhanced Multi-Modal Fusion (PE-MMF) framework that integrates GPR and IRT data to improve delamination detection in reinforced concrete bridge decks. The proposed approach leverages transfer learning, cross-modal attention mechanisms, and gated fusion to enable robust learning from heterogeneous sensor inputs. Furthermore, a systematic feature selection protocol is integrated to identify physically meaningful indicators that remain consistent across different bridges, enhancing generalization capability. The framework is trained and validated using the publicly available SDNET2021 dataset, comprising co-registered GPR and IRT measurements from five in-service bridge decks with verified delamination ground truth. Results demonstrate substantial performance improvements, with average F1-score gains of up to 55% over IRT-based methods and 25% over GPR-based methods across all tested bridges. Comparative analysis against state-of-the-art methods confirmed the superior generalization capability of the proposed multi-modal approach over single-modality approaches. The findings highlight the potential of deep learning-based sensor fusion as a scalable and data-efficient decision-support tool to prioritize regions for detailed physical investigation during long-term infrastructure monitoring. Full article

Saimaluu-Tash I, located in a high-altitude glacial valley in Kyrgyzstan, preserves one of Central Asia’s largest and most culturally significant concentrations of rock engravings. Despite extensive archaeological research, the physical, mechanical, and chromatic properties of the sandstone substrates relevant for conservation assessment remain poorly characterized. This study integrates petrographic microscopy, scanning electron microscopy, colorimetry, and Vickers hardness testing with the digital documentation of twelve engraved blocks to evaluate weathering processes, engraving practices, and long-term preservation. The engravings are carved into arkosic sandstone with carbonate cement, characterized by a weathered surface enriched in clay minerals and covered by a dark surface coating (patina). Weathered surfaces exhibit significantly lower hardness (0.6 ± 0.2 GPa) than unweathered stone (2.8 ± 0.6 GPa), which facilitated the engraving of the petroglyphs by allowing tools to penetrate more deeply into the stone. Colorimetric analyses reveal a strong chromatic contrast between the surface patina and the lighter sandstone exposed by engraving (ΔE ≈ 22.7). This contrast would have enhanced the original visibility of the petroglyphs and highlights potential conservation issues associated with the progressive reformation of this surface layer. Iconographic analysis identifies recurrent themes related to hunting, herding, mobility, animal management, and symbolic spatial practices within a nomadic high-mountain landscape. Overall, the results demonstrate how an integrated material and interpretative approach contributes to understanding rock art production processes. They support preventive and sustainable conservation strategies for vulnerable engraving landscapes shaped by long-term interactions between geological processes and human activity. Full article

Deep learning has been widely applied to denoising ground-penetrating radar (GPR) signals. However, most existing methods lack physical constraints consistent with GPR data characteristics, especially in the frequency domain, leading to the loss of weak reflections and blurred reconstruction. Conventional networks also treat GPR denoising as a generic image restoration task without explicit weak-signal enhancement. To address these issues, this paper proposes a frequency-domain multi-scale loss function to introduce physical constraints into network training. Combined with traditional loss functions, the proposed method effectively improves the fidelity of weak reflection recovery. A multi-domain constrained TransUNet is further developed for GPR noise reduction. Experiments on synthetic data and field GPR data demonstrate that the proposed method achieves stronger robustness and competitive denoising performance. Full article

High-resolution soil moisture monitoring in a lysimeter requires precise Ground-Penetrating Radar (GPR) systems that can provide clean time-domain data for a Full-Waveform Inversion (FWI) algorithm. Using high-speed Radio Frequency System-on-Module (RFSoM) devices provides flexibility in signal generation. To optimize such a system, an appropriate transmit waveform and processing pipeline need to be selected. This paper presents a performance evaluation of three GPR waveforms—impulse, Stepped-Frequency Continuous Wave (SFCW) and non-linear Frequency-Modulated Continuous Wave (FMCW/chirp)—on the same hardware setup. To ensure a fair comparison, all waveforms were tested under an identical total measurement time. Numerical simulations were performed using an electromagnetic model of the system. Physical validation was conducted in an anechoic chamber using a 4 GS/s RFSoM setup and planar elliptical dipole antennas. Simulations showed that both sinewave-based methods provide better signal-to-noise ratios (SNRs) than the impulse GPR, with the non-linear chirp achieving the best results (20.7 dB improvement compared to impulse). Experimental measurements supported these results, showing better SNR across the frequency band for the SFCW and chirp waveforms. Because of its high SNR and simple hardware implementation, the non-linear chirp was identified as the most suitable waveform for this RFSoM-based GPR system. Full article

The increasing generation of waste toner from printers and photocopiers presents significant environmental and disposal challenges. This study investigates the feasibility of utilizing waste toner as a modifier in asphalt binder to enhance performance and sustainability. Bitumen with a penetration grade of 60/70 was modified with waste toner at varying contents (0–30%). The modified binders were evaluated using penetration, ductility, and softening-point tests to assess their physical behavior. Results indicate that increasing toner content reduces penetration and ductility while improving the softening point, indicating enhanced temperature resistance. Furthermore, asphalt mixtures were evaluated using both destructive (Marshall stability) and non-destructive testing (ultrasonic pulse velocity) methods to provide a comprehensive performance assessment. In addition, an artificial neural network (ANN) model was developed to predict and evaluate the performance of toner-modified mixtures. The findings demonstrate that waste toner can be effectively used as a sustainable modifier in asphalt mixtures, thereby improving material performance and reducing environmental impact. Full article

The high penetration of renewable energy has exposed integrated energy systems (IES) to stronger source-load uncertainties. Traditional scheduling methods that primarily pursue economic optimality often fail to account for system regulation margins, which may lead to excessive charging and discharging of energy storage systems, frequent fluctuations in unit output, and insufficient supply–demand matching capability under uncertain operating scenarios. To address these issues, this paper proposes a Flex-TD3 optimal scheduling method for IESs with embedded flexibility rules. First, a regional IES model incorporating photovoltaic generation, wind power, micro-gas turbines, gas boilers, electric chillers, waste heat recovery units, heat exchangers, and battery energy storage systems is established to describe the coupling relationships among electricity, heat, cooling, and gas flows, as well as the operational constraints of key devices. Second, active regulation flexibility indicators are constructed from the perspectives of system upward regulation capability, downward regulation capability, energy storage state health, and electro-thermal decoupling regulation margin. A comprehensive flexibility score is then formulated to characterize the system’s capability to cope with renewable energy fluctuations and load disturbances under the current operating state. Third, the flexibility indicators are embedded into the state space and reward function of the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, and a rule-based physical feasibility mapping mechanism is introduced to modify the raw scheduling actions generated by the agent according to device operational constraints, thereby enhancing the physical consistency and operational safety of the scheduling strategy. Case study results show that, compared with traditional optimal scheduling methods, the proposed method achieves better overall performance in terms of training convergence speed, operational economy, and scheduling stability. It can effectively reduce system operating costs, improve renewable energy accommodation capability, and decrease renewable energy curtailment, supply shortages, and constraint violations. Under uncertain scenarios involving renewable energy prediction errors, load disturbances, and high renewable energy penetration, the proposed method still maintains favorable scheduling performance, demonstrating its effectiveness and robustness. Full article

Biofilms are structured communities of microorganisms embedded in a self-produced extracellular polymeric substance (EPS) matrix, whose development significantly enhances microbial resistance to antibiotics, disinfectants, and host immune defenses, posing major challenges in clinical, industrial, and environmental settings. Compared with planktonic cells, biofilm-associated microorganisms can exhibit up to 10- to 1000-fold increased tolerance to antimicrobial agents, contributing to the persistence of biofilm-associated infections (BAIs). These infections remain difficult to eradicate due to reduced penetration, altered metabolic states, and the presence of dormant or persister cells. Anti-biofilm strategies can be broadly classified into physical approaches (e.g., ultrasound, mechanical stress, and light-based approaches) that target biofilm structure; chemical and enzymatic methods (e.g., EPS-degrading enzymes) that destabilize the matrix; and biological and molecular strategies (e.g., quorum-sensing (QS) inhibitors, anti-virulence agents, bacteriophages, phage-derived antimicrobial molecules, antimicrobial peptides, and natural bioactive compounds) that modulate biofilm development and integrity by targeting regulatory pathways and matrix stability through distinct mechanisms of action. Natural compounds, including lactoferrin, lactoferrin-derived peptides, and probiotic and postbiotic fractions of lactic acid bacteria (LAB), as well as plant-derived metabolites, have shown promising anti-biofilm effects, with efficacy often enhanced through complementary or potentially synergistic interactions. However, despite these advancements, clinical translation remains limited. For example, BAIs account for approximately 80% of chronic infections, with high recurrence rates and therapeutic failure reported in device-associated infections and chronic wounds. These limitations highlight the need for clinically translatable, multimodal approaches that integrate structural biofilm disruption, antimicrobial targeting, and host response modulation to design more effective and sustainable anti-biofilm strategies. Full article

High penetration of distributed photovoltaic (PV) generation has intensified voltage violations and stochastic voltage fluctuations in distribution networks, while existing voltage–var control methods still have limitations in terms of communication dependence, scalability, and edge deployment. To address these issues, this paper proposes a stability-constrained voltage–var self-optimizing control method for distribution networks based on the Bandit-Guided Online Self-Tuning Group Relative Policy Optimization (BOST-GRPO) algorithm. First, based on the LinDistFlow linearized power-flow model, a communication-free, decentralized, and locally observable reinforcement learning control environment is constructed, enabling each node to independently generate reactive power regulation commands using only local voltage measurements. Second, a contraction-mapping-based stability constraint is embedded into the policy output layer, theoretically guaranteeing the local exponential convergence of nodal voltage deviations around the equilibrium point and reducing the risk of voltage instability caused by overly aggressive policy actions. Meanwhile, device capacity constraints are incorporated into the policy output through a tanh-based action mapping, ensuring the physical feasibility of control commands. On this basis, BOST-GRPO realizes the online self-tuning of key hyperparameters within a single training process through a Bandit-guided mechanism, thereby avoiding the repeated training overhead caused by traditional offline hyperparameter tuning. Simulation results on the IEEE 33-bus system show that the proposed method outperforms benchmark reinforcement learning algorithms in final test cost, voltage deviation suppression, steady-state error, and regulation speed. Further tests under sensitivity matrix mismatch, different initial voltage disturbance intensities, and the extended IEEE 69-bus system demonstrate that the proposed method achieves good robustness and scalability. Full article

Hydraulic fracturing in naturally fractured shale reservoirs commonly generates complex mesh-like fracture networks governed by hydraulic fracture–natural fracture interactions, which strongly affect stimulated volume, fracture connectivity, and early-time production. Existing simulation and monitoring-based methods often cannot simultaneously capture interaction mechanisms, rapidly generate field-scale fracture networks, and validate production responses. This study proposes a mechanics-constrained recursive propagation framework. A field-constrained stochastic natural-fracture model is first constructed, an explicit hydraulic fracture–natural fracture interaction criterion is incorporated to identify penetration, opening, and shear slipping, and a fully vectorized bidirectional recursive algorithm is developed to efficiently generate complex fracture networks. The method is applied to a 40-stage fractured horizontal well in the Changqing Oilfield, where the target interval has a porosity of 6.1%, a permeability of 0.1 mD, and a horizontal stress contrast of 7.0 MPa. The simulated network reproduces crossing, arrest, unilateral diversion, and bilateral diversion, and agrees well with microseismic observations. EDFM-based fully implicit flow simulation further shows early-time production deviations of 2–10%. These results demonstrate that the proposed framework can efficiently generate physically plausible field-scale fracture networks for fracturing design, post-fracturing evaluation, and short-term production forecasting. Full article