Search Results (6,759)

Low-carbon scheduling of regional integrated energy systems (RIES) based only on energy-balance models may overlook the physical operating limits of distribution, gas, and heating networks, resulting in a gap between scheduling outcomes and actual operating boundaries. To address this issue, this paper proposes a framework integrating bi-objective scheduling, three-network posterior verification, and closed-loop repair. A mixed-integer linear programming model is first formulated with operating cost and carbon emissions as the two objectives, and an adaptive ε-constraint strategy is used to improve the characterization of the compromise region on the Pareto front. Posterior verification models are then established for the distribution, gas, and heating networks to assess the physical feasibility of representative solutions. When infeasibility is detected, a boundary-shrinking repair mechanism is triggered to iteratively update the scheduling boundaries. Case results show that the adaptive refined strategy improves the resolution of the compromise region by 3.2 times with only a 20.4% increase in computational time. Compared with the cost-optimal solution, the carbon-optimal solution reduces carbon emissions but increases peak purchased electricity from 7.333 MW to 11.1 MW, further tightening the lower-voltage margin of the distribution network. The results show that posterior physical verification and closed-loop repair provide additional support for evaluating and improving the engineering feasibility of RIES scheduling solutions. Full article

The increasing complexity and extended operational lifetimes of modern space infrastructure have significantly intensified the demand for reliable structural health monitoring (SHM) systems. However, the extreme space environment, characterized by radiation exposure, microgravity, ultra-high vacuum, and severe thermal cycling, imposes critical limitations on conventional electrical sensing technologies, leading to reduced measurement accuracy, instability, and long-term degradation. This review presents a comprehensive analysis of fiber Bragg grating (FBG)-based sensing technologies as a promising solution for deformation monitoring in space infrastructure. The study investigates the fundamental operating principles of FBG sensors under space conditions and systematically classifies existing FBG-based SHM architectures, including point-based, multiplexed, long-distance, and hybrid sensing systems. Furthermore, the advantages of FBG sensors—such as immunity to electromagnetic interference, passive operation, and high-resolution multipoint sensing—are critically evaluated in comparison with traditional electrical sensors. In addition, key challenges affecting the performance of FBG systems in space environments are analyzed, including radiation-induced wavelength drift, temperature–strain cross-sensitivity, signal attenuation, and long-term stability issues. The paper also highlights recent advances in interrogation techniques and network architectures that enable reliable in situ and real-time deformation monitoring of space structures. The results demonstrate that FBG-based sensing systems provide a scalable and robust framework for SHM in extreme environments while also revealing existing limitations and open research challenges. This work establishes a structured foundation for the development of next-generation intelligent monitoring systems for space infrastructure. Full article

This work introduces a compact multi-resonant metamaterial absorber designed to achieve efficient electromagnetic absorption over several microwave frequency bands. The proposed configuration is based on a hybrid resonator arrangement that promotes strong electromagnetic interaction and enables multiple resonant modes within a single unit cell. Consequently, six distinct absorption peaks are obtained at 2.4, 5.21, 6.88, 9.77, 12.61, and 14.99 GHz, covering S-, C-, X-, and Ku-band applications. The absorber exhibits high absorption performance, exceeding 97% across most operating frequencies and slightly lower value is observed of 91.13% at 12.61 GHz, which indicates effective impedance matching with free space and efficient energy dissipation mechanisms. The absorption characteristics are further examined through surface current distributions, electric field confinement, and effective medium analysis, demonstrating that the multi-band response originates from the interaction of multiple resonant elements and intrinsic material losses. Moreover, the proposed structure maintains stable performance for different polarization angles and oblique wave incidence, confirming its polarization-insensitive and angularly stable behavior. To validate the design, a prototype is fabricated and experimentally characterized using a free-space measurement setup, showing close agreement with the simulated results. The compact geometry, low fabrication cost, and scalability of the proposed absorber make it a promising candidate for applications such as electromagnetic interference mitigation, radar cross-section reduction, and modern wireless communication systems. Full article

Localized mechanical loading induces complex elastic–plastic interactions in anisotropic crystalline materials. However, quantitative orientation-resolved characterization of residual relative elastic strain heterogeneity remains limited. In this study, high-resolution electron backscatter diffraction was used to map residual in-plane relative elastic strain distributions beneath micro-indents in two annealed body-centered cubic ferritic non-oriented electrical steels, B35AV1900 and 35WW300. Grains oriented near (001), (101), and (111) were analyzed to evaluate the crystallographic effects on residual strain accommodation. Frequency distributions of the in-plane residual relative elastic strain components were constructed, and full width at half maximum values were extracted to quantify strain heterogeneity. The results revealed a pronounced orientation dependence. Near-(001) grains exhibited greater indentation depths and more widely distributed post-indentation deformation features. By contrast, near-(111) grains showed broader residual in-plane relative elastic strain distributions in both alloys. These results indicate that residual strain heterogeneity after unloading is influenced not only by indentation depth but also by crystallographic constraint and orientation-dependent strain redistribution. This study establishes a quantitative orientation-resolved framework for characterizing residual relative elastic strain heterogeneity beneath localized loading. It also provides a basis for linking crystallographic anisotropy, localized deformation, and residual strain redistribution in ferritic electrical steels. Full article

The shift toward more electric aircraft has intensified thermal management challenges due to increased heat load from electrical actuators, power electronics and energy storage systems concentrated within confined fuselage bays. A Conventional Environmental Control System (ECS) alone is not sufficient to dissipate such high localized heat loads. This creates the need for innovative heat dissipation and heat reuse strategies. This paper presents two thermal management concepts evaluated at the Fraunhofer Flight Test Facility. The first, developed in the ORCHESTRA project, integrates a bilge skin heat exchanger with modified ventilation to dissipate elevated heat loads. The second, under investigation in the TheMa4HERA project, focuses on reusing avionics heat to warm the FWD cargo hold, thereby reducing ECS power demand. Both concepts depend on convective heat exchange, characterized using Fraunhofer’s Convective Heat Transfer Meter (CHM) to determine key heat transfer coefficients. In parallel, an aircraft-level thermal model was developed, validated against experimental data and subsequently used for virtual demonstration of a ground test scenario. Full article

Accurate demagnetization fault diagnosis is critical to ensuring the safety and reliability of permanent magnet synchronous motors (PMSMs). However, the number, location, and severity of demagnetized permanent magnets are mutually coupled, leading to a combinatorial explosion of fault patterns. Existing methods are largely limited to idealized assumptions involving single-magnet demagnetization or uniform demagnetization of multiple magnets, making it difficult to characterize the random nature of demagnetization in practical operation. Thus, this paper proposes a precise demagnetization fault diagnosis method based on a novel search coil (SC) configuration, in which only two toroidal-yoke-type search coils are installed in the stator slots. The proposed method partitions the rotor permanent magnets into several modules and categorizes the infinite demagnetization fault patterns into 26 representative patterns, effectively addressing the issue of fault mode explosion. Theoretical analysis and experimental results show that the voltage waveforms of the search coil over a single electrical period exhibit significant and stable differences across the identified patterns. By constructing feature vectors based on these differences, a physically interpretable mapping between the feature vectors and fault patterns is established. Combined with a corresponding pattern recognition algorithm, the proposed method enables fast and accurate differentiation of the 26 patterns without the need for complex machine learning models, thereby achieving precise localization of demagnetized permanent magnets. Simulation and experimental results verify the correctness and effectiveness of the proposed method. Full article

The widespread adoption of photovoltaic systems in residential electrical installations has increased the importance of Automatic Transfer Switches (ATSs) for ensuring power continuity during grid outages. However, many low-cost ATS solutions available on the market prioritize economic efficiency over operational safety, leading to significant risks under fault conditions. This paper investigates a real overvoltage incident in a residential three-phase installation equipped with a photovoltaic inverter and an ATS, which resulted in the failure of multiple electronic loads. The study reconstructs the event and demonstrates that the loss of the neutral conductor during backup operation caused severe phase voltage imbalance, generating overvoltage conditions across lightly loaded phases. A simplified electrical model is used to explain current paths and voltage redistribution under asymmetric loads, highlighting the critical role of correct neutral switching in ATS design. Two commercially available ATS architectures, one based on a changeover-contact mechanism and one employing four-pole miniature circuit breakers, are experimentally evaluated. The evaluation reveals major design deficiencies, including the absence of protective elements for control circuits, reliance on mechanical end-position limiters, and the use of switching devices not intended for frequent source transfer. These shortcomings introduce risks such as uncontrolled actuator operation, overheating, mechanical damage, and potential fire hazards. To overcome these limitations, a new ATS architecture was developed using a phase-monitoring relay, interlocked ABB contactors, and dedicated fuse protection for all control circuits. Detailed laboratory measurements were conducted to characterize contactor switching times and internal relay command delays. By optimizing the command sequence, the proposed ATS achieves predictable, fault-tolerant operation with competitive transfer times, representing a meaningful safety improvement over the evaluated commercial alternatives. The proposed solution is scoped to three-phase residential installations equipped with a hybrid photovoltaic inverter providing a dedicated backup output, operating within TN-S or TN-C-S earthing systems with a maximum grid connection capacity of 21 kW. Full article

Lithium-ion battery thermal runaway has become a key safety hazard restricting the development of electric vehicles. Early precursor signals of thermal runaway are characterized by multi-scale features, weak signal strength and spatial coupling, posing significant challenges for traditional methods in achieving accurate early warning. To solve this problem, a multi-scale spatiotemporal attention network (MSTA-Net) is proposed for battery thermal runaway early warning. First, a systematic feature engineering process is designed, including signal denoising, normalization processing and multi-level feature construction, to fully extract discriminative information from voltage and temperature signals. Then, the MSTA-Net architecture is constructed, which includes three parallel feature extraction branches: local fine perception branch based on 1D depthwise separable convolution to capture transient anomalies, a temporal evolution modeling branch based on bidirectional gated recurrent units to learn long-term trends, and a global spatial dependence branch based on a graph attention network to model the spatial propagation of thermal runaway. Finally, an adaptive fusion gate is designed to dynamically fuse the features of each branch according to the input context. The experimental results on the self-built battery thermal runaway dataset show that the proposed MSTA-Net achieves a recall rate of 98.7%, an average early warning time of 115 s and a false alarm rate of 0 times/h. Compared with traditional machine learning and deep learning models such as Random Forest, LSTM and Transformer, the model has significant advantages in early warning accuracy, timeliness and robustness. Ablation experiments verify the effectiveness of each component of the MSTA-Net. The proposed method can provide reliable early warning of thermal runaway only by using the existing voltage and temperature sensors of the battery management system, which has important engineering application value. Full article

Background: Myocardial fibrosis (MF) is a dynamic remodeling process characterized by excessive extracellular matrix (ECM) deposition, fibroblast activation, and dysregulated matrix turnover. Although initially reparative, persistent fibrotic remodeling promotes myocardial stiffening, electrical instability, and progressive cardiac dysfunction across diverse cardiovascular diseases. Circulating biomarkers reflecting collagen synthesis, degradation, proteolytic regulation, and inflammatory activation have emerged as potential tools for assessing fibrotic activity and risk stratification. Methods: This targeted narrative review was based on manually guided searches of PubMed and Scopus, supplemented by citation chaining and inclusion of landmark mechanistic and translational studies. Publications addressing myocardial extracellular matrix remodeling, circulating fibrosis-related biomarkers and imaging-derived fibrosis phenotypes were selected for qualitative synthesis. Results: Myocardial fibrosis reflects interconnected inflammatory, neurohormonal, oxidative, and extracellular matrix remodeling pathways. Among circulating biomarkers, C-terminal propeptide of procollagen type I (PICP) showed the most consistent association with myocardial collagen burden and adverse outcomes, whereas carboxy-terminal telopeptide of type I collagen (CITP), matrix metalloproteinases (MMPs), tissue inhibitors of metalloproteinases (TIMPs), galectin-3, osteopontin, soluble suppression of tumorigenicity 2 (sST2), and natriuretic peptides provided more context-dependent signals. Standalone interpretation remains limited by restricted cardiac specificity, renal dysfunction, systemic inflammation, assay heterogeneity, and lack of standardized thresholds. Integration with cardiac magnetic resonance (CMR)-derived late gadolinium enhancement (LGE), T1 mapping, and extracellular volume (ECV) may improve biological and structural phenotyping. Conclusions: Circulating biomarkers capture complementary dimensions of myocardial remodeling but cannot replace structural imaging. We propose an updated, hypothesis-generating biomarker–imaging framework integrating inflammatory activation, collagen turnover, matrix quality, hemodynamic stress, and structural imaging to support phenotypic stratification and future validation of antifibrotic strategies. Full article

The low-pressure environment at aircraft cruising altitudes severely degrades lithium battery performance, yet the effectiveness and mechanisms of air-cooling thermal management under such conditions remain poorly understood. This study systematically investigates the coupled thermal, electrical, and material responses of NCM523/graphite ternary batteries under forced air-cooling at three pressures (96 kPa, 77 kPa, 58 kPa) and varying wind speeds (0–10 m/s) during 4C charge/6C discharge cycling. Air cooling reduces the maximum surface temperature by up to 14.2 °C and maintains the temperature difference below 5 °C, even at 58 kPa. An optimal wind speed of 6 m/s extends cycle life by 71% at 58 kPa (from 45 to 77 cycles), suppresses resistance growth, and preserves discharge capacity. Further increasing the wind speed paradoxically accelerates degradation. Post-mortem analyses reveal that appropriate air cooling mitigates cathode particle fragmentation, restores cation mixing (I₀₀₃/I₁₀₄ from 1.07 to 1.63 for 58 kPa), reduces transition metal dissolution, and suppresses solid electrolyte interface (SEI) thickening. This work establishes an optimum air velocity for low-pressure battery cooling and provides mechanistic insights into preserving electrode structural integrity, offering design guidelines for safe battery thermal management in electric aircraft. Full article

The skeletal muscle (SkM) secretome has been widely studied since the establishment of its endocrine function. Extracellular vesicles (EVs) are the most recently identified elements of the SkM secretome. These nano-sized lipid-bound vesicles carry molecular cargo and function as a means of intercellular communication. The effect of exercise on SkM EV micro-RNA cargo (miRNAs) remains a challenge to elucidate. Electrical pulse stimulation (EPS) was applied to C2C12 myotubes at high (30 Hz) and low (2 Hz) frequencies. EVs released during 10 h of stimulation were isolated and characterized and used to treat myoblasts. Their miRNA cargo was sequenced. EVs were used to treat myoblasts (2.19 × 10⁸ EVs per mL) to determine the effects on myoblast migration and differentiation. Sequencing revealed over 300 known miRNAs packaged into myotube EVs. Many were differentially expressed after EPS, either positively or negatively. Muscle-important miRNAs were present (miR-206 was 4.8-fold more prevalent than any other miRNA). EV treatments improved myoblast migration and differentiation without a frequency-specific influence. Gene Ontology analysis based on differentially expressed miRNAs between control and EPS-EVs indicates an effect of EPS frequency on muscle EV signaling. Full article

The growing penetration of electric logistics vehicles (ELVs) poses a significant challenge to electric utility site selection. This paper addresses the problem of joint site selection for electric logistics vehicle charging and swapping stations (CSSs). First, a joint site selection model is introduced to characterize the problem, and an improved genetic algorithm (IGA) is designed to solve this model. Derived from the standard genetic algorithm (SGA), the IGA incorporates local search operations, evolutionary inversion operations, and an elitist preservation strategy to enhance performance. On this basis, small-scale numerical simulations are conducted to determine the optimal parameters, thereby guaranteeing optimal algorithmic efficiency. Subsequently, large-scale numerical simulations are performed, with key indicators recorded including the optimal routing length, battery replenishment frequency, number of stations, number of ELVs, and solution time. Finally, analysis across three efficiency levels demonstrates that joint siting improves distribution efficiency by 39.38%, increases grid electricity sales by 46.89%, and reduces total transportation costs by 26.28%, with the optimization scheme validated across six different numerical scenarios. Overall, the joint site selection proposed in this paper has enhanced the benefits of relevant stakeholders and provided a reference for building a low-carbon transportation chain. Full article

As modern power systems undergo a paradigm shift toward decentralization, driven by substantial investments in Distributed Energy Resources (DERs), Virtual Power Plants (VPPs) have emerged as the primary mechanism for their effective technical and commercial integration. This paper provides a seminal and comprehensive literature review, dissecting the VPP ecosystem through operational, infrastructural, and coordination strategy perspectives. By categorizing VPPs into distinct technical and commercial frameworks, this study critically evaluates their role in optimizing smart grid components, including demand response, multifaceted market structures, cooperative game-theoretic behaviors, and multi-carrier energy systems. The analysis transcends basic infrastructure, focusing on the resolution of fundamental challenges: mitigating carbon emissions and energy costs, characterizing generation uncertainty and asynchrony, and maintaining the dynamic equilibrium between supply and demand. Furthermore, the review explores advanced strategies for incentivizing prosumer engagement, enhancing market pricing transparency, and ensuring transaction integrity within rigorous operational constraints. A significant methodological evolution is identified, highlighting the transition toward advanced mathematical frameworks and data-driven optimization techniques designed to enhance system resilience and operational stability under multifaceted uncertainties. The synthesis reveals that VPP-led sector coupling integrating electricity, thermal, and hydrogen vectors provides a robust pathway for minimizing grid imbalances and diminishing the overall carbon footprint. By evaluating the subject through a multidimensional lens—technical, economic, environmental, and regulatory—this study serves as a critical reference and strategic roadmap for researchers, planners, and policymakers aiming to navigate the complexities of future smart grids and build a sustainable energy ecosystem. Full article

Flat bands exhibit vanishing group velocity and marked sensitivity to lattice geometry, making them a useful setting for studying localization driven by destructive interference. In this work, electrical-circuit simulations are employed to investigate flat-band systems in one, two, and three dimensions. A one-dimensional two-band circuit is first considered, and its flat-band response is characterized through node-to-ground impedance spectra and steady-state voltage distributions. The analysis is then extended to two- and three-dimensional Lieb lattice circuits characterized by sublattice imbalance. In the two-dimensional Lieb circuit, the flat band touches the dispersive bands at a Dirac point, so hybridization with dispersive modes affects the observed localization. Under periodic boundary conditions, wave vector quantization also produces responses that depend on whether the number of unit cells is even or odd. By contrast, in the three-dimensional Lieb circuit, the flat band is spectrally isolated from the dispersive bands, allowing stronger spatial confinement and clearer sublattice selectivity. The one-dimensional, two-dimensional, and three-dimensional models therefore represent three different situations: a singular flat band, a flat band that touches dispersive bands, and a spectrally isolated flat band. Comparing these cases shows how different degeneracy conditions shape impedance responses and localization patterns in electrical circuit systems. At the flat band frequency, the localized voltage response can also be used to generate spatial patterns in both two-dimensional and three-dimensional circuits, pointing to a possible route for spatial mode control of compact localized states in electrical systems. Full article

This paper presents a novel Adaptive Sliding Mode Control (ASMC) strategy for nonlinear multivariable systems subjected to parameter uncertainties and external disturbances. The proposed control scheme guarantees robust and smooth state convergence via an adaptive mechanism that dynamically adjusts the switching gain. Unlike conventional SMC techniques, this adaptive formulation effectively mitigates the chattering phenomenon through a continuously updated boundary layer and eliminates the need for prior knowledge of the uncertainty bounds. The effectiveness of the synthesized controller is validated on an autonomous electric vehicle (AEV) platform, a system characterized by strong dynamic coupling. MATLAB/Simulink (version 2022b) simulations are conducted under various operational scenarios, including load variations and strict trajectory tracking. Comparative results with a traditional SMC demonstrate superior convergence, significant chattering reduction, and an optimized energy consumption profile, leading to a 22% reduction in equivalent CO₂ emissions. This approach provides a viable and energy-efficient control framework for modern autonomous EVs. Full article