Search Results (8,198)

Cosmological filaments, galaxy clusters, and galaxies are magnetized reservoirs of cosmic rays (CRs). The exchange of CRs across these structures is usually modeled assuming that they remain charged and magnetically confined. At high energies, hadronic interactions can convert CR protons to neutrons. This physics is routinely included in air-shower and ultra-high-energy (UHE) CR propagation Monte Carlo simulations used for composition studies but is rarely treated explicitly in propagation models of CR transport and exchange between magnetized reservoirs. CR neutrons are not affected by magnetic fields and can propagate ballistically over kpc-Mpc distances before decaying back into protons, with relativistic time dilation extending their effective decay length. We show how such charged–neutral switching modifies CR confinement and escape in four representative environments: a Milky Way-like galaxy, a starburst galaxy, a galaxy cluster, and a cosmological filament. By solving the transport of a confined CR proton population in each structure using a diffusion/streaming propagation approach with hadronic pp and p$\gamma$ interactions, and treating neutron production and decay as a stochastic Poisson “jump” process, we find that neutron-mediated steps can allow additional CR escape from large-scale cosmological structures at energies where charged-particle transport alone would predict strong CR confinement and attenuation in ambient radiation fields. These effects imply a qualitative shift in how ultra-high-energy CRs are transferred from embedded sources into filaments and voids once intermediate neutron propagation is considered, with consequences for the partitioning of CRs across the large-scale structure of the Universe. Full article

Long-fiber thermoplastic (LFT) materials are a versatile category of composite materials that can be directly compounded (LFT-D) in twin screw extruders and compression molded. Originating in the automotive sector, the LFT-D process is becoming increasingly attractive for other industries where low cycle times, lightweight performance and recyclability are required. The purpose of this work is to summarize mechanical properties and findings from the investigations into LFT-D process–microstructure–property relationships and present a design of experiments (DoE) study based on the current state of the art. Primary parameters from LFT-D compounding, screw speed, fiber roving amount and polymer throughput m_p are chosen as DoE factors. Polyamide 6 (PA6) is reinforced with a glass fiber (GF) mass fraction w_f between w_f = 20% and w_f = 60%. Tensile, flexural and impact properties are chosen as DoE output parameters, characterized and discussed in relation to the state of the art. The unique microstructure of LFT-D materials, especially the existence of a charge and flow area as well as the fiber migration, is considered in the discussion. All mechanical properties characterized have a linear relation to w_f. This study demonstrates the interactive relationship between the main factors and w_f, which significantly influences the mechanical properties. This dependence of w_f on the DoE factors is accounted for in advanced response contour plots proposed in this work. Parameter recommendations for the screw speed are reported by ranges of w_f and polymer throughput for the goal of maximum mechanical properties or low coefficient of variations. At w_f < 30% a low screw speed is recommended to improve most mechanical properties as well as the coefficient of variation. Full article

Reliable estimation of the State of Health (SOH) in lithium-ion batteries is critical to battery system security and dependability. However, existing Physics-Informed Neural Networks (PINNs) have drawbacks like single-feature physical constraints, rigid fixed-weight fusion of multi-feature constraints and insufficient time-series degradation modeling. To solve these problems, this study proposes an Adaptive-Weight PINN (AW-PINN) optimized by the Grey Wolf Optimizer (GWO) algorithm, which features a dual-LSTM parallel structure and takes incremental capacity peaks and charged capacity as dual physical constraints. A weight generator LSTM adaptively learns weights for monotonicity losses without manual intervention, and GWO globally optimizes physical loss weights to balance data fitting accuracy and prediction physical consistency. Validated on ${\mathrm{LiCoO}}_2$, NCA, and NCM batteries from CALCE and Tongji University datasets via comparative, ablation, and small-sample experiments, AW-PINN shows superior predictive performance (average RMSE = 0.0076; MAE = 0.0065; MAPE = 0.0072), robustness, and generalization. It integrates battery degradation physics with deep learning, retaining strong fitting capability while enabling physical interpretability. Full article

Accurate and physically consistent state prediction is essential for shipboard power systems (SPS) operating under dynamic conditions. However, purely data-driven models often exhibit degraded robustness and physically inconsistent outputs when exposed to transient disturbances or limited data coverage. To address these limitations, this paper proposes a physics-constrained hybrid prediction model that integrates a convolutional neural network–bidirectional long short-term memory (CNN–BiLSTM) architecture with wide residual connections (WRC) and a physics-constrained loss (PCL). The proposed modeling approach combines real operational measurement data with high-resolution simulation data to enhance data diversity and improve generalization capability. The CNN–BiLSTM structure captures nonlinear temporal dependencies, while the WRC preserves critical low-level transient electrical features during deep temporal modeling. In addition, multiple physical constraints, including power balance, voltage conversion relationships, and battery state-of-charge (SOC) dynamics, are incorporated into the training process to enforce physically consistent predictions. The model is validated using charging and discharging experiments on a laboratory-scale SPS under both steady-state and transient conditions. Comparative results demonstrate that the proposed approach achieves higher prediction accuracy, improved dynamic stability, and faster recovery following disturbances compared with conventional data-driven models. These results indicate that physics-constrained deep learning provides an effective and interpretable modeling framework for SPS state prediction, supporting digital twin-oriented monitoring and real-time prediction applications. Full article

Stay cables are critical load-bearing components in cable-stayed structures, making corrosion distribution vital for damage diagnosis and maintenance. To address the insufficient characterization of circumferential multi-point corrosion distribution in stay cables, a theoretical model of circumferential multi-point defect magnetic charge for the stay cables was established, and a self-magnetic flux leakage experiment was conducted on 37-wire steel specimens with circumferential corrosion. The effects of corroded wire number (N), corrosion time (T), and circumferential angle number (K) on the axial B_x component of the magnetic flux leakage signal were analyzed. The relationship between the θ-B_x-max peak distribution and corrosion patterns was clarified. Quantitative models for corrosion number (c), center (θ_c), and the cross-sectional corrosion rate (α) were established. The results indicate that c improves the determination of the number of concentrated corrosion sites in the ‘peak platform’ corrosion distribution type. Based on the Lorentz fitting, the maximum prediction error of θ_c is 15.1%, and the prediction accuracy of the cross-sectional corrosion rate α exceeds 90%. The study provides a reference for the quantifiable characterization and evaluation methods of the circumferential multi-point defect distribution in stay cables. Full article

Lithium-ion batteries (LiBs) are fundamental to modern energy systems, particularly in electric vehicle (EV) applications, due to their high energy density, long cycle life, and low self-discharge characteristics. Accurate State-of-Charge (SoC) estimation is essential for ensuring reliable performance, efficient energy usage, and the safety of Battery Management Systems (BMSs). However, the nonlinear and time-varying characteristics of LiBs, along with the difficulty in directly measuring internal states, pose significant challenges for parameter identification and SoC estimation. This study presents an advanced approach based on the Weighted Mean of Vectors optimization algorithm to simultaneously identify the unknown parameters of an extended Thevenin Equivalent Circuit Model (ECM) and estimate the SoC. Unlike previous methods that use static parameters for specific battery modes, the proposed technique accounts for dynamic changes during both charging and discharging operations. The algorithm demonstrates superior adaptability by continuously adjusting model parameters to reflect real-time battery behavior under varying operational conditions. The algorithm also models the relationship between SoC and open-circuit voltage (Voc) using data collected from real lithium-ion cells tested under a controlled load profile in the laboratory. This experimental validation ensures the practical applicability and robustness of the proposed methodology. The simulation results confirm the effectiveness and precision of the proposed approach, showing excellent agreement between measured and estimated values, with minimal errors in both voltage and SoC prediction. The enhanced accuracy achieved through this dynamic parameter identification framework represents a significant advancement in battery state estimation technology. Full article

This study investigates the electro-thermal ageing (ETA) behaviour of neat polyamide-6 (PA6) and PA6/barium titanate (BTO) nanocomposites. Time–to–breakdown measurements were performed at 333 K, 353 K, and 373 K at field strengths between 50 and 90 kV/mm to assess the impact of nanofiller level on lifetime to failure. The ageing experiment showed that moderate amounts of nanofiller improved the electro-thermal endurance while excessive filler addition (20 wt.%) led to faster breakdown. The Dissado–Montanari–Mazzanti (DMM) model was used to evaluate the ageing parameters for neat PA6 and PA6/10 wt.% BTO across all three temperatures. Neat PA6 and PA6/10 wt.% BTO both showed nearly identical activation enthalpy (H/k) values, indicating that the intrinsic thermally activated ageing mechanism of PA6 is preserved in the nanocomposite. Variations in the field-sensitivity parameters (C/k and b) align with an interpretation involving changes in charge transport and interfacial trapping introduced by the addition of BTO. Furthermore, analysis of all filler concentrations confirmed that 1–10 wt.% BTO maintains or slightly improves the time to breakdown, while 20 wt.% significantly accelerated the ageing process. This research forms part of the research programme of DPI, project #852. Full article

The recycling of carbon fiber-reinforced polymers (CFRPs), particularly carbon fiber-reinforced epoxy polymers (CFREPs), is a challenging problem because of their broad application spectrum, the amount of laminates produced per year, and the cost per kg of the carbon fiber fabric. Recently, several papers were published on the recycling of CFREPs through solvothermal methods that allow the recovery of the carbon fiber fabrics with a relatively low environmental impact. In the present paper, for the first time, the effect of the presence of flame retardants is discussed. A carbon fiber-reinforced epoxy polymer (CFREP) charged with P-, Zn-, B- and Al-based flame retardants, supplied by the aerospace industry, was subjected to a double-step solvothermal treatment. The epoxy matrix was successfully dissolved in monoethanolammine after a preswelling step in acetic acid. The experimental results show that the proposed process allows the full recovery of the carbon fabric with its original sizing layer without injury to the fiber. As confirmation, CFREP laminates produced with the recycled carbon fiber fabrics exhibited mechanical properties close to that of laminates obtained from the virgin epoxy/carbon prepreg. Contrary to what is reported in the literature, the present paper also shows that, in the studied case, whilst acetic acid treatment promotes swelling, it also causes the formation of a degraded surface layer that would impede complete removal of the polymeric matrix and full recovery of the carbon fabric if only acetic acid was used. On the basis of the known mechanism of flame retardancy of phosphates and borates, the degraded layer formation is attributed to the acidic character of the acetic acid. It is worth pointing out that the paper suggests, therefore, that the presence of flame retardants may strongly affect the solvothermal processes. Full article

In urban driving cycles, battery electric vehicles are subject to frequent start–stop operations, which lead to substantial braking energy losses. Although fuzzy control (FC) strategies are commonly employed for regenerative braking, their performance is often constrained by subjectively defined membership functions and rules. To address this limitation, this paper proposes an improved FC strategy that is optimized using the particle swarm optimization (PSO) algorithm. Focusing on a front-wheel-drive BEV, a three-input single-output fuzzy controller is developed in accordance with ECE regulations, where braking intensity, battery state of charge (SOC), and vehicle speed serve as inputs, and the motor braking force ratio serves as the output. A co-simulation platform based on AVL-Cruise 2019 and Matlab/Simulink 2017a is established to evaluate the strategy under the New European Driving Cycle (NEDC) and the Worldwide Light Vehicles Test Cycle (WLTC). Additionally, hardware-in-the-loop (HIL) tests are conducted to validate the practical feasibility and accuracy of the optimized strategy. The results demonstrate that the PSO-optimized FC strategy achieves a performance in real-world controllers that is comparable to that observed in a simulation, confirming its real-time applicability. Specifically, under the NEDC, the optimized strategy reduces battery SOC from 0.90 to 0.8795, representing improvements of 0.2515% and 0.4670% over the unoptimized FC strategy and the ideal distribution strategy, respectively. The regenerative braking efficiency is enhanced by 2.45% and 10.48%. Under the WLTC, the final SOC with the optimized strategy is 0.8488, reflecting gains of 0.5202% and 0.8380% over the two reference strategies, while regenerative braking efficiency improves by 2.32% and 8.95%. These findings indicate that the proposed strategy offers a safe and effective solution for improving the regenerative braking performance in electric vehicles. Full article

We study axial perturbations of Reissner–Nordström black holes within the general framework of parity-violating modified gravity theories. We derive the governing equations for a class of frame-dragging perturbations, focusing on the symmetry structure and radial dependence of the perturbed metric component, describing its behavior across three distinct regions: near the singularity ($r\to 0$), between the inner and outer Reissner–Nordström horizons ($r_{-}<r<r_{+}$), and in the asymptotic exterior regime ($r\to \infty$). Using a combination of analytical and numerical methods, we analyze the solutions for varying black hole charge-to-mass ratios ($Q/M$) and angular momentum parameters (l). Key findings include the suppression of perturbations by the electromagnetic field for higher $Q/M$; the emergence of radial resonance-like behavior for specific l values; and a high degree of symmetry for solutions in the extremal limit ($Q/M\sim 1$), attributed to the AdS₂$\times S^2$ near-horizon geometry. The WKB approximation is employed to study the high-l regime, revealing quantized radial resonance modes and singular behavior in the extremal limit. Additionally, we explore the role of boundary conditions and the possibility of a Chern–Simons field $\mathsf{\Theta }$ as the source of the parity violation, showing that consistency and the behavior of the perturbations under time reversal demand a constant field (and thus no actually observable Chern–Simons effects) at leading order. These results provide a basis for further analysis of the stability and dynamical properties of charged black holes in parity-violating theories, with potential experimental signatures in gravitational wave observations. Full article

The removal of water pollutants, specifically the organophosphorus pesticides chlorpyrifos (CHP) and azinphos-methyl (AZM), as well as the dye Rhodamine B (RB), was investigated through the valorization of grape pomace, an abundant agricultural byproduct. For the first time, hydrochars derived from grape pomace were utilized as adsorbents for these contaminants following KOH activation (HCK) and pyrolysis at 400 °C (PHC). The study aimed to evaluate the adsorption performance, determine the optimal conditions, and elucidate the adsorption mechanisms. Physicochemical characterization using SEM, FTIR, BET surface area analysis, stability, and pH_PZC measurements revealed distinct differences in surface morphology, functional groups, porosity, and surface charge. Under optimized conditions, maximum adsorption capacities reached 751.0, 3.98, and 1.39 mg g⁻¹ for RB, CHP, and AZM, respectively, on HCK, and 616.0 (RB), 30.10 (CHP), and 9.15 mg g⁻¹ (AZM) on PHC, indicating that the selected hydrochars efficiently removed the investigated pollutants from water. Kinetic modeling demonstrated pseudo-first-order adsorption for RB and CHP on HCK and pseudo-second-order adsorption for AZM on HCK and all pollutants on PHC. Thermodynamic analysis confirmed that adsorption processes were spontaneous and favorable, with enhancements dependent on temperature. These findings suggest that HCK is particularly effective for cationic dyes, while PHC exhibits greater affinity toward organophosphorus pesticides, offering complementary applications and providing new mechanistic insights into hydrochar-based pollutant removal. Full article

Synchronously enhancing the light response range and electron–hole separation efficiency is essential to improve photocatalytic activity. Herein, we synthesized a Cu-doped ZnIn₂S₄ (ZIS) catalyst with S-vacancy (Cu_n-VZIS) via hydrothermal synthesis, incorporating sulfur vacancies and directionally substituting copper ions for zinc ions. The experimental results elucidate the synergistically photocatalytic mechanism associated with the two types of defects. Both the sulfur vacancies within the structure and the copper doping sites lead to a reduction in the size of the ZnIn₂S₄ unit cell. The sulfur vacancy traps electrons, thereby mitigating the recombination of photogenerated carriers. Meanwhile, the copper ions optimize the carrier migration pathways, enhancing the overall carrier separation efficiency. Consequently, Cu_1.5-VZIS demonstrates a stable and markedly enhanced photocatalytic hydrogen production activity, achieving a performance that is 7.5 times greater than that of pristine ZIS. Our study elucidates the effect of vacancy defects and ion doping on the photogenerated charge dynamics in ZIS, and paves a novel pathway for optimizing carrier dynamics through the concurrent utilization of both types of defects. Full article

Shared e-scooter systems are frequently characterized as zero-emission mobility solutions; however, lifecycle greenhouse gas (GHG) emissions depend on manufacturing, electricity generation, and operational logistics. While conventional life cycle assessment (LCA) studies quantify environmental impacts using static average parameters, they rarely integrate lifecycle emissions into real-time fleet decision-making. This study proposes a formally defined carbon-aware operational framework that integrates ride-level telemetry, time-varying electricity grid carbon intensity, amortized production emissions, and dynamically allocated logistics impacts into a unified optimization architecture. Lifecycle emissions are computed at ride-level granularity and incorporated into charging and rebalancing decisions through a constrained optimization framework. A multi-objective extension is introduced to account for environmental–economic trade-offs. An illustrative simulation of 1000 rides was conducted to evaluate the operational performance of the framework. Under the assumed baseline scenario, the illustrative carbon-aware simulation indicated a potential reduction of up to 24.5% relative to conventional scheduling. Sensitivity analysis across variations in grid carbon intensity, scooter lifetime, energy consumption, and logistics emissions demonstrated reduction outcomes ranging between 18% and 29%, indicating robustness to parameter uncertainty. The study does not present large-scale empirical validation but provides a mathematically formalized decision-support architecture that operationalizes lifecycle assessment within shared micro-mobility fleet management. The results suggest that integrating carbon metrics into operational control may substantially enhance the environmental performance of shared e-scooter systems. Future research should validate the framework using real-world fleet data and incorporate a comprehensive economic assessment. The proposed framework provides a scalable methodological basis for integrating environmental metrics into real-time micro-mobility management and urban sustainability planning. Full article

In recent years, DC-DC partial power converters (PPCs) have become increasingly important in fast-charging architectures for electric vehicles (EVs). Their key feature is that only a fraction of the energy delivered to the battery is processed by the PPC, while the rest is transferred directly, bypassing the conversion stage. This reduces DC-DC conversion losses and improves overall charging efficiency. However, the nonlinear dynamics of these converters can limit performance, especially with model-based controllers such as proportional–integral (PI) controllers. This paper proposes a data-driven control framework for EV fast-charging stations using a DC-DC PPC that is controlled by deep reinforcement learning (DRL). A value-based deep Q-network (DQN) directly selects switching actions and jointly regulates the partial-voltage and output current. The control problem is formulated as a discrete-time Markov decision process, and a two-stage transfer learning scheme ensures safe, efficient deployment. Firstly, the DQN agent is trained in a high-fidelity simulation and then fine-tuned with a small set of experimental data to capture parasitic and modeling errors. The controller is integrated into a constant-current–constant-voltage (CC-CV) charging algorithm and validated over a full charging cycle of a 60 kWh EV battery. The proposed control scheme exhibits a settling time of approximately 2 ms in response to current reference variations while maintaining steady-state errors below 2% in current regulation and below 1% in partial voltage regulation. Simulation results show that the proposed DRL controller has a small steady-state tracking error and improved robustness to reference changes compared with conventional PI and sliding mode controllers. The low computational cost of the trained DQN policy also enables real-time execution on embedded platforms for EV charging. Full article

Ionic polymer–metal composites consist of an ion-conducting polymer–gel membrane sandwiched between two flexible electrodes, representing a class of soft electroactive materials capable of large deformation under low voltage. The gel membrane, swollen with solvent, facilitates ion migration under an electric field, enabling actuation. Tailoring the interfacial architecture between the electrode and the polymer–gel membrane is pivotal for advancing high-performance IPMC actuators. This study presents a comparative investigation of three core–shell nanocomposite electrodes, fabricated via in situ polymerization, for IPMC applications. Among these, the polyaniline-coated multi-walled carbon nanotube composite exhibits a deliberately designed hierarchical structure, with a specific surface area of 32.345 m²·g⁻¹ and a conductive doped polyaniline shell, as confirmed through XPS analysis. This optimized interface enables superior charge storage and transport, endowing the corresponding electrode with a specific capacitance of 40.28 mF·cm⁻² at 100 mV·s⁻¹—3.2 times greater than that of conventional silver-based electrodes—along with a reduced sheet resistance. When integrated with a Nafion ion–gel membrane, the PANI@MWCNT electrode achieves a 67% increase in force density and a larger displacement output compared to standard devices, directly correlated with its enhanced electrical and electrochemical properties. This work highlights the critical role of core–shell interfacial engineering in governing electromechanical performance at the electrode–gel interface and offers a practical design strategy for developing high-performance, cost-effective IPMC actuators for soft robotics, flexible electronics, and related applications. Full article