Search Results (4,668)

With the widespread adoption of electric vehicles (EVs), the deep integration of transportation and power grids has emerged as a significant trend. EV charging stations, acting as dynamic loads, present challenges to real-time power balance and economic dispatch in microgrids, while EVs serving as mobile energy storage units offer new opportunities for system flexibility. To address these issues, this paper proposes a hardware-in-the-loop (HIL) co-design method for vehicle–grid-integrated microgrid energy management systems, covering the entire workflow from simulation to embedded deployment. This method resolves the core challenges of multi-objective optimization algorithm deployment on embedded platforms (i.e., high computational complexity, strict real-time constraints, and heterogeneous communication protocol integration) via deployability analysis, hybrid code generation, real-time task restructuring, and consistency validation. A prototype microgrid system integrating photovoltaic panels, wind turbines, diesel generators, an energy storage system, and EV charging loads was built on the RK3588 embedded platform. An improved multi-objective particle swarm optimization (MOPSO) algorithm is employed to optimize operational costs. Experimental results verify the effectiveness of the proposed co-design method. Compared with traditional rule-based control strategies, the MOPSO algorithm reduces the total daily operating cost of the VGIM system by approximately 50%. After integrating vehicle-to-grid (V2G) scheduling, the operating cost is further reduced. In addition, this method ensures the consistency of algorithm functionality and performance during the migration from HIL simulation to embedded deployment, and the RK3588-based embedded system can complete a single optimization iteration within 60 s, which fully satisfies the real-time requirements of industrial applications. This work provides a feasible technical pathway for the reliable deployment of vehicle–grid-integrated microgrids in practical industrial scenarios. Full article

To address the limited accuracy of power demand prediction for hybrid electric vehicles under complex and dynamic driving conditions, this paper proposes a hybrid prediction approach based on the cascade correction of Long Short-Term Memory networks and Least Squares Support Vector Machines (LSTM-LSSVM-CC). The proposed method adopts a stage-wise modeling framework that exploits the least-squares optimality of LSSVM for low-frequency steady-state signals and the dynamic compensation capability of LSTM for high-frequency non-stationary residuals, thereby achieving complementary feature representation in the frequency domain. Specifically, an LSSVM is first used to construct a baseline regression model that captures stationary components, followed by an LSTM network that performs deep temporal modeling of the residual sequence to correct nonlinear prediction errors. Extensive experiments conducted on three standard driving cycles—CLTC-P, WLTP, and UDDS—demonstrate that the proposed model consistently outperforms conventional methods including LSSVM, RNN, ELMAN, and Random Forest in multi-step predictions, achieving an average RMSE reduction of 28–52% and maintaining correlation coefficients (R²) between 0.87 and 0.99. Particularly under highly dynamic and abrupt load conditions, the model exhibits superior real-time performance and stability while significantly mitigating cumulative prediction errors. These results demonstrate that the proposed LSTM-LSSVM-CC model achieves robust modeling performance of non-stationary time series while balancing prediction accuracy and computational efficiency, providing an effective technical foundation for hybrid vehicle energy management optimization and offering a transferable theoretical framework for time-series prediction in complex systems. Full article

Large-scale and widely distributed air-conditioning (AC) loads can be aggregated into load-type Virtual Power Plants (VPPs) to participate in peak-shaving ancillary services, thereby improving the allocation of demand-side electricity resources. However, current AC aggregation methods primarily focus on meeting peak-shaving instructions and generally employ fixed incentive pricing and proportional capacity allocation, making it difficult to balance user revenue and satisfaction and thereby constraining the flexibility of VPP demand-side regulation. This paper proposes a unified incentive-based demand response scheduling framework for both fixed- and variable-frequency AC loads across industrial, commercial, and residential scenarios. Based on the Equivalent Thermal Parameter model, AC loads are classified into curtailable and shiftable types, with their adjustable boundaries characterized by a Time-of-Use (TOU) elasticity-based interaction willingness model and a fuzzy load transfer rate model, respectively. A three-objective optimization model is established to maximize user revenue while minimizing user dissatisfaction and scheduling error, with incentive pricing and capacity allocation jointly optimized via Non-dominated Sorting Genetic Algorithm III (NSGA-III). Case studies are conducted on a load-type VPP covering three scenarios, namely a large industrial zone, a commercial zone, and a residential zone, under weekday and non-weekday TOU tariffs and three representative 1 h peak-shaving periods. Compared with a fixed-pricing benchmark, the proposed strategy increases total user revenue by 9.4% to 11.4% and reduces weighted average dissatisfaction by 0.27 to 1.92%. The case study results demonstrate that the proposed method can improve the trade-off between user revenue and satisfaction. Full article

Ankle–foot orthoses (AFOs) are widely used in the rehabilitation of patients with neurological or musculoskeletal disorders. However, treatment outcomes may be influenced by incorrect use of the device or by inappropriate orthosis selection. Since many types of AFOs are available, differing in materials, stiffness, and geometry, an objective evaluation tool can support clinical decision-making. This work presents the design, development, and characterization of an instrumented AFO able to quantify relevant gait parameters in an objective way. The proposed device integrates three measurement modalities in a compact wearable structure. Two longitudinal strain gauges estimate ankle plantar- and dorsiflexion angles. Two force-sensitive elements detect foot–ground contact and allow identification of stance and swing phases of the gait cycle. A single inertial measurement unit (IMU) is used to measure lateral shank inclination. The strain-gauge-based angle estimation was validated against a gold-standard motion capture system, achieving a root mean square error of approximately 1.6 degrees and showing higher accuracy than the IMU for plantar/dorsiflexion measurement, while maintaining a simple electronic architecture. The force sensors were validated using a force platform and demonstrated reliable detection of loading and unloading events. Monitoring lateral inclination through the single IMU provides additional information related to balance and potential fall risk. Data are transmitted via Bluetooth Low Energy (BLE) to a custom Python-based application for real-time visualization and recording. Overall, the results validate the electronic instrumentation and demonstrate reliable system performance, indicating that the proposed instrumented AFO represents a promising platform for objective gait assessment and future clinical applications. Full article

Grape pomace represents a widely available agro-industrial by-product in Chile with considerable potential for valorization within circular economy frameworks; however, its functionality as a cosmetic ingredient depends on both grape cultivar and processing strategy. In this study, the direct incorporation of solid grape pomace residues into cream formulations was first evaluated, revealing limitations related to color control, homogeneity, and sensory performance. Subsequently, the influence of varietal origin (Malbec, Merlot, Syrah, and Grenache) on the extraction, stability, color behavior, and functional performance of anthocyanin-rich extracts was investigated for cosmetic applications. pH-standardized color analysis revealed statistically significant (p < 0.05) varietal differences, with Malbec extracts showing superior chromatic stability under acidic and near-neutral conditions, exhibiting lower reduction in a* values across the pH range compared to other varieties. In contrast, Syrah, Grenache, and Merlot showed a more pronounced decrease in red chromaticity, indicating higher sensitivity to pH-induced structural transformations. Although Merlot and Syrah exhibited higher ABTS antioxidant activity, Malbec presented the highest total phenolic content and the most balanced functional profile when considering both stability and color retention. Incorporation of anthocyanin-rich extracts into cosmetic cream formulations demonstrated that a 4.5% (m/v) loading ensured a skin-compatible pH (4.5–5.5), with Malbec-based creams exhibiting superior color stability and formulation performance over time. These findings demonstrate that grape pomace valorization requires variety-specific evaluation and identify extraction as a key enabling step for the development of sustainable, bio-based color-functional cosmetic ingredients. Full article

Per- and polyfluoroalkyl substances (PFASs) include thousands of fluorinated organic compounds of anthropogenic origin. Their extensive use, combined with their high stability, has led to the widespread contamination of water and soil resources. Here, single-step foam fractionation enhanced soil washing was carried out for the remediation of PFAS-contaminated soil. Concentrations of target Perfluoroalkyl Carboxylic Acids (PFCAs) and Perfluoroalkane Sulfonic Acids (PFSAs) were monitored in foam and leachate along the duration of the treatment. Among PFCAs, only long-chain compounds peaked in foam at the beginning of the treatment. This was consistent with the increase in the sorption affinity to the air–water interface with chain length. The same behavior was observed also in PFSAs by comparing PFHXs, PFHpS and PFOS. The fraction of PFCAs still in the leachate after 40 min of treatment was found to decrease with chain length, with PFSAs showing a similar trend. PFAS removal significantly increased with soil particle size, ranging from 48.2 ± 3.2% (fraction < 0.063 µm) to 64.1 ± 1.9% (fraction > 2 mm). Final mass balance analyses detail PFAS distribution among soil, leachate, and foam, providing valuable information for the additional treatment required to destroy the PFAS load extracted from the contaminated soil. Full article

This study addresses a critical methodological gap in evaluating building envelope performance in hot, arid climates, the overreliance on annual energy indicators, which fail to capture transient thermal behavior during peak-load periods. In such environments, instantaneous heat gains, their intensity, and temporal distribution are decisive factors for cooling demand, occupant comfort, and grid stability. To overcome this limitation, a dynamic evaluation framework—the Thermal Adaptation Rating (TAC) system—is proposed. TAC integrates three interrelated indices—peak temperature reduction (ΔT_peak), relative peak cooling load reduction (ΔP_peak, %), and peak thermal delay (Δt_delay), representing thermal damping, load intensity mitigation, and temporal redistribution, respectively. A typical residential building in Karbala was modeled in DesignBuilder using the EnergyPlus engine, with inputs documented and calibration performed against real consumption data following ASHRAE standards (MBE and CV(RMSE)) to ensure reliability. The study examined advanced envelope systems, including thermochromic glass (TG), phase-change materials (PCMs), aerogel materials (AMs), and hybrid combinations. Results revealed that while AM achieved the greatest annual energy savings, its impact on instantaneous cooling load was limited. PCM, by contrast, effectively mitigated and delayed peak loads, enhancing thermal comfort (PMV/PPD). Hybrid systems, particularly TG-PCM, delivered the most balanced performance, simultaneously reducing peak cooling load and shifting its occurrence to reshape the cooling demand curve during critical periods. These findings demonstrate that annual indices alone are insufficient for evaluating envelope performance in extreme climates. Peak-condition analysis, expressed in terms of instantaneous cooling load, as operationalized through TAC, provides a more accurate representation of thermal behavior and offers a practical tool to guide envelope design decisions in hot, dry regions. Full article

Multi-Robot Systems (MRS) demand optimal spatial resource configuration to ensure systemic efficiency in mission-critical applications. Conventional paradigms rely on rigid coverage-first principles, prioritizing exhaustive spatial scanning over rapid target discovery, thereby compromising systemic responsiveness. To bridge this gap, this study proposes the Attraction of Unknown area Centroid for Exploration (AUCE) architecture, a centralized framework designed to simultaneously optimize global exploration efficiency and early-stage target discovery rates. The control framework incorporates a dynamic region planning strategy that adaptively modulates the systemic search focus based on the specific field of view of autonomous agents, alongside an optimized S-shaped trajectory pattern to establish a rigorous balance between localized path simplicity and global coverage. A versatile profit function synthesizing constant and time-varying coefficient strategies explicitly regulates the systemic trade-off between accelerated early-stage target discovery and global path cost minimization. Quantitative simulations demonstrate that AUCE significantly outperforms established methods by mitigating redundant path costs and generating a distinct front-loading effect to accelerate target localization. Subsequent evaluations confirm the framework’s computational scalability in expanded swarms and its systemic adaptability when navigating static obstacles. Full article

With the high penetration of distributed photovoltaics (PV) in distribution areas, transformer capacity limits and source–load fluctuations have become key factors constraining PV accommodation. To accurately assess the PV hosting capacity under energy storage regulation, this paper proposes an assessment method based on operating region analysis. First, a coordinated operation model for the distribution area is established, incorporating the transformer capacity, energy storage constraints, and power balance. On this basis, the calculation boundaries for the PV hosting capacity are discussed in two scenarios: Model 1 ignores power curve uncertainty, characterizing the geometry of the conventional operating region to find the maximum deterministic hosting capacity ($S_1$) that keeps the region non-empty. Model 2 introduces box-type uncertainty sets for the source and load, proposes the concept of a “Self-Balanced Operating Region”, and constructs a robust feasibility determination model ($f_3$) based on a Min–Max–Min structure. To solve this multi-layer nested non-convex model, an iterative algorithm based on duality theory and Benders decomposition is employed to determine the robust hosting capacity under uncertainty ($S_2$) at the critical point where $f_3$ shifts from zero to non-zero. Case studies show that source–load uncertainty leads to a significant contraction of the operating region, and the robust hosting capacity under uncertainty requirements is strictly less than the deterministic hosting capacity ($S_1>S_2$). This method quantifies the reduction effect of uncertainty on the accommodation capability, providing a theoretical basis for planning high-renewable penetration distribution areas and energy storage configuration. Full article

Although bio-hydrogenated diesel (BHD) offers drop-in compatibility and high oxidative stability, its poor lubricity remains a critical barrier to long-term engine deployment. Previous studies have primarily relied on short-term tribological assessments, leaving insufficient empirical data on sustained wear behavior under realistic conditions. This study addresses that gap through a 200 h durability evaluation of BHD–biodiesel blends in a single-cylinder diesel engine under constant load conditions per Thai Industrial Standard TIS 2618-2557. Five fuels, namely diesel, pure BHD, BHD90, BHD70, and pure biodiesel, were tested to identify the critical biodiesel threshold for optimal tribological and oxidative performance. BHD90 (90% BHD + 10% biodiesel) emerged as the optimal formulation, delivering the lowest torque reduction (11.2%) and minimal iron wear particles (101 ppm), while preserving oxidation stability. Biodiesel concentrations exceeding 10% induced accelerated lubricant oxidation through hygroscopic effects, negating the lubricity benefits. Fourier-transform infrared spectroscopy (FTIR) analysis of piston carbon deposits further revealed that higher biodiesel blends produced more oxygenated compounds, whereas pure BHD and diesel generated predominantly aliphatic hydrocarbons. These findings establish a mechanistic relationship between fuel composition, oxidation, and wear under endurance conditions, providing a practical guideline for renewable diesel formulation that balances lubrication performance, oxidation control, and long-term engine durability. Full article

This study focuses on the of key engineering parameters for the repair of shipboard carbon fiber reinforced polymer composite structures using a scarf patch repair configuration. A three-dimensional finite element model was developed to systematically analyze the effects of repair location (center-symmetric, diagonal-asymmetric, and edge-unidirectional) and cut-out depth (2.0 mm, 3.0 mm, and 4.0 mm) on the mechanical response of the repair structure. The results indicate that although the local stress level of the center-symmetric repair is slightly higher, it provides a continuous load transfer path with more balanced stress distribution, demonstrating the best overall mechanical performance. When the cut-out depth is 3.0 mm, the repair structure achieves an optimal balance between stress uniformity and displacement coordination, effectively reducing the risk of early adhesive layer failure and local buckling. This study identifies the optimal parameter combination for scarf patch repairs, providing important theoretical foundations and references for the design of repair processes and the standardization of engineering practices in shipboard composite structures. Full article

The retinal pigment epithelium (RPE) is a long-lived, highly polarised epithelial monolayer that performs essential functions in retinal homeostasis, including outer blood–retina barrier maintenance, visual cycle activity, metabolic exchange, phagocytic clearance of photoreceptor outer segments, and regulation of oxidative and immune balance. Because RPE cells persist for decades under conditions of sustained oxidative, metabolic, and phagocytic stress, this tissue provides a valuable model for examining how long-lived post-mitotic cells preserve function over time and how age-related dysfunction emerges when that balance weakens. Although much of the current literature on RPE ageing has been shaped by age-related macular degeneration (AMD), age-dependent change in the RPE should not be understood solely as a preclinical stage of disease. Rather, the ageing RPE offers a broader framework for studying cellular maintenance under chronic physiological load. In this review, we synthesise current evidence on RPE ageing across four interrelated domains: structural remodelling, mitochondrial and metabolic imbalance, proteostatic and lysosomal burden, and chronic inflammatory dysregulation. Across these processes, ageing in the RPE is expressed less as widespread cell loss than as progressive decline in cellular organisation, buffering capacity, and functional precision. Structural irregularity, altered mitochondrial regulation, incomplete degradative clearance, and persistent low-grade inflammatory signalling together reduce the ability of the RPE to maintain long-term homeostasis and increase vulnerability to age-related retinal dysfunction. We further argue that ageing in the RPE is best understood not as abrupt failure of isolated pathways, but as gradual loss of system coherence among interacting homeostatic systems that remain active while operating under increasing constraint. This view helps integrate diverse cellular and molecular findings and highlights the RPE as an informative model for understanding ageing in long-lived post-mitotic tissues. Full article

Hybrid renewable energy systems, which combine photovoltaic panels, wind turbines, batteries, generators, and grid connections, require careful sizing to balance economic performance, renewable integration, and supply reliability. In this context, this study proposes a deep reinforcement learning (DRL)-based sensitivity analysis framework in which the admissible energy contributions from the diesel generator and the grid are treated as explicit design-control parameters. The objective is to simultaneously minimize the levelized cost of energy, minimize the loss of power supply probability, and maximize the renewable energy fraction. A sensitivity analysis was conducted across different HRES configurations, load profiles, and tau/gamma values. The performance of the DRL approach was compared with that of multi-objective particle swarm optimization and the non-dominated sorting genetic algorithm II under the same study setting. The results indicate that DRL can identify competitive trade-offs, especially under standard load conditions, while also providing insight into how admissible backup-energy constraints reshape techno-economic and reliability compromises. The best trade-offs were observed around intermediate tau and gamma values, suggesting that moderate backup-energy margins are more favorable than extreme values. These findings should be interpreted within the scope of a simulation-based study and provide comparative design-oriented evidence rather than universally transferable design rules. Full article

Accurately determining digging resistance during bucket–soil interaction is crucial for optimizing excavator working devices and power systems. To address measurement difficulties, a numerical simulation model based on the arbitrary Lagrangian–Eulerian (ALE) method was established and verified through excavation tests. Through orthogonal experiments, the influence of excavation parameters was studied, and the optimal compound digging trajectory was determined. The results show that increasing the excavation angle from 36° to 48° decreases the X-direction resistance and moment by 39.48% and 38.85%, respectively, though specific energy consumption (SE) increases. Additionally, optimizing arm movement speed reduces the X-direction resistance and moment. While ensuring the bucket load factor is suitable, reducing arm speed and a horizontal soil push during compound excavation effectively decreases SE. Finally, the optimal balance of digging resistance and SE can be achieved with a 300 mm bucket hydraulic cylinder displacement, a 1.5 s interval for initial arm and bucket movements, and an arm-to-bucket speed ratio of 5.5 for hydraulic cylinders. Full article

The large-scale integration of wind energy introduces increased uncertainty and variability in modern power systems, with direct implications for both system design and operation. In addressing similar aspects, energy storage plays a pivotal role as a key source of system flexibility. However, the design and sizing of storage systems remain challenging, especially under conditions of increased uncertainty. In this context, the present study proposes an alternative methodological framework, based on an inverse sizing pathway, i.e., from operations to design. More specifically, the uncertainty embedded in day-ahead forecasting of residual errors, associated with wind power generation and load demand, is currently exploited as a design-relevant signal, while energy storage is treated explicitly as a risk-hedging mechanism. Forecasting residuals spanning a year of operation are incorporated in the problem through probabilistic modeling, leading to the generation of trajectories that correspond to different risk levels and are managed as design scenarios. Regarding the modeling of uncertainties, the study examines two different strategies, namely a global modeling approach and a k-means clustering strategy. Accordingly, by mapping the interplay between storage capacity, uncertainty levels (or risk tolerance), achieved RES shares and system-level costs, we highlight the role of energy storage as a risk-hedging entity rather than merely a means of energy balancing. Our results to that end demonstrate that the achieved shares of RES exhibit increased sensitivity, even within constrained regions of wind power variation, while storage capacity features distinct zones of hedging value and hedging saturation effects emerging beyond certain storage levels. Moreover, evaluation of the two modeling strategies reflects on their complementary character, with the global modeling approach ensuring continuity and the clustering strategy capturing local asymmetries within different operational regimes. In conclusion, the methodology presented in this study bridges the gap between operational forecasting and long-term system design, offering a risk-aware framework for storage sizing, grounded in actual operational signals rather than relying on stationary historical data and relevant scenarios. Full article