Search Results (8,843)

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

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

Super oxidized water is a disinfectant generated by electrolysis whose effectiveness depends mainly on oxidation–reduction potential and pH. In this study, a 2² factorial Design of Experiments was applied to evaluate the influence of applied potential (8.2–12.2 V) and NaCl concentration (0.05–0.25 wt.%) on the redox properties of SOW, aiming to produce solutions with targeted disinfection profiles. The obtained models showed excellent predictive capacity (R² > 0.99), identifying NaCl concentration as the most influential factor affecting both oxidation–reduction potential and pH. The system enabled the controlled generation of SOW with ORP values ranging from approximately 950 to 1100 mV and pH between ~3.8 and 5.0, with experimental errors below 1.5%. Stability tests demonstrated that oxidation–reduction potential and pH remained within ±25 mV and ±0.15 units, respectively, over 24 weeks of storage. Microbiological evaluation revealed effective antimicrobial activity against Escherichia coli, Staphylococcus aureus, methicillin-resistant Staphylococcus aureus, and Candida albicans, with inhibition halos of up to ~5 mm depending on ORP and microorganism. The results demonstrate that Design of Experiments enables precise adjustment of SOW redox properties, allowing optimization of antimicrobial performance for specific applications. This positions super oxidized water as a flexible, stable, and scalable disinfection technology for industrial and clinical use. Full article

Three–dimensional excitation–emission matrix (3D–EEM) fluorescence spectroscopy is widely applied for the rapid characterization of dissolved organic matter (DOM) in aquatic environments. However, conventional decomposition based on parallel factor analysis (PARAFAC) requires multiple spectra and manual intervention, limiting its applicability for rapid analysis and future online implementation. The purpose of this study is to develop an efficient data–driven method capable of decomposing fluorescence components from a single 3D–EEM spectrum. We propose a conditional single–spectrum decomposition network (CSSD–Net) based on the encoder–decoder model. The encoder extracts fluorescence features from the input spectrum, while the decoder combines these features with conditional information on component count to generate up to five component maps. The component count can be automatically predicted by CSSD–Net or manually specified to support flexible application scenarios. CSSD–Net was trained using publicly available component spectra from the OpenFluor database without PARAFAC preprocessing. Validation on natural water samples demonstrates that the results obtained from CSSD–Net using a single sample are highly consistent with those from PARAFAC using multiple parallel samples, with a mean Tucker’s congruence coefficient (TCC) of 0.9615. These results show that CSSD–Net provides a fast and practical solution for decomposing single 3D–EEM spectra under constrained aquatic scenarios, and it has potential for future near–real–time and in situ applications. Full article

Intelligent bearing fault diagnosis is essential for maintaining the reliability of rotating machinery. However, deploying deep learning models in industrial environments is often constrained by a lack of labeled data, environmental noise, and strict hardware limits. To address these connected challenges, this paper proposes 1D-CNN-Trans, a flexible and resource-efficient hybrid framework. Designed for supervised diagnosis with restricted data, the configurable model combines a compact one-dimensional convolutional neural network (1D-CNN) for local feature extraction, a Transformer encoder for capturing long-range temporal dependencies, and an optional squeeze-and-excitation (SE) module for channel recalibration under favorable conditions. The method is evaluated on two standard mechanical benchmarks under limited sample conditions, controlled additive white Gaussian noise (AWGN), and dynamic non-stationary interference. Experimental results indicate that 1D-CNN-Trans shows improved robustness under interference compared to selected baselines, notably improving accuracy against a standard CNN backbone. Furthermore, findings indicate that while the Transformer ensures noise robustness, channel recalibration (via SE) introduces optimization instability under extreme sparsity and noise. Consequently, we reposition the architecture as a configurable framework where recalibration is conditionally activated. Finally, theoretical complexity analysis is provided to validate the model’s low computational burden, indicating its general feasibility for resource-constrained scenarios. Full article

The energy transition of the building sector requires the implementation of high-efficiency solutions that increase the share of renewable energy sources while addressing environmental, technical, and economic constraints. Among available technologies, solar air collectors represent a simple and robust option for direct thermal energy generation. This study experimentally evaluates the performance of a prototype solar air collector under laboratory and field conditions and compares its thermal energy yield with the electrical output of photovoltaic panels. Under laboratory conditions, the tested solar air collector achieved a maximum thermal power of 1305 W and an air temperature increase exceeding 40 K. Field measurements conducted under near-standard test conditions demonstrated an average thermal efficiency above 60%. Winter analyses confirmed that, despite lower solar irradiance, the system maintained relatively high efficiency, although the total energy yield strongly depended on atmospheric stability. Comparative results showed that, for an equivalent installation area, the solar air collector generated more usable thermal energy than photovoltaic panels under favorable solar conditions. On the other hand, the limited flexibility of direct thermal energy storage reduces the operational versatility of solar air collectors. These findings confirm the technical feasibility of integrating solar air collectors with photovoltaic systems in hybrid renewable installations. Such combined configurations can improve building energy performance and support decarbonization strategies within sustainable development frameworks. Full article

We propose a novel self-coupled optical waveguide (SCOW+) architecture that enhances spectral control in integrated photonic circuits. Derived from the foundational SCOW platform, SCOW+ introduces a tunable ring resonator coupled with an all-pass filter to achieve sharp, periodic transmission dips with adjustable free spectral range and extinction ratio. This hybrid configuration supports multifunctional behavior, enabling the device to operate as a narrowband filter, modulator, or sensor depending on the tuning parameters. The SCOW+ structure leverages self-coupling and phase interference to induce coupled-resonator-induced transparency, offering fine control over spectral features. Using frequency-domain simulations, we validate the spectral response and tunability of SCOW+. Simulation results confirm that the device exhibits flexible tuning capabilities and dynamic reconfiguration of its transmission profile by adjusting ring length and coupling coefficient. SCOW+ enhances spectral shaping without significantly increasing device size. Its modularity and compatibility with standard fabrication processes underscore its potential for scalable integration in silicon photonics platforms. The results of this study highlight the versatility of SCOW-derived architectures and enable compact, tunable photonic components in next-generation integrated systems. Full article

Background: Professional identity formation (PIF) and wellbeing are increasingly being recognised in postgraduate Family Medicine (FM) education. Role models are central to both, yet traditional learning activities often struggle to implement them effectively. Podcasts offer a flexible medium that may support these goals. This study examines the potential of postgraduate medical education (PGME) podcasts, such as the European Young Family Doctor’s Movement (EYFDM) podcast, to promote PIF and wellbeing. Methods: This mixed-methods study analyses podcast use, role modelling effects, and PIF among young general practitioners (GPs). In 2024, 57 participants, including students, FM trainees, and specialists, completed an online questionnaire with quantitative and qualitative items. Descriptive and analytical statistics were combined with qualitative content analysis (Kuckartz). Sentiment analysis was conducted using artificial intelligence, and triangulation enhanced credibility. Results: Within the trainees and specialists of the study population, most participants (70%; 32/46 SPs) reported regularly using podcasts for PGME, and particularly young female GPs in Western Europe. In our study population, 90% (27/30 SPs) agreed that the podcasts broadened their perspective on professional opportunities in FM. Many participants reported reflections on potential career pathways and PIF. Exposure to role models significantly increased motivation to work in FM (χ² (1) = 10.7, p < 0.001). Conclusions: Podcasts may help address gaps in affective competency training, including wellbeing and PIF, while integrating easily into busy routines. Findings suggest a positive influence on career attitudes, with role modelling supporting PIF and motivation in FM. Full article

This paper presents the design, implementation, and experimental validation of a real-time embedded photovoltaic (PV) emulator based on the two-diode model, using a dSPACE DS1103 platform for hardware validation. The proposed system aims to accurately reproduce the electrical behavior of PV modules under varying environmental conditions, including irradiance and temperature variations. The emulator architecture combines a lookup-table-based modelling approach with a programmable DC power source, enabling deterministic real-time execution and efficient implementation. A multi-level control structure is employed, integrating inner-loop regulation, model-based reference generation, and feedback control to ensure accurate tracking of the PV current–voltage (I–V) characteristics. Experimental results demonstrate that the emulator achieves high accuracy, with an approximation error of approximately 1.2% under standard operating conditions. The system exhibits stable dynamic behavior characterized by a time constant of approximately 0.5 s, with performance maintained across different sampling intervals and load conditions. Additional simulations confirm that the two-diode model preserves high accuracy over a temperature range of 15–60 °C, with deviations below 2%. The results highlight that the two-diode model provides an optimal trade-off between modelling accuracy and computational complexity for real-time embedded applications. The proposed emulator offers a flexible and reliable platform for laboratory validation of photovoltaic behavior and provides the foundation for future testing of maximum power point tracking (MPPT) algorithms, power electronic converters, and embedded control strategies under controlled conditions. Full article

Flexible electrolyte-gated synaptic field-effect transistors (EGFETs) have emerged as a promising platform for neuromorphic visual systems, owing to their low-voltage operation, diverse synaptic plasticity, and exceptional mechanical flexibility. In particular, gel-based electrolytes, including hydrogels and ion gels, play a pivotal role as functional gate dielectrics, enabling efficient ion transport and strong ion–electron coupling through electric double-layer (EDL) formation. By leveraging these unique properties at the semiconductor/gel interface, EGFETs can effectively emulate essential biological synaptic behaviors, including short-term and long-term plasticity under optical stimulation. The inherent compatibility of EGFETs with a broad range of semiconductor channels, gel electrolytes, and flexible substrates enables the development of wearable and conformable neuromorphic platforms that seamlessly integrate sensing, memory, and signal processing within a single device architecture. Recent advances in gel material engineering, such as polymer network design, ionic modulation, and nanofiller incorporation, have significantly improved ion transport dynamics, interfacial stability, and device performance. Despite remaining challenges related to ion migration stability, multi-physical field coupling, and large-area device uniformity, these developments have substantially advanced the practical potential of gel-based systems. This review provides a comprehensive overview of the operating mechanisms, gel-based material systems, synaptic functionalities, mechanical reliability, and future prospects of flexible electrolyte-gated visual synapses, highlighting their considerable potential for next-generation intelligent perception and artificial vision technologies. Full article

This project focuses on the development of a 2U CubeSat intended for potential integration into an LEO constellation. The CubeSat is designed to deliver space-based CNS services, supporting the evolving needs of next-generation airspace and global communication networks. The primary objective is to enhance global connectivity and demonstrate how compact satellite platforms can contribute to modern ICNS systems. By leveraging the flexibility, scalability, and cost-efficiency of CubeSat technology, the mission aims to validate the role of small satellites in delivering reliable and responsive CNS capabilities. This approach provides a foundation for future advancements in satellite constellations tailored for airspace management and communication services. Full article

Multi-block structured mesh is widely used for high-precision aerodynamic simulation, but mesh blocking usually requires substantial manual intervention, which is time-consuming and demands a high level of user expertise. In this study, a potential field-based blocking algorithm for mesh generation around an aircraft is proposed, and a corresponding multi-block grid generation workflow is established. First, the hyperbolic partial differential equation (PDE) method is used to march boundary layer grids from the body surface. Next, the potential field is solved on an unstructured background grid, and the grid topology is flexibly designed by adjusting boundary conditions. The gradient lines of the potential field are then determined and employed to partition the external domain into blocks. Finally, the elliptic PDE method is applied to generate structured grids within each sub-block. A low-aspect-ratio flying-wing configuration is adopted as the test case. Structured grids of both H-type and O-type topologies are generated and compared with the benchmark grid released by the China Aerodynamics Research and Development Center (CARDC). The grid quality analysis and aerodynamic calculation results demonstrate that the two generated grids possess good quality, and the computational results show satisfactory agreement with experimental data. The O-type mesh yields more accurate predictions for the lift coefficient and pitching moment coefficients. Furthermore, two test cases, namely a rocket sled and a V-tail aircraft, are presented to demonstrate that the proposed method can flexibly design either O-type or H-type topologies to accommodate different geometric characteristics. In summary, the proposed method enables efficient generation of high-quality multi-block structured grids for the configurations examined in this study. Full article

The rapid advancement of communication technologies exacerbates severe electromagnetic interference (EMI) pollution. Conventional flexible shielding materials rely heavily on non-degradable petroleum-based polymers, aggravating the electronic waste crisis. To address this dual challenge, sustainable biomass-derived TEMPO-oxidized cellulose nanofibrils (TOCNFs) emerge as ideal structural substrates. However, their intrinsic electrical insulation necessitates integrating conductive two-dimensional (2D) MXene, which suffers from severe self-restacking and brittleness. Herein, TOCNFs@MXene hybrid films are manufactured via vacuum filtration and hot-pressing densification. TOCNFs inhibit MXene self-restacking, constructing a highly ordered layered architecture via a dense hydrogen-bonded network. The optimized ultrathin film T5@M20 (~4.92 μm) exhibits an electrical conductivity of 1.09 × 10⁶ ± 5.06 × 10⁴ s m⁻¹ and an X-band shielding effectiveness (SE_Total) of 25.55 dB. Demonstrating an ultrahigh thickness-normalized specific shielding effectiveness (SSE/t) of 51,934.72 dB·cm²·g⁻¹, this sustainable architecture shows exceptional potential for next-generation flexible electronics. Full article

Task scheduling in cloud computing environments is a complex NP-hard problem that requires maximizing resource utilization while satisfying quality-of-service (QoS) constraints. Traditional meta-heuristic algorithms often become stuck in local optima, while single deep reinforcement learning (DRL) models exhibit instability when exploring large-scale solution spaces. To address this, this paper proposes a hybrid scheduling algorithm based on multi-objective sand cat colony optimization (MoSCO). This algorithm utilizes a D3QN network to extract task features and guide population initialization, followed by a multi-objective Sand Cat Swarm Optimization (SCSO) algorithm for refined local search. Results from 50 independent replicate experiments conducted in a simulated cloud environment, coupled with an analysis of the dynamic convergence process, demonstrate that MoSCO exhibits significant superiority and robustness. Scatter plot convergence analysis further confirms that MoSCO’s knowledge injection mechanism effectively overcomes the blind exploration phase of traditional algorithms and successfully breaks through the local optimum bottleneck in the late iteration stages of single reinforcement learning, achieving higher-quality, denser, and more stable convergence. Furthermore, 3D and 2D Pareto front analyses show that MoSCO generates highly competitive, well-distributed non-dominated solutions, offering flexible trade-off options for conflicting objectives. Compared to PureD3QN, H-SCSO, and NSGA-II, MoSCO exhibits the smallest performance fluctuations in box plots. Specifically, MoSCO elevates the average resource utilization of clusters to 92.20%, while reducing the average maximum Makespan and Tardiness to 528 and 4187, respectively. Experimental data confirm that MoSCO effectively balances global exploration with local exploitation, delivering stable, high-quality solutions for dynamic cloud task scheduling. Full article

Recurring socio-temporal events, such as Ramadan in Middle Eastern cities, introduce pronounced non-stationarity in urban traffic demand. During these periods, daytime traffic volumes typically decline, while congestion becomes more severe in the evening around the Iftar (fast-breaking) period and persists into late-night hours, making conventional fixed-time signal plans less effective. An additional challenge is that demand is not only time-varying, but also unevenly distributed across competing movements: attempts to prioritize high-volume phases can inadvertently cause excessive delays—or even starvation—on lower-demand approaches. To address these issues, this study presents an adaptive, regime-aware traffic signal control framework that combines predictive modeling with constrained optimization. Short-term phase-level delays are forecast using Long Short-Term Memory (LSTM) models, and a Model Predictive Control (MPC) scheme then determines the green time allocation at each control cycle through a receding-horizon strategy. The optimization explicitly represents phase interactions by including constraints that prevent excessive delay in competing movements, thereby yielding a balanced and operationally realistic control policy. The approach is validated with one-minute-resolution TomTom delay data from a signalized intersection in Jeddah, Saudi Arabia, covering both Normal and Ramadan conditions. The LSTM models show stable predictive performance, achieving root mean square errors (RMSEs) of 19.8 s under Normal conditions and 17.1 s during Ramadan. In general, the results show that the proposed framework cuts total intersection delay by about 0.3% to 2.8% compared to standard control strategies. Even though these total-delay improvements are small, they come with big drops in delay for lower-demand phases (about 12–20%) and keep the delay increases for higher-demand phases under control. This shows that the method makes the whole process more efficient by fairly spreading out the delay instead of just making one phase better on its own. The results show that combining forecasting with constrained optimization is a strong and useful way to handle changing traffic demand. This is especially true during times of high demand when flexibility, stability, and fairness across movements are all important. Full article