Search Results (4,008)

Laser beam combining is essential for achieving high-power and high-radiance output. However, atmospheric turbulence induces independent tip–tilt aberrations across discrete sub-beams in laser array systems, which severely degrades the concentration of far-field energy. Traditional wavefront sensing techniques are primarily designed for the continuous wavefront of a single laser and are not directly applicable to laser array, whereas indirect optimization-based methods often suffer from slow convergence and limited real-time performance. To address these limitations, this study introduces a tip–tilt aberration compensation system for laser array propagation based on cooperative beacons with a shared-aperture transmit–receive configuration. The primary innovation consists of a modified Shack–Hartmann wavefront sensor (SHWFS) tailored to a discrete multi-beam layout, which facilitates the direct, independent, and simultaneous measurement of tip–tilt aberrations for each sub-beam. In conjunction with a segmented deformable mirror (SDM), the architecture can facilitate real-time closed-loop correction with high bandwidth and high precision. Numerical simulations of a 7-, 19-, and 37-beam laser array, together with validation experiments utilizing a 30-beam configuration, demonstrate that the proposed approach effectively suppresses tip–tilt error induced by turbulence. After closed-loop correction, the Strehl ratio (SR) increases above 0.92 ($r_0=5\mathrm{cm}$), while the beam quality factor β reduces below 1.37 ($r_0=5\mathrm{cm}$). Furthermore, the system retains performance stability as the number of sub-beams increases, demonstrating the scalability of the proposed method. In contrast to conventional approaches designed for a continuous wavefront, the proposed method offers a feasible approach for a discrete laser array system, providing robust and scalable tip–tilt correction under varying atmospheric conditions. Full article

The increasing emphasis on sustainable and decentralized energy has elevated microgrids as a central element of modern power systems. By integrating renewable energy sources, advanced energy storage technologies, and intelligent control strategies, microgrids enhance efficiency, stability, and flexibility and play a vital role in creating resilient and adaptable energy networks. This review provides a comprehensive analysis of Energy Management Systems (EMSs) in microgrids, distinguishing between planning-oriented tools for techno-economic evaluation and control-oriented platforms for real-time operation and optimization. Hierarchical control architectures spanning primary, secondary, and tertiary levels are examined, highlighting their roles in frequency and voltage regulation, load sharing, and economic dispatch. Optimization techniques for EMSs are analyzed across deterministic, stochastic, metaheuristic, and artificial intelligence/machine learning methods, addressing objectives, constraints, uncertainties, and multi-timeframe decision-making. AI-based methods, including supervised learning, deep learning, and reinforcement learning, are highlighted for their ability to enhance predictive control, system autonomy, and operational efficiency, despite their computational demands. Future trends emphasize AI-based predictive control, deep learning for energy forecasting, multi-microgrid coordination, hybrid energy storage management, and cybersecurity enhancements. Overall, an intelligent EMS, combined with innovative technologies, is critical for developing resilient, scalable, and sustainable microgrid solutions that meet the evolving demands of modern energy systems. 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

The transition toward sustainable and low-carbon power systems increasingly depends on the efficient accommodation of high shares of renewable energy while maintaining secure and reliable grid operation. In interconnected power systems, this challenge is intensified by strong cross-regional coupling, tie-line flow violation risks, and the high computational burden of fully coupled probabilistic assessments. To support the sustainable operation of renewable-rich interconnected systems, this paper proposes a probabilistic production simulation method that incorporates risk-aware tie-line exchange optimization. Sequential random sample paths are constructed by considering load fluctuations, renewable energy output uncertainty, and random outages of conventional units. Using cross-regional exchange power as coupling variables, a conditional value-at-risk (CVaR)-based pre-scheduling model is established to control tie-line and interface flow tail risks. Given the scheduled exchange power, cross-regional exchanges are transformed into regional boundary power injections, enabling decoupled sequential probabilistic production simulation for each region. The exchange schedule is then iteratively updated through marginal-value feedback. A four-region interconnected system is used for case-study validation. Results show that the proposed method improves renewable energy accommodation, reduces renewable curtailment, suppresses tie-line flow violation risk, and maintains high reliability assessment accuracy. Compared with the region-decoupled benchmark with fixed exchange power, the proposed method increases the renewable energy accommodation rate from 93.82% to 95.41% and reduces renewable curtailment from 312,162 MWh to 231,284 MWh, while also lowering expected energy not served and loss of load expectation. In addition, under the reported case-study setting, the proposed RC-IEF-PPS reduces the computation time from 5216.24 s for Full-PPS to 4074.63 s, i.e., by 21.9%, while maintaining comparable reliability assessment accuracy. These results indicate that the proposed framework can support the sustainable integration of high-penetration renewable energy by improving clean-energy utilization, operational reliability, and computational tractability in interconnected power systems. Full article

Social Virtual Reality (VR) is emerging as a powerful medium for remote social interaction and collaboration, enabling multiple users to share experiences together while apart. Likewise, recent advances in multimedia technologies have proposed strategically combining diverse content formats and introducing interaction techniques for recreating virtual environments and engaging with them, respectively. This study pioneers the joint exploration of Social VR enhanced with holographic communication, multimodal content integration, and advanced interaction methods to deliver realistic and interactive group visits to reconstructed cultural heritage sites, specifically an existing restaurant–museum. The reconstructed space is further augmented with Points of Interest (PoIs), which can be freely visited and dynamically activated to provide rich contextual and historical information about the venue. The proposed technology and scenario have been evaluated objectively and subjectively. Results from objective tests offer relevant insights into the technical requirements, performance metrics (including bandwidth usage and latency), and overall system stability. Results from subjective tests with 22 participant pairs reveal high levels of user satisfaction, particularly in terms of immersion, presence, togetherness, and interaction quality regardless of whether participants acted as Guides (interacting with the VR environment) or Followers (observing and following the Guide’s actions). Beyond demonstrating feasibility, the findings from this study prove, for the first time, how strategically combining multi-user holoportation with multimodal content and role-based interactions can enable guided, collaborative cultural or touristic visits that preserve social presence while supporting rich exploration and contextual learning. Full article

The joint probability distribution of observable variables from a system is constrained by the underlying causal structure. In the presence of hidden variables, untestable independencies that involve hidden variables lead to testable causally-imposed inequality constraints for observable variables, whose violation can reject the compatibility of a causal structure with data. One type of causally informative inequalities is entropic inequalities, which appear in the space of entropic terms associated with the distribution of observable variables. We derive a new type of minimum information (minInf) entropic inequalities that substantially increases causal inference power. These new entropic inequalities appear when considering the constraints that the causal structure imposes on entropic terms determined by information minimization within families of distributions that preserve sets of marginals shared with the original distribution. We introduce a new family of minInf data processing inequalities and a procedure to recursively combine different types of data processing inequalities to create tighter testable entropic inequalities. We extensively illustrate the applicability of this procedure in the instrumental causal scenario, integrating the new inequalities with standard instrumental entropic inequalities constructed with multivariate instrumental sets. We also provide additional examples with other types of entropic inequalities, such as the Information Causality and Groups-Decomposition inequalities. 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

Cytometry-based single-cell proteomics (CySCP) has emerged as a powerful tool for analyzing cellular heterogeneity at the protein level because of its ability to reveal dynamic cell states and response patterns through high-dimensional protein expression profiling in thousands of individual cells. However, detailed summaries of quantification, processing and analysis of CySCP data remain limited. This review provides comprehensive perspectives on CySCP, including quantification technologies, analysis pipelines, annotation strategies, and resource platforms. Specifically, first, the strengths and limitations of the detection platforms are discussed. Second, comprehensive data processing steps, including compensation, transformation, normalization, batch effect correction, signal cleaning, and doublets, debris or dead cells removal, are described in detail. Third, various strategies for cell type annotation, including manual gating, unsupervised clustering, supervised/semi-supervised classification, and fully automated approaches, are illustrated. Fourth, emerging CySCP databases, as critical resources for facilitating antibody validation, panel optimization, and open-access data sharing, are summarized. In summary, this review provides a comprehensive guide for the use of CySCP to obtain novel biological insights at the single-cell protein level. Full article

The growing need for sustainable aviation propulsion has increased interest in hydrogen fuel cell systems as alternatives to combustion engines. This study presents the modeling, simulation, and optimization of a hybrid hydrogen–electric powertrain for the MIMIQ unmanned aerial vehicle (UAV). A 2 kW proton exchange membrane fuel cell is integrated with a 12S lithium-polymer battery via a DC–DC converter, enabling parallel power sharing and in-flight battery recharging. A MATLAB-based dynamic model was developed using mission power profiles derived from flight data and refined using momentum theory. The developed model was benchmarked through a comparative simulation of a combustion-based hybrid-electric powertrain variant of the same platform, demonstrating consistency in electrical and energetic behavior. Multi-objective optimization using NSGA-II was performed to maximize hover endurance and to minimize energy consumption while maximizing payload over a full mission. Results from this computational framework show that endurance is primarily constrained by hydrogen availability rather than battery capacity, with the fuel cell operating near its optimal efficiency region. The findings indicate that hydrogen–electric architectures offer improved endurance, reduced emissions and better scalability compared to combustion-based systems, supporting their suitability for long-endurance UAV applications. Full article

Aim: The aim of this study was to explore the distribution and driving factors influencing the disparity of health professionals (HPs) at the Centers for Disease Control and Prevention (CDC) in China and to provide a reference for regional health planning and rational allocation of public health resources. Methods: The Gini coefficient was used to measure the equity of HP distribution at CDC sites at the provincial level during 2012–2023 in China. Moran’s I was used to analyze the spatial agglomeration effect, and the geographic detector model was used to explore the factors driving the allocation of HPs at CDC sites in different provinces. Results: The number of HPs at the CDC showed an increasing trend from 2012 to 2023 in China. The average Gini coefficients at the population and geographical areas were 0.16 and 0.58, respectively. The global Moran’s I statistic indicated a notable decline in spatial clustering for the population dimension, decreasing from 0.503 to 0.238; in contrast, spatial clustering for the geographical dimension remained relatively stable, ranging between 0.13 and 0.16. The local Moran’s I statistic revealed that provinces such as Qinghai in the western China consistently exhibited a “low–low” spatial clustering pattern. Six factors were found to explain the variability in the CDC HP distribution based on the 2020 data. In the context of factor interactions, the synergistic effects between education level and health expenditure share (q = 0.9781), and between population aging and per capita GDP (q = 0.9699), substantially exceed the explanatory power attributable to any single factor alone. Conclusions: A significant regional disparity was observed in the distribution of HPs among 31 provinces, with the distribution based on service area being less equitable than that based on population. The shortage of healthcare professionals in the western region is characterized by notably inadequate geographical distribution. Future policy initiatives should prioritize targeted spatial interventions and integrated, multi-factor collaborative strategies. Full article

With the development of scientific machine learning (SciML), the proposal of physics-informed neural networks (PINNs) has provided a powerful paradigm for solving partial differential equations (PDEs). While PINNs perform well in solving high-dimensional PDEs, they perform worse than traditional numerical methods for low-dimensional problems. This discrepancy arose from potential convergence conflicts induced by distinct physical magnitude of loss terms. To decouple the convergence conflicts, we propose a partial derivative guided multi-branch physics-informed neural network (PDGM-PINN). Inspired by SciML, we treat both the solution and partial derivatives as dependent variables to be predicted. The partial derivatives are directly predicted by sub-branches, while the main branch approximates the PDE solution, and all branches share error backpropagation information. Furthermore, we redesign the loss function. The loss of the governing equation is computed with the solution and partial derivatives predicted by the main and sub-branches. Schwarz’s theorem and Kullback–Leibler divergence are incorporated into the loss terms as soft constraints of partial derivatives continuity and residual distributions consistency for the governing equations. We conducted comprehensive experimental evaluations on seven PDEs, and ablation experiments, sensitivity analyses, and complexity analyses were carried out to investigate the rationality of PDGM-PINN. The results demonstrate that PDGM-PINN achieves the best performance among PINN variants with the fewest trainable parameters, effectively avoiding architectural redundancy. Full article

To address the coupled challenges of renewable power volatility, high operating cost, and electrolyzer degradation in grid-connected wind–PV hydrogen production systems, this paper proposes a hierarchical day-ahead scheduling strategy under time-of-use (TOU) electricity prices. The upper layer performs price-responsive economic dispatch to coordinate renewable utilization, battery operation, grid transactions, and aggregate hydrogen-production power with the objective of minimizing lifecycle operating cost. The lower layer introduces a health-aware non-uniform rotation mechanism to allocate the aggregate power command among electrolyzer units, thereby reducing fluctuation exposure and balancing lifetime consumption across the array. Practical constraints, including multi-state electrolyzer operation, unit-commitment logic, battery state-of-charge dynamics, hydrogen storage limits, and system power balance, are explicitly considered. A case study of a wind–PV hydrogen production project in Northern China shows that the proposed strategy shifts electricity purchases to valley-price periods and promotes electricity export during peak-price periods. Compared with the benchmark strategy, hydrogen production during low wind–PV generation periods increases from 342,000 to 381,000 Nm³, the share of fluctuating operating time decreases from 62.5% to 12.5%, and the average daily start–stop frequency declines from 8.0 to 4.8. Consequently, the degradation penalty is reduced by about 40%, and lifecycle operating cost decreases by 27.3%. Full article

Considering that the unmanned aerial vehicles (UAVs) are susceptible to both co-channel interference and malicious jamming with limited onboard battery energy, this paper proposes an energy-harvesting-assisted anti-jamming communication framework for UAV swarm networks. Specifically, we first model the problem as a decentralized partially observable Markov decision process (Dec-POMDP), aiming to achieve a long-term trade-off between data transmission success rate and energy consumption. Then we propose a multi-agent independent advantage actor–critic (IA2C)-based energy-harvesting-assisted anti-jamming communication solution, which enables each cluster head (CH) to learn its transmit channel, power, and energy harvesting time policy independently. By constructing a time-space-based extended Dec-POMDP, the spatiotemporal correlations among neighboring nodes are learned by allowing adjacent agents to share discounted local observations. Extensive simulations show that, compared with the benchmark schemes, the proposed scheme improves the average cumulative reward and average cumulative success rate by 17.26% and 10.37%, respectively, while achieving a higher transmission success rate with lower energy consumption under different numbers of available channels. Full article

The evolution toward sixth-generation (6G) networks envisions humans as active nodes within a fully interconnected digital ecosystem, supported by data collected from in-body and on-body sensors. Since many of these devices are not equipped to connect directly to 6G networks, Wireless Body Area Networks (WBANs) serve as an essential intermediate layer. However, conventional radio-frequency technologies face limitations in terms of energy efficiency, security, and data integrity, motivating the adoption of lightweight security mechanisms. Physical Layer Security (PLS), and in particular Physical Key Extraction (PKE), offers a promising solution by enabling legitimate devices to derive shared cryptographic keys from the reciprocal properties of the communication channel. Galvanic coupling (GC) communication has recently emerged as an on-body transmission technology alternative to radio-frequency (RF), which exploits low-power electrical signals propagating through biological tissue. Building on prior feasibility studies, this work proposes a PKE framework tailored to GC channels, integrating a lightweight key reconciliation method, based on Hamming (7,4) error-correction codes, and evaluating system performance through dedicated reliability and security Key Performance Indicators (KPIs). Results reveal a trade-off shaped by electrode placement and channel quantization parameters. Among the ones tested, the optimal configuration is achieved with a 3 cm transverse inter-electrode spacing at both transmitter and receiver, and a 3 cm longitudinal separation between transmitter and receiver, by quantizing the channel impulse response with two quantization bits. While this work focuses on validating the method in controlled conditions in order to establish a reliable study framework, future developments will focus on enhanced reconciliation, privacy amplification, and analysis of the GC channel considering physiological and environmental variations. Full article

A new control method for efficiency optimization in systems composed of parallel converters is presented in this paper. The proposed methodology considers the individual efficiency surfaces for given ratings of power and voltage and determines the optimum operating point for each converter such that the global system efficiency is maximized throughout the entire operating spectrum. Furthermore, a supervisory control strategy is proposed to manage the power-sharing of the converters according to the optimal surfaces provided by the methodology, enabling a performance enhancement for the system by improving its efficiency. Different approaches can be used to implement the active current sharing (ACS) scheme, and in-depth discussions are provided to guide the designer through the tradeoffs to achieve the desired transient and steady-state behavior for the system. Experimental results show that under light load operation, an improvement of 8.5% is achieved in comparison with a conventional technique of equal power-sharing. This points out that the proposed strategy is especially applicable and can significantly improve the performance of systems powered by batteries or renewable sources. Full article