Search Results (7,931)

We present a reproducible pipeline that converts color images into quantitative fluorescence maps by combining spectral measurement with a linear mixture model. The method is designed specifically for quantitative comparisons of Glo Germ™ on images of hands taken under different experimental conditions with controlled illumination. The emission spectrum of Glo Germ is measured using a spectral photometer and normalized to obtain its spectral power density function. This spectrum is projected into CIE XYZ coordinates and incorporated into a linear mixture model in which each pixel contains contributions from white light, UV-illuminated skin reflectance, and fluorophore emission. Component magnitudes are estimated with non-negative least squares, yielding a grayscale image whose intensity is a monotonic proxy for local fluorophore density. Spatial integration provides an image-level summary proportional to total detected material. Compared with single-channel proxies, the observer suppresses background structure, improves contrast, and remains radiometrically interpretable. Because the method depends only on measurable spectra and linear transforms, it can be reproduced across cameras and extended to other fluorophores. Full article

The wave energy resource along the Azores coast is evaluated for the present (1990–2019) and future (2030–2059) periods using the third-generation wave model WAVEWATCH III, forced by winds and sea-ice cover from the RCP8.5 EC-Earth integration dynamically downscaled with the Weather Research and Forecasting model. The results indicate that the region is characterized by a high-energy wave climate, with mean wave power values typically ranging between 30 and 40 kW/m. A statistical comparison between the two periods shows a moderate reduction in wave energy potential under future conditions, with strong spatial variability. The performance of four wave energy converters (AquaBuoy, Wavestar, Oceantec, and Atargis) is analyzed, revealing significant differences in energy production and capacity factor depending on device–site matching. A techno-economic evaluation is performed by estimating the LCOE, accounting for capital expenditure, operational costs, device lifetime, and annual energy production (AEP). The results demonstrate that economic performance is primarily driven by energy production rather than capital cost alone, and that wave energy exploitation in the Azores remains viable under near-future climate conditions. Full article

The paper shows the methodologies of implementing a high-performance low-power logic control system designed with the use of standard languages like LD and SFC (according to the IEC61131-3 standard) directly in hardware utilizing FPGA devices. The essential idea is to convert the sequential sentences of a language to parallel computations and then map them to a dedicated hardware structure. The flexible graph-based method of language mapping is shown. It enables extracting control and data flow from language sentences. The direct hardware mapping technique enables building not only a high-performance structures but also a low-power implementations. All implementations retain a very short response time consisting of several clock cycles (from 3 to 7). The proposed low-power mapping strategies enable power saving up to 10 times while retaining processing performance. The obtained results are compared with a standard implementation using a benchmark program set. The paper is concluded with a comparison of the performance and energy consumption for the proposed implementation strategies. Full article

The increasing penetration of renewable energy has led to the large-scale integration of power electronic devices into the power grid. In weakly connected grids, such devices are connected to the grid via voltage source converters (VSCs) using grid-forming (GFM) control strategies. Ideally, the point of common coupling (PCC) with the grid is treated as a purely inductive circuit. However, in weak grids, the resistance-to-inductance ratio (R/X) cannot be ignored, which leads to the power coupling problem between active power (P) and reactive power (Q). This phenomenon impedes the precise control of P and Q, potentially resulting in steady-state power deviations and even system instability. Traditional power-decoupling methods based on virtual inductance (VI) have inherent limitations and fail to achieve complete decoupling between P and Q. To address this issue, this paper first analyzes the influencing factors of power coupling through an established power coupling model. Comparisons between the output voltage and the degree of power coupling demonstrate that power decoupling can be achieved by compensating the output voltage. Consequently, an improved power-decoupling strategy based on apparent power feedforward (APPFF) is proposed. The proposed APPFF method realizes complete P-Q decoupling, with a steady-state reactive power error of less than 1% of the rated value. Compared with the PI-decoupling method, the reactive power overshoot is reduced by about 24%, and no additional active power overshoot is introduced. Compared with the conventional virtual inductance method that only reduces coupling by up to 35%, APPFF eliminates the power coupling fundamentally while retaining the reactive power–voltage droop characteristics and fast dynamic response. By directly compensating the reference voltage to the ideal value using apparent power as the feedforward variable, the proposed method is essentially different from the existing voltage/angle compensation schemes. The feasibility and effectiveness of the proposed decoupling method are verified under various working conditions, such as different R/X ratios, line resistances and power references, through both Simulink simulations and experimental results. Full article

Accurate photovoltaic (PV) power forecasting is essential for the efficient operation of residential energy systems and microgrids, as reliable short-term predictions enable improved energy scheduling, demand management, and operational planning in distributed energy environments. In this study, one-hour-ahead forecasting of residential PV power generation is investigated using real-world data collected from multiple households within an Irish energy community. Several deep-learning architectures, including long short-term memory (LSTM), gated recurrent unit (GRU), convolutional neural networks (CNN), CNN–LSTM hybrid networks, and attention-based LSTM models, are evaluated and compared with a seasonal autoregressive integrated moving average (SARIMA) statistical model. A sliding-window approach is employed to transform the PV time series into a supervised learning problem. To ensure statistical robustness, deep-learning models are evaluated using a multi-run framework, and results are reported as mean ± standard deviation based on MAE, RMSE, MAPE, and R² metrics across multiple households. The results indicate that deep-learning models achieve consistently strong forecasting performance, with GRU frequently providing the most reliable predictions across several households. For instance, in House 5, GRU achieved an RMSE of 142.02 ± 1.87 W and an R² of 0.694 ± 0.008, while in Houses 11 and 13 it attained R² values of 0.837 ± 0.002 and 0.835 0.08, respectively. However, performance varied across households, reflecting the influence of data variability and generation patterns on model effectiveness. In comparison, the SARIMA model demonstrated competitive performance and, in certain cases, outperformed deep-learning models. For example, in House 4, it achieved the lowest RMSE of 90.68 W and the highest R² of 0.709. Overall, these findings highlight that while deep-learning models offer greater adaptability and stability, statistical models remain effective for more regular PV generation patterns. Consequently, the study emphasizes the importance of evaluating forecasting models under realistic household-level conditions and demonstrates that both deep-learning and statistical approaches can provide short-term PV forecasting. Full article

Triboelectric nanogenerators (TENGs) have emerged as versatile self-powered platforms for wearable and implantable biomedical sensing, offering an alternative to battery-dependent electronic devices. By converting biomechanical energy from physiological motion into electrical signals, TENGs enable simultaneous energy harvesting and active sensing within flexible, lightweight, and biocompatible architectures. This review summarizes recent advances from 2020 to 2025 in triboelectric nanogenerator (TENG)-based cardiovascular monitoring. The discussion focuses on material systems, device configurations, sensing mechanisms, and applications including pulse detection and cuffless blood pressure estimation. Representative studies are compared to highlight emerging trends in wearable and self-powered sensing technologies. However, differences in experimental conditions, anatomical sites, calibration methods, and signal-processing approaches limit direct comparison of reported performance. In addition, challenges such as subject-specific calibration, motion artifacts, and limited clinical validation remain. Overall, this review highlights current progress and outlines key challenges for future development and translation of TENG-based cardiovascular monitoring systems. Full article

Trust decay in social media is a serious threat to user experience and platform ecology. To solve this problem, this paper focuses on triadic closure in the infrastructure of social networks and explores its mechanism in trust decay prevention. Based on the systematic comparison of the ER random graph, the BA scale-free network, a forest fire model, and complete graph approaches, two core metrics, the trust decay risk index and trust resilience index, are proposed in this paper. Combined with structural indices such as the clustering coefficient, the average path length, and the triangular closure number and its growth rate, the quantitative relationship between network structure evolution and trust decay risk is established. It is found that the forest fire model exhibits optimal trust resilience in structure due to its power-law growth characteristics of high clustering, short path length and triangular closure; the dynamic mechanism of trust decay under different network growth modes is significantly different. The validity of the theoretical framework is further supported by the verification of Sina Weibo attention relationship network data. The analysis framework of network growth evolution based on fusion triangle closure and the risk and resilience indicators defined in this paper provides a computable theoretical tool for understanding and predicting trust evolution in social media from the perspective of network structure. Full article

In order to validate the structure and ascertain its conformity with the stipulated design conditions, the responses and members’ internal forces of the finite element model of structures under artificial earthquakes must be simulated. There are a variety of methodologies to generate the artificial earthquake waveform that corresponds to the design response spectrum. The frequency domain method is intuitive and convenient for generating the artificial earthquake waveform that corresponds to the design response spectrum. However, fluctuations in energy within specific frequency bands influence the acceleration responses across all frequency ranges. This, in turn, impedes the convergence process during the generation of artificial earthquake waveforms. The present study proposes a refined procedure for the generation of artificial earthquake waveforms in the frequency domain. The procedure can be used to generate the artificial earthquake that occurred in the vicinity of the Maanshan Nuclear Power Plant in Taiwan. A comparison of the parameters effect, including the cover range of the weighted function and the peak ground acceleration of the initial guess, were conducted to ascertain the convergence properties of the proposed approach. Full article

The use of millimeter-wave spectrum in fifth-generation (5G) systems is increasing the need for accurate prediction of received power and coverage in real deployment scenarios. In this context, ray tracing (RT) is a promising approach for site-specific analysis, although its reliability depends on how accurately different tools reproduce measurements in complex urban environments. This work presents a comparative assessment at 27 GHz of three RT tools: in-house Exact tool based on Vertical Plane Launching (VPL), Matlab 5G and open-source Sionna RT based on Shooting and Bouncing Rays (SBR). The comparison relies on a large outdoor walk-test campaign, including about 14,725 measurement points collected in a real urban area around a 27 GHz mMIMO base station, using real operator-provided antenna radiation patterns. Measured and simulated power levels are compared using statistical metrics, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and a planning-oriented coverage-rate metric. The results show a reasonable agreement between simulations and measurements, with RMSE and MAE values around 10–12 dB, highlighting tool-specific behaviors related to boundary effects, interaction modeling, and high-power overestimation. This work confirms that RT is a flexible support for 5G preliminary network design, reducing the need for extensive drive tests. Full article

Cultivated land fragmentation has become a critical constraint on regional agricultural sustainable development. Revealing its spatial patterns and driving mechanisms is of great significance for optimizing the utilization and management of cultivated land resources and enhancing regional agricultural productivity. This study focuses on the main grain-producing areas in the middle and lower reaches of the Yangtze River Basin. It constructs a Cultivated Land Fragmentation Index (CLFI) using an integrated method that combines landscape index analysis with an entropy-weighted approach, based on 2023 land-use data. The optimal analytical grain size and extent were determined before employing geographic detectors to identify dominant factors influencing cultivated land fragmentation. The key findings include the following: (1) The appropriate spatial resolution for fragmentation analysis was identified as 330 m, with an optimal analysis extent of 8910 m. (2) CLFI values ranged from 0.001 to 0.973, exhibiting significant spatial heterogeneity. The central plains and northeastern regions demonstrated low fragmentation levels and better contiguous cultivated land distribution, while the western and peripheral areas showed higher fragmentation. A provincial-scale comparison revealed that Jiangxi Province had the highest fragmentation level (0.255), whereas Jiangsu Province had the lowest (0.146). The topographic gradient analysis indicated a decreasing trend from the Guizhou Plateau (0.503) to the North China Plain (0.125), with plateaus and basins showing significantly higher fragmentation than hilly and plain regions. (3) Dominant controlling factors varied among provinces: In provinces with greater topographic relief (Anhui, Hubei, Hunan, Jiangxi), natural factors like elevation, slope gradient, and NDVI primarily controlled fragmentation patterns; in contrast, socioeconomic factors such as nighttime light intensity dominated in Jiangsu Province, characterized by flat terrain and high urbanization. Multi-factor interactions generally enhanced explanatory power regarding spatial patterns, confirming that cultivated land fragmentation is a result of comprehensive multi-factor interactions. This study reveals the spatial distribution characteristics of cultivated land fragmentation at the pixel scale in the study region, providing theoretical foundations and decision-making references for the efficient utilization of cultivated land resources and rural land system reforms. Full article

Cloud-based AI systems increasingly rely on Retrieval-Augmented Generation (RAG) to handle complex, knowledge-intensive queries. However, query decomposition for multi-hop retrieval—traditionally powered by large language models (LLMs)—incurs significant latency and cost, rendering it impractical for large-scale, cost-sensitive cloud deployments. We propose ToR-Lite, a lightweight, generative LLM-free semantic query decomposition framework designed to enhance multi-hop retrieval efficiency in cloud-based AI systems. ToR-Lite employs a novel Word-Window Splitting algorithm that detects semantic breakpoints via sliding window embeddings, effectively decomposing complex queries without expensive LLM inference. Experiments on the MultiHop-RAG benchmark (n = 2255) demonstrate that ToR-Lite achieves +6.03 pp Hits@10 and +0.89 pp Exact Match improvements over the Baseline, while operating 3.18 times faster than LLM-based Adaptive ToR. Retrieval performance correlates monotonically with decomposition granularity: three sub-query decompositions (#Dq = 3) yields a +7.00 pp Hits@10 improvement, confirming that semantic granularity is a key driver of retrieval performance. Comparison with rule-based Baselines confirms that these gains derive from the precision of semantic boundary detection rather than decomposition quantity alone. ToR-Lite delivers nearly twice the retrieval improvement per unit of computational cost, offering a practical and cost-effective solution for latency-sensitive cloud AI deployments. Full article

Improving the efficiency of distribution systems (DSs) through reactive power compensation using shunt capacitor banks is a widely applied practice, as it enhances the voltage profile and reduces operating costs. Owing to the nonlinear nature of DSs, heuristic algorithms—along with other optimization tools—are frequently employed to support techno-economic decision-making in DS design. In this study, we employ the neural population dynamics optimization algorithm (NPDOA), a recently developed heuristic approach inspired by brain neuroscience. The simulation and optimization model adopted in this research is based on quasi-static time-series analysis, which enables the planning problem and DS constraints to be examined from a probabilistic perspective. A comparative analysis with the genetic algorithm (GA) and the whale optimization algorithm (WOA) indicates that NPDOA provides a similar solution with comparable computational time. Specifically, the results show that NPDOA produces a solution only 0.02% higher than GA, with improvement probabilities of 27.42% and 10.94%, respectively. In comparison with WOA, NPDOA yields a solution that is 0.05% lower, with a corresponding probability of improvement of 10.76%. Furthermore, the installation of shunt capacitor banks optimized using NPDOA reduces the net present cost by 33%. Full article

Distributed generation (DG) exhibits inherent volatility and intermittency, and its grid-integration expansion presents formidable challenges to microgrid regulation and control. Conventional control strategies often neglect the uncertainties associated with renewable energy generation and the coordinated management of flexible resources. This paper proposes a multi-time-scale optimal control strategy for microgrids that explicitly accounts for uncertainty. The strategy integrates a collaborative scheduling framework for assets, including electric vehicles (EVs) and energy storage systems, alongside a stochastic optimization model for microgrids that comprehensively incorporates uncertainties from wind and solar power generation, EV operations, and load forecasting errors. The improved Archimedean chaotic adaptive whale optimization algorithm is utilized to solve the optimal scheduling model, while the Latin hypercube sampling (LHS) technique is employed to address uncertainty-related problems in the optimization process. Case study results demonstrate that, in comparison with traditional optimal scheduling strategies, the proposed approach more effectively mitigates uncertainties in real-world operations, reduces microgrid operational risks, achieves a significant reduction in scheduling costs, and concurrently fulfills the dual objectives of microgrid economic efficiency and operational security. Full article

Background/Objectives: Mindfulness-based interventions are widely used to reduce psychological distress. Their long-term neurophysiological correlates remain insufficiently characterized. Using a portable Muse InteraXon^® EEG device, this study aimed to evaluate (i) the extent to which a 12-month combined mindfulness and gratitude-based intervention reduces anxiety, depression, and perceived stress, and (ii) whether these changes are accompanied by corresponding EEG-derived neurophysiological alterations, exploring longitudinal brain–behavior associations. Methods: Fifty participants completed psychological assessments at baseline, 6 months, and 12 months using validated scales (BDI-II, DASS-21, EMAS). A subcohort of 25 participants also underwent EEG recordings with a portable Muse device at the same time points. Longitudinal changes were analyzed using linear mixed-effect models, and exploratory brain–behavior associations were assessed with change-score analyses and Spearman’s correlations with false discovery rate correction. Results: Across the full cohort (n = 50), psychological outcomes showed longitudinal improvements over 12 months, with reductions in BDI-21, DASS-21 depression, anxiety, and stress subscales, and EMAS-State scores (all p < 0.001; linear mixed-effect models). In the EEG subcohort (n = 25), longitudinal analyses showed increased alpha power and reduced beta and gamma power in frontal and temporoparietal regions (pFDR < 0.05), along with a modest decrease in delta power at 12 months, while theta power remained stable. Exploratory analyses showed non-significant trends in the hypothesized directions that did not remain statistically significant after correction for multiple comparisons (e.g., Δalpha vs. Δstate anxiety: ρ ≈ −0.44; Δbeta vs. Δdepression: ρ ≈ 0.43) or after FDR correction. Conclusions: The mindfulness- and gratitude-based intervention was associated with sustained improvements in psychological outcomes and suggests accompanying dynamic modulation of neurophysiology. EEG appears to reflect time-dependent neural adaptation rather than a static predictor of treatment response. Full article

Scandate cathodes have garnered significant attention for their exceptional low-temperature, high-current-density emission characteristics. However, their widespread deployment in vacuum electronic devices is currently hindered by stringent vacuum requirements and susceptibility to ion bombardment. To enhance the engineering applicability of scandate cathodes, this study employs first-principles density functional theory (DFT) to model the surface microstructures of strontium (Sr)–scandium (Sc) co-doped systems. Guided by simulation predictions regarding surface elemental ratios, corresponding emission active materials and cathode samples were fabricated. A systematic comparison between theoretical calculations and experimental measurements reveals a critical trade-off: while increasing Sr content enhances structural stability (indicated by lower formation energies), it concurrently increases the work function. Consequently, an optimal Sr doping level of approximately 2 wt% is identified, which significantly improves emission current density without compromising stability. Cathodes fabricated with this optimized composition were tested in a practical electron gun configuration. Results demonstrate that under low-temperature conditions (1000 °C) and wide-pulse operation (2 ms), the cathode achieves an emission current density of 21.57 A/cm². These findings validate the efficacy of simulation-guided material design and highlight the potential of Sr-doped scandate cathodes for high-power microwave applications. Full article