Search Results (8,907)

The increasing penetration of Doubly Fed Induction Generator (DFIG)-based wind energy systems raises major concerns regarding voltage stability and Fault Ride-Through (FRT) capability under grid disturbances and wind speed variations. This paper proposes a coordinated control framework for a grid-connected DFIG system, where a Static Synchronous Compensator (STATCOM) based on discrete-time deadbeat current control is integrated with a Supercapacitor Energy Storage System (SCES) connected to the DC link through a bidirectional DC-DC converter governed by cascaded Active Disturbance Rejection Control (ADRC). The deadbeat-controlled STATCOM provides fast reactive current injection for voltage support during sag and swell events, while the cascaded ADRC enhances DC-link voltage regulation and suppresses rotor-speed oscillations. Comprehensive MATLAB/Simulink simulations are carried out under variable wind speed and severe grid disturbances up to 80% voltage sag and 50% voltage swell. For voltage regulation, the proposed method is compared with SVC and PI-based STATCOM. In addition, SCES control performance is evaluated by comparing PI, single ADRC, and cascaded ADRC in terms of DC-link voltage overshoot, undershoot, and ripple. The results show clear improvements in voltage response and transient performance. Under a 20% voltage sag, the proposed deadbeat-controlled STATCOM significantly improves the dynamic response, where the undershoot is reduced from 0.125 p.u. (with SVC) to 0.04 p.u., and the settling time is shortened from 0.04 s to 0.025 s. Under a severe 80% sag, the overshoot is limited to 0.02 p.u., compared with 0.13 p.u. for the SVC and 0.15 p.u. for the PI-based STATCOM. Similarly, under a 50% voltage swell, the overshoot is reduced to 0.20 p.u., compared with 0.46 p.u. for the SVC and 0.27 p.u. for the PI-based STATCOM. Regarding the DC-link performance under 80% sag, the proposed cascaded ADRC-based SCES limits the overshoot and undershoot to 6 V and 2 V, respectively, compared with 39 V and 32 V for the PI-based SCES. These results confirm the superior damping, disturbance rejection, and FRT enhancement achieved by the proposed strategy. Full article

The increasing integration of renewable energy sources increases the variability and uncertainty of power systems, requiring advanced prediction-based control strategies. This paper proposes an integrated AutoML–MPC framework for a hybrid renewable energy system (HRES) combining solar and wind generation, biomass, battery energy storage, and a hydrogen chain (electrolyzer and fuel cell). Short-term load and generation forecasts are made using H2O AutoML models, and the energy flow allocation is optimized using model-based control (MPC) formalized in the form of mixed-integer linear programming (MILP). The objective function minimizes electricity imports from the grid and the associated ${\mathrm{C}\mathrm{O}}_2$ emissions, subject to technological constraints. The results obtained showed a clear distribution of short-term (battery) and long-term (hydrogen) storage functions in time: during periods of excess generation, the electrolyzer operated close to nominal mode, and in the deficit phase, the fuel cell was activated, reducing the need for grid imports. The battery ensured fast short-term balancing, while the hydrogen system compensated for the longer-term energy shortage. The forecast models were characterized by high accuracy ($R^2>0.98$), which allowed for reliable planning of energy flows over the MPC horizon. The proposed methodology allows for effective coordination of storage technologies of different time scales, maximum use of renewable generation and reducing the system’s dependence on the external grid. Full article

Sodium-ion batteries (SIBs) have emerged as one of the most promising alternatives to lithium-ion systems, driven by the abundance and low cost of sodium resources as well as the urgent demand for sustainable large-scale energy storage. In recent years, remarkable advances have been achieved in electrode materials, electrolytes, and interfacial engineering, which have significantly improved the electrochemical performance of SIBs. Hard carbons and alloy-type anodes have shown encouraging progress in balancing capacity and stability, while layered oxides, polyanionic compounds, and Prussian blue analogues are leading candidates for cathodes due to their structural diversity and tunable redox properties. Concurrently, the development of advanced liquid and solid electrolytes, together with strategies to control the solid–electrolyte interphase (SEI) and cathode–electrolyte interphase (CEI), is enhancing safety and long-term cycling. Despite these achievements, critical challenges remain, including limited energy density, volumetric expansion in alloying anodes, interfacial instability, and scalability issues. This review provides a comprehensive overview of the fundamental principles, recent material innovations, and failure mechanisms of SIBs, and highlights the current status of industrial progress led by companies such as Faradion, HiNa Battery, CATL, and Tiamat. Finally, future perspectives are discussed, emphasizing the role of sodium-ion technology in grid-scale storage, renewable energy integration, and sustainable battery recycling. By bridging academic advances and industrial development, this article outlines the roadmap toward the commercialization of sodium-ion batteries. Full article

Recent deep exploration in the northern Sichuan Basin has advanced our understanding of Lower Cambrian Canglangpu Formation carbonate reservoirs. However, the characteristics, genesis, and distribution of the reservoir, as well as future exploration targets, remain unclear. Specifically, core and thin-section analyses indicate that these reservoirs are notably tight, with virtually no visible macroporosity and low permeability (0.01–1 mD). However, helium porosity measurements reveal values of 2–5%, suggesting significant storage potential. An integrated approach utilizing optical and scanning electron microscopy (SEM), high-pressure mercury injection capillary pressure (MICP), nuclear magnetic resonance (NMR), and micro-computed tomography (micro-CT) was employed to characterize the pore systems. Quantitative thin-section analysis reveals visible areal porosity markedly lower than helium porosity, indicating predominance of micropores; mercury intrusion and NMR demonstrate that intragranular and intergranular micropores constitute most pore volume, although effectively connected throat sizes remain below 1 µm. Comparative stratigraphic evaluations show that porosity is more developed in the dolomite-rich upper and middle intervals of the depositional cycles, whereas the lower intervals are less porous. Early subaerial exposure promoted dolomitization and dissolution, which facilitated pore development. However, the influence of sediment mixing led to a reduction in porosity. And deep burial subjected the rocks to intense compaction and cementation, destroying most of the primary pore space. Consequently, reservoir quality is ultimately governed by the interplay between the original depositional environment and the later diagenetic history, with paleotopographic highs identified as the most promising exploration targets. These findings establish a predictive framework for reservoir quality in tight carbonate rocks, which holds significant implications for analogous plays worldwide. Full article

Apple firmness, soluble solids content (SSC), and titratable acidity (TTA) were modeled when fruit were kept under cold storage and shelf-life conditions. These attributes are key indicators of fruit quality, storability, and organoleptic properties. Apple fruit of the ‘Irene’ cultivar were stored at 1 °C for 7 months, with quality assessed monthly and after 7 days of shelf life. Models based on the Storage Time Equivalent Value (STEV) were applied to predict firmness, SSC, and TTA as functions of time in cold storage and shelf life. Rates of change were higher during shelf life, with acceleration factors of 5.78 for firmness, 6.50 for SSC, and 5.51 for TTA. Model performance was high (R²_CV = 0.977, RMSE_CV = 1.16 N for firmness; R²_CV = 0.862, RMSE_CV = 0.29 °Brix for SSC; R²_CV = 0.978, RMSE_CV = 0.155 g L⁻¹ for TTA). The proposed approach integrates cold storage and shelf life into a single predictive framework. The unified STEV models, incorporating acceleration factors, show potential for forecasting the shelf life of ‘Irene’ apples. Full article

Photoplethysmography (PPG) has emerged as an attractive modality for non-invasive cardiovascular monitoring due to its low cost, unobtrusive nature, and ubiquity in consumer wearable devices. Despite its potential, existing PPG-based arrhythmia detection systems remain limited in scope: (i) most target only atrial fibrillation, (ii) temporal localization of abnormal segments is rarely provided, and (iii) deep learning models lack explainability, hindering adoption in clinical workflows. We present a comprehensive and fully integrated framework for multi-class arrhythmia detection, segmentation, and explainability based on PPG waveforms, Heart Rate Variability (HRV), and structured clinical metadata. The proposed system introduces a CLIP-style contrastive learning module aligning PPG waveforms with clinical variables and rhythm-state textual descriptions using BioBERT; a multitask U-Net architecture performing 4-class classification and 1D segmentation; a Retrieval-Augmented Generation (RAG) pipeline leveraging Gemini Flash large language models to produce guideline-grounded diagnostic reports; and a real-time Streamlit-based web platform supporting inference, visualization, and database storage. The system significantly improves classification accuracy (from 86.27% to 91.19%) and segmentation Dice (from 0.5815 to 0.7167). These results demonstrate the feasibility of a robust, multimodal, and explainable PPG-based arrhythmia monitoring system for real-world applications. Full article

The rapid advancement of artificial intelligence relies on massive high-quality data, yet increasingly stringent data privacy regulations have exacerbated the problem of data silos. Federated learning enables collaborative training under privacy protection by exchanging model parameters rather than transmitting raw data. Nevertheless, its traditional centralized architecture still suffers from limitations such as single points of failure, lack of trust, and insufficient incentives. The integration of blockchain and federated learning opens new pathways for decentralized, auditable, and secure machine learning systems. This paper systematically reviews research progress in blockchain-enabled federated learning, analyzing technological evolution from three perspectives: system architecture, incentive mechanisms, and privacy enhancement. It further explores critical challenges including efficiency bottlenecks, storage overhead, and the inherent tension between transparency and privacy, while identifying key research directions for building scalable, efficient, and trustworthy decentralized learning systems. Full article

To meet the demands for miniaturization, lightweight design, and high performance in modern electronic systems, advanced 3D TSV technology enables a substantial increase in storage capacity even within physically constrained form factors. This paper proposes a schematic design methodology and system-level integrated modeling approach for a four-layer stacked micro-module based on wafer-level packaging. By leveraging heterogeneous chip fan-out technology and TSV-based vertical stacking, the fabricated DDR3 micro-module achieves a compact footprint of 14 × 9 × 3.5 mm, a storage capacity of 4 GB, and a 64-bit bus width. Compared to conventional board-level mounting, the module reduces the footprint area by 95%. Following comprehensive multi-level testing, the micro-module fully complies with standard protocol requirements, enabling a paradigm shift in form factors for mobile computing devices while enhancing computational density and energy efficiency in data center server applications. Full article

The building sector is responsible for 40% of CO₂ emissions in Portugal, making the integration of renewable energy systems increasingly relevant. Photovoltaic (PV) technologies have become more accessible due to declining levelized costs of energy, and when coupled with battery energy storage systems (BESSs), they can enhance grid independence, reduce household energy expenses, and mitigate peak load stress. However, high upfront costs still limit adoption, particularly among vulnerable communities. This study evaluates the technical, economic, and environmental performance of PV systems, with and without BESSs, compared with an existing solar thermal configuration in a social-housing neighbourhood in Porto, Portugal. Numerical simulations were conducted for three scenarios, optimising system sizing and ensuring hourly energy flow balance between generation, storage, and grid supply. Results indicate that all configurations are technically feasible within Porto’s climate conditions, though with distinct investment needs, payback periods, and CO₂ reduction outcomes. The findings offer practical guidance for designing renewable energy solutions tailored to social housing, supporting both decarbonization goals and long-term mitigation of energy poverty. Full article

A warm-up is a critical procedure in sports science for enhancing muscular performance and optimizing subsequent athletic activities. However, the physiological and athletic performance effects of a warm-up are often transient, diminishing rapidly during the period of inactivity after the warm-up, which is known as the warm-up transition phase. In this study, a multi-functional thermoregulation wearable composite film of graphene–MXene–bacterial cellulose/polyethylene glycol (G-M-BC/PEG) was developed by integrating MXene (a two-dimensional material with good photothermal conversion performance) and graphene into a bacterial cellulose aerogel framework, subsequently impregnated with polyethylene glycol (PEG-2000). The film showed stable structure, efficient solar photothermal conversion and storage (SPCS), and improved mechanical properties. Under 1 sun irradiation, the optimized G-M-BC/PEG wearable film showed excellent SPCS performance, sustaining a temperature plateau of 38–40 °C for 10 min after the xenon lamp was switched off under 1 sun irradiation, with a leakage rate of only 5.32% after five cycles. By constructing a biomimetic sports human body model, the composite aerogel was shown to significantly elevate muscle surface temperature and effectively mitigate heat loss during the transition phase. In the warm-up effectiveness and sports performance tests, the wearable film improved 200 m sprint performance by 0.8% ± 0.4% (p = 0.039). It also maintained subjective thermal sensation during the warm-up transition phase, with no significant decline at 5 or 10 min after the warm-up and a significant decrease only at 15 min (p = 0.02), while thermal comfort remained stable, suggesting improved neuromuscular readiness. This research provided a novel strategy for the fabrication of advanced aerogel-based wearable devices aimed at precision thermal management and athletic performance optimization. Full article

A continuous integration framework and methodology for hydrological modeling is proposed that integrates model sensitivity analysis with real-time sensor tasking to prioritize data collection in regions and periods of high hydrological variability and drive model refinement. The Cahaba River Watershed in central Alabama serves as a case study to develop this approach. To this end, a benchmark Soil and Water Assessment Tool (SWAT) model (30 m DEM) was refined with high-resolution spatial datasets in QGIS, including 1 m DEMs, NLCD land cover, and SSURGO soil data. The refined model significantly enhanced subbasin delineation, increasing granularity from 8 to 99 subbasins, thereby improving representation of slope, runoff, and storage variability across heterogeneous landscapes. Sensitivity analyses were performed to evaluate the influence of DEM resolution and curve number (CN) perturbations on hydrologic responses, including retention, flow partitioning, and dominant flow direction. High-resolution DEMs (≤5 m) captured microtopographic features that strongly affect infiltration and surface runoff, while coarser DEMs (≥20 m) systematically underestimated retention and smoothed hydrologic gradients. The higher-resolution DEMs can be used to selectively improve the model at certain hotspots/areas of higher sensitivity. Localized flow simulations demonstrated that fine-scale terrain data substantially improve model realism, with up to 58% greater retention captured in 10 m DEMs compared to 30 m DEMs. The results confirm that aligning sensor placement and model refinement with spatially explicit sensitivity zones enhances both predictive accuracy and computational efficiency. The proposed continuous integration approach provides a scalable pathway for coupling high-resolution modeling with adaptive sensing in watershed management and supports future integration of real-time data assimilation for continuous model improvement. Full article

Mold infection by Aspergillus and Penicillium spp. in Sithong sweet tamarind (Tamarindus indica L.) during commercial postharvest storage poses quality and food safety risks. However, the current visual detection method, which involves randomly cracking open the pods, is both destructive and laborious. The integration of near-infrared spectroscopy (NIRS) with artificial neural networks (ANN) enables rapid and non-destructive detection while capturing non-linear biochemical–spectral relationships, offering advantages over conventional destructive and linear analytical methods. It was tested as a mold classifier in sweet tamarind pods preserved in commercial ambient conditions (25 °C, 60% relative humidity) for five weeks. Six hundred pods were examined weekly using interactance spectroscopy (800–2500 nm) with six measurement points per pod and four spectral preprocessing methods. The ANN outperformed partial least squares discriminant analysis (PLS-DA) across all storage weeks, peaking at Week 2 with standard normal variate (SNV) preprocessing (prediction accuracy: 85.00%; sensitivity: 0.84; specificity: 0.86; F1-score: 0.85). Advanced tissue degeneration caused spectral heterogeneity, which decreased performance at Week 4 (prediction accuracy: 71.82–76.36%). Principal component loadings identified mold-induced water redistribution and carbohydrate depletion wavelengths at 938, 975–980, and 1035 nm. Week-adaptive calibration is essential for implementation because of the large difference between week-specific model accuracy (up to 85%) and overall storage model accuracy (63.53%). These findings provide a mechanistic underpinning for smaller wavelength-selective sensors and temporally adaptive mold screening systems in commercial tamarind storage. Full article

The transition toward capacity-based network tariffs shifts the primary role of battery energy storage systems (BESS) from traditional energy arbitrage to active peak shaving. This paper presents a mixed-integer linear programming (MILP) optimization model for the co-optimization of both BESS size and operation scheduling for multiple prosumers operating individually and within an energy community (EC). Battery aging is accounted for in the optimization model through the state of health (SOH). The framework is evaluated by a comprehensive techno-economic analysis of BESS integration under Slovenia’s multi-block tariff structure. The results demonstrate that while individual distributed BESS integration is highly profitable, centralized EC BESS financially underperforms. Because centralized BESS cannot directly reduce individual contracted power limits, its profitability relies on energy arbitrage, making the initial investment and double grid fees the primary barriers. Conversely, integrating distributed storage with peer-to-peer (P2P) trading minimizes the required BESS capacity while maintaining profitability. The evaluation also reveals that ECs do not automatically act as socio-economic equalizers, indicated by a stable Gini coefficient. However, a break-even analysis reveals the necessary reduction in capital costs to overcome these hurdles, confirming the strong future viability of centralized EC BESS. Full article

Brewer’s spent grain (BSG) is one of the major by-products of the brewing industry and an abundant lignocellulosic stream with potential for energy recovery and broader biorefinery use. This review evaluates the main BSG-to-energy pathways, including anaerobic digestion (AD), combustion/co-combustion, pyrolysis, gasification, and hydrothermal processes (HTC/HTL), with emphasis on technical performance, environmental aspects, implementation constraints, and integration into brewery systems. Particular attention is given to the effect of BSG heterogeneity, high moisture content, protein and ash composition, and storage instability on process selection and operability. In addition to summarizing pathway-specific evidence, the manuscript proposes a harmonized comparative framework and an integrated technical–economic–environmental interpretation of BSG valorization options. The analysis shows that wet-feed-compatible pathways, especially AD and hydrothermal processing, are generally better aligned with the intrinsic properties of fresh BSG, whereas thermochemical routes usually require more intensive feedstock conditioning and tighter control of ash-related and gas cleaning risks. The review also highlights that long-term operational reliability, scale-up constraints, and utility integration are as important as nominal conversion efficiency when assessing practical deployment. Current evidence suggests that the most realistic implementation strategies are context-dependent and should be selected according to brewery scale, energy demand profile, available heat integration, and acceptable operational risk. Future research should prioritize harmonized reporting, long-term industrial validation, and the development of robust hybrid systems and brewery-integrated biorefinery configurations. Full article

Pavement energy harvesting has been investigated as a means of converting traffic loading, solar radiation, and pavement thermal gradients into usable electricity or heat. This paper reviews 135 publications available through March 2026 and evaluates the field from a pavement engineering perspective. The literature is organized into six technology families: piezoelectric systems, mechanical-electromagnetic systems, triboelectric systems, thermoelectric systems, hydronic/geothermal/solar-thermal pavements, and photovoltaic or pavement-integrated photovoltaic-thermal systems. The review considers not only reported energy output, but also structural compatibility, durability, constructability, maintenance requirements, safety, and deployment conditions. The synthesis shows that the most credible near-term roles of piezoelectric and triboelectric systems are self-powered sensing and other localized low-power functions rather than bulk electricity generation. Mechanical-electromagnetic systems can produce larger event-level output, but their practicality is limited to low-speed and highly controlled settings because they rely on deliberate surface displacement. Thermoelectric systems are mechanically compatible with pavements, yet their performance remains constrained by weak and transient temperature gradients. Hydronic and solar-thermal pavements are presently the most infrastructure-compatible option for large-area energy recovery because they deliver useful heat and align with snow-melting, seasonal storage, and adjacent building-energy applications. Photovoltaic and photovoltaic-thermal pavements offer direct electrical generation, but continued challenges with transparent cover layers, surface friction, durability, fouling, and maintenance still limit broad roadway deployment. Overall, the review indicates that future progress will depend less on maximizing peak output in isolated prototypes and more on integrated pavement-energy design, standardized performance reporting, durability assessment, techno-economic evaluation, and corridor-scale demonstration. Full article