Search Results (17,696)

The determination of brain age based on electroencephalography (EEG) data has become widely developed with the spread of machine learning in recent years. In this research paper, we analyzed 21 articles published no earlier than 2015, focusing particularly on features, machine learning and deep learning models, and the validation process. The studies reviewed presented model performance on EEG data using machine learning or deep learning techniques. Deep convolutional and transformer-based models trained on well-curated features forecasted chronological age most precisely. In newborns, time–frequency and entropy-based characteristics showed good predictive power for the brain age index (BAI) and functional brain age (FBA). Consistently, spectral and nonlinear descriptors ranked among the most informative characteristics. Methodological rigor, meanwhile, differed: only a small number of studies used bias correction techniques, addressed statistical assumptions, or reported external validation. Preprocessing techniques also showed significant variation. Although EEG-based models have good accuracy, problems of interpretability and generalizability restrict their clinical and developmental use. Advancing this discipline will call for biologically based outcome definitions, uniform evaluation systems, and open source processing pipelines. Full article

Thermoelectric generators (TEGs) based on single-walled carbon nanotubes (SWCNTs) offer a promising approach for powering sensors in wearable systems. However, achieving high performance remains challenging because the high thermal conductivity of SWCNTs limits the temperature gradient within the device. We previously developed flexible SWCNT-TEGs with enhanced heat dissipation by dip-coating SWCNTs onto mesh sheets; however, their performance in real wearable environments had not been evaluated. In this study, we demonstrate the practical operation of these SWCNT-TEGs under conditions such as fingertip contact and cap-based wear. The output voltage increased proportionally with the number of fingers touching the device, and a stable voltage of 6.1 mV was obtained when the TEG was mounted on a cap and worn outdoors at 7 °C. These findings highlight the promising potential of flexible SWCNT-TEGs as power sources for next-generation wearable technologies, including human–computer interaction and health monitoring. Full article

Lithium thermal batteries are primary reserve batteries utilizing solid molten salt electrolytes. They are regarded as ideal power sources for high-reliability applications due to their high power density, rapid activation, long shelf life, wide operating temperature range, and excellent environmental adaptability. However, existing electrode systems are limited by insufficient conductivity and the use of high-impedance MgO binders. This results in sluggish electrode reaction kinetics and incomplete material conversion during high-temperature discharge, causing actual discharge capacities to fall far below theoretical values. To address this, FeS₂-CoS₂ multi-component composite cathode materials were synthesized via a high-temperature solid-phase method. Furthermore, two distinct MgO binders were systematically investigated: flake-like MgO (MgO-F) with a sheet-stacking structure and spherical MgO (MgO-S) with a low-tortuosity granular structure. Results indicate that while MgO-F offers superior electrolyte retention via physical confinement, its high tortuosity limits ionic conduction. In contrast, MgO-S facilitates the construction of a wettability-enhanced continuous ionic network, which effectively reduces interfacial impedance and enhances system conductivity. This regulation promoted Li⁺ migration and accelerated interfacial reaction kinetics. This study provides a feasible pathway for improving the electrochemical performance of lithium thermal batteries through morphology-oriented MgO binder regulation. Full article

Accurate short-term forecasting of renewable energy sources is essential for stable and efficient microgrid operation. Existing models primarily focus on either solar or wind prediction, often neglecting their combined stochastic behavior within isolated systems. This study presents a comparative evaluation of three machine-learning models—Random Forest, ANN, and LSTM—for short-term solar and wind forecasting in microgrid environments. Historical meteorological data and power generation records are used to train and validate three ML models: Random Forest, Long Short-Term Memory, and Artificial Neural Networks. Each model is optimized to capture nonlinear and rapidly fluctuating weather dynamics. Forecasting performance is quantitatively evaluated using Mean Absolute Error, Root Mean Square Error, and Mean Percentage Error. The predicted values are integrated into a microgrid energy management system to enhance operational decisions such as battery storage scheduling, diesel generator coordination, and load balancing. Among the evaluated models, the ANN achieved the lowest prediction error with an MAE of 64.72 kW on the one-year dataset, outperforming both LSTM and Random Forest. The novelty of this study lies in integrating multi-source data into a unified ML-based predictive framework, enabling improved reliability, reduced fossil fuel usage, and enhanced energy resilience in remote microgrids. This research used Orange 3.40 software and Python 3.12 code for prediction. By enhancing forecasting accuracy, the project seeks to reduce reliance on fossil fuels, lower operational costs, and improve grid stability. Outcomes will provide scalable insights for remote microgrids transitioning to renewables. Full article

Greenhouse gas emissions are a primary contributor to climate change and the observed rise in global temperatures. To reduce these emissions, renewable energy sources (RESs) must replace fossil fuels in power generation. Because of the mismatch between production and demand, the increase in RES is limited. To address this phenomenon, the addition of renewable energy generation should be accompanied by storage systems. In this paper, the island of Crete is examined for various renewable energy generations and storage capacities using the PowerWorld Simulator software. Four main scenarios are studied in which the installed renewable energy generation is increased to reach substation limits. For every scenario, different renewable energy generation mixes are considered between wind farms and photovoltaics. Furthermore, for all sub-scenarios, different storage capacities are considered, ranging from 1.6 GWh to 12.8 GWh. This study proves that storage systems are mandatory to increase renewable energy penetration. In certain scenarios, a battery energy storage system can further increase renewable energy penetration from 6.15% to 28.07%. Although the battery energy storage system enhanced renewable penetration, increasing transmission line capacities should also be considered regarding the scenario. Full article

With the increasing integration of renewable energy into power grids, voltage source converter-based high-voltage direct current (VSC-HVDC) stations often adopt hybrid grid-following (GFL) and grid-forming (GFM) control strategies to improve adaptability to varying grid strengths. In many existing schemes, the hybrid coefficient changes abruptly, which may produce large transient current overshoots and compromise the safe and stable operation of converters. An adaptive hybrid GFL-GFM control framework equipped with a hybrid coefficient transition regulation is proposed. Small-signal state–space models are established and eigenvalue analysis confirms stability over the considered short-circuit ratio (SCR) range. The regulating method is activated only during coefficient transitions and is inactive in steady-state, thereby preserving the operating-point eigenvalue properties. Dynamic equations of the converter current change rate are derived to reveal the key role of the hybrid-coefficient change rate in driving transient current overshoots, based on which a real-time hybrid coefficient regulating method is developed to shape coefficient transitions. Simulations on a 500 kV/2100 MW VSC-HVDC project demonstrate reduced transient current overshoot and power oscillations during SCR variations, with robustness under moderate parameter deviations as well as representative SCR assessment error and update delay. Full article

PM_2.5 pollution has become one of the major environmental issues in Shandong Province in recent years. High concentrations of PM_2.5 not only reduce atmospheric visibility but also induce respiratory and cardiovascular diseases, and significantly increase health risks. Source apportionment of PM_2.5 is important for policy makers to determine control strategies. This study analyzed regional and sectoral PM_2.5 sources across 17 Shandong cities during the 2017–2018 winter heating season, which is selected because it is representative of severe air pollution with an average PM_2.5 of 65.75 μg/m³ and hourly peak exceeding 250 μg/m³. This air pollution episode aligned with key control policies, where seven major cities implemented steel capacity reduction and coal-to-gas/electric heating, as a baseline for evaluating emission reduction effectiveness. The particulate matter source apportionment technology in the Comprehensive Air Quality Model with extensions (CAMx) was applied to simulate the source contributions to PM_2.5 in 17 cities from different regions and sectors including industry, residence, transportation, and coal-burning power plants. The meteorological fields required for the CAMx model were generated using the Weather Research and Forecasting (WRF) model. The results showed that all cities besides Dezhou city in Shandong Province contributed PM_2.5 locally, varying from 39% to 53%. The emissions from Hebei province have a large impact on the PM_2.5 concentrations in Shandong Province. The non-local industrial and residential sources in Shandong Province accounted for the prominent proportion of local PM_2.5 in all cities. The contribution of non-local industrial sources to PM_2.5 in Heze City was up to 56.99%. As for Zibo City, the largest contribution of PM_2.5 was from non-local residential sources, around 56%. Additionally, the local industrial and residential sources in Jinan and Rizhao cities had relatively more contributions to the local PM_2.5 concentrations compared to the other cities in Shandong Province. Finally, the emission reduction effects were evaluated by applying different reduction ratios of local industrial and transportation sources, with decreases in PM_2.5 concentrations ranging from 0.2 to 26 µg/m³ in each city. Full article

Accurate indoor positioning and navigation remain significant challenges, with audio sensor-based sound source localization emerging as a promising sensing modality. Conventional methods, often reliant on multi-channel processing or time-delay estimation techniques such as Generalized Cross-Correlation, encounter difficulties regarding computational complexity, hardware synchronization, and reverberant environments where time difference in arrival cues are masked. While machine learning approaches have shown potential, their performance depends heavily on the discriminative power of input features. This paper proposes a novel feature extraction method named Short-Time Homomorphic Deconvolution, which transforms multi-channel audio signals into a 2D Time × Time-of-Flight representation. Unlike prior 1D methods, this feature effectively captures the temporal evolution and stability of time-of-flight differences between microphone pairs, offering a rich and robust input for deep learning models. We validate this feature using a lightweight Convolutional Neural Network integrated with a dual-stage channel attention mechanism, designed to prioritize reliable spatial cues. The system was trained on a large-scale dataset generated via simulations and rigorously tested using real-world data acquired in an ISO-certified anechoic chamber. Experimental results demonstrate that the proposed model achieves precise Direction of Arrival estimation with a Mean Absolute Error of 1.99 degrees in real-world scenarios. Notably, the system exhibits remarkable consistency between simulation and physical experiments, proving its effectiveness for robust indoor navigation and positioning systems. Full article

This paper presents a closed-loop price–dispatch framework for park-scale virtual power plants (VPPs) with coupled electric–thermal processes under high penetrations of photovoltaics (PVs) and electric vehicles (EVs). The outer layer clears time-varying prices for operator electricity, operator heat, and user feed-in using an improved particle swarm optimizer with adaptive coefficients and velocity clamping. Given these prices, the inner layer executes a lightweight linear source decomposition with feasibility projection that enforces transformer limits, combined heat-and-power (CHP) and boiler constraints, ramping, energy balances, and EV state-of-charge requirements. PV uncertainty is represented by a small set of scenarios and a conditional value-at-risk (CVaR) term augments the welfare objective to control tail risk. On a typical winter day case, the coordinated setting aligns EV charging with solar hours, reduces evening grid imports, and improves a social welfare proxy while maintaining interpretable price signals. Measured outcomes include 99.17% PV utilization (95.14% self-consumption and 4.03% routed to EV charging) and a reduction in EV charging cost from CNY 304.18 to CNY 249.87 (−17.9%) compared with an all-from-operator benchmark; all transformer, CHP/boiler, and EV constraints are satisfied. The price loop converges within several dozen iterations without oscillation. Sensitivity studies show that increasing risk weight lowers CVaR with modest welfare trade-offs, while wider price bounds and higher EV availability raise welfare until physical limits bind. The results demonstrate an effective, interpretable, and reproducible pathway to integrate market signals with engineering constraints in park VPP operations. Full article

This study investigates 49 gold solidi issued between the 4th and 5th century AD to determine their chemical composition. The coins were first catalogued by recording mass, diameter, and thickness. All specimens underwent non-destructive µ-EDXRF analysis to identify main elements, followed by semi-quantitative fineness evaluation. To validate these results, six coins were randomly micro-sampled: material was dissolved in aqua regia and analysed by ICP-AES for gold quantification and ICP-MS for high precision trace element determination. The non-destructive analyses showed consistently high gold percentages, confirming authenticity and the extensive use of this noble metal during the studied period. Two distinct groups were identified based on the XRF Pt/Pd ratio, suggesting the use of gold from different sources. Comparison of μ-EDXRF and ICP-AES gold contents shows no statistically significant differences; however, this apparent agreement should be interpreted cautiously, as it mainly reflects the limited resolving power of ICP-AES at very high gold concentrations rather than definitive evidence for the absence of surface-related effects. Trace elements analysis detected low concentrations of Cu, Sn, and Pb suggesting the use of alluvial gold for minting. The presence and correlation of terrigenous elements (Al, Ca, Ti, Cr, Mn, Fe, Ni, Zn, Sr) indicate soil as the burial site. Full article

As key driving and supporting components of spacecraft, pod-type space self-deployable structures have terminal pointing accuracy that directly affects overall spacecraft performance. To clarify the influence of the structure’s machining deformation on its pointing accuracy, this study focuses on two key processes, namely laser welding and hot forming. Based on the bionic symmetric structural characteristics of pod-type structures, a laser welding finite element model with a surface Gaussian heat source and a hot forming constitutive model coupled with creep aging were established. An orthogonal experimental design was adopted: for laser welding, three parameters, namely laser power, spot diameter, and welding speed, each with three levels, were selected, and an L₉(3³) orthogonal table was constructed to conduct nine groups of simulations; for hot forming, two parameters, namely processing temperature and holding time, each with three levels, were chosen, and nine groups of simulations were designed based on the first two columns of the L₉(3⁴) orthogonal table. The combined method of residual analysis and analysis of variance was used to quantitatively identify the influence of each process parameter on pointing accuracy. The results show that in laser welding, welding speed has the most significant impact on deformation, followed by laser power, and spot diameter has the least; in hot forming, processing temperature and holding time have similar effects on deformation. Physical machining verification was performed, and the actually measured deformations are 0.164 mm and 0.034 mm, which are close to the simulation results of 0.176 mm and 0.047 mm, meeting the index requirement that the terminal pointing deformation of a single pod structure is less than 0.2 mm. The results can provide a theoretical basis and engineering reference for the actual machining of such structures. Full article

The rise of multidrug-resistant tuberculosis (TB) necessitates alternative therapeutic sources. This study investigated the polyphenolic content and the antioxidant, antimycobacterial, and anti-virulence activities of selected medicinal plants traditionally used to treat TB and related symptoms. Total phenolics, tannins, and flavonoids were quantified using colorimetric assays. Antioxidant capacity was assessed via DPPH and ferric-reducing power assays. Antimycobacterial activity against Mycobacterium smegmatis was evaluated using broth microdilution, growth kinetics, cell constituent leakage, and respiratory chain dehydrogenase inhibition assays. Anti-virulence effects were examined using crystal violet biofilm and swarming motility assays. Tarchonanthus camphoratus showed the highest polyphenolic levels and, together with Combretum hereroense, strong antioxidant activity. Extracts of Senecio macroglossus, Nerium oleander, and Tetradenia riparia displayed potent antimycobacterial activity (MIC = 0.16 mg/mL), characterized by delayed exponential growth, membrane damage, and metabolic inhibition. Tabernaemontana elegans exhibited the weakest activity (MIC > 2.5 mg/mL). Most extracts also significantly impaired motility (12–100%) and early-stage biofilm formation. Polyphenolic-rich plant extracts demonstrated promising antimycobacterial and anti-virulence properties against M. smegmatis, highlighting their potential as leads for developing novel anti-TB agents. Full article

The optimal allocation of Photovoltaic (PV) and wind-based renewable energy sources and Battery Energy Storage System (BESS) capacity is an important issue for efficient operation of a microgrid network (MGN). The impact of the unpredictability of PV and wind generation needs to be smoothed out by coherent allocation of BESS unit to meet out the load demand. To address these issues, this article proposes an efficient Energy Management System (EMS) and Demand Side Management (DSM) approaches for the optimal allocation of PV- and wind-based renewable energy sources and BESS capacity in the MGN. The DSM model helps to modify the peak load demand based on PV and wind generation, available BESS storage, and the utility grid. Based on the Real-Time Market Energy Price (RTMEP) of utility power, the charging/discharging pattern of the BESS and power exchange with the utility grid are scheduled adaptively. On this basis, a Jellyfish Search Algorithm (JSA)-based bi-level optimization model is developed that considers the optimal capacity allocation and power scheduling of PV and wind sources and BESS capacity to satisfy the load demand. The top-level planning model solves the optimal allocation of PV and wind sources intending to reduce the total power loss of the MGN. The proposed JSA-based optimization achieved 24.04% of power loss reduction (from 202.69 kW to 153.95 kW) at peak load conditions through optimal PV- and wind-based DG placement and sizing. The bottom level model explicitly focuses to achieve the optimal operational configuration of MGN through optimal power scheduling of PV, wind, BESS, and the utility grid with DSM-based load proportions with an aim to minimize the operating cost. Simulation results on the IEEE 33-node MGN demonstrate that the 20% DSM strategy attains the maximum operational cost savings of €ct 3196.18 (reduction of 2.80%) over 24 h operation, with a 46.75% peak-hour grid dependency reduction. The statistical analysis over 50 independent runs confirms the sturdiness of the JSA over Particle Swarm Optimization (PSO) and Osprey Optimization Algorithm (OOA) with a standard deviation of only 0.00017 in the fitness function, demonstrating its superior convergence characteristics to solve the proposed optimization problem. Finally, based on the simulation outcome of the considered bi-level optimization problem, it can be concluded that implementation of the proposed JSA-based optimization approach efficiently optimizes the PV- and wind-based resource allocation along with BESS capacity and helps to operate the MGN efficiently with reduced power loss and operating costs. Full article

This paper proposes a dynamic voltage restorer (DVR) based on a new three-phase multilevel inverter (MLI). An integral component of DVRs is the power electronic converter. At medium-to-high voltage levels, MLIs are the ideal converters for DVR applications because lower voltage-rated switches are used to generate high voltages, thus minimizing power losses. The proposed three-phase MLI generates 15 levels of load voltage per phase, using a reduced component count: eight lower-rated semiconductor power switches, four primary DC voltage sources, two auxiliary DC sources, and eight driver circuits per phase. Additionally, each phase features a low-frequency transformer with voltage-boosting and galvanic isolation capabilities. The switching sequence of the proposed MLI is simpler to execute using fundamental frequency control; this methodology provides reduced switching stress and reduced switching losses as merits. Structurally, the proposed MLI is less complex and thus scalable. The proposed DVR, based on three-phase MLI, efficiently offsets power quality problems such as voltage swell, voltage sags, and harmonics for balanced and unbalanced loads. The operational performance of the proposed DVR-MLI is verified by a simulation, using PSCAD software and an experimental prototype. Full article

Achieving high reliability remains the critical challenge for pulsed power supplies (PPS), whose core components are susceptible to severe degradation and catastrophic failure due to long-term operation under electrical, thermal and magnetic stresses, particularly those associated with high voltage and high current. This reliability challenge fundamentally limits the widespread deployment of PPSs in defense and industrial applications. This article provides a comprehensive and systematic review of the reliability challenges and recent technological progress concerning PPSs, focusing on three hierarchical levels: component, system integration, and extreme operating environments. The review investigates the underlying failure mechanisms, degradation characteristics, and structural optimization of key components, such as energy storage capacitors and power switches. Furthermore, it elaborates on advanced system-level techniques, including novel thermal management topologies, jitter control methods for multi-module synchronization, and electromagnetic interference (EMI) source suppression and coupling path optimization. The primary conclusion is that achieving long-term, high-frequency operation depends on multi-physics field modeling and robust, integrated design approaches at all three levels. In summary, this review outlines important research directions for future advancements and offers technical guidance to help speed up the development of next-generation PPS systems characterized by high power density, frequent repetition, and outstanding reliability. Full article