Search Results (301)

This paper presents an adaptive talkative power (TP) framework that enables simultaneous high-efficiency power transfer and reliable data communication under time-varying load conditions. A high-frequency TP-based bidirectional boost converter employing a SiC-based zero voltage switching–quasi square wave (ZVS-QSW) topology is proposed, incorporating closed-loop online efficiency optimization. Data transmission is realized through adaptive switching-frequency modulation at the transmitter, allowing information encoding while preserving optimal power transfer efficiency. To support reliable data detection under unknown and non-constant load conditions, an adaptive receiver architecture is developed that extracts information from output voltage ripple variations induced by frequency modulation. Owing to the nonlinear and complex nature of the ripple characteristics, a supervised machine-learning-based classification approach is employed for data detection, eliminating the need for prior knowledge of converter parameters and overcoming the limitations of conventional maximum-likelihood detection methods. The proposed system is validated through real-time simulations using a dSPACE MicroLabBox system in conjunction with MATLAB/Simulink R2025b. Simulation results demonstrate power transfer efficiencies approaching 98% while enabling reliable and efficient data transmission across a wide range of operating conditions, including varying conversion ratios and dynamic load variations, thereby confirming the effectiveness and robustness of the proposed TP-based power and data transmission scheme. Full article

Introduction: Caffeine is the most widely consumed psychoactive stimulant worldwide and acts primarily through antagonism of adenosine A1 and A2A receptors, thereby reducing sleep pressure and promoting wakefulness. Although its alerting and performance-enhancing effects are well established, its influence on sleep-related electroencephalography (EEG) has been investigated across diverse paradigms with substantial methodological heterogeneity. This systematic and mechanistic review aimed to synthesize human evidence on how caffeine affects sleep architecture, quantitative sleep EEG, and neurophysiological markers of sleep homeostasis, and to interpret these findings within current models of adenosine-mediated sleep–wake regulation. Materials and methods: A systematic search of PubMed/MEDLINE, Web of Science, Scopus, Embase, PsycINFO, ResearchGate, and Google Scholar was conducted for studies published between January 1980 and January 2026, with the final search performed on 10 January 2026. Eligible studies were original human investigations examining caffeine exposure or administration and reporting sleep-related EEG outcomes, including polysomnographic sleep staging, spectral EEG analyses, or other EEG-derived sleep metrics. Two reviewers independently screened records and assessed eligibility, with disagreements resolved by consensus. Data on study design, participant characteristics, caffeine interventions, EEG methodology, and outcomes were extracted using a predefined form. Risk of bias was evaluated using the RoB 2 and ROBINS-I tools. Owing to marked heterogeneity across studies, findings were synthesized narratively within a mechanistic interpretive framework. Results: Thirty-two studies were included. Across highly heterogeneous paradigms—including acute bedtime or evening dosing, daytime or repeated caffeine use before nocturnal sleep, administration during prolonged wakefulness followed by recovery sleep, withdrawal protocols, and ambulatory/home EEG monitoring—the most consistent finding was suppression of low-frequency NREM EEG activity, particularly slow-wave activity and the lowest delta frequencies. Caffeine frequently increased faster EEG activity, including sigma/spindle and beta ranges, producing a lighter, more aroused, and more wake-like sleep EEG profile. These effects were especially prominent during early-night NREM sleep and in recovery sleep after sleep deprivation, where caffeine attenuated the expected homeostatic rebound in low-frequency power. REM-related effects were less consistent, but some studies reported delayed REM timing and subtler alterations in REM EEG. Emerging evidence further suggests that caffeine increases EEG complexity and shifts sleep dynamics toward a more excitation-dominant state. Several studies indicated that quantitative EEG measures were more sensitive than conventional sleep-stage variables in detecting caffeine-related sleep disruption. Dose, timing, habitual caffeine use, withdrawal state, age, circadian context, and adenosinergic genetic variation, particularly involving ADORA2A, moderated the magnitude of effects. We also highlighted the connection between current results and sports and sports science. Conclusions: Caffeine reliably alters the neurophysiological architecture of human sleep in a direction consistent with reduced sleep depth and weakened homeostatic recovery. The overall evidence supports a mechanistic model centered on adenosine receptor antagonism, attenuation of sleep-pressure build-up and expression, and a shift toward greater cortical arousal during sleep. Sleep EEG appears to be a sensitive marker of these effects, often revealing physiological disruption even when conventional sleep architecture changes are modest. Future research should prioritize larger and more diverse samples, pharmacokinetic and pharmacogenetic characterization, and ecologically valid high-resolution sleep monitoring to clarify the real-world and functional consequences of caffeine-induced EEG changes. Full article

To further explore the energy-saving potential of power-split hybrid electric vehicles, this paper addresses issues in traditional Radial Basis Function (RBF) neural network-based vehicle speed prediction methods, which rely solely on time-varying information from historical speed sequences of the host vehicle, leading to problems such as idle overestimation, large local prediction errors, and low prediction accuracy across different time horizons. An improved RBF neural network-based vehicle speed prediction method that integrates multi-sensor information is proposed. This method identifies the driver’s driving intention through a fuzzy inference system, extracts historical speed sequences within a fixed time window in a rolling manner, and integrates inter-vehicle motion characteristic parameters obtained through fusion of millimeter-wave radar and camera data. These multi-dimensional influencing factors are used as inputs to the RBF neural network for vehicle speed prediction. Based on this, an energy management optimization model for the vehicle is established, with the goal of optimizing fuel economy. The model predictive control (MPC) strategy is employed, and the Dynamic Programming (DP) algorithm is used to solve for the real-time optimal torque distribution among various power sources within a limited time horizon. Finally, simulation validation is conducted on the MATLAB/Simulink platform under the CHTC-B driving cycle, CCBC driving cycle, and actual road driving cycle. The results show that, compared with the traditional method adopting Radial Basis Function (RBF) neural network-based vehicle speed prediction and rule-based energy management, the proposed method improves the vehicle’s fuel economy by 4.11%. Full article

The application of ultra-supercritical power plant boilers is becoming increasingly widespread. P92 steel, as a typical material used for boiler main steam pipes, plays a critical role in unit safety, making the detection of its creep damage highly significant. However, existing conventional non-destructive testing methods are difficult to effectively detect creep damage. To address this issue, a magnetoacoustic emission (MAE)–magnetic Barkhausen noise (MBN) composite measurement system is developed, which is adapted to 20 Hz and 0.3 A sine wave excitation to trigger the synchronous pickup of MBN and MAE signals of P92 steel. After collecting signals with different creep life ratios (0%~100%) under working conditions of 650 °C and 100 MPa, time-domain (absolute mean, peak value, etc.) and frequency-domain (bandwidth) features are extracted. In response to the non-monotonicity between the magnetoacoustic features and the creep damage grade, principal component analysis (PCA) is introduced to reduce dimensionality. Different creep levels of samples in the two-dimensional principal component space are presented as clear gradient clustering, achieving the accurate differentiation of creep stages. Research has shown that the MAE-MBN composite system combined with PCA can effectively characterize the creep damage of P92 steel, providing a novel non-destructive detection path for the in-service life assessment of power plant components. Full article

This study proposes a method to quantify uncertainty in the scattering parameter (S-parameter) measurements when using de-embedding techniques. After calibrating the measurement setup with reference standards, de-embedding algorithms are employed to extract the intrinsic S-parameter of the device under test (DUT). This process introduces additional complexity to the uncertainty analysis. This study investigates the sources of uncertainty inherent to vector network analyzer (VNA) measurements. Subsequently, a covariance matrix-based approach is employed to propagate these uncertainties, culminating in the quantification of S-parameter uncertainty. The effectiveness of the proposed is determined by comparing the measured S-parameters of power dividers and couplers to their nominal values, considering parameters such as balance, coupling, and voltage standing wave ratio (VSWR). Additionally, an uncertainty analysis is conducted for the power divider’s S-parameters, tracing the uncertainty sources back to the calibration standards. Full article

Ultrasound has emerged as a versatile and promising tool to enhance and speed up traditional processing operations used by the food industry or to be used as an alternative food-processing method. This review provides an overview of the fundamental principles of sonication and its diverse applications in food processing. The core concepts of acoustic cavitation and the influence of power on processing outcomes are discussed in detail. The design and operation of different ultrasound systems, including direct-contact probe and indirect-contact bath systems, and their respective advantages were reviewed. Furthermore, a wide array of applications were explored, namely extraction, homogenization, degassing and deodorizing, pasteurization and vegetable blanching, drying and dehydration, freezing and thawing, brining and hydration, and cutting, highlighting how ultrasound waves can enhance process efficiency and improve product quality. The review also provides a critical analysis of the challenges and limitations associated with scaling up the technology for industrial use, including potential impacts on food quality, safety considerations, and economic viability. Finally, future perspectives and potential areas for further research are outlined to encourage the broader adoption of this technology in the food sector. Full article

Identifying the time-domain waveform type under broadband conditions is a basic but very challenging task. Traditional methods based on frequency domain or training models generally have the problems of high resource consumption, large delay, and unsuitability for hardware. This paper proposes a time-domain waveform recognition architecture based on an FPGA, which is integrated with multi-feature voting. Several lightweight time domain characteristics, such as high amplitude ratio, symmetry, slope uniformity, slope change rate, and flat-top characteristics, are extracted and directly used for waveform classification. Then classify sine waves, square waves, triangular waves, and noise in the time domain according to the decision-making mechanism of voting. In order to improve reliability under non-ideal conditions, adaptive thresholds and noise perception decision-making logic are used to suppress misclassifications caused by random fluctuations and jitter. The whole engineering design focuses on resource consumption and hardware efficiency, using a fully pipeline FPGA architecture. The experimental results prove that the system has the ability of high-precision identification, low power consumption, and real-time processing in the wide frequency band, providing an efficient and practical solution for embedded waveform recognition applications. Full article

The partial discharge (PD) caused by insulation defects of gas-insulated switchgear (GIS) threatens the secure and stable operation of power systems. Traditional PD pattern recognition methods exhibit limitations due to incomplete information utilization and unresolved correlations among characteristic parameters. Based on the partial discharge mechanisms of GIS, this paper establishes a GIS partial discharge simulation model using the finite element time-domain (FETD) method. The propagation rules and influence factors of ultra-high-frequency (UHF) signals are studied. Furthermore, a PD pattern recognition method based on a deep convolutional neural network (CNN) is proposed. Research results indicate that UHF signals generated by GIS partial discharge are significantly influenced by pulse current waveforms and discharge quantity. The peak-to-peak amplitude of the electric field (Epp) increases linearly with the current amplitude, while it decreases nonlinearly with increasing pulse width. The UHF signal remains a certain value while the pulse width exceeds a critical threshold (4 ns). The proposed CNN-based approach, utilizing full-wave UHF signals, overcomes the shortcomings of traditional methods reliant on manually extracted discrete feature parameters. Compared to other network architectures and optimization algorithms, the ConvNeXt-AdamW model demonstrates superior performance, achieving an average PD pattern recognition accuracy exceeding 96%. Full article

Marine renewable energy could support Costa Rica’s decarbonization pathway, but its offshore resource base and enabling conditions remain poorly characterized in the body of knowledge. This study provides the first integrated assessment of marine energy resources, grid integration opportunities, and governance challenges in Costa Rica. A meta-analysis of 76 technical, legal, and policy sources is combined with qualitative doctrinal analysis, GIS-based multi-criteria evaluation for Ocean Thermal Energy Conversion (OTEC), and satellite and reanalysis data for winds, waves, currents, and sea surface temperature to estimate power densities and extractable energy. Results show a contrast between the Pacific and Caribbean coasts. For instance, on the Northern Pacific coast, there are strong Papagayo winds, and persistent swells yield high offshore wind and wave energy potentials, with technical offshore wind resources of around 14.4 GW and Pacific wave power frequently exceeding 20–25 kW/m with relatively low seasonal variability. Furthermore, twelve OTEC-suitable zones are identified with two priority areas in the southern Pacific that combine steep bathymetry and strong thermal gradients with limited environmental conflicts, but they overlap with sensitive conservation and Indigenous territories. Current energy potential is more localized and modest in the Caribbean coast. The analysis highlights major infrastructural, legal, and social barriers but concludes that marine energy can play a pivotal role in diversifying Costa Rica’s renewable-dominated electricity market. Full article

Many of the film thickness measurements that have been reported in the literature tend to focus on small pipe diameters, which may not be practical for a variety of industrial applications. Additionally, single-point measurements are unable to provide the necessary film thickness data around the circumference of the pipe as well as in the axial direction. This paper aims to experimentally study the behaviour of wavy liquid films, including wave frequency, wave velocity, wave width, and wave spacing. A Multi-Pin Film Sensor (MPFS) was used to extract the thickness of a free-falling liquid film in axial, circumferential, and temporal coordinates. The range of liquid Reynolds number Re_L used was 618–1670. It was found that the power spectral density of the disturbance waves showed a pronounced peak at the modal frequency of 6–8 Hz. The number of disturbance waves was found to be almost independent of Re_L. The axial interfacial wave seemed to travel at a constant velocity while the mean velocity in circumferential direction was negligible. The mean width of the disturbance waves was approximately 17.7% of the pipe diameter. Full article

The uncertainty and enormous potential of wave energy have drawn attention and research efforts on predicting offshore wave behavior to aid wave energy harvesting. The movement of offshore waves generates huge amounts of available renewable energy and creates a unique offshore energy source. Because offshore waves are mainly generated by wind, this paper focused on using wind speed as the main factor to predict offshore wave power density to assist wave energy harvesting. The dynamic behaviors of wave energy were displayed in this paper in a format of wave power density distribution, which was extracted and visualized in MATLAB. The model was reconstruction based on a long short-term memory (LSTM) neural network for one week and 3 h wave power density forecasting, integrated with wind conditions as input in two scenarios. One scenario explored the location effect for wave density forecasting. Another scenario compared the influence of different time series input of the structure. RMSE was used as a criteria estimator of the accuracy. The data period ranges from 1979 to 2019 in the Gulf of Mexico exacted from WaveWatch III. The lowest RMSE among different locations is 0.104, while the different time step scenario has an RMSE of 0.715. Because wind speed data is much easier to get from either hindcast dataset or actual measurement, the proposed method with the resulting accuracy will make the forecasting of wave power density much easier. The method has the ability to be implemented in other wave thriving locations, which fills the gap of forecasting on wave height and period based on buoy data given a lack of measurements, as well as reflecting the correlations between wind speed and wave density, thus providing support for a quantitative correlation model based on a deep-learning-based model. Full article

Slant-path propagation of laser beams through atmospheric turbulence produces beam spreading and jitter that must be rapidly predicted for system design and performance assessment. Existing scaling laws are mainly derived for horizontal paths and single-parameter variations, which limits their accuracy and applicability to realistic engagement geometries. Here, we construct a comprehensive wave-optics database for 1.064 μm truncated Gaussian beams with a 1 m aperture by traversing initial beam quality factor β₀, propagation distance L, elevation angle θ, turbulence strength Cₙ², and tracking jitter. From 46,800 turbulence-only cases, we extract the 63.2% encircled-power expansion factor and quantify the coupled influence of β₀, L, and θ on the turbulence term coefficient A in the scaling expression. A compact 3–10–1 feedforward neural network is trained to map (β₀, L, θ) to A, achieving a coefficient of determination R² = 0.948. Additional simulations without turbulence show that the jitter term coefficient B is nearly invariant over the considered parameter range, with an average value B = 3.69. Combining these results yields a unified scaling law for linear beam spreading on horizontal and slant paths. Comparison with full-wave-optics simulations demonstrates that the proposed law reproduces horizontal-path results and significantly reduces prediction errors at θ = 60° relative to existing models, providing an efficient tool for beam-quality prediction and performance evaluation in atmospheric laser propagation. Full article

The porosity of additive manufacturing components significantly impacts their mechanical properties, thereby limiting their widespread application in engineering. Current porosity assessment predominantly relies on destructive testing, underscoring the urgent need for accurate in situ non-destructive testing methods. In this paper, we propose a novel deep learning-assisted non-destructive testing method for porosity assessment in additive manufacturing components. Our approach leverages the high sensitivity of ultrasonic coda waves to minute internal material changes, combined with the powerful feature extraction capability of deep learning. Experimental results demonstrate that ultrasonic coda waves are sensitive to porosity variations in additive manufacturing components. Due to the porosity of additive manufacturing components involves multi-dimensional micro-structural features, conventional parameters such as the correlation coefficient and relative velocity change cannot establish an effective mapping relationship, despite their variation with porosity, thus precluding accurate inversion. To address this challenge, we propose a coda–convolutional neural network–multi-head attention mechanism network. Ultrasonic coda waves can fully interact with pores inside additive manufacturing components, and their signals are rich in porosity-related features. The introduction of deep learning significantly enhances the ability to extract such features. The trained network achieves high-precision porosity prediction with an accuracy of 98%. Our proposed approach reveals the complementary integration of ultrasonic coda waves and deep learning methods: the former provides high sensitivity to porosity changes, while the latter addresses the limitations of difficult extraction of relevant features and unclear complex mapping relationships. This collaborative framework establishes a new solution for high-precision non-destructive testing of additive manufacturing components. Full article

This study presents a pulley-buoy-accelerated linear wave power generation system and verifies its feasibility and effectiveness through experimental research. Compared with traditional wave power generation systems that rely on three-stage energy conversion, the proposed system eliminates intermediate energy transfer and conversion links, enabling direct extraction of electrical energy from wave-induced motion. Additionally, by incorporating a pulley assembly, the system amplifies the buoy’s motion speed. This enhancement boosts the power output of the linear generator and improves the system’s overall wave energy conversion efficiency. Under laboratory conditions, a small-scale prototype of the system and a swing-type wave generator were constructed. Experimental tests were conducted to examine how three key factors influence the system’s power generation performance: the number of stator coils, wave conditions (wave height and wavelength), and buoy size. The results indicate that three measures can effectively improve both the wave energy conversion efficiency and power generation performance of the pulley-buoy-accelerated system: increasing the number of stator coils, increasing wave height and wavelength, and moderately enlarging the buoy size. These findings offer valuable insights for the practical application and efficient operation of wave power generation systems. Full article

Battery-Free Sensor Nodes (BFSNs) used in Simultaneous Wireless Information and Power Transfer (SWIPT) systems often rely on lightweight communication protocols with minimal security overhead due to strict energy constraints. As a result, conventional protocol-dependent security mechanisms cannot be employed, leaving BFSNs vulnerable to replay, spoofing, and other security threats. This paper explores a protocol-independent security mechanism that enhances BFSN security by exploiting the power wave for controlled backscattering. The method introduces a Manchester-encoded digital private key generated by the BFSN’s low-power microcontroller and backscattered through a polarization-shifting module enabled by a fail-safe RF switch, thereby avoiding the need for a dedicated backscattering rectifier. A LoRaWAN-based BFSN integrating this add-on module was implemented to experimentally validate the approach. Results show successful extraction of the backscattered key with minimal energy overhead (approximately 95 µJ for a 3 ms identification sequence), while the original high-efficiency RF rectifier used for harvesting remains unmodified. The orthogonal polarization between the incoming and backscattered waves additionally reduces clutter and cross-jamming effects. These findings demonstrate that secure identification can be seamlessly incorporated into existing BFSNs without altering their core architecture, offering an easy-to-integrate and energy-efficient solution for improving security in SWIPT-based sensing systems. Full article