Search Results (3,443)

To address the stringent demands of intelligent connected vehicles for high bandwidth, low latency, and highly reliable communication, this paper systematically summarizes the semantic communication technology of the Internet of Vehicles (IoV) based on information “meaning” transmission, covering basic theory, key technologies, application practice and challenge and trends. First, the paper expounds the knowledge driven and task oriented paradigm characteristics of semantic communication and its efficiency advantages in the IoV. Second, in terms of key technologies, semantic extraction achieves efficient feature compression through multimodal fusion and Generative Artificial Intelligence (GAI); semantic coding employs hierarchical codebooks and adaptive strategies to optimize transmission efficiency; semantic transmission leverages deep reinforcement learning for the joint scheduling of resources such as spectrum and power; and semantic decoding utilizes reconstruction networks and GAI to enhance resilience against impairments. Application practices demonstrate that semantic communication can significantly compress image data transmission volume for autonomous driving collaborative perception while maintaining high-fidelity reconstruction under adverse channel conditions. It significantly reduces the communication load and improves the system utility in vehicle-to-infrastructure coordination and in-vehicle service. Despite facing technical challenges such as semantic consistency, dynamic adaptability, and security trustworthiness, future semantic communication will evolve towards deep integration with distributed collaborative knowledge networks, lightweight real-time decision-making agents, and integrated “communication, sensing, and computing” architectures, positioning itself as a key enabling technology for empowering Sixth Generation mobile communication (6G) of intelligent vehicular networks. Full article

Background: Reliable intraoperative tools for donor liver assessment are needed, particularly in the context of steatotic and extended-criteria grafts. While histology remains the reference standard, it is limited by sampling variability and logistical constraints. Preservation fluid may provide a complementary, whole-organ source of biochemical information. Methods: In this single-center prospective exploratory pilot study, liver tissue and preservation fluid were collected from 30 donation-after-brain-death grafts during the back-table procedure. Biochemical parameters, including arginase activity, β-hydroxybutyrate (βHB), acetoacetate, and total protein, were measured using standard assays. Associations with histological steatosis on wedge biopsy were assessed using nonparametric correlation analyses, and paired preservation fluid samples were compared. Results: Most grafts demonstrated absent or mild steatosis; only two exhibited moderate steatosis, and none were severely steatotic. No preservation fluid biomarker showed a statistically significant association with histological steatosis. Weak, non-significant positive correlations were observed for βHB and arginase activity (Spearman r ≈ 0.33–0.35). Protein concentration and arginase activity decreased between start and end samples, whereas ketone body levels remained relatively stable. Conclusions: Preservation fluid biomarker measurement during routine graft preparation is feasible. Although no significant associations with histological steatosis were identified, the observed weak correlations suggest possible associations requiring validation in larger studies. Larger, adequately powered studies, including a broader spectrum of steatosis and clinically relevant outcomes, are required to determine potential clinical applicability. Full article

Stable interactions among electricity, carbon allowance, and fossil fuel markets are essential for sustainable energy transition, because excessive cross-market risk transmission may affect energy affordability, carbon-price credibility, and low-carbon investment signals. This study provides comparative evidence on dynamic connectedness, tail-state shock responses, and return-based complexity in representative Chinese and European benchmark markets. Using daily market data from the Wind database for November 2021–January 2026, the empirical framework combines time-varying parameter vector autoregression (TVP-VAR), quantile vector autoregression and quantile impulse response functions (QVAR/QIRFs), and rolling multifractal detrended fluctuation analysis (MFDFA). The results show that the European benchmark system has a higher absolute connectedness level than the Chinese benchmark system: the full-sample mean total connectedness index (TCI) is 18.75 in Europe and 5.63 in China, while the crisis-period mean TCIs are 25.19 and 12.12, respectively. Post-peak adjustment depends on the reversion metric used: China shows a faster initial half-life decline from the crisis peak, whereas reversion to lower region-specific connectedness thresholds depends on the selected benchmark. Natural-gas-shock QIRFs indicate stronger upper-tail persistence in Europe, whereas China is characterized mainly by short-run directional divergence; supplementary coal-, oil-, and carbon-shock checks show that response patterns are shock-source-dependent. Electricity-return multifractal spectrum width (MFW) does not show stable full-sample explanatory power for TCI, but it provides stage-dependent auxiliary diagnostic information. These findings provide a comparative diagnostic framework for monitoring cross-market systemic risk and supporting sustainability-oriented energy-market governance under low-carbon transition. Full article

Gamma-ray bursts (GRBs) represent the most powerful explosions in the Universe, releasing extreme fluxes of non-thermal radiation across the electromagnetic spectrum. A central enigma in GRB physics remains the mechanism responsible for accelerating electrons, positrons, and hadrons to the required ultra-relativistic energies. Conventional theories primarily invoke diffusive shock acceleration (DSA), magnetic reconnection, and relativistic turbulence. This short review first examines these canonical acceleration methods, then discusses the principles and successes of plasma wakefield acceleration as a powerful future technique for ground-based applications. Finally, we critically analyze the feasibility of applying this mechanism to the cosmological environment of GRBs, exploring why the terrestrial success of wakefield acceleration has not yet been definitively confirmed “on the sky”. Full article

Resource-constrained electromagnetic interference (EMI) management requires a frequency-domain feedback path, while FFT-based full-spectrum processing introduces redundant computation, storage, and data movement for decision tasks. This paper proposes a limit-aware sparse frequency-domain decision engine for internal EMI risk feedback. The engine redefines EMI analysis from spectrum reconstruction to selective exceedance verification and uses randomized spectral reordering, flat-window bucket aggregation, and folded sampling to compress the length-N spectral search into bucket-level observations. Then, by comparing bucket-level amplitude envelopes with local limit envelopes, the method excludes risk-negative buckets, and only uncertain buckets are further refined through phase localization and sequential verification. Degradation experiments involving continuous background uplift, main-harmonic sidebands, and parasitic resonance clusters clarify the applicability boundary of the proposed method, and measured GaN power-converter spectra acquired through an in situ EMI sensing chain remain inside the empirical usable region. RTL evaluation at 100 MHz shows that the proposed design achieves an average decision latency of 6.031 ms. Compared with two FFT baseline implementations, it reduces BRAM usage by 95.17% and 97.59%, dynamic power by 54.0% and 83.0%, and per-decision dynamic energy by 46.3× and 33.3×, respectively. The results show that the proposed decision engine reduces hardware overhead for frequency-domain EMI risk feedback in resource-constrained systems. Full article

As the available spectrum in the conventional C band becomes increasingly limited, ultra-wideband transmission across the O–U wavelength range (1260–1675 nm) provides a promising approach to increasing optical fiber link capacity. To support performance evaluation and preliminary fiber-link selection, this study compares standard single-mode fiber (SMF), pure-silica-core fiber (PSCF), and hollow-core fiber (HCF) links across the O–U bands. A transmission-performance analysis framework was established based on the Gaussian noise (GN) model. Band-specific amplifier parameters and fiber-specific span configurations were incorporated to evaluate transmission reach, optimum launch power, and theoretical capacity. Auxiliary simulations were conducted using VPIphotonics Design Suite 11.1 (VPIphotonics GmbH, Berlin, Germany) for representative C-band cases to examine the consistency of the overall trends predicted by the theoretical analysis. The GN-model analysis and auxiliary simulations show consistent overall trends, indicating that the GN model can serve as a computationally efficient tool for comparative link assessment and preliminary fiber-link selection. Under the unified analytical framework and consistently defined engineering constraints, PSCF offers a clear transmission-reach advantage over conventional SMF, whereas HCF shows greater theoretical power tolerance and capacity potential under the adopted representative parameter assumptions. Under the adopted non-saturated reference operating conditions, the per-channel capacity of HCF is approximately 34–54% higher than that of SMF in the S, C, L, and U bands and also clearly exceeds that of PSCF. These HCF results should be interpreted as model-based theoretical estimates, since practical performance may be affected by loss, dispersion uncertainty, splice/connector loss, bending sensitivity, and mode coupling. Full article

To address the structural complexity, limited detection sensitivity, and environmental susceptibility of the stable operating point in conventional fiber-optic current transformers for low-current detection, this study proposes a fiber-optic current transformer based on the coupling of an extrinsic Fabry–Perot interferometer (EFPI) and a lead zirconate titanate piezoelectric ceramic (PZT). In the proposed sensor, a toroidal magnetic core and an induction winding are used as the current pickup unit to convert the measured alternating current into an induced voltage. This induced voltage directly drives the PZT to generate axial displacement, causing periodic variations in the length of the air Fabry–Perot cavity formed between the fiber end face and the coated quartz diaphragm. As a result, the current signal is converted into an optical interference intensity signal. To prevent the static operating point from deviating from the optimal linear region during EFPI intensity demodulation, a DC-component-feedback-based operating point control method is proposed. By adjusting the driving voltage of the fiber Fabry–Perot tunable filter, the center wavelength of the incident narrowband demodulation light can track the optimal operating point of the interference spectrum, thereby improving the stability of the intensity demodulation process. Experimental results show that the fabricated sensor can generate a stable reflected interference spectrum and exhibits a relatively flat frequency response within the range of 0–7 kHz, indicating its potential for power-frequency current detection under the present laboratory conditions. When the measured current is 0.13 mA, the sensor can still produce a distinguishable sinusoidal output signal. When the measured current increases to 75 mA, obvious nonlinear distortion appears in the output signal, indicating that the sensor is approaching the boundary of its linear detection range. Within the linear operating region, the output peak-to-peak value shows good linearity with the measured current. The results indicate that the proposed EFPI-PZT fiber-optic current transformer has the advantages of a relatively simple structure, clear low-current response, and adjustable structural parameters, providing a reference for the miniaturized design and further development of new fiber-optic current sensors. Full article

Space missions working under harsh heliocentric conditions demand more efficient photovoltaics operating under high solar concentration, high temperatures, and harsh radiation conditions. Although most simulation work has been conducted using the terrestrial AM1.5 spectrum, AM0 high concentrators are of great importance to realistic satellite missions. Though III–V multijunction solar cells are currently the norm in space applications, their efficiency under extremely high solar concentration ratios is not yet optimized to support future space missions. This work designs and numerically optimizes a GaAs VTJ solar cell using SILVACO ATLAS software (5.40.0.R). In the optimization, the thickness of the front and back layers, as well as the doping profile within the emitter, base, and tunnel junction regions, were adjusted. The important PV semiconductor attributes, including the short-circuit current density (Jsc), open-circuit voltage (Voc), fill factor (FF), and efficiency (η), were examined over a concentration factor ranging between 1 and 10,000 suns. The efficiency of the optimized VTJ solar cell increased from 20.4% at 1 sun to 26.0% at 10,000 suns. This is mainly due to the near-linear increase in Jsc and the stable FF, which remains between 87% and 89%. In addition, the solar cell shows a steady increase in Voc between 1.85 V and 2.33 V. An optimized GaAs VTJ solar cell design is a promising component in future space missions, which require high power density and are suited to operating under high heliocentric orbits, such as in the Parker Solar Probe and solar-electric propulsion systems. Full article

Bluetooth Low Energy (BLE) is widely used in Internet of Things (IoT) devices due to its low power consumption and efficient wireless communication. However, BLE-based systems remain vulnerable to signal-level attacks, such as spoofing and signal forgery, which allow adversaries to impersonate legitimate devices and compromise system security. Existing security approaches mainly rely on cryptographic mechanisms or protocol-level features, while conventional signal fingerprinting methods often fail to capture subtle device-specific variations across the frequency spectrum. We propose a deep-learning-based BLE signal fingerprinting framework that uses emphasized spectral data to enhance device authentication. The proposed framework selectively highlights frequency regions exhibiting pronounced hardware-dependent variations using a hybrid filter bank design and extracts spectral features for anomaly-based device identification. Experimental evaluations conducted on BLE signals collected from multiple devices demonstrate that the proposed approach outperforms conventional methods, achieving superior authentication performance. By leveraging emphasized frequency-domain characteristics, we provide an effective authentication method for BLE-based IoT environments. Full article

Sleep disturbances are among the most prevalent and clinically significant features observed across the psychosis spectrum, ranging from clinical high-risk (CHR) mental states to first-episode psychosis (FEP) and chronic schizophrenia. Far from being merely secondary phenomena, sleep difficulties—including insomnia, circadian rhythm disruption, altered sleep architecture, hypersomnia, and nightmare disorder—are increasingly acknowledged as transdiagnostic risk factors that may contribute to symptom severity, cognitive impairment, functional decline, and heightened suicidal risk. This narrative review consolidates current evidence on the epidemiology and neurobiological foundations of sleep disturbances in the psychosis spectrum and critically evaluates available non-pharmacological and digital interventions aimed at targeting sleep as a modifiable clinical outcome. We posit that sleep represents a critical, potentially modifiable intervention target within psychosis and that integrating sleep-focused care into standard clinical pathways may substantially enhance clinical, functional, and safety outcomes throughout the illness spectrum, pending replication in adequately powered randomized controlled trials. Full article

Reliable and rapid forecasting of wind turbine output power is vital for operators, particularly day-ahead and intraday market scheduling and reserve allocation. However, the inherent unpredictability, intermittency, and volatility of wind turbine output make forecasting processes difficult. To address this challenge, this study proposes a Sinusoidal Representation Network (SIREN)-based forecasting model for high-accuracy, rapid direct multi-horizon forecasting of wind turbine output power. SIREN is selected due to the periodic and symmetrical mathematical structure of its sinusoidal activation function, which allows the model to represent both low-frequency trends and high-frequency sudden changes in wind energy data. To improve data quality, compensate for asymmetric fluctuations in wind data, and provide more suitable inputs for SIREN training. Several preprocessing steps are utilized before feeding the data into the model. The proposed preprocessing step includes a moving median filter, robust scaling based on median and interquartile range, Winsorizing clipping, and a Hampel filter to reduce the effects of instantaneous noise, outliers, and local peaks without disrupting temporal continuity. Subsequently, a Savitzky–Golay smoothing is applied to attenuate high-frequency measurement noise while preserving curvature, local peaks, and physically meaningful short-term dynamics in the data. The sliding-window approach is used to formulate the multi-horizon forecasting problem directly, and a direct h-step-ahead forecasting architecture is designed, preserving structural symmetry in the time series. The SIREN is trained and tested using MATLAB with the help of two different datasets: Dataset-1 has a 10 min resolution for 1 year, and Dataset-2 has a 1 h resolution for 15 years. The forecast horizon parameter h is considered separately for each step, and the proposed SIREN is independently trained, validated, and tested for each target horizon while maintaining chronological order. The results demonstrate that the proposed model is able to yield high forecast performance for a wide spectrum of horizons ranging from 10 min to 15 days. The accuracy of the proposed model for Dataset-1 is R² of 99.6%, MSE of 0.085%, MAE of 1.7%, and MAPE of 12%, while for Dataset-2, the accuracy is R² of 98.8%, MSE of 0.3%, MAE of 3.6%, and MAPE of 23%. Ablation and sensitivity analyses are conducted to evaluate the impact of the basic components used in the proposed model on forecasting performance. In addition, combative experiments are performed using traditional time series, ML, and DL forecasting techniques to better assess the contribution of the model. The obtained results show that the SIREN-based direct forecasting approach provides strong learning capability, as well as high forecasting accuracy, for both high-resolution and low-resolution wind power data. Overall, its ability to capture the symmetric and periodic characteristics inherent in wind turbine power data makes it a promising alternative for multi-horizon wind power forecasting applications. Full article

Accurate prediction of remaining useful life (RUL) for lithium-ion batteries remains a critical yet complex challenge due to highly non-linear degradation dynamics and profound data heterogeneity across varying operational profiles. While convolutional neural networks (CNNs) have shown promise in battery health management, traditional architectures struggle with gradient vanishing in deep feature spaces and lack the adaptive capacity to filter early-cycle noise under diverse degradation conditions. To improve robust RUL estimation across heterogeneous benchmark datasets, this paper proposes a deep learning framework that integrates residual connections with dual-attention mechanisms (ResCNN). Specifically, the residual structures effectively mitigate gradient degradation during the extraction of abstract degradation patterns. Concurrently, a synergistic Squeeze-and-Excitation (SE) and Multi-Head Attention module adaptively calibrates channel-wise feature importance and captures long-range temporal dependencies inherent in complex capacity fade processes. The proposed framework is evaluated under a wide spectrum of degradation conditions and distinct cathode systems (LFP and LCO) using both dataset-specific train/validation/test protocols and strict source-to-target cross-dataset transfer tests. Experimental results demonstrate that ResCNN achieves consistently lower prediction errors than baseline models across the evaluated datasets and maintains positive explanatory power on unseen target datasets without target-domain training. Ablation studies further validate the synergistic contribution of each architectural component toward capturing intrinsic battery aging phenomena. Full article

The LLC resonant converter has gained extensive adoption in recent years, primarily owing to its benefits including high efficiency and high power density. However, the intrinsic electrical traits of the LLC converter fail to accommodate operational requirements involving a broad voltage span for both the input and the output. To tackle the operational scenarios of LLC resonant converters characterized by broad input and output voltage ranges, this study examines the gain properties of LLC subjected to both frequency modulation control and phase shift control techniques, correspondingly, and puts forward a hybrid control approach integrating frequency modulation with phase shift strategy. Through the seamless combination of frequency modulation control and phase shift control within one control loop, the issue of system oscillations occurring during the transition among differing control loops is successfully eliminated. As a result, the voltage gain spectrum of the LLC is substantially widened. A high-power LLC simulation model featuring interleaved and parallel configurations, along with an experimental testing rig, were established. The presented hybrid control strategy, which utilizes frequency modulation and phase shift, was investigated via extensive simulations and empirical testing. The obtained simulation results and experimental data exhibit strong alignment, thereby confirming the accuracy and feasibility of the presented full-bridge LLC hybrid control approach designed for extensive input and output voltage variations. Full article

This paper proposes a micro-Doppler flash detection framework for hovering unmanned aerial vehicle (UAV) detection with linear frequency modulated continuous wave (LFMCW) radar under the dual constraints of strong ground clutter and severe thermal noise conditions. In such scenarios, conventional methods fail not only due to the spectral overlap between hovering targets and clutter but also because of the visual disappearance of micro-Doppler features under heavy noise. The framework consists of three sequential modules. A prior-template orthogonal projection (PTOP) module suppresses clutter via a single-step orthogonal projection, preserving the micro-Doppler flash signature without distortion while approximately maintaining the Gaussian noise statistics required for subsequent detection. A flash power spectrum construction module then collapses the periodic blade flash energy onto a sharp spectral peak in a one-dimensional (1D) power spectrum via Gabor transform, power projection, and fast Fourier transform (FFT). A cell-averaging constant false alarm rate (CA-CFAR) detection module with an analytically derived threshold factor finally renders a reliable detection decision. Simulations under a signal-to-clutter ratio (SCR) of $-21$ dB and signal-to-noise ratio (SNR) of $-23$ dB confirm that the proposed framework achieves reliable detection even when the micro-Doppler flash signatures are visually obscured by residual noise in the time–frequency domain. Parametric SNR sweep curves and a two-dimensional (2D) SCR–SNR detection-probability heatmap under a non-stationary clutter model further quantify the practical performance boundaries of the framework. By transforming these concealed periodic features into a sharp spectral peak, the framework provides robust detection performance where conventional range-Doppler and moving target indication (MTI)-based methods both exhibit severe performance degradation. Full article

A Q-switched pulsed laser based on a coupled 7-core Yb-doped fiber with a cavity based on a fiber Bragg grating array has been demonstrated with a maximum energy of microsecond pulses up to 15 μJ at a 1 kHz repetition rate. The lasing spectrum is hybridized so that the laser line maxima of each core are nearly the same, having a negligible spread relative to each other, which is much lower than the wavelength shifts between individual FBGs in the cores. At the same time, the generated power is nearly the same in all the cores. However, when increasing the power beyond the stimulated Raman scattering threshold, the supermodes are destroyed so that the spectra in the cores become increasingly different and less stable, and the output power is mainly concentrated in one of the cores, whereas the pulse shortens significantly to a sub-microsecond duration (300 ns), with damped oscillations appearing at the beginning. The new regimes we demonstrated of the multicore fiber laser are promising for creating powerful pulsed radiation sources with a narrow spectrum. Full article