Search Results (413)

The wave spectrum, as a key statistical feature describing wave energy distribution, is crucial for understanding wave propagation mechanisms and supporting ocean engineering applications. This study, based on ERA5 reanalysis spectrum data, proposes a model combining CNN and xLSTM for rapid gridded wave spectrum prediction over the Bohai and Yellow Seas domain. It uses 2D gridded spectrum data rather than a spectrum at specific points as input and analyzes the impact of various input factors at different time lags on wave development. The results show that incorporating water depth and mean sea level pressure significantly reduces errors. The model performs well across seasons with the seasonal spatial average root mean square error (SARMSE) of spectral energy remaining below 0.040 m²·s and RMSEs for significant wave height (SWH) and mean wave period (MWP) of 0.138 m and 1.331 s, respectively. At individual points, the spectral density bias is near zero, correlation coefficients range from 0.95 to 0.98, and the peak frequency RMSE is between 0.03 and 0.04 Hz. During a typical cold wave event, the model accurately reproduces the energy evolution and peak frequency shift. Buoy observations confirm that the model effectively tracks significant wave height trends under varying conditions. Moreover, applying a frequency-weighted loss function enhances the model’s ability to capture high-frequency spectral components, further improving prediction accuracy. Overall, the proposed method shows strong performance in spectrum prediction and provides a valuable approach for regional wave spectrum modeling. Full article

Terahertz (THz) frequencies, spanning from 0.1 to 1 THz, are poised to play a pivotal role in the development of future 6G wireless communication systems. These systems aim to utilize photonic technologies to enable ultra-high data rates—on the order of terabits per second—while maintaining low latency and high efficiency. In this work, we present a novel photonic method for generating sub-THz vector signals within the THz band, employing a semiconductor optical amplifier (SOA) and phase modulator (PM) to create an optical frequency comb, combined with in-phase and quadrature (IQ) modulation techniques. We demonstrate, both through simulation and experimental setup, the generation and successful transmission of a 0.1 THz vector. The process involves driving the PM with a 12.5 GHz radio frequency signal to produce the optical comb; then, heterodyne beating in a uni-traveling carrier photodiode (UTC-PD) generates the 0.1 THz radio frequency signal. This signal is transmitted over distances of up to 30 km using single-mode fiber. The resulting 0.1 THz electrical vector signal, modulated with quadrature phase shift keying (QPSK), achieves a bit error ratio (BER) below the hard-decision forward error correction (HD-FEC) threshold of ${3.8 \times 10}^{-3}$. To the best of our knowledge, this is the first experimental demonstration of a 0.1 THz photonic vector THz wave based on an SOA and a simple PM-driven optical frequency comb. Full article

Under global warming, persistent extreme heat events (PHEs) in China have increased significantly in both frequency and intensity, posing severe threats to agriculture and socioeconomic development. Combining observational analysis (1961–2019) and numerical simulations, this study investigates the distinct impacts of Northwest Pacific (NWP) and North Atlantic (NA) sea surface temperature (SST) anomalies on PHEs over China. Key findings include the following: (1) PHEs exhibit heterogeneous spatial distribution, with the Yangtze-Huai River Valley as the hotspot showing the highest frequency and intensity. A regime shift occurred post-2000, marked by a threefold increase in extreme indices (+3σ to +4σ). (2) Observational analyses reveal significant but independent correlations between PHEs and SST anomalies in the tropical NWP and mid-high latitude NA. (3) Numerical experiments demonstrate that NWP warming triggers a meridional dipole response (warming in southern China vs. cooling in the north) via the Pacific–Japan teleconnection pattern, characterized by an eastward-retreated and southward-shifted sub-tropical high (WPSH) coupled with an intensified South Asian High (SAH). In contrast, NA warming induces uniform warming across eastern China through a Eurasian Rossby wave train that modulates the WPSH northward. (4) Thermodynamically, NWP forcing dominates via asymmetric vertical motion and advection processes, while NA forcing primarily enhances large-scale subsidence and shortwave radiation. This study elucidates region-specific oceanic drivers of extreme heat, advancing mechanistic understanding for improved heatwave predictability. Full article

This study investigates the use of a custom-built 10 GHz continuous wave micro-Doppler radar system to analyze external vibrations of idling vehicles under various conditions. Scenarios included different gear engagements with one occupant and parked gear with up to four occupants. Motivated by security concerns, such as the threat posed by idling vehicles with multiple occupants, the research explores how micro-Doppler signatures can indicate vehicle readiness to move. Experiments focused on a mid-size SUV, with similar trends seen in other vehicles. Radar data were compared to in situ accelerometer measurements, confirming that the radar system can detect subtle frequency changes, especially during gear shifts. The system’s sensitivity enables it to distinguish variations tied to gear state and passenger load. Extracted features like frequency and magnitude show strong potential for use in machine learning models, offering a non-invasive, remote sensing method for reliably identifying vehicle operational states and occupancy levels in security or monitoring contexts. Spectrogram and PSD analyses reveal consistent tonal vibrations around 30 Hz, tied to engine activity, with harmonics at 60 Hz and 90 Hz. Gear shifts produce impulse signatures primarily below 20 Hz, and transient data show distinct peaks at 50, 80, and 100 Hz. Key features at 23 Hz and 45 Hz effectively indicate engine and gear states. Radar and accelerometer data align well, supporting the potential for remote sensing and machine learning-based classification. Full article

We provide an approach to detect a nitrogen-vacancy (NV) center, which might be a defect in a diamond nanomembrane, using a dual-coupling optomechanical system. The NV center modifies the energy-level structure of a dual-coupling optomechanical system through dressed states arising from its interaction with the mechanical membrane. Thus, we study the photon blockade in the cavity of a dual-coupling optomechanical system in which an NV center is embedded in a single-crystal diamond nanomembrane. The NV center significantly influences the statistical properties of the cavity field. We systematically investigate how three key NV center parameters affect photon blockade: (i) its coupling strength to the mechanical membrane, (ii) transition frequency, and (iii) decay rate. We find that the NV center can shift, give rise to a new dip, and even suppress the original dip in a bare quadratic optomechanical system. In addition, we can amplify the effect of the NV center on photon statistics by adding a gravitational potential when the NV center has little effect on photon blockade. Therefore, our study provides a method to detect diamond nanomembrane defects in a dual-coupling optomechanical system. Full article

Motor imagery (MI) EEG decoding is a key application in brain–computer interface (BCI) research. In cross-session scenarios, the generalization and robustness of decoding models are particularly challenging due to the complex nonlinear dynamics of MI-EEG signals in both temporal and frequency domains, as well as distributional shifts across different recording sessions. While multi-scale feature extraction is a promising approach for generalized and robust MI decoding, conventional classifiers (e.g., multilayer perceptrons) struggle to perform accurate classification when confronted with high-order, nonstationary feature distributions, which have become a major bottleneck for improving decoding performance. To address this issue, we propose an end-to-end decoding framework, MCTGNet, whose core idea is to formulate the classification process as a high-order function approximation task that jointly models both task labels and feature structures. By introducing a group rational Kolmogorov–Arnold Network (GR-KAN), the system enhances generalization and robustness under cross-session conditions. Experiments on the BCI Competition IV 2a and 2b datasets demonstrate that MCTGNet achieves average classification accuracies of 88.93% and 91.42%, respectively, outperforming state-of-the-art methods by 3.32% and 1.83%. Full article

Pulmonary hemorrhage (PH) is a life-threatening complication predominantly affecting preterm infants, particularly those with very low birth weight (VLBW) and fetal growth restriction (FGR). Typically occurring within the first 72 h of life, PH is characterized by acute respiratory deterioration and significant morbidity and mortality. This review synthesizes current evidence on the multifactorial pathogenesis of PH, highlighting the roles of immature pulmonary vasculature, surfactant-induced hemodynamic shifts, and left ventricular diastolic dysfunction. Key risk factors include respiratory distress syndrome (RDS), hemodynamically significant patent ductus arteriosus (hsPDA), sepsis, coagulopathies, and genetic predispositions. Diagnostic approaches incorporate clinical signs, chest imaging, lung ultrasound, and echocardiography. Management strategies are multifaceted and include ventilatory support—particularly high-frequency oscillatory ventilation (HFOV)—surfactant re-administration, blood product transfusion, and targeted hemostatic agents. Emerging therapies such as recombinant activated factor VII and antifibrinolytics show promise but require further investigation. Preventive measures like antenatal corticosteroids and early indomethacin prophylaxis may reduce incidence, particularly in high-risk populations. Despite advancements in neonatal care, PH remains a major contributor to neonatal mortality and long-term neurodevelopmental impairment. Future research should focus on individualized risk stratification, early diagnostic tools, and optimized treatment protocols to improve outcomes. Multidisciplinary collaboration and innovation are essential to advancing care for this vulnerable population. Full article

Using the rolling-shutter mechanism to enhance the signal sampling frequency of Optical Camera Communication (OCC) is a low-cost solution, but its periodic sampling interruptions may cause signal loss, and existing solutions often compromise communication rate and distance. To address this, this paper proposes NoGap-RS, a no-gap sampling method, theoretically addressing the signal loss issue at longer distances from a perspective of CMOS exposure timing. Experiments show that NoGap-OOK, a OCC system based on NoGap-RS and On-Off key modulation, can achieve a communication rate of 6.41 Kbps at a distance of 3 m, with a BER of ${10}^{-5}$ under indoor artificial light. This paper further proposes TickRS, a time slot division method, innovatively addressing the overlap that occurs during consecutive-row exposures to further enhance communication rate. Experiments show that TickRS-CSK, a OCC system based on TickRS and Color-Shift Key, can achieve a communication rate of 20.09 Kbps at a distance of 3.6 m, with a BER of ${10}^{-2}$ under indoor natural light. Full article

Intelligent fault diagnosis for rotary machinery often suffers performance degradation under domain shifts between training and deployment environments. To address this, we propose a robust fault diagnosis framework incorporating three key components: (1) an order-frequency-based preprocessing method to normalize rotational variations, (2) a U-Net variational autoencoder (U-NetVAE) to enhance adaptation through reconstruction learning, and (3) a test-time training (TTT) strategy enabling unsupervised target domain adaptation without access to source data. Since existing works rarely evaluate under true domain shift conditions, we first construct a unified cross-domain benchmark by integrating four public datasets with consistent class and sensor settings. The experimental results show that our method outperforms conventional machine learning and deep learning models in both F1-score and recall across domains. Notably, our approach maintains an F1-score of 0.47 and recall of 0.51 in the target domain, outperforming others under identical conditions. Ablation studies further confirm the contribution of each component to adaptation performance. This study highlights the effectiveness of combining mechanical priors, self-supervised learning, and lightweight adaptation strategies for robust fault diagnosis in the practical domain. Full article

Hydrological processes in river systems are changing due to climate variability and human activities, making it crucial to understand and quantify these changes for effective water resource management. This study examines long-term trends in hydroclimate variables (1990–2022) and land use/land cover (LULC) changes (1988, 2002, and 2022) within the Continental Reach of the Gambia River Basin (CGRB). Trend analyses of the Standardized Precipitation-Evapotranspiration Index (SPEI) at 12-month and 24-month scales, along with river discharge at the Simenti station, reveal a shift from dry conditions to wetter phases post-2008, marked by significant increases in rainfall and discharge variability. LULC analysis revealed significant transformations in the basin. LULC analysis highlights significant transformations within the basin. Forest and savanna areas decreased by 20.57 and 4.48%, respectively, between 1988 and 2002, largely due to human activities such as agricultural expansion and deforestation for charcoal production. Post-2002, forest cover recovered from 32.36 to 36.27%, coinciding with the wetter conditions after 2008, suggesting that climatic shifts promoted vegetation regrowth. Spatial analysis further highlights an increase in bowe and steppe areas, especially in the north, indicating land degradation linked to human land use practices. Bowe areas, marked by impermeable laterite outcrops, and steppe areas with sparse herbaceous cover result from overgrazing and soil degradation, exacerbated by the region’s drier phases. A notable decrease in burned areas from 2.03 to 0.23% suggests improvements in fire management practices, reducing fire frequency, which is also supported by wetter conditions post-2008. Agricultural land and bare soils expanded by 14%, from 2.77 to 3.07%, primarily in the northern and central regions, likely driven by both population pressures and climatic shifts. Correlations between precipitation and land cover changes indicate that wetter conditions facilitated forest regrowth, while drier conditions exacerbated land degradation, with human activities such as deforestation and agricultural expansion potentially amplifying the impact of climatic shifts. These results demonstrate that while climatic shifts played a role in driving vegetation recovery, human activities were key in shaping land use patterns, impacting both precipitation and stream discharge, particularly due to agricultural practices and land degradation. Full article

This paper presents the development and analysis of a planar microfluidic microwave sensor featuring three circular complementary split-ring resonators (CSRRs) fabricated on an RO3035 substrate. The sensor demonstrates enhanced sensitivity in characterizing liquids contained in a fine glass capillary tube by leveraging a novel configuration: a central 5-split-ring CSRR with a drilled hole to suspend the capillary, flanked by two 2-split-ring CSRRs to improve the band-stop filtering effect. The sensor’s performance is benchmarked against another CSRR-based microwave sensor with a similar configuration. High linearity is observed (R² > 0.99), confirming its capability for precise ethanol concentration prediction. Compared to the replicated square CSRR design from the literature, the proposed sensor achieves a 35.22% improvement in sensitivity, with a frequency shift sensitivity of 567.41 kHz/% ethanol concentration versus 419.62 kHz/% for the reference sensor. The enhanced sensitivity is attributed to several key design strategies: increasing the intrinsic capacitance by enlarging the effective area and radial slot width to amplify edge capacitive effects, adding more split rings to intensify the resonance dip, placing additional CSRRs to improve energy extraction at resonance, and adopting circular CSRRs for superior electric field confinement. Additionally, the proposed design operates at a lower resonant frequency (2.234 GHz), which not only reduces dielectric and radiation losses but also enables the use of more cost-effective and power-efficient RF components. This advantage makes the sensor highly suitable for integration into portable and standalone sensing platforms. Full article

High-frequency (HF) communication is critical for applications such as over-the-horizon positioning and ionospheric detection. However, precise time-delay estimation in complex HF channels faces significant challenges from multipath fading, Doppler shifts, and noise. This paper proposes a Modulation Signal-based Adaptive Time-Delay Estimation (MATE) algorithm, which effectively decouples carrier and modulation signals and integrates phase-locked loop (PLL) and delay-locked loop (DLL) techniques. By leveraging the autocorrelation properties of 8PSK (Eight-Phase Shift Keying) signals, MATE compensates for carrier frequency deviations and mitigates multipath interference. Simulation results based on the Watterson channel model demonstrate that MATE achieves an average time-delay estimation error of approximately 0.01 ms with a standard deviation of approximately 0.01 ms, representing a 94.12% reduction in mean error and a 96.43% reduction in standard deviation compared to the traditional Generalized Cross-Correlation (GCC) method. Validation with actual measurement data further confirms the robustness of MATE against channel variations. MATE offers a high-precision, low-complexity solution for HF time-delay estimation, significantly benefiting applications in HF communication systems. This advancement is particularly valuable for enhancing the accuracy and reliability of time-of-arrival (TOA) detection in HF-based sensor networks and remote sensing systems. Full article

Vibrational analysis of peptides in solution and the theoretical determination of the effects of the microenvironment on infrared and Raman spectra are of key importance in many fields of chemical interest. In this work, we present a computational study combining static quantum mechanical calculations with ab initio molecular dynamics simulations to investigate the vibrational behavior of three peptide models in both the gas phase and in explicit water, under non-periodic boundary conditions. The vibrational spectra of the main amide bands, namely amide I-III and A, were analyzed using a time–frequency approach based on the wavelet transform, which allows the resolution of transient frequency shifts and mode couplings along the trajectories. This combined approach enabled us to perform a time-resolved vibrational analysis revealing how vibrational frequencies, especially of the C=O and N–H stretching modes, evolve over time due to dynamical microsolvation. These fluctuations modulate vibrational couplings and lead to spectral broadening and frequency shifts that correlate with the local structuring of the solvent. In conclusion, our results highlight how the proposed protocol allows for the direct connection between vibrational modes and local structural changes, providing a link from the spectroscopic observable to the structure, the peptide backbone, and its microenvironment. Full article

Agricultural organic pollutants have been identified as a key factor contributing to amphibian population decline, particularly during early developmental stages when tadpoles are frequently exposed to neonicotinoids (NEOs) and microplastics (MPs). In this study, Hoplobatrachus rugulosus tadpoles were exposed to imidacloprid (IMI: 0.045, 0.45, and 4.5 mg L⁻¹) and polyethylene-derived MPs (10 mg L⁻¹) from agricultural mulch films, both individually and in combination. We systematically evaluated acute toxicity, bioaccumulation, developmental and oxidative stress responses, and changes in the skin and gut microbiota. The results showed that the 96 h median lethal concentration (LC₅₀) of IMI was 44.8 mg L⁻¹ in the IMI-only group and was 40.5 mg L⁻¹ in the IMI + MPs group, indicating the negligible impact of MPs on acute toxicity. However, in the highest co-exposure group (IMI4.5 + MPs), tadpole body length and weight decreased by 14.7% and 22.6%, respectively, alongside marked changes in oxidative stress, whereby catalase (CAT) and superoxide dismutase (SOD) activities were suppressed, while malondialdehyde (MDA) levels increased by 35%, indicating elevated lipid peroxidation. Furthermore, the micronucleus frequency in erythrocytes was significantly elevated, suggesting genotoxic effects. Microbial community analysis revealed significant shifts in the relative abundance of gut and skin microbiota under IMI + MPs exposure, with a notable enrichment of Proteobacteria, Fusarium, Actinomycetota, and Bacteroidota, indicating the disruption of host–microbiome interactions. This study proposes a comprehensive multi-tiered assessment framework encompassing environmental exposure, bioaccumulation, toxicological endpoints, oxidative stress biomarkers, and microbiome shifts. Our findings provide new mechanistic insights and quantitative evidence on the compound threats posed by IMI and MPs to amphibians in aquatic environments. Full article

This paper presents a comprehensive summary of recent advances in circuit topologies for piezoelectric energy harvesting, leading to self-powered systems (SPSs), covering the full-bridge rectifier (FBR) and half-bridge rectifier (HBR), AC-DC converters, and maximum power point tracking (MPPT) techniques. These approaches are analyzed with respect to their advantages, limitations, and overall impact on energy harvesting efficiency. Th work explores alternative methods that leverage phase shifting between voltage and current waveform components to enhance conversion performance. Additionally, it provides detailed insights into advanced design strategies, including adaptive power management algorithms, low-power control techniques, and complex impedance matching. The paper also addresses the fundamental principles and challenges of converting mechanical vibrations into electrical energy. Experimental results and performance metrics are reviewed, particularly in relation to hybrid approaches, load impedance, vibration frequency, and power conditioning requirements in energy harvesting systems. This review aims to provide researchers and engineers with a critical understanding of the current state of the art, key challenges, and emerging opportunities in piezoelectric energy harvesting. By examining recent developments, it offers valuable insights into optimizing interface circuit design for the development of efficient and self-sustaining piezoelectric energy harvesting systems. Full article