Search Results (12)

This paper presents a compact, high-efficiency electromagnetic transmitter for Controlled-source Audio-frequency Magnetotelluric (CSAMT) applications, operating in the 10–100 kHz range. A novel bipolar square-wave inverter topology is proposed, which directly modulates the transformer’s secondary-side AC output, eliminating conventional rectification and filtering stages. This design reduces system losses (simulated efficiency > 90%) and achieves an approximately 40% reduction in both volume and weight. The power stage uses a full-bridge bipolar inverter topology with SiC-MOSFETs, combined with a high-frequency transformer for voltage gain. Simulation, laboratory testing, and EMI evaluation confirm stable square-wave generation and full compliance with EN55032 Class A standards. Field validation with a CSAMT receiver demonstrates effective signal transmission and high-resolution subsurface imaging, thereby improving the efficiency and portability of urban geophysical exploration. Full article

Transient receptor potential vanilloid 4 (TRPV4) is a calcium-permeable cation channel critical for maintaining intracellular Ca²⁺ homeostasis and is essential in regulating immune responses, metabolic processes, and signal transduction. Recent studies have shown that TRPV4 activation enhances influenza A virus infection, promoting viral replication and transmission. However, there has been limited exploration of antiviral drugs targeting the TRPV4 channel. In this study, we developed the first machine learning model specifically designed to predict TRPV4 inhibitory small molecules, providing a novel approach for rapidly identifying repurposed drugs with potential antiviral effects. Our approach integrated machine learning, virtual screening, data analysis, and experimental validation to efficiently screen and evaluate candidate molecules. For high-throughput virtual screening, we employed computational methods to screen open-source molecular databases targeting the TRPV4 receptor protein. The virtual screening results were ranked based on predicted scores from our optimized model and binding energy, allowing us to prioritize potential inhibitors. Fifteen small-molecule drugs were selected for further in vitro and in vivo antiviral testing against influenza. Notably, glecaprevir and everolimus demonstrated significant inhibitory effects on the influenza virus, markedly improving survival rates in influenza-infected mice (protection rates of 80% and 100%, respectively). We also validated the mechanisms by which these drugs interact with the TRPV4 channel. In summary, our study presents the first predictive model for identifying TRPV4 inhibitors, underscoring TRPV4 inhibition as a promising strategy for antiviral drug development against influenza. This pioneering approach lays the groundwork for future clinical research targeting the TRPV4 channel in antiviral therapies. Full article

The integration of a photodetector that converts optical signals into electrical signals is essential for scalable integrated lithium niobate photonics. Two-dimensional materials provide a potential high-efficiency on-chip detection capability. Here, we demonstrate an efficient on-chip photodetector based on a few layers of MoTe₂ on a thin film lithium niobate waveguide and integrate it with a microresonator operating in an optical telecommunication band. The lithium-niobate-on-insulator waveguides and micro-ring resonator are fabricated using the femtosecond laser photolithography-assisted chemical–mechanical etching method. The lithium niobate waveguide-integrated MoTe₂ presents an absorption coefficient of 72% and a transmission loss of 0.27 dB µm⁻¹ at 1550 nm. The on-chip photodetector exhibits a responsivity of 1 mA W⁻¹ at a bias voltage of 20 V, a low dark current of 1.6 nA, and a photo–dark current ratio of 10⁸ W⁻¹. Due to effective waveguide coupling and interaction with MoTe₂, the generated photocurrent is approximately 160 times higher than that of free-space light irradiation. Furthermore, we demonstrate a wavelength-selective photonic device by integrating the photodetector and micro-ring resonator with a quality factor of 10⁴ on the same chip, suggesting potential applications in the field of on-chip spectrometers and biosensors. Full article

Heavy metal pollution in agricultural land poses significant threats to both the ecological environment and human health. Therefore, the rapid and accurate prediction of heavy metal content in agricultural soil is crucial for environmental protection and soil remediation. Acknowledging the limitations of traditional single linear or nonlinear machine learning models in terms of prediction accuracy, this study developed an ensemble learning model that integrates multiple linear or nonlinear learning models with a random forest (RF) model to improve both the prediction accuracy and reliability. In this study, we selected a typical copper (Cu) polluted area in the Pearl River Delta of Guangdong Province as the research site and collected Cu content data and indoor soil reflectance spectral data from 269 surface soil samples. First, the soil spectral data were preprocessed using Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC), and continuous wavelet transform (CWT) to reduce noise interference. Next, principal components analysis (PCA) was employed to reduce the dimensionality of the preprocessed spectral data, eliminating redundant features and lowering the computational complexity. Finally, based on the dimensionality-reduced data and Cu content, we established a stacked ensemble learning model, where the base models included SVR, PLSR, BPNN, and XGBoost, with RF serving as the meta-model to estimate the soil heavy metal content. To evaluate the performance of the stacking model, we compared its prediction accuracy with that of individual models. The results indicate that, compared to the traditional machine learning models, the prediction accuracy of the stacking model was superior (R² = 0.77; RMSE = 7.65 mg/kg; RPD = 2.29). This suggests that the integrated algorithm demonstrates a greater robustness and generalization capability. This study presents a method to improve soil heavy metal content estimation using hyperspectral technology, ensuring a robust model that supports policymakers in making informed decisions about land use, agriculture, and environmental protection. Full article

With the development of multispectral imaging technology, retrieving soil heavy metal content using multispectral remote sensing images has become possible. However, factors such as soil pH and spectral resolution affect the accuracy of model inversion, leading to low precision. In this study, 242 soil samples were collected from a typical area of the Pearl River Delta, and the Cu content in the soil was detected in the laboratory. Simultaneously, Sentinel-2 remote sensing image data were collected, and two-dimensional and three-dimensional spectral indices were established. Constructing independent decision trees based on pH values, using the Successive Projections Algorithm (SPA) combined with the Boruta algorithm to select the characteristic bands for soil Cu content, and this was combined with Optuna automatic hyperparameter optimization for ensemble learning models to establish a model for estimating Cu content in soil. The research results indicated that in the SPA combined with the Boruta feature selection algorithm, the characteristic spectral indices were mainly concentrated in the spectral transformation forms of TBI2 and TBI4. Full-sample modeling lacked predictive ability, but after classifying the samples based on soil pH value, the R² of the RF and XGBoost models constructed with the samples with pH values between 5.85 and 7.75 was 0.54 and 0.76, respectively, with corresponding RMSE values of 22.48 and 16.12 and RPD values of 1.51 and 2.11. This study shows that the inversion of soil Cu content under different pH conditions exhibits significant differences, and determining the optimal pH range can effectively improve inversion accuracy. This research provides a reference for further achieving the efficient and accurate remote sensing of heavy metal pollution in agricultural soil. Full article

In this experiment, the effect of replacing milk fat with soybean fat body (25%, 50%, 75%, 100%) on the quality, antioxidant capacity and in vitro digestive characteristics of yogurt was investigated while maintaining the total fat content of the yogurt unchanged. The results showed that increasing the substitution amount of soy fat body for milk fat had little effect on the pH and acidity of yogurt during the storage period, while the physicochemical properties, degree of protein gel network crosslinking, saturated fatty acid content, PV value and TBARS value of the yogurt significantly decreased (p < 0.05). Meanwhile, protein content, solids content, unsaturated fatty acid content, tocopherol content and water holding capacity significantly increased (p < 0.05). Flavor analysis revealed that yogurts with soybean oil bodies were significantly different when compared to those without soybean oil bodies (p < 0.05), and yogurt with 25% substitution had the highest sensory score. After in vitro digestion, the free fatty acid release, antioxidant capacity and protein digestibility of soybean oil body yogurt were significantly higher (p < 0.05). The SDS-PAGE results showed that the protein hydrolysis of the soybean oil body yogurt was faster. Therefore, the use of an appropriate amount of soybean oil bodies to replace milk fat is able to enhance the taste of yogurt and improve the quality of the yogurt. Full article

Integrated thin-film lithium niobate (LN) electro-optic (EO) modulators of broad bandwidth, low insertion loss, low cost and high production rate are essential elements in contemporary interconnection industries and disruptive applications. Here, we demonstrated the design and fabrication of a high performance thin-film LN EO modulator using photolithography assisted chemo-mechanical etching (PLACE) technology. Our device shows a 3-dB bandwidth over 50 GHz, along with a comparable low half wave voltage-length product of 2.16 Vcm and a fiber-to-fiber insertion loss of 2.6 dB. The PLACE technology supports large footprint, high fabrication uniformity, competitive production rate and extreme low device optical loss simultaneously, our result shows promising potential for developing high-performance large-scale low-loss photonic integrated devices. Full article

We demonstrate the hybrid integration of a lithium niobate microring resonator with a silicon nitride waveguide in the vertical configuration to achieve efficient light coupling. The microring resonator is fabricated on a lithium niobate on insulator (LNOI) substrate using photolithography assisted chemo-mechanical etching (PLACE). A fused silica cladding layer is deposited on the LNOI ring resonator. The silicon nitride waveguide is further produced on the fused silica cladding layer by first fabricating a trench in the fused silica while using focused ion beam (FIB) etching for facilitating the evanescent coupling, followed by the formation of the silicon nitride waveguide on the bottom of the trench. The FIB etching ensures the required high positioning accuracy between the waveguide and ring resonator. We achieve Q-factors as high as 1.4 × 10⁷ with the vertically integrated device. Full article

We demonstrate the fabrication of single-mode optical waveguides on lithium niobate on an insulator (LNOI) by optical patterning combined with chemomechanical polishing. The fabricated LNOI waveguides had a nearly symmetric mode profile of ~2.5 µm mode field size (full-width at half-maximum). We developed a high-precision measurement approach by which single-mode waveguides were characterized to have propagation loss of ~0.042 dB/cm. Full article

We report the fabrication of crystalline microresonators of high quality (Q) factors with a controllable wedge angle on lithium niobate on insulator (LNOI). Our technique relies on a femtosecond laser assisted chemo-mechanical polish, which allows us to achieve ultrahigh surface smoothness as critically demanded by high Q microresonator applications. We show that by refining the polish parameters, Q factors as high as 4.7 × 10⁷ can be obtained and the wedge angle of the LNOI can be continuously tuned from 9° to 51°. Full article

In recent years, endless link prediction algorithms based on network representation learning have emerged. Network representation learning mainly constructs feature vectors by capturing the neighborhood structure information of network nodes for link prediction. However, this type of algorithm only focuses on learning topology information from the simple neighbor network node. For example, DeepWalk takes a random walk path as the neighborhood of nodes. In addition, such algorithms only take advantage of the potential features of nodes, but the explicit features of nodes play a good role in link prediction. In this paper, a link prediction method based on deep convolutional neural network is proposed. It constructs a model of the residual attention network to capture the link structure features from the sub-graph. Further study finds that the information flow transmission efficiency of the residual attention mechanism was not high, so a densely convolutional neural network model was proposed for link prediction. We evaluate our proposed method on four published data sets. The results show that our method is better than several other benchmark algorithms on link prediction. Full article

In this paper, we develop a technique for realizing multi-centimeter-long lithium niobate on insulator (LNOI) waveguides with a propagation loss as low as 0.027 dB/cm. Our technique relies on patterning a chromium thin film coated on the top surface of LNOI into a hard mask with a femtosecond laser followed by chemo-mechanical polishing for structuring the LNOI into the waveguides. The surface roughness on the waveguides was determined with an atomic force microscope to be 0.452 nm. The approach is compatible with other surface patterning technologies, such as optical and electron beam lithographies or laser direct writing, enabling high-throughput manufacturing of large-scale LNOI-based photonic integrated circuits. Full article