Search Results (336)

Power hardware-in-the-loop (PHIL) creates a safe test environment to connect simulations with real hardware under test (HuT). Therefore, an interface algorithm (IA) must be chosen. The ideal transformer method (ITM) and the partial circuit duplication (PCD) are popular IAs, where a distinction is made between voltage- (V-) and current-type (C-) IAs. Depending on the sample time of the simulator and further delays, simulation accuracy is reduced and instability can occur due to negative feedback in the V-ITM and C-ITM control loops, which makes PHIL operation impossible. In the case of positive feedback, such as with the V-PCD and C-PCD, the delay causes destructive interference, which results in a phase shift and attenuation of the output signal. In this article, a novel damped Smith predictor (SP) for positive feedback PHIL IAs is presented, which significantly reduces destructive interference while allowing stable operation at low linking impedances at V-PCD and high linking impedances at C-PCD, thus reducing losses in the system. Experimental results show a reduction in phase shift by 21.17° and attenuation improvement of 24.3% for V-PCD at a sample time of 100 µs. The SP transfer functions are also derived and integrated into the listed negative feedback IAs, resulting in an increase in the gain margin (GM) from approximately one to three, which significantly enhances system stability. The proposed methods can improve stability and accuracy, which can be further improved by calculating the HuT impedance in real-time and dynamically adapting the SP model. Stable PHIL operation with SP is also possible with SP model errors or sudden HuT impedance changes, as long as deviations stay within the presented limits. Full article

We analyze the high-order above-threshold ionization (HATI) process of a small polyatomic molecule with C₃ symmetry, which is induced by a bicircular strong laser field. This field consists of two coplanar, counter-rotating, circularly polarized components with frequencies $r\omega$ and $s\omega$ where r and s are integers. In our study, we use an improved molecular strong-field approximation to obtain electron energy-angle-resolved and momentum spectra of the BF₃ molecule. We analyze the contributions of individual atoms as well as the impact of molecular symmetries on these spectra. We find that these spectra are significantly affected by the characteristics of the molecule and the laser-field parameters. Furthermore, we observe pronounced interference minima in the HATI spectra. We demonstrate that these minima result from the destructive interference of rescattered wave packets from different atomic centers, and we determine the conditions under which they occur, including two-, three-, and four-center interference. Full article

Potassium (K), a critical macronutrient for the growth and development of Korla fragrant pear (Pyrus sinkiangensis Yu), plays a pivotal regulatory role in sugar-acid metabolism. Furthermore, K exhibits a highly specific response in near-infrared (NIR) spectroscopy compared to elements such as nitrogen (N) and phosphorus (P). Given its fundamental impact on fruit quality parameters, the development of rapid and non-destructive techniques for K determination is of significant importance for precision fertilization management. By measuring leaf potassium content at the fruit setting, expansion, and maturity stages (decreasing from 1.60% at fruit setting to 1.14% at maturity), this study reveals its dynamic change pattern and establishes a high-precision prediction model by combining near-infrared spectroscopy (NIRS) with machine learning algorithms. “Near-infrared spectroscopy coupled with machine learning can enable accurate, non-destructive monitoring of potassium dynamics in Korla pear leaves, with prediction accuracy (R²) exceeding 0.86 under field conditions.” We systematically collected a total of 9000 leaf samples from Korla fragrant pear orchards and acquired spectral data using a benchtop near-infrared spectrometer. After preprocessing and feature extraction, we determined the optimal modeling method for prediction accuracy through comparative analysis of multiple models. Multiplicative scatter correction (MSC) and first derivative (FD) are synergistically employed for preprocessing to eliminate scattering interference and enhance the resolution of characteristic peaks. Competitive adaptive reweighted sampling (CARS) is then utilized to screen five potassium-sensitive bands, specifically in the regions of 4003.5–4034.35 nm, 4458.62–4562.75 nm, and 5145.15–5249.29 nm, among others, which are associated with O-H stretching vibration and changes in water status. A comparison between random forest (RF) and BP neural network indicates that the MSC + FD–CARS–BP model exhibits the optimal performance, achieving coefficients of determination (R²) of 0.96% and 0.86% for the training and validation sets, respectively, root mean square errors (RMSE) of 0.098% and 0.103%, a residual predictive deviation (RPD) greater than 3, and a ratio of performance to interquartile range (RPIQ) of 4.22. Parameter optimization revealed that the BPNN model achieved optimal stability with 10 neurons in the hidden layer. The model facilitates rapid and non-destructive detection of leaf potassium content throughout the entire growth period of Korla fragrant pears, supporting precision fertilization in orchards. Moreover, it elucidates the physiological mechanism by which potassium influences spectral response through the regulation of water metabolism. Full article

For non-destructive testing (NDT) of defects in aluminum alloy plates, traditional ultrasonic contact methods face challenges from high temperatures and liquid couplant contamination. Using electromagnetic acoustic transducers (EMATs), a key issue is that longitudinal waves (L-waves) excited by the butterfly-coil EMATs interfere with the desired shear waves (S-waves) reflected by internal defects. To solve this problem, a simulation–experiment approach optimized the butterfly coil parameters. An FE model visualized the electromagnetic acoustic transducer (EMAT) acoustic field and predicted signals. Orthogonal simulations tested three main parameters: excitation frequency, wire diameter, and effective coil width. Tests on aluminum specimens with artificial defects used the optimized EMAT. Simulated and measured signals showed strong correlation, validating optimal parameters. The results confirmed suppressed L-wave interference and improved defect detection sensitivity, enabling detection of a 3 mm diameter flat-bottomed hole buried 37 mm deep. Full article

Pine wilt disease (PWD), a destructive pine forest disease caused by pine wood nematode (PWN), Bursaphelenchus xylophilus, has led to huge economic losses and ecological environment damage. Thaumatin-like proteins (TLPs) are the products of a complex gene family involved in host defense and a wide range of developmental processes in fungi, plants, and animals. In this study, a tlp gene of B. xylophilus (Bxtlp) (GenBank: OQ863020.1) was amplified via PCR and cloned into the expression vector pET-15b to construct the recombinant vector PET-15b-Bxtlp, which was then transformed into Escherichia coli BL-21(DE3). The recombinant protein was successfully purified using Ni-NTA affinity chromatography. The effect of the Bxtlp gene on the vitality and pathogenicity of PWNs was elucidated through RNA interference (RNAi) and overexpression. Bxtlp dsRNA significantly reduced the feeding, motility, spawning, and reproduction abilities of PWN; shortened its lifespan; and increased the female–male ratio. In contrast, the recombinant BxTLP markedly enhanced the reproductive ability of PWN. In addition, Bxtlp dsRNA increased reactive oxygen species (ROS) content in nematodes, while the recombinant BxTLP was confirmed to have antioxidant capacity in vitro. Furthermore, the bioassays on Pinus thunbergii saplings demonstrated that Bxtlp could significantly influence PWN pathogenicity. Overall, we speculate that Bxtlp affects the pathogenicity of PWNs mainly via regulating ROS levels, the motility, and hatching of PWN. Full article

Given the exceptional capabilities of femtosecond laser processing in achieving high-precision ablation for air-film cooling hole fabrication on turbine blades, it is imperative to develop an advanced monitoring methodology that enables real-time feedback control to automatically terminate the laser upon complete penetration detection, thereby effectively preventing backside damage. To tackle this issue, a spectrum-domain coherent imaging technique has been developed. This innovative approach adapts the fundamental principle of fiber-based Michelson interferometry by integrating the air-film hole into a sample arm configuration. A broadband super-luminescent diode with a 830 nm central wavelength and a 26 nm spectral bandwidth serves as the coherence-optimized illumination source. An optimal normalized reflectivity of 0.2 is established to maintain stable interference fringe visibility throughout the drilling process. The system achieves a depth resolution of 11.7 μm through Fourier transform analysis of dynamic interference patterns. With customized optical path design specifically engineered for through-hole-drilling applications, the technique demonstrates exceptional sensitivity, maintaining detection capability even under ultralow reflectivity conditions (0.001%) at the hole bottom. Plasma generation during laser processing is investigated, with plasma density measurements providing optical thickness data for real-time compensation of depth measurement deviations. The demonstrated system represents an advancement in non-destructive in-process monitoring for high-precision laser machining applications. Full article

State-of-the-art radar systems for the contactless monitoring of vital signs and respiratory diseases are typically based on single-channel continuous wave (CW) technology. This technique allows precise measurements of respiration patterns, periods of movement, and heart rate. Major practical problems arise as CW systems suffer from signal cancellation due to destructive interference, limited overall functionality, and a possibility of low signal quality over longer periods. This work introduces a sophisticated multiple-input multiple-output (MIMO) solution that captures a radar image to estimate the sleep pose and position of a person (first step) and determine key vital parameters (second step). The first step is enabled by processing radar data with a forked convolutional neural network, which is trained with reference data captured by a time-of-flight depth camera. Key vital parameters that can be measured in the second step are respiration rate, asynchronous respiratory movement of chest and abdomen and limb movements. The developed algorithms were tested through experiments. The achieved mean absolute error (MAE) for the locations of the xiphoid and navel was less than 5 cm and the categorical accuracy of pose classification and limb movement detection was better than 90% and 98.6%, respectively. The MAE of the breathing rate was measured between 0.06 and 0.8 cycles per minute. Full article

Accurate assessment of biomass fuel properties is essential for quality control and fair market pricing, particularly when dealing with variable moisture content (MC) in agricultural residues. This study investigates the use of near-infrared (NIR) spectroscopy to predict the lower heating value (LHV) and ash content of sugarcane leaf pellets while minimizing the interference caused by moisture variability. Sixty-two samples were scanned using an NIR spectrometer over three week-long storage periods to get different MCs with the same sample. Additionally, variable selection methods such as a genetic algorithm (GA) and moisture-related wavelength exclusion were explored. The optimal model for LHV prediction was developed using GA-PLS regression (Method II), provided a coefficient of determination (R²) of 0.80, a root mean square error of calibration (RMSEc) of 595.80 J/g, and a ratio of performance to deviation (RPD) of 1.74, indicating fair predictive performance. The ash content model showed moderate accuracy, with a maximum R² of 0.61 and an RPD of 1.40. These findings suggest that the variables selected via GA in Method II were not relevant to MC; as Method II provided the best result, this indicates a low impact of MC, which may influence model construction in the future. Moreover, the findings also highlight the potential of NIR spectroscopy, combined with appropriate spectral preprocessing and wavelength optimization, as a rapid, non-destructive tool for evaluating biomass quality, enabling more precise control in bioenergy production and biomass trading. Full article

Cytoplasmic polyadenylation element-binding proteins (CPEBs) are critical regulators of maternal mRNA translation during oogenesis, yet their roles in insect reproduction remain underexplored. Here, we characterized CmCPEB2, a CPEB homolog in the rice leaf roller Cnaphalocrocis medinalis, a destructive lepidopteran pest insect, and elucidated its role in mating-induced oviposition. The CmCPEB2 protein harbored conserved RNA recognition motifs and a ZZ-type zinc finger domain and was phylogenetically clustered with lepidopteran orthologs. Spatiotemporal expression profiling revealed CmCPEB2 was predominantly expressed in ovaries post-mating, peaking at 12 h with a 6.75-fold increase in transcript levels. Liposome-mediated RNA interference targeting CmCPEB2 resulted in a 52% reduction in transcript abundance, leading to significant defects in ovarian maturation, diminished vitellogenin deposition, and a 36.7% decline in fecundity. The transcriptomic analysis of RNAi-treated ovaries identified 512 differentially expressed genes, with downregulated genes enriched in chorion formation and epithelial cell development. Tissue culture-based hormonal assays demonstrated the juvenile hormone-dependent regulation of CmCPEB2, as JH treatment induced its transcription, while knockdown of the JH-responsive transcription factor CmKr-h1 in the moths suppressed CmCPEB2 expression post-mating. These findings established CmCPEB2 as a juvenile hormone-dependent regulator of mating-induced oviposition that orchestrates vitellogenesis through yolk protein synthesis and ovarian deposition and choriogenesis via transcriptional control of chorion-related genes. This study provides novel evidence of CPEB2-mediated reproductive regulation in Lepidoptera, highlighting its dual role in nutrient allocation and structural eggshell formation during insect oogenesis and oviposition. Full article

Eggshell quality inspection plays a pivotal role in enhancing the commercial value of poultry eggs and ensuring their safety. It effectively enables the screening of high-quality eggs to meet consumer demand for premium egg products. This paper analyzes the surface characteristics, ultrastructure, and mechanical properties of poultry eggshells. It systematically reviews current advances in eggshell quality inspection technologies and compares the suitability and performance of techniques for key indicators, including shell strength, thickness, spots, color, and cracks. Furthermore, the paper discusses challenges in non-destructive testing, including individual egg variations, species differences, hardware precision limitations, and inherent methodological constraints. It summarizes commercially available portable and online non-destructive testing equipment, analyzing core challenges: the cost–accessibility paradox, speed–accuracy trade-off, algorithm interference impacts, and the technology–practice gap. Additionally, the paper explores the potential application of several emerging technologies—such as tactile sensing, X-ray imaging, laser-induced breakdown spectroscopy, and fluorescence spectroscopy—in eggshell quality inspection. Finally, it provides a comprehensive outlook on future research directions, offering constructive guidance for subsequent studies and practical applications in production. Full article

To address the challenges in identifying NAPL contamination within low-permeability clay sites, this study innovatively integrates high-density electrical resistivity tomography (ERT) with a UNet deep learning model to establish an intelligent contamination detection system. Taking an industrial site in Shanghai as the research object, we collected apparent resistivity data using the WGMD-9 system, obtained resistivity profiles through inversion imaging, and constructed training sets by generating contamination labels via K-means clustering. A semantic segmentation model with skip connections and multi-scale feature fusion was developed based on the UNet architecture to achieve automatic identification of contaminated areas. Experimental results demonstrate that the model achieves a mean Intersection over Union (mIoU) of 86.58%, an accuracy (Acc) of 99.42%, a precision (Pre) of 75.72%, a recall (Rec) of 76.80%, and an F1 score (f1) of 76.23%, effectively overcoming the noise interference in electrical anomaly interpretation through conventional geophysical methods in low-permeability clay, while outperforming DeepLabV3, DeepLabV3+, PSPNet, and LinkNet models. Time-lapse resistivity imaging verifies the feasibility of dynamic monitoring for contaminant migration, while the integration of the VGG-16 encoder and hyperparameter optimization (learning rate of 0.0001 and batch size of 8) significantly enhances model performance. Case visualization reveals high consistency between segmentation results and actual contamination distribution, enabling precise localization of spatial morphology for contamination plumes. This technological breakthrough overcomes the high-cost and low-efficiency limitations of traditional borehole sampling, providing a high-precision, non-destructive intelligent detection solution for contaminated site remediation. Full article

In fiber Bragg grating (FBG) sensing demodulation systems, high-precision peak detection is a core requirement for demodulation algorithms. However, practical spectral signals are often susceptible to environmental noise interference, which leads to significant degradation in the accuracy of traditional demodulation methods. This study proposes a self-adaptive K-SVD (SAK-SVD) denoising algorithm based on adaptive window parameter optimization, establishing a closed-loop iterative feedback mechanism through dual iterations between dictionary learning and parameter adjustment. This approach achieves a synergistic enhancement of noise suppression and signal fidelity. First, a dictionary learning framework based on K-SVD is constructed for initial denoising, and the peak feature region is extracted by differentiating the denoised signals. By constructing statistics on the number of sign changes, an adaptive adjustment model for the window size is established. This model dynamically tunes the window parameters in dictionary learning for iterative denoising, establishing a closed-loop architecture that integrates denoising evaluation with parameter optimization. The performance of SAK-SVD is evaluated through three experimental scenarios, demonstrating that SAK-SVD overcomes the rigid parameter limitations of traditional K-SVD in FBG spectral processing, enhances denoising performance, and thereby improves wavelength demodulation accuracy. For denoising undistorted waveforms, the optimal mean absolute error (MAE) decreases to 0.300 pm, representing a 25% reduction compared to the next-best method. For distorted waveforms, the optimal MAE drops to 3.9 pm, achieving a 63.38% reduction compared to the next-best method. This study provides both theoretical and technical support for high-precision fiber-optic sensing under complex working conditions. Crucially, the SAK-SVD framework establishes a universal, adaptive denoising paradigm for fiber Bragg grating (FBG) sensing. This paradigm has direct applicability to Raman spectroscopy, industrial ultrasound-based non-destructive testing, and biomedical signal enhancement (e.g., ECG artefact removal), thereby advancing high-precision measurement capabilities across photonics and engineering domains. Full article

This study presents the development of a discrete algorithm for interpreting ground-penetrating radar (GPR) data in vertically inhomogeneous media for the diagnostics of road structures. Experimental data were obtained using an OKO-2 GPR system, followed by primary radargram processing using the CartScan software. This included noise and interference filtering, as well as the initial estimation of the dielectric permittivity of detected layers. The resulting dataset was used to validate numerical algorithms for solving the forward and inverse problems of geolectrics. The proposed approach is based on minimizing a quadratic misfit functional between the calculated and observed values of the horizontal component of the electromagnetic field. The gradient of the functional required for optimization is obtained via the numerical solution of an adjoint problem. A discrete version of this problem was developed, which satisfies the properties of conservativeness and uniformity according to finite difference theory. The inverse problem reconstruction of dielectric permittivity is considered a non-destructive method for radargram interpretation. Assuming a piecewise-continuous medium structure eliminates the need for computing gradients at material interfaces. The proposed methodology enhances the accuracy and reliability of pavement condition assessment and holds practical value for road infrastructure monitoring. Full article

This study explores the application of near-infrared (NIR) spectroscopy combined with machine learning for the non-destructive detection of aflatoxin in peanuts contaminated by Aspergillus flavus (A. flavus). The key innovation lies in the development of an optimized spectral processing pipeline that effectively overcomes moisture interference while maintaining high sensitivity to low aflatoxin concentrations. NIR spectra were collected from peanut samples at different incubation times within the spectral range of 950 to 1650 nm. Spectral data were preprocessed, and Competitive Adaptive Reweighted Sampling (CARS) selected ten characteristic bands. Correlation analysis was performed to examine the relationships between physicochemical properties, characteristic bands, and aflatoxin content. Three machine learning models—Backpropagation Neural Network (BPNN), Support Vector Machine (SVM), and Random Forest (RF)—were used to predict aflatoxin levels. The SNV-SVM model demonstrated superior performance, achieving calibration metrics (R²_C = 0.9945, RMSE_C = 9.92, RPD_C = 14.59) and prediction metrics (R²_P = 0.9528, RMSE_P = 19.58, RPD_P = 7.01), along with leave-one-out cross-validation (LOOCV) results (R² = 0.9834, RMSE = 11.20). The results demonstrate that NIR spectroscopy combined with machine learning offers a rapid, non-destructive approach for aflatoxin detection in peanuts, with significant implications for food safety and agricultural quality control. Full article

The instability of the geopolitical situation due to the high terrorist danger leads to the need to take into account at the planning stage the capabilities of intruders to perform UAV flight missions. A general method for analyzing the process of cargo delivery by UAVs (Unmanned Aerial Vehicles) to hard-to-reach areas during emergencies has been proposed. This method allows for the evaluation of UAV effectiveness based on the probability of successful cargo delivery within a specified time limit. The method is based on applying topological transformation techniques to stochastic networks. The cargo delivery process is modeled as a stochastic network, followed by the determination of its equivalent function and the use of Heaviside decomposition to calculate the distribution function and the expected delivery time. This presentation of the studied process for the first time made it possible to take into account the impact on the flight mission of the UAV of the destructive impact from the attacker. This approach allows the destructive effects on the UAV from malicious interference to be considered. The input data used for the analysis are parameters that characterize the properties of individual processes within the stochastic network, represented as branches, which are computed using methodologies published in earlier studies. It has been demonstrated that the resulting distribution function of the mission completion time can be accurately approximated by a gamma distribution with a level of precision suitable for practical applications. In this case, the gamma distribution parameters are determined using the equivalent function of the stochastic network. The proposed method can be used by flight planners when scheduling UAV operations in emergency zones, especially in scenarios where there is a risk of malicious interference. Full article