Search Results (777)

Electro-Fenton chemical mechanical polishing primarily regulates the generation of hydroxyl radicals (·OH) via the Fenton reaction through an applied electric field, which subsequently influences the formation and removal of the oxide layer on the workpiece surface, thereby impacting the overall polishing quality and rate. This study employs Pin–Disk friction and wear experiments to investigate the material removal behavior of single-crystal GaN during electro-Fenton chemical mechanical polishing. Utilizing a range of analytical techniques, including coefficient of friction (COF) curves, surface morphology assessments, cross-sectional analysis, and power spectral density (PSD) measurements on the workpiece surface, we examine the influence of abrasives, polishing pads, polishing pressure, and other parameters on the electro-Fenton chemical–mechanical material removal process. Furthermore, this research provides preliminary insights into the synergistic removal mechanisms associated with the electro-Fenton chemical–mechanical action in single-crystal GaN. The experimental results indicate that optimal mechanical removal occurs when using a W0.5 diamond at a concentration of 1.5 wt% combined with a urethane pad (SH-Q13K-600) under a pressure of 0.2242 MPa. Full article

The nitrogen nutrition index (NNI) is a significant agronomic statistic used to assess the nitrogen nutrition status of crops. The use of remote sensing to invert it is crucial for accurately diagnosing and managing nitrogen nutrition in crops during critical periods. This study utilizes the UHD185 airborne hyperspectral imager and the ASD Field Spec3 portable spectrometer to acquire hyperspectral remote sensing data and agronomic parameters of the winter wheat canopy during the nodulation and flowering stages. The objective is to estimate the NNI of winter wheat through a winter wheat nitrogen gradient experiment conducted in Leling, Shandong Province. The ASD spectral reflectance data of the winter wheat canopy were selected as the reference standard and compared with the UHD185 hyperspectral data obtained from an unmanned aerial vehicle (UAV). The comparison focused on analyzing the trends in the spectral curve changes and the spectral correlation between the two datasets. The findings indicated a strong agreement between the UHD185 hyperspectral data and the spectral data obtained by ASD in the range of 450–830 nm. A spectrum index was developed to estimate the nitrogen nutritional index utilizing the bands within this range. The linear model, based on the first-order derivative ratio spectral index (RSI) (FD₆₆₆, FD₈₂₆), demonstrated the highest accuracy in estimating the nitrogen nutrient index in winter wheat. The model yielded R² values of 0.85 and 0.75, respectively, and may be represented by the equation y = −2.0655x + 0.156. The results serve as a benchmark for future utilization of the UHD185 hyperspectral data in estimating agronomic characteristics of winter wheat. Full article

With the rapid development of the semiconductor industry, the demand for high-speed testing in the large-scale production of semiconductor devices and integrated circuit production lines continues to grow. As one of the key tools in semiconductor device performance testing and integrated circuit testing, source measure unit (SMU) plays a crucial role in high-precision transient response testing scenarios. In high-precision measurement scenarios, multiple measurements are often required and averaged to improve measurement accuracy, but this can slow down the measurement speed. This article proposes a measurement acceleration algorithm based on BA-Informer time series prediction to solve the problem of decreased measurement speed in high-precision measurement. On the one hand, this algorithm improves the encoder structure. Traditional time series prediction models may have limitations in handling long-term dependencies and trend extraction. BiRNN is an extended version of recurrent neural network (RNN), which consists of two directional RNN. One forward RNN processes data from the beginning to the end of the sequence, while the other reverse RNN processes data from the end to the beginning of the sequence. In the end, the outputs from both directions are merged at each time step. Compared to traditional one-way RNN, BiRNN can more effectively handle data with before and after dependencies. Based on its characteristics, this article integrates BiRNN into the encoder structure. This algorithm can simultaneously process input sequences from both positive and negative directions, effectively limiting the bidirectional contextual information of data and significantly enhancing the model’s ability to capture time series trends. In this paper, BiRNN is integrated into the encoder structure, and the algorithm can simultaneously process input sequences from both positive and negative directions, more effectively capturing the bidirectional contextual information of data and significantly enhancing the model’s ability to capture time series trends. This improvement enables the model to more accurately grasp the overall trend of data changes during prediction, thereby improving prediction accuracy. On the other hand, an attention discrete cosine transform (ADCT) module is introduced between the encoder and decoder to convert time-domain signals into frequency-domain representations. This not only reveals the spectral characteristics of the signal but also reduces data redundancy and improves the efficiency of subsequent processing by combining attention mechanisms. Finally, the algorithm performance is analyzed by analyzing the output characteristic curves of loads with different properties. The experiment shows that the prediction algorithm and the combination of measurement and prediction method proposed in this article save half of the measurement time by combining measurement and prediction while ensuring the same amount of data obtained, verifying the effectiveness of the proposed method. Full article

Motor impairment is a critical health issue that restricts disabled people from living their lives normally and with comfort. Detecting motor imagery (MI) in electroencephalography (EEG) signals can make their lives easier. There has been a lot of work on detecting two or four different MI movements, which include bilateral, contralateral, and unilateral upper limb movements. However, there is little research on the challenging problem of detecting more than four motor imagery tasks and unilateral lower limb movements. As a solution to this problem, a spectral-spatio-temporal multiscale network (SSTMNet) has been introduced to detect six imagery tasks. It first performs a spectral analysis of an EEG trial and attends to the salient brain waves (rhythms) using an attention mechanism. Then, the temporal dependency across the entire EEG trial is worked out using a temporal dependency block, resulting in spectral-spatio-temporal features, which are passed to a multiscale block to learn multiscale spectral-–spatio-temporal features. Finally, these features are deeply analyzed by a sequential block to extract high-level features, which are used to detect an MI task. In addition, to deal with the small dataset problem for each MI task, the researchers introduce a data augmentation technique based on Fourier transform, which generates new EEG trials from EEG signals belonging to the same class in the frequency domain, with the idea that the coefficients of the same frequencies must be fused, ensuring label-preserving trials. SSTMNet is thoroughly evaluated on a public-domain benchmark dataset; it achieves an accuracy of 77.52% and an F1-score of 56.19%. t-SNE plots, confusion matrices, and ROC curves are presented, which show the effectiveness of SSTMNet. Furthermore, when it is trained on augmented data generated by the proposed data augmentation method, it results in a better performance, which validates the effectiveness of the proposed technique. The results indicate that its performance is comparable with the state-of-the-art methods. An analysis of the features learned by the model reveals that the block architectural design aids the model in distinguishing between multi-imagery tasks. Full article

Background: Sexual characteristics in brain neurophysiological activity are a significant area of research in cognitive neuroscience. As a sport that involves minimal physical movement, shooters remain largely stationary during aiming, facilitating the collection of their neural activity compared to athletes in other sports. Objectives: To investigate the neural characteristics of novice shooters of different genders under competitive conditions. Methods: Sixteen subjects participated in a shooting competition following four weeks of training. Electroencephalogram (EEG) data and behavioral data (shooting scores, aiming curves, and pressure curves) were recorded during the competition, and the power spectral density (PSD) and phase-locking value (PLV) network features were extracted to explore further the correlation between the shooting scores and neural activity. Results: In our sample, (1) there were no significant differences in shooting scores between males and females; (2) there were differences in PSD values across the theta, alpha, alpha-2, beta, and gamma frequency bands between males and females; and (3) there were differences in PLV network properties in the theta, alpha, beta, and gamma frequency bands between males and females. Correlation analysis revealed associations between shooting scores and neural activity in male and female novices. Conclusions: The case study demonstrated that males and females exhibited different neural activity characteristics in the shooting competition, providing a foundation for further investigation into the sex differences in neural activity in shooting competition. Full article

Optical fiber-based acoustic detectors for ultrasound imaging in medical field feature plano-concave Fabry–Perot cavities integrated on fiber tips, realized via dip-coating. This technique imposes constraints on sensor geometry, potentially limiting performance. Lab-on-Fiber technology enables complex three-dimensional structures with precise control over geometric parameters, such as the curvature radius. A careful investigation of the optical and mechanical aspects involved in the sensors’ performances is crucial for determining the design rules of such probes. In this study, we numerically analyzed the impact of curvature on the optical and acoustic properties of a plano-concave cavity using the Finite Element Method. Performance metrics, including sensitivity, bandwidth, and directivity, were compared to planar Fabry–Perot configurations. The results suggest that introducing curvature significantly enhances sensitivity by improving light confinement, especially for cavity thicknesses exceeding half the Rayleigh zone (∼45 μm), reaching an enhancement of 2.5 a L = 60 μm compared to planar designs. The curved structure maintains high spectral quality (FOM) despite 2% fabrication perturbations. A mechanical analysis confirms no disadvantages in acoustic response and bandwidth (∼40 MHz). These findings establish curved plano-concave structures as robust and reliable for high-sensitivity polymeric lab-on-fiber ultrasound detectors, offering improved performance and fabrication tolerance for MHz-scale bandwidth applications. Full article

This study investigated the structural response characteristics of a novel single-point mooring gravity-type deep-water (SPM-GDW) net cage under irregular waves and currents. A hydrodynamic numerical model of the cage was created and validated through model experiments. Based on the validated cage model, the structural response characteristics such as cage motion response, mooring line forces, and floating collar stress were studied, considering the actual operating conditions in the target sea area. The response time history curves, wave height time history, and spectral density statistics were studied and compared. The results showed that the heave motion of the cage was consistent with wave elevation in the vertical direction and mainly influenced by wave conditions. The surge motion of the cage was closely related to the current, with a significant lag effect compared to wave elevation motion. Low-frequency loads under the combined action of waves and currents had a significant impact on the surge motion of the cage. In addition, the mooring line tension and pontoon stress were closely related to the wave elevation, with peak values of tension and stress occurring almost simultaneously with the peak wave elevation. However, the pontoon stress exhibited high-frequency response characteristics while satisfying the wave frequency response trend. It was found that the flow velocity had a significant impact on the spectral density of mooring line tension and pontoon stress in the low-frequency range, with an increase in spectral density values as the flow velocity increased. The structural response characteristics identified in this study provide a computational basis for the optimized design and analysis of single-point mooring gravity-type deep-water cages. Full article

The phosphorus-31 (³¹P) spectral modulus (PSM) is a measure of the metabolic status of cells, tissues, and organs. The PSM can be calculated from ³¹P nuclear magnetic resonance (³¹P NMR) spectra obtained from cell, tissue, or organ preparations. These ³¹P NMR spectra can be a measure of intact living cells, tissues, or organs, or appropriate biochemical extracts of such preparations. The ³¹P NMR spectrum is comprised of signals derived from organophosphate metabolites that resonate from 10 δ to −25 δ on the phosphorus chemical shift δ scale. The PSM is the ratio of the high-energy phosphate to that of the low-energy phosphate spectral integrals. These integrals may be conveniently grouped into high-energy and low-energy spectral regions, respectively, into ³¹P chemical shifts located between −0.13 δ to −25 δ and between 10 δ to −0.13 δ. High-energy phosphates are typically described as providing the energy necessary for the activity of cellular metabolism; chemically, they contain one or more phosphate anhydride bonds. This study demonstrates that, (1) in general, the higher the metabolic activity, the higher the PSM, and (2) the modulus calculation does not require a highly resolved ³¹P spectrum and can be calculated solely from the integral. The PSM was calculated among cells, tissues, and organs considered normal, diseased, and stressed. In diseased (mean 1.29 ± 0.73) and stressed (mean 1.23 ± 0.75) cells, tissues, and organs, PSM values are typically low or low relative to normal cells, tissues, or organs (mean 1.65 ± 0.90), following time-course measurements, in dynamic decline. The PSM is useful in determining the metabolic status of cells, tissues, or organs and can be employed as a calculable numeric assay for determining health status statically or over time. Calculation of the PSM can be carried out with spectra of low signal-to-noise; it relies on the minimal resolution required to detect an integral curve having a clear spectral integral inflection point at ca. −0.13 δ. Detection of an integral curve alone enables the calculation of a PSM even at levels of phosphorus concentration so low as to prevent detection of the individual or groups of metabolites, such as with in vivo or ex vivo cell, tissue, or organ determinations. This study (1) presents the foundations and fundamentals of the PSM, a living index of tissue metabolic health, and (2) demonstrates the use of spectral scan analysis in opening new vistas of biology and medicine for measuring the metabolic status of stressed and diseased tissues at a range of detectable levels for monitoring therapeutic interventions. Full article

Laser-induced breakdown spectroscopy (LIBS) is widely used for online quantitative analysis in industries due to its rapid analysis and minimal damage. However, challenges like signal instability, matrix effects, and self-absorption hinder the measurement accuracy. Recent approaches, including the internal standard method and crater limitation method, aim to improve the stability but suffer from high computational demands or complexity. This study proposes a method to enhance LIBS stability by utilizing craters formed from laser ablation without external cavity assistance. It first improves the plasma temperature calculation reliability using multiple elemental spectral lines, after which electron density calculations are performed. By fitting plasma parameter curves based on laser pulse counts and using a laser confocal microscope for crater analysis, stable plasma conditions were found within crater areas of 0.400 mm² to 0.443 mm² and depths of 0.357 mm to 0.412 mm. Testing with elemental spectral lines of Ti II, K II, Ca I, and Fe I showed a significant reduction in the relative standard deviation (RSD) of the LIBS spectral line intensity, demonstrating an improved signal stability within specified crater dimensions. Full article

Background: Kidney transplantation is a life-saving treatment for end-stage kidney disease, but allograft rejection remains a critical challenge, requiring accurate and timely diagnosis. The study aims to evaluate the integration of Fourier Transform Infrared (FTIR) spectroscopy and machine learning algorithms as a minimally invasive method to detect kidney allograft rejection and differentiate between T Cell-Mediated Rejection (TCMR) and Antibody-Mediated Rejection (AMR). Additionally, the goal is to discriminate these rejection types aiming to develop a reliable decision-making support tool. Methods: This retrospective study included 41 kidney transplant recipients and analyzed 81 serum samples matched to corresponding allograft biopsies. FTIR spectroscopy was applied to pre-biopsy serum samples, and Naïve Bayes classification models were developed to distinguish rejection from non-rejection and classify rejection types. Data preprocessing involved, e.g., atmospheric compensation, second derivative, and feature selection using Fast Correlation-Based Filter for spectral regions 600–1900 cm⁻¹ and 2800–3400 cm⁻¹. Model performance was assessed via area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and accuracy. Results: The Naïve Bayes model achieved an AUC-ROC of 0.945 in classifying rejection versus non-rejection and AUC-ROC of 0.989 in distinguishing TCMR from AMR. Feature selection significantly improved model performance, identifying key spectral wavenumbers associated with rejection mechanisms. This approach demonstrated high sensitivity and specificity for both classification tasks. Conclusions: The integration of FTIR spectroscopy with machine learning may provide a promising, minimally invasive method for early detection and precise classification of kidney allograft rejection. Further validation in larger, more diverse populations is needed to confirm these findings’ reliability. Full article

Vegetable production in intensive protected agriculture systems has evolved due to its intensity and economic importance. Sensors are increasingly common for decision-making in crop management and control of environmental variables, obtaining optimal yields, such as estimating vegetation indices. Innovation and technological advances in unmanned vehicle platforms have improved spatial, spectral, and temporal resolution. However, in protected agriculture systems, the use is limited due to the assumption of having controlled environmental conditions for indeterminate vegetable production. Therefore, sequential monitoring of NDVI is proposed during the 2022 and 2023 agricultural cycles using the Green Seeker^® sensor and agronomic variables. This has created a database to generate predictive models of development and yield as a function of nutrient status. The results obtained indicate high significance levels for the development and NDVI curves in all phenological stages; in contrast to the yield predictive models, this is due to the maximum values (close to one) recorded for NDVI inside the greenhouse in comparison to the yield prediction obtained from the 18th week of harvest. Evaluating the models between NDVI and agronomic variables is not an index that offers certainty in predicting yield in indeterminate crops in protected agriculture production systems. This is due to the constant optimal development in response to controlled environmental conditions, nutrient status, and water supply inside the greenhouse, without the sustainability of yield, which decreases in the final stages of production until production becomes economically unprofitable. Full article

This study employs multi-source remote sensing information and machine learning methods to comprehensively assess the geological background, structural features, alteration anomalies, and spectral characteristics of the Malkansu Manganese Ore Belt in Xinjiang. Manganese mineralization is predicted, and areas with high mineralization potential are delineated. The results of the feature factor weight analysis indicate that structural density and lithological characteristics contribute most significantly to manganese mineralization. Notably, linear structures are aligned with the direction of the manganese belt, and areas exhibiting high controlling structural density are closely associated with the locations of mineral deposits, suggesting that structure plays a crucial role in manganese production in this region. The Area Under the Curve (AUC) values for the Random Forest (RF), Naïve Bayes (NB), and eXtreme Gradient Boosting (XGBoost) models were 0.975, 0.983, and 0.916, respectively, indicating that all three models achieved a high level of performance and interpretability. Among these, the NB model demonstrated the highest performance. By algebraically overlaying the predictions from these three machine learning models, a comprehensive mineralization favorability map was generated, identifying 11 prospective mineralization zones. The performance metrics of the machine learning models validate their robustness, while regional tectonics and stratigraphic lithology provide valuable characteristic factors for this approach. This study integrates multi-source remote sensing information with machine learning methods to enhance the effectiveness of manganese prediction, thereby offering new research perspectives for manganese forecasting in the Malkansu Manganese Ore Belt. Full article

The present study investigates the relationship between the near-infrared (NIR) spectral characteristics of cross-linked polyethylene (XLPE) insulation materials and their macroscopic properties, with the aim of establishing a reference framework for non-destructive material aging analysis. Accelerated thermal aging tests were conducted on samples of XLPE cables. These samples underwent Fourier-transform infrared spectroscopy (FTIR), elongation at break (EAB), and tensile strength (TS) tests. The temporal variation curves of the carbonyl index (CI), EAB, and TS were obtained at aging temperatures of 105 °C, 135 °C, 155 °C, and 180 °C. Additionally, NIR spectroscopy was performed on the aged XLPE samples, producing absorbance curves corresponding to different aging times at these temperatures. The absorption peaks of ‘C-H (-CH₂-)’ (1730 nm/1764 nm) were analyzed to determine their temporal variation patterns. Finally, a correlation analysis was conducted between the NIR results and those of the FTIR, EAB, and TS tests, revealing numerical relationships between NIR characteristic peaks and FTIR, EAB, and TS data. These quantified correlations demonstrate that NIR can effectively represent macroscopic mechanical properties, thereby simplifying the procedures for monitoring material aging and providing valuable results without requiring destructive testing. Results indicate that there is a certain feasibility in replacing traditional cable aging tests with NIR. Full article

Antimonide laser diodes, with their high performance above room temperature, exhibit significant potential for widespread applications in the mid-infrared spectral region. However, the laser’s performance significantly degrades as the emission wavelength increases, primarily due to severe quantum-well hole leakage and significant non-radiative recombination. In this paper, we put up an active region with a high valence band offset and excellent crystalline quality with high luminescence to improve the laser’s performance. The miscibility gap of the InGaAsSb alloy was systematically investigated by calculating the critical temperatures based on the delta lattice parameter model. As the calculation results show, In_0.54Ga_0.46As_0.23Sb_0.77, with a compressive strain of 1.74%, used as the quantum well, is out of the miscibility gap with no spinodal decomposition. The quantum wells exhibit high crystalline quality, as evidenced by distinct satellite peaks in XRD curves with a full width at half maximum (FWHM) of 56 arcseconds for the zeroth-order peak, a smooth surface with a root mean square (RMS) roughness of 0.19 nm, room-temperature photoluminescence with high luminous efficiency and narrow FHWM of 35 meV, and well-defined interfaces. These attributes effectively suppress non-radiative recombination, thereby enhancing internal quantum efficiency in the antimonide laser. Furthermore, a novel epitaxial laser structure was designed to acquire low optical absorption loss by decreasing the optical confinement factor in the cladding layer and implementing gradient doping in the p-type cladding layer. The continuous-wave output power of 310 mW was obtained at an injection current of 4.6 A and a heatsink temperature of 15 °C from a 1500 × 100 μm² single emitter. The external quantum efficiency of 53% was calculated with a slope efficiency of 0.226 W/A considering both of the uncoated facets. More importantly, the lasing wavelength of our laser exhibited a significant blue shift from 3.4 μm to 2.9 μm, which agrees with our calculated results when modeling the interdiffusion process in a quantum well. Therefore, the interdiffusion process must be considered for proper design and epitaxy to achieve mid-infrared high-power and high-efficiency antimonide laser diodes. Full article

Cotton is susceptible to Verticillium wilt (VW) during its growth. Early and accurate detection of VW can facilitate targeted pesticide treatment and reduce the potential spread of the disease. However, accurately detecting VW in cotton before symptoms appear (the asymptomatic period) after infection by Verticillium dahliae remains challenging. This study proposes an early detection method for cotton wilt disease using hyperspectral imaging and recurrence plots (RP) combined with machine learning techniques. First, spectral curves were collected and analyzed under three conditions of cotton plants: healthy, asymptomatic, and symptomatic. Then, the one-dimensional spectral curve was transformed into two-dimensional recurrence plots to enhance the detail differences in the original spectral curve of cotton plants in various states. Hyperspectral recurrence plots contain rich texture information; fifteen texture features were extracted from the spectral recurrence plots using the Gray-Level Gradient Co-occurrence Matrix (GLGCM). Eleven of these texture features showed a strong correlation with the class labels of the cotton plants. In order to reduce redundant information between features, principal component analysis (PCA) was used to extract the first five principal components, which explained 99.02% of the information from the 11 features. The final principal component dataset was then input into KNN, SVM, ELM, and XGBoost classifiers to assess the accuracy of early detection of VW in cotton. The results showed that the XGBoost model, based on the first five principal components obtained from the texture features, achieved accuracy, precision, recall, and F1-score of 96.3%, 95.6%, 96%, and 95.8%, demonstrating a high classification capability. The results of this study confirm the feasibility of converting spectral curves into recurrence plots and extracting image texture features for the accurate identification of VW in cotton during the asymptomatic period. This method also provides a new strategy for early disease detection of cotton and other plants in the future. Full article