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Introduction: The application of selenium could directly or indirectly modulate the activity of antioxidant enzymes in crops, thereby mitigating the detrimental effects of abiotic and biotic stresses on crop health. However, there are few studies on the effects of nano-selenium fertilizer on potato scab caused by Streptomyces spp., potato yield and tuber quality. Methods: We aimed to elucidate the impact of nano-selenium fertilizer on potato disease resistance, yield, tuber quality, antioxidant enzyme activity and rhizosphere soil bacterial communities, and to determine the optimal frequency and growth stages of nano-selenium fertilizer spraying. Results and discussion: The application of nano-selenium fertilizer twice during the seedling stage significantly reduced the disease index of potato scab, enhanced potato yield, tuber quality (dry matter, Vitamin C, crude protein, and selenium content), and antioxidant enzyme activity (glutathione peroxidase, peroxidase, polyphenol oxidase, superoxide dismutase, and phenylalanine ammonia lyase). The diversity of the rhizosphere bacterial community of potatoes subjected to selenium fertilizer spraying at the seedling stage increased significantly, and concurrently, the symbiotic network of rhizosphere bacterial microbiome grew more complex. Beneficial microorganisms such as bacteria of the genus Bacillus were enriched in the rhizosphere soil. The current study provided theoretical support for the exploration of a potato selenium-enriched technology system and supplies scientific guidance for the utilization of nano-selenium. [ABSTRACT FROM AUTHOR]

Fundamentally, the heat source in the ball screw feed system accounts for the thermal behavior of ball screw. Due to the fact that cooling resources are not always sufficient, the main objective of this paper is to allocate cooling resources for multiple heat sources. The network model of ball screw thermal deformation and temperature at the heat source is constructed based on wavelet neural network (WNN). Combined with the mathematical analysis results, the explicit mathematical expression of thermal deformation and heat source under this working condition is deduced, and the explicit mathematical expression of heat source contribution is further obtained. After that, keeping the total heat source contribution constant under this working condition, the particle swarm optimization (PSO) algorithm is used to determine the heat source contribution distribution that can minimize the thermal deformation of the ball screw. Finally, through the design of the ball screw cooling system, the minimum thermal deformation of various heat sources at different cooling ratios under constant cooling flow is explored. The contribution of heat source is basically consistent with the optimal cooling ratio, which not only verifies each other, but also shows that the uniform cooling of heat source can best inhibit thermal deformation. [ABSTRACT FROM AUTHOR]

The most devastating soilborne disease of soybean is Phytophthora root rot (PRR) caused by Phytophthora sojae. Biological control has emerged as an effective method of reducing soilborne diseases. The present study isolated the bacterial strain W82T‐44 from the rhizosphere soil of a resistant soybean variety Williams 82. This strain was identified as Bacillus amyloliquefaciens and exhibited a 92.4% inhibition of mycelial growth of P. sojae. W82T‐44 produced cellulase, siderophore and protease and significantly promoted soybean growth. W82T‐44 treatment, compared to the negative control, was found to significantly (p < 0.05) increase soybean plant height (22.34 vs. 18.47 cm), fresh weight of the whole plant (2.57 vs. 2.05 g), dry weight (0.43 vs. 0.32 g), total root length (266.80 vs. 182.53 cm), root area (27.10 vs. 17.84 cm2) and root volume (1.88 vs. 1.16 cm3). The fermentation filtrate of W82T‐44 had significant inhibitory effects on mycelial growth, oospore formation and cyst germination in P. sojae and additionally promoted zoospore encystment. W82T‐44 possessed genes involved in lipopeptide synthesis, including bacilysin, surfactin, fengycin and bacillomycin. Moreover, it significantly upregulated pathogenesis‐related genes (β‐1,3‐glucanase, nonexpressor of pathogenesis‐related gene 1, chitinase) and genes encoding defence enzymes (polyphenol oxidase, phenylalanine ammonia‐lyase, peroxidase) in soybean roots (p < 0.05). The PRR disease index of soybean treated with the W82T‐44 strain was significantly lower (11.67) than the negative control (40.00; p < 0.05). The present study indicates the potential of B. amyloliquefaciens W82T‐44 as a biological control agent against PRR from various perspectives. [ABSTRACT FROM AUTHOR]

This article presents a cost-effective camera network system that employs neural network-based object detection and stereo vision to assist a pan–tilt–zoom camera in imaging fast, erratically moving small aerial targets. Compared to traditional radar systems, this approach offers advantages in supporting real-time target differentiation and ease of deployment. Based on the principle of knowledge distillation, a novel data augmentation method is proposed to coordinate the latest open-source pre-trained large models in semantic segmentation, text generation, and image generation tasks to train a BicycleGAN for image enhancement. The resulting dataset is tested on various model structures and backbone sizes of two mainstream object detection frameworks, Ultralytics' YOLO and MMDetection. Additionally, the algorithm implements and compares two popular object trackers, Bot-SORT and ByteTrack. The experimental proof-of-concept deploys the YOLOv8n model, which achieves an average precision of 82.2% and an inference time of 0.6 ms. Alternatively, the YOLO11x model maximises average precision at 86.7% while maintaining an inference time of 9.3 ms without bottlenecking subsequent processes. Stereo vision achieves accuracy within a median error of 90 mm following a drone flying over 1 m/s in an 8 m × 4 m area of interest. Stable single-object tracking with the PTZ camera is successful at 15 fps with an accuracy of 92.58%. [ABSTRACT FROM AUTHOR]

Objective: Recognition of auditory brainstem response (ABR) waveforms may be challenging, particularly for older individuals or those with hearing loss. This study aimed to investigate deep learning frameworks to improve the automatic recognition of ABR waveforms in participants with varying ages and hearing levels. Study Design: The research used a descriptive study design to collect and analyze pure tone audiometry and ABR data from 100 participants. Setting: The research was conducted at a tertiary academic medical center, specifically at the Clinical Audiology Center of Tsinghua Chang Gung Hospital (Beijing, China). Methods: Data from 100 participants were collected and categorized into four groups based on age and hearing level. Features from both time‐domain and frequency‐domain ABR signals were extracted and combined with demographic factors, such as age, sex, pure‐tone thresholds, stimulus intensity, and original signal sequences to generate feature vectors. An enhanced Wide&Deep model was utilized, incorporating the Light‐multi‐layer perceptron (MLP) model to train the recognition of ABR waveforms. The recognition accuracy (ACC) of each model was calculated for the overall data set and each group. Results: The ACC rates of the Light‐MLP model were 97.8%, 97.2%, 93.8%, and 92.0% for Groups 1 to 4, respectively, with a weighted average ACC rate of 95.4%. For the Wide&Deep model, the ACC rates were 93.4%, 90.8%, 92.0%, and 88.3% for Groups 1 to 4, respectively, with a weighted average ACC rate of 91.0%. Conclusion: Both the Light‐MLP model and the Wide&Deep model demonstrated excellent ACC in automatic recognition of ABR waveforms across participants with diverse ages and hearing levels. While the Wide&Deep model's performance was slightly poorer than that of the Light‐MLP model, particularly due to the limited sample size, it is anticipated that with an expanded data set, the performance of Wide&Deep model may be further improved. [ABSTRACT FROM AUTHOR]

The problem of machining complex surfaces with non-ball-end cutters by strip-width-maximization machining is formulated as a kind of surface fitting problem in which the tool surface envelope feature line approximates the design surface under the movement transform. The theory of surface envelope−approximation is proposed as a general method for optimizing tool movement in single-contact strip-width-maximization machining of sculptured surfaces with non-ball-end cutters. Based on the surface moving frame, the velocity equations and transformation matrices for the tool motion relative to the workpiece, described by the motion-invariant parameters of the tool surface and design surface, are derived. A functional extremum model for optimizing the tool position ensures continuous and symmetrical motion relative to the workpiece to achieve the highest machining efficiency and accuracy. Finally, a Matlab-based simulation example verifies the machining efficiency and accuracy of the envelope approximation theory. [ABSTRACT FROM AUTHOR]