Vehicle Type Recognition Combining Global and Local Features via Two-Stage Classification

This study proposes a new vehicle type recognition method that combines global and local features via a two-stage classification. To extract the continuous and complete global feature, an improved Canny edge detection algorithm with smooth filtering and non-maxima suppression abilities is proposed. To extract the local feature from four partitioned key patches, a set of Gabor wavelet kernels with five scales and eight orientations is introduced. Different from the single-stage classification, where all features are incorporated into one classifier simultaneously, the proposed two-stage classification strategy leverages two types of features and classifiers. In the first stage, the preliminary recognition of large vehicle or small vehicle is conducted based on the global feature via a k-nearest neighbor probability classifier. Based on the preliminary result, the specific recognition of bus, truck, van, or sedan is achieved based on the local feature via a discriminative sparse representation based classifier. We experiment with the proposed method on the public and established datasets involving various challenging cases, such as partial occlusion, poor illumination, and scale variation. Experimental results show that the proposed method outperforms existing state-of-the-art methods

Observation of helimagnetism in the candidate ferroelectric CrI2_2

CrI$_{2}$ is a quasi-one dimensional (1D) van der Waals (vdW) system that exhibits helimagnetism that propagates along the ribbons. This was determined from neutron time-of-flight diffraction measurements. Below $T_N=17$ K, a screw-like helimagnetic order develops with an incommensurate wavevector of $\mathbf{q} \approx (0.2492,0,0)$ at 8 K. Using density functional theory (DFT)$+U$ calculations, the $J_{1}$-$J_{2}$ model was leveraged to describe the helimagnetism, where $J_{1} (> 0)$ and $J_2 (< 0)$ correspond, respectively, to a ferromagnetic nearest neighbor (NN) and antiferromagnetic next-nearest neighbor (NNN) intrachain interaction. The DFT$+U$ calculations predict that bulk CrI$_2$ in the orthorhombic $Cmc2_1$ crystal structure satisfies the $|J_2| > |J_1|/4$ condition, which favors formation of helimagnetic order.Comment: main pdf file includes supplemen

Research on Practical Teaching of Railway Engineering Specialty Based on Temperature Test of Rubber Sleepers

Experimental teaching plays an important role in cultivating college students' innovative ability. This paper takes the practical teaching of the temperature test of the new rubber sleeper as an example to analyze the current situation and problems of the practical teaching of railway engineering. The specific measures of the new system of practical teaching of railway engineering are put forward: Build a practical teaching curriculum system, improve the practical teaching evaluation mechanism, and promote the sharing of school-enterprise resources, so as to cultivate outstanding railway engineering talents with engineering ability and innovative spirit

The roles of microRNAs in horticultural plant disease resistance

The development of the horticultural industry is largely limited by disease and excessive pesticide application. MicroRNAs constitute a major portion of the transcriptomes of eukaryotes. Various microRNAs have been recognized as important regulators of the expression of genes involved in essential biological processes throughout the whole life cycle of plants. Recently, small RNA sequencing has been applied to study gene regulation in horticultural plants. In this review, we summarize the current understanding of the biogenesis and contributions of microRNAs in horticultural plant disease resistance. These microRNAs may potentially be used as genetic resources for improving disease resistance and for molecular breeding. The challenges in understanding horticultural plant microRNA biology and the possibilities to make better use of these horticultural plant gene resources in the future are discussed in this review

<b>NWB2023_Interdisciplinary research classification based on a combined conceptual-empirical framework</b>

Measuring interdisciplinarity is a significant but challenging task in science quantitative studies. Various indicators have been proposed for measurement, but recent studies showed the majority of these indicators are unsatisfactory and that some even produce contradicting results. This problem is largely due to the fact that interdisciplinarity is a complex and multifaceted concept, and it is difficult for indicators to capture this complexity. Therefore, in this study, we argue for classifying interdisciplinary research (IDR) rather than measuring it directly. A combined conceptual-empirical framework is proposed to classify IDR. Specifically, at the conceptual level, four ideal types of IDR—Synthetic, Discovery, Diffusion, and Background—are provided in terms of their knowledge integration patterns; at the empirical level, bibliometrics based on full-text are used to extract citation features (e.g., citation mentioned, shared, length, and function features) of categories from IMR&D structure to characterize different knowledge integration patterns. Finally, these elected features are fed into deep learning classifiers (e.g., CNN and RNN) to achieve the final result of IDR classification. Our result shows that the number of articles of Discovery and Diffusion types accounted for the largest proportion of the total. The proportion of articles of Synthetic type that well-satisfy the core definition of IDR is slightly lower. The Background type accounted for the lowest. We therefore argue that classifying IDR using a well-designed framework is a feasible and reasonable solution to the current “measurement trap” and may offer the opportunity and foundation for subsequent measurement research of different IDR types.</p

Infrared Image Enhancement Using Convolutional Neural Networks for Auto-Driving

Auto-driving detection usually acquires low-light infrared images, which pose a great challenge to the autopilot function at night due to their low contrast and unclear texture details. As a precursor algorithm in the field of automatic driving, the infrared image contrast enhancement method is of great significance in accelerating the operation speed of automatic driving target recognition algorithms and improving the accuracy of object localization. In this study, a convolutional neural network model including feature extraction and image enhancement modules is proposed to enhance infrared images. Specifically, the feature extraction module consists of three branches, a concatenation layer, and a fusion layer that connect in parallel to extract the feature images. The image enhancement module contains eight convolutional layers, one connectivity layer, and one difference layer for enhancing contrast in infrared images. In order to overcome the problem of the lack of a large amount of training data and to improve the accuracy of the model, the brightness and sharpness of the infrared images are randomly transformed to expand the number of pictures in the training set and form more sample pairs. Unlike traditional enhancement methods, the proposed model directly learns the end-to-end mapping between low- and high-contrast images. Extensive experiments from qualitative and quantitative perspectives demonstrate that our method can achieve better clarity in a shorter time

The Study on Mechanical Strength of Titanium-Aluminum Dissimilar Butt Joints by Laser Welding-Brazing Process

Laser welding&ndash;brazing of 5A06 aluminum to Ti6Al4V titanium in a butt configuration was carried out to discuss the influences of welding parameters on dissimilar joint properties. The effects of laser offset, welding speed, and laser power on the spreading length of the molten aluminum liquid, interface fracture zone width (IFZW), fracture roughness, intermetallic compounds (IMCs) thickness, and tensile strength were also investigated. The microstructure and fracture of the joint were also studied. The results show that the tensile strength of the joint is not only influenced by the thickness and type of IMCs, but also influenced by the spreading ability of the aluminum liquid, the fracture area broken at the Ti/fusing zone (FZ) interface, and the relative area of the brittle and ductile fracture in FZ. A dissimilar butt joint with an IMC thickness of 2.79 &mu;m was obtained by adjusting the laser offset, welding speed, and laser power to 500 &mu;m, 11 mm/s and 1130 W, respectively. The maximum tensile strength of the joint was up to 183 MPa, which is equivalent to 83% of the tensile strength of the 5A06 aluminum alloy

Citrus Canopy SPAD Prediction under Bordeaux Solution Coverage Based on Texture- and Spectral-Information Fusion

Rapid and nondestructive prediction of chlorophyll content and response to the growth of various crops using remote sensing technology is a prominent topic in agricultural remote sensing research. Bordeaux mixture has been extensively employed for managing citrus diseases, such as black star and ulcer disease. However, the presence of pesticide residues in Bordeaux mixture can significantly modify the spectral response of the citrus canopy, thereby exerting a substantial influence on the accurate prediction of agronomic indices in fruit trees. In this study, we used unmanned aerial vehicle (UAV) multispectral imaging technology to obtain remote sensing imagery of Bordeaux-covered citrus canopies during the months of July, September, and November. We integrated spectral and texture information to construct a high-dimensional feature dataset and performed data downscaling and feature optimization. Furthermore, we established four machine learning models, namely, partial least squares regression (PLS), ridge regression (RR), ridge, random forest (RF), and support vector regression (SVR). Our objectives were to identify the most effective prediction model for estimating the SPAD (soil plant analysis development) value of Bordeaux-covered citrus canopies, assess the variation in prediction accuracy between fused features and individual features, and investigate the impact of Bordeaux solution on the spectral reflectance of the citrus canopy. The results showed that (1) the impact of Bordeaux mixture on citrus canopy reflectance bands ranked from the highest to the lowest as follows: near-infrared band at 840 nm, red-edge band at 730 nm, blue band at 450 nm, green band at 560 nm, and red band at 650 nm. (2) Fused feature models had better prediction ability than single-feature modeling, with an average R2 value of 0.641 for the four model test sets, improving by 0.117 and 0.039, respectively, compared with single-TF (texture feature) and -VI (vegetation index) modeling, and the test-set root-mean-square error (RMSE) was 2.594 on average, which was 0.533 and 0.264 lower than single-TF and -VI modeling, respectively. (3) Multiperiod data fusion effectively enhanced the correlation between features and SPAD values and consequently improved model prediction accuracy. Compared with accuracy based on individual months, R improved by 0.013 and 0.011, while RMSE decreased by 0.112 and 0.305. (4) The SVR model demonstrated the best performance in predicting citrus canopy SPAD under Bordeaux solution coverage, with R2 values of 0.629 and 0.658, and RMSE values of 2.722 and 2.752 for the training and test sets, respectively

Do you take off your mask correctly? A survey during COVID-19 pandemic in Ningbo, China.

Guidelines and recommendations from public health authorities related to face masks have been essential for containing the COVID-19 pandemic. A cross-sectional survey was conducted in Ningbo City, China, from April 8 to 12, 2022. We assessed the behavioral differences and correlates of mask usage, primarily mask-removal. We examined public mask-wearing behavior during on-site COVID-19 nucleic acid detection. The survey instrument was developed based on the guidelines issued by the World Health Organization and consisted of demographics, mask-wearing knowledge, and behavior. We analyzed data from 1180 participants; 73.2% demonstrated good mask-wearing knowledge. However, regarding mask-wearing behavior, only 53.7% knew the correct way to remove a mask; 70.3% maintained hand hygiene after touching the outside. Binary logistic regression analyses revealed that health prevention knowledge and free mask distribution were positively associated with two types of mask-wearing behaviors. Most participants used masks during the COVID-19 pandemic; however, mask-removal and hand hygiene were neglected when touching the outside of the mask. More attention must be paid to mask-removal and hand hygiene details. Local health authorities should consider introducing the free distribution of masks