In this paper, the author analyzes the post-bore noise problem in mixed pipe-flow modeling, which uses shock-capturing methods within single-equation frameworks. The study reveals that the origin of these numerical no...
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In this paper, the author analyzes the post-bore noise problem in mixed pipe-flow modeling, which uses shock-capturing methods within single-equation frameworks. The study reveals that the origin of these numerical noises following pipe-filling is the sudden jump from the free-surface gravity wave speed c to a significantly higher constant pipe acoustic wave speed ac during surcharging. This abrupt transition results in an excessively large Laplacian type numerical dissipation, which overwhelms the physical fluxes, reverses their directions, and leads to significant decreases in mass and momentum, particularly at the bore front where the concavity of the conserved variable is not small in a relatively sharp bore profile, and periodically manifest at the bore front following the initial pressurization of a cell. Based on the analyses of the above origin and underlying mechanisms, the author proposes a novel noise-mitigation technique: the post-bore oscillation mitigation (PBOM) approach, which diminishes the concavity of the shock profile by allowing the ventilated cells ahead of the bore to fill more rapidly, and introduces a new smooth transient function for signal wave speed to prevent a sudden jump in the numerical viscosity coefficient. Some preliminary tests validate this proposed noise-mitigation approach.
There has been increasing concern on facilitating the health beneficial by improving neighborhood living conditions. However, the comprehensive health impacts of urban green spaces (UGS) and built environment factors ...
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There has been increasing concern on facilitating the health beneficial by improving neighborhood living conditions. However, the comprehensive health impacts of urban green spaces (UGS) and built environment factors on ischemic heart disease (IHD) has not been fully investigated, occasionally causing inconsistent results. This study investigated how three UGS indicators, i.e., Normalized Difference Vegetation Index (NDVI), Green View Index (GVI), and UGS equity, were associated with IHD risk with distinctive effects. Based on multi-source BE data across 1025 communities in Wuhan, China, a combination of the negative binomial regression model (NBR) was adopted to estimate the effects, with the propensity score matching approach for sensitivity test. The geographically weighted negative binomial regression (GWNBR) was employed to detect the spatial variations of UGS indicators and other built environment factors. The results indicated that NDVI and GVI, along with sky view, open space ratio, and road integration, presented negative association with the IHD risk, while floor area ratio and road intersection density presented positive association with the IHD risk. These findings underscored the importance of differentiating UGS indicators in urban planning. Strategic green infrastructure development should consider both quantity and functional quality to optimize public health outcomes. Dynamic urban greening approaches locally tailored in response to urban conditions are needed.
The breeding of silkworms is a crucial step in silkworm rearing. To breed high quality hybrid silkworm species, it is necessary to obtain silkworm pupae of different sex and species with high purity. This study explor...
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The breeding of silkworms is a crucial step in silkworm rearing. To breed high quality hybrid silkworm species, it is necessary to obtain silkworm pupae of different sex and species with high purity. This study explores the feasibility of simultaneously identifying the sex and species of silkworm pupae using hyperspectral imaging (HSI) technology. Firstly, a total of 400 hyperspectral images of 4 species of silkworm pupae were acquired using the hyperspectral imaging system, and the spectral information within the wavelength range of 400.89-1002.19 nm was extracted. The original spectral data was preprocessed using Savitzky-Golay (SG) smoothing combined with multivariate scattering correction (MSC), and a combined dimensionality reduction method of PCA-t-SNE was constructed to reduce the dimensionality of spectral data, aiming to retain the global and local features of the original spectra as much as possible. Subsequently, multiple classifiers were utilized to achieve the simultaneous identification of the sex and species of silkworm pupae. Among them, classification accuracies of the test set, utilizing PCA-t-SNE combined dimensionality reduction on the WHO-SVM and CNN models, achieve 93.75% and 95%, respectively. The results indicate that the combined dimensionality reduction method of PCA-t-SNE can extract representative spectral information more effectively and improve the classification accuracy. Simultaneously, the CNN model with strong learning ability has great potential in the classification of silkworm pupae. In conclusion, HSI technology can be effectively utilized for the identification of silkworm pupae to accomplish high-precision and complex classification tasks.
A novel maglev deceleration drive system (MDDS) with the advantages of compact structure and high torque is proposed in this paper. The structure and working principle of the MDDS are introduced. Based on the equivale...
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A novel maglev deceleration drive system (MDDS) with the advantages of compact structure and high torque is proposed in this paper. The structure and working principle of the MDDS are introduced. Based on the equivalent magnetic circuit method, the time-varying air-gap magnetic density between the stator teeth and floating ring are obtained. According to the virtual work principle, the maglev forces generated by magnetic pole pairs are derived. The resultant maglev forces acting on the floating ring are deduced by the force model of the floating ring. With finite-element simulation, the validity of analytical model for the air-gap magnetic density is verified. In addition, the superiority of the permanent magnet position in the maglev drive part is demonstrated. Furthermore, the influences of the structure parameters on the resultant maglev forces are analyzed. Results show that the eccentric distance of the floating ring and stator axial length have a great influence on the resultant maglev forces. This research is of great significance to further output torque research and structure optimization of MDDS.
We typically think of the demand volume for a business in a city as a function of basic characteristics, such as the type of business, the quality of the product or service offered and its pricing. In addition, factor...
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We typically think of the demand volume for a business in a city as a function of basic characteristics, such as the type of business, the quality of the product or service offered and its pricing. In addition, factors related to the urban environment, such as population density and accessibility are also crucial and have been considered in the literature. However, these considerations have typically been at the macro level. In this work we are interested in exploring the complementarity between specific (pairs) of venues. Simply put, venue B is complementary to venue A, if customers are more probable to visit venue B after being at venue A. This can increase the traffic for a business beyond the demand expected from the aforementioned factors, and it has been largely ignored in the literature. In this study we take a simulation-based approach to estimate this complementarity. We perform our simulations and analysis on two different spatial levels, namely, the venue level, as well as, the urban area level (e.g., zip code, neighborhood, etc.). The estimated complementarity provides insights for business owners and urban planners that can allow them to satisfy more demand, which consequently can increase the revenue for the businesses, but also can create more convenient urban navigation for city dwellers.
The permanent magnet generator with motion rectifier (MR-PMG) can harvest energy from reciprocating motion, and it has broad application prospects in field of wave energy generation. In the process of bi-directional i...
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The permanent magnet generator with motion rectifier (MR-PMG) can harvest energy from reciprocating motion, and it has broad application prospects in field of wave energy generation. In the process of bi-directional input, radial electromagnetic force has significant impact on vibration of the generator. This paper focuses on radial electromagnetic force and electromagnetic vibration of MR-PMG. Firstly, the air gap flux density and radial electromagnetic force of the MR-PMG are calculated, and finite element method is used to analyze time-space and spectral characteristics of the radial electromagnetic force. Secondly, the natural frequencies of each vibration mode are calculated using modal superposition method and the influence of radial electromagnetic force on generator vibration is obtained by magnetic-structural coupling analysis. Finally, the influence of structural parameters on low-order radial electromagnetic force is discussed. The research results indicate that the generator does not resonance at low speed, and the twice frequency of radial electromagnetic force has the greatest impact on electromagnetic vibration. The radial electromagnetic force of MR-PMG can be reduced by increasing slot notch width, air gap length and polar arc coefficient while reducing the thickness of permanent magnet. The research results can provide theoretical basis for further research on vibration suppression and structural optimization of MR-PMG.
Hair loss affects over 30% of the global population, impacting psychological well-being and social interactions. Robotic hair transplantation has emerged as a pivotal solution, requiring precise hair follicle detectio...
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Hair loss affects over 30% of the global population, impacting psychological well-being and social interactions. Robotic hair transplantation has emerged as a pivotal solution, requiring precise hair follicle detection for effective treatment. Traditional methods utilizing horizontal bounding boxes (HBBs) often misclassify due to the follicles' elongated shapes and varied orientations. This study introduces YOLO-OHFD, a novel YOLO-based method using oriented bounding boxes (OBBs) for improved hair follicle detection in dermoscopic images, addressing the limitations of traditional HBB approaches by enhancing detection accuracy and computational efficiency. YOLO-OHFD incorporates the ECA-Res2Block in its feature extraction network to manage occlusions and hair follicle orientation variations effectively. A Feature Alignment Module (FAM) is embedded within the feature fusion network to ensure precise multi-scale feature integration. We utilize angle classification over regression for robust angle prediction. The method was validated using a custom dataset comprising 500 dermoscopic images with detailed annotations of hair follicle orientations and classifications. The proposed YOLO-OHFD method outperformed existing techniques, achieving a mean average precision (mAP) of 87.01% and operating at 43.67 frames per second (FPS). These metrics attest to its efficacy and real-time application potential. The angle classification component particularly enhanced the stability and precision of orientation predictions, critical for the accurate positioning required in robotic procedures. YOLO-OHFD represents a significant advancement in robotic hair transplantation, providing a robust framework for precise, efficient, and real-time hair follicle detection. Future work will focus on refining computational efficiency and testing in dynamic surgical environments to broaden the clinical applicability of this technology.
This article focuses on discussing the extensive application of data-driven models in industry and the challenges it faces, particularly in dealing with interference and outliers in process data. To this end, the basi...
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This article focuses on discussing the extensive application of data-driven models in industry and the challenges it faces, particularly in dealing with interference and outliers in process data. To this end, the basic stochastic configuration network (SCN) is expanded with the heavy-tailed Laplace distribution in order to effectively address the outliers in industrial data, and a novel robust data-driven modeling method (renamed as Lap-RSC) is proposed. First, the prototype of the SCN learner model is constructed with the stochastic configuration algorithm, which means the hidden nodes number, the input weights, and biases of the SCN learner model are all determined in this step. Second, as the output data are contaminated with the outliers, the expectation-maximization (EM) algorithm is combined with the Laplace distribution to estimate the output weights of the SCN learner model robustly. Furthermore, the mathematical decomposition of the Laplace distribution is introduced in this work, which brings three major advantages: 1) it largely improves the computational efficiency of the Lap-RSC;2) it allows a more clearly and evident explanation of the robustness of the Lap-RSC;and 3) it facilitates the mathematical proof of the convergence property of the Lap-RSC. Finally, the verification results demonstrate the effectiveness of the proposed Lap-RSC.
With the progress of science and technology and the development of sports science, the training methods of athletes are gradually developing towards a more scientific and data-oriented direction. Infrared thermal imag...
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With the progress of science and technology and the development of sports science, the training methods of athletes are gradually developing towards a more scientific and data-oriented direction. Infrared thermal image technology can capture the temperature distribution of human body surface in real time. A large number of infrared thermal image data of athletes under different training intensity and environment are collected and input into the model for training. In the feature extraction stage, CNN can automatically identify the key temperature change region from the infrared thermal image. In the pattern recognition stage, the model can classify and predict the new thermal image data by learning the thermal energy consumption pattern under different training intensity. In the association learning stage, the model associates the thermal image features with the actual thermal energy consumption data, so as to achieve accurate simulation of thermal energy consumption. After a series of experiments and verification, the deep learning model constructed in this study shows high accuracy and reliability in the simulation of thermal energy consumption in infrared thermal images. The model can not only accurately identify the heat energy consumption pattern of athletes during training, but also predict the heat energy consumption that may occur under specific training conditions. The model also has good generalization ability, and can adapt to the heat energy consumption simulation needs of different athletes and different training environments.
In this paper, the nonlocal reverse space-time derivative nonlinear Schr & ouml;dinger equation under nonzero boundary conditions is investigated using the Riemann-Hilbert (RH) approach. The direct scattering prob...
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In this paper, the nonlocal reverse space-time derivative nonlinear Schr & ouml;dinger equation under nonzero boundary conditions is investigated using the Riemann-Hilbert (RH) approach. The direct scattering problem focuses on the analyticity, symmetries, and asymptotic behaviors of the Jost eigenfunctions and scattering matrix functions, leading to the construction of the corresponding RH problem. Then, in the inverse scattering problem, the Plemelj formula is employed to solve the RH problem. So the reconstruction formula, trace formulae, theta\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\theta $$\end{document} condition, and exact expression of the single-pole and double-pole solutions are obtained. Furthermore, dark-dark solitons, bright-dark solitons, and breather solutions of the reverse space-time derivative nonlinear Schr & ouml;dinger equation are presented along with their dynamic behaviors summarized through graphical simulation.
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