Yanxi Liu
Abstract: To address the security threats posed by unauthorized UAV operations, commonly known as "black flights," traditional frequency-hopping signal detection methods often exhibit low accuracy and poor adaptability in environments characterized by low signal-to-noise ratios and complex electromagnetic conditions. This paper provides a systematic review of deep learning-based methods for detecting UAV frequency-hopping signals, followed by a detailed comparative analysis of three representative …
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Zhaojie Li
Abstract: Due to its wide bandgap, high thermal conductivity, and high-temperature, high-frequency, and high-power characteristics, silicon carbide (SiC) has been widely applied, especially in the field of electric vehicles, power conversion and photovoltaic inverters. However, there are still some defects in the utilization of SiC-based semiconductor devices, such as lattice mismatch, material defects, and interface defects. Enhancing the stability and reliability of SiC devices themselves is an urgent …
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Linhan Jiang
Abstract: The enhancement of mechanical arm load-bearing performance under constrained operational conditions represents a persisting conundrum in mechanical engineering, given the profound influence of structural parameters on stress distribution and displacement profiles. In response to this requirement, this investigation introduces a comprehensive optimization paradigm. Initially, computational simulations are executed via ANSYS Workbench, wherein cross-sectional geometry, area proportion, and support …
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Zikun Yu, Xianzi Zeng, Dongrui Xu, Hao Wang, Ziyu Li, Lele Yang
Abstract: This paper details the design and implementation of an omnidirectional mobile platform motion control system built upon a microcontroller-based microprocessor. The mechanical framework employs low-hardness, lightweight aluminum extrusions as base materials, featuring a pentagonal chassis structure integrated with omnidirectional wheels. This configuration ensures agile movement while enabling on-the-spot rotation and high-speed cornering. The execution system innovatively incorporates an …
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Sibo Wang, Shiqi Li, Hao Huang
Abstract: This paper establishes a physical model and designs a robust inversion algorithm for the precise measurement of silicon carbide epitaxial layer thickness. First, under the dual-beam interference assumption, a physical dual-beam interference model for silicon carbide epitaxial layer thickness was developed. This model, based on optical path difference and phase analysis, dynamically calculates refractive index using the Sellmeier dispersion model. It quantifies beam amplitude differences through …
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Jingbin Xue, Yuyang Qi, Dechao Lv
Abstract: Targeting the LC oscillation commonly taught in high school, this study models a series RLC circuit using university-level circuit theory and ordinary differential equations. We derive the analytical solutions for the underdamped, critically damped and overdamped regimes, compare their time responses and energy dissipation, and visualize charge, current, and total energy via MATLAB. The work offers a quantitative, visual reference to bridge high-school and university instruction, and clarifies …
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Zining Nicole Yang, Qinghan Wang
Abstract: As a passive unidirectional flow control device, Tesla valves demonstrate broad application prospects in microfluidics, thermal photovoltaics, and biomedical equipment due to their advantages of no moving parts, easy processing, and scalability. However, current research faces two major bottlenecks: first, the lack of unified performance evaluation metrics, where parameters such as flow rate ratio and pressure drop ratio are often used interchangeably, making results difficult to compare …
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Qijia Xu, Zhengyang Sun, Xidong Yang
Abstract: This study focuses on quantitatively analyzing the relationship between national demographic characteristics and the distribution of cybercrime, aiming to identify the most influential macro-factors. The research first employed Spearman's rank correlation analysis to assess the linear correlations between twelve national indicators—encompassing economic, social, educational, and internet usage metrics—and the cybercrime factor. The results revealed strong linear correlations for GDP per capita, …
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Peng Yang, Dujing Luo, Zihong Zhang
Abstract: The accuracy of Non-Invasive Prenatal Testing (NIPT) relies heavily on sufficient fetal Y chromosome concentration in maternal blood. Identifying key influencing factors is critical for optimizing testing timing and improving diagnostic reliability. This study employed multiple linear and polynomial regression models to explore relationships between Y concentration, gestational age, and maternal BMI. To address repeated measurements and significant individual variations, a linear mixed-effects …
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Mingxiang Mo
Abstract: To address the challenges of uncertain testing time points and the difficulty in determining abnormalities due to individual variations in non-invasive prenatal testing (NIPT), this paper introduces an integrated machine learning framework that amalgamates prediction, optimization, and classification. The framework is designed to identify key factors influencing the quality of testing data and, based on these factors, to offer personalized optimal testing time points and highly accurate …
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Chenyu Yang, Xiang Li
Abstract: This paper addresses the issue of unobservable audience voting data in real-world competition scenarios by proposing a unified modeling framework based on inverse inference. Treating judges' scores and elimination outcomes as observable constraints, the study constructs a Bayesian inverse problem model incorporating ranking rules by introducing a social attention prior. An approximate Bayesian computation method is employed to achieve stable estimation of the underlying voting distribution. …
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Shaobo Xiang
Abstract: This paper constructs a physical-economic coupling model integrating space elevators with traditional rockets for multi-billion-ton Earth-Moon material transport missions. Within a unified framework, it delineates the intrinsic relationships among transport capacity, time cycles, and economic costs. Based on dynamic analysis and energy consumption calculations, a dual-objective planning model targeting total project duration and total cost is established by incorporating unit cost functions and …
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Shengxuan Zhao
Abstract: Nowadays, with the development of artificial intelligence technology, image processing is widely used in many areas such as industry or education field. Python is becoming the de facto standard of choice when developing computer vision software mainly due to its cross platform capability, simple programming style as well as a rich set of open source third party libraries such as OpenCV [1], TensorFlow [2], and PyTorch [3]. Unfortunately, today’s programming practices require tedious coding and …
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Yihe Ma, Xiyuan Wang
Abstract: The Olympic medal table showcases the competitive level of each country, and establishing a medal prediction model based on historical data is crucial for accurately predicting each country's performance at the 2028 Los Angeles Summer Olympics. This article first preprocesses data from the past 10 Olympic Games, selecting 11 features Xi (such as total number of participants, total number of events, etc.) and 4 predictor variables Yi (such as whether medals were won, total number of medals, …
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Jie Peng, Xudong Peng, Jin Xue
Abstract: In response to the problems of environmental degradation, social pressure and economic imbalance caused by excessive tourism in tourist destinations, this paper takes Juneau, Alaska, USA as the research object and constructs a multi-dimensional dynamic analysis model for sustainable tourism development based on dynamic system modeling technology. The number of tourists, the quality of life of residents, carbon emissions and infrastructure pressure were selected as the core indicators. By …
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Haolin Zhou
Abstract: This study focuses on the quantitative association analysis between fetal Y chromosome concentration and maternal indicators, and the determination of the optimal timing for non-invasive prenatal testing (NIPT) based on BMI grouping. The aim is to optimize NIPT strategies and minimize potential risks to pregnant women. Regarding the association characteristics between Y chromosome concentration and maternal indicators such as gestational age and BMI, Pearson correlation analysis first revealed a …
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Keyue Song
Abstract: This study developed a hybrid prediction framework to address the issues of Olympic medal prediction and strategic analysis, with a focus on model architecture, integration, and validation. A combination of linear regression and random forest algorithm was proposed, which was dynamically weighted through grid search optimization. This model combines athlete performance indicators such as participation frequency, historical medal count, national strength indicators, host country advantages, and …
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Tong Yu, ShanRen Xiong, Rui Shen
Abstract: This study aims to clarify the quantitative association patterns between maternal biological indicators and fetal Y chromosome concentration in male fetuses. First, comprehensive preprocessing was performed on 549 male fetal detection datasets, including key missing value deletion, Z-score outlier handling, and construction of derived features such as BMI grouping and gestational age segmentation, to ensure data quality and applicability for modeling. Spearman's rank correlation analysis …
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Xuejian Liu, Yifan Xue, Wenwu Deng
Abstract: In response to the clinical pain point of insufficient accuracy of the unified detection time point due to the difference in body mass index (BMI) of pregnant women in non-invasive prenatal testing (NIPT) for male fetuses, this paper proposes an optimization scheme for the optimal detection time point of NIPT combining FCM fuzzy clustering and TOPSIS methods. Firstly, the original NIPT data of 1082 male pregnant women with fetuses were preprocessed (abnormal samples of BMI and gestational weeks …
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Yuxuan Li, Yijia Liu, Xiyuan Wang
Abstract: To enhance the accuracy of non-invasive prenatal testing and safeguard maternal health, this study mathematically models and investigates the relationship between fetal Y chromosome concentration and maternal characteristics, aiming to provide optimal testing timing recommendations. First, exploratory data analysis was conducted to examine the correlation between fetal Y chromosome concentration and maternal gestational age, BMI, and age. The Shapiro-Wilk normality test confirmed non-normal …
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Xingchen Shi
Abstract: This study aims to deeply analyze the complex spatial linkage mechanism between the growth of artificial intelligence patents in China and regional innovation capacity. Based on panel data from Chinese provinces and municipalities from 2019 to 2023, this research comprehensively employs two spatial econometric methods: the Spatial Durbin Model (SDM) and the Geographically Weighted Regression Model (GWR). The study first reveals significant spatial dependence in regional innovation factors …
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Jianfu Ren, Jiaxu Shi, Huayu Yuan, Gencai Zhang, Haitao Li, Zeyu Wang
Abstract: With the rapid growth of natural gas consumption in my country, flow metering accuracy is crucial for energy trade settlement and transmission and distribution loss control. However, metering errors caused by multiple factors, such as liquefied natural gas vaporization, metering equipment failures, and improper parameter settings, pose challenges to traditional compensation methods that rely on simple mathematical models. This study aims to construct a high-precision natural gas flow metering …
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Qixuan Li
Abstract: Partial differential equations (PDEs) are core tools for characterizing physical laws and are widely used in fluid mechanics, power systems, additive manufacturing, and other fields. However, traditional numerical methods are limited by mesh partitioning, leading to a trade-off between accuracy and efficiency in complex geometric domains and high-dimensional problems. While Physically Informed Neural Networks (PINNs) achieve meshless PDE solutions by embedding prior physical knowledge, they …
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Zhihe Yang
Abstract: This study integrates multi-source satellite-remote-sensed PM2.5 data and provincial vehicle ownership data in China (1998-2023) to construct a high-precision prediction model. We developed a spatiotemporally coupled hybrid model that fuses time series analysis (Prophet) with graph convolutional networks, and incorporates a reinforcement learning framework for dynamic feature optimization. The model achieves an R² of 0.95, with a mean absolute error of 1.97 μ g/m³ and a root mean square error of …
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Zhiwen Wang, Zhipeng Zhu, Junjie Zhang, Junxi Zhang
Abstract: This study employs natural language processing, Kalman filtering, and Gaussian process regression to construct an NLP-KF-GPR multimodal data fusion model, conducting empirical analysis across 31 provinces and municipalities nationwide. Focusing on quantifying the synergistic effects of policy instruments and nonlinear responses to environmental constraints, the findings reveal significant regional heterogeneity in the transmission mechanisms of rural revitalization investment and financing. …
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Jiahui Hu, Zhenzhi Shi
Abstract: Stroke is currently one of the largest causes of death and disability worldwide, hence it needs effective strategies to detect it early. The paper discusses the role that ML techniques may play in predicting stroke risk, along with methodologies, performance metrics, and their clinical implications. There are benefits associated with integrating ML into stroke prediction models: large dataset processing, improved accuracy, and timely risk assessment. In this paper, we have considered five …
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