Zian Huang
Abstract: This study delves deeply into the theoretical basis of the Lorentz force damping mechanism in non-uniform magnetic fields and its application potential in the field of vibration isolation. By establishing the dynamic model of the rectangular coil in the gradient magnetic field and combining it with the numerical simulation system, the nonlinear enhancement relationship between the magnetic field gradient and the damping performance was analyzed. The research verified the significant …
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Zirui Liu
Abstract: This research will focus on understanding gravity, which governs everything from falling apples to orbiting planets. Newton described gravity as a force that pulls on objects, while Einstein later revealed that gravity is the curvature of space itself. Most experiments proving these theories require advanced laboratory equipment, making it difficult for students to test these revolutionary ideas firsthand. My research addresses this problem by creating a simple method that anyone can use to …
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Jia Geng
Abstract: The escape orbit in the three-body system is a crucial topic in the field of astrophysics, helping people to research the chaotic system, stimulate the evolution of some complex celestial systems, and design the spacecraft trajectory. Extensive research has been conducted on this topic, but there are still some limitations. This paper focuses on reviewing and analyzing escape orbits in the problem of three bodies. Specifically, this paper explains the basic mechanics conditions and analyzes …
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Wenjie Wu
Abstract: Three body problem is one of the most complicated dynamical problems in classical mechanics; the difficulty comes from the nonlinear equations and sensitive initial conditions. In this paper, it reviews the disintegration process of Three body system in long-term evolution and discuss the application of statistical mechanics in explaining this phenomenon. Based on the theoretical model, numerical simulation, and observational results, it draws the following conclusions: The most frequent final …
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Yixuan Xu
Abstract: In recent years, as computational power increases, the traditional encryption methods are becoming increasingly vulnerable to attacks and thus have some safety issues. Therefore, the application of quantum communication has become increasingly widespread, and more technologies related to quantum communication have been invented. This article provides an overview of the differences between quantum communication and traditional encryption in terms of their advantages, disadvantages, security, and …
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Yinqi Wu
Abstract: With more attention being put on the dynamic evolution of celestial bodies, the system of galaxy triplets has been considered as an important and helpful dynamic system to understand the gravitational interaction between stars and clusters in the universe. However, the analysis of the chaoticity of the system of galaxy triplets was still not emphasized as much as the system of three point masses. By setting the same initial conditions of L4 on the system of extended masses and the system of …
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Dongqi Cui
Abstract: Non-intrusive human sensing has gained rapid momentum across smart homes, rehabilitation, safety monitoring and other applications. However, heterogeneous evaluation practices hinder fair cross-technology comparison. Thus, this paper focuses on three representative technologies—wearable sensors, radar sensing, and Wi-Fi sensing and consolidates reported findings onto a unified six-dimensional scheme: Accuracy, Response Speed, Cost, Privacy, Convenience, and Coverage. Those heterogeneous metrics …
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Junze Lyu
Abstract: With the rapid development of artificial intelligence technology, service robots are increasingly integrating into daily life scenarios. Achieving efficient and natural human-robot interaction has become a key challenge. The ability to recognize emotions, as a core factor in enhancing the interaction experience, has attracted extensive attention from both the academic and industrial communities. This paper focuses on the emotion recognition problem of service robots, analyzes three key problems …
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Jialin Liu, Jiaqi Liu
Abstract: Hearing is essential for information processing and our safety, especially in traffic. For patients with hearing impairments, sound localization devices help identify threatening sound sources and reduce traffic safety risk. Existing methods typically rely on measuring time delay using microphone arrays. Unfortunately, this method faces critical challenges for dynamic sound sources, posing major threats. Addressing this challenge, we propose a wearable sound localization system that combines …
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Yongxi Zhao
Abstract: Deep learning models have achieved remarkable success in fields such as computer vision, natural language processing, and speech recognition. However, their massive parameter counts and high computational complexity severely restrict their deployment in resource-constrained scenarios like edge devices and embedded systems. As a core model compression technology, neural network pruning achieves model lightweight while maintaining performance by removing redundant connections, neurons, or filters. …
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Hong Tao
Abstract: As a signaling method widely used in communication systems (such as telephone buttons and remote control), the core of Dual-tone multi-frequency (DTMF) signal is to represent a key information through the combination of two specific frequency sine waves. In this paper, a detection algorithm based on fast Fourier transform (FFT) is proposed to meet the detection requirements of DTMF signals. The algorithm uses FFT to convert the time-domain signal into a frequency-domain spectrum after …
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Junhao Yang
Abstract: The Circular Restricted Three-Body Problem (CR3BP) is a simplification of the general three-body problem with essential assumptions: two massive primaries and an infinitesimal third body influenced solely by the gravity of the primaries. The equilibrium points that emerge from the resulting simplified system—Lagrange points—have become crucial locations for space missions due to their special stability characteristics. This article examines the usefulness of these points, specifically L1, L2, …
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Boyuan Sun
Abstract: Single-particle escape in tri-stellar gravitational systems is investigated using large ensembles of direct integrations that sample broad ranges of initial specific energy E and angular momentum L. It constructs outcome maps over (E, L) and interprets their structure with the aid of a co-rotating (synodic) frame, where the Jacobi-like integral and zero-velocity surfaces (ZVS) provide geometric diagnostics of accessible channels near the classical L1,
2 necks. Two outcome regimes emerge …
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Qianen Wang
Abstract: This paper focuses on periodic orbits in the three-body problem, systematically expounding the basic concepts of the three-body problem, classifying typical three-body problems and restricted three-body problems, and defining, characteristics and formation mechanism of periodic orbits. By analyzing typical orbital types such as special solutions to Lagrange points, low-energy periodic orbits, and resonant periodic orbits, combined with the application of the Queqiao satellite in the L2 halo …
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Jiancheng Ma
Abstract: Electroencephalogram (EEG) signals are widely used in brain-computer interface (BCI) systems due to their high temporal resolution and non-invasive nature. However, their low amplitude, susceptibility to noise, and poor spatial resolution often lead to inaccurate classification of brain intentions. To address these limitations, this study explores a multimodal approach that integrates functional near-infrared spectroscopy (fNIRS) with EEG to enhance signal quality and classification accuracy. …
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Zhuoxuan Xiong
Abstract: Affective Brain Computer Interface (BCI) is an interdisciplinary system that integrates neuroscience, psychology, and computer science to detect and interpret human emotional states by analyzing brain and physiological signals. This article illustrates how affective BCI using Electroencephalography (EEG) and other physiological signals to recognize different emotions and diagnose mental diseases. With questionaries and interviews, this closed-loop BCI can personalize a specific treatment option. …
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Yiyuan Zhu
Abstract: Electroencephalography (EEG) presents an approach toward practical emotion recognition by being noninvasive, providing a rich source with precise timing, and showing resilience to interference. This paper aims to present recent developments of emotion recognition systems based on brain-computer interfaces (BCI) using EEG, starting with a view into approaches used in modeling emotional states along with the methodologies used in acquiring, collecting, and preprocessing the datasets, then …
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Xingtong Ou
Abstract: Chronic diseases affecting younger people have become prevalent in contemporary society. Among chronic diseases, cardiovascular and cerebrovascular diseases are predominant, with countless people dying from cardiovascular diseases each year. Long-term heart rate variability (HRV) monitoring can warn of arrhythmia, myocardial ischemia, and other conditions, reducing the mortality rate from acute events by 30%. The advent of non-contact detection technology has made long-term monitoring possible. …
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Tianqi Hu
Abstract: This study aims to develop and implement a low-cost, high-resolution three-dimensional biological sample imaging system based on the light intensity transmission equation (TIE) technology to obtain the three-dimensional phase information of biological samples in a non-destructive way. The system can provide non-staining, real-time three-dimensional reconstruction images through a common optical path design, combined with a standard LED light source, CMOS sensor and optical components. The goal …
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Yangxiao (Olivia) Ou
Abstract: Traditional microscopes provide only 2D intensity information for transparent or unstained samples, limiting access to 3D morphology. Here, we present an in-line lensless digital holographic system using a 532 nm monochromatic light source. The unmodulated direct component serves as the reference wave, and the sample-diffracted component forms the interference, enabling single-frame 3D reconstruction via angular spectrum analysis. The system integrates a board-level CMOS camera with a simplified …
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Hengrui Pan
Abstract: Wireless charging, as an important technology, is constantly being applied in people's lives. However, there is still the problem that coil offset affects power transmission. Currently, solutions such as multi-coil array adaptive compensation are adopted, which to a certain extent increase the transmitted power and reduce energy loss, providing more application fields for wireless power transmission technology. Based on the development of wireless charging technology, this paper sorts out the …
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Zhengbing Hou
Abstract: Reconfigurable intelligent surface (RIS) is introduced as a new technology promising to revolutionize the wireless communication networks, especially for the next generation (6G), due to the fact that RIS can dynamically regulate electromagnetic waves propagation, potentially leading to vast performance improvement and energy-saving. This paper explores the capability of RIS to enhance communication systems in terms of their energy efficiency and signal quality. By incorporating passive, active, …
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Qidi Yuan
Abstract: The Poynting vector provides a rigorous framework for understanding electromagnetic energy flow, revealing that power is transported by fields in space rather than conductors. Although central to Maxwell’s equations, its physical interpretation in everyday circuits is often overlooked. This paper revisits the Poynting vector’s theoretical basis—energy density, Poynting’s theorem, and applicability in static and dynamic regimes—and applies it to three representative systems: a battery–resistor …
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Yize Sun
Abstract: The performance of traditional building temperature control systems is often suboptimal due to the inherent complexity of the control objects and the challenges associated with establishing accurate mathematical models. These traditional systems frequently result in energy inefficiency and compromised comfort levels for occupants. Addressing these shortcomings requires an innovative approach to temperature regulation. After a thorough review of existing indoor temperature regulation systems both …
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Yanqi Huang
Abstract: Against the backdrop of an aging society, the health of middle-aged and elderly individuals has become a growing concern for families. Falls are the leading cause of injury-related deaths, with a significant number of older adults suffering from disabilities or fatalities due to falls each year. Therefore, detecting falls is critically important. Traditional wearable sensors face challenges such as algorithmic limitations and wearability issues, making it difficult to meet the increasing demands …
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Yuhong Li
Abstract: Schlieren imaging technology is a magical tool that can "see" air and temperature fluctuations. Schlieren imaging technology uses the refraction of light to convert tiny changes in fluid substances into visual images, making invisible physical phenomena intuitive and easy to understand. High-end Schlieren imaging systems offered by companies like Holmarc, are designed for advanced applications and come with sophisticated features. These systems can be quite expensive, often costing several …
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Kairon Xia
Abstract: Quantitative assessment of satellite proliferation impacts on Antarctic astronomy reveals that 14.5% (14.14–14.86%) of images from Kunlun station are contaminated, exceeding Hubble's design tolerance over four times and ACS/WFC baselines (10.0%) by 45% and threatening humanity’s ultimate pristine window to the universe, immediate space traffic control is needed to improve the space environment. Using Kunlun Station images (05/2008, n=11477) and Space-Track.org satellite catalogues, we annotated …
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Fanchen Gu
Abstract: With the rapid development of wearable devices, smart medical and other industries, traditional rigid electronic materials are difficult to meet the requirements of modern electronic devices for flexibility and stability. Carbon nanotubes have become an ideal material for flexible electronic devices due to their unique performance characteristics. This paper focuses on the preparation and processing technologies of carbon nanotube flexible electronic materials, including the floating catalyst …
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Wenye Liang
Abstract: The stock market is one of the most important parts of the global economy, and predicting its movements has long been a challenge. Traditional methods relied on mathematical models and historical data, but advances in computing have introduced machine learning and deep learning approaches. This paper reviews four common methods used in stock prediction: Random Forest, XGBoost, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks. Each method has strengths, such as …
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Jiaye Miao
Abstract: We develop an analytic halo mass function (HMF) framework calibrated on the comprehensive Abacus-Summit simulations, achieving 2–3% accuracy for Λ CDM (≲ 5% for extended cosmologies with massive neutrinos, evolving dark energy, and modified power spectra) across 1011--1015M⊙ and 0<z<3 . Unlike universal forms limited to Λ CDM, our MCMC-optimized model explicitly incorporates redshift and cosmology dependence while maintaining physical interpretability. The analytic formulation enables …
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Yufei Wu, Zhirong Zhuang, Ziyou Chen
Abstract: With the acceleration of the global informatization process, cybercrime has increasingly become a serious challenge for the world. The transnational nature of cybercrime makes the response of a single country inadequate, and there is an urgent need for global policy coordination and optimization. Through an in-depth analysis of VCDB data, this paper reveals the distribution characteristics of global cybercrime, and points out the significant differences between developed and developing countries …
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Xuanzheng He
Abstract: This review provides a comprehensive overview of the advancements in deep learning for electroencephalography (EEG) analysis. EEG offers a non-invasive means to monitor brain activity, with applications in brain-computer interfaces (BCIs), clinical diagnostics, emotion recognition, and sleep monitoring. We trace the historical progression from early convolutional neural networks (CNNs) in the mid-2010s to the recent emergence of transformer-based models. Key CNN architectures, such as EEGNet, …
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Rujia Yang
Abstract: Three-dimensional measurement technology is widely used in scientific research and industrial fields. With the advancement of the era, traditional contact measurement methods, due to their low imaging accuracy, slow measurement speed, and limited applicability—being unable to measure elastic or deformable objects—are gradually failing to meet the demands of modern industrial development. As a novel 3D imaging approach, non-contact optical three-dimensional measurement technology offers the …
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Yanshen Zhao
Abstract: Based on the core of Survey on the Application of Reinforcement Learning in Distribution Network Voltage Control, this study systematically organizes RL's application system in distribution network voltage control: it establishes MDP and POMDP modeling frameworks, clarifying the design logic of state, action, and reward functions; classifies and reviews core schemes of value function-based, policy gradient-based, and residual RL, quantitatively comparing algorithm performance via unified …
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Huiyu Hu, Xiuli Wang
Abstract: For the task scheduling problem in multi-agent systems, this paper proposes a collaborative optimization method based on Graph Neural Network and Reinforcement Learning. Firstly, a heterogeneous graph structure is constructed to uniformly model the temporal dependencies, resource competition, and agent capability differences among tasks, and multi-dimensional node features are designed to fully describe the scheduling state. Secondly, the Proximal Policy Optimization algorithm is adopted to …
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Xinran Chen, Xuming Yan, Rongtao Zhang
Abstract: Medal prediction serves as a critical research direction in sports science and data analysis, holding significant implications for optimizing resource allocation and strategic decision-making in competitive sports. This study proposes an innovative hybrid predictive model that integrates hierarchical clustering, LASSO regression, and random forest algorithms. By constructing a purely competition-endogenous multidimensional competitiveness indicator system, the model overcomes the limitations of …
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Yipeng Zhang, Zhuoyu Li, Haosheng He
Abstract: This paper constructs a dynamic weighted comprehensive model that integrates linear regression and random forest algorithms. This model is used to predict the distribution of competitive performance indicators in international comprehensive sports events and analyze the relevant influencing factors. The model innovatively integrates multiple factors. It incorporates historical data and uses a mechanism to assign higher weights to recent performances, so as to better reflect the current …
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Yichen Lin
Abstract: The prediction of multi-objective complex systems has always been one of the significant challenges in academia. The multi-dimensional structure, diverse distribution, and complex temporal dependencies of the data make it difficult for conventional mathematical models to perform effective analysis. This study explores the application of hybrid machine learning algorithms in predicting multi-objective systems. This paper developed an LSTM-Markov model which uses the Markov chain to optimize the …
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Wenqian Zhong, Jingrui Xu, Jiabao Luo
Abstract: In this paper, a two-stage modeling framework integrating multivariate statistical analysis and dynamic correction is proposed to address the problems of lack of variable correlation and insufficient modeling of nonlinear influence mechanism in traditional prediction models in complex systems. The key explanatory variables are first screened out based on feature engineering, and the multiple linear regression prediction model and logistic regression model are constructed respectively. To further …
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Keying Zhang, Jiayu Wu
Abstract: With the rapid development of information technology, machine learning and artificial intelligence have achieved significant breakthroughs in the field of data analysis and prediction, demonstrating great application potential. For example, in the field of sports, ordinary prediction methods can no longer meet the increasingly complex global sports events with a surge in data volume. Taking sports event prediction as a specific application scenario, this paper proposes an advanced integrated …
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Shaonan You, Jing Chen, Liang Wei, Shuyi Zhou
Abstract: This study aims to solve the difficult problem of sports performance prediction and improve the accuracy and reliability of prediction. The improved ARIMA and neural network fusion technology was used to clean and standardize the sports data first, and then the improved ARIMA model was used to capture the time series features, and external factors such as athletes' physical fitness and training level were included in the model, and special cases were predicted by combining Bayes theory. Finally, …
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Junran Wang
Abstract: To address the inherent limitations of traditional time series forecasting models in handling complex real-world scenarios characterized by nonlinear dynamics and multivariate interactions, this paper proposes a novel hybrid prediction framework that synergistically integrates multivariate regression analysis with adaptive polynomial fitting mechanisms. First, outliers and missing values are processed through data cleaning technology. A feature system of many parameters such as event focus, new …
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Zimin Wang, Yiwen Dong, Chenyue Sun
Abstract: This paper focuses on the production decision optimization problem of electronic products, using a variety of models and algorithms to carry out research. Assuming that the defect rate of electronic products follows Bernoulli distribution, a hypothesis testing model is constructed with binomial distribution and normal approximation, and the minimum sample size formula is derived to determine the minimum detection scheme. The 0-1 variable is introduced to construct the unconstrained decision …
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Zhaobin Xu
Abstract: Medical image analysis plays a pivotal role in the early diagnosis of diseases such as skin lesions. However, the scarcity of data and class imbalance significantly hinder the performance of deep learning models. This paper proposes a novel method that leverages the pre-trained Stable Diffusion-2.0 model to generate high-quality synthetic skin lesion images and corresponding segmentation masks. This approach augments training datasets for classification and segmentation tasks. We adapt Stable …
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Yaowei Zhang
Abstract: The research on the strategy optimization of the“Snakes and Ladders”game offers a practical case for applying mathematical and algorithmic theories to real - world decision - making scenarios. It enriches the research methods in the field of game - based decision - making and provides new ideas for solving complex problems in other related fields.This paper addresses the strategy optimization problem in the Snakes and Ladders game by systematically solving four core issues through the …
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Weiping Yang, Tianqi Wu, Xukun Liu, Zhiyun Ma
Abstract: In the context of rapid artificial intelligence advancement, this study explores public perception of autonomous driving technology, exemplified by Baidu's " Apollo Go " autonomous driving taxis in Wuhan. Given the discrepancy between governmental/enterprise optimism and public skepticism, this research aims to analyze public sentiment and concerns towards autonomous vehicles to mitigate risks associated with AI adoption in intelligent driving. Utilizing web text analysis, 2,181 valid Weibo …
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Xin Yu, Haofei Li, Jiangyu Sun
Abstract: Against the backdrop of deep global digital integration, the transnational nature and spatial heterogeneity of cybercrime have intensified governance challenges. This study employs a Geographically Weighted Regression (GWR) model to integrate cybercrime data from 129 countries (including 12 indicators such as crime incidents, success rate, and prevention rate) from the International Telecommunication Union (ITU) 2023 Global Cybersecurity Index (GCI) and the VERIS Community Database (VCDB), …
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Zhaohe Gu, Kaiyi Lei, Mengyuan Xu
Abstract: To address global population aging, this study innovatively integrates statistics and machine learning, analyzing multidimensional data, including economic status, health conditions, family support, and social environment. Logistic regression is employed to elucidate the influencing mechanisms of elderly care model choices, while decision tree and random forest models significantly boost the prediction accuracy to 85%. Key findings indicate that financial capabilities and family support are the …
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Jiayin Song, Xue Shi
Abstract: In the context of the deep penetration of the mobile Internet into campus life, in response to the problems of traditional campus communities such as scattered functions, information silos, and insufficient interactivity, this study takes Dalian Maritime University as a pilot, and designs and implements the ‘Ocean Wave Bay’ multi-terminal interconnected campus social blogging and culture circle platform, which adopts multi-terminal convergence architecture, covering WeChat The platform adopts a …
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Chengyi Song, Wei Yang
Abstract: This research addresses the issues of low efficiency, high error rates in traditional manual railway brake shoe replacement, and the mismatch between existing automated equipment and actual needs due to insufficient technical adaptability. It designs an intelligent management and control system integrating NFC technology and intelligent optimization algorithms, adopting a hierarchical architecture (perception, transmission, decision-making layers). NFC realizes automatic data acquisition; SVM …
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Jiaxu Ding, Zhengyu Huang, Ziyuan Cao
Abstract: In multivariate predictive modeling, traditional models are often difficult to balance prediction accuracy, robustness and interpretability in the face of challenges such as high dimensionality, strong noise, nonlinear coupling and structural uncertainty. To this end, this paper proposes a new modular intelligent modeling framework - KFPS-Net. The framework integrates four key functional modules: temporal denoising, nonlinear feature modeling, causal structure exploration and integration fusion, …
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Xiangjun Wu
Abstract: Cardiovascular diseases (CVDs) persist as a critical global health burden, accounting for over 30% of annual mortality worldwide. While machine learning approaches show promise in early-stage risk identification, existing research predominantly focuses on isolated model validation without systematic comparative analysis. To address this gap, our study conducts a rigorous multi-algorithm evaluation, leveraging Logistic Regression (LR), Random Forest (RF), and XGBoost classifiers trained on …
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Keyuan Wang, Xiaole Ge, Han Li, Xiangyi Liu
Abstract: This paper focuses on multibeam bathymetric technology in the field of oceanographic survey and seabed mapping, and carries out a systematic study on the optimisation of survey line laying scheme. The purpose of this study is to improve the efficiency and accuracy of oceanographic surveys while reducing resource consumption and survey costs, and to reveal the importance of optimizing the survey line layout by analyzing the influence of the rationality of the survey line layout on the …
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Tian'ai Song
Abstract: We propose an end-to-end neural architecture that calibrates stochastic volatility models directly from high-dimensional option panels. A 3-D CNN–Transformer hybrid learns the inverse mapping from implied-volatility surfaces to latent Heston parameters in milliseconds while enforcing no-arbitrage constraints via a differentiable penalty layer. Closed-form gradients of the Fourier pricing formula enable stable training on synthetic and market data. Extensive experiments on SPX and EURO STOXX 50 …
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Jiarui Chen, Zuhua Ji
Abstract: Understanding the equation of state of neutron stars (NSs) is a fundamental challenge in astrophysics and nuclear physics. A first-order phase transition at high densities could lead to the formation of a quark core, significantly affecting NS properties. This review explores observational and theoretical constraints on such transitions from observational effects. X-ray observations, including mass-radius measurements from NICER and spectral features like Quasi-Periodic Oscillations, Cyclotron …
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Ke Tao, Hongyu Zhou, Li Li
Abstract: Based on the invention patent data in the AI field jointly applied by enterprises in Chinese mainland from 2015 to 2024, this paper constructs a dynamic collaborative innovation network and employs the Temporal Exponential Random Graph Model (TERGM) to explore the formation mechanism of enterprises' AI innovation collaborative relationships. Considering both endogenous and exogenous factors affecting network construction, it incorporates structural dependence, temporal dependence, node …
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Qiyuan Xi
Abstract: This study investigates the acoustic performance of microspeakers across various cavity configurations using coupled electromagnetic-mechanical-acoustic (E-M-A) simulations and experimental validation. A modular test setup was developed to accommodate interchangeable microspeaker modules with distinct cavity designs, including front-firing, side-firing, and back cavity geometries. The results demonstrate that side-firing cavities induce a distinct Helmholtz resonance peak in the mid-frequency …
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Zeyuan Du, Hongfei Yu, Xinlan Zeng, Yanglingyun Dong, Yu Wang
Abstract: The paper focuses on predicting electric load using a neural network prediction model for big data analysis. Accurate traffic flow estimation on branch roads, a crucial element for urban traffic management, is explored in the study. Traditional methods face challenges due to the high costs of equipment installation. The paper presents models that utilize main road data to indirectly estimate traffic flow, including linear and piecewise models. Additionally, the study integrates advanced …
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Yipeng Dai, Maolong Huang, Qiye Luo
Abstract: Amid global value chain restructuring and Industry 4.0 transformation, manufacturers face significant challenges in controlling defect rates across multi-stage production processes. This study develops a hybrid optimization model combining Simulated Annealing and Monte Carlo Simulation to balance quality control and cost efficiency. The framework incorporates a comprehensive cost structure covering components, semi-finished/finished products, and defect-related losses, while introducing a …
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Victor Niu Young, Forest Niu Young
Abstract: Facing the sustainable development challenges of Olympics, this study aims to identify the Sports, Disciplines, or Events (SDEs) that best align with the Olympic spirit and the International Olympic Committee’s (IOC) long-term strategy. All 47 SDEs held between 1896 and 2028 were evaluated using the IOC’s six Sports Inclusion Criteria (C1–C6) as primary indicators, each subdivided into 3-4 secondary factors (21 total). Subjective weights for P1–P21 were derived via the Analytic Hierarchy Process …
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Yazhou Gao, Shaoxuan Xie, Liwei Feng
Abstract: This study proposes an optimized production monitoring solution to address these challenges. By employing hypothesis testing, binomial distribution, and Poisson distribution, a sampling inspection method is designed to determine the minimum sample size and defect threshold for detecting defective products. Subsequently, a multi-stage dynamic decision tree model is developed to decompose the complex production process into multiple decision nodes, enabling the identification of the optimal …
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Tongjian Zhang
Abstract: The Yellow River, known as the mother river of the Chinese nation, the study on the variation patterns of its water and sediment flux holds significant theoretical guiding significance for the environment, climate, people's livelihood, and optimal allocation of water resources. This paper first focuses on the annual characteristic evaluation of sediment concentration and water-sediment flux in the Yellow River. Through the analysis of actual monitoring data from the Yellow River hydrological …
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