Jackknife Empirical Likelihood-Based Confidence Intervals for Low Income Proportions with Missing Data

The estimation of low income proportions plays an important role in comparisons of poverty in different countries. In most countries, the stability of the society and the development of economics depend on the estimation of low income proportions. An accurate estimation of a low income proportion has a crucial role for the development of the natural economy and the improvement of people\u27s living standards. In this thesis, the Jackknife empirical likelihood method is employed to construct confidence intervals for a low income proportion when the observed data had missing values. Comprehensive simulation studies are conducted to compare the relative performances of two Jackknife empirical likelihood based confidence intervals for low income proportions in terms of coverage probability. A real data example is used to illustrate the application of the proposed methods

On the parameters of extended primitive cyclic codes and the related designs

Very recently, Heng et al. studied a family of extended primitive cyclic codes. It was shown that the supports of all codewords with any fixed nonzero Hamming weight of this code supporting 2-designs. In this paper, we study this family of extended primitive cyclic codes in more details. The weight distribution is determined. The parameters of the related $2$-designs are also given. Moreover, we prove that the codewords with minimum Hamming weight supporting 3-designs, which gives an affirmative solution to Heng's conjecture

Motion Picture Analysis: A Mechanical Study of Tennis Players during Forehand and Backhand Strokes

Objectives: The purpose of this article is to utilize video images for the examination of lower limb biomechanics in tennis players while executing forehand and backhand strokes, providing a reference for training. Methods: This article provides a brief introduction to forehand and backhand strokes in the sport of tennis. Subsequently, a biomechanical analysis of the lower limbs during forehand and backhand strokes was conducted on ten level 2 tennis players and ten specialized tennis students at XX Sports University. Findings: Level 2 athletes who have undergone a long training exhibited higher linear velocity and joint torque in the lower-limb joints during the preparatory and striking phases of forehand and backhand strokes. Additionally, they exhibited more pronounced surface electromyographic signals in the rectus femoris muscle of the lower limbs. Novelty:The novelty of this article lies in the use of video imagery, a non-contact and non-intrusive method that does not affect the athletes' movements, to study the biomechanics of their lower limbs.   Doi: 10.28991/HIJ-2024-05-01-07 Full Text: PD

Streaming Traffic Flow Prediction Based on Continuous Reinforcement Learning

Traffic flow prediction is an important part of smart transportation. The goal is to predict future traffic conditions based on historical data recorded by sensors and the traffic network. As the city continues to build, parts of the transportation network will be added or modified. How to accurately predict expanding and evolving long-term streaming networks is of great significance. To this end, we propose a new simulation-based criterion that considers teaching autonomous agents to mimic sensor patterns, planning their next visit based on the sensor's profile (e.g., traffic, speed, occupancy). The data recorded by the sensor is most accurate when the agent can perfectly simulate the sensor's activity pattern. We propose to formulate the problem as a continuous reinforcement learning task, where the agent is the next flow value predictor, the action is the next time-series flow value in the sensor, and the environment state is a dynamically fused representation of the sensor and transportation network. Actions taken by the agent change the environment, which in turn forces the agent's mode to update, while the agent further explores changes in the dynamic traffic network, which helps the agent predict its next visit more accurately. Therefore, we develop a strategy in which sensors and traffic networks update each other and incorporate temporal context to quantify state representations evolving over time

Research on transient heat transfer performance of disc brakes for mining motor vehicle

The type of disc brake has excellent efficiency and strong heat dissipation, which are beneficial to the transport capacity for mining motor vehicles. To ensure the reliability of the brake, the two-dimensional heat transfer differential equation model of disc brake is established, considering the dynamic change of convective heat transfer coefficient. The heat boundary conditions are established through the characteristics of brake disc structure and air condition, which is more accuracy than the traditional simplified method. Based on the PDE module in MATLAB, the mathematical model of heat transfer is solved and the transient temperature field is obtained. The calculation results are verified by the temperature field of the brake disc Link3900 NVH test platform. The results show that the research scheme has high computation accuracy, and can provide important basis, new ideas and advanced methods for the brake of mining motor vehicle in related fields