Volume 52

As a matter of fact, near space radiation environment analysis plays a crucial role in near space physics. With this in mind, this study introduces the types of radiation damage in the near-space radiation environment as well as corresponding protective measures. To be specific, the protective strategy for Total Ionizing Dose (TID) and for Displacement Damage (DD) are similar, focusing on material shielding to mitigate the effects. According to the analysis, the guiding principle for the protection strategy against Single Event Effects (SEE) is risk management, aiming to minimize the probability of catastrophic SEE and to detect and mitigate the impact of non-destructive SEE. Based on the analysis, some measures are proposed accordingly. In fact, current strategies for future radiation protection may involve the use of biological membranes to absorb radiation or the application of quantum mechanics principles to eliminate the effects brought about by radiation. These results shed light on guiding further exploration if near space radiation.

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Consumption plays a crucial role in people’s daily lives. In recent years, China’s consumer market has faced urgent needs for consumption transformation and heavy pressure from economic downturn. As an important component of everyday consumption and wealth savings, there is a close relationship between changes in house prices and the consumer market. There has been a heated debate in the academic community regarding the positive or negative relationship between the two. The study keeps focus on the changes of consumer market, attempting to reveal the effects of house price has. This article extracts monthly consumption and housing price data in Shanghai from 2014 to 2024, strictly arranges them in chronological order, and uses the ECM model to handle the instability of time series data. The conclusion of this study is that the rise in housing prices can stimulate consumption. Also house prices have a controlling effect on the fluctuations of the consumer market.

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Gold has a variety of properties, in addition to the commodity properties, gold also has a monetary function. There are many investment values, so the price of gold is also much attention by investors. Many factors may affect the price of gold. In this paper, relevant data from December 2011 through December 2018 have been selected from a number of sources in order to provide as comprehensive a picture as possible of the various factors that may affect the price of gold. An Ordinary Least Squares regression model was used for the analysis. It was found that U.S. stock market conditions, gold mining conditions, U.S. dollar exchange rate, and crude oil-related indices have different correlation results from various perspectives of the index analysis. Besides, there is a significant positive relationship between the price of other precious metals and Gold Price. Meanwhile, The U.S. Economic Related Index has a significant negative impact relationship with Gold Price.

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In recent years, more and more diabetic patients have appeared all over the world, and people have begun to pay more and more attention to this kind of health problems, and it is a necessary thing to understand the influencing factors related to diabetes mellitus. This study explores the key factors that influence the occurrence of diabetes using multivariate logistic regression analysis and can be used to predict diabetes in individuals. The data for the study was obtained from the Kaggle website and various factors affecting diabetes were analyzed and a multivariate logistic regression model was developed to assess the impact of different factors on the risk of developing diabetes. The study found that good lifestyle habits and better basic personal circumstances the lower the risk of developing diabetes. These findings emphasize the importance of individuals focusing on their daily habits and improving their quality of life, which can help individuals reduce their risk of diabetes, and for those who are potentially at risk of developing diabetes, personal information can be used to make predictions and provide appropriate advice to help them change their bad habits.

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The second-hand car market is a hot topic. Buying a second-hand car has advantages in price and many other aspects. Therefore, it is important to establish a good price prediction model. This paper will explore the factors that affect the price of second-hand cars. After analyzing and learning many kinds of literature, this paper establishes a multiple linear regression model and a random forest model and makes a comparative analysis of the model effect. The sum of the square error and R-square value of the random forest are better than the multiple linear regression model. Among the factors affecting the price of second-hand cars, the year of production has the greatest impact on the price, which shows that the age of the year is an important factor in determining the price of second-hand cars. The next most important factor is the number of kilometers traveled, followed by fuel type and transmission type-finally, engine displacement, number of transfers and number of seats. The random forest model established in this paper has better application value to price prediction.

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Gold is one of the most prevalent currencies in the world and its price has a very strong influence in the global financial markets. Gold has safe-haven properties, which can have a significant impact on its demand and price, especially in times of social unrest or financial crisis. Now, the demand for gold by investors has increased dramatically. Therefore, being able to accurately predict the direction of the gold price can help investors to effectively develop investment strategies and risk management measures. The overall objective of this study is to forecast the price of gold futures for the next six months. In this study, the Kaggle website was searched to find the price of gold from 2020 to 2024 and finally the CLOSE price was chosen as the final predicted price. This paper uses the ARIMA model for gold price forecasting. By comparing the RSME size of each model, ARIMA (1, 1, 2) is finally chosen. From the prediction results the price of gold remains stable in the first half of the year and then increases significantly. From the results of the residual test, there is no autocorrelation, and then it is white noise.

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This paper aims to use multiple linear regression model and random forest models to analyze and study the factors affecting the housing price in Boston. The multiple linear regression model describes the relationship between multiple independent variables and one dependent variable through linear equations, and the random forest improves the accuracy and robustness by constructing multiple decision trees and combining their prediction results. To deal with complex nonlinear relationships and high dimensional data. Housing price is an important index to reflect the level and condition of economic and social development of a region, so it is of theoretical value and practical significance to explore its influencing factors and ways and degrees. Multiple factors are selected to analyze the weight and importance of each influencing factor, so as to help the government and decision makers to formulate more accurate policies, promote the stable development of the market, and provide scientific decision-making support for real estate developers, investors and ordinary buyers. In this study, the random forest model based on decision tree was used to clean, select and reduce the acquired housing price data, and to find out the main factors affecting housing price from the perspective of information gain, so as to obtain a relatively complete mathematical model and provide a reference scheme for future research by scholars.

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This paper explores the advancements from the traditional Fast Fourier Transform (FFT) to the Sparse Fast Fourier Transform (sFFT) and their implications for efficient signal processing of large, sparse datasets. FFT has long been a fundamental component in digital signal processing, significantly lowering the runtime of the Discrete Fourier Transform. However, the ingress of big data has necessitated much more efficient algorithms. In contrast, the sFFT exploits the sparsity in the signals themselves to reduce computational demand, and it becomes very efficient. This paper will discuss the theoretical backing of these two developments, FFT and sFFT, and the algorithmic development in both. In addition, it will also discuss the practical applications of both with emphasis on how the latter outperforms the former in large, sparse data. Comparative analysis shows that sFFT has far greater efficiency and noise tolerance, which is of value for network traffic analysis, astrophysical data analysis, and real-time medical imaging. The purpose of this paper is to provide clarity regarding these transformations and their relationship to being paradigms in modern signal analysis.

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The Fourier transform, as a fundamental mathematical tool, plays a pivotal role in quantum mechanics. Its significance extends to wave function analysis, solving the Schrödinger equation, and elucidating the relationship between position and momentum. In this review article, the primary objective is to summarize the diverse applications of the Fourier transform in quantum mechanics. This paper will delve into key applications, highlighting the uncertainty principle, the Planck-Einstein relation, and the Fourier transform solution of the Schrödinger equation. These theorems and relationships not only facilitate the mathematical manipulation of quantum states but also lay the theoretical foundation of quantum mechanics. By exploring these critical aspects, the author aims to provide a comprehensive understanding of how the Fourier transform underpins the core principles of quantum theory, offering valuable insights into the wave particle duality and the probabilistic nature of quantum systems. This discussion will emphasize the essential nature of the Fourier transform in both theoretical development and practical problem-solving within quantum mechanics

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This paper delves into the mechanism of how the Short-Time Fourier Transform (STFT) is used for generating random music. A brief review of the history of stochastic music development is at the outset. The paper contains the principle of audio digitization. This starts with how to convert a continuous audio signal into discrete samples. The Nyquist Theorem plays an important role in that process to preserve signal integrity. The Heisenberg uncertainty principle takes effect as the STFT is applied to covert these samples. It states that when converting the audio signal between the time domain and the frequency domain, the audio signal can only have the properties of one or the other. This paper categorized the audio signals into three categories based on their spectral characteristics. The paper also points out the reason why natural sound effects are always chosen to generate random music due to their inherent complexity and randomness. This paper demonstrates the detail of STFT in generating random music, explaining how to specifically manipulate spectrum analysis to generate random music with practical examples. The paper concludes by discussing the possible future role and direction of STFT in the field of stochastic music.

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