Science and Technology of Engineering, Chemistry and Environmental Protection
ISSN 2959-6157 (Print) ISSN 2960-1339 (Online) Editor-in-chief: Vinod A. Thale （Zhejiang University） Associate Editor: Syed Murawat Abbas Naqvi （Beijing Institute of Technology） Editorial Board Member: Faisal Rafique （Southern University of Science and Technology） Jian Hu （University of Kent） The Journal of Science and Technology of Engineering, Chemistry and Environmental Protection (JSTECEP) is a multidisciplinary, peer-reviewed academic publication dedicated to fostering the next generation of scientific leaders. By providing a professional platform for college students, young researchers, and early-career innovators, JSTECEP bridges the gap between classroom learning and professional academic publishing. Our mission is to democratize the exchange of scientific knowledge. We aim to support emerging scholars by providing a rigorous yet constructive peer-review process that highlights original research, innovative technical solutions, and sustainable developmental practices. Scope of Subjects The journal welcomes submissions that explore the intersections of theory and application across three primary pillars: Engineering: Advancements in mechanical, civil, electrical, and chemical systems, with an emphasis on modern technological integration. Chemistry: Research spanning organic, inorganic, and analytical chemistry, as well as biochemistry and material sciences. Environmental Protection: Innovative approaches to sustainability, climate mitigation, pollution control, and the preservation of natural resources. Why Publish With Us? Support for Emerging Voices: We prioritize high-quality work from students and young professionals who are often underrepresented in legacy journals. Rigorous Peer Review: Every article undergoes a thorough evaluation to ensure technical accuracy and academic integrity. Interdisciplinary Reach: Our broad scope encourages authors to submit work that solves complex, real-world problems involving multiple scientific disciplines. Commitment to Innovation: We celebrate "outside-the-box" thinking and technical creativity that addresses the environmental and industrial challenges of the 21st century. All articles will be submitted to: Google Scholar, Crossref, U.S. Librarian Database, ProQuest (part of Clarivate’s Web of Science), Ulrich's Periodicals Directory, SEMANTIC SCHOLAR, OpenAlex.
Recognition of the Physical Significance of Heisenberg’s Uncertainty Principle.
Heisenberg’s uncertainty principle (HUP) is a central concept in quantum mechanics that reveals inherent limitations in the measurement of microscopic particles and profoundly affects the understanding of the physical world. This paper reviews the physical significance of HUP and its applications and research progress in modern physics. The complexity of measurements in quantum systems is revealed by deriving the uncertainty relation and analysing the wavefunction collapse and the classicalisation of quantum states. This paper summarises the current experimental verification of the principle of measurement uncertainty in different quantum systems and discusses the research progress in improving measurement accuracy through quantum entanglement. Recent studies have shown that controlling quantum entangled states can effectively reduce measurement disturbances and thus improve the measurement accuracy of quantum systems without violating the principle of mismeasurement. Despite the many advances made, there are still challenges to further improve the measurement accuracy in complex quantum systems. Future research will continue to explore the potential for applications in this area.
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Threat Assessment and Defense Strategies for Asteroid Impacts on Earth: Impact Index Model and its Application.
Near-Earth asteroid impacts have the potential to cause catastrophic damage, making it imperative to develop robust defense strategies to mitigate these risks. Understanding Near-Earth asteroid impact dynamics and potential effects becomes crucial for global disaster preparedness and response efforts. This study presents an impact index model designed to guide preliminary defense strategies against near-earth asteroid impacts, which pose significant threats to humanity. The model is informed by numerical simulations and engineering calculations, providing a high-fidelity analysis suitable for fine-grained assessment of impact effects. This paper adopts principal component analysis and factor analysis to optimize the impact index model, catering to asteroids with incomplete observational data. The research investigates the effects of asteroid disintegration, explosion, and tsunami, enhancing the formulation of defense policies and deepening the understanding of impact effects. The study’s results indicate that the proposed model can effectively assist in preemptive strategic planning to mitigate potential disaster losses.
Revealing the secret of dark matter through gravitational lensing.
This paper illustrates the major method of dark matter detecting, which is the gravitational lensing technique. Including details about the observational methods, analyzing techniques and dark matter mapping, this paper demonstrates the mechanism of gravitational lensing. Specifically, this paper illustrates the mechanism of gravitational shear, which is a method of concluding results of observations. Furthermore, convolving method is introduced, which helps form a comprehensive image and reduce the negative effects caused by noises. On top of that, the author has introduced an analyzing method based on the Bayes Theorem, which may be helpful for finding and abandoning unreasonable results. At the end, the author compares the differences and advantages of different analyzing methods, and gives examples of the major methods that are widely used in each field. This paper gives the future prospect of the field, including introduction to the edge cutting projects, references to relative dark matter theory, and several results that have already been found. For example, this paper raised the dark matter distribution of a galaxy taken by the Hubble Space Telescope as a representation for the development of dark matter study. Additionally, the author introduced the GRAVITY model that can analyze the results partially automatically.
Galton Board Experiment: Proof of Central Limit Theorem.
This paper investigates the application of the Central Limit Theorem (CLT) using a Galton board experiment. The Galton board, which produces a binomial distribution of ball positions, serves as a practical model to test the CLT’s assertion that the distribution of sample means approaches a normal distribution as the sample size increases. In this experiment, 30 independent trials were conducted, with 100 balls passing through 10 rows of pegs in each trial. The sample means for each trial were computed, and the resulting sampling distribution was analyzed. A normal distribution curve was fitted to the data, visually demonstrating alignment with CLT predictions. Additionally, statistical tests, including the Shapiro-Wilk test, were applied to rigorously evaluate the normality of the sample means, providing empirical validation for the theoretical model. The findings confirm the applicability of the CLT to the Galton board, as the sampling distribution closely follows a normal pattern, highlighting the theorem’s generalizability even when the original data follows a binomial distribution.
Unveiling the Influencing Factors of Advertisement and Study by Linear Regression Approach.
The linear regression method is important in natural science. This method is widely used in economics, data science, management, medicine, biology, sociology and so on. This statistic method helps people to find the relationship between inputs and outputs and classify massive data. In the following article, some basic theories, formulas and real problems with solutions will be mentioned. This essay firstly introduces some methods about linear regression, and then use the formula and basic theories to calculate and analyze the relationship between independent and dependent variables of some real massive data. Finally, it can be found that all of data have relationship and people can use these conclusions to predict or give a suitable strategy, so it is really useful for people to prepare solutions ahead of schedule. It is one of the most fundamental methods in dealing with data, because the model of linear regression is quite easy, and provide understandable mathematical formulas, then create some scientific results.
The Principle and Application Prospect of Optical Temperature Measurement.
The principle of optical thermometry not only changes the cognition of deterministic worldview in classical physics but also lays an important foundation for quantum mechanics. The challenge is still to reach places that are difficult to reach or that have an impact on the measurement environment. In this paper, the physical meaning of the principle of optical thermometry and its application and research progress in modern physics are reviewed. The fundamental limitation in quantum measurement is revealed. In this paper, the principle of temperature measurement is based on the characteristic that the emission spectrum of the excited fluorescent material changes with the temperature. The most important characteristic of fluorescence spectrum temperature measurement is that it overcomes the limitations of traditional temperature measurement. In this paper, the basic theory of blackbody radiation, especially Planck’s law radiation, is briefly introduced and the close relationship between radiant energy and temperature is emphasized. In the third part, Raman spectroscopy and its basic principle are studied. The last part of this paper focuses on the temperature measurement method’s in-depth study of its principle and application. The theoretical basis of the method, including the radiation law and optical properties, is described in detail. The photometric temperature measurement method has a wide application prospect in the fields of industrial production process monitoring, fire early warning, scientific research experiments, and so on. Future research can further optimize the photometric temperature measurement technology, improve its adaptability and reliability, and provide more effective means for temperature measurement.
Research for Global Carbon Emissions Influence Factor and Sustainable Reduction.
Today, carbon emission pollution is particularly serious. Greenhouse gases are produced all the time in every part of people’s lives, which directly leads to catastrophic global warming. Controlling carbon emissions is related to the fate of the entire earth, and countries around the world need to study the most effective and lowest-cost emission reduction methods as soon as possible. In this regard, this article comprehensively reviews the factors affecting global carbon emissions and analyzes the rising trend of carbon emissions in recent years. The article focuses on the mechanisms of major sources of carbon emissions such as transportation, urbanization, and industrial production, and their impact on the environment. The study found that the combustion of fossil fuels produces a large amount of carbon dioxide emissions and forest reclamation is the main reason for the increase in greenhouse gas concentrations, which directly leads to climate change. In addition, the article also analyzes the potential impact of urbanization. This article emphasizes the importance of reducing carbon emissions through global cooperation, technological innovation, and policy intervention to promote sustainable development.
Analysis of the Principle, Facilities and Applications for Quantum Entanglements.
Quantum mechanics is a field of physics that became important from the late 19th century to the start of the 20th century and its non-relativistic part seemed to be completed by von Neumann in 1932. However, the EPR paradox by Einstein, Podolsky and Rosen pointed out a strange feature of quantum mechanics known as quantum entanglement, which represented that people are unable to consider the existence of global states of composite system as a product of the states of separate subsystems. The phenomenon promotes to a further study of quantum and a new faster way of communication. The article introduces the discovery and the early research of the quantum entanglement, how the theory about the quantum entanglement was corrected, the fabrication of the entangled particles, and the application of the quantum entanglements phenomena to the quantum communication. The research aims to discuss the importance of the study of the quantum entanglement at current stage.
Analysis of the Principle, Applications and Improvements of Foldable Structure for Deep-space Exploration.
As a matter of fact, deployable structures, a technology that has been widely used in deep-space exploration since the 1960s, are receiving more attention due to its applications in various conditions. With this in mind, this article aims to illustrate several space deployable structures available in recent years that have been adopted in the deep-space exploration. To be specific, the main content that have been applied in the deep-space exploration includes deployable antenna, deployable solar arrays and deployable surfaces. In this study, after introduction of the structures, some explanations about the operation of these unique equipment are made. According to the analysis, the current limitations for the state-of-art designs are also clarified. At the same time, the future prospects are demonstrated based on the evaluations. In conclusion, the article tries to give a brief review of the already existing technology and makes an effort to further explore the future potential of these structures.
Progress in photocatalytic reduction of CO2 to CO by MOF materials.
By harnessing the power of photocatalysis, there is a promising pathway to develop sustainable carbon cycles, which can significantly reduce our reliance on fossil fuels and advance the goal of sustainable energy development. MOFs, with their ordered porous structures, have shown tremendous potential in photocatalytic CO2 reduction. These materials are characterized by their excellent structural tunability, large surface areas, and outstanding CO2 capture capabilities, making them ideal candidates for photocatalytic reactions. In the context of CO2 reduction, the photocatalytic performance of MOFs is critically influenced by their light absorption properties, material design, and CO2 capture efficiency. In recent years, researchers have made significant progress in exploring and developing MOF materials specifically for reducing CO2 to carbon monoxide (CO). This paper reviews some of the most representative MOF materials in this domain, discussing their performance, advantages, and the challenges they face in practical applications. By synthesizing these research outcomes, we can gain a deeper understanding of the operational mechanisms of MOFs, providing valuable insights and guidance for future research directions.
Study on Grid Stability of Electric Vehicle Bidirectional Charging Device Based on Bidirectional Power Electronic Converter.
With the increasing number of electric vehicles (EVs), V2G (Vehicle-to-Grid) and G2V (Grid-to-Vehicle) technologies have garnered growing attention as essential methods for energy flow between EVs and the power grid. This study examines the impact of EV charging devices, based on bidirectional power electronic converters, on grid stability. As EVs become more widely adopted, V2G and G2V technologies are extensively applied to enable bidirectional energy flow between EVs and the grid. This study analyzes different topologies of power electronic converters and explores how optimized control strategies can enhance grid voltage and frequency stability. The research shows that bidirectional charging devices provide significant advantages in addressing voltage fluctuations and frequency instability, especially in peak shaving and valley filling, effectively easing grid load. Furthermore, future developments in bidirectional charging devices will focus on intelligent control and synergistic optimization with renewable energy to improve grid stability and power quality. The research methods include analysis and comparison of different converter topologies. Results indicate that bidirectional charging devices have a significant effect in resolving grid instability issues, supporting the further advancement of smart grids).
Evaluation of the Effectiveness of Influenza Vaccination among Primary School Students in Fangshan District, Beijing.
Influenza (hereinafter referred to as “influenza”) is an acute respiratory infectious disease caused by influenza virus, which is seriously harmful to human health. Its antigenicity is variable and spreads rapidly. It can cause seasonal epidemics every year. People are generally susceptible to influenza virus. Pregnant women, infants, the elderly, patients with chronic diseases and other high-risk groups are more serious after infected with influenza virus. Influenza outbreaks are easy to occur in schools, kindergartens, nursing homes and other places where people gather. Annual influenza vaccination is an effective means to prevent influenza and reduce the burden of influenza related severe illness and death. It can reduce the health hazards caused by influenza related diseases and the run on medical resources. Therefore, the data of free influenza vaccination among primary school students in Fangshan District of Beijing in 2023 and the survey data of influenza clustering in the influenza epidemic season from 2023 to 2024 were selected to evaluate the vaccination rate and the protection effect of influenza vaccine.
Advances and Application Prospects of Spectrometers and Their Key Technologies.
Spectrometers are key devices for analyzing the structure and properties of substances. They have been widely applied in various fields such as chemistry, physics, biology, environmental monitoring, and astronomy. This article aims to review the classification, core technologies, application areas, and development trends of spectrometers. The article elaborates on the working principles, classification, and key technologies of spectrometers, and discusses their application in different fields and future development directions. In addition, this article also classifies different types of spectrometers, including monochromatic spectrometers, polychromatic spectrometers, infrared spectrometers, etc., and analyzes their respective advantages and disadvantages. The article particularly emphasizes the latest advancements in light source technology, spectrometry, detection technology, and data processing technology. At the same time, this article also outlines the extensive application of spectrometers in various fields.
Impact of Heterogeneous Data on Financial Market Volatility: A Generalized Additive Model Approach.
Financial market volatility is driven by a complex interplay of factors, many of which exhibit nonlinear relationships with market behavior. Traditional linear models often struggle to adequately capture these intricate dynamics, particularly when analyzing heterogeneous data sources such as stock prices, interest rates, and macroeconomic indicators. In this paper, a nonlinear regression approach is proposed, specifically using generalized additive models, to better understand the impact of such diverse data on market volatility. By incorporating data from multiple sources, including historical stock prices and interest rates, significant nonlinear relationships between stock returns and interest rates with market volatility are revealed in this paper. The smooth terms for log returns show that volatility increases sharply during periods of negative returns, particularly during market downturns, which is consistent with the widely observed phenomenon of volatility clustering. By accounting for these nonlinear relationships, the model provides valuable insights into how varying economic conditions impact market stability, informing risk management strategies and policymaking.
Applications of Harmonic Analysis in Quantum Computing.
The quantum Fourier transformation has been demonstrated to be a useful tool in dealing with quantummechanical problems. This paper discusses how quantum Fourier transformation is applied in finding the period of functions within Shor’s algorithm, allowing the algorithm to be much more efficient in factorizing large integers than classic algorithms. This paper also discusses how Grover’s algorithm uses the concept of harmonic process, where amplitude amplification is likened to harmonic oscillation for improving the efficiency of searching unsorted data base. The paper further explores the application of harmonic analysis in quantum error correction, particularly in stabilizer codes. The paper also introduces wavelet analysis as an alternative approach for detecting and correcting localized quantum errors. Finally, the paper extends the discussion into the potential future directions of harmonic analysis in quantum computing, such as extending Fourier transformation to non-abelian groups and using spherical harmonics in quantum algorithms which may optimize current quantum algorithms and solve currently unsolved quantum questions.
Stock Market Price Analysis and Prediction of Financial Industry Based on the Time Series Method.
With the increasing complexity of financial markets and the rapid growth of data volumes, accurately predicting stock prices has become crucial for ensuring market stability and guiding investment decisions. Although traditional time series analysis models have been widely applied, they still face research gaps in prediction accuracy due to the high nonlinearity and dynamics of the market. This paper introduces the application of time series analysis methods in stock market price prediction within the financial industry. This paper aims to enhance prediction accuracy and timeliness. The study provides a detailed overview of the advantages of both traditional models and modern deep learning techniques. The latter offers a new perspective for prediction through its capabilities in nonlinear modeling and capturing long-term dependencies. Representative stocks in the financial industry are selected for empirical analysis, demonstrating the performance of models that combine time series analysis with deep learning techniques in terms of prediction accuracy and stability. Meanwhile, the paper points out current research deficiencies, such as model optimization, improvement in generalization ability, and interdisciplinary integration, as future directions. With the backdrop of big data, it is hoped that through the rational integration of time series analysis and deep learning techniques, stronger support can be provided for financial market stability and scientific decision-making.
Multiple Regression Analysis of the Factors Influencing the Sales of New Energy Vehicles in China.
In recent years, the global climate crisis and energy crisis have become increasingly serious, prompting countries to strongly support the development of clean energy technology. New energy vehicles represent a significant avenue for transforming and upgrading the automobile industry worldwide, offering environmental protection and energy-saving advantages. This thesis addresses the vital factors influencing the volume of new energy vehicle sales in China. Within an empirical research framework, multiple regression analysis is used. This study begins with the background of the new energy vehicle market, recognizing factors such as policy incentives, technological progress, and charging infrastructure improvements, and then proposes research hypotheses. By collecting relevant data, the study uses multiple linear regression models to evaluate the influence of various factors on both the demand and supply sides of sales. The results show that the amount of subsidy in vehicle purchases and charging time significantly affects sales, while factors like price and technological progress do not hold as much weight. Based on these findings, the thesis suggests policy recommendations such as increasing and improving vehicle purchase subsidies and enhancing charging facilities for the healthy development of the new energy vehicle industry. Finally, the limitations of this study, such as the limited data scope, the problem of multicollinearity, and the failure to consider the effects of external macroeconomic factors, are summarized. The paper concludes with a discussion of potential avenues for future research.
The Relationship Between Interaction and Student Achievement on Online Education Platforms.
Online education has become an indispensable component of modern teaching, and the study of the relationship between its interactive modalities and academic performance is significant for the optimization of teaching strategies. Although existing research has explored the impact of online interaction on learning outcomes, an in-depth analysis targeting university students in specific regions still warrants further investigation. Therefore, the present study employed a survey methodology to collect data on the usage of online education platforms and academic performance from 324 university students at Jiangxi Vocational College of Mechanical & Electrical Technology. Descriptive statistical analysis was then conducted on the collected data. The results indicate a positive correlation between online learning and academic performance, with only a small number of students reporting that online learning has harmed their grades. While it is not possible to assert a direct causal relationship between the time and pattern of online interactive learning and grades, it is evidence that increased viewing of live courses and interactive participation does help students enhance their problem-solving abilities and significantly impacts their academic performance. Future research could explore the specific effects of different interactive tools and methods within online education platforms on academic performance, as well as how to design more effective online teaching activities to improve students’ motivation and grades. Additionally, with the advancement of artificial intelligence and big data technologies, future studies could leverage these technologies for a more in-depth analysis of online learning behaviors, providing support for personalized online teaching.
A Machine Learning-Based Prediction Study for Type 2 Diabetic Mellitus.
Diabetes mellitus is one of the major diseases in the world. The pathogenesis is complex and difficult to cure, and accurate prediction of the risk of diabetes can help improve the treatment rate. Machine learning, as an important branch of artificial intelligence, can discover potential relationships between data, to efficiently carry out disease prediction and risk assessment. In this paper, a logistic regression prediction model was constructed, and an authoritative dataset was used as the research object, including 8 medical characteristics as the variables for predicting diabetes. First, some data pretreatment steps were taken to eliminate any invalid or missing data, and then the model was trained the prediction test set was carried out, and finally, the five main evaluation indicators of the model were obtained. The results showed that the logistic regression model had an accuracy rate of 0.95, a precision rate of 0.85, a recall rate of 0.63, an F1 Score of 0.73, and an AUC Value of 0.96. The results show that the prediction model has good stability and accuracy, can effectively predict diabetes, and has certain potential clinical application value.
The Analysis of Nanjing Air Quality by Using Multiple-Linear Regression Model.
For the sake of industrial evolution, booming population, increased vehicles, and so forth, the air quality index (AQI) needs to be emphasized. This research paper takes Nanjing’s AQI as the survey background. This essay has deployed the multiple linear regression model to help analyze the basic circumstances of the AQI by using monthly data on six basic factors. The results show that the most significant factor of Nanjing’s AQI is the density of O3. What’s more, NO2 plays the role of the negative correlation and there exists a linear relationship between the variables. To improve or maintain the “generally good” circumstances of the AQI, it’s better to take measures like enlarging the vegetation, reducing the activities of incomplete incendiary, and increasing the usage of public transport... Additionally, this essay’s goal is to raise the awareness of the public and point out the mainly significant factors, that can offer evidence for the proceeding survey and measures.
Linear Regression Analysis Between the Height of Parents and Children.
Height is a subject of interest in understanding familial relationships and growth patterns. A sample of 20 boys and 20 females, ages 15 to 19, together with their parents, were gathered for the study, which examines the link between parents’ height and that of their offspring. By treating the parents’ height as the independent variable (X) and the children’s height as the dependent variable (Y), regression models were developed to predict height differences across genders. The models were also cross-tested to compare their predictive power. The optimal regression equations yielded relatively low R² values: 0.096 for the height of father and son, 0.122 for mother and son, 0.078 for father and daughter, and 0.091 for mother and daughter. Despite the relatively low R² values, these findings underscore the complex nature of height inheritance, suggesting that multiple genetic and environmental factors contribute to height variations. To give a more thorough picture of the factors influencing height, subsequent research might examine these extra variables.
The Analysis of Factors Influencing New Energy Vehicle Stocks.
With the global emphasis on environmental protection and the reduction of non-renewable energy sources, the new energy automobile industry is developing rapidly, and its stock performance has become the focus of investors’ attention. This paper mainly analyses the influencing factors of new energy automobile stocks. By combing the relevant literature and combining the international background of the new energy industry, this paper investigates that the influencing factors of new energy automobile stocks are multiple and compounded, including the macroeconomic environment, the industry development trend, the policy support as well as the enterprise’s factors. Investors should take these factors into account when investing and analyze them in conjunction with actual data to make informed decisions while paying close attention to market dynamics and adjusting their investment strategies on time. In addition, market competition, enterprise market share, and competitiveness will also affect the stock price, this paper takes the top three enterprises in China’s new energy vehicle market share in 2023 as an example, they all have strong competitiveness and profitability, and their stock prices are relatively high. This paper shows that the performance of new energy vehicle stocks is affected by a variety of factors, and these factors may change with the development of the industry.
From Data to Prediction: A Study on China’s GDP Forecasting.
This study utilizes the Least Absolute Shrinkage and Selection Operator (LASSO) regression model, implemented through the R programming language, to refine GDP forecasting methods in China. The analysis centers on the function of the three main monetary indicators—M0, M1, and M2—using time-series data from reliable sources, including the People’s Bank of China and the National Bureau of Statistics. These variables have been identified as significant drivers of GDP fluctuations. By leveraging LASSO’s ability to select relevant variables and control for overfitting, the model achieves a streamlined and accurate approach to real-time economic forecasting. The results highlight the importance of money supply factors in predicting GDP and emphasize the LASSO model’s efficiency in enhancing traditional forecasting techniques. Furthermore, this study offers valuable insights for policymakers and business strategists, who may use these findings to guide economic planning. Integrating modern statistical methods with classical economic models, this research also paves the way for future exploration into global economic forecasting.
Multivariate Statistical Analysis of Influencing Factors of Student Achievement.
The performance of students has a close relationship and far-reaching impact on the long-term development of the entire education community. It is not only related to objective factors such as the school’s teaching style and teachers but also to students‘ families and personal factors. This article aims to study the factors that affect students’ performance. This article selects 500 students, uses questionnaires to obtain data, and then selects about 100 students from 500 people for further quantitative survey. The scoring system is adopted, allowing these students to choose the influence of factors that affect their academic performance to score, and then linear regression is adopted. The model is analyzed and the results are obtained. The results show that learning environment, learning habits, psychological state, family education, and IQ have a relatively high correlation with students‘ grades. Student grades not only involve the living standards of students and parents but also have a close relationship with the whole society.
Application of Linear Regression on GDP and International Foreign Exchange Earnings.
GDP is the most significant measure for assessing a nation’s or region’s economic standing and level of development, which is also the core indicator of national accounting. The rapidly developing tourism industry contributes significantly to GDP, and as China’s international status continues to improve, foreign exchange earnings from international tourism also continue to rise. This paper uses The National Bureau of Statistics’ data release to measure the four important variables associated with GDP and the three variables associated with international tourism foreign exchange earnings. By constructing a multiple linear regression model, eliminating multiple covariance and using software correction, it is found that there is a positive correlation between GDP and total social consumer goods and net exports, and a positive correlation between foreign exchange earnings from international tourism and the number of international routes of civil aviation. Relevant suggestions are also put forward, such as enhancing the country’s economic strength and globalization, building a strong national brand and promoting consumer upgrades by expanding trade with foreign countries, thus improving product quality and meeting consumer demand for a high quality of life. This will not only help to attract more foreign tourists, but will also contribute to sustained GDP growth.
Exploring the Impacts of Accelerated Expansion on Galaxy Dynamics.
The contribution of this work is an in-depth analysis of the effects induced by the accelerated expansion of the universe on the dynamics of galaxies, paying particular attention to the contribution of dark energy and alternative cosmological models. It analyzes the role that these ingredients play in the formation and evolution of galaxies within hydrodynamical simulations and by using theoretical modeling. It gives, in detail, the rather insignificant contribution of dark energy towards the process involved in galaxy formation, emphasizing that the action takes effect only in relatively late stages of cosmic developments. The role of black hole feedback is critically reviewed and demonstrates its contribution towards the quenching of star formation in high-mass galaxies. The paper also discusses, as an alternative, a theoretical model based on Tsallis entropy, which shows another approach to explaining both cosmic acceleration and the phenomenon of flat galactic rotation curves without the use of traditional dark matter or dark energy. The Grüneisen parameter is also presented as a thermodynamical tool that connects cosmic expansion to condensed matter physics, allowing new insights on the physical processes that underlie galaxy dynamics. Combining these models, in this paper we deepen the knowledge on the complex interaction of forces at play for driving galaxies towards their observed behavior.
Harmonic Effects on the Operation of Power Electronic Devices.
This study examines the effects of harmonics on power systems and their equipment, focusing on and exploring the application of different types of filters in harmonic suppression. Harmonics, a significant concern in modern power systems, leads to waveform distortion, increased losses, and reduced system efficiency. These include passive filters (PPFs), active filters (APFs) and optimisation strategies for hybrid filters. By analysing current harmonic optimisation techniques, further improvements in control algorithms and measures to optimise energy consumption and reduce costs are proposed to increase the adaptability and efficiency of filters in complex power environments. In addition, this research helps to advance the development of filtering techniques. It gives ideas that can be applied to developing renewable energy systems and smart grids, as harmonic suppression is vital in improving overall power quality and system reliability. The results of this study could guide future developments in power system optimisation to ensure smoother operation in an increasingly complex and interconnected grid.
Enhancing Neural Vocoders with Fourier Transform: A Frequency-Domain Approach to Improved Speech Synthesis.
This paper introduces a frequency-domain approach to enhance neural vocoders, addressing limitations in capturing high-frequency details essential for natural and clear speech synthesis. By integrating Short-Time Fourier Transform preprocessing, the method provides two key benefits. It offers a richer, frequency-detailed input, enabling the vocoder to capture finer spectral elements for improved synthesis quality. It also facilitates targeted noise reduction, refining output clarity. Additionally, frequency-domain enhancements during generation allow selective amplification of key frequencies (e.g., 3–6 kHz). The integration of these techniques not only significantly improves the clarity and naturalness of synthesized speech but also reduces artifacts that commonly affect highfrequency content. By leveraging both traditional signal processing methods and deep learning, this framework enhances the vocoder‘s ability to accurately reproduce challenging speech spectra, providing a balanced approach that can generalize across different acoustic environments. Combining Fourier-based processing with neural networks, this approach pushes the boundaries of vocoder quality, improving both naturalness and intelligibility. These advancements set a new standard in speech synthesis, offering broader applications in audio processing.
Research on Frontier Applications of MEMS Technology in Robotics.
Microelectromechanical systems (MEMS) are critical in advancing modern robotics by offering compact, lowpower, and high-precision solutions. This paper explores the frontier applications of MEMS in robotics, focusing on key areas such as motion control, environmental sensing, and future technological integration with artificial intelligence (AI). MEMS sensors and actuators have significantly improved the precision and efficiency of robots in various industries, including autonomous vehicles, industrial automation, and medical robotics. The combination of MEMS and AI opens new possibilities for autonomous decision-making and adaptive robotics, while micro-robots equipped with MEMS hold great potential in fields like biomedicine. The results of this study show that MEMS devices significantly enhance robots‘ adaptability in dynamic environments, and their integration with AI can lead to greater autonomy. Moreover, the research suggests that MEMS-based micro-robots could revolutionize precision surgery and drug delivery applications. This paper highlights MEMS technology’s current state and prospects in revolutionizing robotics.
Design and Optimization of VLSI Architectures for Artificial Neural Networks: Innovations, Challenges, and Future Directions.
With the rapid development of artificial technology, artificial neural networks (ANN) have come into people’s vision. Artificial neural network is a computing system that mimics the workings of the human brain. It is widely used in information recognition, natural language processing and predictive analysis. It uses electronic components to form nodes. These nodes are similar to human neurons: each node is connected with multiple nodes in adjacent levels with different weights. Very large scale integrated circuit (VLSI) technology plays a crucial role in optimizing the hardware implementation of ANN. This paper introduces the design principle of VLSI architecture for ANN. Using a simple ANN model- Perceptron as an example, the optimization design of tree adder and accumulator is discussed. In addition, several advanced design techniques and hardware-specific optimization strategies of VLSI technology in practical ANN applications will be discussed. Lastly, this paper summarizes the challenges that VLSI may face in the development of neural networks.