As a nation that is quickly developing, China’s construction industry faces a variety of issues. Despite the size and technical complexity of some Chinese projects, the old paper documents and two-dimensional drawing techniques are no longer adequate to meet demand. China’s construction sector has enormous potential. Therefore, effective digital technology and the best project management are crucial for the growth of China’s construction business. Throughout the whole life cycle of a construction project, including all phases of planning, design, building, operation, and maintenance, Building Information Modelling (BIM) can be used. This study intends to investigate the use of BIM in project management in China with a particular emphasis on how BIM impacts project design, construction, and operation. By analysing the successful cases of BIM in China’s project management, the prospect and development of BIM in China’s construction industry are discussed. In the whole process of project management, BIM improves quality, efficiency, sustainability and maintainability through various applications. It is intended to provide China’s construction industry with a tool to promote the modernization and long-term growth of the industry.

With the increasingly scarce energy resources on earth and the enhancement of people’s awareness of environmental protection, high performance building (HPB) has become a trend pursued by all countries while providing comfortable space for people and protecting good natural environment. This paper discusses the optimal application of HPB in various countries, and analyses its correct application in the face of China’s national conditions and its role in improving the value of buildings. Through the research and analysis of relevant literatures, the following important factors of performance building are summarized: integration of urban block resources, building design and construction, evaluation criteria of efficient building, occupant experience, green energy efficiency and sustainability. These factors play a key role in the construction and operation of HPB. On the basis of further shortening the gap between construction and operation and optimizing the standards and performance of HPBs, they not only improve the actual value of the buildings themselves, but also have a positive impact on the natural environment and human health. HPB is an indispensable part of human development and progress in the future.

Uncertain disasters such as typhoons can affect buildings. Single-storey industrial building is an important type of factory building. In this paper, the reliability of a single storey industrial building under wind load is studied by taking one typical factory in Hunan, China as an example. Firstly, finite element method is used to analyse the structure in the range of linear elasticity. Then, based on Monte Carlo simulation, the probability of failure of the structure under wind load is obtained. The results show that the probability of damage is relatively small, which is also in line with the fact that inland areas are not easily affected by typhoons. In order to obtain a deeper understanding of its reliability, the structural fragility curve considering the variability of steel strength is also studied. The results showed that the smaller the variability of the steel, the more beneficial it is for the reliability of the structure.

With the continuous acceleration of China's industrialization process and urbanization construction, traditional engineering management models have gradually exposed various shortcomings and shortcomings, unable to adapt to the development of the new era. Therefore, exploring innovative models in engineering management has become particularly important. This article focuses on analyzing the characteristics and shortcomings of three traditional engineering management models that are widely used worldwide at present, and proposes corresponding analysis methods for them. Firstly, further improve the organizational system from the perspective of management system and establish a modern enterprise system and a responsible person system to fundamentally ensure the practicality of project management. Secondly, based on Building information modeling (BIM) technology, modeling and information collection are carried out for different management stages to achieve transparent and visual management, Further improve construction quality from a technical perspective, ensure construction progress, and improve management level. In summary, this article analyzes the problems of traditional models and analyzes the application of innovative models from two aspects. However, there are still issues that need to be addressed, such as different situations in different regions and insufficient information and data collection, in order to truly achieve the application of innovative models in engineering management.

When artificial intelligence (AI) begins to intervene, the production and lifestyles of various industries have also undergone great changes. The civil engineering construction industry has taken this opportunity to carry out the industry transformation, from traditional civil engineering construction to the intelligent construction of civil engineering with the participation of AI. Through the dynamic tracking and data analysis of construction sites and buildings through AI, the life safety of construction workers can be ensured by improving efficiency and ensuring quality. This paper analyzes the feature of intelligent construction, and discusses the current situation of intelligent construction and the application progress of intelligent construction in civil engineering construction, including Building information modeling (BIM) technology, Internet of Things and big data technology, AI technology, virtual reality technology, three-dimensional scanning technology, intelligent equipment and construction robots. At the same time, the problems and disadvantages of AI in the construction field are analyzed, and the application prospect of intelligent construction technology in future engineering construction is forecasted.

The core objective of this undertaking revolves around digital circuits and Field-programmable gate arrays (FPGAs), focusing on the design and implementation of a digital clock capable of showcasing real-time hours, minutes, and seconds. To ensure accurate time tracking, the project ingeniously employs a MOD 60 counter, dedicated specifically for counting both minutes and seconds, while a separate MOD 24 counter is harnessed to track hours. These counters serve as the backbone of the clock’s accurate time-keeping capability. To translate this raw digital data into an easily interpretable format for users, the project incorporates a seven-segment display, ensuring that the time can be read intuitively at a glance. The entire architecture and logic of the digital clock is artfully crafted using Verilog HDL, a versatile programming language revered for its aptness in hardware description and simulation. To bring the clock to life and rigorously test its functionality, the Quartus platform is utilized. This renowned platform not only facilitates the efficient translation of the Verilog HDL code into tangible digital circuitry but also offers a robust environment for simulation, ensuring the clock operates flawlessly in real-world scenarios.

In the evolution of integrated circuit technology, chip size and performance enhancement stand as paramount and challenging domains of progress. Yet, a dearth of foundational simulations and comparisons for introductory purposes exists. Consequently, this study delves into an introduction of distinct advanced integrated circuit (IC) technologies: CMOS, FinFET, and CNTFET, dissecting their merits and limitations. Subsequently, a preliminary simulation is executed to authenticate specific characteristics inherent to these IC technologies. Discoveries indicate that as IC transistors scale down, there are marked improvements in transistor performance, encompassing aspects such as switching speed, noise immunity, power efficiency, and heat dissipation. Further, a simulation grounded on a NAND gate substantiates certain traits in CMOS and FinFET, specifically switching speed, propagation delay, and noise margin. The results illustrate a superior performance of FinFET over CMOS. Additionally, as CMOS technology scales, its efficacy enhances. Nonetheless, the present research and simulations hold potential uncertainties and constraints, paving avenues for more refined investigations in the future.

Nowadays, an increasing number of students are opting to study abroad in order to acquire more advanced knowledge and pursue a superior educational environment. In many foreign countries, the option to apply for school dormitories is only available during the first year of university or graduate school. At other times, international students have to search for rented apartments or apply to stay with local host families. However, when studying abroad for an extended period, purchasing a property can potentially result in significant savings compared to renting. Therefore, this study focuses on comparing three types of machine learning techniques: multiple linear regression, Random Forest, and XGboost in predicting house prices in the United States. This research could provide reference for families studying abroad or property investors. Based on the preliminary findings of this study so far, it can be concluded that the XG-boost model demonstrates the highest accuracy and stability among these three methods.

The extensive utilization of fossil fuels by humanity has led to notable ecological degradation alongside a surge in productivity. The ensuing climate change, a result of global warming, poses a grave threat to human survival. A significant contributor to global warming is the emission of abundant greenhouse gases, with carbon dioxide being the most prevalent. Addressing global warming necessitates the identification and adoption of cleaner, alternative fuels to diminish carbon dioxide emissions. Sustainable Aviation Fuel (SAF) emerges as a prime alternative in this context. Chemically akin to conventional and fossil fuels, SAF originates from cleaner sources, offering a reduction in carbon dioxide emissions upon combustion. This paper highlights the importance of SAF as a viable strategy to mitigate CO2 emissions resulting from fossil fuel combustion. The paper also examines different SAF synthesis approaches, such as Fischer-Tropsch, Hydrogenated fatty acid esters and fatty acids (HEFA), and Alcohol-to-Jet (ATJ) processes. In summary, challenges such as high production costs, raw material price fluctuations, and the need for supportive policies hinder SAF's widespread adoption. To address climate change and reduce aviation emissions, further research, technological advancements, government incentives, and collaborative efforts within the aviation industry are crucial.

Now that new energy vehicles are developing well, Tesla’s stock forecast has research value. This report focuses on predicting and analysing Tesla stock price returns using Long Short-Term Memory (LSTM) models. Deep learning models like LSTM can handle large amounts of data and make predictions about future stock dynamics. In this research, historical stock prices of Tesla Inc. are utilized as input data. The LSTM model is used to train and test the data, and subsequently provides results on its accuracy. For comparison, both Linear Regression and Random Forest models have also been used. The results indicate that the LSTM model has better performance than the other models in predicting short-term stock price movements. The result is evaluated by MSE, MAE and RMSE. However, Stock prices are extremely susceptible to economic, market, and political factors, so the predictions of the LSTM model cannot play an important role in actual investment.

This paper focuses on the prediction of Bitcoin prices and returns based on the Long Short Term Memory (LSTM) neural network model, to better consider the impact of time factors. Since Bitcoin has long dominated the digital currency trading market, many researchers have completed many Bitcoin prediction results, including the screening of optimal features, comparison of prediction models and classification of prediction problems. Based on previous work, this article adds a Bitcoin revenue forecast section, presenting the results in the form of charts and data to provide more intuitive trends and more accurate performance. This paper uses LSTM as the experimental model, and uses the Bitcoin transaction history data set with timestamps as the original input. After a specific normalization method, the original model is trained, and then the subsequent transaction data is predicted. Compare it with the real value in the data set to get the final experimental results show that in this prediction problem, the performance of LSTM is slightly better than Autoregressive Integrated Moving Average (ARIMA) and eXtreme Gradient Boosting (XGBoost); on the other hand, compared with price prediction based on real values for prediction, the prediction fluctuations of return are more obvious and more realistic, providing better reference value.

Control systems play a crucial role in modern engineering and technology, and their stability and performance are vital for the success of various applications. This paper aims to explore the application and performance analysis of proportional feedback control (P control) in DC motors and integral control (PI control) in speed control. The following section provides an exposition of the fundamental principles underpinning P control and PI control, alongside an exhaustive account of their practical implementations within the Tinkercad and Octave software environments. The simulations carried out in Tinkercad serve as the basis for evaluating step responses associated with varying values, leveraging a 1Hz function generator. Subsequent analysis pertains to proportional-integral control through the utilization of Octave's PZmap and root locus methodologies, with specific regard to their implications for system stability and control performance in the context of speed control. The experimental outcomes reveal the aptitude of P control in scenarios demanding rapid responses, while establishing the superiority of PI control in the context of steady-state error mitigation. In the experimental analysis conducted, an evident trend emerged as Kp values were systematically increased within the framework of proportional feedback control. The primary observation related to the reduction in system response times, along with the concurrent rise in overshooting tendencies.

The primary focus of this article is on Uber Technologies Inc (Uber) and aims to provide an in-depth forecast of Uber's future stock price. The article introduces Uber company from various dimensions, including Uber's unique business model, leveraging on the sharing economy, enable individuals to use personal vehicles as a means of generating income. This article will be built around the ARIMA model, which is employs this robust model to generate forecasts that offer insights into Uber's stock performance. Before the forecasting process the article will comprehensively describe the data selection process and offer a clear explanation of how the ARIMA model works including the equations. To enhance the effectiveness and reliability of the prediction results, In this article, data visualization is used to present different data representations. The predicted results are presented in the form of graphs and charts, to make a more visual representation of the data and to present and interpret the prediction results effectively.

Forecasting house price index is a useful and classic problem in real estate and investment fields. Predicting house price index in a region not only helps investors make sensible decisions but also aids the government in promulgating policy. This paper will use some simple forecasting models (mean model, naïve model, drift model, linear model and ARIMA model) in forecast test part and by seeing the average value of their residuals and checking whether the distribution of the residuals approximates the normal distribution, select the one with the highest accuracy among them for the final prediction. Multiple linear regression is also used to find if there is relationship between predicted data and possible influencing factors (such as income, unemployment rate and population) and then use the factors that have strong correlation with predicted data to optimize our forecasts and provide a more accurate prediction for the house price index in California in the next few years.

The Bitcoin price was chosen as the research subject, and the observation period was set from January 2015 to September 2023. An ARIMA time series model was constructed to forecast the trading price. The results indicate that the optimal model for fitting the trading price is ARIMA (3, 2, 8). This model takes into account trends, seasonality, and other factors that may impact the price of Bitcoin. By analyzing the historical data, the model was able to accurately predict the short-term fluctuations in Bitcoin’s trading price. Based on this, short-term predictions were made for Bitcoin’s trading price in the next year. Recommendations were then provided by combining the forecast results with the economic development situation in the post-pandemic era. The recommendations suggest that Bitcoin has become a low-quality asset and is no longer suitable for diversifying one’s investment portfolio, but rather focus on the development of physical industries and adjust one’s investment portfolio in a timely manner.

In a rapidly changing global economic landscape, the level of unemployment remains one of the most important indicators of economic health, social stability and political dynamics. Due to the complexity and diversity of the unemployment problem, there are still some shortcomings and controversies in the existing research. Therefore, the aim of this study is to explore and predict unemployment in depth. This study mainly explores the history, current situation and influencing factors of unemployment statistics in depth, so as to make corresponding predictions and analyses. The research methods include literature review and R for relevant statistics and prediction. According to the forecast results, the world unemployment problem is still relatively serious, and the unemployment rate of the world population will remain relatively high in the coming years. However, due to the existence of various force majeure factors, the future unemployment rate may still show a relatively large increase or decline. This study demonstrated the applicability of the ARIMA model in predicting the values of this variable. These models do a good job of capturing patterns and trends, and the predicted values are reliable. At the same time, this study breaks through the traditional thinking, explores the causes of unemployment from multiple angles and analyzes the future social situation, which also has certain implications for further exploring the unemployment problem.

This essay will focus on the comparison of the Homogenous Charged Compression Ignition engine and the Diesel engine. Diesel engine is one of the most widely used engines in the world. It uses fossil fuel such as the gasoline to get power. What is more, it does not need a spark to ignite the fuel. It uses compression method to burn the fuel. However, there is also some problems for this kind of engine, such as the greenhouse effect and the heat loss for this kind of engine. Compared to the Diesel engine, the Homogenous Charged Compression Ignition engine can mix the fuel into a correct ratio before burning. This can improve the efficiency of burning process. It can also improve the energy it released to get more power. There are several solutions to the problem. For example, to reduce the bad effect caused by the fossil fuel, people started to develop the hydrogen fuel. There are also some limitations of the hydrogen, such as expensive cost for producing the hydrogen and the hard storage of hydrogen. Use hydrogen to replace the fuel will face some problems. As a result, improving the efficiency of burning fossil fuel will also be a good choice. The essay also focuses on the scandal of diesel engines, mainly talks about the scandal of the diesel-engine transportation. Diesel engines are the most widely used engine in the world, they will release wastes, pollution and some toxic gases when they are working. Have these scandals been fixed?

In contemporary times, an increasing number of individuals are encountering health-related issues stemming from extended periods of sitting, primarily attributable to improper sitting posture. Hence, the introduction of an intelligent seating solution into the market is imperative and holds significant potential for commercial success. Concerns regarding the health of children, namely their spinal development, as well as the need for sedentary workers to maintain a healthy sitting posture, have become significant issues for consumers. Currently, there are certain items in the market, such as backrests, which aim to partially correct sitting posture. However, these products have limitations in terms of requiring attachment or wearing, as well as lacking sufficient data support. Consequently, the user experience and scalability of these products are considerably inadequate. Based on the analysis of consumer big data by China Business News data (CBNData), there has been a notable surge in the market’s appetite for premium corrective goods in recent years. There exists a market demand for products that are closely associated, specifically smart seats. Currently, researchers have conducted investigations on the recognition and reminder systems pertaining to sitting posture. This paper aims to conduct a comprehensive analysis and evaluation of the fundamental prerequisites and advancements in intelligent seating systems by employing literature analysis and review methods.

Mechanical processing occupies a very important position in China’s industrial production, and it has realized the rapid development and growth of China’s national economy. However, the accuracy of loader machining in China is still in the awkward situation of stopping. Therefore, engineers should continue to explore various factors affecting loader machining in order to improve the quality of workpieces in practice to provide a strong guarantee for the mechanical processing of China’s loader machining. There are many factors affecting the machining accuracy of the loader, among which the main factors are three aspects: geometric accuracy of the process system, force deformation of the process system, and thermal deformation of the process system. The reasons for these three errors are not the same, but they all affect the quality of the loader parts to a certain extent. In this article, the author will explain the influencing factors and measures of machining accuracy one by one.

A vortex generator is a compact device that can be installed on automobiles and airplanes to enhance aerodynamic efficiency. The use of vortex generators on automobiles and airplanes is briefly described in this study. It begins by outlining the working theories and categorizations of vortex generators, which reactivate the boundary layer to postpone airflow separation. Second, this study examines the use of vortex generators on automobiles and trucks, positioned at the back of these vehicles to reduce air drag and improve fuel efficiency. Studies show that vortex generators can increase vehicle fuel economy by 10% to 20%. This study examines how vortex generators improve airplane efficiency and lift by controlling the airflow above the wings. In general, if a vortex generator’s design is appropriate for automobiles and airplanes, it can improve aerodynamic performance. This article lists several sources regarding the use of vortex generators and their benefits. In short, the future development of vortex generators will tend to be intelligent, material optimization, and environmental protection, aiming to provide more efficient, reliable, and intelligent fluid control solutions, and bring greater economic and social benefits to various industrial applications.