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.

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.

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.

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.