Construction progress risk management of construction projects is faced with the complexity, dynamics and uncertainty of multi-source risk factors. Traditional methods rely on a single indicator or empirical judgment, making it difficult to systematically reveal the risk transmission mechanism. Therefore, an integrated assessment model integrating work breakdown structure (WBS), risk breakdown structure (RBS) and uncertainty measurement theory (UMT) is proposed in this paper. A four-class risk assessment system including 18 key risk factors is constructed, covering four causes: organization management, resource supply, technology implementation and external environment. The risk level is divided into four levels I (extremely high risk) to IV (low risk). Through the combination of expert scoring method and entropy weight method to determine the weight of each index, establish the risk factor adjacency matrix. Taking DK-1 project of Rongchuang Yuhe Chenyuan as the empirical object, the uncertainty measurement function of single index is constructed by Matlab platform, and the measurement evaluation matrix of each risk factor is calculated. The results show that UMT evaluation method can effectively deal with the risk assessment problem under the condition of incomplete information, realize the organic integration of qualitative analysis and quantitative evaluation, and provide a new technical path for dynamic identification and scientific management of complex project schedule risk. In practical cases, the method improves the accuracy of risk identification by about 30% compared with traditional methods.

In mechanical processing workshops, fire hazards gravely threaten safety and property. Existing assessment methods fail to comprehensively account for multi-factor interactions. This study innovatively applies the Bayesian network model to this field for the first time. The model, featuring 20 tertiary and 5 secondary nodes, covers hazardous gas storage, operational norms, and equipment conditions. Empirical analysis reveals a 0.16 probability of fire due to improper hazardous gas cylinder storage at the secondary level. Sensitivity analysis shows that "uninspected oxygen and acetylene cylinders upon entry" significantly impacts "non-standard gas welding operations", while hose aging and backfire preventer damage also strongly influence risks. Forward inference identifies key fire-causing factors, with probabilities up to 0.22 at the tertiary level and 0.3 at the secondary level. Reverse inference indicates improper hazardous chemical cylinder storage as the most likely cause during a fire. This study confirms that non-standard operations and equipment aging are pivotal. The Bayesian network's multi-level design and bidirectional inference enable precise risk quantification, offering a scientific basis for safety management and paving the way for dynamic evaluations.

With the development of society, the concerns of the public have shifted to the aspects of physical health and environmental protection. This leads to a huge demand for biosensors. At the same time, 3D printing has been improving for several years. In this review, the Various categories of 3D printing and their accessibility on biosensors are discussed. There are 7 main types of 3D printing. When each type of technique is applied to biosensors, advantages and disadvantages will emerge. In the final, vat photopolymerization is found to be the most suitable way to apply mature biosensor solutions, which are for the commercial market. And material extrusion and material jetting are good ways to help experimenters build simple sensor prototypes in the early stages of research and development. By aligning 3D printing methodologies with biosensor design goals, this review contributes to accelerating innovation in healthcare, environmental monitoring, and point-of-care diagnostics, addressing pressing global challenges.

The prediction of Olympic medals is complex due to the multi-dimensional influencing factors and the non-linear feature relationships. Therefore, a combined model capable of handling both non-linear feature relationships and capturing time series trends is required. Specifically, this study predicts the medal table for the 2028 Los Angeles Olympics based on a Random Forest-ARIMA combined model. After cleaning and integrating past data using non-negative least squares in a "property division" manner, the United States, China, and Japan are predicted to rank first, second, and third respectively, with a total of 92, 61, and 33 medals, and 37, 28, and 14 gold medals respectively. The United States, China, and Brazil are expected to continue their upward trends, while the United Kingdom will maintain a stable number of medals. Australia, Italy, and Japan have experienced greater medal fluctuations in recent Olympics and are expected to continue this trend. SAM is most likely to win its first medal, with a probability of approximately 72.3%, and three countries are expected to win their first medals.

This study constructs a hybrid prediction framework that integrates random forest and quantile regression. The study first integrates multi-dimensional variables reflecting trends and attribute characteristics to construct the input feature system for the prediction model. By leveraging the ensemble learning mechanism of the random forest regression model to integrate the output results of multiple decision trees, the framework captures the distribution patterns of the data to achieve predictions for continuous variables. Additionally, the quantile regression model is introduced to select feature variables, quantify model uncertainty by setting quantiles, and generate prediction intervals. Furthermore, the study extends the application scope by incorporating a random forest classifier and conducts correlation analysis to uncover positive associations between feature variables and target variables, verifying the significant impact of multi-dimensional inputs on prediction outcomes and enhancing the model's interpretability. This framework enhances data analysis and prediction capabilities through multi-model collaboration and multi-method comprehensive analysis, demonstrating its applicability in related data processing tasks and providing a reference framework for similar studies.

Against the backdrop of the digitization and intelligence of traditional medicine, this study employs the collaborative technology of optical character recognition and natural language processing to address the inheritance challenge of the Dunhuang medical literature "fu Xing Jue Zang-fu Organ Medication Method Yao", and constructs a "prescription - drug" knowledge graph. It mainly expounds the processes of data preprocessing, knowledge extraction and fusion, as well as graph construction. Through the application of the two techniques, the role of the two techniques in the transformation of Dunhuang medicine was revealed. The research results show that the integrated architecture of the two technologies not only has significant value in medical research, but also provides a new paradigm for the modernization, inheritance and innovation of Dunhuang medicinet.

Parallel imported cars, as a structural supplement to the automotive industry, rarely involve manufacturing and are more inclined towards trade. Due to the particularity of its industry, its ability to bear the cost of testing and certification is not as strong as that of traditional automobile manufacturing enterprises. Previously, the China National Accreditation Service for Conformity Assessment (CNCA) implemented a policy of using data review instead of actual testing for destructive testing of parallel imported vehicles, which can reduce their testing costs. Since the cancellation of the data review system in 2024, the cost of parallel imported car certification has increased and the certification declaration has significantly decreased. At the same time, considering the opportunity for CAE, which has been widely used in the field of automotive design, to gradually enter the testing field, the author envisions that if CAE is introduced to replace the destructive testing project of certification testing, the certification cost of parallel imported cars will be greatly reduced, and the constraints of the industry will be lifted accordingly. Therefore, this article aims to study the introduction of CAE into parallel import vehicle certification to replace the certification technology of destructive testing in type testing. The author believes that this is highly forward-looking.

In multi-task complex systems, how to achieve efficient task scheduling and optimal resource allocation under multiple conditions such as resource constraints, process constraints and time window limitations is a key issue that urgently needs to be solved in the fields of systems engineering and intelligent control. For typical scheduling scenarios with strong process dependence, multi-stage processes and heterogeneous resource structures, this paper proposes a priority-driven scheduling model (PRISM). Firstly, by constructing the temporal dependency graph model of the task process, the integer programming method is adopted to uniformly represent the task execution status and resource occupation. On this basis, a multi-dimensional priority evaluation function was innovatively designed. Combined with the dynamic resource state vector and the bidirectional scheduling control mechanism, the collaborative optimization of task execution periods and resource allocation was achieved. To deal with the problems of timing conflicts and resource competition in complex scenarios, an adaptive adjustment algorithm based on difference detection is proposed, which effectively guarantees the feasibility and robustness of the scheduling scheme. The experimental part was verified on a typical high-density task set. The results show that the proposed method can effectively improve the scheduling completion rate and resource utilization efficiency, and has good conflict repair ability and engineering adaptability. This method has the advantages of strong universality, stable scheduling performance and high deployability, and is suitable for the research of scheduling optimization problems in multi-type process systems.

During the 2024 Paris Olympics, both individual events and the overall medal tally have garnered significant attention. As a key indicator of a nation's competitive strength, the number of Olympic medals serves as a critical reference for countries preparing for the Games. This paper addresses three core questions: how to predict the medal rankings for the 2028 Los Angeles Olympics, the likelihood of countries that have never won medals securing their first, and the impact of Olympic events on medal rankings. By combining the Random Forest algorithm and the LSTM model to predict feature variables (such as gender ratio and number of participants), a high-precision medal prediction model was constructed, achieving an R² score of 0.929 and an MSE of 1.78098. For countries that have never won a medal, nine countries were selected, and their probability of winning a medal in 2028 was predicted. Additionally, the Random Forest Regressor model was used to analyze the importance of sports events, identify key events for each country, and reveal the strategic impact of host country event selection. The conclusions indicate that this model provides a scientific basis for countries to optimize their Olympic training strategies and resource allocation.

The rapid increase in the installed capacity of renewable energy generation has led to the phenomenon of "abandoned solar power" and "abandoned wind power." Some questions, for example, bidirectional power flow, three-phase unbalance, harmonics and voltage crossings can affect the safe operation of the grid. In order to solve the dilemma, the power grid usually uses energy storage devices to store the renewable energy on the spot. In this paper, according to the characteristics of all-vanadium redox flow batteries and regional environmental factors, such as irradiance and ambient temperature, a photovoltaics power generation system model with an energy storage device is established. The problem of capacity allocation in the photovoltaic system is studied. At the same time, the operating cost of the system is evaluated from an economic point of view, and finally the configuration of energy storage and PV capacity with the lowest system operating cost is derived.