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Cardiovascular and cerebrovascular diseases are among the leading causes of death and disability worldwide. Despite continuous advancements in medical technology, the diagnosis and treatment of these diseases still face many unresolved challenges. The emergence of artificial intelligence and machine learning provides new approaches to addressing these issues. The American Heart Association’s 2024 scientific statement highlights the applications and challenges of artificial intelligence and machine learning in clinical scenarios, including imaging analysis, electrocardiogram interpretation, bedside monitoring, wearable devices, genomics, and electronic health records. This paper aims to introduce these aspects and analyze the advantages of artificial intelligence and machine learning in the field of cerebrovascular diseases, providing a reference for the future development of artificial intelligence and machine learning in this area in China. [ABSTRACT FROM AUTHOR]

Clinical prediction models can predict individual outcomes or risks based on effect evidence obtained from the population, serving as a powerful tool linking evidence-based medicine and precision medicine. With the continuous advancement of data measurement, storage, interoperability, and analytical techniques, the application prospect for clinical prediction models is becoming increasingly clear. However, it also faces many problems and challenges. This paper aims to provide an overview of the fundamental concepts, application scenarios, current dilemmas, and development opportunities of clinical prediction models [ABSTRACT FROM AUTHOR]

[Purpose/Significance] Accurately measuring the influence of technical topics is crucial for decision-makers to understand the developmental trends in the technology sector. It is also an important link in identifying emerging, cutting-edge, and disruptive technical topics. Traditional methods of measuring technical topic influence are significantly affected by the latency of patent data approval and citations, lack a forward-looking perspective on the potential influence of technical topics, and suffer from insufficient semantic richness in the extraction of technical topics. This paper presents a method for measuring technical topic influence based on PhraseLDA-SNA and machine learning. It aims to mitigate the impact of delays in patent data approval and citation, while improving the interpretability and accuracy of the results in assessing technical topic influence. [Method/Process] In this study the explicit and implicit determinants of technical topic influence were first analyzed, based on which an index system for measuring technical topic influence was constructed. Then, the PhraseLDA model was used to extract semantically rich technical topics from a large corpus of pre-processed patent texts and to compute the topic-patent association probabilities. PhraseLDA-SNA enhances the semantic richness of technical topic extraction and deepens the analysis of topic content. Machine learning methods leverage their robust data processing and analysis capabilities to predict the high citation potential of patents related to the topics. This research integrates PhraseLDA-SNA and machine learning methods to accurately measure the significance and advanced nature of technical topics in promoting field development, thereby achieving an accurate measurement of the influence of technical topics. Finally, an empirical study was conducted in the field of cellulose biodegradation to compare the high-impact technical topics identified by the proposed method with those identified by the traditional method. Several experts with high academic influence and extensive experience in cellulose biodegradation research were invited to evaluate the high-impact technical topics identified in this study, thus validating the effectiveness of the proposed method. [Results/Conclusions] Compared with the traditional method, the technical topic influence measurement approach based on PhraseLDA-SNA and machine learning reveals more in-depth content. Moreover, this method also analyzes the importance and leading nature of technical topics, which shows superiority in quantitative analysis. Comparing the distribution of high-impact technical topic-related patents identified by the two methods across different years, the topics identified by the proposed method had a higher association ratio in the most recent data, indicating a significant reduction in the impact of patent data approval and citation delays. [ABSTRACT FROM AUTHOR]

With the increasing diversity and complexity of material requirements for underground water conservancy and water pipeline networks, the efficient and convenient design of special materials to meet individual needs through machine learning has become a hot topic of concern. Traditional supervised learning methods are all based on a large dataset to train models, but obtaining large datasets for special materials required in deeply buried underground water pipeline networks and high-end military equipment, such as rare and high-entropy alloys, etc. requires extremely high cost and a long period. To solve this problem, we propose a small sample expansion model-RX-SMOGN, using XGBoost and RFECV algorithms for feature screening. We enrich the dataset with the SMOGN algorithm. In this paper, the phase structure of high-entropy alloys is used as the research object, and traditional machine learning models are trained to predict them to verify the effectiveness of the RX-SMOGN model. From the results of 5-fold cross-verification and 4 evaluation indicators, it can be seen that the RX-SMOGN model fully improves the performance of the machine learning model, provides a more convenient method for alloy material design, and fully improves the efficiency of alloy material design. [ABSTRACT FROM AUTHOR]

The complexity of modern war is increasing，and the rapid operational mission planning is of great importance to improve the efficiency of command and control. This paper presents a joint operational Task Matrix （TM） model，which is a theoretical method for mission planning; A belief network model is put forward to describe the relationship among the elements in TM model ;A naive bayesian learning method for belief network is designed; A mechanism of imagination is put forward to speed up the learning process ; A search algorithm named Deep Minimum Threat Generation Tree（DMTGT） is proposed, which can efficiently calculate task priority by balancing search error and search speed. Finally, the validity of above models and algorithms is verified by simulation experiments. [ABSTRACT FROM AUTHOR]

In order to solve the problem of dynamic evaluation of operational effectiveness of space-based information support equipment system, this paper presents a method of operational effectiveness evaluation of space-based information support equipment system based on dynamic Bayesian network. Firstly, the data samples are obtained by the operational effectiveness evaluation model of system dynamics, then the dynamic network is established based on the static Bayesian network operational effectiveness evaluation model, and the dynamic Bayesian network operational effectiveness evaluation model is established by EM parameter learning algorithm. Finally, the operational effectiveness of space-based information support equipment system is analyzed. Through this model, the relationship and influence degree of many influencing factors in the space- based information support equipment system are deeply analyzed in the battle scenario of attacking moving ship targets in the sea direction. At the same time, simulation experiments verify the effectiveness and feasibility of this method in the operational effectiveness evaluation of space-based information support equipment system, which can provide solid technical support for the operational effectiveness evaluation of space-based information support equipment system. [ABSTRACT FROM AUTHOR]

The production and sales of high-quality fruits are inseparable from fruit quality testing, while traditional fruit quality testing methods have low precision, high cost, poor timeliness, and strong destructiveness. With the continuous advancement of science and technology in recent years, low-cost and high-efficiency non-destructive testing technology for fruit quality has been developed rapidly. Among them, hyperspectral imaging technology has gradually become a research hotspot. This paper summarizes the technical principle, application and development status of this technology in the field of fruit quality, and discusses its performance potential, existing problems, development trend and application prospects in the field of non-destructive testing of fruit quality. On the whole, hyperspectral imaging technology can non-destructively and efficiently realize the effective detection of various fruits and various quality indicators, such as maturity, sugar content, acidity, red index, etc.; Limited by hardware technology, its development focuses on data mining, that is, in the case of limited hardware development, accurate analysis results can be obtained through continuously updated and optimized targeted algorithms; on the other hand, expensive equipment, complex data processing, and poor model universality are the main problems that this technology needs to be further optimized and improved; Its future development will be based on efficient data processing of cloud computing and artificial intelligence, research and development of hyperspectral detection equipment for fruit quality with a wider range of applications, and multi-source comprehensive non-destructive testing. With the continuous development of technology, hyperspectral imaging technology has broad application prospects in non-destructive testing of fruit quality, and will become one of the important means of fruit quality testing in the future. [ABSTRACT FROM AUTHOR]

Gastrointestinal stromal tumor (GIST) is the most common mesenchymal tumor in the gastrointestinal tract, with complex biological characteristics and varying risks, and the treatment methods and prognosis of patients with different risks are quite different; therefore, early diagnosis and risk assessment are crucial for its precision treatment. In recent years, CT radiomics, as an emerging imaging technology, can transform traditional CT image features into a large number of data, thereby reflecting the inherent heterogeneity of GIST and even correlating with its gene expression features. This paper reviews the research progress of CT radiomics in the diagnosis and prediction of GIST with the help of machine learning. The current CT radiomics can not only be used for the differential diagnosis of GIST and other gastric diseases, but also for the risk evaluation of GIST. Furthermore, pathological analysis and gene diagnosis can be performed based on CT images, and then the first-line treatment effect and longterm prognosis can be predicted. At present, various prediction models constructed by combination of CT radiomics and clinical information have been well verified in the specific practice of different clinical problems, showing broad application prospects. However, in the specific clinical application process, different methods of sample data collection and processing, differences in the selection of machine learning algorithms, and the selection of 2D or 3D images all affect the specific effectiveness of CT radiomics. Hence, unified and standardized application rules for radiomics has to be established. [ABSTRACT FROM AUTHOR]

This paper made a comprehensive literature review on the topic of applying machine learning methods to shield tunnelling parameter predictions, stratum predictions ahead of a tunnel face, surface settlements prediction, shield attitude deviation and tool wear predictions. The selections of machine learning methods and the associated input and output parameters were analyzed, and the shortcomings and challenges of existing research were summarized. Some prospects were given, including the model generalization, multi-source heterogeneous data compression and assimilation, data-physics-based intelligent shield tunnelling, big data in shield tunnelling, in order to provide reference and guidance for the future research and engineering practice. [ABSTRACT FROM AUTHOR]

In the field of tumor diagnosis, the AI-assisted diagnosis system can accurately distinguish tumor attributes and malignant tumor stages, thereby prolonging the survival time of patients. Taking breast tumor as an example, this paper proposes an artificial intelligence-assisted diagnosis model based on supervised learning to solve the problem of overfitting caused by the large amount of data in the feature extraction process. When extracting features, effective feature dimensionality reduction is achieved by introducing hierarchical clustering analysis, and the classified feature data is used as the feature input of the artificial neural network model, so as to realize the effective training of the classifier. The experimental results show that the accuracy and AUC value of the proposed algorithm are improved compared with the control algorithm, indicating that the model can not only solve the problem of overfitting caused by the description of massive feature regions, but also enhance the generalization of the artificial intelligence-aided diagnosis system. The ability to achieve high-precision differentiation of mammography-targeted breast tumors. [ABSTRACT FROM AUTHOR]

[Purpose/Significance] This paper proposes a fine-grained automatic extraction method for document structure based on PDF layout features, in order to realize fine-grained organization of literature resources and meet the increasingly growing needs of users for accurate information services. [Method/Process] The method takes full advantage of machine learning in information classification, which can automatically analyze, identify and extract the chapter title of unstructured PDF documents based on layout features. And according to the coordinate positioning of chapter titles, the body content is automatically matched to the subordinated position of the title with paragraph as the minimum granularity, and the fine-grained extraction and identification of the full text of the document is finally realized. [Results/Conclusions] The test result shows that the average accuracy of automatic extraction can reach 80%. The method of fine-grained extraction of unstructured PDF documents proposed has practical significance and application prospect, and the data processing system designed based on the underlying method has been put into practical application, which will greatly liberate us from the mechanical drudgery of chapter structure extraction tasks. [ABSTRACT FROM AUTHOR]