Amorphous metal dry-type transformer (AMDT) is a kind of green and energy-saving power distribution equipment, which is widely used in power distribution systems in various fields. The material cost and loss of AMDT a...
详细信息
The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure-function coupling (SFC) a valuable indicator for early de...
详细信息
The progression of Alzheimer's disease (AD) involves complex changes in brain structure and function that are driven by their interaction, making structure-function coupling (SFC) a valuable indicator for early detection of AD. Static SFC refers to the overall structure-function interaction, whereas dynamic SFC refers to transient coupling variations. In this study, we aimed to assess the potential of combining static and dynamic SFC with machine learning (ML) for the early detection of AD. We analyzed a discovery cohort and an external validation cohort, including AD, mild cognitive impairment (MCI), and healthy control (HC) groups. Then, we quantified differences between static SFC and dynamic SFC at different stages of AD progression. Feature selection was performed using ElasticNet. A Gaussian naive Bayes (GNB) classifier was used to test the ability of SFC to classify AD stages. We also analyzed the correlations between SFC features and early AD physiological biomarkers. Static SFC increased with AD progression, whereas dynamic SFC showed greater variability and decreased stability. Using SFC features selected by ElasticNet, the GNB classifier achieved high performance in differentiating between the HC and MCI stages (area under the curve [AUC] = 91.1%) and between the MCI and AD stages (AUC = 89.03%). Significant correlations were found between SFC features and physiological biomarkers. The combined use of SFC features and ML has strong potential value for the accurate classification of AD stages and significant potential value for the early detection of AD. This study demonstrates that combining static and dynamic SFC with ML provides a novel perspective for understanding the mechanisms of AD and contributes to improving its early detection.
With the evolution of existing modeling languages and the emergence of more and more new modeling languages, it is necessary to rapidly build the corresponding software modeling tools with good quality. However, model...
详细信息
With the evolution of existing modeling languages and the emergence of more and more new modeling languages, it is necessary to rapidly build the corresponding software modeling tools with good quality. However, modeling tools for larger modeling languages are usually diversity in function and complexity in implementation technology. Taking building modeling tools as a domain,this paper presents an approach to building software modeling tools based on metamodeling and product line technologies. The paper provides the concept system of the approach and a feature model from diverse functions of modeling tools in order to specify the commonality and variability of the tools by deeply making the domain analysis,discusses the design and implementation of a general tool framework that provides the conveniences for reusing components and generating code for components, and specifies the mapping between the feature model and the components for modeling tools.
To enrich the theoretical system of multi-mode mechanisms, a classification method and a determination method are proposed in this paper. From the perspective of configuration transformation, the multi-mode mechanisms...
详细信息
To enrich the theoretical system of multi-mode mechanisms, a classification method and a determination method are proposed in this paper. From the perspective of configuration transformation, the multi-mode mechanisms are divided into two types: one based on lockable joints and the other based on the principle of bifurcated motion. Furthermore, the mechanisms based on the principle of bifurcated motion are categorized into two types: one based on the variable mobility branch and the other based on constraint singularity generated by branches. The principles of classification are expounded and the determination method is developed. The proposed classification and the determination methods of the multi-mode mechanism provide new insights for their analysis.
This paper proposes a generalizable, end-to-end deep learning-based method for relative pose regression between two images. Given two images of the same scene captured from different viewpoints, our method predicts th...
详细信息
In the field of computer vision, the task of unsupervised person re-identification presents significant challenges. The features extracted by mainstream unsupervised methods are greatly affected by background noise an...
详细信息
Unmanned aerial vehicle is considered one of the most promising technologies. In the multi-UAV systems, a great challenge is to control multiple UAVs remotely by one person without being limited by distance. In this p...
详细信息
Due to their diverse species and complex morphology, the automatic identification of marine plankton has always been a challenging task. In response to this problem, this study proposes an innovative deep learning-bas...
详细信息
To solve the problem of low efficiency in extracting features and key protocol fields in traditional machine learning-based classification techniques and the vulnerability to sensitive data leakage, federated learning...
详细信息
暂无评论