The article discusses the need for a lightweight software architecture evaluation framework that can address practitioners' concerns. Specifically, the proposed framework uses process mining and Petri nets to analyze security and performance in software development's early and late stages. Moreover, the framework has been implemented in six case studies, and the results show that it is a feasible and effective solution that can detect security and performance issues in complex and heterogeneous architecture with less time and effort. Furthermore, the article provides a detailed explanation of the framework's features, factors, and evaluation criteria. Additionally, this article discusses the challenges associated with traditional software architecture documentation methods using Unified Modeling Language diagrams and the limitations of code alone for creating comprehensive Software Architecture models. Various methods have been developed to extract implicit Software Architecture from code artifacts, but they tend to produce code-oriented diagrams instead of Software Architecture diagrams. Therefore, to bridge the model-code gap, the article proposes a framework that considers existing Software Architecture in the source code as architectural components and focuses on Software Architecture behaviors for analyzing performance and security. The proposed framework also suggests comparing Software Architecture extracted by different Process Mining algorithms to achieve consensus on architecture descriptions, using visualizations to understand differences and similarities. Finally, the article suggests that analyzing the previous version of a system's Software Architecture can lead to improvements and deviations from planned Software Architecture can be detected using traceability approaches to aid software architects in detecting inconsistencies.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10918206 | PMC |
http://dx.doi.org/10.1016/j.heliyon.2024.e26969 | DOI Listing |
Brief Bioinform
November 2024
Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, No. 15 Shangxiadian Road, Cangshan District, Fuzhou 350002, China.
Spatial transcriptomics (ST) technologies enable dissecting the tissue architecture in spatial context. To perceive the global contextual information of gene expression patterns in tissue, the spatial dependence of cells must be fully considered by integrating both local and non-local features by means of spatial-context-aware. However, the current ST integration algorithm ignores for ST dropouts, which impedes the spatial-aware of ST features, resulting in challenges in the accuracy and robustness of microenvironmental heterogeneity detecting, spatial domain clustering, and batch-effects correction.
View Article and Find Full Text PDFBackground: Hematoxylin and eosin (H&E) staining is widely considered to be the gold-standard diagnostic tool for histopathology evaluation. However, the fatty nature of some tissue types, such as breast tissue, presents challenges with cryo-sectioning, often resulting in artifacts that can make histopathologic interpretation and correlation with other imaging modalities virtually impossible. We present an optimized on-block H&E staining technique that improves contrast for identifying collagenous stroma during cryo-fluorescence tomography (CFT) sectioning.
View Article and Find Full Text PDFBMC Med Inform Decis Mak
January 2025
Department of Nutritional and Metabolic Psychiatry, The Affiliated Brain Hospital, Guangzhou Medical University, No. 36 Fangcun Mingxin Road, Liwan District, Guangzhou, 510370, China.
Background: The practical application of infectious disease emergency plans in mental health institutions during the ongoing pandemic has revealed significant shortcomings. These manifest as chaotic management of mental health care, a lack of hospital infection prevention and control (IPC) knowledge among medical staff, and unskilled practical operation. These factors result in suboptimal decision-making and emergency response execution.
View Article and Find Full Text PDFJ Orthop Surg Res
January 2025
Department of Medical Equipment, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Background: Adolescent idiopathic scoliosis (AIS) is a complex three-dimensional deformity, and up to now, there has been no literature reporting the analysis of a large sample of X-ray imaging parameters based on artificial intelligence (AI) for it. This study is based on the accurate and rapid measurement of x-ray coronal imaging parameters in AIS patients by AI, to explore the differences and correlations, and to further investigate the risk factors in different groups, so as to provide a theoretical basis for the diagnosis and surgical treatment of AIS.
Methods: Retrospective analysis of 3192 patients aged 8-18 years who had a full-length orthopantomogram of the spine and were diagnosed with AIS at the First Affiliated Hospital of Zhengzhou University from January 2019 to March 2024.
Comput Biol Med
January 2025
Department of Software Engineering, University of Engineering and Technology-Taxila, 47050, Punjab, Pakistan. Electronic address:
Lung cancer remains a significant health concern worldwide, prompting ongoing research efforts to enhance early detection and diagnosis. Prior studies have identified key challenges in existing approaches, including limitations in feature extraction, interpretability, and computational efficiency. In response, this study introduces a novel deep learning (DL) framework, termed the Improved CenterNet approach, tailored specifically for lung cancer detection.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!