Application of Heterogeneous Network Oriented to NoSQL Database in Optimal Postevaluation Indexes of Construction Projects

With the advent of the Internet Web 2.0 era, storage devices used to store website data are developing at an ever-increasing high-growth rate and a diversified trend. The focus on the structured data storage model has reduced the responsiveness of traditional relational databases to data changes. NoSQL database is scalable, has a powerful and flexible data model and a large amount of data, and has an increasing application potential in the memory field. Heterogeneous networks are composed of third-party computers, network equipment, and systems. Network types are usually used for other protocols to support other functions and applications. The research on heterogeneous networks can be traced back to the BARWAN project that started in 1995 at the University of California, Berkeley. The project leader RHKatz merged multiple types of nested networks for the first time to form heterogeneous network requirements for various future terminal services. Construction engineering refers to an engineering entity formed by installing pipelines and equipment that support the construction of various houses and ancillary facilities. “House construction” refers to projects with roofs, beams, columns, walls, and foundations that can form internal spaces to meet people’s needs in production, living, learning, and public activities. Among them, the engineering evaluation index is a statistical index used to evaluate and compare the quality and effects of social and economic activities through the use of equipment, such as capital turnover rate and employee labor efficiency. It is the exchange of corporate performance evaluation content and the expression of corporate performance evaluation content.

Deep Learning-Assisted Repurposing of Plant Compounds for Treating Vascular Calcification: An In Silico Study with Experimental Validation

Background. Vascular calcification (VC) constitutes subclinical vascular burden and increases cardiovascular mortality. Effective therapeutics for VC remains to be procured. We aimed to use a deep learning-based strategy to screen and uncover plant compounds that potentially can be repurposed for managing VC. Methods. We integrated drugome, interactome, and diseasome information from Comparative Toxicogenomic Database (CTD), DrugBank, PubChem, Gene Ontology (GO), and BioGrid to analyze drug-disease associations. A deep representation learning was done using a high-level description of the local network architecture and features of the entities, followed by learning the global embeddings of nodes derived from a heterogeneous network using the graph neural network architecture and a random forest classifier established for prediction. Predicted results were tested in an in vitro VC model for validity based on the probability scores. Results. We collected 6,790 compounds with available Simplified Molecular-Input Line-Entry System (SMILES) data, 11,958 GO terms, 7,238 diseases, and 25,482 proteins, followed by local embedding vectors using an end-to-end transformer network and a node2vec algorithm and global embedding vectors learned from heterogeneous network via the graph neural network. Our algorithm conferred a good distinction between potential compounds, presenting as higher prediction scores for the compound categories with a higher potential but lower scores for other categories. Probability score-dependent selection revealed that antioxidants such as sulforaphane and daidzein were potentially effective compounds against VC, while catechin had low probability. All three compounds were validated in vitro. Conclusions. Our findings exemplify the utility of deep learning in identifying promising VC-treating plant compounds. Our model can be a quick and comprehensive computational screening tool to assist in the early drug discovery process.

When teachers Google physical literacy: A cartography of controversies analysis

How physical literacy (PL) is presented on ‘the web’ (i.e. Google) has implications for how health and/ physical education (H/PE) teachers and coaches engage with and understand the concept, and ultimately how it is made to act in practice. This research sheds light on the type of PL content they are likely to encounter in their search via the web. Utilising Venturini's ‘cartography of controversies’ method, the top 100 Google search results for PL were analysed to observe and describe how PL is presented on the web, by whom and in the name of what. Findings show that PL has been ‘framed’ on the web by a heterogeneous network of actors who present different viewpoints, ideologies and suggested practices for PL within and across the contextual ‘spheres’ of education, sport and health. Further, the findings highlight how Google's algorithms prioritise and privilege particular PL viewpoints and ideologies. Consequently, variations in understanding and practices will be evident between H/PE teachers and coaches who only engage with the first page of Google results (top one to 10 URLs) and those who read more broadly. Rather than relying on Google's algorithms or policymakers’ interpretations of PL that commonly serve the interests of the sport and health ‘spheres’ we suggest that H/PE teachers and coaches need to act as ‘knowledge brokers’ and thus be reflexive and aware of the multiple versions of PL that are presented on the web. This is especially important if they use the web as a form of professional development.

USING FEDERATED MACHINE LEARNING FOR ACCESS POINT SELECTION IN THE HETEROGENEOUS NETWORK

The problem of selecting a wireless access network in a highly heterogeneous environment has been analyzed and solved. A network selection model based on the analysis of a wireless network environment using a federated reinforcement machine learning system is proposed. A model has been developed to estimate the theoretical average capacity available to the user in a highly heterogenic access network. The effectiveness of the proposed method was evaluated using a series of experiments. The article is concluded with a discussion regarding the applicability of the proposed method for IMT-2020 and IMT-2030 networks.