Advances in Thermal Image Analysis for the Detection of Pregnancy in Horses Using Infrared Thermography

Infrared thermography (IRT) was applied as a potentially useful tool in the detection of pregnancy in equids, especially native or wildlife. IRT measures heat emission from the body surface, which increases with the progression of pregnancy as blood flow and metabolic activity in the uterine and fetal tissues increase. Conventional IRT imaging is promising; however, with specific limitations considered, this study aimed to develop novel digital processing methods for thermal images of pregnant mares to detect pregnancy earlier with higher accuracy. In the current study, 40 mares were divided into non-pregnant and pregnant groups and imaged using IRT. Thermal images were transformed into four color models (RGB, YUV, YIQ, HSB) and 10 color components were separated. From each color component, features of image texture were obtained using Histogram Statistics and Grey-Level Run-Length Matrix algorithms. The most informative color/feature combinations were selected for further investigation, and the accuracy of pregnancy detection was calculated. The image texture features in the RGB and YIQ color models reflecting increased heterogeneity of image texture seem to be applicable as potential indicators of pregnancy. Their application in IRT-based pregnancy detection in mares allows for earlier recognition of pregnant mares with higher accuracy than the conventional IRT imaging technique.

Fibrosis Protein-Protein Interactions from Google Matrix Analysis of MetaCore Network

Protein–protein interactions is a longstanding challenge in cardiac remodeling processes and heart failure. Here, we use the MetaCore network and the Google matrix algorithms for prediction of protein–protein interactions dictating cardiac fibrosis, a primary cause of end-stage heart failure. The developed algorithms allow identification of interactions between key proteins and predict new actors orchestrating fibroblast activation linked to fibrosis in mouse and human tissues. These data hold great promise for uncovering new therapeutic targets to limit myocardial fibrosis.

Fibrosis protein-protein interactions from Google matrix analysis of MetaCore network

Protein-protein interactions is a longstanding challenge in cardiac remodeling processes and heart failure. Here we use the MetaCore network and the Google matrix algorithms for prediction of protein-protein interactions dictating cardiac fibrosis, a primary causes of end-stage heart failure. The developed algorithms allow to identify interactions between key proteins and predict new actors orchestrating fibroblast activation linked to fibrosis in mouse and human tissues. These data hold great promise for uncovering new therapeutic targets to limit myocardial fibrosis.

Designing AI for Explainability and Verifiability: A Value Sensitive Design Approach to Avoid Artificial Stupidity in Autonomous Vehicles

One of the primary, if not most critical, difficulties in the design and implementation of autonomous systems is the black-boxed nature of the decision-making structures and logical pathways. How human values are embodied and actualised in situ may ultimately prove to be harmful if not outright recalcitrant. For this reason, the values of stakeholders become of particular significance given the risks posed by opaque structures of intelligent agents. This paper explores how decision matrix algorithms, via the belief-desire-intention model for autonomous vehicles, can be designed to minimize the risks of opaque architectures. Primarily through an explicit orientation towards designing for the values of explainability and verifiability. In doing so, this research adopts the Value Sensitive Design (VSD) approach as a principled framework for the incorporation of such values within design. VSD is recognized as a potential starting point that offers a systematic way for engineering teams to formally incorporate existing technical solutions within ethical design, while simultaneously remaining pliable to emerging issues and needs. It is concluded that the VSD methodology offers at least a strong enough foundation from which designers can begin to anticipate design needs and formulate salient design flows that can be adapted to the changing ethical landscapes required for utilisation in autonomous vehicles.

Impact of COVID-19 and Related Forced Digitalization Processes on the Competitiveness of Higher Education Institutions and Organizations

Research background: One of the areas that has undergone major changes in the processes of its activities and has had a strong impact on the change in social institutions is the field of education. The sharp transition to distance education and a number of technical, informational and human problems led to a significant complication of educational and other processes (scientific, innovative, entrepreneurial, etc.). Purpose of the article: The article raises the problem of the impact of the coronavirus pandemic on global social and public institutions. The purpose of the article is to identify the factors of ensuring the competitiveness of universities, as the least protected by state support, in the context of forced digitalization against the background of the covid-19 pandemic. Methods: The paper uses mechanisms for assessing the competitiveness of universities in the new reality of the pandemic and its consequences, implemented on the basis of a modified McKinsey matrix and matrix algorithms for evaluating priority vectors. Findings & Value added: The analysis showed that the universities that have the greatest independence in the system face the greatest difficulties in carrying out their activities in the context of a pandemic. As scientific growth can be considered, the results of the analysis of the transition to distance learning processes that have combined higher education systems in different countries, and if earlier most universities competed at the regional or country level, now they are forced to enter into global competition with foreign universities.

Research on Analysis and Modeling Methods for LBLOCA in Nuclear Power Plants Based on Autonomous LOCA Analysis Platform ARSAC

In order to meet the demand of continuous innovation of technologies and the general trend of autonomous nuclear power plants design and export of nuclear power plants, it is necessary to develop an autonomous LOCA analysis platform and corresponding analysis methods for the most complex design basis accidents. In this paper, the characteristics of LOCA analysis platform ARSAC, designed by Nuclear Power Institute of China, and the code ARSAC-K which meets the requirements of the US Federal Code 10 CFR 50.46 Appendix K model are introduced as well as a set of LOCA analysis methods and modeling methods. Based on the international advanced LOCA analysis code development concept, the code ARSAC has made new breakthroughs in matrix algorithms, key thermal hydraulic models and so on. Validation work has also been carried out in-depth. A set of advanced LOCA analysis methods has been developed using code ARSAC-K and advanced power plant parameter sampling methods. Analysis on LBLOCA of nuclear power plants with code ARSAC-K was performed, and the impact of different modeling methods on the LOCA analysis results was studied. To ensure the rationality and conservativeness of the analysis results, a set of reasonable and conservative modeling methods is fixed on the basis of a large number of sensitivity analyses for subsequent analysis and calculation. In the future, a lot of optimization work will be done to improve the LOCA code and corresponding methods.