ANALYSIS OF EXISTING SOLUTIONS FOR IMPROVING THE MEASURING AND COMPUTING COMPLEXES OF HEAT SUPPLY NETWORKS

In the presented article, the team of authors considers the existing methods and the main modern technical solutions that are currently implemented in different countries in the diagnosis of heat supply networks. There is a selection of the main directions in the development and design of heat supply networks, which have already been implemented or supported by scientific teams from different countries. Various methods and technical features of diagnostics are reviewed, strengths and weaknesses of the presented solutions are highlighted. The reviewed works were subjected to detailed analysis, which revealed the presence of a high interest of the scientific and industrial community in the integration and improvement of existing digital technologies in the development of heat supply systems, which would be closely related to forecasting and modeling processes in this industry. The team of authors highlights the main vectors for the development of this sector, citing an example of a significant increase in the degree of digitalization of final products, which makes it possible to use data analytics to obtain effective technical solutions regarding heat supply networks. Separately, the positive experience of different countries in this industry is noted when using neural networks not only in the design of heat supply networks, but also as a target industry as a whole. Assumptions are put forward about the need for a detailed analysis of the existing foreign and domestic experience, as well as scientific developments in this area, in order to determine the most suitable technical solutions on the territory of the Russian Federation, which will take into account the climatic characteristics of the country and be based on methods of large data analysis, computer vision and simulation. modeling.

The social representations of patients of Quechua origin with Type 2 Diabetes about their disease and treatment in two hospitals in Cusco

Objectives: To achieve an approximation the social representations of patients about their disease. and about its treatment. Methodology: This is a qualitative study on social representations based on the interpretive paradigm and through an in-depth interview, for which a guide of topics or categories was used, based on the objectives of the study. The study population was made up of patients treated in the Endocrinology Units of the Antonio Lorena and Regional Hospitals of Cusco, diagnosed with type 2 diabetes and of Quechua origin evidenced by their mother tongue. The sample is non-probabilistic for convenience, the representativeness of the discourse was sought for this, reaching 30 interviews based on the saturation criterion. The information analysis included 1. The transcription (from oral Quechua to written Quechua) and the translation of the interviews and 2. The computerized processing of the interviews, for which purpose the RQDA (Research qualitative data analysis) computer program was used. Results: The ideas that patients have about the cause of their disease and the changes that it produces reflect the influence of Modern Medicine and Andean and Popular Medicine. On the other hand, for the treatment of their disease, they consider it useful to combine the medications that have been indicated in the health service with the resources of Andean and Popular Medicine (medicinal herbs and other natural products). Conclusion: The patients in our study have an intercultural approach to approach and treat.

Using Probabilistic Movement Primitives in Analyzing Human Motion Differences Under Transcranial Current Stimulation

In medical tasks such as human motion analysis, computer-aided auxiliary systems have become the preferred choice for human experts for their high efficiency. However, conventional approaches are typically based on user-defined features such as movement onset times, peak velocities, motion vectors, or frequency domain analyses. Such approaches entail careful data post-processing or specific domain knowledge to achieve a meaningful feature extraction. Besides, they are prone to noise and the manual-defined features could hardly be re-used for other analyses. In this paper, we proposed probabilistic movement primitives (ProMPs), a widely-used approach in robot skill learning, to model human motions. The benefit of ProMPs is that the features are directly learned from the data and ProMPs can capture important features describing the trajectory shape, which can easily be extended to other tasks. Distinct from previous research, where classification tasks are mostly investigated, we applied ProMPs together with a variant of Kullback-Leibler (KL) divergence to quantify the effect of different transcranial current stimulation methods on human motions. We presented an initial result with 10 participants. The results validate ProMPs as a robust and effective feature extractor for human motions.

Topic-based Video Analysis

Manual processing of a large volume of video data captured through closed-circuit television is challenging due to various reasons. First, manual analysis is highly time-consuming. Moreover, as surveillance videos are recorded in dynamic conditions such as in the presence of camera motion, varying illumination, or occlusion, conventional supervised learning may not work always. Thus, computer vision-based automatic surveillance scene analysis is carried out in unsupervised ways. Topic modelling is one of the emerging fields used in unsupervised information processing. Topic modelling is used in text analysis, computer vision applications, and other areas involving spatio-temporal data. In this article, we discuss the scope, variations, and applications of topic modelling, particularly focusing on surveillance video analysis. We have provided a methodological survey on existing topic models, their features, underlying representations, characterization, and applications in visual surveillance’s perspective. Important research papers related to topic modelling in visual surveillance have been summarized and critically analyzed in this article.

A Metamodel and Framework for Artificial General Intelligence From Theory to Practice

This paper introduces a new metamodel-based knowledge representation that significantly improves autonomous learning and adaptation. While interest in hybrid machine learning/symbolic AI systems leveraging, for example, reasoning and knowledge graphs, is gaining popularity, we find there remains a need for both a clear definition of knowledge and a metamodel to guide the creation and manipulation of knowledge. Some of the benefits of the metamodel we introduce in this paper include a solution to the symbol grounding problem, cumulative learning and federated learning. We have applied the metamodel to problems ranging from time series analysis, computer vision and natural language understanding and have found that the metamodel enables a wide variety of learning mechanisms ranging from machine learning, to graph network analysis and learning by reasoning engines to interoperate in a highly synergistic way. Our metamodel-based projects have consistently exhibited unprecedented accuracy, performance, and ability to generalize. This paper is inspired by the state-of-the-art approaches to AGI, recent AGI-aspiring work, the granular computing community, as well as Alfred Korzybski’s general semantics. One surprising consequence of the metamodel is that it not only enables a new level of autonomous learning and optimal functioning for machine intelligences, but may also shed light on a path to better understanding how to improve human cognition.

Estimated average calcium – magnesium content in day meal in a sample of students from Faculty of Medicine of Sarajevo University

Nutrients are chemical substances obtained from food. They have different roles in body. Some are used as energy source, some as structural materials, and regulating agents. Nutrients may reduce the risks of some diseases. There are some recommendations about dietary intake of these nutrients for optimal health. This study aimed to estimate average calcium and magnesium content in day meal in a sample of students from Faculty of Medicine of Sarajevo University. A cross-sectional study was conducted during academic 2015/16 year at Faculty of Medicine of Sarajevo University. The survey covered 44 students. The research instrument was a self-administered questionnaire, by which 3-Day Diet Record was provided. The average intakes of calcium and magnesium were estimated using Nutritional analysis computer program (Nutrics Professional Nutrition Analysis Software). On daily level average intake of calcium was 718.39±311.14 mg in total sample and average intake of magnesium was 292.57±310.10 mg in total sample. Average Ca/Mg ratio was 2.45. In our sample cheese was top source of calcium with Ca/Mg ratio of 32.5, and bread was top source of magnesium with Ca/Mg ratio of 3.1. These results emphasize the importance of monitoring the food nutrition facts in order to achieve adequate nutrients intake.

Analysis of a High Step-Down DC/DC Converter Topology with a Single Inductor

This paper presents a novel high step-down dc/dc converter topology with a single inductor for 48V data center applications. Distinguished from conventional dc/dc converter topologies with a single inductor, the proposed converter topology is designed by series connecting a step-down cross-connected Fibonacci converter with a buck converter. By combination of these converter modules, the proposed converter topology offers a high voltage gain, viz. 1/48×, without cascade connection. Hence, high power efficiency is provided by the proposed single inductor topology. The characteristics of the proposed converter topology are investigated by theoretical analysis, computer simulations, and experiments. In the given theoretical analysis and the performed simulations, the proposed converter topology demonstrates higher power efficiency than the conventional converter topologies with a single inductor. Furthermore, the validity of the proposed topology is confirmed by the experiments.