Artificial Neural Network for Prediction of Seat-to-Head Frequency Response Function During Whole Body Vibrations in the Fore-and-Aft Direction

Vibrations while driving, regardless of their intensity and shape, have the most obvious effect of reducing driving comfort. Seat-to-head frequency response function (STHT) is a complex relationship resulting from the movement of the head due to the action of excitation on the seat in the form of vibrations in the seat/head interface. In this research, an artificial neural network model was developed, which aims to simulate the STHT function through the body of the subjects based on the data obtained experimentally. The experiments were conducted with twenty healthy male volunteers, who were exposed to single-axis fore-and-aft random broadband vibration. All the results of the experiment were recorded on the basis of which the artificial neural network (ANN) was trained. The developed ANN model has the ability to predict STHT values in the range of trained values both when changing the anthropometric measures of the subjects and changes in the input characteristics of vibrations. The mathematical models based on recurrent neural networks (RNN) used in this paper show with high accuracy STHT values in case there exists prior information about the anthropometric measures of the subjects and the input characteristics of vibrations. The results show that the expensive real-time simulations could be avoided by using reliable neural network models

A Novel Approach of Determining the Risks for the Development of Hyperinsulinemia in the Children and Adolescent Population Using Radial Basis Function and Support Vector Machine Learning Algorithm

Hyperinsulinemia is a condition with extremely high levels of insulin in the blood. Various factors can lead to hyperinsulinemia in children and adolescents. Puberty is a period of significant change in children and adolescents. They do not have to have explicit symptoms for prediabetes, and certain health indicators may indicate a risk of developing this problem. The scientific study is designed as a cross-sectional study. In total, 674 children and adolescents of school age from 12 to 17 years old participated in the research. They received a recommendation from a pediatrician to do an OGTT (Oral Glucose Tolerance test) with insulinemia at a regular systematic examination. In addition to factor analysis, the study of the influence of individual factors was tested using RBF (Radial Basis Function) and SVM (Support Vector Machine) algorithm. The obtained results indicated statistically significant differences in the values of the monitored variables between the experimental and control groups. The obtained results showed that the number of adolescents at risk is increasing, and, in the presented research, it was 17.4%. Factor analysis and verification of the SVM algorithm changed the percentage of each risk factor. In addition, unlike previous research, three groups of children and adolescents at low, medium, and high risk were identified. The degree of risk can be of great diagnostic value for adopting corrective measures to prevent this problem and developing potential complications, primarily type 2 diabetes mellitus, cardiovascular disease, and other mass non-communicable diseases. The SVM algorithm is expected to determine the most accurate and reliable influence of risk factors. Using factor analysis and verification using the SVM algorithm, they significantly indicate an accurate, precise, and timely identification of children and adolescents at risk of hyperinsulinemia, which is of great importance for improving their health potential, and the health of society as a whole

Smart strategies for the transition in coal intensive regions

The TRACER project supports a number of coal-intensive regions around Europe to design (or re-design) their Research and Innovation (R&I) strategies in order to facilitate their transition towards a sustainable energy system. The TRACER consortium consists of different target regions: South East Bulgaria, North West Bohemia - Czech Republic, Lusatian Lignite District - Germany, Western Macedonia - Greece, Upper Silesian Coalfield - Poland, West Region, Jiu Valley - Romania, Wales – UK, Kolubara - Serbia, Donetsk - Ukraine. Core activities of TRACER include the implementation of an EDP (Entrepreneurial Discovery Process) to mobilise a wide range of stakeholders in each target region to develop an appropriate governance structure and to bring regional stakeholders together to discuss and agree on a shared vision and priorities for coal transition. R&I strategies, industrial roadmaps and decision support tools will be developed jointly with key stakeholders of the TRACER target regions. Further TRACER activities include the identification and analysis of best practice examples of successful and ambitious transition processes in coal intensive regions, a detailed assessment of social, environmental and technological challenges, the elaboration of guidelines on how to mobilise investment as well as dedicated activities to stimulate R&I cooperation among coal intensive regions in Europe and beyond

Innovation in Hyperinsulinemia Diagnostics with ANN-L(atin square) Models

Hyperinsulinemia is a condition characterized by excessively high levels of insulin in the bloodstream. It can exist for many years without any symptomatology. The research presented in this paper was conducted from 2019 to 2022 in cooperation with a health center in Serbia as a large cross-sectional observational study of adolescents of both genders using datasets collected from the field. Previously used analytical approaches of integrated and relevant clinical, hematological, biochemical, and other variables could not identify potential risk factors for developing hyperinsulinemia. This paper aims to present several different models using machine learning (ML) algorithms such as naive Bayes, decision tree, and random forest and compare them with a new methodology constructed based on artificial neural networks using Taguchi’s orthogonal vector plans (ANN-L), a special extraction of Latin squares. Furthermore, the experimental part of this study showed that ANN-L models achieved an accuracy of 99.5% with less than seven iterations performed. Furthermore, the study provides valuable insights into the share of each risk factor contributing to the occurrence of hyperinsulinemia in adolescents, which is crucial for more precise and straightforward medical diagnoses. Preventing the risk of hyperinsulinemia in this age group is crucial for the well-being of the adolescents and society as a whole

Internet Addiction among Secondary School Students Conditioned By Gender and Age

Modern forms of addiction are present very much in adolescents today. Internet dependency is a form of addiction that manifests itself as an individual's state of affairs. The use of the Internet has become the most important activity in life in relation to other everyday tasks and activities, to this extent and in that way, to isolate him from other social activities and to bring harmful consequences both to himself and to his family and the environment. Characteristic cases in adolescents that occur are insomnia, family disagreements, delays in school, or the absence and neglect of school obligations, nervousness, fatigue, physical changes such as neglecting personal hygiene, weight loss or other obesity, and etc. The number of Internet users and their addicts is growing every day. With this research, we want to determine whether it depends on gender and age and to what extent does it exist among high school students

Internet Addiction among Secondary School Students Conditioned By Gender and Age

Modern forms of addiction are present very much in adolescents today. Internet dependency is a form of addiction that manifests itself as an individual's state of affairs. The use of the Internet has become the most important activity in life in relation to other everyday tasks and activities, to this extent and in that way, to isolate him from other social activities and to bring harmful consequences both to himself and to his family and the environment. Characteristic cases in adolescents that occur are insomnia, family disagreements, delays in school, or the absence and neglect of school obligations, nervousness, fatigue, physical changes such as neglecting personal hygiene, weight loss or other obesity, and etc. The number of Internet users and their addicts is growing every day. With this research, we want to determine whether it depends on gender and age and to what extent does it exist among high school students

Risk Assessment and Determination of Factors That Cause the Development of Hyperinsulinemia in School-Age Adolescents

Background and Objectives: Hyperinsulinemia and insulin resistance are not synonymous; if the risk of developing insulin resistance in adolescents is monitored, they do not necessarily have hyperinsulinemia. It is considered a condition of pre-diabetes and represents a condition of increased risk of developing DM (diabetes mellitus); it can exist for many years without people having the appropriate symptoms. This study aims to determine the risk of developing hyperinsulinemia at an early age in adolescents by examining which factors are crucial for its occurrence. Materials and Methods: The cross-sectional study lasting from 2019 to 2021 (2 years) was realized at the school children’s department in the Valjevo Health Center, which included a total of 822 respondents (392 male and 430 female) children and adolescents aged 12 to 17. All respondents underwent a regular, systematic examination scheduled for school children. BMI is a criterion according to which respondents are divided into three groups. Results: After summary analyzes of OGTT test respondents and calculated values of HOMA-IR (homeostatic model assessment for insulin resistance), the study showed that a large percentage of respondents, a total of 12.7%, are at risk for hyperinsulinemia. The research described in this paper aimed to use the most popular AI (artificial intelligence) model, ANN (artificial neural network), to show that 13.1% of adolescents are at risk, i.e., the risk is higher by 0.4%, which was shown by statistical tests as a significant difference. Conclusions: It is estimated that a model using three different ANN architectures, based on Taguchi’s orthogonal vector plans, gives more precise and accurate results with much less error. In addition to monitoring changes in each individual’s risk, the risk assessment of the entire monitored group is updated without having to analyze all data