Simple Summary: Ageing is the strongest cancer risk factor, and men and women exhibit disparate risk profiles in terms of incidence and survival. DNA methylation is known to strongly vary by age and sex. Epigenetic drift refers to age-related DNA methylation changes and the tendency for increasing discordance between epigenomes over time, but it remains unknown to what extent the epigenetic drift might contribute to cancer risk and survival. The aims of this study were to identify age-associated, sex-associated and sexually dimorphic age-associated (age-by-sex-associated) DNA methylation markers and investigate whether age- and age-by-sex-associated markers are associated with cancer risk and survival. Our study, which used a total of 3,215 matched case-control pairs with DNA methylation in pre-diagnostic blood, is the first large study to examine the association between sex-specific epigenetic drift and cancer development and progression. The results may be useful for cancer early diagnosis and prediction of prognosis. Abstract: To investigate age- and sex-specific DNA methylation alterations related to cancer risk and survival, we used matched case-control studies of colorectal (N=835), gastric (N=170), kidney (N=143), lung (N=332), prostate (N=869) and urothelial (N=428) cancers, and mature B-cell lymphoma (N=438). Linear mixed-effects models were conducted to identify age-, sex- and age-by-sex-associated methylation markers using a discovery (controls) - replication (cases) strategy. Replication was further examined using summary statistics from Generation Scotland (GS). Associations between replicated markers and risk of and survival from cancer were assessed using conditional logistic regression and Cox models (hazard ratios [HR]), respectively. We found 32,659, 23,141 and 48 CpGs with replicated associations for age, sex and age-by-sex, respectively. The replication rates (GS summary data) for these CpGs were 94%, 86% and 91%, respectively. Significant signals for cancer risk and survival were identified at some individual age-related CpGs. There was a strong negative trend in the association between epigenetic drift and risk of colorectal cancer. Two CpGs overlapping TMEM49 and ARX genes were associated with survival of overall (HR=0.91, P=7.7x10-4) and colorectal (HR=1.52, P=1.8x10-4) cancer, respectively, with significant age-by-sex interaction. Our results may provide markers for cancer early detection and prognosis prediction.

The network model is a powerful tool in the study of localization-delocalization transitions and has been used to describe a variety of topological systems without crystalline symmetry. Here, the authors show that network models can also realize topological phases protected by point-group symmetries. The latter lead to the formation of a higher-order topological phase characterized by midgap modes present at the corners of the system.

This paper investigates the influence of electric vehicle charging station variations for the cases with and without supplementary renewable sources integration, concentrating on symmetry and voltage stability of the network. The study was performed on a realistic low voltage network using is the load flow analysis in DIgSILENT Power Factory software and P-V method. The analysis is based on defined variations for analysis of the baseline variation and electric vehicles with and no additional source as the PV system. It was demonstrated that the complementary operation of EVs and PV can, if planned properly, improve the power system voltage quality parameters.

This research analyzes prediction of student’s success in related courses on Universities. Syllabus of the Faculty of Information Technologies of University "Dzemal Bijedic" in Mostar contains linked courses which are conditioned by each-other. These courses which are pre-requisite to others are in some cases on the same academic years and some of them are in following year. In this research authors proposed regression analysis of student’s success dependency on two courses on first year of study. Correlational analysis indicated existence of moderate correlation. Regression analysis showed that the proposed model indicates weak determination of correlation between subjects. One variant of regression equation is rejected since independent variable was not significant. Regression equation y = ax is accepted.

The solidifying steel follows highly nonlinear thermo-mechanical behavior depending on the loading history, temperature, and metallurgical phase fraction calculations (liquid, ferrite, and austenite). Numerical modeling with a computationally challenging multiphysics approach is used on high-performance computing to generate sufficient training and testing data for subsequent deep learning. We have demonstrated how the innovative sequence deep learning methods can learn from multiphysics modeling data of a solidifying slice traveling in a continuous caster and correctly and instantly capture the complex history and temperature-dependent phenomenon in test data samples never seen by the deep learning networks.

This paper examines the Internet of Things (IoT) as a critical area of interest to regulatory authorities. Although the IoT technologies are driven by market forces, without facilitation from the regulatory authority IoT rollout would be challenging. IoT is a major trend with enormous possibilities, potential advantages, and side effects. Since regulatory authorities have a key role in customer protection, fostering innovation and growth, the outdated or nonexistent regulatory framework for the IoT could be one of the barriers for the IoT long term growth and avoiding the side effects could be difficult to achieve.

This paper proposes power generation forecasting for photovoltaic power plants by using Adaptive Neuro-Fuzzy Inference Systems library in MATLAB and considering meteorological factors. Renewable energy sources (RES) introduce compensation instability problems in the grid hence forecasting methods are considered. Especially important for grid operators is a day ahead forecasting as it can reduce negative imbalance price. Means of ensuring the balance reliability of the power system in terms of RES integration are presented. The installation of charging stations for electric vehicles or use of hydrogen technologies and modern storage systems can provide grid balance. In addition, decreasing the deviation of the current (real) value from the predicted value of power generation is a way to compensate for power unbalance.

This paper develops a Generalized Linear Model using the Negative Binomial Regression with log link function to analyze the effects of mobility trends and seasons on COVID-19 cases. The data of four European countries was used, namely Austria, Greece, Italy, and Czech Republic. The dataset includes daily observations of registered COVID-19 cases, and the data of six types of mobility trends: retail and recreation, grocery and pharmacy, parks, transit stations, workplaces, and residential mobility for the period Feb 15 - Nov 15, 2020. The results suggest that the number of COVID-19 cases differs between seasons and different mobility trends.

Every year installed capacity of renewable energy sources in the World and Ukraine increases. This paper presents a method of determining of technical condition of the photovoltaic model (PVM) with the usage of neuro-fuzzy modeling. The relevance of the transition from traditional to renewable energy sources (RES) is investigated in the article. The most popular RESs for Ukraine and the world are highlighted. The tendency of change of electricity generation by photovoltaic stations is analyzed. Peculiarities in functioning of the electric network employing RES are considered.The optimality criterion components of the power system (PS) normal mode with high level of photovoltaic power plants integration is presented. Technical condition of the PVM was estimated by means of residual resource coefficient. PVM residual resource coefficient which considers the values of all diagnostic parameters was determined using ANFIS library in MATLAB.

The aim of this paper is to analyze the lightning protection model of a photovoltaic power plant, which is of great importance, in order to guarantee the smooth work of the system and avoid errors and damage to the equipment. Atmospheric discharges affect the proper operation of photovoltaic sources and their installation, including sensitive equipment. Determining the need for lightning protection and assessing the success of risk analysis are the first steps to adopt appropriate lightning protection measures. The paper assesses surges due to lightning strikes and the required protection measures based on the results of risk analysis and protection costs. Also, external and internal lightning protection systems, selection of equipment characteristics, and earthing systems are discussed. The lightning protection model was analyzed using the SCIT (Shield) software, and the risk analysis was processed in the Sparkta software.

The paper analyzes the rotation averaging problem as a minimization problem for a potential function of the corresponding gradient system. This dynamical system is one generalization of the famous Kuramoto model on special orthogonal group SO(3), which is known as the non-Abelian Kuramoto model. We have proposed a novel method for finding weighted and unweighted rotation average. In order to verify the correctness of our algorithms, we have compared the simulation results with geometric and projected average using real and random data sets. In particular, we have discovered that our method gives approximately the same results as geometric average.

Web real-time communication (WebRTC) is an open framework that enables real-time voice, video and text communication among browsers. The WebRTC allows collection of large amounts of statistics through browser-embedded tools which can be used to evaluate quality of experience (QoE). This paper focuses on webrtc-internals as Google Chrome browser-embedded tool for collecting WebRTC statistics. The objective is to consider whether webrtc-internals statistics can be used for QoE prediction of WebRTC video calls. A number of experiments were performed and completed with end-user questionnaire in order to collect webrtc-internals statistics and mean opinion scores (MOS). Multiple linear regression (MLR) was used to quantify the relationship between selected webrtc-internals statistics and QoE in order to propose the QoE prediction model for WebRTC video call.

This article presents proportional navigation(PN) and its few variants used in modern tactical missile guidance. This article develops 6-DOF mathematical model and an autopilot for PN guided missile. Full Simulink simulation and animation of PN navigation in three dimensions is shown and discussed.

The aim of this research is to implement Computer Vision technologies on existing published concept proposed by the same author in previous researches "Collaborative and Non-Collaborative Dynamic Path Prediction for Mobile Agents Collision Detection with Dynamic Obstacles". Author proposed usage of Computer Vision technologies in order to increase independency of single robotic units in the swarm. This new method and algorithm is based on analysis of behavior of human objects and its implementation in form of functional method and algorithm which can be used in mobile robotics. In prior research papers, several new terms are proposed and explained such as Metamorphous Hyperspace, Relevant predicted collision time, Coefficient of agility etc. The method implements human behavior in mobile robotics in a way it allows full decentralization of collision detection and ensures many other advantages starting from minimizing network traffic to simplifying inclusion of additional agents in relevant workspace. Algorithm requires a negligible amount of resources allowing mobile agents to exploit more resources for additional tasks. This method and algorithm can be implemented in all kinds of vehicles: ground, naval or airborne objects. Experimental model using Computer Vision technology OpenCV library is implemented and experimental result are described in this paper.

This paper presents the health chatbot application created on the Chatfuel platform. This application allows people to interact with the health chatbot in the same way as they do with other people. The health chatbot identifies their symptoms through a series of queries and guides them to decide whether or not to go to doctor. Such application can be of great benefit to people who are not sure whether their symptoms are transients or require a response to a doctor for detailed tests. It also offers advice to users on minor illnesses, and in that way, encourages people to take appropriate measures to stay healthy, which is a great example of promoting a healthy life. For the purpose of this research, an end-user survey was created and conducted with aim to collect the users’ opinion regarding the acceptance and usage motivation of health chatbot. The results showed good acceptance and usage motivation of health chatbot.