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 Since regulatory authorities have a key role in customer protection, fostering innovation and growth, the outdated or nonexistent regulatory framework for the IoT could be one of the barriers for the IoT long term growth and avoiding the side effects could be difficult to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 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.

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.

In recent years, the number of requests for connection of PV systems to the medium voltage distribution network has been significantly increasing. In order to approve a connection, it is necessary to comprehensively consider the integration impacts on the grid. In this paper, a two-step quasi-dynamic network simulation in DIgSILENT PowerFactory automated by Python scripting is proposed. In the first step, based on statistics of meteorological parameters and consumer load profiles, long-term hourly sequences of PV system power production and load of all consumers were artificially generated. In the second step, a Monte Carlo simulation applied to IEEE 33-bus system with an integrated PV system was performed. As illustrative results, the voltage profile and active power losses are shown and discussed.

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 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.

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.

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.

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.

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.

Contact centers are an operationally complex element of a company and play a major role in the experience of its customers. By offering relevant and quick responses to questions and prompt problem solving, a company can achieve a better customer experience. Contact centers generate huge amounts of very useful data, which are often underused, misused, or even not used at all. Our research aims to apply data research techniques to the problem of creating customer profiles in the contact center. Customer profiling mechanisms should provide an explicit set of information about the observed customer's preferences, interests, and behavior patterns. Based on the attributes contained in the customer profile, the system makes decisions in terms of choosing the right contact center agent by anticipating the needs of the observed customer. In our paper, the customer profile is based on the extraction of his properties from log information about used services, behavior patterns, and other general characteristics of each customer. The purpose of our research is to determine which attributes are the most relevant for creating a customer profile and how to evaluate them.

The paper presents recommendations for a responsive user interface (UI) implementation. Google guidelines for UI implementation - Material Design, are briefly presented and compared against the Nielsen’s design principles. We have identified limitations in preserving interaction design principles while implementing Material Design Guidelines. With objective to achieve flexible and responsive layouts respective improvement recommendations are discussed. Sample design case is presented, illustrated by screens before and after the implementation of the recommendations on Android devices with different resolutions.

PurposeThis study shows various pathways manufacturers can take when embarking on digital servitization (DS) journeys. It builds on the DS and modularity literature to map the strategic trajectories of product–service–software (PSSw) configurations.Design/methodology/approachThe study is exploratory and based on the inductive theory building method. The empirical data were gathered through a workshop with focus groups of 15 servitization manufacturers (with 22 respondents), an on-site workshop (in-depth case study), semi-structured interviews, observations and document study of archival data.FindingsThe DS trajectories are idiosyncratic and dependent on design architectures of PSSw modules, balancing choices between standardization and innovation. The adoption of software systems depends on the maturity of the industry-specific digital ecosystem. Decomposition and integration of PSSw modules facilitate DS transition through business model modularity. Seven testable propositions are presented.Research limitations/implicationsWith the small sample size from different industries and one in-depth case study, generalizing the findings was not possible.Practical implicationsThe mapping exercise is powerful when top management from different functional departments can participate together to share their expertise and achieve consensus. It logs the “states” that the manufacturer undergoes over time.Originality/valueThe Digital Servitization Cube serves as a conceptual framework for manufacturers to systematically map and categorize their current and future PSSw strategies. It bridges the cross-disciplinary theoretical discussion in DS.