Abstract In patients with pulmonary embolism (PE), the D-Dimer assay is commonly utilized as part of the diagnostic workup, but data on D-Dimer for early risk stratification and short-term mortality prediction are limited. The purpose of this study was to determine D-Dimer levels as a predictive biomarker of PE outcomes in younger (<50 years of age) compared to older patients. We conducted retrospective analysis for 930 patients diagnosed with PE between 2015 and 2019 as part of the Serbian University Multicenter Pulmonary Embolism Registry (SUPER).All patients had D-Dimer levels measured within 24 hours of hospital admission. The primary outcome was mortality at 30 days or during hospitalization. Patients were categorized into two groups based on age (≤ 50 and >50 years of age). Younger patients constituted 20.5% of the study cohort. Regarding all-cause mortality, 5.2% (10/191)of patients died in group under the 50 years of age; the short-term all-causemortality was 12.4% (92/739) in older group.We have found that there was significant difference in plasma D-Dimer level between patients ≤ 50 years of age and older group (>50), p= 0.006.D-Dimer plasma level had good predictive value for the primary outcome in younger patients (c-statistics 0.710; 95% CI, 0.640-0.773; p<0.031). The optimal cutoff level for D-Dimer to predict PE-cause death in patients aged > 50 years was found to be 8.8 mg/l FEU(c-statistics 0,580; 95% CI 0.544-0.616; p=0.049). In younger PE patients, D-Dimer levels have good prognostic performance for 30-day all-cause mortalityand concentrations above 6.3 mg/l FEU are associated with increased risk of death. D-Dimer in patients aged over 50 years does not have predictive ability for all-caused short-term mortality. The relationship between D-Dimer and age in patients with PE may need further evaluation.

Although homeostasis is a commonly accepted concept, there is incontrovertible evidence that biological processes and functions are variable and that variability occurs in cycles. In order to explain and understand dysregulation, which has not been embraced by homeostatic principles, the allostatic model has emerged as the first serious challenge to homeostasis, going beyond its homeostatic roots. Circadian rhythm is the predominant variation in the body, and it is a pattern according to which many physiological and pathological events occur. As there is strong experimental and clinical evidence that blood pressure fluctuations undergo circadian rhythm, there is equally strong evidence that targeted time therapy for hypertension provides a better outcome of the disease. The research has gone even further throughout the development and approval process for the use of pulsatile drug release systems, which can be considered as an option for an even more convenient dosage regimen of the medicines needed.

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.

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.

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.

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 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.

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 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 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.