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.

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.

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.

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.

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.

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.

Background Establishing a regional/national/international registry of patients suffering from chronic obstructive pulmonary disease (COPD) is essential for both research and healthcare, because it enables collection of comprehensive real-life data from a large number of individuals. Objective The aim of this study was to describe characteristics of COPD patients from the Serbian patient registry, and to investigate actual differences of those characteristics among the COPD phenotypes. Methods The Serbian registry of patients with COPD was established in 2018 at University of Kragujevac, Faculty of Medical Sciences, based on an online platform. Entry in the Registry was allowed for patients who were diagnosed with COPD according to the following criteria: symptoms of dyspnea, chronic cough or sputum production, history of risk factors for COPD and any degree of persistent airflow limitation diagnosed at spirometry. Results In the Serbian COPD registry B and D GOLD group were dominant, while among the COPD phenotypes, the most prevalent were non-exacerbators (49.4%) and then frequent exacerbators without chronic bronchitis (29.6%). The frequent exacerbator with chronic bronchitis phenotype was associated with low levels of bronchopulmonary function and absolute predominance of GOLD D group. Anxiety, depression, insomnia, hypertension and chronic heart failure were the most prevalent in the frequent exacerbator with chronic bronchitis phenotype; patients with this phenotype were also treated more frequently than other patients with a triple combination of the most effective inhaled anti-obstructive drugs: long-acting muscarinic antagonists, long-acting beta 2 agonists and corticosteroids. Conclusion In conclusion, the data from the Serbian registry are in line with those from other national registries, showing that frequent exacerbators with chronic bronchitis have worse bronchopulmonary function, more severe signs and symptoms, and more comorbidities (especially anxiety and depression) than other phenotypes. Other studies also confirmed worse quality of life and worse prognosis of the AE-CB phenotype, stressing importance of both preventive and appropriate therapeutic measures against chronic bronchitis.

The bio-recognition capabilities of materials-specific peptides offer a promising route to obtaining and organizing 2D nanosheet materials in aqueous media. Although significant advances have been made for graphene, little is currently understood regarding how to apply this strategy to hexagonal boron nitride (h-BN) due to a lack of knowledge regarding peptide/h-BN interactions. Here, one of the few peptide sequences known with affinity for h-BN, BP7, is the focus of mutation studies and bio-conjugation. A combination of experimental methods and modeling reveals the importance of Tyrosine in peptide/h-BN interactions. This residue is identified as the key anchoring species, which is then leveraged via bio-conjugation of BP7 to a fatty acid to create new interfacial properties. Specific placement of the fatty acid in the bio-conjugate results in dramatic manipulation of the surface-bound biotic overlayer to generate a highly viscoelastic interface. This viscoelasticity is a consequence of the fatty acid binding, which also down-modulates Tyrosine contact to h-BN, resulting in presentation of the extended peptide to solution. In this orientation, the biomolecule is available for subsequent bioconjugation, providing new pathways to programmable organization and conjugation of h-BN nanosheets in liquid water.

Vulvar squamous cell carcinoma (VSCC) comprises two distinct etiopathological subtypes: i) Human papilloma virus (HPV)-related VSCC, which arises via the precursor high grade squamous intraepithelial lesion (HSIL); and ii) HPV-independent VSCC, which arises via precursor, differentiated vulvar intraepithelial neoplasia (dVIN), driven by TP53 mutations. However, the mechanism of carcinogenesis of VSCC is poorly understood. The current study aimed to gain insight into VSCC carcinogenesis by identifying differentially expressed genes (DEGs) for each VSCC subtype. The expression of certain DEGs was then further assessed by performing immunohistochemistry (IHC) on whole tissue sections of VSCC and its precursors. Statistical analysis of microarrays was performed on two independent gene expression datasets (GSE38228 and a study from Erasmus MC) on VSCC and normal vulva. DEGs were identified that were similarly (up/down) regulated with statistical significance in both datasets. For HPV-related VSCCs, this constituted 88 DEGs, and for HPV-independent VSCCs, this comprised 46 DEGs. IHC was performed on VSCC (n=11), dVIN (n=6), HSIL (n=6) and normal vulvar tissue (n=7) with i) signal transducer and activator of transcription 1 (STAT1; an upregulated DEGs); ii) nuclear factor IB (NFIB; a downregulated DEG); iii) p16 (to determine the HPV status of tissues); and iv) p53 (to confirm the histological diagnoses). Strong and diffuse NFIB expression was observed in the basal and para-basal layers of normal vulvar tissue, whereas NFIB expression was minimal or completely negative in dVIN and in both subtypes of VSCC. In contrast, no discernable difference was observed in STAT1 expression among normal vulvar tissue, dVIN, HSIL or VSCC. By leveraging bioinformatics, the current study identified DEGs that can facilitate research into VSCC carcinogenesis. The results suggested that NFIB is downregulated in VSCC and its relevance as a diagnostic/prognostic biomarker deserves further exploration.

Road transportation is one of the largest sectors of greenhouse gas (GHG) emissions affecting climate change. Tackling climate change as a global community will require new capabilities to measure and inventory road transport emissions. However, the large scale and distributed nature of vehicle emissions make this sector especially challenging for existing inventory methods. In this work, we develop machine learning models that use satellite imagery to perform indirect top-down estimation of road transport emissions. Our initial experiments focus on the United States, where a bottom-up inventory was available for training our models. We achieved a mean absolute error (MAE) of 39.5 kg CO2 of annual road transport emissions, calculated on a pixel-by-pixel (100 m2) basis in Sentinel-2 imagery. We also discuss key model assumptions and challenges that need to be addressed to develop models capable of generalizing to global geography. We believe this work is the first published approach for automated indirect top-down estimation of road transport sector emissions using visual imagery and represents a critical step towards scalable, global, near-real-time road transportation emissions inventories that are measured both independently and objectively.