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.

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 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.

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 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.

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.

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.

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.

Acetone is a metabolic byproduct found in the exhaled breath and can be measured to monitor the metabolic degree of ketosis. In this state, the body uses free fatty acids as its main source of fuel because there is limited access to glucose. Monitoring ketosis is important for type I diabetes patients to prevent ketoacidosis, a potentially fatal condition, and individuals adjusting to a low-carbohydrate diet. Here, we demonstrate that a chemiresistor fabricated from oxidized single-walled carbon nanotubes functionalized with titanium dioxide (SWCNT@TiO2) can be used to detect acetone in dried breath samples. Initially, due to the high cross sensitivity of the acetone sensor to water vapor, the acetone sensor was unable to detect acetone in humid gas samples. To resolve this cross-sensitivity issue, a dehumidifier was designed and fabricated to dehydrate the breath samples. Sensor response to the acetone in dried breath samples from three volunteers was shown to be linearly correlated with the two other ketone bodies, acetoacetic acid in urine and β-hydroxybutyric acid in the blood. The breath sampling and analysis methodology had a calculated acetone detection limit of 1.6 ppm and capable of detecting up to at least 100 ppm of acetone, which is the dynamic range of breath acetone for someone with ketosis. Finally, the application of the sensor as a breath acetone detector was studied by incorporating the sensor into a handheld prototype breathalyzer.

Although the inclusion of individuals with lived experience is encouraged within the research process, there remains inconsistent direct involvement in many mental health fields. Within the eating disorders field specifically, there is a very strong and increasing presence of lived experience advocacy. However, due to a number of potential challenges, research undertaken in consultation or in collaboration with individuals with lived experience of an eating disorder is scarce. This paper describes the significant benefits of the inclusion of individuals with lived experience in research. The specific challenges and barriers faced in eating disorders research are also outlined. It is concluded that in addition to existing guidelines on working with lived experience collaborators in mental health research, more specific procedures are required when working with those with eating disorders.

Deep learning (DL) is an efficient method for botnet attack detection. However, the volume of network traffic data and memory space required is usually large. It is, therefore, almost impossible to implement the DL method in memory-constrained Internet-of-Things (IoT) devices. In this article, we reduce the feature dimensionality of large-scale IoT network traffic data using the encoding phase of long short-term memory autoencoder (LAE). In order to classify network traffic samples correctly, we analyze the long-term inter-related changes in the low-dimensional feature set produced by LAE using deep bidirectional long short-term memory (BLSTM). Extensive experiments are performed with the BoT-IoT data set to validate the effectiveness of the proposed hybrid DL method. Results show that LAE significantly reduced the memory space required for large-scale network traffic data storage by 91.89%, and it outperformed state-of-the-art feature dimensionality reduction methods by 18.92–27.03%. Despite the significant reduction in feature size, the deep BLSTM model demonstrates robustness against model underfitting and overfitting. It also achieves good generalisation ability in binary and multiclass classification scenarios.