Model-based product line engineering applies the reuse practices from product line engineering with graphical modeling for the specification of software intensive systems. Variability is usually described in separate variability models, while the implementation of the variable systems is specified in system models that use modeling languages such as SysML. Most of the SysML modeling tools with variability support, implement the annotation-based modeling approach. Annotated product line models tend to be error-prone since the modeler implicitly describes every possible variant in a single system model. To identifying variability-related inconsistencies, in this paper, we firstly define restrictions on the use of SysML for annotative modeling in order to avoid situations where resulting instances of the annotated model may contain ambiguous model constructs. Secondly, inter-feature constraints are extracted from the annotated model, based on relations between elements that are annotated with features. By analyzing the constraints, we can identify if the combined variability- and system model can result in incorrect or ambiguous instances. The evaluation of our prototype implementation shows the potential of our approach by identifying inconsistencies in the product line model of our industrial partner which went undetected through several iterations of the model.

Abstract Serology is critical for understanding pathogen-specific immune responses, but is fraught with difficulty, not least because the strength of antibody (Ab) response varies greatly between individuals and mild infections generally generate lower Ab titers1–3. We used robust IgM, IgG and IgA Ab tests to evaluate anti-SARS-CoV-2 responses in individuals PCR+ for virus RNA (n=105) representing different categories of disease severity, including mild cases. All PCR+ individuals in the study became IgG-positive against pre-fusion trimers of the virus spike (S) glycoprotein, but titers varied greatly. Elevated IgA, IL-6 and neutralizing responses were present in intensive care patients. Additionally, blood donors and pregnant women (n=2,900) sampled throughout the first wave of the pandemic in Stockholm, Sweden, further demonstrated that anti-S IgG titers differed several orders of magnitude between individuals, with an increase of low titer values present in the population at later time points4,5. To improve upon current methods to identify low titers and extend the utility of individual measures6,7, we used our PCR+ individual data to train machine learning algorithms to assign likelihood of past infection. Using these tools that assigned probability to individual responses against S and the receptor binding domain (RBD), we report SARS-CoV-2-specific IgG in 13.7% of healthy donors five months after the peak of spring COVID-19 deaths, when mortality and ICU occupancy in the country due to the virus were at low levels. These data further our understanding of antibody responses to the virus and provide solutions to problems in serology data analysis. Significance statement Antibody testing provides critical clinical and epidemiological information during an emerging disease pandemic. We developed robust SARS-CoV-2 IgM, IgG and IgA antibody tests and profiled COVID-19 patients and exposed individuals throughout the outbreak in Stockholm, Sweden, where full societal lockdown was not employed. As well as elucidating several disease immunophenotypes, our data highlight the challenge of identifying low IgG titer individuals, who comprise a significant proportion of the population following mild/asymptomatic infection, especially as antibody titers wane following peak responses. To provide a solution to this, we used SARS-CoV-2 PCR+ individual data to develop machine learning approaches that assigned likelihood of past infection to blood donors and pregnant women, improving the accuracy and utility of individual and population-level Ab measures.

Model-based product line engineering applies the reuse practices from product line engineering with graphical modeling for the specification of software intensive systems. Variability is usually described in separate variability models, while the implementation of the variable systems is specified in system models that use modeling languages such as SysML. Most of the SysML modeling tools with variability support, implement the annotation-based modeling approach. Annotated product line models tend to be error-prone since the modeler implicitly describes every possible variant in a single system model. To identifying variability-related inconsistencies, in this paper, we firstly define restrictions on the use of SysML for annotative modeling in order to avoid situations where resulting instances of the annotated model may contain ambiguous model constructs. Secondly, inter-feature constraints are extracted from the annotated model, based on relations between elements that are annotated with features. By analyzing the constraints, we can identify if the combined variability- and system model can result in incorrect or ambiguous instances. The evaluation of our prototype implementation shows the potential of our approach by identifying inconsistencies in the product line model of our industrial partner which went undetected through several iterations of the model.

The research was conducted on a sample of 70 respondents-swimmers aged 13-15 years of swimming clubs from Sarajevo Canton/Federation of BiH, with the aim of determining the significance and magnitude of the impact of selected basic motor skills on the implementation of specific motor tasks in swimming (navigability in place, sliding length with reflection from water, start from starting block, parallel). The study used 10 variables to assess basic motor skills, which were the input or predictor set of variables, and three variables to assess the efficiency of specific motor tasks in swimming as a criterion, each variable from the battery of specific motor tasks was considered as a criterion on the predictor set of basic-motor variables. Three mini regression analyzes were applied to determine the statistical significance and relative influence of basic motor skills on the realization of specific motor tasks in swimming (buoyancy in place, length of sliding with reflection from water, start from the starting block, parallel). The results of regression analyzes indicate that the greatest influence on the overall efficiency in the implementation of specific motor tests in swimming, looking at all criterion variables together, from the set of basic-motor variables, as a predictor set, show the following variables: stick twist-MFLISK MFLPRK, plantar flexion-MFLPL, long jump from place-MFESDM, agility on the ground-MKOKNT and shelter in lying-MRCZTL. The results obtained in this research can be useful for teachers and swimming trainers who work with younger age categories for the purpose of better programming of training work and selection of training content.

In the modern days, the amount of the data and information is increasing along with their accessibility and availability, due to the Internet and social media. To be able to search this vast data set and to discover unknown useful data patterns and predictions, the data mining method is used. Data mining allows for unrelated data to be connected in a meaningful way, to analyze the data, and to represent the results in the form of useful data patterns and predictions that help and predict future behavior. The process of data mining can potentially violate sensitive and personal data. Individual privacy is under attack if some of the information leaks and reveals the identity of a person whose personal data were used in the data mining process. There are many privacy‐preserving data mining (PPDM) techniques and methods that have a task to preserve the privacy and sensitive data while providing accurate data mining results at the same time. PPDM techniques and methods incorporate different approaches that protect data in the process of data mining. The methodology that was used in this article is the systematic literature review and bibliometric analysis. This article identifieds the current trends, techniques, and methods that are being used in the privacy‐preserving data mining field to make a clear and concise classification of the PPDM methods and techniques with possibly identifying new methods and techniques that were not included in the previous classification, and to emphasize the future research directions.

An increasing amount of sensory data, often of confidential nature, is exchanged day by day: from the sensor and actuator layers over smart gateways to the business logic and analytics level. Robust yet efficient security measures play an essential role in this interaction. However, the complexity of securely connecting different building blocks of a distributed, multi-layered systems is considerable. Security methodologies are often applied at a late stage of system development, posing problems such as inappropriate security levels, performance issues, and longer time-to-market cycles. Addressing possible security properties already in the design phase of a security-critical system helps to mitigate these problems. In this paper, we discuss a distributed, multi-layered IoT data collection system that enables data aggregation and exchange from the embedded level up to different cloud instances while supporting end-to-end secured communication. The system was designed in the course of a case study where we used a design-space-exploration tool for identifying secure processes in regard to key management and distribution. Based on our analysis results, a distributed proof of concept was developed. Subsequently, the most critical processes of the individual layers were evaluated regarding security and execution speed.

The popularity of railway transportation has been on the rise over the past decades, as it has provided safe, reliable, and highly available service. One of the main challenges this domain has been facing is reducing the costs of preventive maintenance and improving operational efficiency.In this paper, we aim at enabling the monitoring and analysis of collected signal data from a train propulsion system. The main idea is to monitor and analyze collected signal data gathered during the regular operation of the propulsion control unit or data recorded during the regular train tests in the real-time simulator. To do so, we have implemented a solution to enable train signal data collection and its storage for further analysis purposes. In our analysis, we focus on identifying signal anomalies and predicting potential failures using MathWorks tools. Two machine learning techniques, unsupervised and supervised learning, are implemented. Additionally, in this paper, we have investigated ways of how data can be efficiently managed.

Introdução: as pessoas com COVID-19 apresentarão na sua maioria formas leves a moderadas da doença e permanecerão no seu domicílio sob acompanhamento telefónico. A pessoa deve manter acompanhamento especializado levando à otimização do seu processo de cura, sem complicações associadas, responsáveis por reinternamentos. Objetivo: identificar os ganhos sensíveis aos cuidados de enfermagem de reabilitação com um programa de telereabilitação numa pessoa com COVID 19 ao nível da dispneia, ansiedade e depressão e fluxo expiratório. Método: estudo de abordagem quantitativa, tipo estudo de caso. Refere-se a um caso de uma pessoa com 53 anos com COVID 19 com internamento hospitalar seguido de alta com isolamento domiciliário. Foi feita uma intervenção com recurso a telereabilitação, através de 4 vídeos. Foram atendidos os princípios éticos em investigação. Resultados: foram evidenciados ganhos na capacitação da pessoa a nível do controlo da dispneia, na redução da ansiedade e depressão e no fluxo aéreo. Conclusão: o recurso à telereabilitação em contexto de COVID 19 pode trazer benefícios na capacitação da pessoa no controlo de sintomas, permitir a recuperação da pessoa no seu domicílio e evitar o internamento hospitalar. Palavras-chave: Infeção por coronavírus; Telereabilitação; Reabilitação respiratória; Enfermagem em Reabilitação;

Popularity and use of dietary supplements are constantly growing. Dietary supplements are food products intended to supplement the usual diet and are concentrated source of nutrients or other substances with nutritional or physiological effecst. The purpose of the Paper is to determine frequency of presence of cadmium, lead and mercury metals in dietary supplements based on protein and amino acids that were analyzed during 2018 and 2019 at the Public Health Institute of Republic of Srpska in Banja Luka. Content of metal was determined by the Atomic Absorption Spectrophotometry method. No health defective samples were identified by public health control, but due to modern frequent use of dietary supplements in various population groups (children, adolescents, pregnant women, athletes, etc.), the aim of the Paper is to raise people’s awareness of the risks, such as heavy metals and artificial sweeteners, colors, prohormones and other chemical risks from dietary supplements since they may be associated with chronic health risks.

Urbanization alters natural hydrological processes and enhances runoff, which affects flood hazard. Interest in nature-based solutions (NBS) for sustainable mitigation and adaptation to urban floods is growing, but the magnitudes of NBS effects are still poorly investigated. This study explores the potential of NBS for flood hazard mitigation in a small peri-urban catchment in central Portugal, prone to flash floods driven by urbanization and short but intense rainfall events typical of the Mediterranean region. Flood extent and flood depth are assessed by manually coupling the hydrologic HEC-HMS and hydraulic HEC-RAS models. The coupled model was run for single rainfall events with recurrence periods of 10–, 20–, 50–, and 100–years, considering four simulation scenarios: current conditions (without NBS), and with an upslope NBS, a downslope NBS, and a combination of both. The model-simulation approach provides good estimates of flood magnitude (NSE = 0.91, RMSE = 0.08, MAE = 0.07, R2 = 0.93), and shows that diverting streamflow into abandoned fields has positive impacts in mitigating downslope flood hazard. The implementation of an upslope NBS can decrease the water depth at the catchment outlet by 0.02 m, whereas a downslope NBS can reduce it from 0.10 m to 0.23 m for increasing return periods. Combined upslope and downslope NBS have a marginal additional impact in reducing water depth, ranging from 0.11 m to 0.24 m for 10– and 100–year floods. Decreases in water depth provided by NBS are useful in flood mitigation and adaptation within the peri-urban catchment. A network of NBS, rather than small isolated strategies, needs to be created for efficient flood-risk management at a larger scale.

The rules and regulations inherent to the design pressures and scantlings of high-speed powercrafts are numerous, and regularly reviewed. Recently, the new ISO 12215-5:2019 made notable changes to the way high-speed crafts are analysed, including extending the acceleration experienced up to 8 g in certain circumstances. Nevertheless, despite the multiple iterations and variety of regulatory bodies, the seminal work undertaken on planing crafts throughout the 1960s and 1970s remains the foundation of any rule-based design requirement. Consequently, this paper investigates an array of recently published rules though a comparative design case study, the current state-of-the-art across a number of regulations, and the ultimate impact on scantlings. The study reveals that, despite divergence in intermediate calculations and assumptions, similar requirements are ultimately achieved. Eventually, discussion on the comparison undertaken and future trends in high-speed marine vehicles is provided, tackling the relevance of classical planing theory in light of contemporary innovations.

Bosnia and Herzegovina (B&H) possess many natural resources that can be exploited for the development of medical tourism. The offer of medical tourism in B&H is focused on spa tourism. B&H has 16 registered spa-centers offering different types of services. This study provides a complete overview of the assessment of the current state of spa-centres using expert decision-making and methods of multi-criteria analysis. An innovative and novel MCDM model based on integration of the FUCOM (full consistency method) and fuzzy MARCOS (Measurement of Alternatives and Ranking according to COmpromise Solution) methods was used. The model consists of 16 alternatives and eight sustainable criteria. The results of this research have shown that the spa-centers of Ilidža near Sarajevo, Fojnica and Vrućica have the best assessments of the current situation and prerequisites for sustainable business. These spa-centers should be a benchmark to other spas providing direction on how to improve their business to be more sustainable and competitive in the market. These results were confirmed by a sensitivity analysis with two approaches used. The first approach was to compare the results obtained by the fuzzy MARCOS method with other fuzzy methods, and the second approach was to examine the influence of the application of different weights on the final ranking of the spa. The results of this study can serve spa-canter managers to understand the position of their spa-centers in order to exploit advantages they have and eliminate the shortcomings to improve their business.

This article analyses the robust stability and performance of the disturbance observer (DOb) based digital motion control systems in the discrete-time domain. It is shown that the phase margin and the robustness of the digital motion controller can be directly adjusted by tuning the nominal plant model and the bandwidth of the observer. However, they have the upper and lower bounds due to robust stability and performance constraints as well as the noise sensitivity. The constraints on the design parameters of the DOb change when the digital motion controller is synthesized by measuring different states of a servosystem. For example, the bandwidth of the DOb is limited by the noise sensitivity and waterbed effect when the velocity and position measurements are employed in the digital robust motion controller synthesis. The robustness constraint due to the waterbed effect is removed when the DOb is implemented by acceleration measurement. The design constraints on the nominal plant model and the bandwidth of the observer are analytically derived by employing the generalized Bode integral theorem in discrete time. The proposed design constraints allow one to systematically synthesize a high-performance DOb-based digital robust motion controller. The experimental results are given to verify the proposed analysis and synthesis methods.

Ambient air pollution is one of eight global risk factors for deaths and accounts for 38.44 all causes death rates attributable to ambient PM pollution, while in Bosnia and Herzegovina, it is 58.37. We have estimated health endpoints and possible gains if two policy scenarios were implemented and air pollution reduction achieved. Real-world health and recorded PM pollution data for 2018 were used for assessing the health impacts and possible gains. Calculations were performed with WHO AirQ+ software against two scenarios with cut-off levels at country-legal values and WHO air quality recommendations. Ambient PM2.5 pollution is responsible for 16.20% and 22.77% of all-cause mortality among adults in Tuzla and Lukavac, respectively. Our data show that life expectancy could increase by 2.1 and 2.4 years for those cities. In the pollution hotspots, in reality, there is a wide gap in what is observed and the implementation of the legally binding air quality limit values and, thus, adverse health effects. Considerable health gains and life expectancy are possible if legal or health scenarios in polluted cities were achieved. This estimate might be useful in providing additional health burden evidence as a key component for a clean air policy and action plans.

A modest fraction of current global stimulus funds can put the world on track to achieve Paris Agreement goals Governments around the globe are responding to the coronavirus disease 2019 (COVID-19)–related economic crisis with unprecedented economic recovery packages (1), which at the time of writing surpassed USD 12 trillion. Several influential voices, including the United Nations (UN) secretary-general, heads of state, companies, investors, and central banks, have called for post–COVID-19 economic recovery efforts to be used to catalyze the necessary longer-term transformation toward a more sustainable and resilient society. Here we shine a light on the opportunity for these investments to support a green recovery by inventorying and classifying the latest information on governments' fiscal stimulus plans (1) and comparing the size of these measures to estimates of low-carbon energy investment needs compatible with the 2015 UN Paris Agreement. We show that low-carbon investments to put the world on an ambitious track toward net zero carbon dioxide emissions by mid-century are dwarfed by currently announced COVID-19 stimulus funds. But marked differences across countries and regions at differing stages of development emphasize the role that international support and global partnership must play to create conditions that enable a global climate-positive recovery.