Sison amomum L. (Apiaceae) was recorded for the first time in Bosnia and Herzegovina during a fieldwork in the vicinity of the city of Tuzla (northeast Bosnia) in September 2019. This study reports the newly discovered localities and presents a short morphological description of the species.

In the last few years, gambling and betting opportunities have been increasing in Bosnia and Herzegovina, leading to an increase in  pathological gambling. Pathological gambling is considered by experts to be an instinct control disorder, i.e. the inability to refrain from an instinct that at the same time causes pleasure to a person, but is also dangerous for them and the people around them as it seriously disrupts not only the physical, emotional, mental and material state of the individual, but affects their families and friends also. In order to examine the most common forms of gambling and betting as well as socio-demographic aspects and their characteristics among Bosniaks in BiH, in the period April-May 2019, on a sample of N = 1520 respondents, a survey was conducted in 62 municipalities in BiH. Respondents ranged from 18-77 years old, of whom 568 were women and 927 were men. While defining the sample for the purposes of this research, a two-stage stratification was performed, namely: stratification at the administrative level of Bosnia and Herzegovina and the type of settlement, respecting the principle of proportional representation of municipalities within entity/cantons. Data was collected via a structured face-to-face interviews with closed questions to which handwritten answers were given. The data collection was done through the Network of Youth and Directorate for Religious Affairs of the Riyasat of the Islamic Community in Bosnia and Herzegovina. The results suggest persisting differences in the perception of the most common forms of gambling and betting, where bingo, disposable lottery tickets/scrapers, lotto and sports betting are perceived to be the most common. The most widespread types of gambling are influenced by the socio-demographic variables such as gender, place of residence, level of education, age and monthly household income.

A Portaria n. 443, de 17 de dezembro de 2014, reconhece 2.113 espécies da flora brasileira como ameaçadas de extinção e as classifica em quatro categorias de ameaça (extintas na natureza, criticamente em perigo, em perigo, vulnerável). As unidades de conservação desempenham importante papel para a manutenção das populações dessas espécies. Porém, essas populações podem ser afetadas mediante a influência antrópica. Nesse sentido, o presente trabalho teve por objetivo avaliar os padrões estruturais e ecológicos de espécies ameaçadas de extinção ocorrentes na Floresta Nacional do Jamari, RO, após exploração madeireira. Foram marcadas, de forma aleatória, 12 parcelas de 0,5ha em duas unidades de produção anual na unidade de manejo florestal III. Nas parcelas, inventariaram-se todos os indivíduos arbóreos com DAP ≥ 10,0cm. De posse da lista da composição florística obtida da amostra, foram selecionadas para o estudo as espécies constantes no livro vermelho de espécies ameaçadas de extinção e as espécies de importância econômica que ocorrem de forma restrita, de acordo com o plano de manejo da unidade de conservação. Foram gerados os descritores fitossociológicos para as espécies, taxa de mortalidade, ingresso, crescimento, padrões de distribuição espacial e estrutura diamétrica. Observaram-se na composição florística da amostra seis espécies classificadas como vulnerável (VU) e uma espécie de importância econômica com ocorrência restrita. As espécies, na sua maioria, apresentaram ocorrência rara. Não houve alterações significativas na estrutura após exploração, porém é importante monitorar e avaliar a necessidade de tratamentos silviculturais, principalmente para as espécies que constam no plano de manejo florestal.

Blockchain (BC) and artificial intelligence (AI) are often utilized separately in energy trading systems (ETSs). However, these technologies can complement each other and reinforce their capabilities when integrated. This article provides a comprehensive review of consensus algorithms (CAs) of BC and deep reinforcement learning (DRL) in ETS. While the distributed consensus underpins the immutability of transaction records of prosumers, the deluge of data generated paves the way to use AI algorithms for forecasting and address other data analytic-related issues. Hence, the motivation to combine BC with AI to realize secure and intelligent ETS. This study explores the principles, potentials, models, active research efforts and unresolved challenges in the CA and DRL. The review shows that despite the current interest in each of these technologies, little effort has been made at jointly exploiting them in ETS due to some open issues. Therefore, new insights are actively required to harness the full potentials of CA and DRL in ETS. We propose a framework and offer some perspectives on effective BC-AI integration in ETS.

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.

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.

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.

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.

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.

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.

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;