This study evaluated the phytoremediation potential of eight native plant species on heavy metal polluted soils along the Spreča river valley (the northeast region of Bosnia and Herzegovina). Plants selected for screening were: ryegrass (Lolium perenne L.), common nettle (Urtica dioica L.), mugwort (Artemisia vulgaris L.), wild mint (Mentha arvensis L.), white clover (Trifolium repens L.), alfalfa (Medicago sativa L.), dwarf nettle (Urtica urens L.) and yarrow (Achillea millefolium L.). All aboveground parts of selected native plants and their associated soil samples were collected and analysed for total concentration of Ni, Cr, Cd, Pb, Zn and Cu. The bioaccumulation factor for each element was also calculated. The levels of Cr (90.9–171.1 mg/kg) and Ni (80.1–390.5 mg/kg) in the studied soil plots were generally higher than limits prescribed by European standards, indicating that the soils in the Spreča river valley are polluted by Cr and Ni. Among the eight screened plant species, no hyperaccumulators for toxic heavy metals Ni, Cr, Cd and Pb were identified. However, the concentrations of toxic heavy metals in the above-ground parts of Artemisia vulgaris L. and Trifolium repens L. were significantly higher than in the other studied plants, indicating that both plant species are useful for heavy metal removal.  

Corrosion of reinforcement is one of the main problems related to the durability of reinforced concrete structures. This can cause cracks and a separation of the protective layer, as well as reducing strength and structural stiffness, which can result in numerous human casualties. Visual inspection is a standard method of assessing the condition of reinforced concrete structures whose limitations, such as time, interpretability, accessibility, etc., may affect its effectiveness. Therefore, damage determination methods based on dynamic parameters are becoming more and more prominent in the assessment of damage to reinforced concrete structures. The aim of this paper is to review the literature regarding the determination of corrosion of reinforcement by methods based on dynamic parameters, and to identify future research to develop a method that would detect corrosion problems in time through a continuous system of structural health monitoring.

We present an event-triggered observer design for linear time-invariant systems, where the measured output is sent to the observer only when a triggering condition is satisfied. We proceed by emulation and we first construct a continuous-time Luenberger observer. We then propose a dynamic rule to trigger transmissions, which only depends on the plant output and an auxiliary scalar state variable. The overall system is modeled as a hybrid system, for which a jump corresponds to an output transmission. We show that the proposed event-triggered observer guarantees global practical asymptotic stability for the estimation error dynamics. Moreover, under mild boundedness conditions on the plant state and its input, we prove that there exists a uniform strictly positive minimum inter-event time between any two consecutive transmissions, guaranteeing that the system does not exhibit Zeno solutions. Finally, the proposed approach is applied to a numerical case study of a lithium-ion battery.

In the world economy, small and medium sized entreprises (SMEs) dominate in the number of overall enterprises (90-99% of all enterprises, depending on the definition used) and in economic contributions (GDP growth, productivity, job creation, innovation, level of competition, etc.) (Lundström i Stevenson, 2001). Because small businesses generate jobs, tax revenue, functional products, charitable donations, technological development, and social contributions to communities, their success and sustainability are important for social and economic development. In addition to the impact on public health, coronavirus disease 2019 (COVID-19) caused a major economic shock and the greatest consequences were felt by the small and medium-sized enterprises. Due to the crisis caused by the Covid-19 pandemic, countries and their companies are facing major problems of human and business capacities sustainability. Although governments have enacted private sector policies, there are constraints that have direct implications for economic growth potential. In this paper, we investigate the impact of COVID-19 on SMEs in Bosnia and Herzegovina, focusing on the impact of the Law for mitigation negative economic consequences, better known as the Crown-Law. We first examined how the companies performed this year compared to the previous year, and then we examined whether there were barriers to the implementation of the Crowv-Law and if so, whether they were internal or external. The results of this research point to the fact that the Crown-Law is not good enough. The measures are not in line with the strategic needs of SMEs, there is a time limit and the measures are short-term. The SME development strategy should be coordinated based on the mechanism of public-private dialogue. SMEs need business services to improve their competitiveness (information, consulting, training, accounting, legal services, advertising, marketing, technical and technological services, including testing standards and certification requirements abroad, product upgrades, etc.). The results of this research provide some information of the business results and expectations of SMEs in times of crisis, while offering insight into measures designed to aid recovery. The results highlight the role that the length of the crisis will play in determining its final impact, which policymakers should consider when considering the scale of interventions needed. On the other side, the Covid-19 pandemic has opened up new challenges, but also opportunities for SMEs, such as technological advances that create new products and transform almost every phase of the business from manufacturing to marketing, procurement and logistics. Currently, only a small part of the SME sector is able to recognize and seize these opportunities and meet the challenges.

Regression analysis is a widely used statistical technique to build a model from a set of data on two or more variables. Linear regression is based on linear correlation, and assumes that change in one variable is accompanied by a proportional change in another variable. Simple linear regression, or bivariate regression, is used for predicting the value of one variable from another variable (predictor); however, multiple linear regression, which enables us to analyse more than one predictor or variable, is more commonly used. This paper explains both simple and multiple linear regressions illustrated with an example of analysis and also discusses some common errors in presenting the results of regression, including inappropriate titles, causal language, inappropriate conclusions, and misinterpretation.

Because of the benefits for all stakeholders, corporate social responsibility (CSR) is a concept that is now becoming increasingly popular and is also an integral part of doing business in the modern world. Through applying CSR, the business sector contributes to sustainable development and companies use it as their competitive advantage because it represents not only a desirable but also a necessary form of behavior in all business activities. The aim of this paper was to investigate the level of CSR policies implementation in small, medium, and large companies that are operating in Bosnia and Herzegovina. The research instrument is adapted in order to analyze the application of particular CSR policies: workplace policies, environmental policies, marketplace policies, and community policies. The study also analyzed the possession standards related to the CSR implementation practice. For the purposes of the survey, data were collected from 90 companies operating in Bosnia and Herzegovina. The collection of primary data was conducted using the online survey technique. Based on the conducted research, it has been obtained that companies in Bosnia and Herzegovina, regardless of the size of the companies, apply certain forms of CSR policies in their operations. The analysis also found that companies in Bosnia and Herzegovina attach different importance to certain CSR policies. The results related to the selection of CSR areas, the directing of financial resources in CSR, as well as the method of decision-making, and the degree of application of norms and standards, indicate that companies in Bosnia and Herzegovina do not have a strategic approach to CSR. Economic responsibility is the primary responsibility of companies in Bosnia and Herzegovina, but following current global trends, it is necessary to work to improve other dimensions and areas of CSR. The lack of significant differences in practices of CSR companies in Bosnia and Herzegovina, with regard to their size and giving the most importance to economic aspects of CSR, indicates an insufficient understanding of the modern approach to CSR from the side of the management of the companies. Government as well as the non-profit sector should support companies in implementing CSR policies to become more environmentally and socially responsible. Nevertheless, although there is no strategic approach, it can be concluded that companies in Bosnia and Herzegovina implement certain CSR policies. Based on the research results, the paper provides an overview of CSR policies in Bosnia and Herzegovina and offers some implications guidelines for managers, as well as guidelines for future CSR research.

In this paper we deal with decision-making processes in monitoring with the use of new technological solutions. This is an area where decision-makers in monitoring face a large number of different challenges and need appropriate specific knowledge. We give an example of a method for making complex decisions. Here we propose the application of the semantic web and knowledge bases that can provide decision-makers with a quick access to the necessary knowledge in the decision-making process. To update some of the knowledge we will use the Protégé editor, an open source platform. Our goal is not to update all the necessary knowledge needed by those who make decisions in monitoring, but only to propose a new concept to their faster fullfilment and more efficient use.

Coronavirus disease 2019 (COVID-19) antiviral response in a pan-tumor immune monitoring (CAPTURE) (NCT03226886) is a prospective cohort study of COVID-19 immunity in patients with cancer. Here we evaluated 585 patients following administration of two doses of BNT162b2 or AZD1222 vaccines, administered 12 weeks apart. Seroconversion rates after two doses were 85% and 59% in patients with solid and hematological malignancies, respectively. A lower proportion of patients had detectable titers of neutralizing antibodies (NAbT) against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOC) versus wild-type (WT) SARS-CoV-2. Patients with hematological malignancies were more likely to have undetectable NAbT and had lower median NAbT than those with solid cancers against both SARS-CoV-2 WT and VOC. By comparison with individuals without cancer, patients with hematological, but not solid, malignancies had reduced neutralizing antibody (NAb) responses. Seroconversion showed poor concordance with NAbT against VOC. Previous SARS-CoV-2 infection boosted the NAb response including against VOC, and anti-CD20 treatment was associated with undetectable NAbT. Vaccine-induced T cell responses were detected in 80% of patients and were comparable between vaccines or cancer types. Our results have implications for the management of patients with cancer during the ongoing COVID-19 pandemic. Turajlic and colleagues assess longitudinal antibody and cellular immune responses against SARS-CoV-2 variants of concern in patients with cancer, following either recovery from SARS-CoV-2 infection or vaccination, in two back-to-back reports from the CAPTURE study.

Patients with cancer have higher COVID-19 morbidity and mortality. Here we present the prospective CAPTURE study, integrating longitudinal immune profiling with clinical annotation. Of 357 patients with cancer, 118 were SARS-CoV-2 positive, 94 were symptomatic and 2 died of COVID-19. In this cohort, 83% patients had S1-reactive antibodies and 82% had neutralizing antibodies against wild type SARS-CoV-2, whereas neutralizing antibody titers against the Alpha, Beta and Delta variants were substantially reduced. S1-reactive antibody levels decreased in 13% of patients, whereas neutralizing antibody titers remained stable for up to 329 days. Patients also had detectable SARS-CoV-2-specific T cells and CD4+ responses correlating with S1-reactive antibody levels, although patients with hematological malignancies had impaired immune responses that were disease and treatment specific, but presented compensatory cellular responses, further supported by clinical recovery in all but one patient. Overall, these findings advance the understanding of the nature and duration of the immune response to SARS-CoV-2 in patients with cancer. Turajlic and colleagues assess longitudinal antibody and cellular immune responses against SARS-CoV-2 variants of concern in patients with cancer, following either recovery from SARS-CoV-2 infection or vaccination, in two back-to-back reports from the CAPTURE study.

This paper presents an overview and preliminary results of the HEKTOR (Heterogeneous Autonomous Robotic System in Viticulture and Mariculture) project. An survey of applications of a heterogeneous cooperative autonomous robotic system, consisting of aerial, surface and underwater vehicles, in mariculture scenarios is presented. Target mariculture applications of the HEKTOR robotic system are autonomous fish net cage inspection (biofouling and damage detection) as well as biomass estimation. Furthermore, detailed description of the acquired autonomous vehicles is given, namely unmanned aerial vehicle and remotely operated vehicle, as well as the catamaran-shaped autonomous surface vehicle that is developed in the scope of the project.

Soil respiration is a significant contributor to the global emissions of CO2 and is governed by many soil factors. Reliable estimates of CO2 emission on different scales (e.g., field, regional level) are hard to obtain due to the expressed spatial and temporal variability of the CO2 flux. This study aims to investigate the spatial variability of CO2 flux and soil properties in soybean cropland on Fluvisols (Croatia). The field measurements and soil samples were taken in a regular sampling grid (2 × 2 m) with 44 points in total and the spatial variability was assessed using the kriging and cokriging techniques. The soil CO2 flux showed relatively high spatial heterogeneity, ranging from 0.03 mg/m2s to 0.40 mg/m2s. The soil organic matter content (SOM), soil water content (SWC), and soil temperature (ST) had the lower variability ranging from 2.09% to 2.52%, from 27.7% to 46.8%, and from 13.7 °C to 18.2 °C, respectively. The spatial dependence was high for CO2 flux and ST, moderate for SOM, and low for SWC. The incorporation of the auxiliary variables increased the precision of the estimations for CO2 flux, SOM, and SWC. Kriging was the most accurate method for the spatial prediction of ST. The SWC was associated as the most important factor of the CO2 fluxes, indicated by their significant negative correlation, and the highest increase of the prediction precision during spatial modeling. However, more robust co-variates should be incorporated in future models to further increase the precision.

One of the major application areas of highly automated vehicles is the problem of Automated Valet Parking (AVP). In this work, we analyze solutions and compare performances of RRT (rapidly exploring random tree) based approaches in the context of the AVP problem, which can also be applied in a more general low-speed autonomy context. We present comparison results using both simulation and real-life experiments on a representative parking use case. The results indicate better suitability of RRTx and RRV for utilization in typical AVP scenarios. The main contributions of this work lie in real-life experimental validation and comparisons of RRT approaches for use in low-speed autonomy.