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With the increasing use of renewable energy sources and decentralized power systems, certain challenges have emerged in meeting consumers' electrical energy demands. The intermittent nature of renewable energy generation means that it cannot always align with consumers' needs, resulting in periods of excess energy production when it is not required. To bridge this gap between production and consumption, energy storage systems are necessary. This paper defines the work of an isolated microgrid, which consists of renewable sources (wind and PV) for energy production, households with electric vehicles as consumers, and a combined storage system. This storage system is made from batteries, hydrogen storage, and a control system that defines the best use of the storage. Stored energy is utilized through fuel cells to generate electricity for consumption when renewable sources cannot meet the demand. This paper presents the principles of electrolysis and models of individual elements within such a system, as well as the definition and principle of control of the system functionality based on rules and conditions. The proposed control system aims to increase the energy storage lifecycle by deciding when and how to utilize which type of storage and define a self-sufficient microgrid based on renewable sources of production. An economic analysis of the storage part of the system was carried out in which the levelized cost of energy stored and the NPC of the storage systems are calculated. A simulation of the system's operation is conducted using one-hour measurements of wind turbines, solar panels, and household consumption in Serbia. To analyze the system's behavior, a one-week time horizon is considered. [ABSTRACT FROM AUTHOR]

Sarcoidosis is a granulomatous disease of unknown etiology that can affect almost any organ. Although the acute form can have spontaneous regression, a certain number of patients can have a chronic form, which leads to an increase in mortality and a decrease in the quality of life. Considering that the risk factors are still unknown, we wanted to compare the characteristics of patients with acute and chronic forms of sarcoidosis in Serbia in order to determine significant differences between them with hopes of contributing to everyday clinical practice. A total of 2380 patients treated in our clinic were enrolled in this study. They were separated into the following two groups: 1126 patients with acute form and 1254 patients with chronic form. They were further compared by gender, smoking status, radiological status, exposition, biomarkers for sarcoidosis, organ involvement, and other comorbidities; the distribution of patients according to regions of Serbia was also noted. Statistical significance was found in radiological findings (p < 0.001), biomarkers (calcium in 24 h urine p < 0.001; chitotriosidase p = 0.001), and the affliction of organs (p < 0.001). The differences noted in this paper could help improve our understanding of this disease. [ABSTRACT FROM AUTHOR]

In this paper, an approach is proposed for assessing the performance of experts in the group from two perspectives: (1) individual consistencies and (2) deviations from the group decision. The quality of performance of the experts is based on combining the standard and rough analytic hierarchy process (AHP) with the technique for order of preference by similarity to the ideal solution (TOPSIS). The statistical method CRITIC is used to derive weights for the TOPSIS method before the experts are assessed based on demonstrated consistency and deviations from the group. Common performance indicators, such as consistency ratio, Euclidean distance, compatibility, and Spearman's correlation coefficient, are proposed for re-grouping experts before making the final decisions. A genetic algorithm enables the efficient solving of this complex clustering problem. Implementing the described approach and method can be useful in comparable assessment frameworks. A critical aspect is conducting a thorough pre-assessment of the competence of potential decision makers, often referred to as experts who may not consistently exhibit apparent expertise. The competence of decision makers (which does not have to be associated with compatibility) is evidenced by selected consistency parameters, and in a way, a pre-assessment of their competence follows Plato's 'government of the wise' principle. In the presented study, the compatibility of individuals in the group with the collective position (group decision) is measured by parameters related to their compatibility with the group solution and statistical deviation while ranking decision elements. The proposed multi-model-based approach stands out for its resilience in conducting thorough pre-assessment of the quality (competence) of potential decision makers, often regarded as experts who might not consistently display evident expertise. The wetland study area in Serbia is used as an example application, where seven measures for reducing the risk of drought were evaluated by twelve experts coming from different sectors and with different backgrounds and expertise. [ABSTRACT FROM AUTHOR]

The global scientific community is intensively promoting energy-plus buildings. Following the leading world trends, this paper presents a new energy-plus building concept—elevational earth-sheltered buildings with three different types of horizontal overhang photovoltaic-integrated panels: wooden support columns covered with clay tiles, steel pipes as support columns covered with sheet steel, and concrete support columns with concrete coverage. In this instance, the specific multi-numerical case study building model for the city of Kragujevac (located in central Serbia with favorable climatic conditions) was performed over 7 months (from 1 October to 30 April), taking into account the soil temperature, the effects of solar shading, the performance of the heating system—a ground source heat pump—and the characteristics of the artificial and automatic lighting control system. The simulation results show that the optimal depth of a horizontal overhang (energy-plus status) depends on the occupant's habits, in addition to meteorological conditions. The presented methodology can be used for any other location, both in Europe and the world. [ABSTRACT FROM AUTHOR]

Many methods exist for solving the problem of evaluating efficiency in different processes. They are divided into two basic groups, parametric and non-parametric methods, which can have significant differences in the results. In this study, the authors consider the process of assessing the business climate depending on realized foreign investments. Due to the expected difference in efficiency assessment using different approaches, the goal of this paper is to create an optimization model of an ensemble for efficiency assessment that uses both types of methods with the aim of creating a symmetrical approach that achieves better results than each type of method individually. The proposed solution simultaneously analyzes the impact of different factors on foreign investments in order to determine the most important factors and thus enable each local government to ensure the best possible efficiency in this process. The innovative idea of this study is in the inclusion of classification and feature selection methods of machine learning to fulfill the set goal. Our research, focused on a specific case study in various cities across the Republic of Serbia, evaluated the effectiveness of that process. This study extends previous research and confirms the published results, highlighting the advantages of the newly proposed model. [ABSTRACT FROM AUTHOR]

The Balkan region is rich in limestone deposits, which have created one of the largest hotspots of subterranean biodiversity. This paper gives an overview of the diversity of subterranean terrestrial arthropods in Resava Cave in eastern Serbia. This cave is protected and has the status of a natural monument. At the same time, it is one of the most visited caves in Serbia and its surroundings. Our study comprises the results of three years of biospeleological investigations of the famous Serbian cave in combination with data from the few available literature sources on the arthropod fauna of the cave. The arthropod samples were collected both manually and with pitfall traps. A total of 107 arthropod species from the four major subphyla were registered in the cave: 66 species of Hexapoda, 27 species of Chelicerata, 11 species of Myriapoda and three species of Crustacea. For four troglobitic, 16 troglophilic and 87 trogloxenic species recorded in the cave, descriptions of their microhabitats and information on their distribution in the cave are given. Considering the medium size of Resava Cave and the lack of permanent water flow in the two main levels on the one hand, and the large number of arthropod species recorded on the other, the cave is relatively rich in hypogean terrestrial arthropod fauna compared to other caves in Serbia that have been biospeleologically studied so far. [ABSTRACT FROM AUTHOR]

Sustainable consumption of construction materials is an important segment of sustainable development goals towards reducing climate change. Since the consumption of natural aggregates raises environmental concerns, there is an increasing demand for use of recycled aggregates (RAs), as it enhances social and environmental benefits and creates a market opportunity. This paper presents the practice of using recycled construction and demolition waste (CDW) in the Belgrade city area (Serbia) as a resource. Two groups of CDW from Vinča landfill site near Belgrade are analyzed: raw material before, and RAs after, construction of a recycling facility on site. Comprehensive characterization is performed (including particle size distribution, density, water and organic pollutants content, various mechanical resistances, flakiness index, etc.) and compilation of samples analyzed and compared to show a holistic overview. The test outputs in both groups show acceptable values and meet required standards, indicating that recycled CDW generated in the Belgrade area can be used as a substitute to natural aggregates. In addition to that, the environmental and economic benefits from this use as a substitute are analyzed and discussed, proving the substantial income from sold Ras and the landscaping benefits, as well as ecological and economic benefits from energy savings. [ABSTRACT FROM AUTHOR]

Heterozygous familial hypercholesterolaemia (FH) is among the most common genetic metabolic lipid disorders characterised by elevated low-density lipoprotein cholesterol (LDL-C) levels from birth and a significantly higher risk of developing premature atherosclerotic cardiovascular disease. The majority of the current pediatric guidelines for clinical management of children and adolescents with FH does not consider the impact of genetic variations as well as characteristics of vascular phenotype as assessed by recently developed non-invasive imaging techniques. We propose a combined integrated approach of cardiovascular (CV) risk assessment and clinical management of children with FH incorporating current risk assessment profile (LDL-C levels, traditional CV risk factors and familial history) with genetic and non-invasive vascular phenotyping. Based on the existing data on vascular phenotype status, this panel recommends that all children with FH and cIMT ≥0.5 mm should receive lipid lowering therapy irrespective of the presence of CV risk factors, family history and/or LDL-C levels Those children with FH and cIMT ≥0.4 mm should be carefully monitored to initiate lipid lowering management in the most suitable time. Likewise, all genetically confirmed children with FH and LDL-C levels ≥4.1 mmol/L (160 mg/dL), should be treated with lifestyle changes and LLT irrespective of the cIMT, presence of additional RF or family history of CHD. [ABSTRACT FROM AUTHOR]

In this paper, we complete pioneering research that indicates the very low frequency (VLF) signal amplitude and phase noise reductions, and short-period wave excitations and attenuations as new potential earthquake precursors. We consider changes in the VLF signal broadcast in Italy by the ICV transmitter and recorded in Serbia that start a few tens of minutes before earthquakes. The sampling interval of the analyzed data is 0.1 s. The main objectives of this study are (1) to complete this research in the time and frequency domains during the periods of the four earthquakes analyzed in the previous studies, and (2) to define the parameters of the VLF signal amplitude and phase in both domains that should be further examined in statistical analyses of the aforementioned potential earthquake precursors. In the first part of this study, we analyze the ICV signal amplitude in the frequency domain during the period around three earthquakes that occurred in November 2010 near the considered signal propagation path. Here, we apply the Fourier transform to the relevant recorded data. In the second part, we compare characteristics of the signal amplitude and phase noise reductions in the time domain, and wave excitations and attenuations in the frequency domain. The results of these comparisons indicate the parameters that should be analyzed in subsequent studies to confirm the connection of the considered VLF signal changes with seismic activity before earthquakes, and potentially establish procedures for their detection are: (a) the start and end times of the noise reductions in the time domain and the excited/attenuated waves in the frequency domain, (b) the differences in the corresponding times, and (c) the wave periods of wave excitations of both the signal amplitude and phase. [ABSTRACT FROM AUTHOR]

This paper presents a novel count time-series model, named integer-valued Split-BREAK process of the first order, abbr. INSB(1) model. This process is examined in terms of its basic stochastic properties, such as stationarity, mean, variance and correlation structure. In addition, the marginal distribution, over-dispersion and zero-inflation properties of the INSB(1) process are also examined. To estimate the unknown parameters of the INSB(1) process, an estimation procedure based on probability generating functions (PGFs) is proposed. For the obtained estimators, their asymptotic properties, as well as the appropriate simulation study, are examined. Finally, the INSB(1) process is applied in the dynamic analysis of some real-world series, namely, the numbers of serious traffic accidents in Serbia and forest fires in Greece. [ABSTRACT FROM AUTHOR]

Retail supply chains are key on any market. Their significance has long been recognized in the literature and in practice. Various factors such as pandemics, economic crises, wars, and natural disasters have further increased interest in this area. However, the most recent research has focused more on resilience, sustainability, energy consumption, and a circular economy, while the efficiency of logistics processes has been almost completely overlooked. Logistics process efficiency in retail supply chain is a fundamental principle without which all mentioned performances cannot have desired values. This gap is precisely the main motivation of this research. In this paper, research models in literature are presented which can be used, with some modifications, to measure the efficiency of the retail supply chain. The models were based on the data envelopment analysis (DEA) approach. Four main groups were identified: standard DEA models, efficiency decomposition models, network models, and game-theory-based models. In the second part of the paper, various approaches were tested on a real example of a trading company operating in Serbia. Seven supply chains were observed, each consisting of a distribution center (DC) and retail store (RS). Variables used were the number of pallet places, logistics costs, number of deliveries, accuracy of deliveries, and turnover. The results showed the advantages and disadvantages of different approaches in real examples. The main contributions of this paper lie in unique approaches to measuring the efficiency of the retail supply chain. The paper creates an excellent foundation for future research and measurements on real systems, which is equally useful for researchers and industry experts. [ABSTRACT FROM AUTHOR]

This paper presents a model for determining fuzzy evaluations of partial indicators of the availability of continuous systems at coal open pits using a neuro-fuzzy inference system. The system itself is a combination of fuzzy logic and artificial neural networks. The system availability is divided into partial indicators. By combining the fuzzy logic and artificial neural networks, a model is obtained that has the ability to learn and uses expert judgment for that learning. This paper deals with the ECC system (bucket wheel excavator-conveyor-crushing plant) of the open pit Drmno-Kostolac, which operates within the Electric Power Company of Serbia. The advantage of a model of this type is that it does not rely on the historical experiences of experts and usual predicted values for the fuzzy evaluation of partial indicators, which are based on the assumption that similar systems affect availability in a similar way. The fuzzy evaluation of partial indicators is based on historical data for the specific system for which the model was created. As such, it can more accurately predict continuous systems availability on the basis of expert evaluations in the appropriate time period. Another advantage of this model is that the availability is estimated on a quarterly basis, which gives a more accurate view because it uses a smaller time period with more similar characteristics and, thus, includes certain external influences which are related to the quarterly meteorological conditions. [ABSTRACT FROM AUTHOR]

Hyperinsulinemia is a condition characterized by excessively high levels of insulin in the bloodstream. It can exist for many years without any symptomatology. The research presented in this paper was conducted from 2019 to 2022 in cooperation with a health center in Serbia as a large cross-sectional observational study of adolescents of both genders using datasets collected from the field. Previously used analytical approaches of integrated and relevant clinical, hematological, biochemical, and other variables could not identify potential risk factors for developing hyperinsulinemia. This paper aims to present several different models using machine learning (ML) algorithms such as naive Bayes, decision tree, and random forest and compare them with a new methodology constructed based on artificial neural networks using Taguchi's orthogonal vector plans (ANN-L), a special extraction of Latin squares. Furthermore, the experimental part of this study showed that ANN-L models achieved an accuracy of 99.5% with less than seven iterations performed. Furthermore, the study provides valuable insights into the share of each risk factor contributing to the occurrence of hyperinsulinemia in adolescents, which is crucial for more precise and straightforward medical diagnoses. Preventing the risk of hyperinsulinemia in this age group is crucial for the well-being of the adolescents and society as a whole. [ABSTRACT FROM AUTHOR]

The validity of the results obtained within different permanent GNSS reference station networks (GNSS Network) must be periodically controlled using criteria that are generally known from statistical analyzes or prescribed by International Standards. Procedures for evaluating the uncertainty of measurements are defined in accordance with the purpose of the GNSS Network. The authors of this paper want to point out the need to establish requirements for periodical and systematical control of GNSS coordinates within the same permanent GNSS Network and control of GNSS coordinates between different permanent GNSS Networks measured on the same/unique point on the ground. This paper presents control procedures for three permanent GNSS reference station Networks established and operating in the Republic of Serbia. Special attention is on the analysis of data consistency within one permanent GNSS Network and the mutual consistency of GNSS data between different networks. The paper aims to promote reliance on the different GNSS Networks and contains suggestions on how GNSS Networks may prove that they are performing competently and that they can provide valid results for field measurements. Particularly highlighted is the need to plan and implement measures related to increasing the effectiveness of the GNSS system, achieving improved results, and preventing negative effects while performing field measurements. The paper presents the results for comparison, selected according to the rules for creating a Digital Cadastral Map features, i.e., points, lines, and polygon. The results for comparing point features are the GNSS coordinates. The results for comparing line features are the lengths of the line, i.e., distances, and the results for comparing polygon features are the areas of the polygons. [ABSTRACT FROM AUTHOR]

Simple Summary: The domestic donkey is an endangered equid species. There are 199 donkeys in the Special Nature Reserve "Zasavica", out of the estimated 1000 that are assumed to exist in Serbia. The aim of this study was to assess the exposure of donkeys in the Special Nature Reserve "Zasavica" to some of the most significant viral pathogens and establish the seroprevalence of equine infectious anemia, African horse sickness, equine herpesvirus type 1 infection, equine influenza and equine viral arteritis. In tested blood samples, antibodies against equine herpesvirus type 1 and equine influenza virus (subtype H3N8) were found, while all samples tested negative for African horse sickness virus, equine infectious anemia virus and equine arteritis virus. To our knowledge, this study represents the first results of detection of these pathogens in the population of domestic donkeys in Serbia. The paper presents the findings of specific antibodies in the blood sera of donkeys against the following viruses: equine infectious anemia virus (EIAV), African horse sickness virus (AHSV), equine herpesvirus type 1 (EHV-1), equine influenza virus subtype H3N8 (EIV) and equine arteritis virus (EAV). The analyses were conducted during the year 2022. From a total of 199 donkeys bred in "Zasavica", blood was sampled from 53 animals (2 male donkeys and 51 female donkeys), aged 3 to 10 years. Specific antibodies against EIAV were not detected in any of the tested animals using the agar-gel immunodiffusion (AGID) assay. No specific antibodies against AHSV, tested by enzyme-linked immunosorbent assay (ELISA), or antibodies against EAV, tested by the virus neutralization test (VNT) and ELISA were detected in any of these animals. A positive serological result for EHV-1 was determined by the VNT in all animals, with antibody titer values ranging from 1:2 to 1:128, while a very low antibody titer value for EIV (subtype H3N8) of 1:16 was determined in 18 donkeys using the hemagglutination inhibition test (HI test). [ABSTRACT FROM AUTHOR]

In previous years, significant attempts have been made to enhance computer-aided diagnosis and prediction applications. This paper presents the results obtained using different machine learning (ML) algorithms and a special type of a neural network map to uncover previously unknown comorbidities associated with chronic diseases, allowing for fast, accurate, and precise predictions. Furthermore, we are presenting a comparative study on different artificial intelligence (AI) tools like the Kohonen self-organizing map (SOM) neural network, random forest, and decision tree for predicting 17 different chronic non-communicable diseases such as asthma, chronic lung diseases, myocardial infarction, coronary heart disease, hypertension, stroke, arthrosis, lower back diseases, cervical spine diseases, diabetes mellitus, allergies, liver cirrhosis, urinary tract diseases, kidney diseases, depression, high cholesterol, and cancer. The research was developed as an observational cross-sectional study through the support of the European Union project, with the data collected from the largest Institute of Public Health "Dr. Milan Jovanovic Batut" in Serbia. The study found that hypertension is the most prevalent disease in Sumadija and western Serbia region, affecting 9.8% of the population, and it is particularly prominent in the age group of 65 to 74 years, with a prevalence rate of 33.2%. The use of Random Forest algorithms can also aid in identifying comorbidities associated with hypertension, with the highest number of comorbidities established as 11. These findings highlight the potential for ML algorithms to provide accurate and personalized diagnoses, identify risk factors and interventions, and ultimately improve patient outcomes while reducing healthcare costs. Moreover, they will be utilized to develop targeted public health interventions and policies for future healthcare frameworks to reduce the burden of chronic diseases in Serbia. [ABSTRACT FROM AUTHOR]

The rapid evolution of wireless communication technologies (such as fifth-generation (5G) cellular networks) in the last years has allowed connecting different objects (from wearable electronics to vehicles) and people through communication networks, and at the same time, has led to widespread deployment of base stations. Along with this growth, questions about the potential adverse effects on human health due to electromagnetic fields (EMFs) from base station antennas have also been raised. In this paper, we focus on the assessment of EMFs in automobiles during short (between cities) and long (between countries) trips on several European roads. Comprehensive measurement campaigns were carried out in several European countries: Austria, Bulgaria, Croatia, Hungary, Italy, Slovenia, and the Republic of Serbia. The results show that the median total electric field is 0.23–0.24 V/m in Bulgaria, Croatia, Hungary, Italy, and the Republic of Serbia. In Austria and Slovenia, the median is 0.28–0.31 V/m. Austria demonstrated the highest value for the total electric field, at 17.4 V/m. [ABSTRACT FROM AUTHOR]

Ironwort (Sideritis montana L.), mountain germander (Teucrium montanum L.), wall germander (Teucrium chamaedrys L.), and horehound (Marrubium peregrinum L.) are species widely distributed across Europe and are also found in North Africa and West Asia. Because of their wide distribution they express significant chemical diversity. For generations, these plants have been used as medical herbs for treating different aliments. The aim of this paper is to analyze volatile compounds of four selected species that belong to the subfamily Lamioideae, family Lamiaceae, and inspect scientifically proven biological activities and potential uses in modern phytotherapy in relation to traditional medicine. Therefore, in this research, we analyze the volatile compounds from this plants, obtained in laboratory by a Clevenger-type apparatus, followed by liquid–liquid extraction with hexane as the solvent. The identification of volatile compounds is conducted by GC-FID and GC-MS. Although these plants are poor in essential oil, the most abundant class of volatile components are mainly sesquiterpenes: germacrene D (22.6%) in ironwort, 7-epi-trans-sesquisabinene hydrate (15.8%) in mountain germander, germacrene D (31.8%) and trans-caryophyllene (19.7%) in wall germander, and trans-caryophyllene (32.4%) and trans-thujone (25.1%) in horehound. Furthermore, many studies show that, in addition to the essential oil, these plants contain phenols, flavonoids, diterpenes and diterpenoids, iridoids and their glycosides, coumarins, terpenes, and sterols, among other active compounds, which affect biological activities. The other goal of this study is to review the literature that describes the traditional use of these plants in folk medicine in regions where they grow spontaneously and compare them with scientifically confirmed activities. Therefore, a bibliographic search is conducted on Science Direct, PubMed, and Google Scholar to gather information related to the topic and recommend potential applications in modern phytotherapy. In conclusion, we can say that selected plants could be used as natural agents for promoting health, as a source of raw material in the food industry, and as supplements, as well as in the pharmaceutical industry for developing plant-based remedies for prevention and treatment of many diseases, especially cancer. [ABSTRACT FROM AUTHOR]

This paper introduces a parameter-free clustering-based approach to detecting critical traffic road segments in urban areas, i.e., road segments of spatially prolonged and high traffic accident risk. In addition, it proposes a novel domain-specific criterion for evaluating the clustering results, which promotes the stability of the clustering results through time and inter-period accident spatial collocation, and penalizes the size of the selected clusters. To illustrate the proposed approach, it is applied to data on traffic accidents with injuries or death that occurred in three of the largest cities of Serbia over the three-year period. [ABSTRACT FROM AUTHOR]

The 2019 European Open Data Directive identifies geospatial data as data that could have a major impact on human activities (high-value data, HVD) and advocates its provision as open data (OD), i.e., without barriers to access and re-use. Although Croatia has implemented OD policies to support the provision of open data, many geospatial data are still not available, or if available, their level of openness ranks Croatia lower than Slovenia and Serbia on some ranking lists. Benchmarking tools have proven to be a powerful tool in identifying barriers in OD. This paper, therefore, benchmarks the level of openness and provision of geospatial HVD in Croatia, Slovenia and Serbia, using the extended and modified Global Open Data Index methodology (GODI Plus). It is expected that this will provide an answer to the status of OD policies and government engagement in OD in Croatia and identify good OD practices among the three countries analyzed. Furthermore, the results will be a baseline benchmark for future HVD analyses. The results reveal low data openness for Croatia and Serbia, high data openness for Slovenia, and a low level of government engagement in all three proposed countries. [ABSTRACT FROM AUTHOR]

Open pit coal mining affects surrounding populated areas, resulting in terrain surface deformation. Surface deformation should be monitored as often as possible to control deformations and prevent potential incidents. This paper analyzes time series deformation estimated from the Sentinel-1 satellite images using the Persistent Scatterer Interferometry method to monitor subsidence rates caused by open pit mining activities. It is possible to measure deformations using classical geodetic methods, but those are rarely used in practice because they are time-consuming and expensive for application in large areas. Using the open access radar images from the Sentinel-1 mission, 513 images from the repository were downloaded between October 2016 and the end of December 2020. We present the processing steps in detail in order to establish a workflow for the automated processing of vertical displacement estimation using open source tools; a total of 402 images were processed: 215 images belonged to the ascending satellite orbit, 187 images belonged to the descending orbit, and 111 images were rejected because of adverse weather conditions. The PS InSAR technique has never been used for the mines of the Republic of Serbia or for land surveying practices related to deformation monitoring. The results based on the Sentinel-1 images were compared with results from geodetic leveling and with neotectonic uplift trends. The trend lines of vertical displacement obtained from PS and corresponding leveling are significantly similar (a Pearson correlation of 85% with a p-value of 0.015). The final evaluation reported results of vertical displacements at the leveling benchmark of −3.4 mm/year with the PS InSAR method and −2.7 mm/year with the leveling method. A comparison of the PS vertical displacements with a settlement model fits reasonably, suggesting that the measurements are valid. As four years of PS time series data is insufficient to establish undisputable conclusions on the neotectonics uplift, extending the time series (covering at least a decade) implies that this approach will become attractive in future neotectonic uplift trend estimations. This study illustrates not only the ability of Sentinel-1 data in mapping vertical deformations, but the obtained results could also be used for geohazard monitoring and land monitoring in general for the area of interest. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to develop possible scenarios for reducing carbon dioxide (CO2) emissions in Serbia by switching from coal-fired to natural gas-fired electricity generation by the end of 2050. Accordingly, the challenges are to establish scenarios and identify measures that are best suited to all Western Balkan countries. In particular, this paper proposes a number of energy mix scenarios that offer some options for reducing CO2 emissions while maintaining an economic way of consumption. Projections of these reduction scenarios are obtained by combining the Kaya identity with the emission factor method. A simplified methodology based on sensitivity analysis is used as a source of data to estimate the nonstatistical uncertainty limits for the projections of the reduction scenarios. The sensitivity analysis is carried out using historical data from Serbia for the period from 1990 to 2019. In addition, a direct verification of the proposed methodology is performed based on historical data for 2020. The developed scenarios 1 and 2 show that CO2 emissions could be reduced by 10.94% and up to 74.44% from baseline in 1990, respectively. The obtained results are also contrasted with the data available for some other countries with similar experiences. Finally, it is found that the developed scenarios are achievable only at significantly decreased levels of coal-fired electricity generation. [ABSTRACT FROM AUTHOR]

For the interpretation of digital entrepreneurship, the context in which the entrepreneurial process takes place plays an important role. The context emphasizes that to analyze the entrepreneurial process, it is no longer sufficient to analyze only the entrepreneurial actions, but also the environment in which the entrepreneurial process takes place. The aim of this paper is to analyze the part of the context in which the entrepreneurial process takes place in the Republic of Serbia, Montenegro, Bosnia and Herzegovina and Hungary. Following the methodology of The European Index of Digital Entrepreneurship Systems (EIDES), the paper analyzes the system framework conditions, in the scale-up phase of the enterprise life cycle, in the digital dimension. The aim of this paper is a comparative analysis of three countries in transition with one country belonging to the European Union. After identifying the most developed factors, as well as the most underdeveloped factors, the paper discusses the conditions that influenced the results achieved in the four previously listed countries and why their improvement is important. [ABSTRACT FROM AUTHOR]

Gastronomy, as a part of cultural heritage, has exceptional potential in tourism, and its key representatives and conservationists/guardians are hospitality facilities that provide food services. Vojvodina (the Republic of Serbia) is a region inhabited by more than 30 ethnic minorities that have nurtured their cultural heritage and have been incorporating it into gastronomy for many years. The subject of this paper is the gastronomy of ethnic groups in Vojvodina and its significance for tourism development from the point of view of hospitality workers as important actors in the sustainability of heritage. One of the motives behind this study is the twelfth UN sustainable development goal (SDGs) defined in 2015, which refers to providing sustainable forms of consumption and production and which emphasizes the development and application of tools for monitoring the impact that sustainable development has on tourism that promotes local culture and products (12b). The aim of this study was to obtain data on the preservation of heritage, that is, on authenticity within the region/area and ethnic groups, and then to perform a valorization of dishes and define steps on how to make gastronomic heritage a more visible tourist attraction, from the perspective of sustainability. Our survey included a sample of 508 respondents, all employees in the hospitality industry. The obtained results were statistically processed. The research showed that the Južnabačka district has the greatest importance in tourism from the aspect of the implementation, preservation, and sustainability of gastronomic heritage in tourism. Among the ethnic groups, the Vojvodina Hungarians place the greatest importance on the preservation of gastronomy, which includes dishes such as goulash and uses ingredients such as river fish. The research led to the conclusion that those in the hospitality industry are of the opinion that gastronomic heritage should be promoted through activities such as tourist exposure, marketing activities, and promoting the diversity of authentic food offers in catering facilities. [ABSTRACT FROM AUTHOR]

Inflammation is a natural protective response of the human body to a variety of hostile agents and noxious stimuli. Standard anti-inflammatory therapy includes drugs whose usage is associated with a number of side effects. Since ancient times, natural compounds have been used for the treatment of inflammation. Traditionally, the use of medicinal plants is considered safe, inexpensive, and widely acceptable. In Serbia, traditional medicine, based on the strong belief in the power of medicinal herbs, is the widespread form of treatment. This is supported by the fact that Serbia is classified as one of 158 world centers of biodiversity, which confirms that this country is a treasure of medicinal herbs. Some of the most used herbs for the treatment of inflammations of various causes in Serbian tradition are yarrow, common agrimony, couch grass, onion, garlic, marshmallow, common birch, calendula, liquorice, walnut, St. John's wort, chamomile, peppermint, white willow, sage, and many others. The biological activity and anti-inflammatory effect of selected plants are attributed to different groups of secondary biomolecules such as flavonoids, phenolic acids, sterols, terpenoids, sesquiterpenes, and tannins. This paper provides an overview of plants with traditional anti-inflammatory use in Serbia with reference to available studies that examined this effect. Plants used in traditional medicine could be a powerful source for the development of new remedies. Therefore intensive research on the bioactive potential of medicinal plants in each region should be the focus of scientists around the world. [ABSTRACT FROM AUTHOR]

An optimization model which determines optimal spatial allocation of wind (WPPs) and PV power plants (PVPPs) for an energy independent power system is developed in this paper. Complementarity of the natural generation profiles of WPPs and PVPPs, as well as differences between generation profiles of WPPs and PVPPs located in different regions, gives us opportunity to optimize the generation capacity structure and spatial allocation of renewable energy sources (RES) in order to satisfy the energy needs while alleviating the total flexibility requirements in the power system. The optimization model is based on least squared error minimization under constraints where the error represents the difference between total wind and solar generation and the referent consumption profile. This model leverages between total energy and total power requirements that flexibility resources in the considered power system need to provide in the sense that the total balancing energy minimization implicitly bounds the power imbalances over the considered time period. Bounding the power imbalances is important for minimizing investment costs for additional flexibility resources. The optimization constraints bound the installed power plant capacity in each region according to the estimated technically available area and force the total energy production to equal the targeted energy needs. The proposed methodology is demonstrated through the example of long-term RES planning development for complete decarbonization of electric energy generation in Serbia. These results could be used as a foundation for the development of the national energy strategy by serving as a guidance for defining capacity targets for regional capacity auctions in order to direct the investments in wind and solar power plants and achieve transition to dominantly renewable electricity production. [ABSTRACT FROM AUTHOR]

Simple Summary: This research aims to evaluate the possibility of introducing modern methods for the selection of dairy cattle in Serbia. A total of eight models were applied to estimate the genetic values and their accuracy based on the data of milk yield, milk fat yield, milk fat content, milk protein yield and milk protein content. A total of 6041 animals were included, and of them, 2565 had data for milk production traits. Two cases were studied, the first when only the first lactations were observed, and the second when all lactations were observed using a repeatability model. For the formation of a genomic relationship matrix, 58 K SNP data were used for a total of 1491 cows. Higher accuracy was obtained using models with repeated measurements. Multivariate analysis with repeated measurements showed the best results. An ssGBLUP methodology showed promising results. Expanding the genotyped population is necessary to achieve better accuracy. In this paper, six univariate and two multivariate best linear unbiased prediction (BLUP) models were tested for the estimation of breeding values (BV) in Holstein Friesian cattle in Serbia. Two univariate models were formed using the numerator relationship matrix (NRM), four using the genomic relationship matrix (GRM). Multivariate models contained only an NRM. Two cases were studied, the first when only first lactations were observed, and the second when all lactations were observed using a repeatability model. A total of 6041 animals were included, and of them, 2565 had data on milk yield (MY), milk fat yield (FY), milk fat content (FC), milk protein yield (PY) and milk protein content (PC). Finally, out of those 2565 cows, 1491 were genotyped. A higher accuracy of BV was obtained when using a combination of NRM and GRM compared to NRM alone in univariate analysis, while multivariate analysis with repeated measures gave the highest accuracy with all 6041 animals. When only genotyped animals were observed, the highest accuracy of the estimated BV was calculated by the ssGBLUPp model, and the lowest by the univariate BLUP model. In conclusion, the current breeding programs in Serbia should be changed to use multivariate analysis with repeated measurements until the optimal size of the reference population, which must include genotyping data on both bulls and cows, is reached. [ABSTRACT FROM AUTHOR]

The main objective of this research was to investigate the relationship between flexible working arrangements (FWA) and turnover intentions (TI), as well as the mediation effect of employee engagement (EE) in the relationship between flexible working arrangements and turnover intentions. The main research question is: what is the nature of the effect of flexible working arrangements on employees' turnover intention, and the role of employee engagement in this relationship? The methodology of the paper consists of theoretical (literature review) and empirical parts (field research). The empirical research was performed on a sample of 514 highly educated employees from service sector organizations that operate in the Republic of Serbia. Sample collection lasted from January to October 2022, via Google Forms. The proposed relationships were tested by using the PLS-SEM method, with the application of the SmartPLS software. The main findings of the research are that there are direct positive effects of flexible work arrangements and employee engagement on turnover intentions, and that employee engagement has an indirect effect on the relationship between flexible work arrangements and turnover intentions. A partial mediation was found. Employees who are offered flexible work arrangements decrease their turnover intentions when they are more engaged at work. [ABSTRACT FROM AUTHOR]

This paper presents an empirically study on the attitudes of female university students at all Teacher Education Faculties in Serbia (TEFS). For the purposes of this study, a survey was prepared to be completed by students online, and virtual exponential non-discriminative snowball sampling was applied. The independent variables were religion, major subjects, year of study, age, the completion of secondary schooling, whether an optional subject was studied during previous schooling, and whether female students were employed. The dependent variables were the respondents' attitudes to religious instruction and civic education. The sample included 372 students from all TEFS, and the research was conducted in the period from 15 May to 8 June 2022. The data were processed with the aid of nonparametric statistics. The results showed that religion did not contribute to differences in students' attitudes and opinions regarding the method of performing religious instruction and civic education, but that some other factors contributed to it, such as previous experience with these subjects and whether the respondent had completed secondary schooling. These results may be interpreted in the light of the weaknesses of the confessional model, that is, the lack of knowledge regarding the basic paradigms of other confessions, which is of great importance for countries such as Serbia which have numerous national minorities and religious communities. [ABSTRACT FROM AUTHOR]

The opportunity for large amounts of open-for-public and available data is one of the main drivers of the development of an information society at the beginning of the 21st century. In this sense, acquiring knowledge from these data using different methods of machine learning is a prerequisite for solving complex problems in many spheres of human activity, starting from medicine to education and the economy, including traffic as today's important economic branch. Having this in mind, this paper deals with the prediction of the risk of traffic incidents using both historical and real-time data for different atmospheric factors. The main goal is to construct an ensemble model based on the use of several machine learning algorithms which has better characteristics of prediction than any of those installed when individually applied. In global, a case-proposed model could be a multi-agent system, but in a considered case study, a two-agent system is used so that one agent solves the prediction task by learning from the historical data, and the other agent uses the real time data. The authors evaluated the obtained model based on a case study and data for the city of Niš from the Republic of Serbia and also described its implementation as a practical web citizen application. [ABSTRACT FROM AUTHOR]

The paper presents and analyzes the state-of-the-art machine learning techniques that can be applied as a decision-support system in the estimation of resource consumption in the construction of reinforced concrete and prestressed concrete road bridges. The formed database on the consumption of concrete in the construction of bridges, along with their project characteristics, was the basis for the formation of the assessment model. The models were built using information from 181 reinforced concrete bridges in the eastern and southern branches of Corridor X in Serbia, with a value of more than 100 million euros. The application of artificial neural network models (ANNs), models based on regression trees (RTs), models based on support vector machines (SVM), and Gaussian processes regression (GPR) were analyzed. The accuracy of each model is determined by multi-criterion evaluation against four accuracy criteria root mean square error (RMSE), mean absolute error (MAE), Pearson's linear correlation coefficient (R), and mean absolute percentage error (MAPE). According to all established criteria, the model based on GPR demonstrated the greatest accuracy in calculating the concrete consumption of bridges. According to the study, using automatic relevance determination (ARD) covariance functions results in the most accurate and optimal models and also makes it possible to see how important each input variable is to the model's accuracy. [ABSTRACT FROM AUTHOR]

Climate factors have an impact on plant life cycle, yield, productivity, economy and profitability of agricultural production. There are not a lot of studies on understanding of influence of climate factors variation on maize yield in agro-ecological conditions of Serbia. The aim of this paper is analysis of variation of climatic factors over a long-time period, as well as assessment of impact of the examined climate parameters on maize yield in two localities in the Republic of Serbia. For the analysis of climatic factors (temperature, precipitation, sunshine, humidity) in the region of Central Serbia and Vojvodina, the data of meteorological stations Kragujevac and Sombor during two thirty-year periods (1961–1990 and 1991–2020) were used. In order to determine the existence and strength of the relationship between the observed climatic factors and maize yield, a correlation analysis of these indicators for the period 2005–2020 years, was performed. In the period 1991–2020, the average values of temperature were annually increased for 0.046 °C in Kragujevac and for 0.05 °C in Sombor, and in the same period the average value of sunshine on an annual level was increased for 1.3 h in Kragujevac and for 5.01 h in Sombor, 2020 in comparison to average values in period of 1961–1990. The humidity was decline annually for 1.3 in Kragujevac and for 3.4 in Sombor in period 1991–2020 in comparison to average humidity in period of 1961–1990. The results of the correlation analysis showed that the maize yield was significantly lower in the years with expressed high temperatures and precipitation deficit. Based on these studies, established effect of climate change on maize yield and that this demand developing adaptation agricultural practice through creating maize hybrids and varieties with greater adaptability and improvement of agrotechnic measure. [ABSTRACT FROM AUTHOR]

The financial systems of the West Balkan countries are mainly based on banks. For an efficient financial system, it is of immense importance that the banks operating in it do so with high levels of efficiency. Furthermore, efficiency is needed when it comes to involving banking institutions in the financial flows in order to maintain sustainability of the financial construction. The aim of this paper is to determine whether there is a difference in efficiency between the considered countries and thus to show which changes the decisionmakers have to make in order to improve the efficiency of their banking systems. We analyze data from the revised financial statements of all banks operating in Albania, Bosnia and Herzegovina, Montenegro, North Macedonia and Serbia for the period from 2015 to 2019, using loans and investment as input variables and interest income, non-interest income and net income as output variables. The results obtained from the output-oriented DEA model with a variable return to scale have shown higher efficiency levels in North Macedonia, Bosnia and Herzegovina and Montenegro, while Serbia and Albania show lower efficiency. The individual analyses have shown that in order to improve efficiency levels, improvement is needed more in the correction of the investment amounts then in loan placements. [ABSTRACT FROM AUTHOR]

This research paper defines service value as the multidimensional overall user evaluation of the efficiency of a particular service in the context of the benefits they provide, measured through satisfaction and loyalty of consumers and, in this particular case, of entrepreneurs. By distributing a questionnaire comprising a set of descriptive questions and a five-point Likert scale, the authors managed to collect 281 valid responses from entrepreneurs in Serbia. Structural equation modelling was used to evaluate the impact of service value factors on satisfaction and, ultimately, loyalty. Following a confirmatory factor analysis, the authors found that quality, personal relationship, expediency, leadership, and perceived costs significantly impacted the bank users' satisfaction, which is ultimately converted into lasting loyalty towards the dominant bank used in a daily routine or running personal business. Even though new forms of business are more and more focused on the individual and subjective attitude towards banking service, a large number of determinants still need to be identified. In this research, all hypotheses about the influencing factors were confirmed. [ABSTRACT FROM AUTHOR]

This paper describes one of the non-linear (and non-stationary) stochastic models, the GSB (Gaussian, or Generalized, Split-BREAK) process, which is used in the analysis of time series with pronounced and accentuated fluctuations. In the beginning, the stochastic structure of the GSB process and its important distributional and asymptotic properties are given. To that end, a method based on characteristic functions (CFs) was used. Various procedures for the estimation of model parameters, asymptotic properties, and numerical simulations of the obtained estimators are also investigated. Finally, as an illustration of the practical application of the GSB process, an analysis is presented of the dynamics and stochastic distribution of the infected and immunized population in relation to the disease COVID-19 in the territory of the Republic of Serbia. [ABSTRACT FROM AUTHOR]

Fungal diversity is one of the most important indicators of overall forest biodiversity and its health. However, scarce information exists on the state of macrofungal communities of mountain forests in Serbia, making it one of the countries with the least-published mycological data in the Mediterranean and Balkan region of Europe. This paper presents the results of the first comprehensive, long-term study of macrofungal communities in some of the most important mountain forest ecosystems in Serbia (Tara, Kopaonik and Vidlič). In the course of three consecutive years, the sampling of five permanent experimental plots resulted in 245 species of macrofungi, classified into three functional groups (terricolous saprothrophs, lignicolous, and mycorrhizal fungi). Special attention was given to protected and indicator species, which point out the great value of studied forest habitats and the importance of their conservation. It was found that precipitation, habitat humidity, and temperature significantly influence the occurrence and distribution, primarily of mycorrhizal and lignicolous group of fungi. Thus, the continuation of long-term monitoring is crucial in order to more precisely determine which groups/species of macrofungi would, and to what extent they would, adapt to a rapidly changing climate. [ABSTRACT FROM AUTHOR]

Technological development has brought humanity to the era of an information society in which information is the main driver. This implies existing large amounts of data from which knowledge should be extracted. In this sense, artificial intelligence represents a trend applied in many areas of human activity. This paper is focused on ensemble modeling based on the use of several machine learning algorithms, which enable the prediction of the risk to human health due to the state of atmospheric factors. The model uses two multi-agents as a technique of emergent intelligence to make a collective decision. The first agent makes a partial decision on the prediction task by learning from the available historical data. In contrast, the second agent does the same from the data available in real-time. The proposed prediction model was evaluated in a case study related to the city of Niš, Republic of Serbia, and showed a better result than each algorithm separately. It represents a reasonable basis for further upgrading both in the scope of different groups of the atmospheric parameters and in the methodological sense, as well as technically through implementation in a practical web citizen service. [ABSTRACT FROM AUTHOR]

Strategic Environmental Assessment (SEA) is one of the most important instruments for directing the strategic planning process toward the sustainable development goals in various areas of human activity. This also applies to the field of waste management. By applying SEA in waste management planning, it is possible to see the benefits and consequences of the proposed changes in space that will occur during the implementation of strategic planning concepts and based on that make appropriate decisions respecting the capacity of the space where the planned activities are implemented. The paper presents the application of SEA for the National Waste Management Program with all its spatial, organizational, energy, environmental, and other solutions, and the way they are included in the specific method of multicriteria evaluation in SEA. The specificity of the methodological approach indicates the need for equal consideration of environmental and socio-economic aspects of development and a clear presentation of the results obtained in order to make optimal decisions in waste management planning at the national level. The National Waste Management Program in Serbia, which legislation in the field of environmental protection and waste management is harmonized with EU legislation and directives, was chosen for the case study. The obtained results indicate the importance of an interdisciplinary approach in the evaluation of strategic solutions in the field of waste management, which is achieved by specific choice of environmental and socio-economic SEA goals and indicators as a basis for valorization of the proposed concept of waste management. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a chemical composition and quantities of mining and processing waste landfills material developed during historical mining and smelting. After detailed inspection, it was found that approximately 2.6 gigatons of the waste had been deposited at 1650 sites, covering almost 65 km2. More than half of this material, 55%, is characterized as conventional mining waste, 37% belongs to the processing tailings, and 8% to metallurgical waste. Most of these tailing sites are unclaimed, presenting a source of contamination for nearby communities. According to the literature data collected and additional chemical analyses, in accordance with zero-waste philosophy, about 42 promising locations (c. 270 million tons) could be selected, where various advanced eco-innovative methods of recovery could possibly apply. The areas with the highest prospective recovery are Serbia and Kosovo. In accordance with the metal prices achieved in March 2022, it is estimated that the recovery of tailings could bring up to 18,100 million USD, which is much more compared to the prices of March 2020—10,600 million USD—when the commodity market was governed by the COVID-19 restrictions. In addition to the commercial value of the metals, the environmental aspect should not be forgotten after the application of reuse and recycling concepts. [ABSTRACT FROM AUTHOR]

In current practice, the remediation of landslides has shown that the biggest problem is the increase in the number of works, and therefore the price of the works. This is due to several factors, including characteristic of the soil, such as the collapse (collapse) of the surrounding ground around the main slide during landslide remediation. Unless these soil erosion effects are taken into account, recovery costs will overrun, which can jeopardize the planned budget. This paper presents a multi-criteria optimization of landslide remediation using the PROMETHEE method and determines the optional number of walls for the additional soil erosion. In a case study on examples of real landslides in the Republic of Serbia, the application of the method is presented and appropriate conclusions are drawn. [ABSTRACT FROM AUTHOR]

In this paper we will present an analysis of the current state of practice in project management in the energy sector in Serbia, wherein the mentioned analysis is based on the results of original research on project maturity in this field. This paper includes a literature review about the current situation in the Serbian energy system, the basic concepts and significance of the research of project maturity in organizations, the basic concepts related to the projects in the energy sector, as well as the general state of project management in Serbia. After this, an analysis of the results of the research is presented, which is structured according to the elements of project maturity included in the research, and the methods of analysis. The analysis of the results includes a general analysis as well as statistical and cluster analysis. At the end, based on the analysis results, recommendations for improvements in the area of project management in the energy sector in Serbia are presented. [ABSTRACT FROM AUTHOR]

Increased concentrations of heavy metals in ash can adversely affect themicrobiological and pedogenetic processes in soil. The aim of this paper is to determine the impact of ash from unburned coal generated in the Kostolac and Gacko coal basins on the quality of soil in the surrounding environment. The investigation included the surface soil layer that was sampled and tested during 2016 and 2017. A total of 30 samples of Kostolac soil and 9 samples of Gacko soil were analyzed for the content of 8 heavy metals: Cu, Pb, Cd, Zn, Hg, As, Cr and Ni. The analyses were carried out by inductively coupled plasma mass spectrometry (ICPMS) technique according to the EPA 6020A method and the following conclusions were made: Kostolac coal ash affects the quality of the surrounding soil in terms of Ni, Cu and Cr as evidenced by the moderately strong correlation of the Ni-Cu pair (k = 0.71), as well as the Cu-Cr pair (k = 0.73) and strong correlation of the Ni-Cr pair (k = 0.82), while the high recorded concentration of Pb, Hg, As and Zn is attributed to other sources of pollution, such as the traffic network and intensive farming activities, and in some cases, its impact is only local. All recorded concentrations of heavy metals are within the remediation values. The effect of ash on soil contamination in the surroundings of the Gacko coal basin is limited to Ni and Cd, with a strong correlation coefficient of this pair (k = 0.82). The recorded overrun of maximum allowed concentration of Cr is evidenced in only 2 samples, and in terms of this element the contamination of the Gacko soil can be considered to be local. It is concluded that prevailing winds play a part in soil pollution. Cluster analysis showed that Ni, Cr and Zn have very similar values in analyzed soil samples from both basins, while a cluster composed of only Hg, in the case of Gacko, indicates lower contamination with Hg compared to the other heavy metals. [ABSTRACT FROM AUTHOR]

This paper presents an approach to dust and noise environmental impact assessment and control in Serbian mine planning theory and practice. Mine planning defines the model of mining operations, production and processing rates, and ore excavation and dumping scheduling, including spatial positioning for all these activities. The planning process then needs to assess the impact of these mining activities on environmental quality. This task can be successfully completed with contemporary models for assessment of suspended particles dispersion and noise propagation. In addition to that, this approach enables verification of the efficiency of suggested protection measures for reduction or elimination of identified impact. A case study of dust and noise management at the Bor copper mine is presented, including the analysis of the efficiency of planned protection measures from dust and noise, within long-term mine planning at the Veliki Krivelj and Cerovo open pits of the Bor copper mine. [ABSTRACT FROM AUTHOR]

Determining the spatial position of wind turbines is the initial and most important phase in the development of a wind farm project. In this sensitive phase, all potential problems that may arise in the later stages of project development should be prevented by means of spatial and urban planning instruments. This makes it possible to achieve maximum use of the potential of wind in a particular space and, thus, fulfil the technical and economic requirements of the project while respecting the goals of environmental protection in that same area, through preventive protection. Therefore, it is essential, even at the earliest planning and development stage of a wind farm project, for the requirements that are important for optimal spatial solutions to be balanced. In this process, strategic environmental assessment (SEA) is a support to the planning process and an invaluable instrument for finding optimal spatial solutions for the possible key spatial impacts of wind power with regard to noise, shadow flicker, ornithofauna and chiropterofauna. The weakness of SEA can be seen in its predominant application of expert qualitative methods that bring with them subjectivity, since they depend on expert knowledge and skills. This paper presents the aspect of noise impact assessment and its inclusion in the SEA for the Maestrale Ring wind farm in Serbia. The results of the research indicate how it is possible to achieve the principle of objectivity in the process of multicriteria expert evaluation by including the results of a partial impact assessment of the noise from wind farms, using results obtained from software modeling of the spatial dispersion of wind turbine noise in the SoundPlan 8.1 software package in the SEA process. These quantitative results predicting the noise level were used in a semi-quantitative method of multicriteria evaluation in the SEA through the definition of criteria to determine the ranking of impacts, which is elaborated in the paper. The results also show the significant of the contribution of applying a methodological approach based on a combination of qualitative and quantitative evaluation methods in SEA. These methods positively affect the application of the principle of preventive protection through the optimal selection of the number and position of wind turbines on one hand and the objectivity of drawing conclusions based on which strategic decisions are made in the final phase of the SEA process, on the other. [ABSTRACT FROM AUTHOR]

There is not a single country in the world that is so rich that it can remove all level crossings or provide their denivelation in order to absolutely avoid the possibility of accidents at the intersections of railways and road traffic. In the Republic of Serbia alone, the largest number of accidents occur at passive crossings, which make up three-quarters of the total number of crossings. Therefore, it is necessary to constantly find solutions to the problem of priorities when choosing level crossings where it is necessary to raise the level of security, primarily by analyzing the risk and reliability at all level crossings. This paper presents a model that enables this. The calculation of the maximal risk of a level crossing is achieved under the conditions of generating the maximum entropy in the virtual operating mode. The basis of the model is a heterogeneous queuing system. Maximum entropy is based on the mandatory application of an exponential distribution. The system is Markovian and is solved by a standard analytical concept. The basic input parameters for the calculation of the maximal risk are the geometric characteristics of the level crossing and the intensities and structure of the flows of road and railway vehicles. The real risk is based on statistical records of accidents and flow intensities. The exact reliability of the level crossing is calculated from the ratio of real and maximal risk, which enables their further comparison in order to raise the level of safety, and that is the basic idea of this paper. [ABSTRACT FROM AUTHOR]

Entrepreneurial ecosystems are the main driver of the widespread trend of digitalization, and they open opportunities for the advancement of the digital economy. The digital economy makes its progress through innovative enterprises that can ensure global progress. In order to effectively use the opportunities that open up the process of digitalization, information is needed on how much the Republic of Serbia is able to support the process of discovering entrepreneurship, which is stimulated by digitalization, which is the subject of this paper. The aim of this paper is to analyze the digital entrepreneurial system of the Republic of Serbia, as well as to identify indicators that hinder the development of this system, using appropriate instruments and methods that will be presented in detail in the paper. The results have demonstrated that the starting point for improvement of the digital entrepreneurial system is in the field of finance, with a particular focus on companies in the startup and stand-up phases. Furthermore, a comparative analysis will showcase the digital entrepreneurial system of the Republic of Serbia and the member states of the European Union, where it will be seen that the digital entrepreneurial system of the Republic of Serbia is lagging behind the member states of the European Union in its growth and development. The results will serve as the starting point for policymakers to improve the process of digitalization and the digital entrepreneurial system as a whole. The results show the starting point for the improvement of entrepreneurship in the Republic of Serbia, that is, how small, and medium-sized enterprises can be encouraged on the path to their successful management. [ABSTRACT FROM AUTHOR]

The main motivation to conduct the study presented in this paper was the fact that due to the development of improved solutions for prediction risk of bleeding and thus a faster and more accurate diagnosis of complications in cirrhotic patients, mortality of cirrhosis patients caused by bleeding of varices fell at the turn in the 21th century. Due to this fact, an additional research in this field is needed. The objective of this paper is to develop one prediction model that determines most important factors for bleeding in liver cirrhosis, which is useful for diagnosis and future treatment of patients. To achieve this goal, authors proposed one ensemble data mining methodology, as the most modern in the field of prediction, for integrating on one new way the two most commonly used techniques in prediction, classification with precede attribute number reduction and multiple logistic regression for calibration. Method was evaluated in the study, which analyzed the occurrence of variceal bleeding for 96 patients from the Clinical Center of Nis, Serbia, using 29 data from clinical to the color Doppler. Obtained results showed that proposed method with such big number and different types of data demonstrates better characteristics than individual technique integrated into it. [ABSTRACT FROM AUTHOR]

Local self-government has the task of enabling stable economic development, in addition to enabling a normal quality of life for citizens. This is why the state government should provide guidelines that will improve the local business climate, and by doing so enable local economic development. This can be done through the introduction of a business-friendly certification procedure, which is influenced by uncertain inputs and influences many output factors. Each local government has the important task of determining its rank of efficiency in this process. A number of methodologies developed to solve this problem are generally divided into two groups: Parametric and non-parametric. These two groups of methodologies could provide quite different results. Therefore, the purpose of this paper was to create a model using both approaches to achieve a balanced symmetrical approach that produces better results than each approach individually. For this purpose, the paper describes a multicriteria decision aid-based model of optimization to evaluate the effectiveness of this process, integrating classification, data envelopment analysis, and stochastic frontier analysis, as well as its application in a case study of business-friendly certification in the Republic of Serbia. [ABSTRACT FROM AUTHOR]

The European standard on transport logistics and services in public passenger transport EN 13816 is based on a relationship between the perception of users and transport carriers throughout the groups of criteria taken as a basis for observation in this paper. The constant development and improvement of services in order to achieve sustainability of passenger transport is an imperative on the one hand and a challenge on the other. This is highly evident in persons with disabilities who are faced with many physical and social barriers related to access to rail transport. In this paper, a new model for the selection of criteria for the quality of passenger service in rail transport, from the perspective of persons with disabilities as the main category of passengers, has been created. The survey has covered 168 criteria classified in several groups and the entire territory of Serbia. In order to select the most important criteria, a new model that implies the integration of Full Consistency Method and a Rough Power Heronian aggregator has been developed. The development of a new aggregator enables more accurate decision-making in the process of group decision-making. The results obtained in this paper show that the most important criteria according to importance are Accessibility, Availability, Security, Time, Customer care, Information, Comfort, Environmental impact. Based on the criteria obtained for the service quality of rail transport for persons with disabilities, railway carriers will be able to change and improve the existing services, content, characteristics, equipment of railway stations and vehicles. [ABSTRACT FROM AUTHOR]

Many analyses of the perturbed ionospheric D-region and its influence on the propagation of ground-based and satellite signals are based on data obtained in ionospheric remote sensing by very low/low frequency (VLF/LF) signals. One of the most significant causes of errors in these analyses is the lack of data related to the analysed area and time period preceding the considered perturbation. In this paper, we examine the influence of the estimation of the quiet ionosphere parameters on the determination of the electron density (Ne) and total electron content in the D-region (TECD) during the influence of a solar X-ray flare. We present a new procedure in which parameters describing the quiet ionosphere are calculated based on observations of the analysed area by a VLF/LF signal at the observed time. The developed procedure is an upgrade of the quiet ionospheric D-region (QIonDR) model that allows for a more precise analysis of the D-region intensively perturbed by a solar X-ray flare. The presented procedure is applied to data obtained in ionospheric remote sensing by the DHO signal emitted in Germany and received in Serbia during 30 solar X-ray flares. We give analytical expressions for the dependencies of the analysed parameters on the X-ray flux maximum at the times of the X-ray flux maximum and the most intense D-region perturbation. The results show that the obtained Ne and TECD are larger than in the cases when the usual constant values of the quiet ionosphere parameters are used. [ABSTRACT FROM AUTHOR]