The Covid-19 pandemic is undoubtedly a global crisis, which has had a negative impact on all economic activities. Tourism, as an extremely sensitive branch, is unquestionably the most affected sector. The Republic of Croatia, as well as Bosnia and Herzegovina, has continuously had an increase in tourist traffic in the number of tourist arrivals and overnight stays until the moment of the pandemic crisis. The aim of this paper is to consider the consequences on tourist flows on the example of Bosnia and Herzegovina – Tuzla County and the Republic of Croatia – SisakMoslavina County. The results of the research, conducted through a questionnaire, will detect the problems in the pandemic crisis faced by economic operators in tourism, and we will try to find answers on how to overcome this health crisis with the least possible consequences for tourism, improve the efficiency of tourist boards, as initial affirmers and promoters of tourism.

Dysphagia or swallowing dysfunction is any impairment in the swallowing function that may cause difficulty or discomfort in initiating or transferring the bolus from the oral cavity into the stomach. Dysphagia can cause the bolus to reroute into the airway, known as aspiration, which can lead to more adverse outcomes such as pneumonia or even death. Videofluoroscopic swallowing study (VFSS) is the gold standard procedure for dysphagia diagnosis. In VFSS, trained clinicians calculate swallowing kinematics and inspect pathophysiological processes in a frame-by-frame manner. Though effective, VFSS evaluation is time-consuming, prone to subjectivity in judgment, and human error. In this study, we present a cascaded pipeline that employs various deep learning algorithms to automate VFSS analysis to identify swallowing abnormalities. The pipeline initially segments the VFSS video into static and dynamic frames which include all the relevant features of swallowing for the subsequent VFSS analysis tasks. These tasks include pharyngeal swallow segmentation, hyoid bone tracking, bolus segmentation, and aspiration detection. The pipeline starts with a shallow neural network (NN) that differentiates between static and dynamic VFSS frames with a 98% accuracy using spatio-temporal features from TV-L1 optical flow. Then, a Single Shot Multi-box Detector (SSD) model localizes the hyoid bone body with a mean average precision (mAP) of 40% at an intersection over union (IOU) of 0.5 in a fast and beyond average performance even when the hyoid bone is occluded by the mandible. So far, the developed automated pipeline has shown comparable performance to the manual analysis performed by trained clinicians.

Difference approaches to the study of excited states have undergone a renaissance in recent years, with the development of a plethora of algorithms for locating self-consistent field approximations to excited states. Density functional theory is likely to offer the best balance of cost and accuracy for difference approaches, and yet there has been little investigation of how the parametrization of density functional approximations affects performance. In this work, we aim to explore the role of the global Hartree-Fock exchange parameter in tuning accuracy of different excitation types within the framework of the recently introduced difference projected double-hybrid density functional theory approach and contrast the performance with conventional time-dependent double-hybrid density functional theory. Difference projected double-hybrid density functional theory was demonstrated to give vertical excitation energies with average error and standard deviation with respect to multireference perturbation theory comparable to more expensive linear-response coupled cluster approaches ( J. Chem. Phys.2020, 153, 074103). However, despite benchmarking of local excitations, there has been no investigation of the methods performance for charge transfer or Rydberg excitations. In this work we report a new benchmark of charge transfer, Rydberg, and local excited state vertical excitation energies and examine how the exact Hartree-Fock exchange affects the benchmark performance to provide a deeper understanding of how projection and nonlocal correlation balance differing sources of error in the ground and excited states.

This research aims to examine the traffic noise levels and to improve the performances of the Calculation of Road Traffic Noise model (C.R.T.N.) by applying the statistical multiple linear regression approach. Research methods included traffic noise level measurements with a noise measuring device in an urban area, using a sampling method in different periods. An evaluation of the measured data and prediction results was performed. Based on the predicted values of the C.R.T.N. model and coefficient of determination (R2), multi-linear regression was carried out to determine statistically significant parameters. The obtained multi-linear regression equation defined a new form of C.R.T.N. model. When applying the new improved version based on the C.R.T.N. model, higher accuracy of prediction is achievable. It can be seen that by applying multi-linear regression, the obtained prediction values are acceptably equated with field measurements in the chosen research environment. So, in this way, the differences between the predicted values of the noise level and the values measured in the field were minimized. Finally it can be concluded that when applying the new improved version based on the C.R.T.N. model, higher accuracy of prediction is achievable.

Traditional fuel-powered vehicle emissions have long been recognized as a major barrier to a sustainable environment, and their minimization could ensure both economic support for the sustainable societal fundament and pollution prevention. Electrifying light-duty vehicle fleets, such as taxis, could provide a financial return as well as bring significant economic and environmental improvements. This paper offers a ranked selection of electric vehicles that are presently available on the market, as reviewed by taxi service representatives, as well as their own evaluation of the criteria that influence this selection. This paper provides stability and support when making decisions by deploying stepwise weight assessment ratio analysis and a modified standard deviation method for calculating the subjective and objective weights of the criteria, as well as performing sensitivity analysis to determine how a particular criterion affects the multi-attributive border approximation area. A comparison ranking of the alternatives discovered how a change in the weight value of one of the criteria affected the ranking of the electric vehicle alternatives. According to the research, led by the battery capacity criterion and its values, the Volkswagen ID.3 Pro has the best results and is the taxi of choice in the Brčko District of Bosnia and Herzegovina. Furthermore, the research has demonstrated that the development of electric vehicles for taxi service purposes should strive to extend the range of these vehicles while reducing the battery charging time.

Today’s economic systems are, on the one hand, exposed to various risks and uncertainties with their trends changing almost daily, while on the other hand, they represent an extremely complex system with a large number of sustainable influential parameters. The challenge is to model macroeconomic parameters and achieve sustainability, yet also satisfy conflict situations with an increased level of uncertainty. The aim of this paper is to create an appropriate functional model by examining the mutual influence of various macroeconomic factors. It assesses a total of four scenarios considering mutual influences of: FDI (foreign direct investments), GDP (gross domestic product), imports, exports, inflation rate, RER (real exchange rate) and employment rate as defined parameters. First, the DEA (Data envelopment analysis) model was applied to determine the initial efficiency of two countries, Bosnia and Herzegovina (BIH) and Serbia, for the period 2005–2020. Then, PCA (Principal Component Analysis) was applied in combination with DEA to obtain more precise values. In addition, IMF SWARA (Improved Fuzzy Stepwise Weight Assessment Ratio Analysis) was applied to define weight coefficients of macro-economic parameters. Finally, the CRADIS (compromise ranking of alternatives from distance to ideal solution) model was applied for the final ranking of part of decision-making units. This developed, integrated model can be classified as a mathematical method for economic analysis and gives extended opportunities for solving different problems. The results show that 2009, 2013 and 2016 were the most favorable years in terms of the conditions set when it comes to Bosnia and Herzegovina, and 2012, 2014 and 2016 when it comes to Serbia. These years have been singled out and can be a benchmark for further handling and modeling of macroeconomic elements. In addition, correlation was tested using statistical coefficients.

The use of digital teaching resources became widespread and very helpful during the COVID‐19 pandemic as an alternative to a traditional course with cadavers. Technologies such as augmented reality (AR), virtual reality (VR), 3D models, video lectures and other online resources enable three‐dimensional visualization of the anatomical structures and allow students to learn more interactively. The aim of this study was to compare students' performance in the traditional anatomical courses in teaching neuroanatomy and technology‐based learning methods such as video lectures, 3D models and 3D printed specimens. Four groups of first‐year students of Veterinary Faculty established for the practical classes during the academic year 2021/2022 took part in this research. The total number of students participating in this research was 72. Each group attended separately the theoretical lecture with a demonstration based on a different technique; the control group used formalized specimens, while the three experimental groups used video lectures, 3D models and 3D printed specimens, respectively. Subsequently, all groups completed the same questionnaire testing their short‐term memory of the neuroanatomical structures. After four weeks students were tested for their long‐term memory of the neuroanatomy lecture with the follow‐up test containing an identical list of questions. The test scores using video lectures and 3D printed models were significantly higher compared with the group that learned in the traditional way. This study suggests that alternative approaches such as technology‐based digital methods can facilitate memorization of anatomical terms and structures in a more interactive and sensory engaging way of learning.

Dripping rainfall simulators are important instruments in soil research. However, a large number of non-standardized simulators have been developed, making it difficult to combine and compare the results of different studies in which they were used. To overcome this problem, it is necessary to become familiar with the design and performances of the current rainfall simulators. A search has been conducted for scientific papers describing dripping rainfall simulators (DRS) and papers that are thematically related to the soil research using DRS. Simulator design analysis was performed integrally, for simulators with more than one dripper (DRS>1) and with one dripper (DRS=1). Descriptive and numerical data were extracted from the papers and sorted by proposed categories, according to which the types and subtypes of used simulators are determined. The six groups of elements that simulators could consist of have been determined, as well their characteristics, representation and statistical analyses of the available numerical parameters. The characteristics of simulators are analyzed and presented, facilitating the selection of simulators for future research. Description of future simulators in accordance to the basic groups of simulator elements should provide all data necessary for their easier replication and provide a step closer to the reduction of design diversification and standardization of rainfall simulators intended for soil research.

Systemic sclerosis-associated interstitial lung disease (SSc-ILD) is rare, poorly understood, with heterogeneous characteristics resulting in difficult diagnosis. We aimed to systematically review evidence of soluble markers in peripheral blood or bronchoalveolar lavage fluid (BALF) as biomarkers in SSc-ILD. Method Five databases were screened for observational or interventional, peer-reviewed studies in adults published between January 2000 and September 2021 that assessed levels of biomarkers in peripheral blood or BALF of SSc-ILD patients compared with healthy controls. Qualitative assessment was performed using Critical Appraisal Skills Programme (CASP) checklists. Standardised mean difference (SMD) in biomarkers were combined in random-effects meta-analyses where multiple independent studies reported quantitative data. Results 768 published studies were identified; 38 articles were included in the qualitative synthesis. Thirteen studies were included in the meta-analyses representing three biomarkers: KL6, SP-D and IL-8. Greater IL-8 levels were associated with SSc-ILD in both peripheral blood and BALF, overall SMD 0.88 (95% CI 0.61 to 1.15; I2=1%). Greater levels of SP-D and KL-6 were both estimated in SSc-ILD peripheral blood compared with healthy controls, at an SMD of 1.78 (95% CI 1.50 to 2.17; I2=8%) and 1.66 (95% CI 1.17 to 2.14; I2=76%), respectively. Conclusion We provide robust evidence that KL-6, SP-D and IL-8 have the potential to serve as reliable biomarkers in blood/BALF for supporting the diagnosis of SSc-ILD. However, while several other biomarkers have been proposed, the evidence of their independent value in diagnosis and prognosis is currently lacking and needs further investigation. PROSPERO registration number CRD42021282452.

The human population is growing every year and naturally so is the need for resources. The most essential resource, water, is in danger of scarcity, both from pollution and increased use. The aim of this study is to reduce the usage of drinkable water in the agriculture sector with the use of Artificial Intelligence, specifically for green areas of undemanding flora (grass in front of buildings, houses, etc.). Conventional ways of irrigation for these green areas are human-operated, regulated by a scheduled timer, sensor directed, or some combination of those. Sensor-directed irrigation with the help of humans has proven to be efficient. This study will show how artificial intelligence replaces sensors and human labor. Using soil moisture sensors, and weather station data (rainfall, humidity, wind strength, wind direction, temperature), the artificial neural network is trained first to show with which data soil moisture data correlates the most, and after that with the data collected for one month is trained to know what is the relative moisture of soil based on current weather station data, so we can set the trigger for the irrigation system to start irrigating the fields. With this study, the need for human labor in means of controlling irrigation and sensor maintenance will be cut out, so a much cheaper and more efficient model for irrigation is achieved.

Wastewater-based epidemiology (WBE) is based on the analysis of human metabolic excretion products (biomarkers) of xenobiotics in wastewater, to gain information about various lifestyle and health aspects of a population in an evidence-based manner. Due to the complex wastewater matrix and trace level occurrence of human biomarkers in the sewage network, it is crucial to have sensitive analytical procedures available. Additionally, to improve the value of WBE as a complementary epidemiological source, there is increasing pressure on the analysis of more compounds, more locations, and more samples. A high-throughput method based on 96-well Oasis MCX solid phase extraction (SPE), requiring less influent wastewater (2 mL), was developed in accordance with the European Medicine Agency guidelines. Validation was successful for 28 parent drug and metabolites of antidepressants, opioids and drugs of abuse. The selection of biomarkers and quantification limit was chosen to be relevant for WBE and was predominantly 10 ng/L or below. The final method was successfully applied to 24h composite samples of October 2019 (n=27), obtained from urban wastewater treatment plant Leuven (Belgium).

We use simplicial complexes to model simple games as well as weighted voting games where certain coalitions are considered impossible. Topological characterizations of various ideas from simple games are provided, as are the expressions for Banzhaf and Shapley-Shubik power indices for weighted games. We calculate the indices in several examples of weighted voting games with unfeasible coalitions, including the U.S. Electoral College and the Parliament of Bosnia-Herzegovina.

We consider the problem of maximum-likelihood estimation in linear models represented by factor graphs and solved via the Gaussian belief propagation algorithm. Motivated by massive Internet of Things (IoT) networks and edge computing, we set the above problem in a clustered scenario, where the factor graph is divided into clusters and assigned for processing in a distributed fashion across a number of edge computing nodes. For these scenarios, we show that an alternating Gaussian belief propagation (AGBP) algorithm that alternates between inter- and intracluster iterations, demonstrates superior performance in terms of convergence properties compared to the existing solutions in the literature. We present a comprehensive framework and introduce appropriate metrics to analyze the AGBP algorithm across a wide range of linear models characterized by symmetric and nonsymmetric, square, and rectangular matrices. We extend the analysis to the case of dynamic linear models by introducing the dynamic arrival of new data over time. Using a combination of analytical and extensive numerical results, we show the efficiency and scalability of the AGBP algorithm, making it a suitable solution for large-scale inference in massive IoT networks.