The most common type of renal cell carcinoma (RCC) is clear cell renal cell carcinoma (ccRCC), which has a high metastatic potential. Even though the International Metastatic RCC Database Consortium risk model is conventionally utilized for selection and stratification of patients with metastatic RCC (mRCC), there remains an unmet demand for novel prognostic and predictive markers. The goal of this study was to analyze the expression of Vascular endothelial growth factor (VEGF), Cluster of Differentiation 31 (CD31) to determine microvessel density, and Angiopoietin-1 (Ang-1) in primary kidney tumors, as well as their predictive and prognostic value in patients with metastatic ccRCC (mccRCC) who were treated with first-line sunitinib. The study included 35 mccRCC patients who were treated with first-line sunitinib in period between 2009 and 2019. Immunofluorescence was used to examine biomarker expression in tissue specimens of the primary tumor and surrounding normal kidney tissue. Median disease-free survival (DFS) was longer in patients with negative and low tumor VEGF score than in patients with medium tumor VEGF score (p ═ 0.02). Those with low tumor CD31 expression had a longer median DFS than patients with high tumor CD31 expression (p ═ 0.019). There was no correlation between Ang-1 expression and DFS. The expression of biomarkers in normal kidney tissue was significantly lower than in tumor tissue (p < 0.001). In conclusion, higher VEGF scores and greater CD31 expression were associated with longer DFS, but neither of these biomarkers correlated with progression-free survival or overall survival.

Filamentary resistance switching, or ReRAM, devices based on oxides suffer from device-do-device and cycle-to-cycle variability of electrical characteristics (electroforming voltages, set and reset voltages, resistance levels and cycling endurance). These are largely materials issues related to the microstructure of the switching oxide. Here we outline strategies to engineer the electrical performance of silicon oxide ReRAM by controlling the oxide microstructure at the nanometre scale through approaches including engineered interfaces and ion implantation. We demonstrate control over the distribution of switching voltages, electroforming voltages, and stable multilevel resistance states.

The virus SARS-Co V -2 that has caused a pandemic of COVID-19 in 2019 is still a major concern for health care systems. The reason for this is the fact that the outcome of the disease is difficult to predict, as deadly complications can occur in all people. Diagnosing COVID-19 relies on polymerase chain reaction (PCR) testing and antigen testing, both of which require special referral. The aim of this study was to develop artificial intelligence (AI) expert system which will facilitate COVID-19 diagnosis based on parameters that can be readily collected from blood specimens. The database contains 1000 samples, divided into 2 categories: (1) healthy and (2) sick subjects The following parameters were used: CRP, LDH, SE, AST, ALT, D-dimer and IL-6. The sensitivity of the developed system was 100%, specificity 98.33%, and accuracy 99.67%, on the basis of which we can conclude that the use of AI in the diagnosis of COVID19 has a significant potential.

Diagnosis of anemia is a time intensive and medically expensive procedure requiring a multitude of tests to establish a final diagnosis. Classification is an even more complex procedure that often takes years to complete thus delaying proper treatment and worsening the prognosis. This paper presents the application of machine learning, K-nearest neighbors (KNN), in order to diagnose and classify anemia. Monitoring parameters used as input for diagnosis were: age, sex, ferritin, transferrin, vitamin B12, erythrocyte count, iron, folic acid, hemoglobin, while the parameter relevant for classification was MCV. The results of the study indicate significant possibilities for the application of this system in the field of medical diagnostics.

Pulmonary emphysema is a complicated disease caused by irreversible damage to the wall of the pulmonary alveoli and causes 5% of the total mortality worldwide. This paper presents the development of an artificial neural network (ANN) for the diagnosis of pulmonary emphysema. Following biomarkers were used for the development of the ANN: AAT (alphal-antitrypsin), FEV1 (forced expiratory volume in 1 second), FVC (forced vital capacity) and FEV1/FVC (ratio forced expiratory volume in 1 second / forced vital capacity). The dataset consisted of 300 patients: 210 healthy subjects and 90 subject with disease. The neural network has 4 input parameters and 1 output parameter. For the final architecture, a neural network with 13 neurons in hidden layer was chosen based on the training results. The developed ANN has shown good performance and has a potential for use in this field.

This paper focuses on the problem of diagnosing polycystic ovary syndrome (PCOS), which is one of the leading disorders of the female endocrine system. Although the incidence of this syndrome is quite high, physicians and patients still often encounter problems in their detection, as well as with the ineffectiveness of prescribed therapy. For the development of expert system, a database containing following parameters was used: oligo ovulation, anovulation, free testosterone, free androgen index (FAI), calculated bioavailable testosterone, androstendione, dehydroepiandrosterone, ovarian volume, number of follicles, obesity. The presented dataset contains 1000 samples distributed in two categories: (1) heatlhy subjects and (2) subjects with disease. The purpose of the developed system is to classify instances with polycystic ovary syndrome using artificial neural networks (ANN s). The overall performance evaluation of the system resulted with accuracy of96.1 %, sensitivity of96.8% and specificity of90% indicating significant potential of ANNs in this field. Since the system predicted a total of 157 positive and 23 negative, this leads us to the result that the sensitivity of our system is 96.8%, specificity 90% and accuracy 96.1 %.

The aim of this paper is to investigate the state of performance of audit firms registered in the Republic of Serbia, as well as the factors that manage performance. The research is based on the entire population of audit firms, based on financial reports for the period 2019-2020. that are publicly available. The performance of audit firms will be investigated from the aspect of profitability performance, where the indicators of return on assets nad return on equity is most often used. Performance analysis will be presented through descriptive statistical analysis. Performance management will be analyzed in terms of the impact of independent factors on ROA and ROE. The following will be defined as independent factors: the size of the audit firm, liquidity, indebtedness, belonging to the big four, growth and the like. The research of the influence of independent factors on the dependent variables of the performance of audit firms will be conducted on the basis of regression analysis. The results of descriptive analysis should indicate the state and trend of performance of audit firms, while the results of regression analysis should indicate the nature of the factors that drive the performance of audit firms.

This paper presents an Artificial Nerual Network (ANN) for identification of postmenopausal women who are at high risk for developing osteopathy. While 800 patients took part in the study, 180 were used for network training. The following parameters were used: T-score (from −2,5 to −4), Age, Blood calcium level (<1,9 mmol/L), Blood vitamin D level (<20 ng/ml), Hip fracture, Spine fracture, Joint fracture, Glucocorticoids use, Smoking status, and BMI. The network has 10 input parameters and 1 output parameter. For the final architecture of expert system, a neural network with 20 neurons in hidden layer was chosen based on the training results. The signal from each neuron from hidden layer is directed to neuron in output layer, where this neuron processes the signal and gives desired output of the network. The sensitivity was 97,5%, specificity 70%, and accuracy 94,44%.

The success of a company depends on the employees, so the challenge for managers is to monitor their needs continuously and find ways to encourage them to work and achieve goals. By using a combination of compatible material and non-material techniques within motivation strategies, managers link long-term company goals and rewarding employees for work and achievements. The aim of this paper is to get insight into the used motivation techniques and strategic approach to motivation in companies in the Federation of BiH (FBiH). The survey was conducted in early 2019 and covered 63 companies. The most commonly used material motivation techniques are salaries, bonuses, and paid leave, and the most commonly used nonmaterial techniques are appropriate working hours, information on work results and the possibility of advancement. Almost half of the managers state that there are established rules for motivating employees in their companies, slightly more than ¼ point out that there is an established plan for motivating employees that is continuously implemented. Only a part of the surveyed companies, have a continuous, systematic way of monitoring employee motivation. Assessing motivation and taking corrective action is most often carried out by top management, two or more times a year. The results indicate that some companies in the FBiH have not yet realized that the human factor is a key factor in achieving better business results. In order for motivation to be truly effective, it must be approached in a planned and continuous manner.

Education and society always lag behind technical state of the art achievements. General computer literacy needed decades to become the part of public acceptance after computers become available. Smart phones enters our life and becomes an extension of the human body yet we still do not know how to properly apply them in education. Artificial intelligence is an exciting technology that adapts educational experiences to different learning groups, teachers and tutors. Intelligent Management Systems (IMS) are not a novelty in education though. There have been many experiments, but they have all somehow stalled due to immature technology or misinterpretation. We can now see a new impetus for AI in education, and its impact will soon be very noticeable. In education, AI can: personalize learning, connect and create innovative learning content, perform tutoring in intelligent tutoring systems, is used to help pupils with special needs, help teachers assess, give students access to learning content, and translate educational content from different languages, removing language barriers. This article will explore the different possibilities of using AI in education and its use in education.

By delivering end-to-end latencies down to 5ms, data rates of up to 20Gbps, and ultra-high reliability of 99.999%, 5G is extending the capabilities of numerous industry verticals, including the Transport & Logistics (T&L). As the T&L industry has a pivotal role in modern production and distribution systems, it is expected to leverage 5G technology to significantly increase efficiency and safety in the T&L operations, through automating and optimizing processes and resource usage. However, to be able to truly benefit from 5G, the design, the development, as well as the management, of T&L services need to specify and include 5G connectivity requirements, and the features that are tailored to the specific T&L use cases. To this end, in this paper we introduce the concept of Network Applications (NetApps), as the fundamental building blocks of T&L services in 5G, which simplify the composition of complex services, abstracting the underlying complexity and bridging the knowledge gap between the vertical stakeholders, the network experts, and the application/service providers, while specifying service-level information (vertical specific) and 5G requirements (5G slices and 5G Core services). In this paper, we exemplify the concept of NetApps leveraging one of the VITAL-5G use cases, which provides faster and safer operations of vessels in the port of Galati, the largest port on the Danube River.

The COVID-19 pandemic has accelerated the process of digital transformation of higher education institutions. In a very short period, teachers and students abruptly switched to digital environments, which they had not used until then. As online teaching is very different from traditional teaching, teachers and students are faced with numerous new challenges. Online teaching requires a specific environment that primarily implies the availability of adequate technology as well as the skills that both teachers and students should have. Some higher education institutions have completely switched to online mode, while others have practiced a combined (online and offline) mode. The aim of this paper is, based on a questionnaire developed by Bernard et al. (2007), to examine the level of online skills, readiness for online learning and learning initiatives, attitudes about online learning, as well as the desire for online interaction with teachers and colleagues by the surveyed students.

The primary focus of this paper review is to summarize the most important facts and findings regarding the use of Artificial Intelligence (AI) in the modeling, processing and analysis of biomedical data and to give an insight on the contributions of AI, Machine learning and Deep learning to the field of medicine. This study compiled and analyzed work published in the period between 1986 and 2021 related to the use of AI in medicine, its various applications and historical development, with a focus on papers published from 2015 until today, due to the accumulation and development of newer technologies. Out of a total of 117 papers reviewed, 52 were selected for a more detailed analysis and presented in a table summarizing the key points, advances, advantages and disadvantages of AI, its subfields and algorithms. The goal of this paper was to extract the most famous AI learning algorithms, past and current, and focus on the methods of modeling, processing and analysis by which these algorithms operate and perform tasks in order to help doctors and experts better understand the underlying mechanisms behind biological processes, and in some cases, even replace humans in data classification, identification, diagnosis and prediction of different conditions associated with diseases.