It seems completely impossible today to imagine life without mass media. The development of modern technologies has made mass media available to everyone. Mass media, in our case the press, sometimes try to interest and excite their users with their sensational headlines, sometimes make them think, and sometimes even entertain. However, it is very important to keep in mind that each medium shapes the message according to its own language, and an integral part of the languages of the mass media are usually different stylistic and linguistic means. Following earlier analyses of journalistic style, this paper will offer a stylistic-linguistic analysis exclusively of titles, as important and recognizable features of journalistic style, whose task is to be particularly effective in provoking the reader's attention and therefore can appear in the form of rhetorical questions, phrases, already established journalistic templates, etc. , which reflects their expressiveness. In addition, the analysis of the headlines of daily newspapers from the territory of bosnia and herzegovina (osloboenje, nezavisne novine and dnevni list) confirmed that on the phoneticphonological level, for example, there is a difference in writing foreign names, i. E. Names of clubs, awards, proper names, etc. Among these three newspapers, that on the morphological plan, verbs are most often used in the present and future, that the superlative forms of adjectives are often represented, that syntaxic, lexical-semantic figures are used that give the title a stronger, stylistically colored meaning - elliptical sentences, metaphor , metonymy with, of course, an unavoidable lexicon of foreign origin where anglicisms and latinisms dominate, along with some words of oriental origin, and among the dominant functions of the titles are referential (informative), popularistic, expressive and aesthetic.

Monitoring, evaluation and assessment of students' knowledge are inseparable processes that subject teachers apply almost daily in their educational work. Experiences from teaching practice point to the fact that the evaluation of student achievements is one of the most sensitive areas of a teacher's work. The processes of assessment, evaluation and reporting on student achievements are often in the focus of the professional public and as such are the subject of numerous discussions and misunderstandings. The biggest shortcomings of the evaluation system in the area of Herzegovina-Neretva Canton at the level of primary education are the absence of clear criteria according to which student achievements and the desired level of objectivity are evaluated, which leads to encouraging negative patterns of motivation and learning among students. Evaluating students in class is a complex process, and in that process teachers can help themselves and students only if they create an effective system of recording, monitoring and checking students’ knowledge and skills within their subject. The paper describes in detail the essential elements of the evaluation process, namely evaluation methods, sources used in the evaluation process, evaluation and assessment criteria. This paper presents the knowledge and thoughts of the authors, through clear presentations of concrete examples for improving the process of monitoring, checking and evaluating students’ knowledge in the teaching of Biology, Bosnian language and literature, and English language, based on modern reform requirements.

This paper performs a comparative evaluation of various generations of Controller Area Network (CAN), including the classical CAN, CAN Flexible Data-Rate (FD), and CAN Extra Long (XL). We utilize response-time analysis for the evaluation. In this regard, we identify that the state of the art lacks the response-time analysis for CAN XL. Hence, we discuss the worst-case transmission times calculations for CAN XL frames and incorporate them to the existing analysis for CAN to support response-time analysis of CAN XL frames. Using the extended analysis, we perform a comparative evaluation of the three generations of CAN by analyzing an automotive industrial use case. In crux, we show that using CAN FD is more advantageous than the classical CAN and CAN XL when using frames with payloads of up to 8 bytes, despite the fact that CAN XL supports higher bit rates. For frames with 12-64 bytes payloads, CAN FD performs better than CAN XL when running at the same bit rate, but CAN XL performs better when running at a higher bit rate. Additionally, we discovered that CAN XL performs better than the classical CAN and CAN FD when the frame payload is over 64 bytes, even if it runs at the same or higher bit rates than CAN FD.

Epilepsy is a life threatening neurological disorder. The person with epilepsy suffers from recurrent seizures. Sudden emission of electrical signal in the nerves of the human brain is called seizure event. The most widely used method for diagnosing epilepsy is analysing electroencephalogram signals in short called as EEG signals collected from the scalp of the patient. The EEG data are normally used for seizure detection. If the recurrent seizure signals are detected in the input EEG dataset, then it can be considered as the presence of epilepsy disorder. Manual inspection of seizure signals in the EEG data is a laborious process. An automated system is very crucial for the neurologists to identify seizures. In this paper, an automated seizure detection method is presented using deep learning method, pre‐trained convolutional neural network architecture. Freely available EEG dataset from Temple University Hospital database is used for the study. The pre‐trained CNN networks, VGGNet and ResNet are used for classifying the seizure activities from non‐seizure activities. CNNs are extremely good in learning the features of the input data. A very large dataset from TUH is provided as input to the multiple layers of CNN model. The same data is fed to VGGNet and ResNet models. The results of CNN, VGGNet and ResNet models are assessed using performance metrics accuracy, AUC, precision and recall. All the three models gave extremely good performance compared to state‐of‐the‐art works in the literature. In comparison VGGNet performed with little higher results giving 97% accuracy, 96% AUC, 97% precision and 79% recall.

The complexity of Industry 4.0 systems makes it challenging to manually synthesize and optimize architecture-level decisions, such as service deployment and composition. To address this challenge, we propose an approach that utilizes a satisfiability modulo theories (SMT) solver to (semi-)automate service deployment and composition synthesis. Thereby, we conceptualize, formalize and implement the complete workflow from input service workflows to valid and optimized service deployments and compositions. We demonstrate the practical application of our approach in an Industry 4.0 scenario, specifically the flexible production of new products. We evaluate the effectiveness of the proposed approach by investigating its benefits in terms of engineering effort, complexity of the resulting IEC 61499-based applications, and improvement of communication coupling quality attribute (QA).

Design of manufacturing system involves various architectural design decisions (ADDs), including software and hardware component allocation, production service composition, plant layout determination, and production planning. These ADDs gain significance in Industry 4.0 (I4.0) scenarios due to the frequent need for architecture synthesis to achieve adaptable and flexible production processes. Additionally, synthesizing ADDs in conjunction with each other increases the decisions’ impact on system quality attributes (QAs). The increasing importance of architecture synthesis and optimization (ASO) in I4.0 necessitates an overview of ADDs within this context. This paper expands on the previous work by creating a landscape of I4.0-related ADDs based on insights from the BaSys 4.0 research project series. The landscape describes relevant ADDs and their relationships, and suggests multi-dimensional ASO workflows. Mapping the ADD landscape to BaSys 4.0 research enhances idea generation and the exploration of existing solutions in ASO.

Graft-versus-host disease (GVHD) after allogeneic hematopoietic cell transplantation is a major cause of nonrelapse morbidity and mortality. Although ruxolitinib is now approved for the treatment of steroidrefractory GVHD, to date, no agent added to corticosteroids has been shown to improve outcomes compared with corticosteroids alone. In this issue of Blood Advances, Al Malki et al presented the results of a multicenter phase 2 study that tested whether the addition of natalizumab, a humanized antibody against the α4 subunit of α4β7 integrin, would improve the outcomes of new-onset acute GVHD. The primary end point was a complete response after 28 days, defined as the clinical resolution of GVHD in the target organs.

Introduction: The coronavirus disease 2019 (COVID-19) pandemic has caused a worldwide emergency. The disease is characterized primarily by symptoms of the respiratory system, but also by systemic inflammation. Since the onset of the disease, there has been a need for biomarkers to predict the severity of the clinical picture and the outcome of the disease. The aim of this study is to evaluate systemic inflammatory markers for predicting severity of COVID-19. Methods: The study was conducted at the Sarajevo Canton Health Center on a total of 170 adults suffering from COVID-19. 70 subjects had mild clinical picture, while the control group consisted of 100 subjects with moderate clinical picture. The results of complete and differential blood counts, C-reactive protein (CRP), and systemic inflammatory indexes (SII) (neutrophil/lymphocyte ratio [NLR], derived NLR [dNLR], platelet/lymphocyte ratio [PLR], and SII) were used to compare the groups. IBM SPSS Ver. 23 was used for statistical analysis and data processing. Results: The proportion of male patients in the group with a milder clinical picture was higher than the proportion of male patients with a moderate clinical picture, p = 0.016. The values of leukocytes and neutrophils were higher in patients with a moderate clinical picture (p = 0.006 and p < 0.001, respectively). The values of all inflammatory indexes (NLR, dNLR, PLR and SII) were higher in patients with a moderate clinical picture of COVID-19 than in patients with a mild clinical picture (p < 0.001 for NLR, dNLR, and SII; p = 0.023 for PLR). In the research, patient age showed no correlation and CRP showed no correlation with SII. Conclusion: SII show higher values in patients with a moderate compared with a mild clinical picture of COVID-19. These parameters can be cost-effective and useful indicators in patient classification, diagnosis, and probably in monitoring patients with COVID-19.

This paper presents some results of analysis of using several types of common Ethernet cables (LAN cables) in last section of access networks. The main goal of the article is to answer the question whether (if so, under what conditions and to what extent) we should consider the type of specific Ethernet cable when using it in a FTTB environment. Four branded and two unbranded CAT5e Ethernet cables are used for measurements. Additionally, a DSL cable with diameter of 0.4 mm is used for comparison purposes. The results are collected and mutually compared under similar loop conditions (good loop). All of the results of measurements are collected in operating conditions.

The aim of the paper is the quality of video streaming analysis in cases of using different video codecs in the environment of distributed computer systems with different QoS (Quality of Service). For the purposes of the analysis, several scenarios were set up in which video encoded with different codecs is transmitted by a virtual video streaming server to virtual clients. For each of the scenarios, an environment with different QoS (packet losses, latency, jitter) was simulated and the quality of the received video stream was evaluated for each video codec. The quality of the received decoded video stream was calculated using SSIM (Structural Similarity) and VMAF (Video Multimethod Assessment Fusion) video objective metrics and compared to the original video stream.

Cryptocurrencies represent a new form of property that exists only on the Internet. The first cryptocurrency to appear on the global market was Bitcoin, and its appearance is linked to the first financial crisis in 2008. The market capitalization of Bitcoin grew very quickly and in 2017 reached the highest capitalization in history (334 billion US dollars). Later, there was an expansion of new cryptocurrencies, of which there are currently over 1500 (e.g. Ethereum, Tether, BNB, USD Coin, etc.). The emergence of cryptocurrencies as a new concept affects the change in the perception of payments and money as a means of payment in general. The development of the global cryptocurrency market is indeed rapid and dynamic, and it is becoming an increasingly popular method of payment. Since the emergence of cryptocurrencies is not related to central banks, the need to change the classical systems and economic policies of countries is also expressed. The dynamic development of the mentioned market takes place in parallel with the development of information technologies, so there is a trend of capital outflow from classic capital markets to emerging global cryptocurrency markets. The question arises of the survival of traditional banks in the future, as more users trust cryptocurrencies. Global cryptocurrency markets are expected to expand more and more in the future. The paper will analyze the development of the global cryptocurrency market based on a sample of five of the most significant cryptocurrencies today.

In multi-attribute group decision-making (MAGDM), the attributes can be placed into independent groups based on their properties through partitioning. First, the partitioned dual Hamy mean (PDHM) operator is introduced, along with its essential properties. This operator integrates these separate groups while preserving the relationships between the attributes within each group. Furthermore, the partitioned Hamy mean (PHM) and the PDHM operators are also constructed in the generalized orthopair fuzzy environment, namely the q-rung orthopair fuzzy PHM (q-ROFPHM), the q-rung orthopair fuzzy PDHM (q-ROFPDHM), and their weighted forms. Their essential properties are verified to ensure the validity of the proposed aggregation operators (AOs). Subsequently, a new MAGDM approach is developed, employing the proposed AOs. The MAGDM problem of selecting the best person is examined. Moreover, the research includes a sensitivity analysis in three directions and a comparative analysis of the proposed MAGDM approach with five different approaches. The findings indicate that applying attribute partitioning in the proposed approach mitigates the adverse impact of irrelevant attributes, leading to more feasible and reliable outcomes. Additionally, a practical case study focuses on selecting a suitable industry for investment among the five available options. This case study demonstrates the approach’s effectiveness by considering five distinct qualities and results that make the Internet industry the best place to invest. Furthermore, a comparative analysis with four similar papers is also performed, indicating that the developed method’s results are more reliable and consistent.