Courses in principles of digital computers usually begin with elementary logic circuits, proceed through increasingly complex ones, and often end with the design of a central processing unit. Such processors are typically simpler to explain than commercial microprocessors, but also often have very limited capabilities. The educational processor presented in this paper has a good balance between simplicity and capabilities. All its instructions, including multiplication and bit rotation, are executed in one clock cycle. The method of encoding and decoding instructions is quite simplified so that the encoding of instructions can be done even manually without tables, and the decoder unit is a simple forwarding of parts of the instruction word to the control bits of multiplexers. The processor is symmetric around the number 16: it has 16 three-operand instructions, 16 registers, each register is 16 bits wide, as well as the address and data bus. It is simulated at the logic gates level, a Verilog implementation on FPGA, and an emulated computer run by an implementation of a Forth interpreter written partly in its machine language.

Introduction: Stress among students is a growing problem. As emotional stress increases, the limbic structures and hypothalamus are stimulated, activating the gamma efferent system, which ultimately leads to an increase in muscle tone or additional muscle activity that can become repetitive behaviors such as bruxism. The aim of the study was to investigate the stress level that students are exposed to, to determine the difference between students in terms of gender, faculty, and year of study, and to evaluate the possible relationship between stress level and self-reported bruxism in college students during the pandemic COVID-19. Methods: In April 2022, a cross-sectional study was conducted on a sample of students from the Faculty of Dentistry and the Faculty of Pharmacy at the University of Sarajevo (BiH). The students answered a questionnaire consisting of two parts: The first part contained questions on basic personal data and data on self-reported bruxism and the second part contained questions on the perceived stress scale (PSS). Results: The study included 756 students from both faculties. Analysis of stress levels among students revealed higher stress levels. Female students were more likely to be under stress than male respondents. Students in the Faculty of Pharmacy were more likely to be stressed than students in the Faculty of Dentistry. At the Faculty of Pharmacy, there was no difference in stress levels between the different years of study, while at the Faculty of Dentistry, the individual score for PSS was highest among 1st-year students. A high prevalence (46.8%) of self-reported bruxism was found among students in both faculties. Conclusion: A slight positive correlation between self-reported bruxism and stress suggests that it is important to implement stress management strategies during academic education and to prevent bruxism and its consequences.

Detecting phishing attacks is not straightforward, since there are many obstacles that derive from language complexity and technical aspects. Studying phishing attacks and other related issues heavily relies on computer datasets, i.e. digital corpora that reflect these linguistic and technical intricacies. Diverse studies using phishing datasets have been performed, but mainly for the English language. Research for other languages is scarce, and especially for not widely spoken languages. For the Croatian language there is an evident lack of corpora that are essential for diverse analyses and for constructing models that are capable of recognizing phishing attacks and protecting users. These datasets are necessary for natural language processing and building machine learning workflows, where results largely depend on corpora that must be specifically crafted for this purpose. Therefore, creating high-quality domain-specific corpora is of great importance in the domain of information security. Such corpora can be employed for teaching purposes in various courses in higher education, and could be analyzed in numerous ways in order to understand the underlying principles of phishing attack strategies. The aim of this paper is to demonstrate the entire process of data acquisition and corpus creation for the phishing detection domain. In addition, an analysis of the corpus is presented with regard to different aspects, such as descriptive attributes, terminology characteristics, metadata and language.

In post pandemic era where companies already adopted digital agendas in their everyday business, conservative businesses as insurance companies must intensify activities in creating new values and use of the potential innovations. Insurance companies have to follow new age users, developments in society and new economic laws with new demands for insurers. As ITIL4 describes an operating model for the delivery of tech-enabled products and services, the importance of its adoption significantly increases. The way this adoption helps insurance companies is described in this paper. Trends in IT operations, such as agile approach are also considered.

Autistic children often have difficulties in executive functions (EF). These difficulties can, in turn, affect their everyday functioning. It is less clear in what way EF are affected by the severity of autism symptoms in children. We hypothesize that autism severity level does not have the same effect across the different components of EF. In this study, we examined how EF are affected by the autism severity level in a sample of 52 autistic children aged 4–7 years (mean age‐ 5.4 years, SD‐ 0.9 years). EF were measured through teachers' reports on the Behavior Rating Inventory of Executive Functions‐ Preschool Version. Autism severity level was measured with the Social Communication Questionnaire‐ Current Form. The results of this study showed that autism severity level impacted two EF, namely Planning and Working memory, and did not affect three EF components: Inhibition, Shifting, and Emotional Control. These results indicate that the cool or cognitive EF are more affected by autism severity level than hot EF. We conclude the article with suggestions for improving EF in autistic children.

The purpose and idea of the paper is to define a methodological framework for the comparative assessment of the carbon footprint of virtual remote work and the footprint of an autonomous electric vehicle for physical mobility to the workplace. The methodology is based on the remote work service, as a typical representative of information and communication solutions with potentially significant opportunities to reduce emissions in the area of physical mobility. On the other hand, autonomous electric vehicles cause less greenhouse gas emissions than diesel cars, even when powered by engines with lower carbon emissions, but we still don’t know if it is more environmentally friendly to use digital teleworking services instead of electric autonomous vehicles for trips to the workplace. In the proposed methodology, special attention will be focused on the analysis of emission variables for existing consumption technologies of autonomous vehicles. The originality and value of the work consists in the fact that the results of the work offer an original comparative procedure for determining the value of emission footprint of the physical mobility of an autonomous electric vehicle in relation to the footprint of the virtual mobility of telecommuting.

Missing values handling in any collected data is one of the first issues that must be resolved to be able to use that data. This paper presents an approach used for missing values interpolation in PurpleAir particle pollution sensor data, based on a correlation of the measurements from the observed locations with the measurements from its neighboring locations, using KNIME Analytics Platform. Results of our experiments with data from five locations in Bosnia & Herzegovina, presented in this paper, show that this approach, which is relatively simple to implement, gives good results. All modeling and experiments were conducted using KNIME Analytics Platform.

Support channels represent a unique opportunity to improve customer satisfaction by offering a consistent experience in resolving customer issues. Several surveys show that customers have raised their standards of customer support services. While only a few years ago customers willingly waited a long time to speak with one of the service agents and were patient for their problem to be resolved, today’s customers have very limited patience and want a solution to the problem immediately. Customers don’t want to settle for a mediocre support channel experience. Support channels must provide superior service capacities so that customers see that the company values their choice and time. Efficient management of support centers implies accurate modeling of customer behavior on hold. The subject of our research is the application of data research techniques for predicting customer behavior in support channels. In this paper, we apply machine learning methods to predict customer behavior. Based on historical data in the service system, we use classification algorithms to predict customer patience in service channels.

Software processes consist of a complex set of activities required to deliver software products within predicted quality, costs, and deadlines. To accomplish such goals, a software organization needs a quality and mature software process as a prerequisite for success. Adopting Software Capability Maturity Model Integration (CMMI) represents a well-known path in the pursuit of mature software processes. However, its implementation is a subject of a permanent effort that implies different approaches and methods, and often leads to unsuccessful or limited success, though. This is especially emphasized in small software companies given the dynamic environment influenced by different factors, including insufficient resources, changes in technology, and staff turnover. In this paper, a case study of a small software company implementing software process improvement is presented. In a tailored approach to process improvement, a specific method using the balanced scorecard as input into the IDEAL model has been designed, enabling a narrow link between business goals and specific improvement goals. The results show that the software process and selected performance indicators were improved, and suggest the potential of the proposed approach in small organizations.

In the aftermath of the recent pandemic, organizations around the world had the opportunity to assess the benefits and drawbacks of allowing the bulk of their employees to work from home (WFH). As a result, many organizations realize that by using technology, it is possible to shift a significant percentage of their workforce to permanently function from any location without being physically present at a designated workplace. Although the economic benefits for organizations that allow WFH seem to be clear, how factors related to perceptions of employees such as their work motivation (WM) and their work-life balance (WLB) caused by blurred boundaries between work and family at home are not clearly understood. Therefore, the primary goal of this study is to determine how WFH impacts WLB through the possible mediating effects of work-family conflict (WFC) and WM. A cross-sectional survey instrument was developed using Likert type measurement scales that were adopted from top-tier journals. The data was collected through convenient sampling from 249 managerial and non-managerial employees in Omani business organizations. The relationships were tested through structural equation modeling. The results indicate that WFH increases WFC and WM, while the relationship between WFH and WLB is mediated by WFC, but not by WM. The findings of this study have implications for both theory and practice.

To assess the prevalence, clinical characteristics, and outcomes of patients with heart failure (HF) with or without moderate to severe aortic valve disease (AVD) (aortic stenosis [AS], aortic regurgitation [AR], mixed AVD [MAVD]).

Key account management (KAM) is a strategic approach that focuses on developing and retaining long-term relationships with key customers. In today’s business world, where competition is fierce and disruption is the norm, KAM has become increasingly important for companies looking to maintain a competitive edge. In Bosnia and Herzegovina, many companies are beginning to recognize the value of KAM and are implementing KAM strategies to improve their financial performance. To better understand the impact of KAM on financial performance in Bosnia and Herzegovina, ongoing research is being conducted to identify the influence of key account management orientation on company financial performance in different industry sectors. The research has collected data from several companies in various industries, with each company being considered as a unit of analysis. To ensure the reliability and validity of the research instrumentation, a validated and reliable questionnaire was used, and item total reliability and confirmatory factor analysis were employed to test the reliability and validity of the constructs. The analysis of the data collected will be done using the structural equation modeling (SEM) technique, which will allow the researchers to identify the effects of key account management orientation on the company’s financial performance. The researchers expect to find statistically significant evidence supporting the impact of KAM orientation on the financial performance of companies in Bosnia and Herzegovina. The findings of this research could have important implications for companies looking to improve their financial performance through the implementation of KAM strategies. By demonstrating the impact of KAM on financial performance, the research could encourage more companies in Bosnia and Herzegovina to adopt KAM strategies and help them gain a competitive edge in their respective industries.

The Internet of things (IoT) is getting more and more intrusive into our lives until the day comes when everything becomes connected to the Internet. Due to the limited resources and heterogeneous Internet of Things (IoT) devices, the traditional means of protection are useless and cannot be used to protect these devices. The most important security risks, their causes and the consequences of their occurrence have been listed, scheduled and categorized. The study concluded that there are real security risks that cannot be ignored, and they need to find innovative solutions to eliminate them or reduce their damage to a minimum. This paper showed the main risks addressed in previews research, and outlined the gaps in this field of technology, also producing a brief summary information about the most important solution to avoid many threats against the IoT field.