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.

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.

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.

The aim of this work is to optimize the design of a sensor-actuator measurement system for identifying local variations in the magnetic permeability of cut steel sheets. Before the identification problem, i.e. finding the material parameters causing the measurement data, is solved, the design of the measurement setup should first be improved in order to increase the identifiability of the material distribution. For the objective function of the design optimization the Fisher information matrix (FIM) is used, which allows to quantity the amount of information that the measurements carry about the unknown parameters. To evaluate the magnetic field values depending on various design parameters and material distributions, a 2D magneto-static problem is solved by the finite element method. Due to the high numerical effort arising with evaluating the FIM and thus calculating the forward model many times within the optimization procedure, a surrogate model of the sensor-actuator system has been trained in advance and is used to predict the magnetic flux densities.

Understanding physical effects occurring, for example, in the electromagnetic field, can be challenging. To ease the learning experience, it is beneficial to visualize and encourage interaction with the physical field. Augmented Reality (AR) can serve as a tool to visualize naturally invisible fields to help students understand physical effects. In this paper, we present a workflow to incorporate field results stemming from a FEM tool or simple analytical solutions into an augmented reality (AR) experience. We focus on providing a simple framework for educators to integrate this tool into school or university teaching. We present a workflow to process simulation results for AR and provide source material through a template and a guide so that educators can quickly translate their projects into (augmented) reality. The basis for this project is the game engine Unity, which can be used free of charge for educators. Combined with other free or open-source programs for visualization and preparation like openCFS and Paraview, this setup can be used freely by anybody for education.

This study analyzes financial literacy in Bosnia and Herzegovina by considering three areas: interest, inflation, and diversification, with financial literacy as a multi-dimensional construct consisting of financial knowledge and financial skills. Using a cross-sectional questionnaire-based survey, 638 valid responses were collected from working-age individuals (18-65 years old). Financial knowledge and skills were analyzed through a prism of several demographic factors, including age, education, household income, and gender. Welch's F tests, ANOVA with Brown-Forsythe, LSD post hoc tests, and Welch's t-tests were performed to test the hypotheses. The findings provide evidence of moderate financial literacy. Similarly to previous studies, financial knowledge and skills partially depend on the respondent's age, education, household income, and gender. The study contributes to the current literature by taking a much-needed non-functional approach to examining financial literacy, focusing not only on financial knowledge but also on often neglected financial skills and providing insight into the unique context of Bosnia and Herzegovina.

The purpose of this study was to compare attitudes toward sexting using vignettes. Participants were 49 university students (ages 19-26) who participated in five online focus group discussions and responded to five written vignettes describing sexting. The five vignettes about sexting were composed of five types of sexting experiences: 1) sexting under intimate partner pressure, 2) revenge sexting, 3) consensual sexting with intimate partner, 4) sexting under peer pressure, and 5) sexting to flirt with others. Students gave their opinions on the vignettes presented. Revenge sexting was perceived as a behaviour more negative than any other type of sexting experience. The vignette that depicted sexting with an intimate partner in a long-distance relationship was perceived as the least negative of all types of sexting. The results of this study aim to inspire future studies to use vignettes as a methodological tool to determine youths' attitudes, beliefs, and opinions about sexting.

Diabetes is a widespread disease, suffered by millions, including children. Treatment of diabetes type 1 and sometimes even type 2, entails multiple blood glucose checks and insulin injections per day, and can thus be extremely exhausting, especially for very young children. Open-loop systems of insulin delivery, insulin pumps, used today commercially, require human interaction which can lead to low blood glucose control due to human mistakes. Fully automated closed-loop systems of artificial pancreas, as one-hormone as well as dual-hormone systems, are being developed. This paper is the literature survey of the latest research on the automated closed-loop artificial pancreas. The objective of this paper is to explore the development of devices and techniques to facilitate the daily life of diabetic patients with emphasis on the latest research on the topic. From so-called pens to open-loop systems of insulin pumps, closed-loop systems with user interaction -hybrid closed-loop, to the latest fully automatized closed-loop -artificial pancreas. In total 300 articles are reviewed from which 150 articles are retained for the literature survey and 50 are analysed in this literature review.

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.