Sleep is essential to physical and mental health. However, the traditional approach to sleep analysis—polysomnography (PSG)—is intrusive and expensive. Therefore, there is great interest in the development of non-contact, non-invasive, and non-intrusive sleep monitoring systems and technologies that can reliably and accurately measure cardiorespiratory parameters with minimal impact on the patient. This has led to the development of other relevant approaches, which are characterised, for example, by the fact that they allow greater freedom of movement and do not require direct contact with the body, i.e., they are non-contact. This systematic review discusses the relevant methods and technologies for non-contact monitoring of cardiorespiratory activity during sleep. Taking into account the current state of the art in non-intrusive technologies, we can identify the methods of non-intrusive monitoring of cardiac and respiratory activity, the technologies and types of sensors used, and the possible physiological parameters available for analysis. To do this, we conducted a literature review and summarised current research on the use of non-contact technologies for non-intrusive monitoring of cardiac and respiratory activity. The inclusion and exclusion criteria for the selection of publications were established prior to the start of the search. Publications were assessed using one main question and several specific questions. We obtained 3774 unique articles from four literature databases (Web of Science, IEEE Xplore, PubMed, and Scopus) and checked them for relevance, resulting in 54 articles that were analysed in a structured way using terminology. The result was 15 different types of sensors and devices (e.g., radar, temperature sensors, motion sensors, cameras) that can be installed in hospital wards and departments or in the environment. The ability to detect heart rate, respiratory rate, and sleep disorders such as apnoea was among the characteristics examined to investigate the overall effectiveness of the systems and technologies considered for cardiorespiratory monitoring. In addition, the advantages and disadvantages of the considered systems and technologies were identified by answering the identified research questions. The results obtained allow us to determine the current trends and the vector of development of medical technologies in sleep medicine for future researchers and research.

Background: Patients with acute pulmonary embolism (PE) may have various types of atrial fibrillation (AF). The role of AF in hemodynamic states and outcomes may differ between men and women. Methods: In total, 1600 patients (743 males and 857 females) with acute PE were enrolled in this study. The severity of PE was assessed using the European Society of Cardiology (ESC) mortality risk model. Patients were allocated into three groups according to their electrocardiography recordings taken during hospitalization: sinus rhythm, new-onset paroxysmal AF, and persistent/permanent AF. The association between the types of AF and all-cause hospital mortality was tested using regression models and net reclassification index (NRI) and integrated discrimination index (IDI) statistics with respect to sex. Results: There were no differences between the frequencies of the types of AF between men and women: 8.1% vs. 9.1% and 7.5% vs. 7.5% (p = 0.766) for paroxysmal and persistent/permanent AF, respectively. We found that the rates of paroxysmal AF significantly increased across the mortality risk strata in both sexes. Among the types of AF, the presence of paroxysmal AF had a predictive value for all-cause hospital mortality independent of mortality risk and age in women only (adjusted HR, 2.072; 95% CI, 1.274–3.371; p = 0.003). Adding paroxysmal AF to the ESC risk model did not improve the reclassification of patient risk for the prediction of all-cause mortality, but instead enhanced the discriminative power of the existing model in women only (NRI, not significant; IDI, 0.022 (95% CI, 0.004–0.063); p = 0.013). Conclusion: The occurrence of paroxysmal AF in female patients with acute PE has predictive value for all-cause hospital mortality independent of age and mortality risk.

We present constraints on the left-handed dimension-6 interactions that contribute to semileptonic and leptonic decays of K, D, pions and to nuclear beta decay. We employ the flavour covariant description of the effective couplings, identify universal CP phases of New Physics and derive constraints from decay rates and CP-odd quantities. As a result, we can predict the maximal effects of such flavoured NP in D decays from stringent K decay constraints and vice-versa.

RATIONALE Tobacco smoking and air pollution are primary causes of chronic obstructive pulmonary disease (COPD). However, only a minority of smokers develop COPD. The mechanisms underlying the defense against nitrosative/oxidative stress in non-susceptible smokers to COPD remain largely unresolved. OBJECTIVES To investigate the defense mechanisms against nitrosative/oxidative stress that possibly prevent COPD development or progression. METHODS Four cohorts were investigated: (1) sputum samples (healthy, n=4; COPD, n=37), (2) lung tissue samples (healthy, n=13; smokers without COPD, n=10; smoker+COPD, n=17), (3) pulmonary lobectomy tissue samples (no/mild emphysema, n=6) and (4) blood samples (healthy, n=6; COPD, n=18). We screened 3-nitrotyrosine (3-NT) levels, as indication of nitrosative/oxidative stress, in human samples. We established a novel in vitro model of a cigarette smoke extract (CSE)-resistant cell line and studied 3-NT formation, antioxidant capacity, and transcriptomic profiles. Results were validated in lung tissue, isolated primary cells and an ex vivo model using adeno-associated virus-mediated gene transduction and human precision-cut lung slices (hPCLS). MEASUREMENTS AND MAIN RESULTS 3-NT levels correlate with COPD severity of patients. In CSE-resistant cells, nitrosative/oxidative stress upon CSE was attenuated, paralleled by profound upregulation of heme-oxygenase-1 (HO-1). We identified carcinoembryonic-antigen-related-cell-adhesion-molecule-6 (CEACAM6) as a negative regulator of HO-1-mediated nitrosative/oxidative stress defense in human alveolar type 2 epithelial cells (hAEC2). Consistently, inhibition of HO-1 activity in hAEC2 increased the susceptibility towards CSE-induced damage. Epithelium-specific CEACAM6 overexpression increased nitrosative/oxidative stress and cell death in hPCLS upon CSE treatment. CONCLUSIONS CEACAM6 expression determines the hAEC2 sensitivity to nitrosative/oxidative stress triggering emphysema development/progression in susceptible smokers.

The Message Queuing Telemetry Transport (MQTT) protocol is one of the most widely used IoT protocol solutions. In this work, we are especially interested in open-source MQTT Broker implementations (such as Mosquitto, EMQX, RabbitMQ, VerneMQ, and HiveMQ). To this end, we engineer a network testbed to experimentally benchmark the performance of these implementations in an edge computing context with constrained devices. In more detail, we engineer an automated deployment and orchestration of the containerized MQTT broker implementations, with support for deployment across either moderately powerful AMD64 devices, or more resource constrained ARM64 devices. The proposed MQTT implementations are evaluated in terms of overhead response time and different payload sizes. Results showed that the hardware platform used as well as the message size, and the network parameters (latency, packet loss and jitter) have a significant impact on the performance differences between the brokers. All results, software tools and code are fully reproducible and free and open source.

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.

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.

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.

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.

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.