Abstract Introduction: With advancements in sensor and communication technologies, sleep monitoring is moving out of specialized clinics and into everyday homes. Extracting sleep-related data using far less complicated tools and procedures is possible than polysomnography. Respiratory and cardiovascular data are extracted from the signals such as the electrocardiogram (ECG), photoplethysmogram (PPG), and ballistocardiogram (BCG) to identify the aberrant respiratory events of apnea/hypopnea as well as to estimate sleep parameters. However, due to the different sleeping positions, such systems lack accuracy and/or complicated sensor network topology. In this work, we proposed an optimal topology of forcesensitive resistor (FSR) sensors to simplify the system design by identifying the region of interest for estimating cardiorespiratory parameters with minimal error. The sensors are deployed under the mattress and located on the bed frame. Methods: We proposed a low-cost, unobtrusive, non-invasive, and reliable solution with robust signal processing algorithms for cardiorespiratory measurements and automatic signal validation based on signal quality. The solution is established based on a multi-physical layer (MPL) and sensor interfaces coping with the embedded system’s specifications, and signal processing is performed onboard with two independent and simultaneous pipelines for heart rate and respiratory rate using discrete wavelet transform (DWT) and bandpass filter, respectively. Results: We identified the three most contributing FSR sensors forming a triangle shape covering the left upper side of the subject (in the supine position) as the region of interest. We reduced the mean absolute error (MAE) to as low as 3.94 and 2.35 for heart rate and respiratory rate. Conclusions: The approach with the topology of triangle-shaped performs appropriately in estimating the cardiorespiratory parameters in all four regular sleeping positions, i.e. supine, prone, left lateral, and right lateral.

This paper presents the energy and CO2 saving potential of existing district heating energy system. Analysed system fully rely on fuel oil, with significant energy losses, increased fuel consumption and CO2 emission resulting from outdated and oversized system and fuel with high greenhouse emission factor. Heat generation and thermal energy distribution systems efficiency are assessed, showing that overall system efficiency is 48.5%. System environmental impact is shown via absolute CO2 and specific CO2 emission per heated surface area and useful energy. The study proposes retrofit measures to improve system efficiency, reduce fuel consumption, introduce low-emission fuels, and lower the system’s environmental impact. The study finds that the implementation of these measures could reduce system energy consumption by 42.7%, absolute CO2 emissions by 52%, and specific CO2 indicators as well, highlighting the potential for reducing the environmental impact of district heating systems while meeting users energy needs.

Background: Left atrial (LA) myopathy with paroxysmal and permanent atrial fibrillation (AF) is frequent in chronic coronary syndromes (CCS) but sometimes occult at rest and elicited by stress. Aim: This study sought to assess LA volume and function at rest and during stress across the spectrum of AF. Methods: In a prospective, multicenter, observational study design, we enrolled 3042 patients [age = 64 ± 12; 63.8% male] with known or suspected CCS: 2749 were in sinus rhythm (SR, Group 1); 191 in SR with a history of paroxysmal AF (Group 2); and 102 were in permanent AF (Group 3). All patients underwent stress echocardiography (SE). We measured left atrial volume index (LAVI) in all patients and LA Strain reservoir phase (LASr) in a subset of 486 patients. Results: LAVI increased from Group 1 to 3, both at rest (Group 1 = 27.6 ± 12.2, Group 2 = 31.6 ± 12.9, Group 3 = 43.3 ± 19.7 mL/m2, p < 0.001) and at peak stress (Group 1 = 26.2 ± 12.0, Group 2 = 31.2 ± 12.2, Group 3 = 43.9 ± 19.4 mL/m2, p < 0.001). LASr progressively decreased from Group 1 to 3, both at rest (Group 1 = 26.0 ± 8.5%, Group 2 = 23.2 ± 11.2%, Group 3 = 8.5 ± 6.5%, p < 0.001) and at peak stress (Group 1 = 26.9 ± 10.1, Group 2 = 23.8 ± 11.0 Group 3 = 10.7 ± 8.1%, p < 0.001). Stress B-lines (≥2) were more frequent in AF (Group 1 = 29.7% vs. Group 2 = 35.5% vs. Group 3 = 57.4%, p < 0.001). Inducible ischemia was less frequent in SR (Group 1 = 16.1% vs. Group 2 = 24.7% vs. Group 3 = 24.5%, p = 0.001). Conclusions: In CCS, rest and stress LA dilation and reservoir dysfunction are often present in paroxysmal and, more so, in permanent AF and are associated with more frequent inducible ischemia and pulmonary congestion during stress.

Indoor air quality monitoring is vital for ensuring high-quality healthcare services and minimizing the presence of harmful pollutants and environmental factors that could potentially impact on the well-being of individuals in hospitals. To address this need, the authors developed the transparent robot (TR): an integrated sensorized platform designed for indoor environmental sensing. This Internet-of-Things (IoT)-based platform serves as a modular system that can be installed on robotic platforms, enabling both static and dynamic monitoring of indoor spaces. In the context of a smart hospital, the TR can be integrated with the hospital's software architecture. It collaborates to generate a secure dataset of monitored data and can promptly notify healthcare professionals about any parameters that fall outside acceptable level. By utilizing this IoT-based device's features, hospitals can ensure a safer environment. The system's effectiveness and usability were preliminary demonstrated, showcasing its potential for further development; for instance, by incorporating additional sensors and algorithms, the TR can provide a probabilistic estimation of the likelihood of certain conditions based on the sampled environmental parameters.

Next-generation mobile communication systems are planned to support millimeter Wave (mmWave) transmission in scenarios with high-mobility, such as in private industrial networks. To cope with propagation environments with unprecedented challenges, data-driven methodologies such as Machine Learning (ML) are expected to act as a fundamental tool for decision support in future mobile systems. However, high-quality measurement datasets need to be made available to the research community in order to develop and benchmark ML-based methodologies for next-generation wireless networks. We present a reliable testbed for collecting channel measurements at sub-6 GHz and mmWave frequencies. Further, we describe a rich dataset collected using the presented testbed. Our public dataset enables the development and testing of innovative ML-based channel simulators for both sub-6GHz and mmWave bands on real-world data. We conclude this paper by discussing promising experimental results on two illustrative ML tasks leveraging on our dataset, namely, channel impulse response forecasting and synthetic channel transfer function generation, upon which we propose future exploratory research directions. The original dataset employed in this work is available on IEEE DataPort (https://dx.doi.org/10.21227/3tpp-j394), and the code utilized in our numerical experiments is publicly accessible via CodeOcean (https://codeocean.com/capsule/9619772/tree).

Cilj: Prikazati najčešće razloge odbijanja potencijalnih darivatelja krvi po spolu i usporediti ih između gradova Osijeka i Zagreba. Ispitanici i metode: Istraživanje je provedeno po principu retrospektivne studije tijekom 2021. godine iz baze podataka Hrvatskog zavoda za transfuzijsku medicinu u Zagrebu i Kliničkom zavodu za transfuzijsku medicinu u Osijeku. Korišten je χ2-test, dok je statistička analiza napravljena pomoću programskog sustava MedCalc (inačica 14.12.0, MedCalcSoftware) uz razinu značajnosti od P < 0,05. Rezultati: Hrvatski zavod za transfuzijsku medicinu u Zagrebu kao najčešći razlog odbijanja potencijalnih darivatelja krvi navodi sniženu koncentraciju hemoglobina zbog koje je odbijeno 7 119, od kojih 2 583 (36 %) muškarca i 4 536 (64 %) žena. Također, Klinički zavod za transfuzijsku medicinu u Osijeku odbio je najviše potencijalnih darivatelja krvi zbog snižene koncentracije hemoglobina, njih 660, od kojih 235 (36 %) muškaraca i 425 (64 %) žena. Nakon obrade podataka nema statistički značajne razlike u odbijanju potencijalnih darivatelja krvi zbog hemoglobina u Zagrebu i Osijeku uspoređujući ih po spolu (P = 0,729). Zbog krvnog tlaka odbijenih potencijalnih darivatelja krvi u Zagrebu bilo je 3 855, 2 375 (62 %) muškaraca i 1 480 (38 %) žena. Odbijenih potencijalnih darivatelja krvi zbog krvnog tlaka u Osijeku je bilo 369, 141 (38 %) muškarac i 228 (62 %) žena. Statistički značajna razlika u odbijanju potencijalnih darivatelja krvi zbog krvnog tlaka u gradovima po spolu postoji (P < 0,001). Zaključak: Najčešći razlozi odbijanja dobrovoljnih darivatelja krvi tijekom 2021. godini u Hrvatskom zavodu za transfuzijsku medicinu u Zagrebu i Kliničkom zavodu za transfuzijsku medicinu u Osijeku jesu snižena koncentracija hemoglobina te krvni tlak kod obaju spolova.

At the beginning of 2020, few people could imagine that the new coronavirus, COVID-19, would impact that many aspects of our lives and change the content, structure, and teaching methods we knew before. Many language teachers (LT) worldwide who had been effectively implementing face-to-face instruction had to make an abrupt transition to online education, something they were not trained for or had experience with. The present study aims to discover whether LT successfully delivered online instruction and whether online teaching during the first online period impacted students’ learning habits. Using a specifically designed questionnaire, students who study at several public universities from Bosnia and Herzegovina (B&H), the Republic of North Macedonia (RNM), and Türkiye (TUR) were asked to evaluate their teachers’ professional adaptation and success during the first “emergency online teaching semester”, and to reflect on their learning habits during this period and the changes they personally experienced. The results of the study revealed that students in the three countries approached and evaluated their teachers’ pedagogical skills as appropriate for online teaching in the first COVID-19 period in remarkably similar ways. However, the impact on students’ learning habits is, to a certain extent, different in these countries. The findings of the study might provide relevant input to rethink the teaching profession in terms of competencies, means of instruction, and strategies for coping with processes that affect teaching. Education will not be the same in a post-pandemic world, we must use the knowledge we have gained, and the suggestions made by our students to enhance our educational systems.

Introduction: Prediabetes is a state of impaired glucose homeostasis manifested either by an increase in fasting glucose or a decrease in glucose tolerance. Prediabetes carries a high risk of developing diabetes and cardiovascular complications. Aim: to determine the prevalence of prediabetes in pre-obese and obese individuals and the ten-year risk of developing T2DM in these subjects. Methods: The study was conducted in the family medicine clinics of the Educational Center for Family Medicine (ECFM) of the Banja Luka Health Center and the Tesliü Health Center. The study included pre-obese and obese patients older than 18 years of age, with a body mass index greater than 27.0 kg/m2. The research instrument was a questionnaire with sociodemographic data, a questionnaire for assessing the risk of developing diabetes mellitus type 2 (FINDRISC), and patient anthropometric measurements, arterial blood pressure measurements and laboratory findings (fasting glucose, lipid status, HbA1c) were performed. Results: The research included 264 patients. The prevalence of prediabetes in obese patients is 28.03%, while in pre-obese it is 21.21%. Subjects with a larger waist circumference are more likely to have prediabetes. With increasing age, the chance of getting prediabetes increases, over 30% of obese and pre-obese people who are over 70 years old have prediabetes. In relation to gender, women have a higher chance of developing prediabetes. Data from the FINDRISC questionnaire showed that 1.52% of patients from the pre-obese category have a 50% chance of developing diabetes within ten years, while 13.6% of obese patients have a 50% chance of developing T2DM within ten years. Conclusion: The role of family medicine doctors is in early identification of patients with prediabetes and diabetes, reduction of risk factors and possibly pharmacological treatment of these patients.