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).

Among Enterococcus spp, only the virulence gene harboring strains of Enterococcus faecalis and E. faecium are associated with human infections, including urinary tract infections (UTI), pelvic, blood, intraabdominal, and skin and soft tissue infections (SSTI). Over the past decades, enterococcal antimicrobial resistance has escalated in many regions of the world, leading to ominous outcomes. The rising incidence of Healthcare-Associated Infections (HCAIs) secondary to Vancomycin-resistant strain (VRE) resulted in high morbidity and mortality, as well as substantial challenges in control, prevention, and management). The aim of this study is to examine the antimicrobial resistance of E. faecalis and E. faecium species in different human samples. The study included 184 clinical samples over a period of 6 months. E. faecalis was identified in 95.65% and E. faecium in 4.35% of cases. E. faecalis isolates showed resistance to gentamicin in 40.9% of cases and to ampicillin in 1.7% of cases. Resistance to nitrofurantoin and ciprofloxacin was observed in 6.1% and 35.7% of E. faecalis isolates. VRE was isolated in 1.1% of E. faecalis isolates tested for this antibiotic. Resistance of E. faecium isolates to ampicillin and gentamicin was observed in 87.5% of cases in both antibiotics. All urinary isolates of E. faecium were resistant to ciprofloxacin. All E. faecium isolates were sensitive to vancomycin. Based on the results of our study, the growing importance of Enterococcus spp. as a causative agent of hospital infections and infections in the general population, and its antimicrobial resistance to various drugs were observed.

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.

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.

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.

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.

Robots are commonly envisioned as assisting older adults in physical tasks or providing companionship. But there has been less focus on helping older adults achieve more intangible, but equally important, aspects of wellness, such as a feeling of purpose and meaning in life. Here, we share our experiences working and learning together with older adults on developing a robot that can support their achievement of ikigai---meaning or purpose in life.