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fully rely on fuel oil, with significant energy losses, increased fuel consumption and CO2 emission resulting from outdated and oversized system and fuel with high greenhouse emission factor.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.

Highlights • Microarray analysis of dorsal root ganglia from mice subjected to collagen antibody-induced arthritis (CAIA) and controls revealed that arthritis leads to differential expression of 120 circular RNA genes.• Bioinformatical analysis indicates that altered levels of circRNAs in DRG is associated with sensitization-related processes.• Microarray or RT-qPCR analysis showed increased levels of circVps13 and circMicall1 in the inflammatory phase and circNufip1 the late “post-inflammatory” phase in DRG from mice subjected to CAIA.

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).

Abstract The aim of this study was to examine the seroprevalence of Leptospira spp. in dogs and red foxes in the entity of the Republic of Srpska, Bosnia and Herzegovina, after heavy rainfall and floods in 2014 and for the two years thereafter. The seroepidemiological study involved testing serum samples from dogs (n = 98) and foxes (n = 112) using MAT (microscopic agglutination test). Antibodies to at least one Leptospira spp. serovar were found in 52.04% of the tested dogs. The dog seroprevalence in 2014 (81.25%) was significantly higher than in 2015 (51.42% p <0.0001) and 2016 (22.5% p<0.05). The highest seroprevalences were for serovars Australis (76.47%), Bratislava (70.58%), Sejroe (66.67%) and Autumnalis (45.09%). Antibodies to at least one Leptospira spp. serovar were detected in 34.82% of the examined red foxes. In 2015, the fox seroprevalence was significantly higher (52.94%) than in 2016 (6.82%) (p <0.0001). The highest seroprevalences were for serovars Sejroe (64.10%), Bratislava (48.72%), Australis (43.59%) and Bataviae (25.64%). The high seroprevalence of Leptospira spp. in dogs and foxes determined during this study indicates the importance of these carnivores in maintaining leptospirosis in the study area, and the potential risk of infection for humans and other animal species that come into contact with these canids. The results obtained indicate that heavy rainfall and intense floods can result in increased Leptospira spp. infection in these canids.

Objective: The main objective of this research is to determine the prevalence and characteristics of neurological manifestations in hospitalized patients with SARS-CoV-2 infection. Methods: A cross-sectional study was conducted. 572 hospitalized patients at the COVID Department of Pulmonology of the Mostar University Clinical Hospital in the six-month period from October 31, 2020, to April 30, 2021, were included. We analyzed the incidence of neurological manifestations and the influence of comorbidities and metabolic syndrome on stroke incidence in COVID-19 patients. We analyzed hospital length of stay and mortality in patients with and without neurological manifestations. The research was conducted with respect to all the determinants of the Helsinki Declaration. Results: 572 patients, 351 men (61.4%), and 221 women (38.6%) were included. A fatal outcome was present in a quarter of patients (25.3%). Neurological manifestations were found in 163 patients (28.5%). Myalgia was the most common (16.1%). The following were reported: headache (9.6%), loss of taste (7.34%), loss of smell (6.8%), and vertigo (2.5%). There was a significant difference regarding loss of smell between males and females (p=0.04). The cerebrovascular incident was present in 2.97% of patients and was more frequent in the group of patients with metabolic syndrome. Patients with neurological manifestations had a longer hospital stay, but it was not statistically significant (p=0.9319). The presence of neurological manifestations in general did not influence the mortality rate. Conclusion: Patients with SARS-CoV-2 infection can present with neurologic findings such as myalgia, headache, loss of smell or taste, vertigo, as well as cerebrovascular incidents. Patients with neurological manifestations had longer hospital stays, but the presence of neurological manifestations in general did not influence the mortality rate.

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.