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Artykuł jest przyczynkiem do badań hagiologicznych w zakresie poszukiwania odpo­wiedzi na pytanie o specyfikę doświadczenia mistycznego. Jest opracowaniem teologiczno-hagiograficznym, dostarcza bowiem danych biograficznych, które stanowią źródło do dalszych badań hagiologicznych w różnych aspektach. Źródła franciszkańskie ukazują Świętą Klarę jako „kobietę eucharystyczną”, której życie można nazwać egzystencją paschalną. Tym obrazem jest ukazany charyzmat franciszkański, skupiony na misterium Wcielenia i Zmartwychwstania. Osiągnięciem badań przyczynkowych, ukazanych w artykule, jest wskazanie korelacji między dziejami życia człowieka świątobliwego a teologią będącą refleksją nad ogółem wydarzeń i sytuacji, w których Bóg objawia swoje misterium i wprowadza w nie człowieka.

In the context of smart campuses, effective emergency management is crucial for ensuring the safety and well-being of students, staff, and visitors. This paper presents a comprehensive support tool designed to enhance emergency management on smart campuses, integrating a low-cost people-counting system based on cameras and Raspberry Pi devices. It introduces a newly designed architecture and user interfaces that enhance the functionality and user experience of a smart campus disaster management system. Finally, a usability evaluation has been carried out to validate the brand-new user interfaces devoted to emergency management.

Urban noise is one of the most serious and underestimated environmental problems. According to the World Health Organization, noise pollution from traffic and other human activities, negatively impact the population health and life quality. Monitoring noise usually requires the use of professional and expensive instruments, called phonometers, able to accurately measure sound pressure levels. In many cases, phonometers are human-operated; therefore, periodic fine-granularity city-wide measurements are expensive. Recent advances in the Internet of Things (IoT) offer a window of opportunities for low-cost autonomous sound pressure meters. Such devices and platforms could enable fine time-space noise measurements throughout a city. Unfortunately, low-cost sound pressure sensors are inaccurate when compared with phonometers, experiencing a high variability in the measurements. In this paper, we present RaveGuard, an unmanned noise monitoring platform that exploits artificial intelligence strategies to improve the accuracy of low-cost devices. RaveGuard was initially deployed together with a professional phonometer for over two months in downtown Bologna, Italy, with the aim of collecting a large amount of precise noise pollution samples. The resulting datasets have been instrumental in designing InspectNoise, a library that can be exploited by IoT platforms, without the need of expensive phonometers, but obtaining a similar precision. In particular, we have applied supervised learning algorithms (adequately trained with our datasets) to reduce the accuracy gap between the professional phonometer and an IoT platform equipped with low-end devices and sensors. Results show that RaveGuard, combined with the InspectNoise library, achieves a 2.24% relative error compared to professional instruments, thus enabling low-cost unmanned city-wide noise monitoring.

The global population is progressively entering an aging phase, with population aging likely to emerge as one of the most-significant social trends of the 21st Century, impacting nearly all societal domains. Addressing the challenge of assisting vulnerable groups such as the elderly and disabled in carrying or transporting objects has become a critical issue in this field. We developed a mobile Internet of Things (IoT) device leveraging Ultra-Wideband (UWB) technology in this context. This research directly benefits vulnerable groups, including the elderly, disabled individuals, pregnant women, and children. Additionally, it provides valuable references for decision-makers, engineers, and researchers to address real-world challenges. The focus of this research is on implementing UWB technology for precise mobile IoT device localization and following, while integrating an autonomous following system, a robotic arm system, an ultrasonic obstacle-avoidance system, and an automatic leveling control system into a comprehensive experimental platform. To counteract the potential UWB signal fluctuations and high noise interference in complex environments, we propose a hybrid filtering-weighted fusion back propagation (HFWF-BP) neural network localization algorithm. This algorithm combines the characteristics of Gaussian, median, and mean filtering, utilizing a weighted fusion back propagation (WF-BP) neural network, and, ultimately, employs the Chan algorithm to achieve optimal estimation values. Through deployment and experimentation on the device, the proposed algorithm’s data preprocessing effectively eliminates errors under multi-factor interference, significantly enhancing the precision and anti-interference capabilities of the localization and following processes.

Non-Typhoidal Salmonellas (NTSs) are diffused worldwide. In Italy, more than 3500 cases are notified each year, but despite this, data about salmonellosis are scarce. Our multi-center, retrospective, descriptive study selected 252 patients with positive cultures for Salmonella spp. (feces 79.8%, blood 8.7%, feces and blood 7.5%), aiming to describe the epidemiological and clinical characteristics of this population. Bacteremic infections constituted 16.3% (41/252) of patients. Extreme ages (≤12 and ≥65 years old) accounted for 79.7%, but only elder ages were strongly associated with bacteremic infections (aOR 5.78). Invasive infections had a 7-fold higher mortality rate than non-invasive disease (9.8% vs. 1.4%, p = 0.013), with an overall rate of 2.8%. The two more represented serogroups were O:4 (52.8%) and O:9 (22.2%). The O:9 serogroup was strongly associated with a higher frequency of invasive infection (aOR 2.96, 95% CI 1.17–7.63). In particular, S. napoli, an emerging serovar in Europe, accounted for 31.7% of bacteremic infections and only 9.5% of non-bacteremic ones (p < 0.001). Antibiotic microbial resistance (AMR) observed a steep increasing trend and was detected in 60.4% of cases (122/202): amoxicillin/clavulanate, ampicillin, and gentamicin were most commonly involved (26.7%, 21.8%, 14.3%, respectively), while TMP/SMX, ciprofloxacin, and ceftriaxone were