On exploiting Data Visualization and IoT for Increasing Sustainability and Safety in a Smart Campus

In a world that is getting increasingly digital and interconnected, and where more and more physical objects are integrated into the information network (Internet of Things, IoT), Data Visualization can facilitate the understanding of huge volumes of data. In this paper, we present the design and implementation of a testbed where IoT and Data Visualization have been exploited to increase the sustainability and safety of the Cesena (Smart) Campus. In particular, we detail the overall system architecture and the interactive dashboard that facilitates the management of the campus premises and the timetabling. Exploiting our system, we show how we can improve the campus sustainability (in terms of energy saving) and safety (considering the COVID-19 restrictions and regulations)

On combining Big Data and machine learning to support eco-driving behaviours

A conscious use of the battery is one of the key elements to consider while driving an electric vehicle. Hence, supporting the drivers, with information about it, can be strategic in letting them drive in a better way, with the purpose of optimizing the energy consumption. In the context of electric vehicles, equipped with regenerative brakes, the driver\u2019s braking style can make a significant difference. In this paper, we propose an approach which is based on the combination of big data and machine learning techniques, with the aim of enhancing the driver\u2019s braking style through visual elements (displayed in the vehicle dashboard, as a Human\u2013Machine Interface), actuating eco-driving behaviours. We have designed and developed a system prototype, by exploiting big data coming from an electric vehicle and a machine learning algorithm. Then, we have conducted a set of tests, with simulated and real data, and here we discuss the results we have obtained that can open interesting discussions about the use of big data, together with machine learning, so as to improve drivers\u2019 awareness of eco-behaviours

Barriers in the management of cancerrelated pain and strategies to overcome them: findings of a qualitative research involving physicians and nurses in Italy

Introduction and aims. There are many barriers and obstacles that even today lead to an inadequate treatment of cancer-related pain. The aim is to describe the experiences of a group of Italian physicians and nurses as far as the nature of these barriers is concerned and the possible tools to be used to overcome them. Material and method. We run 5 focus groups with 42 healthcare professionals (11 physicians, 31 nurses) working in 5 hospitals in Italy. The findings of the focus groups were analysed according to the “Content Analysis” method. Results. Five main items emerged: the importance of communication, the need for education regarding pain therapy, the ethnic/cultural/religious differences, the mutual trust and support within the working group, the daily challenges. Conclusion. In harmony with the most recent literature, physicians and nurses voice above all their need for an education more directly aimed at overcoming the prevailing barriers rooted in ignorance, prejudice and fears

Palatosquisis en la especie canina

La palatosquisis o paladar secundario hendido es una de las anomalías congénitas más corrientes que presentan los cachorros alnacimiento. Se describen las técnicas más habituales de corrección y la utilizada por los autores.Paiaiosquisis or secondary cleft palate is one of the most frequent congenital abnormalities shown by newborn pups. Commonly used correction techniques and that used by the authors are described

Analysis of 19 Minerals and Cortisol in Red Deer Hair in Two Different Areas of the Stelvio National Park: A Preliminary Study

The aim of the study was to perform an investigation on the concentration of 19 minerals and cortisol in red deer (Cervuselaphus) hair, a matrix that is easy to collect with non-invasive and painless sampling, able to represent an integrative values of long-term substance concentrations, and able to give useful information, also when performed on dead animals, given its extreme stability over time. In the study thirty-five animals were included, coming from two different sides of a valley in the Stelvio National Park, where official water analysis had pointed out elevated concentrations of As in one of the two orographic sides. Hair cortisol concentrations were measured using a RIA(Radio Immuno Assay), while minerals were detected using ICP-MS (Inductively Coupled Plasma- Mass Spectrometry). Results showed a negative relationship between cortisol and some mineral concentrations (Li, Co, As, Cd, Cr and Tl) and significant differences in some mineral concentrations between park areas (Al, Co, Cu, Cd and Ni). As, Cr and cortisol differences approached statistical significance. This preliminary study represents a step forward in the study of wildlife allostatic load and a valid method for applications in wildlife management programs, in environmental studies and in public health programs

Perception of nurses’ professional identity during the first wave of COVID-19 pandemic infections

Background and aim of the work: The main purpose of this study is to investigate on the experience of nurses working in the Covid-19 area focusing on their role’s perception. In particular, we explored the nurses’ perception of job satisfaction in relation to the images sent back by public opinion through the mass media and social communication channels. During the first wave of Covid-19 nurses have acquired media visibility, but their feeling is represented more by the discomfort of finding themselves suddenly glorified in the face of a lack of professional, social, and economic recognition. Materials and methods: A Mix-Method methodology and convenience sampling was adopted, on the population of professionals and students in post-graduate specializations, belonging to the Department of Medicine and Surgery of the University of Parma, and by nurses from the ASST-Bergamo Est, Lombardia Italy, who worked in the Covid emergency during the first wave of the pandemic, from February 2020 to May 2020. In the quantitative phase Stamm’s Professional Quality of Life Scale - ProQOL was administered to 89 respondents through a Google Form, In the qualitative phase, 3 Focus Groups were conducted on a total of 17 students. Results: At the ProQOL questionnaire, a moderate score was found in the Compassion Satisfaction scale (CF = 38.28) and in the Secondary Traumatic Stress subscale (STS-24.33), while low values emerged in the Burnout subscale (BO = 16.02). Five specific topics emerged from the focus groups: Professional collaboration, Job satisfaction, Nurse’s personal skills, Failure to protect the public image and the nursing profession. Conclusions: The professional collaboration, union with the work team, sense of solidarity, job satisfaction, professional growth, and awareness of one’s role seem to have worked favorably on Compassion Satisfaction, while keeping Compassion Fatigue levels under control

Efficient Parallel Statistical Model Checking of Biochemical Networks

We consider the problem of verifying stochastic models of biochemical networks against behavioral properties expressed in temporal logic terms. Exact probabilistic verification approaches such as, for example, CSL/PCTL model checking, are undermined by a huge computational demand which rule them out for most real case studies. Less demanding approaches, such as statistical model checking, estimate the likelihood that a property is satisfied by sampling executions out of the stochastic model. We propose a methodology for efficiently estimating the likelihood that a LTL property P holds of a stochastic model of a biochemical network. As with other statistical verification techniques, the methodology we propose uses a stochastic simulation algorithm for generating execution samples, however there are three key aspects that improve the efficiency: first, the sample generation is driven by on-the-fly verification of P which results in optimal overall simulation time. Second, the confidence interval estimation for the probability of P to hold is based on an efficient variant of the Wilson method which ensures a faster convergence. Third, the whole methodology is designed according to a parallel fashion and a prototype software tool has been implemented that performs the sampling/verification process in parallel over an HPC architecture