A Smart and Secure Logistics System Based on IoT and Cloud Technologies

Recently, one of the hottest topics in the logistics sector has been the traceability of goods and the monitoring of their condition during transportation. Perishable goods, such as fresh goods, have specifically attracted attention of the researchers that have already proposed different solutions to guarantee quality and freshness of food through the whole cold chain. In this regard, the use of Internet of Things (IoT)-enabling technologies and its specific branch called edge computing is bringing different enhancements thereby achieving easy remote and real-time monitoring of transported goods. Due to the fast changes of the requirements and the difficulties that researchers can encounter in proposing new solutions, the fast prototype approach could contribute to rapidly enhance both the research and the commercial sector. In order to make easy the fast prototyping of solutions, different platforms and tools have been proposed in the last years, however it is difficult to guarantee end-to-end security at all the levels through such platforms. For this reason, based on the experiments reported in literature and aiming at providing support for fast-prototyping, end-to-end security in the logistics sector, the current work presents a solution that demonstrates how the advantages offered by the Azure Sphere platform, a dedicated hardware (i.e., microcontroller unit, the MT3620) device and Azure Sphere Security Service can be used to realize a fast prototype to trace fresh food conditions through its transportation. The proposed solution guarantees end-to-end security and can be exploited by future similar works also in other sectors

Leveraging Internet of Things and Distributed Ledger Technology for Cold Chain Management in Freight Transportation

The management of the cold chain in transportation is a crucial issue as it affects the quality and safety of perishable goods such as food, pharmaceuticals, and medical supplies. To ensure proper temperature control and monitoring during transportation, the certification of data is essential. Traditional methods for data management, like paper-based records and manual processes, can be unreliable and cause errors, leading to potential food waste. The integration of Internet of Things and Distributed Ledger Technology (DLT) presents a promising solution for the challenges in cold chain management and can help to improve the overall efficiency of the process. Internet of Things devices can gather and transmit sensor data in real-time, while DLT can ensure the authenticity and immutability of the data. The combination of these technologies can offer a transparent and reliable solution for cold chain management, improving the safety and quality of perishable goods during transportation. Additionally, DLT eliminates the need for intermediaries, enabling direct exchange of data between stakeholders, leading to greater efficiency and accountability. In this paper, we propose a system architecture focused on the cold chain management in transportation that integrates Internet of Things and DLT. In such a system, the Internet of Things component provides real-time data collection whereas the DLT component ensures a secure data management

Architetture al Cubo. Edizione 2019

il volume è interamente dedicato all'architettura sacra contemporanea, proponendo una selezione di edifici visitati da docenti, autori e studenti nel corso dell'anno accademico 2019-2020, di interviste e di contributi teorico-critici su un tema cruciale della progettazione architettonica.the volume is dedicated to contemporary sacred architecture, proposing a selection of buildings visited by teachers, authors and students during the 2019-2020 academic year, interviews and theoretical-critical contributions on a crucial theme of architectural design

Residual respiratory impairment after COVID-19 pneumonia

Abstract Introduction: The novel coronavirus SARS-Cov-2 can infect the respiratory tract causing a spectrum of disease varying from mild to fatal pneumonia, and known as COVID-19. Ongoing clinical research is assessing the potential for long-term respiratory sequelae in these patients. We assessed the respiratory function in a cohort of patients after recovering from SARS-Cov-2 infection, stratified according to PaO2/FiO2 (p/F) values. Method: Approximately one month after hospital discharge, 86 COVID-19 patients underwent physical examination, arterial blood gas (ABG) analysis, pulmonary function tests (PFTs), and six-minute walk test (6MWT). Patients were also asked to quantify the severity of dyspnoea and cough before, during, and after hospitalization using a visual analogic scale (VAS). Seventy-six subjects with ABG during hospitalization were stratified in three groups according to their worst p/F values: above 300 (n = 38), between 200 and 300 (n = 30) and below 200 (n = 20). Results: On PFTs, lung volumes were overall preserved yet, mean percent predicted residual volume was slightly reduced (74.8 ± 18.1%). Percent predicted diffusing capacity for carbon monoxide (DLCO) was also mildly reduced (77.2 ± 16.5%). Patients reported residual breathlessness at the time of the visit (VAS 19.8, p < 0.001). Patients with p/F below 200 during hospitalization had lower percent predicted forced vital capacity (p = 0.005), lower percent predicted total lung capacity (p = 0.012), lower DLCO (p < 0.001) and shorter 6MWT distance (p = 0.004) than patients with higher p/F. Conclusion: Approximately one month after hospital discharge, patients with COVID-19 can have residual respiratory impairment, including lower exercise tolerance. The extent of this impairment seems to correlate with the severity of respiratory failure during hospitalization

Asthma in patients admitted to emergency department for COVID-19: prevalence and risk of hospitalization

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Psychological Distress After Covid-19 Recovery: Reciprocal Effects With Temperament and Emotional Dysregulation. An Exploratory Study of Patients Over 60 Years of Age Assessed in a Post-acute Care Service

To study the long-term psychological effects of Covid-19 disease, we recruited 61 patients older than 60 years of age and administered the Kessler questionnaire K10 to assess psychological distress and classify them according to mental health risk groups. Patients' affective temperaments were assessed with the 39-item form of the Temperament Evaluation of Memphis, Pisa, Paris, and San Diego (TEMPS-A-39) and emotional dysregulation with the Difficulties in Emotion Regulation Scale (DERS). Patients were divided in two samples according to their scores on the K10, i.e., a high likelihood of psychological distress group (N = 18) and a low likelihood of psychological distress group (N = 43). The two groups differed on their gender composition, in that more women (N = 11) were in the former and more men in the latter (N = 29) (χ2 = 4.28; p = 0.039). The high likelihood of psychological distress group scored higher on the Cyclothymic (3.39 ± 3.45 vs. 0.93 ± 1.08, p < 0.001) and the Depressive (2.28 ± 2.82 vs. 0.65 ± 1.09, p = 0.01) affective temperaments of the TEMPS and on the lack of Impulse control (12.67 ± 4.04 vs. 9.63 ± 3.14, p = 0.003) and lack of Clarity (15.00 ± 5.56 vs. 9.85 ± 4.67, p = 0.004) scales of the DERS. Our results show that having had Covid-19 may be related with high likelihood for psychological distress in advanced-age people and this may in turn be associated with impaired emotional regulation and higher scores on depressive and cyclothymic temperaments