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The IEEE Audio Video Bridging (AVB) technology is nowadays under consideration in several automation domains, such as, automotive, avionics, and industrial communications. AVB offers several benefits, such as open specifications, the existence of multiple providers of electronic components, and the real-time support, as AVB provides bounded latency to real-time traffic classes. In addition to the above mentioned properties, in the automotive domain, comparing with the existing in-vehicle networks, AVB offers significant advantages in terms of high bandwidth, significant reduction of cabling costs, thickness and weight, while meeting the challenging EMC/EMI requirements. Recently, an improvement of the AVB protocol, called the AVB ST, was proposed in the literature, which allows for supporting scheduled traffic, i.e., a class of time-sensitive traffic that requires time-driven transmission and low latency. In this paper, we present a schedulability analysis for the real-time traffic crossing through the AVB ST network. In addition, we formally prove that, if the bandwidth in the network is allocated according to the AVB standard, the schedulability test based on response time analysis will fail for most cases even if, in reality, these cases are schedulable. In order to provide guarantees based on analysis test a bandwidth over-reservation is required. In this paper, we propose a solution to obtain a minimized bandwidth over-reservation. To the best of our knowledge, this is the first attempt to formally spot the limitation and to propose a solution for overcoming it. The proposed analysis is applied to both the AVB standard and the AVB ST. The analysis results are compared with the results of several simulative assessments, obtained using OMNeT++, on both automotive and industrial case studies. The comparison between the results of the analysis and the simulation ones shows the effectiveness of the analysis proposed in this work.

In Industrial Internet of Things (IoT) applications, when the network size increases and different types of flows share the bandwidth, the demand for flexible and efficient management of the communication network is compelling. In these scenarios, under varying workload and flow priorities, the combined use of Software-Defined Networking (SDN) and Network Virtualization (NV) is a promising solution, as such techniques allow to reduce the network management complexity. This work presents the PrioSDN Resource Manager (PrioSDN_RM), a resource management mechanism based on admission control for virtualized SDN-based networks. The proposed combination imposes bounds on the resource utilization for the virtual slices, which therefore share the network links, while maintaining isolation from each other. The presented approach exploits a priority-based runtime bandwidth distribution mechanism to dynamically react to load changes (e.g., due to alarms). The paper describes the design of the approach and provides experimental results obtained on a real testbed.

Background Group B Streptococcus (GBS) is a major cause of sepsis and meningitis in newborns. The Centers for Disease Control and Prevention (CDC) recommends to pregnant women, between 35 and 37 weeks of gestation, universal vaginal-rectal screening for GBS colonization, aimed at intrapartum antibiotic prophylaxis (IAP). The latter is the only currently available and highly effective method against early onset GBS neonatal infections. Since the onset of the coronavirus disease 2019 (COVID-19) pandemic, the preventive measures implemented to mitigate the effects of SARS-CoV-2 infection led to the reduction in the access to many health facilities and services, including the obstetric and perinatal ones. The purpose of the present study was to evaluate the prevalence of maternal GBS colonization, as well as use of IAP and incidence of episodes of neonatal GBS infection when antibiotic prophylaxis has not been carried out in colonized and/or at risk subjects, in a population of pregnant women during (years 2020–2021) and after (year 2022) the COVID-19 pandemic, also with the aim to establish possible epidemiological and clinical differences in the two subjects’ groups. Methods We retrospectively analyzed the clinical data of pregnant women admitted to, and delivering, at the Gynaecology and Obstetrics Unit, Department of Sciences for Health Promotion and Mother and Child Care, of the University Hospital of Palermo, Italy, from 01.01.2020 to 31.12.2022. For each of them, we recorded pertinent socio-demographic information, clinical data related to pregnancy, delivery and peripartum , and specifically execution and status of vaginal and rectal swab test for GBS detection, along with eventual administration and modality of IAP. The neonatal outcome was investigated in all cases at risk (positive maternal swabs status for GBS, either vaginal or rectal, with or without/incomplete IAP, preterm labor and/or delivery, premature rupture of membranes ≥ 18 h, previous pregnancy ended with neonatal early onset GBS disease [EOD], urine culture positive for GBS in any trimester of current gestation, intrapartum temperature ≥ 38 °C and/or any clinical/laboratory signs of suspected chorioamnionitis). The data concerning mothers and neonates at risk, observed during the pandemic (years 2020–2021), were compared with those of both subjects’ groups with overlapping risk factors recorded in the following period (year 2022). The chi squared test has been applied in order to find out the relationship between pregnant women with GBS colonization receiving IAP and outcome of their neonates. Results The total source population of the study consisted of 2109 pregnant women, in addition to their 2144 newborns. Our analysis, however, focused on women and neonates with risk factors. The vaginal-rectal swab for GBS was performed in 1559 (73.92%) individuals. The test resulted positive in 178 cases overall (11.42% of those undergoing the screening). Amongst our whole sample of 2109 subjects, 298 women had an indication for IAP (vaginal and/or rectal GBS colonization, previous pregnancy ended with neonatal GBS EOD, urine culture positive for GBS in any trimester of current gestation, and unknown GBS status at labor onset with at least any among delivery at < 37 weeks’ gestation, amniotic membranes rupture ≥ 18 h and/or intrapartum temperature ≥ 38.0 °C), and 64 (21.48%) received adequate treatment; for 23 (7.72%) it was inadequate/incomplete, while 211 (70.8%) did not receive IAP despite maternal GBS colonization and/or the presence of any of the above mentioned risk factors. Comparing the frequency of performing vaginal-rectal swabs in the women admitted in the two time periods, the quote of those screened out of the total in the pandemic period (years 2020–2021) was higher than that of those undergoing GBS screening out of the total admitted in the year 2022 (75.65% vs. 70.38%, p  = 0.009), while a greater number (not statistically significant, p  = 0.12) of adequate and complete IAP was conducted in 2022, than in the previous biennium (26.36 vs. 18.62%). During the whole 3 years study period, as expected, none of the newborns of mothers with GBS colonization and/or risk factors receiving IAP developed EOD. Conversely, 13 neonates with EOD, out of 179 (7.3%) born to mothers with risk factors, were observed: 3 among these patients’ mothers performed incomplete IAP, while the other 10 did not receive IAP. Neither cases of neonatal meningitis, nor deaths were observed. The incidence rate in the full triennium under investigation, estimated as the ratio between the number of babies developing the disease out of the total of 2144 newborns, was 6.06‰; among those born to mothers with risk factors, if comparing the two time periods, the incidence was 8.06% in the pandemic biennium, while 5.45% in the following year, evidencing thus no statistical significance ( p  = 0.53). Conclusions The present study revealed in our Department an increased prevalence of pregnant women screened for, and colonized by GBS, in the last decade. However, an overall still low frequency of vaginal-rectal swabs performed for GBS, and low number of adequate and complete IAP despite the presence of risk factors have been found, which did not notably change during the two time periods. Moreover, significant EOD incidence rates have been reported among children of mothers carrying risk factors, although also in this case no statistically significant differences have been observed during and after the pandemic. Such data seem to be in contrast to those reported during the COVID-19, showing a decrease in the access to health facilities and increased mortality/morbidity rates also due to the restrictive measures adopted to mitigate the effects of the pandemic. These findings might be explained by the presence within the same metropolitan area of our Department of a COVID hospital and birthing center, which all the patients with SARS-CoV-2 infection referred to, and likely leading to a weaker concern of getting sick perceived by our patients. Although IAP is an easy procedure to implement, however adherence and uniformity in the management protocols are still not optimal. Therefore, the prophylactic measures adopted to date cannot be considered fully satisfactory, and should be improved. Better skills integration and obstetrical-neonatological collaboration, in addition to new effective preventive tools, like vaccines able to prevent invasive disease, may allow further reduction in morbidity and mortality rates related to GBS perinatal infection.

Background The lives of many children and adolescents are today increasingly influenced by new technological devices, including smartphones. The coronavirus disease 2019 (COVID-19) pandemic occurred in a time of outstanding scientific progress and global digitalization. Young people had relevant adverse psychological and behavioral effects due to the COVID-19 pandemic, mainly related to infection control measures, which led them to spend more time at home and with major use of technological tools. The goal this study proposes is to evaluate health and social outcomes of smartphone overuse among Italian children and adolescents during the COVID-19 pandemic, analyzing patterns and aims of utilization, as well as the eventual presence and degree of addiction. Methods This study was based on a self-report and anonymous questionnaire, which was administered to 184 Italian school-age (6–18 years) children and adolescents during the second wave of the COVID-19 pandemic. The test was electronically (email, whatsapp) explained and sent by pediatricians either directly to older children (middle and high school), or indirectly, through the help of teachers, to younger ones (primary school). All participants spontaneously and voluntarily joined the present study. The survey was made by 4 sections, and designed to know and outline modalities (frequency, patterns and aims) of smartphone use, adverse outcomes, and related parental behaviors, also in order to reveal the eventual occurrence and degree of addiction. The same information, related to the pre-epidemic period, was also investigated and analyzed. Results The data obtained revealed a significantly greater adhesion to the questionnaire by females, likely reflecting higher attention and interest than boys to initiatives relating to health education. Our study showed more frequent smartphone use among Italian children and adolescents during the COVID-19 pandemic, compared to the pre-epidemic period. This may be related to the social distancing measures adopted during the months under investigation. The present survey also outlined the changing patterns and aims in the use of smartphones among young people, which allowed to limit some effects of the crisis. Indeed, they were used for human connection, learning and entertainment, providing psychological and social support. Finally, it was observed a significant increase of overuse and addiction. This led to many clinical (sleep, ocular and musculoskeletal disorders), psychological (distraction, mood modification, loss of interest) and social (superficial approach to learning, isolation) unfavorable outcomes. Conclusions Pediatricians and health care professionals should be aware of the potential risks related to inappropriate use of smartphones. They should monitor, in cooperation with parents, possible associated adverse effects, in order to early recognize signs and symptoms suggestive, or at high risk, for addiction. They must carry out, as well, the necessary interventions to prevent and/or lower the detrimental impact of smartphone overuse on children and adolescents’ health, oriented to sustain adequate physical and psychological development as well as social relationships.

Automated driving requires correct perception of the surrounding environment in any driving condition. To achieve this result, not only are many more sensors than in current Advanced Driver Assistant Systems (ADAS) needed, but such sensors are also of different types, such as radars, ultrasonic sensors, LiDARs, and video cameras. Given the high number of sensors and the bandwidth requirements of some of them, high-bandwidth automotive-grade networks are required. Ethernet technology is a suitable candidate, as it offers a broad selection of automotive-grade Ethernet physical layers, with transmission speeds ranging from 10 Mbps to 10 Gbps. In addition, the Time-Sensitive Networking (TSN) family of standards offers several features for Ethernet-based networks that are suitable for automotive communications, such as high reliability, bounded delays, support for scheduled traffic, etc. In this context, this paper provides an overview of Ethernet-based in-car networking and discusses novel trends and future developments in automotive communications.

This study presents a novel approach to passive human counting in indoor environments using Bluetooth Low Energy (BLE) signals and deep learning. The motivation behind this research is the need for non-intrusive, privacy-preserving occupancy monitoring in sensitive indoor settings, where traditional camera-based solutions may be unsuitable. Our method leverages the deformations that BLE signals undergo when interacting with the human body, enabling occupant detection and counting without requiring wearable devices or visual tracking. We evaluated three deep neural network models—Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and a hybrid CNN+LSTM architecture—under both classification and regression settings. Experimental results indicate that the hybrid CNN+LSTM model outperforms the others in terms of accuracy and mean absolute error. Notably, in the regression setup, the model can generalize to occupancy values not present in the fine-tuning dataset, requiring only a few minutes of calibration data to adapt to a new environment. We believe that this approach offers a valuable solution for real-time people counting in critical environments such as laboratories, clinics, or hospitals, where preserving privacy may limit the use of camera-based systems. Overall, our method demonstrates high adaptability and robustness, making it suitable for practical deployment in diverse indoor scenarios.

The Deterministic and Synchronous Multichannel Extension (DSME) of the IEEE 802.15.4 standard was designed to fulfill the requirements of commercial and industrial applications. DSME overcomes the IEEE 802.15.4 limitation on the maximum number of Guaranteed Time Slots (GTS) in a superframe and it also exploits channel diversity to increase the communication reliability. However, DSME suffers from scalability problems, as its multi-superframe structure does not efficiently handle GTS in networks with a high number of nodes and periodic flows. This paper proposes the enhanceD DSME (D-DSME), which consists of two extensions that improve the DSME scalability and reliability exploiting a GTS within the multi-superframe to accommodate multiple flows or multiple retransmissions of the same flow. The paper describes the proposed extensions and the performance results of both OMNeT simulations and experiments with real devices implementing the D-DSME.

Passive Human Sensing (PHS) is an approach to collecting data on human presence, motion or activities that does not require the sensed human to carry devices or participate actively in the sensing process. In the literature, PHS is generally performed by exploiting the Channel State Information variations of dedicated WiFi, affected by human bodies obstructing the WiFi signal propagation path. However, the adoption of WiFi for PHS has some drawbacks, related to power consumption, large-scale deployment costs and interference with other networks in nearby areas. Bluetooth technology and, in particular, its low-energy version Bluetooth Low Energy (BLE), represents a valid candidate solution to the drawbacks of WiFi, thanks to its Adaptive Frequency Hopping (AFH) mechanism. This work proposes the application of a Deep Convolutional Neural Network (DNN) to improve the analysis and classification of the BLE signal deformations for PHS using commercial standard BLE devices. The proposed approach was applied to reliably detect the presence of human occupants in a large and articulated room with only a few transmitters and receivers and in conditions where the occupants do not directly occlude the Line of Sight between transmitters and receivers. This paper shows that the proposed approach significantly outperforms the most accurate technique found in the literature when applied to the same experimental data.

Passive human sensing approaches based on the analysis of the radio signals emitted by the most common wireless communication technologies have been steadily gaining momentum during the last decade. In this context, the Bluetooth technology, despite its widespread adoption in mobile and IoT applications, so far has not received all the attention it deserves. However, the introduction of the Bluetooth direction finding feature and the application of Artificial Intelligence techniques to the processing and analysis of the wireless signal for passive human sensing pave the way for novel Bluetooth-based passive human sensing applications, which will leverage Bluetooth Low Energy features, such as low power consumption, noise resilience, wide diffusion, and relatively low deployment cost. This paper provides a reasoned analysis of the data preprocessing and classification techniques proposed in the literature on Bluetooth-based remote passive human sensing, which is supported by a comparison of the reported accuracy results. Building on such results, the paper also identifies and discusses the multiple factors and operating conditions that explain the different accuracy values achieved by the considered techniques, and it draws the main research directions for the near future.