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The advent of the Internet of Things (IoT) paradigm highlighted the importance of core technologies at the root of any distributed systems, as wireless communications, cloud computing and big data analysis. Today, companies and organizations, pursuing the so-called Industry 4.0 (r)evolution, are moving toward smart factories, smart workforce, and intelligent transportation. The use of IoT technologies for connecting smart vehicles is identified by the IoV acronym, i.e. the Internet of Vehicle. In this work, the authors address the characterization of a distributed measurement platform for vehicle fleet management, focusing on time-related performance. In particular, a commercially available solution has been considered and real-world use cases have been identified, showing obtainable performance: with default configuration, an average overall delay less than 11 s has been estimated.

In the last years, the mobility sector is undergoing a significant transformation. Electric Vehicles (EVs) are progressively replacing traditional Internal Combustion Engines (ICEs), thanks to the lack of direct Green House Gas (GHG) and pollutant emissions: a great step toward decarbonized and sustainable energy systems. Nevertheless, the growing diffusion of EVs is also introducing relevant concerns, particularly about the placement and installation of Electric Vehicle Supply Equipment (EVSE). The total amount of electric power required by each charge point is a serious limiting issue: EVSE can be installed only in those locations where the distribution grid is able to feed it. The research project MoSoRe addresses this problem by proposing a multimodal EVSE able to work both in islanded and grid-tied modes, thanks to the use of a local photovoltaic systems, an electrochemical energy storage system, and an auxiliary generator. In this research work, the architecture of the multimodal EVSE, the requirements in terms of the types of charging sockets, power sources, and the related control strategies, will be discussed in detail, along with the required communication interfaces. The strategies used for the power management of both the loads and generators of the multimodal EVSE were validated in a simulation environment.

The climate changes are progressively influencing the energetic policies in every field. The mobility sector is also under a deep transformation: the Internal Combustion Engine (ICE) vehicles are progressively replaced by Electric Vehicles (EVs). In the case of the Hybrid Electric Vehicles (HEVs), the traditional engine coexists with the electric one, reducing the overall environmental impact of the vehicles. Currently, only a limited number of EVs are fully electric, the so called Battery Electric Vehicles (BEVs), but their impact on the distribution grid is increasing day by day. BEVs require the availability of Electric Vehicle Supply Equipment (EVSE) to recharge on-board battery. Each of the charging point of a EVSE could typically feed up 22 kW active power (or more in the case of mode 4, according to IEC 61851–1 standard), more than the consumption of three private houses, for approximately a couple of hours (the modern BEVs are equipped generally with a 50 kWh on-board battery to guarantee a useful range). Thus, the installation of EVSEs represents a critical task for Distribution System Operators (DSOs): adequate power supply lines should be identified to be able to install an EVSE. The target of the MoSoRe project is to design a multimodal EVSE able to exploit the energy provided by renewable generators in order to improve sustainability of EV charging and to minimize its impact on the power grid. In that case, a proper charging of the EVs requires the EVSE to work in coordinate mode: a strong interaction between the DSO, the EVSE and the customer is needed. The target of the proposed research work is to investigate the is to analyze the communication requirements of the EVSE in order to support coordinate charge of multimodal EVSE. The behavior of the proposed multimodal EVSE has been modeled in simulation environment, demonstrating the validity of the proposed approach to reduce the impact of EVs on the distribution grid in case of a coordinate charge is implemented.

One of the most widespread applications of IoT technologies is smart metering. Despite remote metering in smart grids is not a novelty, the advent of Low Power Wide Area Network (LPWAN) wireless technologies allowed to extend the concept to gas and water distribution. However, these battery-supplied meters must ensure an operating lifetime longer than 10 years without any maintenance, thus leading to battery oversizing. This work focuses on LoRaWAN and proposes an innovative adaptive strategy for equalizing the message time duration in order to maximize the battery exploitation. A simulator has been purposely designed to evaluate the performance in a real-world scenario, considering a node density of hundreds of nodes per square km. The simulation results demonstrate that a better exploitation of the battery is possible, ensuring an increase by 10 times of the amount of the amount of useful user data bytes transferred by meters.

Background and objectives: We studied a large series of ductal carcinoma in situ with microinvasion (MIDC) an infrequent disease whose diagnosis and management are not well defined. Methods: 17,431 cases of breast carcinoma were treated between 2011 and 2016 by ten Italian Breast Units. Our analysis included diagnostic and clinic-pathological characteristics, surgical management, and the use of adjuvant therapies. Results: 15,091 cases (86.6%) were infiltrating carcinomas (IC), 2107 (12.1%) ductal carcinoma in situ (DCIS), and 233 (1.3%) MIDC. Age at diagnosis did not differ between DCIS and MIDC. MIDC were usually larger and expressed more frequently biologically aggressive features (higher Ki67 values, hormone receptor negativity and HER2/neu over-expression) (p < 0.01). Axillary lymph nodes were involved in 25 MIDC cases (12%), but >3 lymph nodes were involved in two cases only (1%). At multivariable analysis, only lymphovascular invasion (LVI) was associated with lymph node status (p < 0.01). Hormone therapy was prescribed in 388/1462 DCIS cases (26.5%), in 84/200 MIDC cases (42%), and in 11,086/14,188 IC cases (84.7%) (p < 0.01). Chemotherapy was administered in 28/190 MIDC cases (14.7%), and in 4080/11,548 IC cases (35.3%) (p < 0.001). Conclusions: This is one of the largest studies of MIDC reported in the literature. Approximately 10% of DCIS harbor one or more foci of MIDC, and the latter often expresses aggressive biological features. LVI is a predictor of axillary node involvement, but this is infrequent and usually limited. Conservative surgery is performed less often than in DCIS, and adjuvant chemotherapy is less frequently utilized compared to IC.

An early Alzheimer Disease (AD) diagnosis is fundamental for maximizing the effectiveness of treatment administration. Unfortunately, distinguish AD from other neurodegenerative dementias, such as Frontotemporal Dementia (FTD) is not trivial. Transcranial Magnetic Stimulation (TMS) emerged as an effective non-invasive, easy to apply and not time-consuming solution. TMS-based techniques generally require expensive ad hoc clinical equipment that suffer from poor flexibility and user friendliness in defining the specific diagnostic protocol. In this work, a low-cost BeagleBone Black single board computer (BBB-SBC) has been used to implement all the functionalities required to manage a TMS-based instrument. Timeliness of signal generation is guaranteed by dedicated programmable real-time units hosted by the BBB-SBC System on Chip. Web-based interface, complemented by IoT-like features, provide a high degree of versatility and permit the execution of many different diagnostic protocols. In particular, the experimental validation confirms timing error in the sub-microsecond range, more than enough for the considered application.

Location based services (LBS) have emerged as an active area of research aimed to filter and organize the information present on the internet. This work deals with LBS applied to industrial Internet of Things (IoT) applications, ranging from distributed measurement systems (DMS) to distributed human -machine interface (HMI). The goal is to investigate the available techniques to determine the location of assets (e.g., sensors and actuators) and then to characterize some application scenarios. In detail, the use cases have been implemented exploiting the eLUX Living Laboratory in Brescia, Italy, which resembles a smart grid, smart living and Industry 4.0 real scenario thanks to the numerous installed plants (including renewable sources, indoor sensory systems and electric vehicles). The ranging performance of beacon based and ultrawide band -based localization principles have been experimentally assessed.

Background: Immune Activation (IA) has been previously documented in both pregnant (PG) and non-PG HIV-1 infected (HIV+) women as well as in HIV- uninfected PG women; the latter as a result of the fetal allograft. To determine whether the combined effects of HIV and pregnancy result in increased IA and whether IA is associated with Microbial Translocation (MT), we performed a prospective, longitudinal, controlled study during pregnancy and the postpartum (PP) period. Methods: HIV+ PG women had biomarkers of IA and MT tested at 12-20 weeks (T1), and 24-36 weeks (T2) of pregnancy and at 6-8 weeks Postpartum (T3). HIV+, non-PG women were tested at comparable time points. HIV- PG women were tested at T1 only. HIV+ women were not started on antiretroviral therapy (ART) until T1. Biomarkers of IA assessed included: CD4DR+, CD4CD38+, CD4DR+CD38+, CD8DR+, CD8CD38+, and CD8DR+CD38+. Biomarkers of MT included LPS, sCD14, and 16SrDNA. Results: 30 HIV+PG women, 18 HIV+ non-PG and 10 HIV-PG were enrolled. In the HIV+ women, there were no differences in median age, viral load, % or absolute CD4 at entry. Significant differences between T1 and T2 and between T1 and T3 were noted in CD8DR+CD38+ in HIV+PG women after ART. CD4DR+, CD4DR+CD38+, and CD8DR+ decreased post ART in HIV+PG women but a decline in IA was less evident in HIV+ non-PG. LPS decreased post ART by T3 in both HIV+PG and HIV+ non-PG groups; 16SrDNA was elevated at all time points in both groups when compared to control values, and declined post ART in the HIV+PG group. A subgroup of HIV-PG at T1 had IA and MT as evidenced by several IA markers and increased LPS. Conclusion: The degree of IA and MT was similar among HIV+PG and HIV+ non-PG women followed longitudinally. There was no incremental increase due to the combined effects of HIV and pregnancy. Several markers of IA and MT (LPS, 16SrDNA) decreased post ART. IA and MT occurred in a subgroup of HIV-PG women during the 1st trimester. Further study must be done to confirm whether MT consistently occurs in some healthy women during PG.

Information and operational technologies merge into the so-called industrial Internet of Things, which is one of the basic pillars of the Industry 4.0 paradigm. Roughly speaking, yet-to-come services will be offered in the automation scenario by industrial devices having an internet connection for sharing data in the cloud. Currently, most efforts are in the development of protocols able to ensure horizontal interoperability among heterogeneous applications. Consequently, poor attention is devoted to time-related performance. In this paper, a new, full software, platform-independent approach is proposed for experimentally evaluating the delay in transferring information across local and intercontinental routes by applications leveraging on messaging middleware. The application is realized using the node-RED web-based framework, due to its availability on different platforms; the widely accepted message queue telemetry transport protocol has been chosen thanks to its low overhead and complexity. For sake of completeness, five different, private and public, brokers are used. The adopted industrial-grade hardware, complemented by global positioning system time reference, permits an overall synchronization and timestamping accuracy of a few milliseconds. The vast measurement campaign highlighted that, generally, quality of service (QoS) type 1 offers low end-to-end delay (average value less than 0.5 s) with reduced variability (0.1 s). However, the maximum end-to-end one-way delay ranges from 1 s for QoS 0 to less than 1.5 s for fully acknowledged QoS 2.

This paper investigates the ultimate limits of White Rabbit (WR), an high-accuracy time distribution system based on field-programmable gate array (FPGA). The knowledge of such limits is essential for new emerging applications that are evaluating WR. In this paper, we identify and study the key elements in the WR synchronization: the digital dual mixer time difference phase detector and the Gigabit Ethernet transceiver. The benchmarks and experimental analysis of these key elements allow us to determine the WR switch (WRS) performance limits and evaluate their evolution with newer FPGAs. The identified performance limits are achievable by the present-day generation of WRS. The ultimate limits of short-term synchronization performance due to FPGA implementation have been derived through analysis and then demonstrated using the existing WRS enhanced with an additional daughterboard. This combination (WRS and daughterboard) achieves a tenfold improvement in terms of phase noise, jitter, and short-term stability with respect to the current WR performance. Both phase detectors and Gigabit transceivers have a similar phase noise contribution equal to a short-term stability of modified Allan deviation 4E−13 at $\tau = 1$ s (dominated by flicker phase modulation noise).

BACKGROUND: Assays to estimate human immunodeficiency virus (HIV) reservoir size require large amounts of blood, which represents a drawback especially in pediatric settings. We investigated whether HIV-antibody repertoire could estimate the viral reservoir size. Moreover, we assessed the magnitude of HIV-antibody response as a predictor of time of antiretroviral therapy (ART) initiation. METHODS: Human immunodeficiency virus-antibody responses to 10 different viral proteins were evaluated by HIV Western blot (WB) kit and a WB score was assigned to each patient. Patients were classified in 2 subgroups based on the timing of ART initiation (early treated [ET], 0–24 weeks and late treated [LT], >24 weeks). Human immunodeficiency virus-deoxyribonucleic acid (DNA) was quantified using real-time quantitative polymerase chain reaction on total peripheral blood mononuclear cells. Logistic regression and principal component analysis were built on these data to test the ability of WB score to predict the expected value of HIV-DNA and the timing of ART initiation. RESULTS: Sixty-nine perinatally HIV-infected children were evaluated. Reduced HIV-specific antibody responses and lower size of HIV-DNA were observed in ET compared with LT patients (P < .001 and P = .02, respectively). We found that WB score correlates with HIV-DNA (P = .032) and timing of ART initiation (P < .001). Based on the logistic regression analysis, we found that WB score can predict the HIV-DNA size and the timing of ART initiation with an Akaike information criterion of −118.13 and −151.51, respectively. CONCLUSIONS: Western blot score can estimate HIV-DNA size and timing of ART initiation in long-term virally suppressed children. This rapid, inexpensive, and easily reproducible tool can provide useful information to identify potential candidates for HIV remission studies.

Combination antiretroviral therapies (cART) can lead to normal life expectancy in HIV-infected persons, and people aged >50 yrs represent the fastest growing HIV group. Although HIV and aging are independently associated with impaired humoral immunity, immune status in people aging with HIV is relatively unexplored. In this study influenza vaccination was used to probe age associated perturbations in the B cell compartment of HIV-negative “healthy controls” (HC) and virologically controlled HIV-infected participants on cART (HIV) (n=124), grouped by age as young (

HIV-infected patients of all ages frequently underperform in response to seasonal influenza vaccination, despite virologic control of HIV. The molecular mechanisms governing this impairment, as well as predictive biomarkers for responsiveness, remain unknown. This study was performed in samples obtained prevaccination (T0) from HIV-infected children who received the 2012–2013 seasonal influenza vaccine. Response status was determined based on established criterion for hemagglutination inhibition titer; participants with a hemagglutination titer ≥1:40 plus a ≥4-fold increase over T0 at 3 wk postvaccination were designated as responders. All children had a history of prior influenza vaccinations. At T0, the frequencies of CD4 T cell subsets, including peripheral T follicular helper (pTfh) cells, which provide help to B cells for developing into Ab-secreting cells, were similar between responders and nonresponders. However, in response to in vitro stimulation with influenza A/California/7/2009 (H1N1) Ag, differential gene expression related to pTfh cell function was observed by Fluidigm high-density RT-PCR between responders and nonresponders. In responders, H1N1 stimulation at T0 also resulted in CXCR5 induction (mRNA and protein) in CD4 T cells and IL21 gene induction in pTfh cells that were strongly associated with H1N1-specific B cell responses postvaccination. In contrast, CD4 T cells of nonresponders exhibited increased expression of IL2 and STAT5 genes, which are known to antagonize peripheral Tfh cell function. These results suggest that the quality of pTfh cells at the time of immunization is important for influenza vaccine responses and provide a rationale for targeted, ex vivo Ag-driven molecular profiling of purified immune cells to detect predictive biomarkers of the vaccine response.

The current perspective about urban development expects 70% of energy consumption will be concentrated in the cities in 2050. In addition, a growing density of people in the urban context leads to the need for increased security and safety for citizens, which imply a better lighting infrastructure. Smart solutions are required to optimize the corresponding energy effort. In developing countries, the cities&rsquo, lighting is limited and the lighting world map is strongly significant about the urban density of the different areas. Nevertheless, in territories where the illumination level is particularly high, such as urban contexts, the conditions are not homogenous at the microscale level and the perceived security is affected by artificial urban lighting. As an example, 27.2% of the families living in the city of Milan, ombardy Region, Italy, consider critical the conditions of lighting in the city during the night, although the region has diffused infrastructure. The paper aims to provide a local illuminance geographic information system (GIS) mapping at the neighborhood level that can be extended to the urban context. Such an approach could unveil the need to increase lighting to enhance the perceived safety and security for the citizens and promote a higher quality of life in the smart city. Lighting mapping can be matched with car accident mapping of cities and could be extended to perceived security among pedestrians in urban roads and green areas, also related to degradation signs of the built environment. In addition, such an approach could open new scenarios to the adaptive street lighting control used to reduce the energy consumption in a smart city: the perceived security of an area could be used as an additional index to be considered during the modulation of the level of the luminosity of street lighting. An example of a measurement set-up is described and tested at the district level to define how to implement an extensive monitoring campaign based on an extended research schema.

Recently, designers of the home automation framework are looking to the Internet of Things (IoT) model to make it easier to deploy devices capable of gathering data from various plants. This situation is pushing a further evolution: from the conventional building automation, where the logic is determined by the installer or by the customer, to a cognitive system, capable of learning from the customers’ preferences what the best plant configuration would be. The aim of this research work is to identify issues related to the implementation of cognitive solutions during the renovation process of buildings. The paper proposes a cognitive framework to help the operation and management phases of buildings, by means of the digitalization of the supply chain of the construction industry, from the single building aspect to the entire construction cycle. The paper presents the design of smart building elements with integrated capability to generate a massive amount of data for monitoring the energy and maintenance status of renovated buildings. The IoT paradigm has been used to link the physical word of building elements with the cognitive layer. The proposed approach has been applied and validated into a real test case.

The Internet of Things applied to Industry (IIoT) is one of the pillars of Industry 4.0. The connection to Internet of all the industrial devices, and the storage (sharing) of enormous amount of data inside Cloud database, enable the development of analysis algorithms for the delivery of new "high value" services. However, the industrial market may require response with short predictable latency (real-time response) even for supervision and optimization functions. Often today, the estimation of the latency of new services is not taken into account. For all these reasons, this work introduces an experimental measurement procedure for investigating the impact of Cloud database services on the communication delay from the production line to the Cloud and the way back (feedback). In this paper, a use case demonstrates the feasibility of the proposed test methodology. In details, the considered use case is a predictive maintenance system with Siemens S7 industrial automation controller sending data to a Cloudant database inside the IBM Bluemix platform. The use case results show that the IIoT solutions based on Cloud database services can be easily evaluated, compared (and optimized) thanks to the proposed approach.

It is common for elderly to experience a decrease in health conditions, limiting independence. Very often, hospitalization is not required and at home assistance is a more effective solution. Ambient assisted living technologies can help in mitigating the need for continuous supervision, enabling the elderly to easily look for help in case of emergency. In this work, the well-known and widespread LoRaWAN communication infrastructure is evaluated for possible complementing indoor and outdoor positioning solutions, in order to alert a remotely-connected caregiver about accidents (e.g., falls). The choice of LoRaWAN is dictated by the capability to implement both private and public infrastructures. Satellite-based systems are addressed for outdoor localization, whereas the demanding task of indoor localization is solved by means of Ultra Wide Band technology. Results demonstrated sub-meter error in a typical indoor scenario, with average communication latency on the order of 700 ms.

The requirements of distributed systems implemented using IoT paradigms include the sharing of a common time reference. When the applications are intended for industrial scenarios, the desired time synchronization uncertainty decreases, due to the real-time needs of this kind of systems. Currently, the most diffused way to obtain a time reference for an Internet connected device is to use NTP (Network Time Protocol) for querying time servers. This paper is focused on industrial IoT applications and it is aimed to evaluate the impact that NTP synchronization performance and NTP throughput may have on the real-time host network. Analytical formulas for estimating the NTP impact are introduced. The work is complemented by the presentation of a use case that shows the use of NTP for the synchronization of a large industrial plant at field level. The results highlight the threat to the real-time behavior of the automation system caused by the uncontrolled peaks of traffic due to NTP. Last, some design considerations for mitigation of the impact are discussed.

Perinatally HIV-infected children (PHIV), despite successful antiretroviral therapy, present suboptimal responses to vaccinations compared to healthy-controls (HC). Here we investigated phenotypic and transcriptional signatures of H1N1-specific B-cells (H1N1-Sp) in PHIV, differentially responding to trivalent-influenza-vaccine (TIV), and HC. Patients were categorized in responders (R) and non-responders (NR) according to hemagglutination-inhibition-assay at baseline and 21 days after TIV. No differences in H1N1-Sp frequencies were found between groups. H1N1-Sp transcriptional analysis revealed a distinct signature between PHIV and HC. NR presented higher PIK3C2B and NOD2 expression compared to R, confirmed by downregulation of PIK3C2B in resting-memory of R after H1N1 in-vitro stimulation. In conclusion this study confirms that qualitative rather than quantitative analyses are needed to characterize immune responses in PHIV. These results further suggest that higher PIK3C2B in H1N1-Sp of NR is associated with lower H1N1 immunogenicity and may be targeted by future modulating strategies to improve TIV responses in PHIV.

In the last few years, the technology re-evolution has deeply transformed several aspects of everyday life. For sure, one technology with a strong impact is the so-called Internet of Things (IoT). The IoT paradigm made it possible to break down the data barrier between the vertical domains on which the traditional information and communication technology (ICT) world was organized. Recently, the designers of home automation systems have begun looking to the IoT paradigm to ease the deployment of systems that are able to collect data from different plants. Such a situation has driven further evolution from the traditional automation system, where logic is defined by the programmer or by the user, to a cognitive system that is able to learn from the user&rsquo, habits regarding what should be the best configuration of plants. Several countries are funding renovations of public and private buildings for improving energy efficiency. Generally, such renovations are only focusing on the structure of the building and of its energy performance (e.g., the thermal envelope, window units, air-conditioning plants, and renewable generators) and largely ignoring the use of intelligent devices. On the contrary, scientific literature and practice have demonstrated that the wider use of IoT sensors, as well as distributed and remote intelligence, is fundamental to optimize energy consumption. This research work aimed to identify issues due the application of cognitive solutions during the renovation phase of buildings. In particular, the paper presents a cognitive architecture to support the operation and management phases of buildings, thanks to the massive digitalization of the entire supply chain of the construction sector from the single building element to the entire construction process. Such an architecture is capable of combining data from the IoT sensors and actuators of smart objects installed during the renovation phase, as well as legacy building automation systems. As an indication of the capability of the proposed solution, an intelligent window device was developed and validated. Within the Energy, Life Styled, and Seismic Innovation for Regenerated Buildings (ELISIR) project, window units are equipped with sensors to monitor indoor and outdoor condition behaviours of users. In addition, windows are able to react to changes in the environment by means of actuators that enable motorized opening and shading. Thanks to the cognitive layer designed in the project, the window is able to automatically define the best rules for opening and shading by using the local controller to satisfy user&rsquo, s habits and energy efficiency targets. The cognitive layer defines the appropriate rules for opening and shading using the decision tree algorithm applied to the data generated by the sensors in order to infer users&rsquo, preferences. For this research, two prototypes of the window units were installed in two offices of Politecnico di Milano, Italy. The accuracy of this algorithm to classify the users&rsquo, behaviour and preferences was found to be around 90%, considering an observation interval of two months.

In the last years, the industrial automation has experienced a deep transformation known as Industry4.0, and it is driven by Internet of Things (IoT) paradigm. The IoT-based automation is based on well-defined data models, which make easy the interaction among devices. Generally, the data generated by IoT sensors are elaborated to obtain value added services (such as predictive maintenance), exploiting cloud services and remote servers. An accurate timestamp of the data generated by sensors is required to maintain an adequate level of such services: an “easy” task in the case of a new deployment, but a nightmare when existing plants or machinery are retrofitted. In this case, the data are timestamped at cloud level, using the remote time. In such situations, a knowledge of the sense of time of cloud services is fundamental to guarantee the quality of data elaboration. The target of the research is an experimental characterization and a comparison of time awareness of different commercial cloud service providers (i.e. Amazon AWS, Google Cloud and Microsoft Azure). The characterization highlights as, generally, the performance provided by different platform is comparable each other. The time offset of NTP (Network Time Protocol) clients running on different Virtual Machines (VMs) has an uncertainty ranging from 0.05 ms up to 0.6 ms depending by the client configuration. Such results demonstrate that extreme care must be taken when using the time of remote VMs.

Early initiation of antiretroviral therapy (ART) in vertically HIV-infected children limits the size of the virus reservoir, but whether the time of treatment initiation (TI) can durably impact host immune responses associated with HIV infection is still unknown. This study was conducted in PBMC of 20 HIV-infected virally suppressed children on ART (mean age 9.4 y), classified as early treated (ET; age at ART initiation ≤0.5 y, n = 14) or late treated (LT; age at ART initiation 1–10 y, n = 6). Frequencies and functions of Ag-specific CD4 (CD40L+) and CD8 (CD69+) T cells were evaluated by intracellular IL-2, IFN-γ, and TNF-α production with IL-21 in CD4 or CD107a, granzyme B and perforin in CD8 T cells following stimulation with HIV gp140 protein (ENV) or GAG peptides by multiparameter flow cytometry. ET showed a higher proportion of cytokine-producing ENV- and GAG-specific CD4 and CD8 T cells compared with LT. In particular, ET were enriched in polyfunctional T cells. RNA sequencing analysis showed upregulation of immune activation pathways in LT compared with ET. Our results suggest that timing of TI in HIV-infected children has a long-term and measurable impact on the quality of the HIV-specific T cell immune responses and transcriptional profiles of PBMC, reinforcing the importance of early TI.

Breast cancer treatment has deeply changed in the last decades, since clinical and oncological cure cannot be achieved without patient's satisfaction in term of aesthetic outcomes. Several methods have been proposed to objectively assess these results. However, Italian breast centers have not yet agreed on measurable, reproducible and validated aesthetic outcome indicators to monitor their performance.The study was designed and conducted by Senonetwork, a not-for-profit association of Italian breast centers. Ten breast centers were selected based on specific eligibility criteria. This multicentre observational prospective study recruited 6515 patients with diagnosis of in situ or invasive breast cancer who underwent breast surgery in the years 2013-2016. Thirteen indicators of aesthetic results and of related quality of care were analyzed. Data collection and analysis were conducted using a common study database.On average, seven out of ten centers were able to collect data on the proposed indicators with a proportion of missing values 25%. By expert consensus based on study results, some seven indicators have been defined as "mandatory" while the remaining six have been defined as "recommended" because they require further refinement before they can be proposed for monitoring aesthetic outcomes or because there are doubts on the feasibility of data collection. The minimum standard is reached for 5 of 13 indicators. This finding and the wide range between centers reveal that there is ample room for improvement.From the present study useful measurable aesthetic parameters have emerged, leading to the definition of target objectives that breast centers can use for benchmarking and improvement of quality of care.

It is widely known that physical activity helps preventing several diseases. However, unsupervised training often results in low exercise quality, ineffective training, and, in worst cases, injuries. Automatic tracking and quantification of exercises by means of wearable devices could be an effective mean for the monitoring of exercise correctness. As a consequence, such devices could help motivating people, thus improving the quantity of performed physical exercise, with positive effects on users’ health conditions. However, despite the availability of several commercial devices, the performance and effectiveness are not well documented. This work proposes a new solution for fitness workout supervision exploiting machine learning techniques, in particular Linear Discriminant Analysis for analyzing data coming from wearable Inertial Measurement Units. Efforts have been done in order to reduce the computational requirements, thus assuring compatibility in perspective of embedded implementation. The experimental tests carried out to assess the proposed approach performance showed an accuracy in exercise detection over 93% and error in exercise counting less than 6%.

The Smart City (SC) paradigm is based on the integration of Information and Communication Technology (ICT) into the urban asset, for the optimal management of the energies and resources. The Internet of Thing (IoT) technology seems the proper solution to achieve this target, thanks to its capability to abstract the object in the real world. The deployment of IoT devices at different level in urban infrastructures is causing the presence of thousands of intelligent devices, large part of them with unused computational capabilities. Such devices could be integrated in a cluster in order to share the unused resources with other devices with limited computational resources. The use of a cluster of IoT Sensors has several benefits, including, but not limited to: high availability, sharing of computational resources, reduced response time with the respect of centralized cloud computing solution. The main bottleneck of this approach is represented by the communication infrastructure, typically based on wireless connection and, thus with a limited available bandwidth. The aim of the work related to this paper is to analyze the impact the communication infrastructure has on computational performance of a cluster of IoT sensors. An experimental set-up for the characterization of the performance of a cluster of low-cost off-the-shelf devices has been described. The experimental validation highlighted as the network infrastructure is loaded only during the data transfer and the maximum network load, with a cluster of ten IoT nodes is approximately 2 Mb/s with the considered benchmark.

Anti-retroviral therapy (ART) improves life expectancy in people living with HIV (PWH), but it remains unclear how chronic HIV infection affects normal aging of the immune system. Plasma cell-free protein expression and immune phenotypes were assessed in blood from ART treated PWH (19–77yrs, n=106) and age-matched, HIV-negative controls (HC, n=103). Age-associated parameters were identified in HC and PWH independently by univariate Spearman correlation (p Plasma biomarker analysis resulted in 8-parameter inflammatory index that was higher in PWH compared to HC and correlated with age. Combining flow and plasma data generated 277 and 491 age-associated parameters (out of total 1,357) in HC and PWH, respectively. PWH exhibited different immunological profiles associated with age compared to HC. However, CD38 expression on T cells declined with age in both HC and PWH. Predictive modeling using age-associated parameters in HC generated a 14-parameter signature with 76.9% accuracy to predict chronological age in HC, however this model failed to predict age in PWH (18.6% accuracy) highlighting the effect of HIV infection on immunological aging. A 25-parameter model (IMmunological Age Prediction, IMAP-25) however showed 70% and 53% accuracy in HC and PWH and was used to show an accelerated aging rate in PWH by 5.6 yrs compared to HC. We applied a statistical approach to generate immune signatures, easily monitored in human blood samples, that could be used as an indicator of one’s ‘immunological age’ during ART-treated HIV infection.

Distributed measurement and control systems often rely on time synchronization protocols over Ethernet. Recently, it has been shown that estimating and compensating for not only time offsets, but also frequency skews is of crucial importance to meet the accuracy requirements of high-end applications. In this respect, this paper presents a new technique capable of: 1) measuring the jitter of multiple reference signals received by a single node over different network paths and 2) combining them in order to synthesize a downstream signal with a more stable frequency than using a single reference. The proposed algorithm can exploit the native frequency transport of Synchronous Ethernet and relies on a module for outlier identification and removal, a Kalman filter, and a linear quadratic regulator that drives a local voltage-controlled oscillator. The algorithm performance has been evaluated through both simulations and experiments using a prototype platform based on a field-programmable gate array. Simulation and experimental results confirm that the proposed approach can strongly decrease the jitter and wander of the generated signal. For instance, the time deviation is reduced on average by about 30% when three stable input references are merged and by several orders of magnitude when one of the input signals is affected by a large frequency step.

The installation of Power Quality (PQ) meters is becoming mandatory in many countries to face the problems introduced by the increasing number of renewable energy sources with intermittent generation. Unfortunately, the current situation is unbalanced: PQ meters are installed on High Voltage (HV) transmission lines and sometimes on Medium Voltage (MV) lines, while the highest number of renewable generators is connected to Low Voltage (LV) grid. In this paper, a novel measurement system for the analysis of voltage dips is proposed. If compared with existing similar systems, the proposed distributed measuring system has the following new characteristics: it is composed of a network of PQ meters on HV, MV and also on LV lines; it uses a heterogeneous communication infrastructure to interconnect meters; it has a cost-effective network-based time synchronization instead of costly GPS solutions; it implements a distributed algorithm to determine the origin of PQ problems. This novel approach overcomes scalability issue, which currently is limiting a larger penetration of PQ systems. The proposed distributed system for monitoring of voltage dips has been deployed in a real distribution grid in Italy. The paper reports analyses on data collected during an entire year. Voltage dips have been correlated and analyzed thanks to the time synchronization of the system.

In the Industry 4.0 the communication infrastructure is derived from the Internet of Things (IoT), and it is called Industrial IoT or IIoT. Smart objects deployed on the field collect a large amount of data which is stored and processed in the Cloud to create innovative services. However, differently from most of the consumer applications, the industrial scenario is generally constrained by time-related requirements and its needs for real-time behavior (i.e., bounded and possibly short delays). Unfortunately, timeliness is generally ignored by traditional service provider, and the Cloud is treated as a black box. For instance, Cloud databases (generally seen as &ldquo, Database as a service&rdquo, &mdash, DBaaS) have unknown or hard-to-compare impact on applications. The novelty of this work is to provide an experimental measurement methodology based on an abstract view of IIoT applications, in order to define some easy-to-evaluate metrics focused on DBaaS latency (no matter the actual implementation details are). In particular, the focus is on the impact of DBaaS on the overall communication delays in a typical IIoT scalable context (i.e., from the field to the Cloud and the way back). In order to show the effectiveness of the proposed approach, a real use case is discussed (it is a predictive maintenance application with a Siemens S7 industrial controller transmitting system health status information to a Cloudant DB inside the IBM Bluemix platform). Experiments carried on in this use case provide useful insights about the DBaaS performance: evaluation of delays, effects of involved number of devices (scalability and complexity), constraints of the architecture, and clear information for comparing with other implementations and for optimizing configuration. In other words, the proposed evaluation strategy helps in finding out the peculiarities of Cloud Database service implementations.

In this paper a general model for the estimation of the uncoordinated charging costs of Electric Vehicles (EVs) in the presence of distributed and intermittent generation, and variable electricity tariffs is presented. The proposed method aims at estimating the monthly average cost of uncoordinated charging of a single EV depending on the hour at which the EV is plugged into the EV Supply Equipment (EVSE). The feasibility and relevance of the proposed model is verified by applying the considered cost estimation method to a suitable use case. A single EV charging service offered at a public building equipped with a Photovoltaic (PV) system has been considered as reference case. The proposed model has been applied to the PV production and loads consumption data collected during one year, and the results of the study compared with the Time-Of-Use (TOU) electricity tariff. The application of the proposed model identified noticeable deviations among the computed EV charging costs and the reference TOU profile, with differences up to 40%, depending on the considered month and on the time of charging during the day. It can be concluded that such model could be used to properly detect opportunities of energy savings, and to define dedicated EV price signals that could help to promote the optimal use of distributed energy resources.

The availability of low-power wide area networks solutions opens new scenario in many application sectors. Among these technologies, long-range wide area network (LoRaWAN) was originally designed for consumer internet-of-things systems, but now it is investigated for distributed measurement systems applied to more demanding applications, including industry. In order to fulfill real-time requirements, the LoRaWAN end devices (EDs) are frequently requested to share a common timebase (i.e., to be synchronized). This paper deals with the tradeoff between the desired synchronization uncertainty and the energy available in the ED, which is usually battery powered. A new approach based on two algorithms for a posteriori synchronization and uncertainty estimation is proposed. After experimental validation of the proposed algorithms, a set of optimal energy/uncertainty tradeoff curves is obtained with the help of a specific tradeoff algorithm. Finally, the proposed tradeoff methodology is applied to two use cases concerning the design of: an industrial time division multiple access system and a predictive maintenance framework.

Memory B cells (MBC) respond to secondary antigen challenge to protect against infection and to boost immunity following vaccinations. Despite effective treatment, chronic HIV infection disturbs MBCs by reducing numbers and altering functionality due to hyper-activation and increased apoptosis leading to suboptimal antibody responses against common infectious agents. We used single cell gene expression analysis to evaluate antigen-specific memory B cells in peripheral blood of virally-suppressed HIV-infected individuals and healthy controls stratified by serum H1N1 antibody response 3 weeks post-administration of the seasonal trivalent inactivated influenza vaccine. We used a fluorescent probe to isolate influenza H1N1-specific B cells and a multiplexed and targeted RT-PCR approach to measure expression levels of 96 genes involved in B cell activation and function. Gene profiling revealed a 4-gene predictive signature containing the phosphoinositide-3 kinase (PI3K) inhibitor, PTEN, for identifying antigen-specific MBC from HIV-infected individuals compared to healthy controls. Gene co-expression analysis showed that in addition to overexpression of PTEN, there was increased co-expression of type I interferon-associated genes with PTEN on single cell level in HIV compared to controls. This study highlights the persistent defects in MBC from HIV-infected individuals and points to the PI3K signaling pathway as a target for potential immune intervention.

An efficient energy management requires an exchange of information among different energy grids, such as electricity, gas, and heating. This paper analyzes the open metering system (OMS), a set of protocols used to provide the communication in a multiutility grid, with special focus on its capability of distributing the time information over the infrastructure. Currently, synchronization accuracy on the order of seconds is more than enough for the traditional metering applications, while more advanced power quality and monitoring systems may require synchronization on the order of hundreds of microsecond or less (as stated by the national and international normative for dynamically controlling loads or renewable power generator on the local microgrid). In this paper, advantages and limits of the current standard, when wireless M-bus (wM-bus) is used as a physical layer, are highlighted by means of the analysis of the synchronization uncertainty sources, and by the experimental evaluation. Next, a custom synchronization method is applied for the first time, since the current OMS standard does not propose any solutions. The analysis and the experimental characterization indicate that the synchronization limit on the last-mile segment of wM-bus, using off-the-shelf components, is on the order of 1.8 $\mu \text{s}$ . The proposed methodology can easily be extended to other communication protocols adopting similar architectures.

The ongoing transition towards the distributed generation paradigm is significantly affecting the way modern Distribution Networks (DNs) must be designed and operated. New functions are being introduced year over year, by following the increasing complexity of grid codes. As a consequence, the adoption of smart protection systems is becoming a key-enabling solution for the application of advanced control and protection mechanisms, such as those implemented by Interface Protection Systems (IPSs). In this case, particularly when complex legacy systems are considered, the replacement of existing devices with intelligent units is indeed often not sufficient, and proper communication infrastructures must be deployed to meet the required coordination capabilities, thus increasing the overall costs. To overcome this drawback, in this study the use of a wireless network based on the LoRaWAN technology is proposed to implement the coordination of IPSs. Based on the real use-case of the Engineering campus of the University of Brescia, the coordination of independent low voltage generators connected to the same medium voltage point of common coupling with the main grid is examined in terms of the required coverage range and communication latency. Finally, the scalability of the proposed solution is discussed by referring to the estimated data throughput, and by considering the LoRaWAN duty cycle limitations.

The recent advances in Information and Communication Technology (ICT) are laying down the foundations of future energy systems. The growing availability of reliable and cost-effective ICT solutions are indeed offering the opportunity for the implementation of advanced monitoring and control functions over Distributed Energy Resources (DERs), thus fostering the transition towards decentralized and decarbonized energy systems. The integration of ICT in electrical equipment (such as power converters, metering devices, and protections) is essential for the monitoring and the coordinated control of heterogeneous energy resources, particularly in intermittent and distributed scenarios. Nevertheless, the implementation of such control approaches relies, in the end, on the availability of proper communication infrastructures. The adoption of wired infrastructures (such as field-bus, ethernet, or fiber optic systems) may however imply high installation costs, particularly for complex end-users' systems. To cope with this issue, in this study the use of a wireless network based on the LoRaWAntechnology is proposed to implement the monitoring and the coordinated control of dispersed DERs. A proper communication architecture is proposed and discussed in terms of the required coverage range and latency. Finally, a scalability analysis is also presented, which aims at evaluating the number of devices that can be supervised by a single LoRaWAngateway, by considering the expected data throughput, and the duty cycle limitations.

One of the most interesting application of data analysis to industry is the real-time detection of anomalies during production. Industrial IoT paradigm includes all the components to realize predictive systems, like the anomaly detection ones. In this case, the goal is to discover patterns, in a given dataset, that do not resemble the “normal” behavior, to identify faults, malfunctions or the effects of bad maintenance. The use of complex neural networks to implement deep learning algorithm for anomaly detection is very common. The position of the deep learning algorithm is one of the main problem: this kind of algorithm requires both high computational power and data transfer bandwidth, rising serious questions on the system scalability. Data elaboration in the edge domain (i.e. close to the machine) usually reduce data transfer but requires to instantiate expensive physical assets. Cloud computing is usually cheaper but Cloud data transfer is expensive. In this paper a test methodology for the comparison of the two architectures for anomaly detection system is proposed. A real use case is described in order to demonstrate the feasibility. The experimental results show that, by means of the proposed methodology, edge and Cloud solutions implementing deep learning algorithms for industrial applications can be easily evaluated. In details, for the considered use case (with Siemens controller and Microsoft Azure platform) the tradeoff between scalability, communication delay, and bandwidth usage, has been studied. The results show that the full-cloud architecture can outperform the edge-cloud architecture when Cloud computation power is scaled.

Medical cyber-physical systems (MCPS) have been appeared as a possible approach for detecting and mitigating human errors. In this context, a MCPS in which medical and non-medical devices are connected could promptly detect potential risky or wrong procedures that deviate from standardized approaches. In this work, such a paradigm has been expanded to prototypical diagnostic devices. A distributed platform of such instruments is proposed in order to avoid misdiagnosis: diagnostic instrument send data to a message-oriented middleware, thus allowing technicians and doctors to remotely access test-related information for further processing and for creating an historical database. A Broker has been used for collecting data and distribute them to cloud database and to users. Two of the most diffused protocols, i.e., MQTT and AMQP, have been considered. An experimental setup has been developed to verify performance; time-related metrics confirm that the proposed approach has an end-to-end delay on few hundreds of milliseconds even for geographical scale networks.

IoT networks for data gathering in the buildings allow to control and manage the operational phase of the systems for ventilation and IAQ, optimizing the energy flows and the indoor comfort conditions. The concept of Cognitive Building steers the implementation of such networks in the assets considering the sensors as scattered systems to inform and actuate the adaptation strategies which are crucial when variables have to be included in the process management. Variables as weather, occupancy flows during the day, energy production by renewable energies, energy storage strategies, affect the indoor conditions, the rate of use of the HVAC systems and the energy management of the used/storage resources. The eLUX lab at the Smart Campus of the University of Brescia is a pilot building in the field and it has been monitoring since 2017. The indoor conditions monitoring could unveil critical situations defined by temperature, humidity and indoor air quality (IAQ) in the educational spaces and envisage strategies and scenarios related to energy demand defined by the occupancy stream. The IoT network collects data about indoor air quality in the different spaces and it is used to verify and increase the accuracy on occupancy estimation. The HVAC management referred to the effective occupancy can enable an energy management process based on user-centred approach empowering an increment of the comfort hours facing critical situations and it is possible to promote actuation strategies preserving energy efficiency and IAQ (e.g. increase ventilation to decrease the CO2 concentration, decrease temperature and control relative humidity in the indoor spaces by window opening or modulation of the fans and dehumidification systems activation). The educational spaces have been adopted as case studies to analyse the actual indoor conditions and come up with a detailed description of the profiles of use (i.e. occupancy, lighting, equipment, HVAC, CO2) supporting effective management policies. The paper describes the analyses on the data collected to understand when and how the indoor conditions can be improved to preserve the learning performance of the users. The research addresses one of the main topics of the eLUX living lab.

The increasing presence of intermittent distributed generation (DG) based on renewable energy sources (RESs) is heavily affecting the operation and management of modern distribution grids. Even though the use of distributed energy storage systems (DESSs) and the integration of advanced demand response (DR) programs are expected to mitigate this problem, security, safety, economic, and technical reasons are limiting the tests of these mechanisms in working systems. The test in simulation and emulation environments has proved to be an effective solution for the development of advanced control and protection schema, but their use for the simulation of the typically unpredictable behavior of end-users seems at least questionable. This paper proposes a testing framework for the analysis of experimental control strategies of distributed energy systems. The framework, based on a service-oriented architecture, can be easily interconnected to different systems, including renewable generators and storage systems. This framework can be used to analyze the performance of real energy systems, working in near-to-real conditions. The framework has been used for a pilot test on a subset of the facilities of the eLUX laboratory of the University of Brescia. The proposed framework allows analyzing the energy performance of this system by means of proper key performance indicators (KPIs). The efficiency analysis of a battery energy storage system (BESS) is presented, by proposing KPIs to evaluate the effect of different control strategies. Further KPIs are also used to evaluate the effectiveness of the BESS for improving the energy self-consumption of a photovoltaic system.

In recent years, energy-savings policies have affected many aspects of everyday life. Considering a typical building, the heating, ventilation, and air conditioning (HVAC) system is the most energy-consuming system. This consideration is especially true for large public-access buildings, such as schools, and public administrations. In these cases, the energy saving of buildings depends on the capability to optimise the behaviour of the HVAC. Typically, the HVAC control system is based on static models of the building, which consider the average occupancy rate of each of the rooms. On the contrary, in this research work, a cognitive system based on an occupancy rate model that is able to take into consideration user habits and indoor air quality (IAQ) provided by IoT sensors is considered for the control of HVAC systems. This approach has been applied to the eLUX lab building of the University of Brescia, Italy. Data provided by IoT IAQ sensors (temperature, relative humidity, CO2) is used to refine the results of the occupancy rates for the models of the rooms of this building. The experimental results show, as in 22.15 % of the samples, the CO2 concentration exceeded the 1,000 ppm threshold of the perception of fresh air and good conditions.

The Internet of Things paradigm aims to connect a very large number of smart objects located all around the world for increasing the knowledge about the environment. Low-power wireless communications are compulsory to reduce the smart object invasiveness and ensure reasonable duration of autonomous node. For this reason, several Low-Power Wide-Area Network (LPWAN) technologies have been proposed in the recent past, because of their large range and low consumption. Most notably, the LoRaWAN solution emerged as the most accepted LPWAN and several research works appeared, discussing its advantages and limitations. However, very few, if any, discuss timing performance in a real scenario, considering not only the wireless frontend but also the wired backend. In this work, the authors implement a real use case based on a public LoRaWAN infrastructure and propose metrics to formally evaluate the delays at the application level (i.e. considering the whole infrastructure as a black box). The result is an average delay in the range 400-700 ms, and the proof that the delay is directly dependent on both the LoRaWAN backend and the connection speed of information endpoints.

Antigen-primed cluster of differentiation (CD) 4+ T follicular helper (Tfh) cells interact with B cells in the germinal centers (GCs) of lymph nodes to generate vaccine-induced antibody (Ab) responses. In the circulation, peripheral Tfh (pTfh) cells, a subset of memory CD4 T cells, serve as surrogates for GC Tfh because of several functional and phenotypic similarities between them. We investigated features of H1N1 influenza antigen-specific pTfh (Ag.pTfh) in virologically controlled HIV+ volunteers on antiretroviral therapy (ART) and healthy control (HC) participants selected from a seasonal influenza vaccine responsiveness study. Selection of the participants was made based on age, defined as young (18–40 y) and old (>60 y) and on their classification as a vaccine responder (VR) or vaccine nonresponder (VNR). VRs demonstrated expansion of CD40L+ and CD69+ Ag.pTfh, with induction of intracellular interleukin 21 (IL-21) and inducible costimulator (ICOS) post vaccination; these responses were strongest in young HC VRs and were less prominent in HIV+ individuals of all ages. Ag.pTfh in VNRs exhibited dramatically different characteristics from VRs, displaying an altered phenotype and a cytokine profile dominated by cytokines IL-2, tumor necrosis factor alpha (TNF-α), or IL-17 but lacking in IL-21. In coculture experiments, sorted pTfh did not support the B cell IgG production in VNRs and were predominantly an inflammatory T helper 1 (Th1)/T helper 17 (Th17) phenotype with lower ICOS and higher programmed cell death protein 1 (PD1) expression. Induction of IL-21 and ICOS on Ag.pTfh cells are negatively affected by both aging and HIV infection. Our findings demonstrate that dysfunctional Ag.pTfh cells with an altered IL-21/IL-2 axis contribute to inadequate vaccine responses. Approaches for targeting inflammation or expanding functional Tfh may improve vaccine responses in healthy aging and those aging with HIV infection., This study identifies qualities of flu vaccine-specific T follicular helper cells that are important for vaccine responses, and delineates deficiencies that are associated with impaired vaccine responses in biologic aging and HIV infection in humans.

In the last decade, the growth of the economic, automotive and technological sectors, has been notable. Alongside this growth, emerges the term ”Industry 4.0” which is used to represent the current Industrial Revolution. This revolution involves different areas: from manufacturing to healthcare. Industry 4.0 can create value during the entire product lifecycle, promoting customer feedback and having the entire product history throughout its life. The automatic communication between vehicle and factory was facilitated, allowing the accomplishment of different analysis regarding vehicles, such as the identification of a behavioral pattern through historical driver usage, fuel consumption, maintenance indicators and so on. This would allow the prevention of critical issues and undesired behaviors as it prevents the automakers from losing contactwiththe vehicle after the purchase. This paper aims to propose a customer feedback platform for vehicle manufacturing in Industry 4.0 context, capable of collecting and analyzing, through an OBD-II scanner the sensors available in vehicles, with the purpose of assisting in the management, prevention, and mitigation of different vehicular problems. An intercontinental experiment conducted in locations within Brazil and Italy show the feasibility of the platform and the potential to use preliminary results in order to improve the vehicle manufacturing.

Architecture, Engineering and Construction (AEC) industry is today strongly related to technologies for the gathering, storing and mining of big data. The use of data is paramount in today management and design is also interpreting this dictat as a new and crucial paradigm. A building is no longer a static body of material but a dynamic flow of data driving the new concept of the building as a service provider to users. The evolution of automated and smart building is cognitive, which means a learning building adaptable to users' needs and preferences and to changing conditions (i.e. weather, energy cost, occupancy profile, activities, etc.). The information is collected by sensors and referred to indoor conditions and settings, power consumption, user' flows and preferences to extend the user experience and to save energy. Such information should be machine readable and real-timed analyzed by artificial intelligence. Thus, the performance of the cognitive system relies on the quality and the reliability of the information collected by sensors. The information system should be made conscious of metrological issues through the use of proper metadata. This approach has been used to manage the sensors data in the case study of a building of University of Brescia.

The rising concerns about global warming and environmental pollution are increasingly pushing towards the replacement of road vehicles powered by Internal Combustion Engines (ICEs). Electric Vehicles (EVs) are generally considered the best candidates for this transition, however, existing power grids and EV management systems are not yet ready for a large penetration of EVs, and the current opinion of the scientific community is that further research must be done in this field. The so-called Vehicle-to-Grid (V2G) concept plays a relevant role in this scenario by providing the communication capabilities required by advanced control and Demand-Side Management (DSM) strategies. Following this research trend, in this paper the communication requirements for the DSM of EVs in urban environments are discussed, by focusing on the mobile communication among EVs and smart grids. A specific system architecture for the DSM of EVs moving inside urban areas is proposed and discussed in terms of the required data throughput. In addition, the use of a Low-Power Wide-Area Network (LPWAN) solution—the Long-Range Wide Area Network (LoRaWAN) technology—is proposed as a possible alternative to cellular-like solutions, by testing an experimental communication infrastructure in a real environment. The results show that the proposed LPWAN technology is capable to handle an adequate amount of information for the considered application, and that one LoRa base station is able to serve up to 438 EVs per cell, and 1408 EV charging points.

OBJECTIVE: Antibody responses are often impaired in old age and in HIV-positive (HIV+) infection despite virologic control with antiretroviral therapy but innate immunologic determinants are not well understood. DESIGN: Monocytes and natural killer cells were examined for relationships to age, HIV infection and influenza vaccine responses. METHODS: Virologically suppressed HIV+ (n=139) and HIV-negative (HIV−) (n=137) participants classified by age as young (18–39 years), middle-aged (40–59 years) and old (≥60 years) were evaluated preinfluenza and postinfluenza vaccination. RESULTS: Prevaccination frequencies of inflammatory monocytes were highest in old HIV+ and HIV−, with old HIV+ exhibiting higher frequency of integrin CD11b on inflammatory monocytes that was correlated with age, expression of C-C chemokine receptor-2 (CCR2) and plasma soluble tumor necrosis factor receptor-1 (sTNFR1), with inverse correlation with postvaccination influenza H1N1 antibody titers. Higher frequencies of CD11b(+) inflammatory monocytes (CD11b(hi), >48.4%) compared with low frequencies of CD11b(+) inflammatory monocytes (

In the last years, the energy savings policies are affecting several aspects of the everyday life, from the introduction of renewables and the use of Electric Vehicles, down to the adoption of more efficient lightening systems. Considering a typical building, one of the most energy consuming plant is the Heating, Ventilation and Air Conditioning (HV AC) system. This consideration is especially true for large public-access buildings, such as schools, universities and public administrations. In these cases, the energy saving of buildings depends on the capability to optimize the behavior of the HV AC. Typically, the HV AC control system is based on static models of the building, which consider an average occupancy rate of each of the rooms. On the contrary, in this research work, a Cognitive Building approach has been considered. The Energy Management System (EMS) is able to control and to regulate the HV AC system, considering an occupancy rate model able to take into consideration user’ habits and the indoor air quality (IAQ), provided in near real-time by IoT sensors. This approach has been applied to the eLUX lab building of the campus of the University of Brescia, Italy. Data provided by IAQ sensors (temperature, relative humidity, CO 2 ) are used to refine the results of occupancy rate models of rooms of this building. The experimental results show as in the 22.15 % of the samples, the CO 2 concentration overcame the 1000 ppm threshold of perception of fresh air and good condition.

The emerging Internet of Things paradigm is based on smart objects that, exploiting ubiquitous interconnectivity, will be able to interact with the surrounding environment paving the way to innovative services. Wireless links are a must and in the recent past we saw the larger and larger diffusion of novel Low Power Wide Area Network (LPWAN) technologies, implementing one-hop topology. The typical application scenario is smart metering in smart city applications, where fixed devices send readings sporadically over a long distance to a data concentrator. In particular, the LoRaWAN solution has been widely accepted by both the industrial and the academic worlds. In this paper, authors evaluate performance of a real world LoRaWAN application in a large area (several km2), considering node mobility as well. Results confirm the capability of LoRaWAN to cope with low speed applications. Moreover, the purposely designed experiments evaluate delays in receiving updates from mobile nodes in an urban scenario. Short reaction time enables new services as people (children) and assets tracking during crowded events.

In this paper, we present a conceptual study on a Virtual Power Plant (VPP) architecture for the optimal management of Distributed Energy Resources (DERs) owned by prosumers participating in Demand-Side Management (DSM) programs. Compared to classical VPP architectures, which aim to aggregate several DERs dispersed throughout the electrical grid, in the proposed VPP architecture the supervised physical domain is limited to single users, i.e., to single Points of Delivery (PODs) of the distribution network. The VPP architecture is based on a service-oriented approach, where multiple agents cooperate to implement the optimal management of the prosumer’s assets, by also considering different forms of Demand Response (DR) requests. The considered DR schemes range from Price-Based DRs to Event-Based DRs, covering both the normal operating functions and the emergency control requests applied in modern distribution networks. With respect to centralized approaches, in this study the control perspective is moved from the system level to the single prosumer’s level, who is allowed to independently provide flexible power profiles through the aggregation of multiple DERs. A generalized optimization model, formulated as a Mixed-Integer Linear Programming (MILP) problem, is also introduced. Such a model is able to compute the optimal scheduling of a prosumer’s assets by considering both DR requests and end-users’ requirements in terms of comfort levels while minimizing the costs.