Università degli studi di Modena e Reggio Emilia

This paper is focused on Wireless Sensor Network (WSN) leveraging on Bluetooth Low Energy (BLE) connectivity for low energy applications which is fault tolerant versus communication path failures. The topic is important to create a robust sensorized environment to be applied in industrial context or smart infrastructure to enable scheduled monitoring with low power consumption applications. Currently BLE applications are mainly thought for smart home solutions, health care and positioning systems. In those applications the BLE nodes are continuously supplied by external power suppliers. Our goal is to design a self-configuring network with a synchronized deep sleep behavior, aimed to optimize the energy consumption, with an overall active time interval constraint optimized with a data-driven method. The aim is to find a tradeoff between the on time and the ability to collect all the nodes data, pursuing a low power consumption. Our research is based on BLE protocols, interaction between edge systems for data collection and cloud system for data analysis and software agent optimization system. The paper analyses different configurations and describes the possible optimization algorithm to be used for the software agent design, in order to reach a fine-tuned control to improve the fault tolerance and fault diagnosis of the system. Finally experimental results are compared with the estimates obtained via a software simulation tool implemented for this architectural pattern.

Objectives: Non-alcoholic fatty liver disease (NAFLD) is strongly associated with cardiovascular disease in the general population. We aimed to assess the impact of NAFLD and liver fibrosis on intermediate-high cardiovascular risk in people living with HIV. Methods: We included people living with HIV from three cohorts. NAFLD and significant liver fibrosis were defined using transient elastography: controlled attenuation parameter ≥288 dB/m and liver stiffness measurement ≥7.1 kPa, respectively. Cardiovascular risk was assessed with the atherosclerotic cardiovascular disease (ASCVD) risk estimator in patients aged between 40 and 75 years and categorised as low if <5%, borderline if 5%–7.4%, intermediate if 7.5%–19.9% and high if ≥20% or with the presence of a previous cardiovascular event. Patients with hepatitis B and/or hepatitis C virus co-infection, alcohol abuse and unreliable transient elastography measurements were excluded. Predictors of intermediate-high cardiovascular risk were investigated in multivariable analysis by logistic regression and also by stratifying according to body mass index (BMI; cut-offs of 25 and 30 kg/m2) and age (cut-off of 60 years). Results: Of 941 patients with HIV alone included, 423 (45%), 128 (13.6%), 260 (27.6%) and 130 (13.8%) were categorised as at low, borderline, intermediate and high ASCVD risk, respectively. Predictors of intermediate-high ASCVD risk were NAFLD (adjusted odds ratio [aOR] 2.11; 95% confidence interval [CI] 1.40–3.18; p < 0.001), liver fibrosis (aOR 1.64; 95% CI 1.03–2.59; p = 0.034), duration of HIV (aOR 1.04; 95% CI 1.02–1.06; p < 0.001), and previous exposure to thymidine analogues and/or didanosine (aOR 1.54; 95% CI 1.09–2.18; p = 0.014). NAFLD was also associated with higher cardiovascular risk in normoweight patients (aOR 2.97; 95% CI 1.43–6.16; p = 0.003), in those with BMI <30 kg/m2 (aOR 2.30; 95% CI 1.46–3.61; p < 0.001) and in those aged <60 years (aOR 2.19; 95% CI 1.36–3.54; p = 0.001). Conclusion: Assessment of cardiovascular disease should be targeted in people living with HIV with NAFLD and/or significant liver fibrosis, even if they are normoweight and young.

BIM (Building Information Modeling) is an object-oriented approach for the digital modeling of buildings and infrastructures, a centralized system for sharing technical information among all Stakeholders during the entire life cycle of the structure. BIM is at the same time a tool for planning, evaluation, monitoring and documentation; however, some constraints determine a potential gap between represented reality and actual reality. This gap is determined by the fact that the information within the system is updated manually by expert designers, losing substantial synchronization with the real system under analysis. The risk is also to have a so extensive dataset to make its update management complex for a human user. Currently, to achieve direct interaction between civil structures and digital modeling systems, the scientific community is carrying out researches for the application of digital twin concepts in the field of construction automation. In particular, studies about the integration between IoT sensor networks and BIM start to be explored. The project described in this paper aims to propose such integration with a specific focus on air quality parameters monitoring, leveraging on scalable approaches. The data updating system is based on a distributed IoT sensor network, designed and implemented using free and open-source software; the communication protocols are all based on open broker-based technologies, like MQTT, and the data dashboard aimed to visualize real-time data and historical data series are based on web applications over HTTP. The research project led to the creation of a sensorized and BIM-modeled hub which demonstrates the validity of the concept.

Current guidelines recommend use of a diagnostic algorithm to assess disease severity in cases of suspected nonalcoholic fatty liver disease (NAFLD). We applied this algorithm to HIV-monoinfected patients.

The complexity of modern industrial production systems and the persistent connectivity at the local level of manufacturing resources by means of IoT technologies are encouraging the study of increasingly flexible and reconfigurable control methods. Currently industrial software agents are mainly used for controlling job-shop organized systems, relying on negotiation mechanisms between Product agents and Resource agents. In this paper instead, we aim to develop a distributed production line controller, using agent-based technology for designing decentralized and communicating local state machines. The objective of the study is to obtain a global control system deriving from the interaction and exchange of information between software agents associated with each machine on the production line. The controller implemented has the purpose of eliminating the central infrastructure, generally present in industrial automation systems: the goal is to move the decision-making process locally by associating intelligent entities to each machine. The advantage of decentralization lies in the possibility of having a modular, flexible and reconfigurable line controller concept: indeed, there is no supervisor aware of the entire system to control. The agents can also collaborate for a line detection in case of very complex line without there being statistical evidence of performance deterioration. To validate the functionality of this approach, experimental tests were conducted in a virtual environment.