Time-Sensitive Networking to Improve the Performance of Distributed Functional Safety Systems Implemented over Wi-Fi

Industry 4.0 has significantly improved the industrial manufacturing scenario in recent years. The Industrial Internet of Things (IIoT) enables the creation of globally interconnected smart factories, where constituent elements seamlessly exchange information. Industry 5.0 has further complemented these achievements, as it focuses on a human-centric approach where humans become part of this network of things, leading to a robust human–machine interaction. In this distributed, dynamic, and highly interconnected environment, functional safety is essential for adequately protecting people and machinery. The increasing availability of wireless networks makes it possible to implement distributed and flexible functional safety systems. However, such networks are known for introducing unwanted delays that can lead to safety performance degradation due to their inherent uncertainty. In this context, the Time-Sensitive Networking (TSN) standards present an attractive prospect for enhancing and ensuring acceptable behaviors. The research presented in this paper deals with the introduction of TSN to implement functional safety protocols for wireless networks. Among the available solutions, we selected Wi-Fi since it is a widespread network, often considered and deployed for industrial applications. The introduction of a reference functional safety protocol is detailed, along with an analysis of how TSN can enhance its behavior by evaluating relevant performance indexes. The evaluation pertains to a standard case study of an industrial warehouse, tested through practical simulations. The results demonstrate that TSN provides notable advantages, but it requires meticulous coordination with the Wi-Fi MAC layer protocol to guarantee improved performance

A Comprehensive Review on Time Sensitive Networks with a Special Focus on Its Applicability to Industrial Smart and Distributed Measurement Systems

The groundbreaking transformations triggered by the Industry 4.0 paradigm have dramati-cally reshaped the requirements for control and communication systems within the factory systems of the future. The aforementioned technological revolution strongly affects industrial smart and distributed measurement systems as well, pointing to ever more integrated and intelligent equipment devoted to derive accurate measurements. Moreover, as factory automation uses ever wider and complex smart distributed measurement systems, the well-known Internet of Things (IoT) paradigm finds its viability also in the industrial context, namely Industrial IoT (IIoT). In this context, communication networks and protocols play a key role, directly impacting on the measurement accuracy, causality, reliability and safety. The requirements coming both from Industry 4.0 and the IIoT, such as the coexistence of time-sensitive and best effort traffic, the need for enhanced horizontal and vertical integration, and interoperability between Information Technology (IT) and Operational Technology (OT), fostered the development of enhanced communication subsystems. Indeed, established tech-nologies, such as Ethernet and Wi-Fi, widespread in the consumer and office fields, are intrinsically non-deterministic and unable to support critical traffic. In the last years, the IEEE 802.1 Working Group defined an extensive set of standards, comprehensively known as Time Sensitive Networking (TSN), aiming at reshaping the Ethernet standard to support for time-, mission-and safety-critical traffic. In this paper, a comprehensive overview of the TSN Working Group standardization activity is provided, while contextualizing TSN within the complex existing industrial technological panorama, particularly focusing on industrial distributed measurement systems. In particular, this paper has to be considered a technical review of the most important features of TSN, while underlining its applicability to the measurement field. Furthermore, the adoption of TSN within the Wi-Fi technology is addressed in the last part of the survey, since wireless communication represents an appealing opportunity in the industrial measurement context. In this respect, a test case is presented, to point out the need for wirelessly connected sensors networks. In particular, by reviewing some literature contributions it has been possible to show how wireless technologies offer the flexibility necessary to support advanced mobile IIoT applications

A learning model for battery lifetime prediction of LoRa sensors in additive manufacturing

Today, an innovative leap for wireless sensor networks, leading to the realization of novel and intelligent industrial measurement systems, is represented by the requirements arising from the Industry 4.0 and Industrial Internet of Things (IIoT) paradigms. In fact, unprecedented challenges to measurement capabilities are being faced, with the ever-increasing need to collect reliable yet accurate data from mobile, battery-powered nodes over potentially large areas. Therefore, optimizing energy consumption and predicting battery life are key issues that need to be accurately addressed in such IoT-based measurement systems. This is the case for the additive manufacturing application considered in this work, where smart battery-powered sensors embedded in manufactured artifacts need to reliably transmit their measured data to better control production and final use, despite being physically inaccessible. A Low Power Wide Area Network (LPWAN), and in particular LoRaWAN (Long Range WAN), represents a promising solution to ensure sensor connectivity in the aforementioned scenario, being optimized to minimize energy consumption while guaranteeing long-range operation and low-cost deployment. In the presented application, LoRa equipped sensors are embedded in artifacts to monitor a set of meaningful parameters throughout their lifetime. In this context, once the sensors are embedded, they are inaccessible, and their only power source is the originally installed battery. Therefore, in this paper, the battery lifetime prediction and estimation problems are thoroughly investigated. For this purpose, an innovative model based on an Artificial Neural Network (ANN) is proposed, developed starting from the discharge curve of lithium-thionyl chloride batteries used in the additive manufacturing application. The results of experimental campaigns carried out on real sensors were compared with those of the model and used to tune it appropriately. The results obtained are encouraging and pave the way for interesting future developments

An IoT Measurement System Based on LoRaWAN for Additive Manufacturing

The Industrial Internet of Things (IIoT) paradigm represents a significant leap forward for sensor networks, potentially enabling wide-area and innovative measurement systems. In this scenario, smart sensors might be equipped with novel low-power and long range communication technologies to realize a so-called low-power wide-area network (LPWAN). One of the most popular representative cases is the LoRaWAN (Long Range WAN) network, where nodes are based on the widespread LoRa physical layer, generally optimized to minimize energy consumption, while guaranteeing long-range coverage and low-cost deployment. Additive manufacturing is a further pillar of the IIoT paradigm, and advanced measurement capabilities may be required to monitor significant parameters during the production of artifacts, as well as to evaluate environmental indicators in the deployment site. To this end, this study addresses some specific LoRa-based smart sensors embedded within artifacts during the early stage of the production phase, as well as their behavior once they have been deployed in the final location. An experimental evaluation was carried out considering two different LoRa end-nodes, namely, the Microchip RN2483 LoRa Mote and the Tinovi PM-IO-5-SM LoRaWAN IO Module. The final goal of this research was to assess the effectiveness of the LoRa-based sensor network design, both in terms of suitability for the aforementioned application and, specifically, in terms of energy consumption and long-range operation capabilities. Energy optimization, battery life prediction, and connectivity range evaluation are key aspects in this application context, since, once the sensors are embedded into artifacts, they will no longer be accessible

Driver Drowsiness Detection: A Machine Learning Approach on Skin Conductance

The majority of car accidents worldwide are caused by drowsy drivers. Therefore, it is important to be able to detect when a driver is starting to feel drowsy in order to warn them before a serious accident occurs. Sometimes, drivers are not aware of their own drowsiness, but changes in their body signals can indicate that they are getting tired. Previous studies have used large and intrusive sensor systems that can be worn by the driver or placed in the vehicle to collect information about the driver’s physical status from a variety of signals that are either physiological or vehicle-related. This study focuses on the use of a single wrist device that is comfortable for the driver to wear and appropriate signal processing to detect drowsiness by analyzing only the physiological skin conductance (SC) signal. To determine whether the driver is drowsy, the study tests three ensemble algorithms and finds that the Boosting algorithm is the most effective in detecting drowsiness with an accuracy of 89.4%. The results of this study show that it is possible to identify when a driver is drowsy using only signals from the skin on the wrist, and this encourages further research to develop a real-time warning system for early detection of drowsiness

Design, synthesis and preliminary biological evaluation of 3-cyclopropyl-4-phenoxy-1H-pyrazole derivatives as small molecular ligands of RAGE

Receptor for advanced glycation end products (RAGE) is a multiligand receptor belonging to the immunoglobulin superfamily and plays a crucial role in the development of many human diseases such as neurodegenerative diseases, diabetes, cardiovascular diseases and cancer.1 RAGE is involved in a number of cell processes such as neuroinflammation, apoptosis, proliferation and autophagy, and therefore it is of considerable interest as a promising drug target for innovative therapeutic approaches. It consists of an extracellular region, a short hydrophobic transmembrane spanning region, and a highly charged amino acid cytoplasmatic tail. The extracellular region contains a signal peptide, followed by one N-terminal V-type immunoglobulin domain and two C-type (C1 and C2) immunoglobulin domains.2 RAGE is able to interact with a large number of pro-inflammatory and regulatory molecules, such as advanced glycation end-products (AGEs), quinolinic acid, beta amyloid (A\u3b2), high mobility group box 1 (HMGB1), S100/calgranulin family proteins.3,4 However, due to the structural heterogeneity of these endogenous ligands, little is known about the key pharmacophore elements for ligand-RAGE interaction and the specific mode of binding. On these grounds, we aimed at designing new small molecules able to bind the VC1 extracellular domains of RAGE, in order to clarify the structural features that account for RAGE affinity and activation, and to identify new drug-like compounds. Following a process of structural simplification of known pyrazole-5-carboxamide RAGE ligands,1 we planned a set of novel derivatives characterized by a variously functionalized 3-cyclopropyl-4-phenoxy-1H-pyrazole scaffold (Figure 1). The design and synthesis of the new putative RAGE ligands will be presented and discussed, together with the results of their in vitro screening by means of a surface plasmon resonance (SPR)-based assay to estimate their binding ability to the RAGE extracellular domain. References 1. Bongarzone S., Savickas V., Luzi F., Gee A. D. J. Med. Chem. 2017, 60, 7213-7232. 2. Hudson B. I., Carter A. M., Harja E., Kalea A. Z., Arriero M., Yang H., Grant P. J., Schmidt A. M. FASEB J. 2008, 22, 1572-1580. 3. Xue J., Rai V., Singer D., Chabierski S., Xie J., Reverdatto S., Burz D. S., Schmidt A. M., Hoffmann R., Shekhtman A. Structure 2011, 19, 722\u2013732. 4. Koch M., Chitayat S., Dattilo B. M., Schiefner A., Diez J., Chazin W. J., Fritz, G. Structure 2010, 18, 1342-1352

Is physical training contraindicated in patients with deep vein thrombosis during cardiac rehabilitation?

admitted to cardiac rehabilitation programs after acute coronary syndromes, episodes of acute congestive heart failure, and cardiac revascularization. A common clinical problem in these patients is to decide whether to start or continue physical training or not, given the risk of pulmonary embolism. Until definite evidence becomes available, careful patient selection and inpatient supervision may avoid the a priori withdrawal of such an important core component of cardiac rehabilitation programs

[Standards and outcome measures in cardiovascular rehabilitation. Position paper GICR/IACPR].

Despite major improvements in diagnostics and interventional therapies, cardiovascular diseases remain a major health care and socio-economic problem in Italy. Costs and resources required are increasing in close correlation to both the improved quality of care and to the population ageing. There is an overwhelming evidence of the efficacy of cardiac rehabilitation (CR) in terms of reduction in morbidity and mortality after acute cardiac events. CR services are by definition multi-factorial and comprehensive. Furthermore, systematic analysis and monitoring of the process of delivery and outcomes is of paramount importance. The aim of this position paper promoted by the Italian Association for Cardiovascular Prevention and Rehabilitation (GICR-IACPR) is to provide specific recommendations to assist CR staff in the design, evaluation and development of their care delivery organization. The position paper should also assist health care providers, insurers, policy makers and consumers in the recognition of the quality of care requirements, standards and outcome measure, quality and performance indicators, and professional competence involved in such organization and programs. The position paper i) include comprehensive CR definition and indications, ii) describes priority criteria based on the clinical risk for admission to both inpatient or outpatient CR, and iii) defines components and technological, structural and organizing requirements for inpatient or outpatient CR services, with specific indicators and standards, performance measures and required professional skills. A specific chapter is dedicated to the requirements for highly specialized CR services for patients with more advanced cardiovascular diseases

Cost-effectiveness of HBV and HCV screening strategies:a systematic review of existing modelling techniques

Introduction: Studies evaluating the cost-effectiveness of screening for Hepatitis B Virus (HBV) and Hepatitis C Virus (HCV) are generally heterogeneous in terms of risk groups, settings, screening intervention, outcomes and the economic modelling framework. It is therefore difficult to compare cost-effectiveness results between studies. This systematic review aims to summarise and critically assess existing economic models for HBV and HCV in order to identify the main methodological differences in modelling approaches. Methods: A structured search strategy was developed and a systematic review carried out. A critical assessment of the decision-analytic models was carried out according to the guidelines and framework developed for assessment of decision-analytic models in Health Technology Assessment of health care interventions. Results: The overall approach to analysing the cost-effectiveness of screening strategies was found to be broadly consistent for HBV and HCV. However, modelling parameters and related structure differed between models, producing different results. More recent publications performed better against a performance matrix, evaluating model components and methodology. Conclusion: When assessing screening strategies for HBV and HCV infection, the focus should be on more recent studies, which applied the latest treatment regimes, test methods and had better and more complete data on which to base their models. In addition to parameter selection and associated assumptions, careful consideration of dynamic versus static modelling is recommended. Future research may want to focus on these methodological issues. In addition, the ability to evaluate screening strategies for multiple infectious diseases, (HCV and HIV at the same time) might prove important for decision makers