Low-cost MEMS accelerometers are increasingly used for vibration-based condition monitoring, but their high-frequency performance is often limited by the way they are mechanically mounted. In many practical implementations, the coupling between the sensor and the monitored structure is poorly controlled or only marginally addressed, leading to mounting-induced resonances that can compromise broadband measurements. This paper presents a low-cost wideband vibration sensing system based on a MEMS accelerometer and an epoxy-encapsulated mounting solution specifically designed to improve mechanical coupling and shift parasitic resonances toward higher frequencies. Unlike approaches that mainly focus on signal processing, the proposed system experimentally addresses the mechanical integration of the sensor as a key requirement for reliable vibration measurements. The sensing unit is combined with embedded spectral feature extraction, which reduces data dimensionality by retaining the most relevant frequency components and provides compact feature vectors suitable for Artificial Intelligence (AI) and Machine Learning (ML) based condition monitoring. Finite element simulations, laboratory frequency-response tests, and automotive measurements on different engine configurations show that the proposed approach improves the dynamic behavior of the sensor and captures distinctive vibration signatures for automotive and industrial diagnostic applications.

This paper presents the reliability assessment of an IoT-based sensor node designed for detecting combustible gas leaks in residential environments. Building on a previously published design that integrates low-power micromachined (Micro-Electro-Mechanical Systems, MEMS) pellistors and electrochemical Volatile Organic Compounds (VOC) sensors, this study evaluates the node’s long-term robustness and stability under both realistic and accelerated operating conditions. The system employs a dual-sensor strategy in which the VOC sensor acts as a sentinel, activating the pellistor only when necessary, thereby optimizing power consumption and extending battery life. BLE and LoRa communication capabilities support flexible deployment and real-time data transmission. To ensure suitability for safety-critical applications, we conducted comprehensive reliability testing, including accelerated life tests and environmental stress testing in compliance with IEC 60068 standards. The results confirm the system’s ability to maintain consistent performance and data integrity under thermal, mechanical, and chemical stress, demonstrating its robustness for prolonged operation in demanding environments. Overall, this work underscores the importance of rigorous reliability validation for IoT-based safety devices and positions the proposed solution as a significant step toward enhancing residential gas safety, with potential applications in broader industrial monitoring scenarios.

BACKGROUND:The Covid-19 pandemic has had significant consequences on the well-being of individuals worldwide. However, some subgroups may be more affected, presenting numerous distressing psychological symptoms such as depression, anxiety and stress. The present study aims to explore the needs and concerns of the most vulnerable populations during the pandemic. METHODS:Online focus groups were conducted with representatives of five vulnerable populations: frontline and non-frontline healthcare workers, young adults, older people, and entrepreneurs. The interviews with each group were transcribed, and an inducted theme analysis, based on the Health Belief Model, was conducted to identify recurring themes and build and implement a tailored intervention. RESULTS:Qualitative differences between groups emerged in risk perception, one of the main domains of the Health Belief Model, while similarities emerged in the perceived cost of the pandemic. Other themes that emerged included vaccination intention, future expectations, protective measures usage, and information searching. CONCLUSIONS:The findings of the present study may help shape a Health Belief Model-based intervention to promote well-being and the use of protective measures during pandemics.

This paper presents the research activity finalized to the development of a complete wearable sensors network for health monitoring in workplaces with particular reference to confined workspaces. The proposed network will incorporate gas sensors, vibration sensors, and sensors to provide real-time monitoring and analysis of various physiological parameters related to the well-being and safety of individuals in work environments. Furthermore, with slight modifications, the proposed solution can be adapted to environmental monitoring, soft robots, and athletes monitoring, expanding its potential applications and impact. Moreover, sensor network data can be collected and analysed to enable predictions of possibly unusual/hazardous environmental and personal conditions to alert for rapid intervention. In this paper, the first prototype of a complete wearable sensor network for the monitoring of environmental conditions, worker heart rate and mechanical vibrations, is described.

This paper presents a novel battery-operated IoT sensor node tailored for the accurate detection of combustible gas leaks in residential environments. Integrated with innovative sensor technologies, i.e. micromachined membrane pellistors and with low-power VOC sensors, the device ensures efficient gas monitoring. The electronics was designed with low power circuitry and in such away as to be versatile with respect to the power supply. Powered by Lithium Thionyl Chloride batteries guarantees an energy self-sufficiency longer than one year. It also permits to significantly further prolong this lifespan, by exploiting a novel measurement policy based on sensor triggering. Upon detecting odorizer concentration exceeding a threshold, based on VOC concentration continuous monitoring, the device activates the pellistor for combustible gas detection. The proposed approach minimizes power consumption and maximizes responsiveness, extending the device's lifespan significantly. Additionally, the device utilizes LoRa and BLE connectivity, transmitting data to a LoRa remote gateway daily under normal conditions, increasing the transmission data packet rate during gas leak events and allowing for prompt BLE local interrogation. Given the application context, linked to the measurement of gas concentrations well under the lower explosive limit, the sensors adopted in the device were characterized starting from gas mixtures with known concentrations, showing performances adequate for the application context.

The safety of an operator working in a hazardous environment is a recurring topic in the technical literature of recent years, especially for high-risk environments such as oil and gas plants, refineries, gas depots, or chemical industries. One of the highest risk factors is constituted by the presence of gaseous substances such as toxic compounds such as carbon monoxide and nitric oxides, particulate matter or indoors, in closed spaces, low oxygen concentration atmospheres, and high concentrations of CO2 that can represent a risk for human health. In this context, there exist many monitoring systems for lots of specific applications where gas detection is required. In this paper, the authors present a distributed sensing system based on commercial sensors aimed at monitoring the presence of toxic compounds generated by a melting furnace with the aim of reliably detecting the insurgence of dangerous conditions for workers. The system is composed of two different sensor nodes and a gas analyzer, and it exploits commercial low-cost commercially available sensors.

Mobile health platforms have shown promise in the management of various mental health conditions (including stress, anxiety, and depression) and cognitive behavioral strategies emerged as a popular and effective option offered by the platforms. This paper presents the protocol of a study aimed to test the effectiveness of a mobile platform that uses cognitive-behavioral strategies for stress self-management in the Tuscany region (Italy). The mobile app is adapted to the specific needs of each vulnerable population for which it is designed: young and older people, healthcare professionals, entrepreneurs. The app will be evaluated on the following outcomes: (i) perceived susceptibility and severity of the pandemic situation, perceived benefits, and costs of preventive health behaviors, (ii) knowledge about Covid-19 preventive behaviors and negative consequences of social distancing, (iii) stress and psychopathological symptoms (i.e., anxiety, depression, and post-traumatic stress symptoms) and cognitive distortions. If successful, we expect that the platform could give various groups clinical benefits by providing symptom self-monitoring and early intervention, consolidating the number of mental health programs available, and decreasing barriers to treatment-seeking. This population-level approach has the potential to improve mental health outcomes in pandemic periods for many people.

In this article, the availability and reliability of a remote video monitoring system for offshore sea farming plants are studied and tested in laboratory. The scope of the system is to ensure a video surveillance infrastructure so to supervise breeding cages along with the fish inside them, in order to contrast undesired phenomena like fish poaching as well as cages damages. The system is installed on a cage floating structure: it is mainly composed of an IP camera that is controlled by a Raspberry Pi Zero which is the core of the system. Images are streamed thanks to a 3G/4G dongle, while the overall system is powered via two photovoltaic panels charging a backup battery. Simulations are carried out considering two seasonal functioning periods (i.e., winter and summer): each of them is characterised by temperature trends defined according to the average temperatures of the system deployment site, 8 km offshore the city of Piombino, Italy. In order to optimise power consumption without hindering application scenario requirements, the system operates according to a duty cycle of 2 minutes out of 15 (i.e., 8 minutes of operation per hour). The performances of the system are then tested in laboratory exploiting a climatic chamber so to simulate different environmental conditions: variations on image quality are then analysed in order to identify possible dependencies on critical situations related to specific temperature and relative humidity values and to the presence of salt in the air.

Long Range Wide Area Network (LoRaWAN) has rapidly become one of the key enabling technologies for the development of Internet of Things (IoT) architectures. A wide range of different solutions relying on this communication technology can be found in the literature: nevertheless, the most part of these architectures focus on single task systems. Conversely, the aim of this paper is to present the architecture of a LoRaWAN infrastructure gathering under the same network different typologies of services within one of the most significant sub-systems of the Smart City ecosystem (i.e., the Smart Waste Management). The proposed architecture exploits the whole range of different LoRaWAN classes, integrating nodes of growing complexity according to the different functions. The lowest level of this architecture is occupied by smart bins that simply collect data about their status. Moving on to upper levels, smart drop-off containers allow the interaction with users as well as the implementation of asynchronous downlink queries. At the top level, Video Surveillance Units (VSUs) are provided with machine learning capabilities for the detection of the presence of fire nearby bins or drop-off containers, thus fully implementing the Edge Computing paradigm. The proposed network infrastructure and its subsystems have been tested in a laboratory and in the field. This study has enhanced the readiness level of the proposed technology to Technology Readiness Level (TRL) 3.

In this paper we describe the system realized for the FIDES project, a research project financed by Tuscany Region in Italy to promote the decentralization of monitoring operations on chronic patients or elderly people. The aim of the project was the implementation of a platform provided with a set of devices measuring all the main vital parameters, to be positioned inside the pharmacies. The parameters, once measured, became available both to the user, allowing him to check his health situation, and to the doctors, allowing the remote monitoring of a patient. The system has been designed to allow the user to perform autonomously part of the measurements, in order to reduce as much as possible the impact of the system on the everyday life of the pharmacy.

In this paper we describe an innovative system providing services for tourism based on Near Field Communication technology. This work is part of the SIESTA project, co-funded by the Tuscany Region in Italy, to develop applications relying on mobile phones to support tourists visiting cities of art. Near Field Communication mixes the functionalities of RFID systems with the communication capabilities of common mobile phones, opening the way to new uses of the phones, in particular in the fields of Mobile Payment and Mobile Ticketing. In the SIESTA project two different kinds of applications are studied and then joined together to implement a system allowing the user to perform both M-Ticketing operations, with the mobile phone turned into a multipurpose electronic ticket, and M-Payment operations, with the phone turned into an electronic wallet for micro-payment operations. In particular the system focuses on mobile ticketing automatization for events and services, and on mobile micro-payment of services like bus tickets.

In this paper we describe a new e-service provisioning model based on digital TV as the most familiar way to favor digital inclusion (e-inclusion) and social inclusion and to reduce the digital gap between elderly and disabled people and the information society. Different D-TV platforms are considered and several input and output interfaces for user interaction are analyzed and tested. Finally, a new interesting scenario involving a "hidden PC" into the living room scene as enhanced media centre allows to adopt new interfaces and devices towards an easier and more familiar user interaction.

This paper introduces different standards implemented in existing Digital Terrestrial Television Broadcasting systems to allow the fruition of interactive services and applications through digital Set Top Boxes. It focuses on the interoperability issue between the Brazilian and the European architectures. In fact, despite in Brazil the GEM specification has been designed to foster wide content compatibility across a range of interactive platforms, it has never come to a final implementation and deployment. As a result the interoperability issue has been deeply explored in the BEACON project and an innovative system architecture has been developed to deploy t-learning services across Europe and Brazil, providing integration of those systems that were not able to interoperate until nowadays. This work is an important step in the direction of standards' interoperability. As a result, MHP and Ginga NCL-Lua implementation appeared to be the very best choice to deliver interactive services in an interoperable mode between European and Brazilian digital television.

Ageing of population and the percentage of disabled people are constantly growing as well as the digital gap between them and the information society In this paper we propose a new e-service provisioning model adopting TV as the most familiar channel to reach these audiences and to favor digital inclusion (e-inclusion) and social inclusion The convergence of different DTT, IPTV and IPHN technologies allows to adopt new Interfaces and input/output devices for an easier and more familiar interaction with this enhanced platform

In the heterogeneous scenario of standards supporting the deployment of Interactive Television, interoperability seems to be the key issue to grant a deeper market penetration of the digital television in everyday life as well as adequate return of investments to the actors involved in the value chain. This paper presents an innovative architecture to support application interoperability across different frameworks (i.e. DVB-T, ISDB-T, ACAP, ISDTV, etc.) based on GEM standard, promoting the implementations of iTV XML-based application available worldwide. The theoretical approach introduced in the document will be evaluated in a real learning scenario, both in Europe and in Brazil; the paper presents preliminary results in terms of functionalities and user interface to be evaluated and tuned, according to the user centered design approach.