The science of scale for violence prevention: a new agenda for family strengthening in low- and middle-income countries

Ending all violence against children by 2030 is a core part of Sustainable Development Goals 5 and 16. A number of promising violence reduction strategies have been identified in research studies. However, we lack an understanding of the implementation and impact of these programs in respect to their delivery at a large scale or within existing service systems, particularly in low- and middle-income countries (LMICs). We advocate for greater collaboration between researchers, policymakers, donors, governments, non-governmental organizations, and program managers and staff to study how violence prevention programs operate on a large scale. We describe a new initiative aiming to foster such collaborations in the field of family strengthening programs

An End to End Indoor Air Monitoring System Based on Machine Learning and SENSIPLUS Platform

In the framework of indoor air monitoring, this paper proposes an Internet of Things ready solution to detect and classify contaminants. It is based on a compact and low-power integrated system including both sensing and processing capabilities. The sensing is composed of a sensor array on which electrical impedance measurements are performed through a microchip, named SENSIPLUS, while the processing phase is mainly based on Machine Learning techniques, embedded in a low power and low resources micro controller unit, for classification purposes. An extensive experimental campaign on different contaminants has been carried out and raw sensor data have been processed through a lightweight Multi Layer Perceptron for embedded implementation. More complex and computationally costly Deep Learning techniques, as Convolutional Neural Network and Long Short Term Memory, have been adopted as a reference for the validation of Multi Layer Perceptron performance. Results prove good classification capabilities, obtaining an accuracy greater than 75% in average. The obtained results, jointly with the reduced computational costs of the solution, highlight that this proposal is a proof of concept for a pervasive IoT air monitoring system

Metrological characterization of a novel microsensor platform for activated carbon filters monitoring

Nowadays, an increasing concern about people and environmental health and safety is spreading all over the world, and technologies such as efficient monitoring systems are furthered. In the field of air quality, filtering systems, especially based on activated carbons (ACs), are commonly used. In most cases, their state of health is not monitored, and their time of life is based on statistical and a priori evaluations. This paper proposes a novel microsensor platform for the real-time monitoring of AC filters based on the impedance measurements during gas exposition. A metrological characterization of the novel proposed instrument is provided in this paper, by comparing its output to a reference RLC meter. A calibration and adjustment procedure is developed in order to analyze the device measurement capabilities and obtain correction coefficients. Experiments with different gas typologies are presented, and the analysis of filters impedance dynamics is provided

Sensiplus: an innovative solution for health and safety applications

Nowadays, health and safety are some of the most central topics all over the world in order to improve people life quality and well–being. In this field, an innovative integrated system based on a two layer architecture is presented: the Hardware Layer is the SENSIPLUS chip, a smart sensor IoT ready node endowed with on board sensors and implementing the Electrical Impedance Spectroscopy; the Software Layer deals with measurement acquisition and analysis. The sensing and measuring capabilities of the chip are an enormous potential for the whole platform, with the possibility to be adopted in many different applications. In particular, the SENSIPLUS chip has been involved in three specific applications, all addressed to the warranty of people health and safety: a) Contaminants detection and recognition in air; b) Contaminants detection and recognition in water. The SENSIPLUS chip, developed by the Italian company Sensichips s.r.l. [1] in collaboration with the research group headed by the professor Paolo Bruschi of the University of Pisa, is marked by a very low power consumption (1.5 mW), includes a complete universal sensor interface into a tiny silicon CMOS integrated circuit of only 3mm x 3mm in size. It is endowed with a versatile analog front end, electrical impedance spectroscopy measuring technique and different serial digital interfaces (as SPI and I2C). Furthermore, it is endowed with on-chip sensors as temperature, humidity and gas sensors

The effects of autistic traits and academic degree on visuospatial abilities

In the present study, we were interested to investigate how autistic traits (including systemizing and empathy) and academic degree influence individuals’ visuospatial abilities. To this end, 352 university students completed the Autism Spectrum Quotient (AQ), the Empathy Quotient, the Systemizing Quotient (SQ) and visuospatial tests measuring figure disembedding and mental rotation of two-dimensional figures. Engineering-design students (architecture and engineering) were the most accurate in disembedding and mentally rotating figures, followed by students of physical sciences (computer science, chemistry, physics, etc.) and fact-based humanities (languages, classics, law); biological (psychology and neuroscience, etc.) and systems-based social scientists (economics and commerce) were the least accurate. Engineering-design students also showed higher SQ scores with respect to the other four academic degree subjects, with students of biological sciences showing lower SQ scores. Importantly, results from a path analysis revealed that SQ (but not AQ) exerted an indirect effect on figure disembedding and mental rotations through the influence of the academic degree. Thus, the present findings reveal shady differences in systemizing degree and visuospatial performance within systemizing-based degree subjects. Implications for education are discussed