2Loud? Monitoring traffic noise with mobile phones

The World Health Organization has recently focused attention on guidelines for night noise in urban areas, based on significant medical evidence of the adverse impacts of exposure to excessive traffic noise on health, especially caused by sleep disturbance. This includes serious illnesses, such as hypertension, arteriosclerosis and myocardial infarction. 2Loud? is a research project with the aim of developing and testing a mobile phone application to allow a community to monitor traffic noise in their environment, with focus on the night period and indoor measurement. Individuals, using mobile phones, provide data on characteristics of their dwellings and systematically record the level of noise inside their homes overnight. The records from multiple individuals are sent to a server, integrated into indicators and shared through mapping. The 2Loud? application is not designed to replace existing scientific measurements, but to add information which is currently not available. Noise measurements to assist the planning and management of traffic noise are normally carried out by designated technicians, using sophisticated equipment, and following specific guidelines for outdoors locations. This process provides very accurate records, however, for being a time consuming and expensive system, it results in a limited number of locations being surveyed and long time between updates. Moreover, scientific noise measurements do not survey inside dwellings. In this paper we present and discuss the participatory process proposed, and currently under implementation and test, to characterize the levels of exposure to traffic noise of residents living in the vicinity of highways in the City of Boroondara (Victoria, Australia) using the 2Loud? application

On the impact of mobility on battery-less RF energy harvesting system performance

The future of Internet of Things (IoT) envisions billions of sensors integrated with the physical environment. At the same time, recharging and replacing batteries on this infrastructure could result not only in high maintenance costs, but also large amounts of toxic waste due to the need to dispose of old batteries. Recently, battery-free sensor platforms have been developed that use supercapacitors as energy storage, promising maintenance-free and perpetual sensor operation. While prior work focused on supercapacitor characterization, modelling and supercapacitor-aware scheduling, the impact of mobility on capacitor charging and overall sensor application performance has been largely ignored. We show that supercapacitor size is critical for mobile system performance and that selecting an optimal value is not trivial: small capacitors charge quickly and enable the node to operate in low energy environments, but cannot support intensive tasks such as communication or reprogramming; increasing the capacitor size, on the other hand, enables the support for energy-intensive tasks, but may prevent the node from booting at all if the node navigates in a low energy area. The paper investigates this problem and proposes a hybrid storage solution that uses an adaptive learning algorithm to predict the amount of available ambient energy and dynamically switch between two capacitors depending on the environment. The evaluation based on extensive simulations and prototype measurements showed up to 40% and 80% improvement compared to a fixed-capacitor approach in terms of the amount of harvested energy and sensor coverage

Preliminary tests on a wireless sensor network for pervasive dust monitoring in construction sites

One of the critical aspects in health and safety is the control of fine particle emissions from demolition and construction activities. Such exposure is very often the cause of professional illnesses causing a relevant economic burden for welfare and insurance institutions, besides harming workers. Hence this paper performs a feasibility study of a realtime control system of fine particle concentration on construction sites. It was conceived as a ZigbeeTM based wireless, pervasive and non-invasive system, which is easy to deploy over the site and relatively cheap. Dust sensors were interfaced with the system and calibrated in the laboratory. The prototype is described in detail and tested under controlled and real conditions, in order to determine its potential for application. The prototype was shown to be an excellent tool to support health and safety inspectors, to provide in real-time a broad map of dust concentration over the whole extension of the site, provided that calibration coefficients are worked out for the various types of dust which can be encountered on the site

The interplay of maternal and offspring obesogenic diets: the impact on offspring metabolism and muscle mitochondria in an outbred mouse model

Consumption of obesogenic (OB) diets increases the prevalence of maternal obesity worldwide, causing major psychological and social burdens in women. Obesity not only impacts the mother’s health and fertility but also elevates the risk of obesity and metabolic disorders in the offspring. Family lifestyle is mostly persistent through generations, possibly contributing to the growing prevalence of obesity. We hypothesized that offspring metabolic health is dependent on both maternal and offspring diet and their interaction. We also hypothesized that the sensitivity of the offspring to the diet may be influenced by the match or mismatch between offspring and maternal diets. To test these hypotheses, outbred Swiss mice were fed a control (C, 10% fat, 7% sugar, and n = 14) or OB diet (60% fat, 20% sugar, and n = 15) for 7 weeks and then mated with the same control males. Mice were maintained on the same corresponding diet during pregnancy and lactation, and the offspring were kept with their mothers until weaning. The study focused only on female offspring, which were equally distributed at weaning and fed C or OB diets for 7 weeks, resulting in four treatment groups: C-born offspring fed C or OB diets (C » C and C » OB) and OB-born offspring fed C or OB diets (OB » C and OB » OB). Adult offspring’s systemic blood profile (lipid and glucose metabolism) and muscle mitochondrial features were assessed. We confirmed that the offspring’s OB diet majorly impacted the offspring’s health by impairing the offspring’s serum glucose and lipid profiles, which are associated with abnormal muscle mitochondrial ultrastructure. Contrarily, maternal OB diet was associated with increased expression of mitochondrial complex markers and mitochondrial morphology in offspring muscle, but no additive effects of (increased sensitivity to) an offspring OB diet were observed in pups born to obese mothers. In contrast, their metabolic profile appeared to be healthier compared to those born to lean mothers and fed an OB diet. These results are in line with the thrifty phenotype hypothesis, suggesting that OB-born offspring are better adapted to an environment with high energy availability later in life. Thus, using a murine outbred model, we could not confirm that maternal obesogenic diets contribute to female familial obesity in the following generations

DisKnow: a social-driven disaster support knowledge extraction system

This research is aimed at creating and presenting DisKnow, a data extraction system with the capability of filtering and abstracting tweets, to improve community resilience and decision-making in disaster scenarios. Nowadays most people act as human sensors, exposing detailed information regarding occurring disasters, in social media. Through a pipeline of natural language processing (NLP) tools for text processing, convolutional neural networks (CNNs) for classifying and extracting disasters, and knowledge graphs (KG) for presenting connected insights, it is possible to generate real-time visual information about such disasters and affected stakeholders, to better the crisis management process, by disseminating such information to both relevant authorities and population alike. DisKnow has proved to be on par with the state-of-the-art Disaster Extraction systems, and it contributes with a way to easily manage and present such happenings.info:eu-repo/semantics/publishedVersio

MYCN repression of Lifeguard/FAIM2 enhances neuroblastoma aggressiveness

Neuroblastoma (NBL) is the most common solid tumor in infants and accounts for 15% of all pediatric cancer deaths. Several risk factors predict NBL outcome: age at the time of diagnosis, stage, chromosome alterations and MYCN (V-Myc Avian Myelocytomatosis Viral Oncogene Neuroblastoma-Derived Homolog) amplification, which characterizes the subset of the most aggressive NBLs with an overall survival below 30%. MYCN-amplified tumors develop exceptional chemoresistance and metastatic capacity. These properties have been linked to defects in the apoptotic machinery, either by silencing components of the extrinsic apoptotic pathway (e.g. caspase-8) or by overexpression of antiapoptotic regulators (e.g. Bcl-2, Mcl-1 or FLIP). Very little is known on the implication of death receptors and their antagonists in NBL. In this work, the expression levels of several death receptor antagonists were analyzed in multiple human NBL data sets. We report that Lifeguard (LFG/FAIM2 (Fas apoptosis inhibitory molecule 2)/NMP35) is downregulated in the most aggressive and undifferentiated tumors. Intringuingly, although LFG has been initially characterized as an antiapoptotic protein, we have found a new association with NBL differentiation. Moreover, LFG repression resulted in reduced cell adhesion, increased sphere growth and enhanced migration, thus conferring a higher metastatic capacity to NBL cells. Furthermore, LFG expression was found to be directly repressed by MYCN at the transcriptional level. Our data, which support a new functional role for a hitherto undiscovered MYCN target, provide a new link between MYCN overexpression and increased NBL metastatic properties

Congestion control in constrained Internet of Things networks

The Internet of Things (IoT) is a growing technology that remotely connects multiple devices (ranging across many fields and applications) over the Internet. The scalability of an IoT network mandates a reliable transport infrastructure. Traditional transport control protocol (TCP) control protocol is unsuitable for such domain, mainly due to energy and power consumption reasons. A lighter version of TCP, light weight IP (lwIP) provides a promising solution for current and projected future scalable IoT infrastructures. However, the original lwIP is just a simple mapping of the protocol, without insight into the IoT specific requirements. This paper examines the lwIP congestion control mechanism and addresses its shortcomings. In particular, a detailed examination is devoted to the various metrics such as retransmission time-outs and its back-off epochs, the congestion window behaviour and progress in the absence (and presence) of congestion. In particular, we propose a set of novel algorithms to address both the IoT constraints nature (light-weight) as well as keeping up with scalability in IoT network size and performance. A detailed simulation study has been conducted to endorse the viability of our proposed set of algorithms for next-generation IoT networks

The "Smart Ring" Experience in l'Aquila (Italy): Integrating Smart Mobility Public Services with Air Quality Indexes

This work presents the "City Dynamics and Smart Environment" activities of the Smart Ring project, a model for the smart city, based on the integration of sustainable urban transport services and environmental monitoring over a 4–5-km circular path, the "Smart Ring", around the historical center of l'Aquila (Italy). We describe our pilot experience performed during an experimental on-demand public service electric bus, "SmartBus", which was equipped with a multi-parametric air quality low-cost gas electrochemical sensor platform, "NASUS IV". For five days (28–29 August 2014 and 1–3 September 2014), the sensor platform was installed inside the SmartBus and measured air quality gas compounds (nitrogen dioxide, carbon oxide, sulfur dioxide, hydrogen sulfide) during the service. Data were collected and analyzed on the bases of an air quality index, which provided qualitative insights on the air status potentially experienced by the users. The results obtained are in agreement with the synoptic meteorological conditions, the urban background air quality reference measurements and the potential traffic flow variations. Furthermore, they indicated that the air quality status was influenced by the gas component NO 2 , followed by H 2 S, SO 2 and CO. We discuss the features of our campaign, and we highlight the potential, limitations and key factors to consider for future project designs

Sensor Selection to Improve Estimates of Particulate Matter Concentration from a Low-Cost Network

Deployment of low-cost sensors in the field is increasingly popular. However, each sensor requires on-site calibration to increase the accuracy of the measurements. We established a laboratory method, the Average Slope Method, to select sensors with similar response so that a single, on-site calibration for one sensor can be used for all other sensors. The laboratory method was performed with aerosolized salt. Based on linear regression, we calculated slopes for 100 particulate matter (PM) sensors, and 50% of the PM sensors fell within ±14% of the average slope. We then compared our Average Slope Method with an Individual Slope Method and concluded that our first method balanced convenience and precision for our application. Laboratory selection was tested in the field, where we deployed 40 PM sensors inside a heavy-manufacturing site at spatially optimal locations and performed a field calibration to calculate a slope for three PM sensors with a reference instrument at one location. The average slope was applied to all PM sensors for mass concentration calculations. The calculated percent differences in the field were similar to the laboratory results. Therefore, we established a method that reduces the time and cost associated with calibration of low-cost sensors in the field