An Energy-autonomous Wireless Sensor Network Development Platform

Internet-of-things enabled applications are increasingly popular and are expected to spread even more in the next few years. Energy efficiency is fundamental to support the widespread use of such systems. This paper presents a practical framework for the development and the evaluation of low-power Wireless Sensor Networks equipped with energy harvesting, aiming at energy-autonomous applications. An experimental case study demonstrates the capabilities of the solution

A new distributed framework for integration of district energy data from heterogeneous devices

The introduction of ”smart” low-cost sensing (and actuating) devices enabled the recent diffusion of technological products within the ”Internet of Things” paradigm. In a city district context, such devices are crucial for visualization and simulation of energy consumption trends, to increase the energy distribution network efficiency and promote user awareness. Nevertheless, to unlock the potential of this technology, many challenges have to be faced at district level due to the current lack of interoperability between heterogeneous data sources. In this work, we introduce an original infrastructure model, which efficiently manage and integrate district energy data

Novel insights into RNAi off-target effects using C. elegans paralogs

<p>Abstract</p> <p>Background</p> <p>In the few years since its discovery, RNAi has turned into a very powerful tool for the study of gene function by allowing post-transcriptional gene silencing. The RNAi mechanism, which is based on the introduction of a double-stranded RNA (dsRNA) trigger whose sequence is similar to that of the targeted messenger RNA (mRNA), is subject to off-target cross-reaction.</p> <p>Results</p> <p>We use a novel strategy based on phenotypic analysis of paralogs and predict that, in <it>Caenorhabditis elegans</it>, off-target effects occur when an mRNA sequence shares more than 95% identity over 40 nucleotides with the dsRNA. Interestingly, our results suggest that the minimum length necessary of a high-similarity stretch between a dsRNA and its target in order to observe an efficient RNAi effect varies from 30 to 50 nucleotides rather than 22 nucleotides, which is the length of siRNAs in <it>C. elegans</it>.</p> <p>Conclusion</p> <p>Our predictive methods would improve the design of dsRNA and ultimately the use of RNAi as a therapeutic tool upon experimental verification.</p

Distributed software infrastructure for evaluating the integration of photovoltaic systems in urban districts

Nowadays, the adoption of renewable energy sources distributed across the city is crucial for planning and developing the future Smart City. An accurate simulation and modelling of energy sources, such as Photovoltaic Panels (PV), is necessary to evaluate both economical and environmental benefits. With the growth of renewable sources in the city simulations of energy production became crucial for the DSO for evaluating retrofits or for network balancing events. In this paper, we present a software infrastructure for simulating the solar radiation and estimating the energy production of a district. The infrastructure simulates the PV production and evaluates the integration of such systems considering real electricity consumption data. In its core, the proposed solution models the behaviours of PV systems taking into account the digital surface of rooftops and sub-hourly meteorological data (e.g. solar radiation and temperature) to compute real-sky conditions. Then, such information is used to feed a model of the hardware components of PV systems to gain more accurate estimations of energy production in the district in real-sky conditions

Comparing different solutions for testing resistive defects in low-power SRAMs

Low-power SRAM architectures are especially sensitive to many types of defects that may occur during manufacturing. Among these, resistive defects can appear. This paper analyzes some types of such defects that may impair the device functionalities in subtle ways, depending on the defect characteristics, and that may not be directly or easily detectable by traditional test methods, such as March algorithms. We analyze different methods to test such defects and discuss them in terms of complexity and test time

The energy efficiency management at urban scale by means of integrated modelling

Innovative technologies such as ICTs are recognized as being a key player against climate change and the use of sensors and actuators can efficiently control the whole energy chain in the Smart Thermal Grids at district level. On the other side, advances on 3D modelling, visualization and interaction technologies enable user profiling and represent part of the holistic approach which aims at integrating renewable energy solutions in the existing building stock. To unlock the potentiality of these technologies, the case study selected for this research focuses on interoperability between Building Information Models (BIM), GIS (Geographic Information System) models and Energy Analysis Models (EAM) for designing Renewable Energy Strategies (RES) among the demonstrator. The objectives aims at making a whole series of data concerning the energy efficiency and reduction at district level usable for various stakeholders, by creating a District Information Model (DIM). The described system also integrates BIM and district level 3D models with real-time data from sensors to analyse and correlate buildings utilization and provide real-time energy-related behaviours. An important role is played by the energy simulation through the EAM for matching measured and simulated data and to assess the energy performance of buildings starting from a BIM model or shared data. With this purpose interoperability tests are carried out between the BIM models and quasi-steady energy analysis tools in order to optimize the calculation of the energy demand according to the Italian technical specification UNI TS 11300. Information about the roofs slope and their orientation from the GIS model are used to predict the use of renewable energy – solar thermal and PV – within the selected buildings (both public and private) of the demonstrator in Turin, Italy. The expected results are a consistent reduction in both energy consume and CO2 emissions by enabling a more efficient energy distribution policies, according to the real characteristics of district buildings as well as a more efficient utilization and maintenance of the energy distribution network, based on social behaviour and users attitudes and demand. In the future the project will allow open access with personal devices and A/R visualization of energy-related information to client applications for energy and cost-analysis, tariff planning and evaluation, failure identification and maintenance, energy information sharing in order to increase the user’s awareness in the field of energy consumption