A Wildfire Prediction Based on Fuzzy Inference System for Wireless Sensor Networks

The study of forest fires has been traditionally considered as an important application due to the inherent danger that this entails. This phenomenon takes place in hostile regions of difficult access and large areas. Introduction of new technologies such as Wireless Sensor Networks (WSNs) has allowed us to monitor such areas. In this paper, an intelligent system for fire prediction based on wireless sensor networks is presented. This system obtains the probability of fire and fire behavior in a particular area. This information allows firefighters to obtain escape paths and determine strategies to fight the fire. A firefighter can access this information with a portable device on every node of the network. The system has been evaluated by simulation analysis and its implementation is being done in a real environment.Junta de Andalucía P07-TIC-02476Junta de Andalucía TIC-570

ENVIRONMENTAL EXERGY ANALYSIS OF WASTEWATER TREATMENT PLANTS

This work evaluates the environmental impact of Wastewater Treatment Plants (WTP) based on data generated by the exergy analysis, calculating and applying environmental impact indexes for two WTP located in the Metropolitan Area of São Paulo. The environmental impact of the waste water treatment plants was done by means of evaluating two environmental impact exergy based indexes: the environmental exergy efficiency (ηenv,exerg) and the total pollution rate (Rpol,t). The environmental exergy efficiency is defined as the ratio of the exergy of the useful effect of the WTP to the total exergy consumed by human and natural resources, including all the exergy inputs. That relation is an indication of the theoretical potential of future improvements of the process. Besides the environmental exergy efficiency, it is also used the total pollution rate, based on the definition done by Makarytchev (1997), as the ratio of the destroyed exergy associated to the process wastes to the exergy of the useful effect of the process. The analysis of the results shows that this method can be used to quantify and also optimise the environmental performance of Wastewater Treatment Plants

Drafting 'better regulation": The economic cost of regulatory complexity

Different public agencies are seeking to draft ?better regulation?. Complex or poorly drafted norms are more difficult for economic agents to implement, tending to erode economic efficiency. The literature has so far concentrated on the analysis of regulatory complexity as a phenomenon related to the ?quantity? of norms. This article guides the process of adopting new regulations, taking into account that norms can also be complex due to new ?qualitative? reasons such as linguistic ambiguity or relational structure (references between legal documents). To perform the analysis, we develop new indicators for legibility and regulatory interconnectedness. Specifically, we construct a new database (RECOS ? REgulation COmplexity in Spain) by extracting information from 8171 norms (61 million words) which comprise the regulations of all the Spanish Autonomous regions. Our analysis reveals the relationship between measures of ?qualitative? complexity and relevant economic (productivity) and institutional (judicial efficacy) variables. This researc

Dimethyl sulphide in some Australian red wines

DMS levels in Cabernet Sauvignon wine from the Coonawarra area of southeast South Australia were shown to vary from 42 to 910 μg l-1. Results indicate that the levels are not dependent on age but rather vary from vintage to vintage. This study does not discount the development of DMS with bottle age as it is an 'historical' survey but suggests that the levels of DMS are vintage related and may depend on viticultural practices and vinification techniques. The odour threshold for DMS in Cabernet Sauvignon was 0.07 μl l-1 (60μg l -1)

What can ecosystem models tell us about the risk of eutrophication in the North Sea?

Eutrophication is a process resulting from an increase in anthropogenic nutrient inputs from rivers and other sources, the consequences of which can include enhanced algal biomass, changes in plankton community composition and oxygen depletion near the seabed. Within the context of the Marine Strategy Framework Directive, indicators (and associated threshold) have been identified to assess the eutrophication status of an ecosystem. Large databases of observations (in situ) are required to properly assess the eutrophication status. Marine hydrodynamic/ecosystem models provide continuous fields of a wide range of ecosystem characteristics. Using such models in this context could help to overcome the lack of in situ data, and provide a powerful tool for ecosystem-based management and policy makers. Here we demonstrate a methodology that uses a combination of model outputs and in situ data to assess the risk of eutrophication in the coastal domain of the North Sea. The risk of eutrophication is computed for the past and present time as well as for different future scenarios. This allows us to assess both the current risk and its sensitivity to anthropogenic pressure and climate change. Model sensitivity studies suggest that the coastal waters of the North Sea may be more sensitive to anthropogenic rivers loads than climate change in the near future (to 2040)

Molecular gas at supernova local environments unveiled by EDGE

CO observations allow estimations of the gas content of molecular clouds, which trace the reservoir of cold gas fuelling star formation, as well as to determine extinction via H$_2$ column density, N(H$_2$). Here, we studied millimetric and optical properties at 26 supernovae (SNe) locations of different types in a sample of 23 nearby galaxies by combining molecular $^{12}$C$^{16}$O (J = 1 $\rightarrow$ 0) resolved maps from the EDGE survey and optical Integral Field Spectroscopy from the CALIFA survey. We found an even clearer separation between type II and type Ibc SNe in terms of molecular gas than what we found in the optical using H$\alpha$ emission as a proxy for current SF rate, which reinforces the fact that SNe Ibc are more associated with SF-environments. While A$_V$ at SN locations is similar for SNe II and SNe Ibc, and higher compared to SNe Ia, N(H$_2$) is significantly higher for SNe Ibc than for SNe II and SNe Ia. When compared to alternative extinction estimations directly from SN photometry and spectroscopy, we find that our SNe Ibc have also redder color excess but showed standard Na I D absorption pseudo-equivalent widths ($\sim$1 \AA). In some cases we find no extinction when estimated from the environment, but high amounts of extinction when measured from SN observations, which suggests that circumstellar material or dust sublimation may be playing a role. This work serves as a benchmark for future studies combining last generation millimeter and optical IFS instruments to reveal the local environmental properties of extragalactic SNe.Comment: MNRAS accepted, 17 pages, 8 Figures, 4 Table

On the criticality of inferred models

Advanced inference techniques allow one to reconstruct the pattern of interaction from high dimensional data sets. We focus here on the statistical properties of inferred models and argue that inference procedures are likely to yield models which are close to a phase transition. On one side, we show that the reparameterization invariant metrics in the space of probability distributions of these models (the Fisher Information) is directly related to the model's susceptibility. As a result, distinguishable models tend to accumulate close to critical points, where the susceptibility diverges in infinite systems. On the other, this region is the one where the estimate of inferred parameters is most stable. In order to illustrate these points, we discuss inference of interacting point processes with application to financial data and show that sensible choices of observation time-scales naturally yield models which are close to criticality.Comment: 6 pages, 2 figures, version to appear in JSTA

Supporting future scholars of engaged research

Researchers in the UK are taking on new roles and responsibilities to meet the requirements of an expanded agenda for generating and evidencing social and economic impacts from research. Within this wider context, culture change programmes have identified learning as an important driver of change. Here we outline a professional development programme designed to train postgraduate researchers studying environmental sciences in core engagement, influence and impact, governance and organization skills for research. We argue that training is an important step in further catalysing progressive culture change. However, our research- and experience-informed critical reflections in supporting researchers suggest that there is still significant work to be done: (1) to offer consistent messages to researchers at all grades about social impacts from research and (2) to ensure that engagement is seen as an aspirational activity, embedded within research