Abstract
The increase of global competitiveness, the ability of organizations to effectively use information technologies, and to focus on innovation and creativity are recognized as being important. In this context, the hypothesis of resorting to known creativity techniques or adaptations to help innovation in the field of Software Engineering appears to be challenging. This paper proposes a methodology for introducing creativity and innovation techniques in the Requirements Engineering process in order to build more agile and efficient Information Systems. The method uses a variety of creative techniques that are thought to be appropriate to the different stages of the process and is inspired by existing creative problem-solving methods and techniques, in particular in the Creative Problem-Solving Process, Productive Thinking Model and the Creative Potentiation Method. The study of the method allowed its application, through the use of various creativity techniques, in a real context in a social institution - the Social Center for Support to the Community of São Domingos. The application of the methodology allowed the identification of new opportunities that allowed the organization to devise service delivery strategies that were more suited to the needs of people.

Abstract

Abstract
Radon gas is the largest source of public exposure to naturally occurring radioactivity. However, radon is also a useful tracer for understanding atmospheric processes, assessing the accuracy of chemical transport models, and enabling integrated emissions estimates of greenhouse gases. A sound metrological system for low level atmospheric radon observations is therefore needed for the benefit of the atmospheric, climate and radiation protection research communities. To this end, here we present a new calibration method for activity concentrations below 20 Bq m(-3) and a prototype of the first portable radon monitor capable of achieving uncertainties of 5% (at k = 2) at these concentrations. Compliance checking of policy-driven regulations regarding greenhouse gas (GHG) emissions is an essential component of climate change mitigation efforts. Independent, reliable 'top down' methods that can be applied consistently for estimating local- to regional-scale GHG emissions (such as the radon tracer method (RTM)) are an essential part of this process. The RTM relies upon observed radon and GHG concentrations and measured or modeled radon fluxes. Reliable radon flux maps could also significantly aid EU member states comply with European COUNCIL DIRECTIVE 2013/59/EURATOM. This article also introduces the traceRadon project, key aims of which include outlining a standardized approach for application of the RTM, creating infrastructure with a traceability chain for radon concentration and radon flux measurements, and developing tools for the validation of radon flux models. Since radon progeny dominate the terrestrial gamma dose rate, the planned traceRadon activities are also expected to improve the sensitivity of radiation protection early warning networks because of the correlation known to exist between radon flux and ambient equivalent dose rates.

Abstract
There is an increasing interest to study the interactions between atmospheric electrical parameters and living organisms at multiple scales. So far, relatively few studies have been published that focus on possible biological effects of atmospheric electric and magnetic fields. To foster future work in this area of multidisciplinary research, here we present a glossary of relevant terms. Its main purpose is to facilitate the process of learning and communication among the different scientific disciplines working on this topic. While some definitions come from existing sources, other concepts have been re-defined to better reflect the existing and emerging scientific needs of this multidisciplinary and transdisciplinary area of research.

Abstract

Abstract
<p>The Dansgaard-Oeschger (DO) events are one of the most striking examples of abrupt climate change in the Earth's history, representing temperature oscillations of about 8 to 16 degrees Celsius within a few decades. DO events have been studied extensively in paleoclimatic records, particularly in ice core proxies. Examples include the Greenland NGRIP record of oxygen isotopic composition.<br>This work addresses the anticipation of DO events using machine learning algorithms. We consider the NGRIP time series from 20 to 60 kyr b2k with the GICC05 timescale and 20-year temporal resolution. Forecasting horizons range from 0 (nowcasting) to 400 years. We adopt three different machine learning algorithms (random forests, support vector machines, and logistic regression) in training windows of 5 kyr. We perform validation on subsequent test windows of 5 kyr, based on timestamps of previous DO events' classification in Greenland by Rasmussen et al. (2014). We perform experiments with both sliding and growing windows.<br>Results show that predictions on sliding windows are better overall, indicating that modelling is affected by non-stationary characteristics of the time series. The three algorithms' predictive performance is similar, with a slightly better performance of random forest models for shorter forecast horizons. The prediction models' predictive capability decreases as the forecasting horizon grows more extensive but remains reasonable up to 120 years. Model performance deprecation is mostly related to imprecision in accurately determining the start and end time of events and identifying some periods as DO events when such is not valid.</p>