Abstract
A Mach-Zehnder sensor based on a large knot fiber resonator with a diameter of a few millimeters is designed using a single long taper. The long taper of some centimeters is fabricated with a CO2 laser technique. In air, light cannot couple between adjacent sections in the knot, and no signal is observed. However, in liquid, light is less confined and there is coupling between adjacent sections of the knot, resulting in a phase difference and consequent interference. The Mach-Zehnder is formed by the two contact points in the knot. The refractive index sensing of liquid compounds is achieved by monitoring the wavelength shift of the spectra. A sensitivity of 642 +/- 29 nm/refractive index unit (RIU) is achieved for refractive index sensing in the range of 1.3735-1.428 with a resolution of 0.009 RIU. For temperature sensing, a sensitivity of -42 +/- 9 pm/degrees C is observed. A low influence of temperature in the refractive index change is observed: 6.5 x 10(-5) RIU/degrees C.

Abstract
The secure integration of renewable generation into modern power systems requires an appropriate assessment of the security of the system in real-time. The uncertainty associated with renewable power makes it impossible to tackle this problem via a brute-force approach, i.e. it is not possible to run detailed online static or dynamic simulations for all possible security problems and realizations of load and renewable power. Intelligent approaches for online security assessment with forecast uncertainty modeling are being sought to better handle contingency events. This paper reports the platform developed within the iTesla project for online static and dynamic security assessment. This innovative and open-source computational platform is composed of several modules such as detailed static and dynamic simulation, machine learning, forecast uncertainty representation and optimization tools to not only filter contingencies but also to provide the best control actions to avoid possible unsecure situations. Based on High Performance Computing (IIPC), the iTesla platform was tested in the French network for a specific security problem: overload of transmission circuits. The results obtained show that forecast uncertainty representation is of the utmost importance, since from apparently secure forecast network states, it is possible to obtain unsecure situations that need to be tackled in advance by the system operator.

Abstract
We propose a new dynamic modeling framework for credit risk assessment that extends the prevailing credit scoring models built upon historical data static settings. The driving idea mimics the principle of films, by composing the model with a sequence of snapshots, rather than a single photograph. In doing so, the dynamic modeling consists of sequential learning from the new incoming data. A key contribution is provided by the insight that different amounts of memory can be explored concurrently. Memory refers to the amount of historic data being used for estimation. This is important in the credit risk area, which often seems to undergo shocks. During a shock, limited memory is important. Other times, a larger memory has merit. An application to a real-world financial dataset of credit cards from a financial institution in Brazil illustrates our methodology, which is able to consistently outperform the static modeling schema.

Abstract
One of the most common knee joint disorders is known as osteoarthritis which results from the progressive degeneration of cartilage and subchondral bone over time, affecting essentially elderly adults. Current evaluation techniques are either complex, expensive, invasive or simply fails into detection of small and progressive changes that occur within the knee. Vibroarthrography appeared as a new solution where the mechanical vibratory signals arising from the knee are recorded recurring only to an accelerometer and posteriorly analyzed enabling the differentiation between a healthy and an arthritic joint. In this study, a vibration-based classification system was created using a dataset with 92 healthy and 120 arthritic segments of knee joint signals collected from 19 healthy and 20 arthritic volunteers, evaluated with k-nearest neighbors and support vector machine classifiers. The best classification was obtained using the k-nearest neighbors classifier with only 6 time-frequency features with an overall accuracy of 89.8% and with a precision, recall and f-measure of 88.3%, 92.4% and 90.1%, respectively. Preliminary results showed that vibroarthrography can be a promising, non-invasive and low cost tool that could be used for screening purposes. Despite this encouraging results, several upgrades in the data collection process and analysis can be further implemented.

Abstract
Urban traffic emissions have been increasing in recent years. To reverse that trend, restrictive traffic measures can be implemented to complement national policies. We have proposed a methodology to assess the impact of three restrictive traffic measures in an urban arterial by using a microsimulation model of traffic and emissions integrated platform. The analysis is extended to some alternative roads and to the overall network area. Traffic restriction measures provided average reductions of 45%, 47%, 35%, and 47% for CO2, CO, NOx, and HC, respectively, due to traffic being diverted to other roads. Nevertheless, increases of 91%, 99%, 55%, and 121% in CO2, CO, NOx, and HC, respectively, can be expected on alternative roads.

Abstract
Recent advances in mobile device technology have triggered research on using their aggregate computational and/or storage resources to form edge-clouds. Whilst traditionally viewed as simple clients, smart-phones and tablets today have hardware resources that allow more sophisticated software to be installed, and can be used as thick clients or even thin servers. Simultaneously, new standards and protocols, such as Wi-Fi Direct and Wi-Fi TDLS (Tunneled Direct Link Setup), have been established that allow mobile devices to talk directly with each other, as opposed to over the Internet or across Wi-Fi access points. This can, potentially, lead to ubiquitous, low-latency, device-to-device (D2D) communication. In this paper, we study whether D2D protocols can support mobile-edge clouds by benchmarking different protocols and configurations for a specific application. The results show that decentralized device-to-device techniques can be used to efficiently disseminate multimedia contents while diminishing contention in the wireless infrastructure, allowing for up to 65% traffic reduction at the access points.