Abstract
In our day to day, we often experience a sense of being tired due to mental or physical workload. Along with that, there is also a feeling of degrading performance, even after the completion of simple tasks. These mental states however, are often not felt consciously or are ignored. This is an attitude that may result in human error, failure, and may lead to potential health problems together with a decrease in quality of life. States of acute mental fatigue may be detected with the close monitoring of certain indicators, such as productivity, performance and health indicators. In this paper, a model and prototype are proposed to detect and monitor acute acute fatigue, based on non-invasive Human-computer Interaction (HCI). This approach will enable the development of better working environments, with an impact on the quality of life and the work produced. © 2014 Springer International Publishing Switzerland.

Abstract
The deployment of thousands of tiny devices inter-networked together and accessible through the Internet is the result of the increasing trend towards enabling the concepts of Internet-of-Things. As these devices may be scattered in a unplanned way, a routing protocol is needed. The RPL protocol is the IETF proposed standard protocol for IPv6-based multi-hop Wireless Sensor Networks (WSN). RPL requires that communication paths go through a central router which may provide suboptimal paths, making no distinction of the applications the nodes run. To address these issues, an Application-Driven extension to RPL is proposed which enables the increase of the WSN lifetime by limiting the routing and forwarding functions of the network mainly to nodes running the same application. This paper evaluates the proposed solution coded in ContikiOS by means of Cooja simulations, and compares it against regular RPL. Simulation results confirm that the proposed solution provides lower energy consumption, lower end-to-end delays, and lower total number of packets transmitted and received.

Abstract
Traffic prediction is a fundamental tool that captures the inherent behavior of a network and can be used for monitoring and managing network traffic. Online traffic prediction is usually performed based on large historical data used in training algorithms. This may not be suitable to highly volatile environments, such as cloud computing, where the coupling between observations decreases quickly with time. We propose a dynamic window size approach for traffic prediction that can be incorporated with different traffic predictions mechanisms, making them suitable to online traffic prediction by adapting the amount of traffic that must be analyzed in accordance to the variability of data traffic. The evaluation of the proposed solution is performed for several prediction mechanisms by assessing the Normalized Mean Square Error and Mean Absolute Percent Error of predicted values over observed values from a real cloud computing data set, collected by monitoring the utilization of Dropbox.

Abstract
Expression Atlas (http://www.ebi.ac.uk/gxa) is a value-added database providing information about gene, protein and splice variant expression in different cell types, organism parts, developmental stages, diseases and other biological and experimental conditions. The database consists of selected high-quality microarray and RNA-sequencing experiments from ArrayExpress that have been manually curated, annotated with Experimental Factor Ontology terms and processed using standardized microarray and RNA-sequencing analysis methods. The new version of Expression Atlas introduces the concept of 'baseline' expression, i.e. gene and splice variant abundance levels in healthy or untreated conditions, such as tissues or cell types. Differential gene expression data benefit from an in-depth curation of experimental intent, resulting in biologically meaningful 'contrasts', i.e. instances of differential pairwise comparisons between two sets of biological replicates. Other novel aspects of Expression Atlas are its strict quality control of raw experimental data, up-to-date RNA-sequencing analysis methods, expression data at the level of gene sets, as well as genes and a more powerful search interface designed to maximize the biological value provided to the user.

Abstract

Abstract
Acoustic sensing is nowadays a very demanding field which plays an important role in modern society, with applications spanning from structural health monitoring to medical imaging. Fiber-optics can bring many advantages to this field, and fiber-optic acoustic sensors show already performance levels capable of competing with the standard sensors based on piezoelectric transducers. This review presents the recent advances in the field of fiber-optic dynamic strain sensing, particularly for acoustic detection. Three dominant technologies are identified - fiber Bragg gratings, interferometric Mach-Zehnder, and Fabry-Pérot configurations - and their recent developments are summarized. © 2014 The Author(s).