Abstract
We propose a concept drift detection method utilizing statistical change detection in which a drift detection method and the Page-Hinkley test are employed. Our method enables users to annotate clustering results without constructing a model of drift detection for every input. In our experiments using synthetic data, we evaluated our proposed method on the basis of detection delay and false detection, also revealed relations between the degree of drift and parameters of the method.

Abstract
The paper is concerned with the comparative analysis of interactive devices. It compares two devices by checking systematically a set of template properties that are designed to explore important interface characteristics. The two devices are designed to support similar tasks in a clinical setting. The devices differ as a result of judgements based on a range of considerations including software. Variations between designs are often relatively subtle and do not always become evident through even relatively thorough user testing. Notwithstanding their subtlety, these differences may be important to the safety or usability of the device. The illustrated approach uses formal techniques to provide the analysis. This means that similar analysis can be applied systematically.

Abstract
An integrate-and-fire modulator (IFM) is designed for power scavenging systems like: Wireless Sensor Network (WSN) and Radio Frequency Identification (RFID) sensor tags. The circuit works with a clock in order to be able to be synchronized with microprocessors, which must be used to reconstruct the signal. The modulator is simulated using 130nm CMOS technology and the resulting power consumption is around 14nW at a clock frequency of 10 kHz. The OTA individually dissipates roughly 13nW. Signal reconstruction resulted in a 9.2 ENOB.

Abstract
Genuine concerns regarding air pollution, climate change, and dependence on unstable and expensive supplies of fossil fuels have lead policy makers and researchers to search for alternatives to conventional petroleum-fueled combustion power plants with the purpose to reduce greenhouse gas emission. This leads to an urgent need to substitute them with alternate generating capacity or reduce the consumption during peak periods, or both. One of the options for power generation is the use of renewable energy resources, which can inject power to the grid deprived of greenhouse gas emissions. But, from the load point of view, the renewable energy resources capacity is not sufficient to supply all the required power. These points to the necessity of innovative methods, able to diminish energy consumption in different sectors, but also with the aim of reducing the domestic customer's total energy costs, greenhouse gas emissions and energy demand, especially during on-peak, while always considering the end user preferences. Hence, this paper analyses model predictive control (MPC) application in domestic appliances with the purpose of energy optimization. In this context, the research theme is focused on the relation between MPC weighting adjustment and the minimization of energy consumption. Three domestic loads are used for MPC tuning evaluation: water heater (WH), room temperature control by conditioner (AC) and refrigerator (RF).

Abstract
An important dimension of complex networks is embedded in the weights of its edges. Incorporating this source of information on the analysis of a network can greatly enhance our understanding of it. This is the case for gene co-expression networks, which encapsulate information about the strength of correlation between gene expression profiles. Classical un-weighted gene co-expression networks use thresholding for defining connectivity, losing some of the information contained in the different connection strengths. In this paper, we propose a mining method capable of extracting information from weighted gene co-expression networks. We study groups of differently connected nodes and their importance as network motifs. We define a subgraph as a motif if the weights of edges inside the subgraph hold a significantly different distribution than what would be found in a random distribution. We use the Kolmogorov-Smirnov test to calculate the significance score of the subgraph, avoiding the time consuming generation of random networks to determine statistic significance. We apply our approach to gene co-expression networks related to three different types of cancer and also to two healthy datasets. The structure of the networks is compared using weighted motif profiles, and our results show that we are able to clearly distinguish the networks and separate them by type. We also compare the biological relevance of our weighted approach to a more classical binary motif profile, where edges are unweighted. We use shared Gene Ontology annotations on biological processes, cellular components and molecular functions. The results of gene enrichment analysis show that weighted motifs are biologically more significant than the binary motifs.

Abstract
MOOCs are a recent phenomenon, although given their impact, they have been subject to a large debate. Several questions have been raised by researchers and educators alike regarding their sustainability, both economically and as an efficient mode of education provision. In this paper we contribute to this discussion by presenting a case study of the MOOC on Lived Experiences of Climate Change, which piloted the iMOOC pedagogical model developed at Universidade Aberta (UAb), the Portugese Distance Learning University. The iMOOC is a hybrid model which incorporates elements from existing MOOCs but adds other features drawn from UAb's experience with online learning and aims at better integrating in the larger context of the institutional pedagogical culture. The iMOOC implied also an integration of platforms - Moodle and Elgg. The pilot course had more than one thousand registrations, and it was the largest MOOC course on Portuguese language delivered so far. We discuss the effort required to design and deliver the course, the technological solution developed, and the results obtained. We registered a moderate effort to create and run the course, ensured by internal staff from the University. The technological solution was a success: an integrated architecture combining well-established, well-tested open software. The completion rate was 3.3%, but the high success of this innovative learning experience was demonstrated by the active involvement of about 50% of the registered participants, that followed the course until the end. Lessons learned from this experience and future research on the field are also discussed.