Abstract
Extended Manufacturing Environments (EMEs) are nowadays growing due to the increase on Distributed and Virtual Enterprises, which led to an emergent need to apply scheduling approaches accordingly. This can be achieved in several different ways, namely by putting forward new approaches or by trying to adapt existing ones. In this paper the adaptation of some existing scheduling methods is proposed for solving a two stage manufacturing scheduling problem, and an illustrative example is presented. Several approaches were analyses, namely through the use of the ANOV A and the Post Hoc Scheffe's test, that demonstrated the superior performance of one of the proposed methods.

Abstract
In this article, we review the state-of-the-art techniques in mining data streams for mobile and ubiquitous environments. We start the review with a concise background of data stream processing, presenting the building blocks for mining data streams. In a wide range of applications, data streams are required to be processed on small ubiquitous devices like smartphones and sensor devices. Mobile and ubiquitous data mining target these applications with tailored techniques and approaches addressing scarcity of resources and mobility issues. Two categories can be identified for mobile and ubiquitous mining of streaming data: single-node and distributed. This survey will cover both categories. Mining mobile and ubiquitous data require algorithms with the ability to monitor and adapt the working conditions to the available computational resources. We identify the key characteristics of these algorithms and present illustrative applications. Distributed data stream mining in the mobile environment is then discussed, presenting the Pocket Data Mining framework. Mobility of users stimulates the adoption of context-awareness in this area of research. Context-awareness and collaboration are discussed in the Collaborative Data Stream Mining, where agents share knowledge to learn adaptive accurate models. Conflict of interest: The authors have declared no conflicts of interest for this article. For further resources related to this article, please visit the .

Abstract

Abstract
To analyze the simultaneous effects of sociodemographic variables and time on each food group contribution to total Portuguese elderly household food availability. Four cross sectional Portuguese Household Budget Surveys were used. A multivariate analysis of variance (MANOVA), using a general linear model (GLM), was applied to analyze the simultaneous effects of sociodemographic variables and time. Portuguese population. Nationally representative samples of households with members aged a parts per thousand yen 65 years were selected and categorized as solitary elderly female, solitary elderly male, or couple (one elderly female and one elderly male). Samples included 1,967 households in 1989-1990, 2,219 households in 1994-1995, 2,533 households in 2000-2001 and 2,441 households in 2005-2006. The simultaneous effects of sociodemographic variables and time were significant for all food groups (P < 0.001). The highest contribution for the total household food availability was found for cereals, potatoes, alcoholic beverages, nonalcoholic beverages and fruits. The effects were large for "household food availability" and medium for "elderly household type", "urbanization degree", "income", "food expenses" and "eating out expenses". Solitary elderly male households had the highest proportion of cereals and alcoholic beverages, whilst solitary elderly female households had higher availability of milk/milk products and fruits. Households located in urban areas had higher contribution of milk/milk products while rural, had higher contribution of potatoes. The simultaneous effect of the studied variables on food group contribution to total household food availability can be considered when addressing dietary recommendation for providing an insight into the motivations associated with food purchases.

Abstract
A new type of fibre-optic refractive index sensor based on a long period fibre grating (LPFG) coated with a titanium dioxide (TiO2) thin film was demonstrated. The wavelength shift of the attenuation bands of this LPFG sensor to changes in the refractive index of the external media from 1.30 to 1.64 RIU was investigated. In order to optimize the sensor the TiO2 thin film thickness deposited around the LPFGs was varied from 10 to 80 nm. It was found that the TiO2 thin film increases the wavelength sensitivity of the LPFG to changes in the surrounding refractive index for values lower and higher than the cladding refractive index. As opposed to the bare LPFG it was shown the possibility to monitor refractive indices lower and higher than cladding refractive index tailoring the TiO2 thickness. An average wavelength sensitivity of 5250 nm/RIU was achieved in the range 1.444 to 1.456 RIU for a TiO2 thickness of 50 nm. In the region between 1.46 and 1.48 RIU the average sensitivity of about 825 nm/RIU was measured for a 40 nm thick film.

Abstract
Existing adaptive educational hypermedia systems have been using learning resources sequencing approaches in order to enrich the learning experience. In this context, educational resources, either expository or evaluative, play a central role. However, there is a lack of tools that support sequencing essentially due to the fact that existing specifications are complex. This paper presents Seqins as a sequencing tool of digital educational resources. Seqins includes a simple and flexible sequencing model that will foster heterogeneous students to learn at different rhythms. The tool communicates through the IMS Learning Tools Interoperability specification with a plethora of e-learning systems such as learning management systems, repositories, authoring and automatic evaluation systems. In order to validate Seqins we integrate it in an e-learning Ensemble framework instance for the computer programming learning domain.