Abstract
ObjectiveThe capacity of hair to absorb water causes changes in its physical and cosmetic properties under different environmental conditions. Hence, the control of hair volume in variable relative humidity settings is an important topic in cosmetics. The behaviour of two types of hair, Caucasian and Asian, was studied regarding their volume change in different relative humidity conditions. The ability of a peptide as a hair volume treatment was evaluated in two climate control formulations. MethodsTresses of the two types of hair were tested in two relative humidity (RH) conditions: (A) variable relative humidity (2h 40% RH, followed by 2h 90% RH and 2h of 40% RH), and (B) continuous high relative humidity (90% RH for 6h). Changes in the hair tress volume were assessed throughout time. Hair treated with two climate control formulations, with and without a peptide (KP peptide), were tested under the two relative humidity conditions. ResultsCaucasian hair had a higher change in volume compared to the Asian hair in variable and high relative humidity conditions. The hair volume increase when subject to high air humidity, and it was lower with the incorporation of a peptide into climate control formulations. ConclusionCaucasian hair showed higher volume than Asian hair when submitted to both relative humidity conditions. The incorporation of the peptide into the climate control formulations, a base (mostly composed of water approximate to 94%) and an ethanolic, was found to reduce the volume of Caucasian hair tresses. The presence of the peptide improved the hair volume change more than 60% in high relative humidity conditions.

Abstract

Abstract
Quality of Service (QoS) is an important enabler for communication in industrial environments. The Arrowhead Framework was created to support local cloud functionalities for automation applications by means of a Service Oriented Architecture. To this aim, the framework offers a number of services that ease application development, among them the QoSSetup and the Monitor services, the first used to verify and configure QoS in the local cloud, and the second for online monitoring of QoS. This paper describes how the QoSSetup and Monitor services are provided in a Arrowhead-compliant System of Systems, detailing both the principles and algorithms employed, and how the services are implemented. Experimental results are provided, from a demonstrator built over a real-time Ethernet network. © 2017 IEEE.

Abstract
This paper provides an overview of the development of a selforiented solar mirror (SOSM) project within the European Project Semester (EPS) at Instituto Superior de Engenharia do Porto (ISEP). While the main objective of the EPS@ISEP project-based educational framework is to foster teamwork, communication, interpersonal and problem solving skills in an international, multidisciplinary engineering environment, the goal of the SOSM is to track and reflect the Sun radiation onto a pre-defined area. In the spring of 2017 a group of five students chose to develop a proof-of-concept domestic SOSM called SUNO. The students undertook project supportive modules in Ethics, Sustainability, Marketing and Project Management together with project coaching meetings to assist the development of SUNO. The paper details this process, describing the initial project definition, the research of current technologies, the designing, the manufacturing and testing of the SUNO prototype, and discusses what the students gained from this learning experience. © 2017 Association for Computing Machinery.

Abstract
It is common knowledge that history repeats itself! Maybe not literally, but patterns of behaviour likely dependent of the human nature, are probably prone to repeat themselves. So, one may wonder if looking back could help us prepare for a better future. Moreover, by looking back at the history of people and societies, we should all be able to have a better understanding of why things happen the way they do. This seldom happens, and when it does, it is happening within very limited circle of the society such as scholars and some politician circles, rarely overflowing to the whole society.The point is that, what we see today is not very different from what has happened in the past. Let us go back to November 13, 1460, the day Prince Henry the Navigator, passed away in Sagres, leaving Portugal with an enormous debt. Despite that fact, Prince Henry was the “guiding force behind Portugal’s assimilation of nautical knowledge and its vast extension of maritime exploration for nearly four decades” (Kock, 2003, p.59). It is interesting that by that time intellectual property was already being managed. (...)

Abstract
A comparison between co-training and self-training method for single-target regression based on multiples learners is performed. Data streaming systems can create a significant amount of unlabeled data which is caused by label assignment impossibility, high cost of labeling or labeling long duration tasks. In supervised learning, this data is wasted. In order to take advantaged from unlabeled data, semi-supervised approaches such as Co-training and Self-training have been created to benefit from input information that is contained in unlabeled data. However, these approaches have been applied to classification and batch training scenarios. Due to these facts, this paper presents a comparison between Co-training and Self-learning methods for single-target regression in data streams. Rules learning is used in this context since this methodology enables to explore the input information. The experimental evaluation consisted of a comparison between the real standard scenario where all unlabeled data is rejected and scenarios where unlabeled data is used to improve the regression model. Results show evidences of better performance in terms of error reduction and in high level of unlabeled examples in the stream. Despite this fact, the improvements are not expressive.