Abstract
Stress can negatively impact one's health and well-being, however, despite the recent evolution in stress assessment research methodologies, there is still little agreement about stress conceptualization and assessment. In an attempt to summarize and reflect on this evolution, this paper aims to systematically review research evidence of ecological approaches on psychophysiological stress assessment. Thus, a literature search of electronic databases was conducted spanning 22 years (1990-2012) and 55 studies were reviewed. Studies were considered for inclusion if they contemplated both psychological and physiological measures of stress under ecological settings. This review focuses on five themes: methodology terminology, research population, study design, measurement, and technology. Findings support the need to use a common methodology terminology in order to increase scientific rigor. Additionally, there seems to be an increasing tendency for the use of these methods by multidisciplinary teams among both clinical and nonclinical populations aiming to understand the relationship between stress and disease. Most of the studies reviewed contemplated a time-based protocol and different conceptualizations of stress were found, resulting in the use of different subjective measures. Findings reinforce the importance of combining subjective and objective measures while also controlling for possible time-or situation-dependent confounders'. Advances in technology were evident and different assessment techniques were found. The benefits and challenges of ecological protocols to assess stress are discussed and recommendations for future research are provided, aiming to overcome previous limitations and advance scientific knowledge in the area.

Abstract
Solving complex optimization problems with genetic algorithms (GAs) with custom computing architectures is a way to improve the execution time of this metaheuristic, which is known to consume considerable amounts of time to converge to final solutions. In this work, we present a scalable computing array architecture to accelerate the execution of cellular GAs (cGAs), a variant of genetic algorithms which can conveniently exploit the coarse- grain parallelism afforded by custom parallel processing. The proposed architecture targets Xilinx FPGAs and is used as an auxiliary processor of an embedded CPU (MicroBlaze). To handle different optimization problems, a high- level synthesis (HLS) design flow is proposed where the problem- dependent operations are specified in C++ and synthesised to custom hardware, thus requiring a minimum knowledge of digital design for FPGAs. The minimum energy broadcast (MEB) problem in wireless ad hoc networks is used as a case study. An existing software implementation of a GA to solve this problem is ported to the proposed computing array to demonstrate its effectiveness and the HLS- based design flow. Implementation results in a Virtex- 6 FPGA show significant speedups, while finding solutions with improved quality.

Abstract
Sectorization means dividing a whole into parts (sectors), a procedure that occurs in many contexts and applications, usually to achieve some goal or to facilitate an activity. The objective may be a better organization or simplification of a large problem into smaller sub-problems. Examples of applications are political districting and sales territory division. When designing/comparing sectors some characteristics such as contiguity, equilibrium and compactness are usually considered. This paper presents and describes new generic measures and proposes a new measure, desirability, connected with the idea of preference. © 2015, Springer International Publishing Switzerland.

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Articles successfully submitted to and presented at IMC 2015: The International Management Conference

Abstract
Many embedded multi-core systems incorporate both dataflow applications with timing constraints and traditional real-time applications. Applying real-time scheduling techniques on such systems provides real-time guarantees that all running applications will execute safely without violating their deadlines. However, to apply traditional real-time scheduling techniques on such mixed systems, a unified model to represent both types of applications running on the system is required. Several earlier works have addressed this problem and solutions have been proposed that address acyclic graphs, implicit-deadline models or are able to extract timing parameters considering specific scheduling algorithms. In this paper, we present an algorithm for extracting real-time parameters (offsets, deadlines and periods) that are independent of the schedulability analysis, other applications running in the system, and the specific platform. The proposed algorithm: 1) enables applying traditional real-time schedulers and analysis techniques on cyclic or acyclic Homogeneous Synchronous Dataflow (HSDF) applications with periodic sources, 2) captures overlapping iterations, which is a main characteristic of the execution of dataflow applications, 3) provides a method to assign offsets and individual deadlines for HSDF actors, and 4) is compatible with widely used deadline assignment techniques, such as NORM and PURE. The paper proves the correctness of the proposed algorithm through formal proofs and examples.

Abstract