HYDRA: Distributed Multi-Objective Optimization for Designers

Architectural design problems can be quite involved, as there is a plethora of – usually conflicting – criteria that one has to address in order to find an optimal, performative solution. Multi-Objective Optimization (MOO) techniques can thus prove very useful, as they provide solution spaces which can traverse the different trade-offs of convoluted design options. Nevertheless, they are not widely used as (a) they are computationally expensive and (b) the resulting solution space can be proven difficult to visualize and navigate, particularly when dealing with higher dimensional spaces. This paper will present a system, which merges bespoke multi-objective optimization with a parametric CAD system, enhanced by supercomputing, into a single, coherent workflow, in order to address the above issues. The system architecture ensures optimal use of existing compute resources and enables massive performance speed-up, allowing for fast review and delivery cycles. The application aims to provide architects, designers and engineers with a better understanding of the design space, aiding the decision-making process by procuring tangible data from different objectives and finally providing fit (and sometimes unforeseen) solutions to a design problem. This is primarily achieved by a graphical interface of easy to navigate solution spaces of design options, derived from their respective Pareto fronts, in the form of a web-based interactive dashboard. Since understanding high-dimensionality data is a difficult task, multivariate analysis techniques were implemented to post-process the data before displaying it to end users. Visual Data Mining (VDM) and Machine Learning (ML) techniques were incorporated to facilitate knowledge discovery and exploration of large sets of design options at an early design stage. The system is demonstrated and assessed on an applied design case study of a master-planning project, where the benefits of the process are more evident, especially due to its complexity and size

Effect of allopurinol in addition to hypothermia treatment in neonates for hypoxic-ischemic brain injury on neurocognitive outcome (ALBINO): Study protocol of a blinded randomized placebo-controlled parallel group multicenter trial for superiority (phase III)

Background: Perinatal asphyxia and resulting hypoxic-ischemic encephalopathy is a major cause of death and long-term disability in term born neonates. Up to 20,000 infants each year are affected by HIE in Europe and even more in regions with lower level of perinatal care. The only established therapy to improve outcome in these infants is therapeutic hypothermia. Allopurinol is a xanthine oxidase inhibitor that reduces the production of oxygen radicals as superoxide, which contributes to secondary energy failure and apoptosis in neurons and glial cells after reperfusion of hypoxic brain tissue and may further improve outcome if administered in addition to therapeutic hypothermia. Methods: This study on the effects of ALlopurinol in addition to hypothermia treatment for hypoxic-ischemic Brain Injury on Neurocognitive Outcome (ALBINO), is a European double-blinded randomized placebo-controlled parallel group multicenter trial (Phase III) to evaluate the effect of postnatal allopurinol administered in addition to standard of care (including therapeutic hypothermia if indicated) on the incidence of death and severe neurodevelopmental impairment at 24 months of age in newborns with perinatal hypoxic-ischemic insult and signs of potentially evolving encephalopathy. Allopurinol or placebo will be given in addition to therapeutic hypothermia (where indicated) to infants with a gestational age 65 36 weeks and a birth weight 65 2500 g, with severe perinatal asphyxia and potentially evolving encephalopathy. The primary endpoint of this study will be death or severe neurodevelopmental impairment versus survival without severe neurodevelopmental impairment at the age of two years. Effects on brain injury by magnetic resonance imaging and cerebral ultrasound, electric brain activity, concentrations of peroxidation products and S100B, will also be studied along with effects on heart function and pharmacokinetics of allopurinol after iv-infusion. Discussion: This trial will provide data to assess the efficacy and safety of early postnatal allopurinol in term infants with evolving hypoxic-ischemic encephalopathy. If proven efficacious and safe, allopurinol could become part of a neuroprotective pharmacological treatment strategy in addition to therapeutic hypothermia in children with perinatal asphyxia. Trial registration: NCT03162653, www.ClinicalTrials.gov, May 22, 2017

Motivation and treatment engagement intervention trial (MotivaTe-IT): The effects of motivation feedback to clinicians on treatment engagement in patients with severe mental illness

Background: Treatment disengagement and non-completion poses a major problem for the successful treatment of patients with severe mental illness. Motivation for treatment has long been proposed as a major determinant of treatment engagement, but exact mechanisms remain unclear. This current study serves three purposes: 1) to determine whether a feedback intervention based on the patients' motivation for treatment is effective at improving treatment engagement (TE) of severe mentally ill patients in outpatient psychiatric treatment, 2) to gather insight into motivational processes and pos

A genetic algorithm for the one-dimensional cutting stock problem with setups

This paper investigates the one-dimensional cutting stock problem considering two conflicting objective functions: minimization of both the number of objects and the number of different cutting patterns used. A new heuristic method based on the concepts of genetic algorithms is proposed to solve the problem. This heuristic is empirically analyzed by solving randomly generated instances and also practical instances from a chemical-fiber company. The computational results show that the method is efficient and obtains positive results when compared to other methods from the literature. © 2014 Brazilian Operations Research Society

A life-course and time perspective on the construct validity of psychological distress in women and men. Measurement invariance of the K6 across gender

<p>Abstract</p> <p>Background</p> <p>Psychological distress is a widespread indicator of mental health and mental illness in research and clinical settings. A recurrent finding from epidemiological studies and population surveys is that women report a higher mean level and a higher prevalence of psychological distress than men. These differences may reflect, to some extent, cultural norms associated with the expression of distress in women and men. Assuming that these norms differ across age groups and that they evolve over time, one would expect gender differences in psychological distress to vary over the life-course and over time. The objective of this study was to investigate the construct validity of a psychological distress scale, the K6, across gender in different age groups and over a twelve-year period.</p> <p>Methods</p> <p>This study is based on data from the Canadian National Population Health Survey (C-NPHS). Psychological distress was assessed with the K6, a scale developed by Kessler and his colleagues. Data were examined through multi-group confirmatory factor analyses. Increasing levels of measurement and structural invariance across gender were assessed cross-sectionally with data from cycle 1 (n = 13019) of the C-NPHS and longitudinally with cycles 1 (1994-1995), 4 (2000-2001) and 7 (2006-2007).</p> <p>Results</p> <p>Higher levels of measurement and structural invariance across gender were reached only after the constraint of equivalence was relaxed for various parameters of a few items of the K6. Some items had a different pattern of gender non invariance across age groups and over the course of the study. Gender differences in the expression of psychological distress may vary over the lifespan and over a 12-year period without markedly affecting the construct validity of the K6.</p> <p>Conclusions</p> <p>This study confirms the cross-gender construct validity of psychological distress as assessed with the K6 despite differences in the expression of some symptoms in women and in men over the life-course and over time. Findings suggest that the higher mean level of psychological distress observed in women reflects a true difference in distress and is unlikely to be gender-biased. Gender differences in psychological distress are an important public health and clinical issue and further researches are needed to decipher the factors underlying these differences.</p

Effective local evolutionary searches distributed on an island model solving bi-objective optimization problems

Using multiple local evolutionary searches, instead of single and overall search, has been an effective technique to solve multi-objective optimization problems (MOPs). With this technique, many parallel and distributed multi-objective evolutionary algorithms (dMOEAs) on different island models have been proposed to search for optimal solutions, efficiently and effectively. These algorithms often use local MOEAs on their islands in which each local search is considered to find a part of optimal solutions. The islands (and the local MOEAs), however, need to communicate to each other to preclude the possibility of converging to local optimal solutions. The existing dMOEAs rely on the central and iterative process of subdividing a large-scale population into multiple subpopulations; and it negatively affects the dMOEAs performance. In this paper, a new version of dMOEA with new local MOEAs and migration strategy is proposed. The respective objective space is first subdivided into the predefined number of polar-based regions assigned to the local MOEAs to be explored and exploited. In addition, the central and iterative process is eliminated using a new proposed migration strategy. The algorithms are tested on the standard bi-objective optimization test cases of ZDTs, and the result shows that these new dMOEAs outperform the existing distributed and parallel MOEAs in most cases

An empirical assessment of the properties of inverted generational distance on multi- and many-objective optimization

The inverted generational distance (IGD) is a metric for assessing the quality of approximations to the Pareto front obtained by multi-objective optimization algorithms. The IGD has become the most commonly used metric in the context of many-objective problems, i.e. those with more than three objectives. The averaged Hausdorff distance and IGD+ are variants of the IGD proposed in order to overcome its major drawbacks. In particular, the IGD is not Pareto compliant and its conclusions may strongly change depending on the size of the reference front. It is also well-known that different metrics assign more importance to various desired features of approximation fronts, and thus, they may disagree when ranking them. However, the precise behavior of the IGD variants is not well-understood yet. In particular, IGD+, the only IGD variant that is weakly Pareto-compliant, has received significantly less attention. This paper presents an empirical analysis of the IGD variants. Our experiments evaluate how these metrics are affected by the most important factors that intuitively describe the quality of approximation fronts, namely, spread, distribution and convergence. The results presented here already reveal interesting insights. For example, we conclude that, in order to achieve small IGD or IGD+values, the approximation front size should match the reference front size.SCOPUS: cp.kinfo:eu-repo/semantics/publishe