Existential Psychotherapies: Similarities and Differences Among the Main Branches

Authors agree that a range of different existential therapies exist. However, not much has been written about what is characteristic and distinctive of each existential therapy, and the few claims that have been made are mainly hypothetical. Practitioners from the four main branches of existential therapy were asked about the authors and texts that have most influenced their practice and the practices they considered most characteristic of existential therapy. From all over the world, 29 daseinsanalysts, 82 existential-humanistic, 573 existential-phenomenological, and 303 logotherapy and/or existential analysis practitioners participated in this study. Data show that the scope of influence of an author is pretty much limited to the branch he or she is related to and only a few authors, in particular Frankl and Yalom, influence practitioners from all four branches. Five categories of practice are shared among the main existential branches as the most characteristics of existential therapy, with phenomenological practices being the most shared category: But the frequency of each of these categories of practice differs significantly depending on respondents’ training or affiliated branch. Data corroborate the idea of different existential therapies, with logotherapy and/or existential analysis being the most markedly different branch of them all

Effect of the curing time on the numerical modelling of the behaviour of a chemically stabilised soft soil

The ability of the Modified Cam Clay (MCC) model combined with the Von Mises (VM) model, considering the effect of curing time on the enhancement of the mechanical properties of a chemically stabilised soft soil is examined. The evolution of the strength and stiffness over time is based on the results of undrained compressive strength (UCS) tests carried out for different curing times (from 28 days to 360 days). Initially, the MCC/VM models associated with the effect of curing time are validated by CIU triaxial tests, for curing times of 28 and 90 days. Finally, the behaviour of an embankment built on a soft soil reinforced with deep mixing columns is predicted based on the previously validated models. The results show that the increase of curing time of the DMCs slightly decreases the settlement obtained with a curing time of 28 days

Online hyper-evolution of controllers in multirobot systems

In this paper, we introduce online hyper-evolution (OHE) to accelerate and increase the performance of online evolution of robotic controllers. Robots executing OHE use the different sources of feedback information traditionally associated with controller evaluation to find effective evolutionary algorithms and controllers online during task execution. We present two approaches: OHE-fitness, which uses the fitness score of controllers as the criterion to select promising algorithms over time, and OHE-diversity, which relies on the behavioural diversity of controllers for algorithm selection. Both OHE-fitness and OHE-diversity are distributed across groups of robots that evolve in parallel. We assess the performance of OHE-fitness and of OHE-diversity in two foraging tasks with differing complexity, and in five configurations of a dynamic phototaxis task with varying evolutionary pressures. Results show that our OHE approaches: (i) outperform multiple state-of-the-art algorithms as they facilitate controllers with superior performance and faster evolution of solutions, and (ii) can increase effectiveness at different stages of evolution by combining the benefits of multiple algorithms over time. Overall, our study shows that OHE is an effective new paradigm to the synthesis of controllers for robots.info:eu-repo/semantics/acceptedVersio

Evolutionary Robotics

info:eu-repo/semantics/publishedVersio

Evolutionary online behaviour learning and adaptation in real robots

Online evolution of behavioural control on real robots is an open-ended approach to autonomous learning and adaptation: robots have the potential to automatically learn new tasks and to adapt to changes in environmental conditions, or to failures in sensors and/or actuators. However, studies have so far almost exclusively been carried out in simulation because evolution in real hardware has required several days or weeks to produce capable robots. In this article, we successfully evolve neural network-based controllers in real robotic hardware to solve two single-robot tasks and one collective robotics task. Controllers are evolved either from random solutions or from solutions pre-evolved in simulation. In all cases, capable solutions are found in a timely manner (1 h or less). Results show that more accurate simulations may lead to higher-performing controllers, and that completing the optimization process in real robots is meaningful, even if solutions found in simulation differ from solutions in reality. We furthermore demonstrate for the first time the adaptive capabilities of online evolution in real robotic hardware, including robots able to overcome faults injected in the motors of multiple units simultaneously, and to modify their behaviour in response to changes in the task requirements. We conclude by assessing the contribution of each algorithmic component on the performance of the underlying evolutionary algorithm.info:eu-repo/semantics/publishedVersio

A parameter for quantitative analysis of plasticity induced crack closure

Numerical models have been successfully developed to predict plasticity induced crack closure (PICC). However, despite the large research effort a full understanding of the links between physical parameters, residual plastic wake and PICC has not been achieved yet. The plastic extension of material behind crack tip, Δyp, obtained by the integration of vertical plastic deformation perpendicularly to crack flank, is proposed here to quantify the residual plastic field. The values of Δyp and PICC were obtained numerically in a M(T) specimen using the finite element method. An excellent correlation was found between PICC and Δyp which indicates that this parameter controls the phenomenon, and can be used to quantify the effect of physical parameters. An empirical model was developed to predict PICC assuming that the residual plastic field is a set of vertical plastic wedges, that the linear superposition principle applies and that the influence of a particular wedge exponentially decreases with distance to crack tip. The model was applied successfully to predict PICC for different residual plastic fields which provided an additional validation of Δyp as the parameter controlling PICC

R-HybrID: Evolution of agent controllers with a hybridisation of indirect and direct encodings

Neuroevolution, the optimisation of artificial neural networks (ANNs) through evolutionary computation, is a promising approach to the synthesis of controllers for autonomous agents. Traditional neuroevolution approaches employ direct encodings, which are limited in their ability to evolve complex or large-scale controllers because each ANN parameter is independently optimised. Indirect encodings, on the other hand, facilitate scalability because each gene can be reused multiple times to construct the ANN, but are biased towards regularity and can become ineffective when irregularity is required. To address such limitations, we introduce a novel algorithm called R-HybrID. In R-HybrID, controllers have both indirectly encoded and directly encoded structure. Because the portion of structure following a specific encoding is under evolutionary control, R-HybrID can automatically find an appropriate encoding combination for a given task. We assess the performance of R-HybrID in three tasks: (i) a high-dimensional visual discrimination task that requires geometric principles to be evolved, (ii) a challenging benchmark for modular robotics, and (iii) a memory task that has proven difficult for current algorithms because it requires effectively accumulating neural structure for cognitive behaviour to emerge. Our results show that R-HybrID consistently outperforms three stateof-the-art neuroevolution algorithms, and effectively evolves complex controllers and behaviours.info:eu-repo/semantics/publishedVersio

Corrections to the Boltzmann mean free path in disordered systems with finite size scatterers

The mean free path is an essential characteristic length in disordered systems. In microscopic calculations, it is usually approximated by the classical value of the elastic mean free path. It corresponds to the Boltzmann mean free path when only isotropic scattering is considered, but it is different for anisotropic scattering. In this paper, we work out the corrections to the so called Boltzmann mean free path due to multiple scattering effects on finite size scatterers, in the s-wave approximation, ie. when the elastic mean free path is equivalent to the Boltzmann mean free path. The main result is the expression for the mean free path expanded in powers of the perturbative parameter given by the scatterer density.Comment: 12 page

Architectural design digital change: interactivity policy

Several researches have been focused on digital architecture historical perspectives of the design throughout the sixties. This paper purposes a different view based on the influence of art, science and computation in architecture that contributed to the use of interactivity in architectural design. The aim is to describe the evolution of interactive CAD from MIT’s Project CAD and Ivan Sutherland’s Sketchpad to the early digital architectural design pioneers: Steven Coons, Gyorgy Kepes and Nicholas Negroponte.info:eu-repo/semantics/publishedVersio

Childhood Obesity and Impact on the Kidney

Obesity is known to be associated with a myriad of cardiovascular and metabolic comorbidities. In children, several longitudinal studies have shown that obesity consequences start early in life and accompany the obese child into adulthood, implying a higher risk of adverse cardiovascular events. More recently, data related to the possible role of obesity in the risk of kidney disease in adults, independently of diabetes, has started to become more available. In children, the evidence is scarcer, but it has also been acknowledged that obesity acts as a risk factor for disease progression when kidney impairment already exists, thereby increasing the risk of death among children with end-stage renal disease (ESRD). Besides this, there is also evidence that otherwise healthy overweight and obese children have a significant increase in the risk of all-cause ESRD later in life. The potential mechanisms underlying this association need to be further discussed in order to allow the setting in motion of preventive strategies to halt chronic kidney disease development and progression