Generating sequential space-filling designs using genetic algorithms and Monte Carlo methods

In this paper, the authors compare a Monte Carlo method and an optimization-based approach using genetic algorithms for sequentially generating space-filling experimental designs. It is shown that Monte Carlo methods perform better than genetic algorithms for this specific problem

Finite size scaling of current fluctuations in the totally asymmetric exclusion process

We study the fluctuations of the current J(t) of the totally asymmetric exclusion process with open boundaries. Using a density matrix renormalization group approach, we calculate the cumulant generating function of the current. This function can be interpreted as a free energy for an ensemble in which histories are weighted by exp(-sJ(t)). We show that in this ensemble the model has a first order space-time phase transition at s=0. We numerically determine the finite size scaling of the cumulant generating function near this phase transition, both in the non-equilibrium steady state and for large times.Comment: 18 pages, 11 figure

SU8 etch mask for patterning PDMS and its application to flexible fluidic microactuators.

Over the past few decades, polydimethylsiloxane (PDMS) has become the material of choice for a variety of microsystem applications, including microfluidics, imprint lithography, and soft microrobotics. For most of these applications, PDMS is processed by replication molding; however, new applications would greatly benefit from the ability to pattern PDMS films using lithography and etching. Metal hardmasks, in conjunction with reactive ion etching (RIE), have been reported as a method for patterning PDMS; however, this approach suffers from a high surface roughness because of metal redeposition and limited etch thickness due to poor etch selectivity. We found that a combination of LOR and SU8 photoresists enables the patterning of thick PDMS layers by RIE without redeposition problems. We demonstrate the ability to etch 1.5-μm pillars in PDMS with a selectivity of 3.4. Furthermore, we use this process to lithographically process flexible fluidic microactuators without any manual transfer or cutting step. The actuator achieves a bidirectional rotation of 50° at a pressure of 200 kPa. This process provides a unique opportunity to scale down these actuators as well as other PDMS-based devices.BG is a Doctoral Fellow of the Research Foundation—Flanders (F.W.O.), Belgium. MDV acknowledges support from the ERC starting grant HIENA (no. 337739)

Universal Prediction Distribution for Surrogate Models

International audienceThe use of surrogate models instead of computationally expensive simulation codes is very convenient in engineering. Roughly speaking, there are two kinds of surrogate models: the deterministic and the probabilistic ones. These last are generally based on Gaussian assumptions. The main advantage of probabilistic approach is that it provides a measure of uncertainty associated with the surrogate model in the whole space. This uncertainty is an efficient tool to construct strategies for various problems such as prediction enhancement, optimization or inversion.In this paper, we propose a universal method to define a measure of uncertainty suitable for any surrogate model either deterministic or probabilistic. It relies on Cross-Validation (CV) sub-models predictions. This empirical distribution may be computed in much more general frames than the Gaussian one. So that it is called the Universal Prediction distribution (UP distribution).It allows the definition of many sampling criteria. We give and study adaptive sampling techniques for global refinement and an extension of the so-called Efficient Global Optimization (EGO) algorithm. We also discuss the use of the UP distribution for inversion problems. The performances of these new algorithms are studied both on toys models and on an engineering design problem

Does touch matter? The impact of stroking versus non-stroking maternal touch on cardio-respiratory processes in mothers and infants

The beneficial effects of touch in development were already observed in different types of skin-to-skin care. In the current study, we aimed at studying potential underlying mechanisms of these effects in terms of parasympatho-inhibitory regulation. We examined the specific impact of affective maternal stroking versus non-stroking touch on the cardio-respiration of both mothers and infants in terms of respiratory sinus arrhythmia (RSA). We compared a 3-min TOUCH PERIOD (stroking or non-stroking touch) with a baseline before (PRE-TOUCH) and after (POST-TOUCH) in 45 dyads (24 stroking/21 non-stroking touch) with infants aged 4–16 weeks. We registered mother-infant ECG, respiration and made video-recordings. We calculated RR-interval (RRI), respiration rate (fR) and (respiratory corrected) RSA and analyzed stroking mean velocity rate (MVR) of the mothers. ANOVA-tests showed a significant different impact on infants' respiratory corrected RSA of stroking touch (increase) versus non-stroking touch (decrease). Further, during and after stroking touch, RRI significantly increased whereas fR significantly decreased. Non-stroking touch had no significant impact on infants' RRI and fR. In the mothers, RRI significantly decreased and fR significantly increased during the TOUCH PERIOD. The mothers' MVR occurred within the range of 1–10 cm/s matching with the optimal afferent stimulation range of a particular class of cutaneous unmyelinated, low-threshold mechano-sensitive nerves, named c-tactile (CT) afferents. We suggest CT afferents to be the a potential missing link between the processing of affective touch and the development of physiological and emotional self-regulation. The results are discussed with regard to the potential role of CT afferents within the building of early self-regulation as part of a multisensory intuitive parenting system and the importance to respect this ecological context of an infant in research and clinical applications

Comparison of Stochastic Methods for the Variability Assessment of Technology Parameters

This paper provides and compares two alternative solutions for the simulation of cables and interconnects with the inclusion of the effects of parameter uncertainties, namely the Polynomial Chaos (PC) method and the Response Surface Modeling (RSM). The problem formulation applies to the telegraphers equations with stochastic coefficients. According to PC, the solution requires an expansion of the unknown parameters in terms of orthogonal polynomials of random variables. On the contrary, RSM is based on a least-square polynomial fitting of the system response. The proposed methods offer accuracy and improved efficiency in computing the parameter variability effects on system responses with respect to the conventional Monte Carlo approach. These approaches are validated by means of the application to the stochastic analysis of a commercial multiconductor flat cable. This analysis allows us to highlight the respective advantages and disadvantages of the presented method

Thermodynamics of histories for the one-dimensional contact process

The dynamical activity K(t) of a stochastic process is the number of times it changes configuration up to time t. It was recently argued that (spin) glasses are at a first order dynamical transition where histories of low and high activity coexist. We study this transition in the one-dimensional contact process by weighting its histories by exp(sK(t)). We determine the phase diagram and the critical exponents of this model using a recently developed approach to the thermodynamics of histories that is based on the density matrix renormalisation group. We find that for every value of the infection rate, there is a phase transition at a critical value of s. Near the absorbing state phase transition of the contact process, the generating function of the activity shows a scaling behavior similar to that of the free energy in an equilibrium system near criticality.Comment: 16 pages, 7 figure

Characterising the muscle anabolic potential of dairy, meat and plant-based protein sources in older adults

The age-related loss of skeletal muscle mass and function is caused, at least in part, by a reduced muscle protein synthetic response to protein ingestion. The magnitude and duration of the postprandial muscle protein synthetic response to ingested protein is dependent on the quantity and quality of the protein consumed. This review characterises the anabolic properties of animal-derived and plant-based dietary protein sources in older adults. While approximately 60 % of dietary protein consumed worldwide is derived from plant sources, plant-based proteins generally exhibit lower digestibility, lower leucine content and deficiencies in certain essential amino acids such as lysine and methionine, which compromise the availability of a complete amino acid profile required for muscle protein synthesis. Based on currently available scientific evidence, animal-derived proteins may be considered more anabolic than plant-based protein sources. However, the production and consumption of animal-derived protein sources is associated with higher greenhouse gas emissions, while plant-based protein sources may be considered more environmentally sustainable. Theoretically, the lower anabolic capacity of plant-based proteins can be compensated for by ingesting a greater dose of protein or by combining various plant-based proteins to provide a more favourable amino acid profile. In addition, leucine co-ingestion can further augment the postprandial muscle protein synthetic response. Finally, prior exercise orn-3 fatty acid supplementation have been shown to sensitise skeletal muscle to the anabolic properties of dietary protein. Applying one or more of these strategies may support the maintenance of muscle mass with ageing when diets rich in plant-based protein are consumed