Scale-effects on mean and standard deviation of the mechanical properties of condensed matter: an energy-based unified approach

The size effects on the mean values of the mechanical properties of condensed matter and on the related variances are analysed by means of a unified approach based on the multiscale character of energy dissipation. In particular, the scaling law for fragmentation energy density is obtained taking into account the self-similarity of fragments. It is based on a generalization of the three classical comminution laws that has been performed to evaluate the energy dissipation, computing volume and surface area of the particles for one- two- and three-dimensional fragmented objects. The result is general and can be applied to different fractal energy dissipation mechanisms, e.g., plasticity. Based on this approach, the scaling laws for mean and standard deviation values of the main mechanical properties of materials can be derived, like Young's and shear elastic moduli, ultimate normal and shear stresses and strains, fracture energy and toughness.     

Effects of interstitial oxygen on microstructure and mechanical properties of Ti-48Al-2Cr-2Nb with fully lamellar and duplex microstructures

The effect of added oxygen in the range of 1000 to 4000 wt ppm on the microstructures of a Ti-48Al-2Cr-2Nb alloy has been investigated and compared to the microstructures for a high-purity alloy. For specimens cooled from theα phase, interstitial oxygen stabilizes the lamellar microstructure with respect toγ grains, with higher than equilibrium values for theα ₂ volume fraction. For specimens cooled from theα +γ phase field, the lamellar microstructure still tends to be favored by oxygen, but it is found that the phase volume fractions are not significantly different from equilibrium values. This suggests that interstitial O essentially reduces the kinetics of theα toα +γ transformation. Thus, interstitial oxygen will have a strong effect on microstructures obtained by continuous cooling fromα, but significantly less on those, such as the duplex microstructure, obtained by long treatment in theα +γ phase field. In general, increased O content is well correlated with reduced ductility. Finally, the role of interstitial oxygen on this phase transformation is discussed.     

Semi-supervised model adaptation for statistical machine translation

Statistical machine translation systems are usually trained on large amounts of bilingual text (used to learn a translation model), and also large amounts of monolingual text in the target language (used to train a language model). In this article we explore the use of semi-supervised model adaptation methods for the effective use of monolingual data from the source language in order to improve translation quality. We propose several algorithms with this aim, and present the strengths and weaknesses of each one. We present detailed experimental evaluations on the French–English EuroParl data set and on data from the NIST Chinese–English large-data track. We show a significant improvement in translation quality on both tasks.     

Mathematical determination of feeding patterns and its consequence on correlational studies

The cell cycle regulatory proteins, which include cyclin-dependent kinases (cdks), cdk inhibitors (CKIs), cyclins, and the pRB, and E2F families of proteins, constitute a network of interacting factors which govern exit from or passage through the mammalian cell cycle. While the proteins within these families have similar structural characteristics, each family member exhibits distinct expression patterns during embryogenesis and distinct biological activities. In order to begin to understand the tissue-specific roles of these interacting factors, we determined the expression pattern and activity of the pRB, E2F, cyclin, cdk, and CKI families of cell cycle regulatory proteins during retinoic acid-induced (neuronal pathway) and DMSO-induced (cardiac muscle pathway) differentiation of the pluripotent murine embryonal carcinoma cell line, P19. We demonstrate here that P19 terminal differentiation causes lineage-specific changes in the expression and activity of distinct members of the E2F, pRB, cyclin, and CKI families. Furthermore, dynamic changes in the activities of these cell cycle regulatory proteins occur through several overlapping mechanisms, culminating in repression of DNA-binding activity by all of the E2F family members as cells terminally differentiate.     

Coping with uncertainty: police strategies for resilient decision-making and action implementation

This study uses a hostage negotiation setting to demonstrate how a team of strategic police officers can utilize specific coping strategies to minimize uncertainty at different stages of their decision-making in order to foster resilient decision-making to effectively manage a high-risk critical incident. The presented model extends the existing research on coping with uncertainty by (1) applying the RAWFS heuristic (Lipshitz and Strauss in Organ Behav Human Decis Process 69:149–163, 1997) of individual decision-making under uncertainty to a team critical incident decision-making domain; (2) testing the use of various coping strategies during “in situ” team decision-making by using a live simulated hostage negotiation exercise; and (3) including an additional coping strategy (“reflection-in-action”; Schön in The reflective practitioner: how professionals think in action. Temple Smith, London, 1983) that aids naturalistic team decision-making. The data for this study were derived from a videoed strategic command meeting held within a simulated live hostage training event; these video data were coded along three themes: (1) decision phase; (2) uncertainty management strategy; and (3) decision implemented or omitted. Results illustrate that, when assessing dynamic and high-risk situations, teams of police officers cope with uncertainty by relying on “reduction” strategies to seek additional information and iteratively update these assessments using “reflection-in-action” (Schön 1983) based on previous experience. They subsequently progress to a plan formulation phase and use “assumption-based reasoning” techniques in order to mentally simulate their intended courses of action (Klein et al. 2007), and identify a preferred formulated strategy through “weighing the pros and cons” of each option. In the unlikely event that uncertainty persists to the plan execution phase, it is managed by “reduction” in the form of relying on plans and standard operating procedures or by “forestalling” and intentionally deferring the decision while contingency planning for worst-case scenarios.     

A microfluidic optical beam steerer

This paper reports the fabrication and characterization of a prototype microfluidic device that can act as a periodic beam steerer. The prototype is formed by a simple T-junction followed by a serpentine channel that allows generation of a periodical segmented flow of air and water bubbles. If light hits the channel wall with a suitable angle, it can be either transmitted or reflected by the segmented flow, giving rise to an alternating beam steerer. The duty cycle, switching frequency, and overall stability and reproducibility of this prototype system are presented and discussed.     

Probabilistic consensus clustering using evidence accumulation

Machine Learning - Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering algorithms. In the evidence...     

Binarised regression tasks: methods and evaluation metrics

Some supervised tasks are presented with a numerical output but decisions have to be made in a discrete, binarised, way, according to a particular cutoff. This binarised regression task is a very common situation that requires its own analysis, different from regression and classification—and ordinal regression. We first investigate the application cases in terms of the information about the distribution and range of the cutoffs and distinguish six possible scenarios, some of which are more common than others. Next, we study two basic approaches: the retraining approach, which discretises the training set whenever the cutoff is available and learns a new classifier from it, and the reframing approach, which learns a regression model and sets the cutoff when this is available during deployment. In order to assess the binarised regression task, we introduce context plots featuring error against cutoff. Two special cases are of interest, the \( UCE \) and \( OCE \) curves, showing that the area under the former is the mean absolute error and the latter is a new metric that is in between a ranking measure and a residual-based measure. A comprehensive evaluation of the retraining and reframing approaches is performed using a repository of binarised regression problems created on purpose, concluding that no method is clearly better than the other, except when the size of the training data is small.