Distributed machining control and monitoring using smart sensors/actuators

The study of smart sensors and actuators led, during the past few years, to the development of facilities which improve traditional sensors and actuators in a necessary way to automate production systems. In another context, many studies have been carried out aimed at defining a decisional structure for production activity control and the increasing need of reactivity leads to the autonomization of decisional levels close to the operational system. We study in this paper the natural convergence between these two approaches and we propose an integration architecture, dealing with machine tool and machining control, that enables the exploitation of distributed smart sensors and actuators in the decisional system.     

Discovery of an Allosteric Ligand Binding Site in SMYD3 Lysine Methyltransferase

SMYD3 is a multifunctional epigenetic enzyme with lysine methyltransferase activity and various interaction partners. It is implicated in the pathophysiology of cancers but with an unclear mechanism. To discover tool compounds for clarifying its biochemistry and potential as a therapeutic target, a set of drug-like compounds was screened in a biosensor-based competition assay. Diperodon was identified as an allosteric ligand; its R and S enantiomers were isolated, and their affinities to SMYD3 were determined (K_D=42 and 84 μM, respectively). Co-crystallization revealed that both enantiomers bind to a previously unidentified allosteric site in the C-terminal protein binding domain, consistent with its weak inhibitory effect. No competition between diperodon and HSP90 (a known SMYD3 interaction partner) was observed although SMYD3–HSP90 binding was confirmed (K_D=13 μM). Diperodon clearly represents a novel starting point for the design of tool compounds interacting with a druggable allosteric site, suitable for the exploration of noncatalytic SMYD3 functions and therapeutics with new mechanisms of action.     

Eucalyptol is an Attractant of the Redbay Ambrosia Beetle,Xyleborus Glabratus

The redbay ambrosia beetle, Xyleborus glabratus, is an invasive wood-boring beetle that has become established in the southeastern United States. The beetle transmits the causal pathogen of lethal laurel wilt to susceptible host trees, which include redbay, an important forest community species, and avocado, a valuable food crop. By examining odors of redbay wood, we developed an artificial lure that captured X. glabratus in redbay forests. Eucalyptol was a critical component of the blend for beetle attraction, and eucalyptol alone in large quantities attracted X. glabratus. Furthermore, eucalyptol stimulated boring by X. glabratus into paper arenas. The results suggest that eucalyptol contributes to host selection behavior of X. glabratus and may be useful for management of this pathogen vector.     

Water retention and gas relative permeability of two industrial concretes

This experimental study aims at identifying the water retention properties of two industrial concretes to be used for long term underground nuclear waste storage structures. Together with water retention, gas transfer properties are identified at varying water saturation level, i.e. relative gas permeability is assessed directly as a function of water saturation level S_w. The influence of the initial de-sorption path and of the subsequent re-saturation are analysed both in terms of water retention and gas transfer properties. Also, the influence of concrete microstructure upon water retention and relative gas permeability is assessed, using porosity measurements, analysis of the BET theory from water retention properties, and MIP. Finally, a single relative gas permeability curve is proposed for each concrete, based on Van Genuchten–Mualem's statistical model, to be used for continuous modelling approaches of concrete structures, both during drying and imbibition.     

Thermodiffusion‐Assisted Pyroelectrics—Enabling Rapid and Stable Heat and Radiation Sensing

Sensors for monitoring temperature, heat flux, and thermal radiation are essential for applications such as electronic skin. While pyroelectric and thermoelectric effects are suitable candidates as functional elements in such devices, both concepts show individual drawbacks in terms of zero equilibrium signals for pyroelectric materials and small or slow response of thermoelectric materials. Here, these drawbacks are overcome by introducing the concept of thermodiffusion‐assisted pyroelectrics, which combines and enhances the performance of pyroelectric and ionic thermoelectric materials. The presented integrated concept provides both rapid initial response upon heating and stable synergistically enhanced signals upon prolonged exposure to heat stimuli. Likewise, incorporation of plasmonic metasurfaces enables the concept to provide both rapid and stable signals for radiation‐induced heating. The performance of the concept and its working mechanism can be explained by ion–electron interactions at the interface between the pyroelectric and ionic thermoelectric materials.     

Segmentation of multiple sclerosis lesions in brain MRI: A review of automated approaches

Automatic segmentation of multiple sclerosis (MS) lesions in brain MRI has been widely investigated in recent years with the goal of helping MS diagnosis and patient follow-up. However, the performance of most of the algorithms still falls far below expert expectations. In this paper, we review the main approaches to automated MS lesion segmentation. The main features of the segmentation algorithms are analysed and the most recent important techniques are classified into different strategies according to their main principle, pointing out their strengths and weaknesses and suggesting new research directions. A qualitative and quantitative comparison of the results of the approaches analysed is also presented. Finally, possible future approaches to MS lesion segmentation are discussed.     

Subspace Procrustes Analysis

Procrustes analysis (PA) has been a popular technique to align and build 2-D statistical models of shapes. Given a set of 2-D shapes PA is applied to remove rigid transformations. Later, a non-rigid 2-D model is computed by modeling the residual (e.g., PCA). Although PA has been widely used, it has several limitations for modeling 2-D shapes: occluded landmarks and missing data can result in local minima solutions, and there is no guarantee that the 2-D shapes provide a uniform sampling of the 3-D space of rotations for the object. To address previous issues, this paper proposes subspace PA (SPA). Given several instances of a 3-D object, SPA computes the mean and a 2-D subspace that can model rigid and non-rigid deformations of the 3-D object. We propose a discrete (DSPA) and continuous (CSPA) formulation for SPA, assuming that 3-D samples of an object are provided. DSPA extends the traditional PA, and produces unbiased 2-D models by uniformly sampling different views of the 3-D object. CSPA provides a continuous approach to uniformly sample the space of 3-D rotations, being more efficient in space and time. We illustrate the benefits of SPA in two different applications. First, SPA is used to learn 2-D face and body models from 3-D datasets. Experiments on the FaceWarehouse and CMU motion capture (MoCap) datasets show the benefits of our 2-D models against the state-of-the-art PA approaches and conventional 3-D models. Second, SPA learns an unbiased 2-D model from CMU MoCap dataset and it is used to estimate the human pose on the Leeds Sports dataset.     

Sugar-Pucker Force-Induced Transition in Single-Stranded DNA

The accurate knowledge of the elastic properties of single-stranded DNA (ssDNA) is key to characterize the thermodynamics of molecular reactions that are studied by force spectroscopy methods where DNA is mechanically unfolded. Examples range from DNA hybridization, DNA ligand binding, DNA unwinding by helicases, etc. To date, ssDNA elasticity has been studied with different methods in molecules of varying sequence and contour length. A dispersion of results has been reported and the value of the persistence length has been found to be larger for shorter ssDNA molecules. We carried out pulling experiments with optical tweezers to characterize the elastic response of ssDNA over three orders of magnitude in length (60–14 k bases). By fitting the force-extension curves (FECs) to the Worm-Like Chain model we confirmed the above trend:the persistence length nearly doubles for the shortest molecule (60 b) with respect to the longest one (14 kb). We demonstrate that the observed trend is due to the different force regimes fitted for long and short molecules, which translates into two distinct elastic regimes at low and high forces. We interpret this behavior in terms of a force-induced sugar pucker conformational transition (C3′-endo to C2′-endo) upon pulling ssDNA.     

Synthesis of probabilistic models for quality-of-service software engineering

An increasingly used method for the engineering of software systems with strict quality-of-service (QoS) requirements involves the synthesis and verification of probabilistic models for many alternative architectures and instantiations of system parameters. Using manual trial-and-error or simple heuristics for this task often produces suboptimal models, while the exhaustive synthesis of all possible models is typically intractable. The EvoChecker search-based software engineering approach presented in our paper addresses these limitations by employing evolutionary algorithms to automate the model synthesis process and to significantly improve its outcome. EvoChecker can be used to synthesise the Pareto-optimal set of probabilistic models associated with the QoS requirements of a system under design, and to support the selection of a suitable system architecture and configuration. EvoChecker can also be used at runtime, to drive the efficient reconfiguration of a self-adaptive software system. We evaluate EvoChecker on several variants of three systems from different application domains, and show its effectiveness and applicability.