Controlling the Critical Temperature in Mg1−x Al x B2

Here we report synthesis and characterization of high quality samples of the superconducting system Mg_1−x Al _x B₂, with a novel optimized preparation method, in a wide range of Al concentration. All the physical properties of the samples can be tuned continuously with increasing Al content. We have characterized the samples for their structural, morphological and magnetic properties with high resolution XRD, SEM-EDS and susceptibility measurements. We have prepared a large number of Al doped MgB₂ in the range 0<x<0.57. All the samples are bulk superconductors and the critical temperature show a smooth and regular behavior as a function of x. This work was supported by European project 517039 “Controlling Mesoscopic Phase Separation” (COMEPHS) (2005). An erratum to this article can be found at     

Savoring Versus Dampening: Self-Esteem Differences in Regulating Positive Affect.

Five studies examined the hypotheses that when people experience positive affect, those low in self-esteem are especially likely to dampen that affect, whereas those high in self-esteem are especially likely to savor it. Undergraduate participants' memories for a positive event (Study 1) and their reported reactions to a success (Study 2) supported the dampening prediction. Results also suggest that dampening was associated with worse mood the day after a success (Study 2), that positive and negative affect regulation are distinct, that self-esteem is associated with affect regulation even when Neuroticism and Extraversion are controlled (Studies 3 and 4), and that self-esteem may be especially important for certain types of positive events and positive affect regulation (Study 5). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Mangrove forests as a nature-based solution for coastal flood protection: Biophysical and ecological considerations

Nature-based coastal protection is increasingly recognised as a potentially sustainable and cost-effective solution to reduce coastal flood risk. It uses coastal ecosystems such as mangrove forests to create resilient designs for coastal flood protection. However, to use mangroves effectively as a nature-based measure for flood risk reduction, we must understand the biophysical processes that govern risk reduction capacity through mangrove ecosystem size and structure. In this perspective, we evaluate the current state of knowledge on local physical drivers and ecological processes that determine mangrove functioning as part of a nature-based flood defence. We show that the forest properties that comprise coastal flood protection are well-known, but models cannot yet pinpoint how spatial heterogeneity of the forest structure affects the capacity for wave or surge attenuation. Overall, there is relatively good understanding of the ecological processes that drive forest structure and size, but there is a lack of knowledge on how daily bed-level dynamics link to long-term biogeomorphic forest dynamics, and on the role of combined stressors influencing forest retreat. Integrating simulation models of forest structure under changing physical (e.g. due to sea-level change) and ecological drivers with hydrodynamic attenuation models will allow for better projections of long-term natural coastal protection.     

Virtual reality for collaborative e-learning

In the past, the term e-learning referred to any method of learning that used electronic delivery methods. With the advent of the Internet however, e-learning has evolved and the term is now most commonly used to refer to online courses. A multitude of systems are now available to manage and deliver learning content online. While these have proved popular, they are often single-user learning environments which provide little in the way of interaction or stimulation for the student. As the concept of lifelong learning now becomes a reality and thus more and more people are partaking in online courses, researchers are constantly exploring innovative techniques to motivate online students and enhance the e-learning experience. This article presents our research in this area and the resulting development of CLEV-R, a Collaborative Learning Environment with Virtual Reality. This web-based system uses Virtual Reality (VR) and multimedia and provides communication tools to support collaboration among students. In this article, we describe the features of CLEV-R, its adaptation for mobile devices and present the findings from an initial evaluation.     

Multimedia ontology population through semantic analysis and hierarchical deep features extraction techniques

Knowledge and Information Systems - The rapid increase of available data in different complex contexts needs automatic tasks to manage and process contents. Semantic Web technologies represent the...     

Hybrid prediction-optimization approaches for maximizing parts density in SLM of Ti6Al4V titanium alloy

Journal of Intelligent Manufacturing - It is well known that the processing parameters of selective laser melting (SLM) highly influence mechanical and physical properties of the manufactured...     

Using a neural network for predicting the average grain size in friction stir welding processes

In the paper the microstructural phenomena in terms of average grain size occurring in friction stir welding (FSW) processes are focused. A neural network was linked to a finite element model (FEM) of the process to predict the average grain size values. The utilized net was trained starting from experimental data and numerical results of butt joints and then tested on further butt, lap and T-joints. The obtained results show the capability of the AI technique in conjunction with the FE tool to predict the final microstructure in the FSW joints.     

Semantics-driven annotation of patient-specific 3D data: a step to assist diagnosis and treatment of rheumatoid arthritis

In the digital era, patient-specific 3D models (3D-PSMs) are becoming increasingly relevant in computer-assisted diagnosis, surgery training on digital models, or implant design. While advanced imaging and reconstruction techniques can create accurate and detailed 3D models of patients’ anatomy, software tools that are able to fully exploit the potential of 3D-PSMs are still far from being satisfactory. In particular, there is still a lack of integrated approaches for extracting, coding, sharing and retrieving medically relevant information from 3D-PSMs and use it concretely as a support to diagnosis and treatment. In this article, we propose the SemAnatomy3D framework, which demonstrates how the ontology-driven annotation of 3D-PSMs and of their anatomically relevant features (parts of relevance) can assist clinicians to document more effectively pathologies and their evolution. We exemplify the idea in the context of the diagnosis of rheumatoid arthritis of the hand district, and show how feature extraction tools and semantic 3D annotation can provide a rich characterization of anatomical landmarks (e.g., articular facets, prominent features, ligament attachments) and pathological markers (erosions, bone loss). The core contributions are an ontology-driven part-based annotation method for the 3D-PSMs and a novel automatic localization of erosion and quantification of the OMERACT RAMRIS erosion score. Finally, our results have been compared against a medical ground truth.