Dynamic analysis of walls strengthened with composite materials

The bidirectional dynamic behavior of walls strengthened with composite materials is studied. For that purpose, a multi-layered high order finite element is developed. The finite element accounts for the bidirectional (plate-type) dynamic behavior and for the interfacial interaction between the adhesively bonded components. The formulation uses a viscoelastic first order shear deformation orthotropic plate theory for the independent modeling of the existing wall and the composite layers and a high order theory for the displacement fields of the adhesive layers. The Finite element framework simplifies the coupling with adjacent structural elements and the use of standard computational procedures. The convergence of the formulation and two numerical examples are studied. The first case studies the response of a strengthened wall to a step base acceleration. The second case studies a wall built in a surrounding frame and strengthened on the outer face. The numerical study examines the capabilities of the model and reveals some of the unique aspect of the dynamic response, including the effects of the orthotropy and orientation of the strengthening system. It also highlights the potential of the high order finite element to become a platform for the modeling and dynamic analysis of the strengthened wall.     

“Designer” scaffolds for tissue engineering and regeneration

Tissue engineering entails the in vitro or in vivo generation of replacement tissues from cells with the aid of supporting scaffolds and stimulating biomolecules, in order to provide biological substitutes for restoration and maintenance of human tissue functions. In this review, we summarize the main classes of degradable polymeric scaffolds, natural and synthetic ones, and the evolution made in this field from adaptation of materials in clinical use to the fabrication of “designer” scaffolds.     

Advanced Technologies for Engineering Tissue Mimetics

Micro- and nanotechnologies have become central to fields ranging from tissue and cellular biophysics to regenerative medicine toward the creation of more complex and progressive tissues. In this article, we overview some of the new technological concepts in this field for recapitulating the native extracellular matrix, designing dynamic microenvironments, and the incorporation of small-scale devices for actuating and monitoring engineered tissue performances.     

Enantiomeric Control of Intrinsically Chiral Nanocrystals

The chiral aspect of inorganic crystals that crystallize in chiral space groups has been largely ignored until recently, partly due to difficulties in characterizing the chiroptical properties of bulk crystals, and also due to the difficulty in separating (sub)micrometer-scale chiral crystal enantiomers. In recent years, the colloidal synthesis of intrinsically chiral nanocrystals (NCs) of several chiral inorganic compounds with significant enantiomeric excess has been demonstrated. This is achieved through the use of chiral molecular ligands, which bind to the atomic/ionic components of the crystals, preferentially forming one crystal enantiomorph. Here, recent progress on several aspects of these NCs is described, including the connection between ligand structure and its ability to direct NC handedness, chiral amplification in the synthesis leading to enantiopure NC samples, spontaneous symmetry breaking, the formation of NCs with chiral shapes, the connection between lattice and shape chirality and mixed contributions of atomic-scale and shape chirality to the chiroptical properties.     

Temperature Dependence of the STM Morphology and Electronic Level Structure of Silver‐Containing DNA

Understanding the effect of external conditions, temperature in particular, on novel nanomaterials is of great significance. The powerful ability of scanning tunneling microscopy (STM) to characterize topography and electronic levels on a single molecule scale is utilized herein to characterize individual silver‐containing poly(dG)–poly(dC) DNA molecules, at different temperatures. These measurements indicate that the molecule is a truly hybrid metal–organic nanomaterial with electronic states originating from both the DNA and the embedded silver. The temperature dependence of this density of states (DOS) leads to the temperature dependent STM apparent height of the molecule—a phenomenon that has not been observed before for other complex nanostructures.     

Anytime similarity measures for faster alignment

Image alignment refers to finding the best transformation from a fixed reference image to a new image of a scene. This process is often optimizing a similarity measure between images, computed based on the image data. However, in time-critical applications state-of-the-art methods for computing similarity are too slow. Instead of using all the image data to compute similarity, one could use only a subset of pixels to improve the speed, but often this comes at the cost of reduced accuracy. These kinds of tradeoffs between the amount of computation and the accuracy of the result have been addressed in the field of real-time artificial intelligence as deliberation control problems. We propose that the optimization of a similarity measure is a natural application domain for deliberation control using the anytime algorithm framework. In this paper, we present anytime versions for the computation of two common image similarity measures: mean squared difference and mutual information. Off-line, we learn a performance profile specific to each measure, which is then used on-line to select the appropriate amount of pixels to process at each optimization step. When tested against existing techniques, our method achieves comparable quality and robustness with significantly less computation.     

Collaboration within large groups in the classroom

The purpose of this paper is to show how a large group of students can work collaboratively in a synchronous way within the classroom using the cheapest possible technological support. Making use of the features of Single Display Groupware and of Multiple Mice we propose a computer-supported collaborative learning approach for big groups within the classroom. The approach uses a multiple classification matrix and our application was built for language-learning (in this case Spanish). The basic collaboration mechanism that the approach is based upon is “silent collaboration,” in which students—through suggestions and exchanges—must compare their ideas to those of their classmates. An exploratory experimental study was performed along with a quantitative and qualitative study that analyzed ease of use of the software, described how the conditions for collaborative learning were achieved, evaluated the achievements in learning under the defined language objectives, and analyzed the impact of silent and spoken collaboration. Our initial findings are that silent collaboration proved to be an effective mechanism to achieve learning in large groups in the classroom.     

Unsupervised named-entity extraction from the Web: An experimental study

The KnowItAll system aims to automate the tedious process of extracting large collections of facts (e.g., names of scientists or politicians) from the Web in an unsupervised, domain-independent, and scalable manner. The paper presents an overview of KnowItAll's novel architecture and design principles, emphasizing its distinctive ability to extract information without any hand-labeled training examples. In its first major run, KnowItAll extracted over 50,000 class instances, but suggested a challenge: How can we improve KnowItAll's recall and extraction rate without sacrificing precision?This paper presents three distinct ways to address this challenge and evaluates their performance. Pattern Learning learns domain-specific extraction rules, which enable additional extractions. Subclass Extraction automatically identifies sub-classes in order to boost recall (e.g., “chemist” and “biologist” are identified as sub-classes of “scientist”). List Extraction locates lists of class instances, learns a “wrapper” for each list, and extracts elements of each list. Since each method bootstraps from KnowItAll's domain-independent methods, the methods also obviate hand-labeled training examples. The paper reports on experiments, focused on building lists of named entities, that measure the relative efficacy of each method and demonstrate their synergy. In concert, our methods gave KnowItAll a 4-fold to 8-fold increase in recall at precision of 0.90, and discovered over 10,000 cities missing from the Tipster Gazetteer.