Current Issue : January - March Volume : 2011 Issue Number : 1 Articles : 6 Articles
A new simulation method has been developed and used to model mechanical properties of materials at many different length scales, from the nanoscale where an atomic description is appropriate, through a mesoscale where dislocation based descriptions may be useful, to macroscopic length scales. In some materials, such as nanocrystalline metals, the range of length scales is compressed and a polycrystalline material may be simulated at the atomic scale. It is observed how the grain boundaries contribute actively to the deformation. At grain sizes below 5-10 nm deformation in the grain boundaries dominate over the traditional dislocation-based deformation mechanisms. This results in a reversal of the normal grain size dependence of the yield stress. It is shown that the material becomes softer when the grain size is reduced....
The development of nanodevices for agriculture and plant research will allow several new applications, ranging from treatments with agrochemicals to delivery of nucleic acids for genetic transformation. But a long way for research is still in front of us until such nanodevices could be widely used. Their behaviour inside the plants is not yet well known and the putative toxic effects for both, the plants directly exposed and/or the animals and humans, if the nanodevices reach the food chain, remain uncertain. In this work we show that magnetic carboncoated\r\nnanoparticles forming a biocompatible magnetic fluid (bioferrofluid) can easily penetrate through the root in four different crop plants (pea, sunflower, tomato and wheat). They reach the vascular cylinder, move using the transpiration stream in the xylem vessels and spread through the aerial part of the plants in less than 24 hours. Accumulation of nanoparticles was detected in wheat leaf trichomes, suggesting a way for excretion/detoxification. This kind of studies is of great interest in order to unveil the movement and accumulation of nanoparticles in plant tissues for assessing further applications in the field or laboratory....
Hydroxyapatite (HAp) is the most promising bioceramic material for orthopedic applications because of its similarity in chemical structure to living bone and teeth(with wide porosity ranges).Several methods are there to produce pours HAp. The simplest method involves the incorporation of volatile compounds during the heating process. Here HAp synthesized from used egg shells is used as the raw materials and some organic additives like naphthalene are used as the ortganic additives. Porous HAp ceramics with porosity up to 53% have been successfully and rapidly fabricated. The porosity of the ceramics can be controlled by adjusting the starting material, green density, sintering time, or temperature. Pore size can also be adjusted. The addition of organic additives like naphthalene in the green specimens help in adjusting porosity....
Background: Forearm surface electromyography (EMG) has been in use since the Sixties to feedforward control active hand prostheses in a more and more refined way. Recent research shows that it can be used to control even a dexterous polyarticulate hand prosthesis such as Touch Bionics's i-LIMB, as well as a multifingered, multi-degree-of-freedom mechanical hand such as the DLR II. In this paper we extend previous work and investigate the robustness of such fine control possibilities, in two ways: firstly, we conduct an analysis on data obtained from 10 healthy subjects, trying to assess the general applicability of the technique; secondly, we compare the baseline controlled condition (arm relaxed and still on a table) with a \"Daily-Life Activity\" (DLA) condition in which subjects walk, raise their hands and arms, sit down and stand up, etc., as an experimental proxy of what a patient is supposed to do in real life. We also propose a cross-subject model analysis, i.e., training a model on a subject and testing it on another one. The use of pre-trained models could be useful in shortening the time required by the subject/patient to become proficient in using the hand.\r\n\r\nResults: A standard machine learning technique was able to achieve a real-time grip posture classification rate of about 97% in the baseline condition and 95% in the DLA condition; and an average correlation to the target of about 0.93 (0.90) while reconstructing the required force. Cross-subject analysis is encouraging although not definitive in its present state.\r\n\r\nConclusion: Performance figures obtained here are in the same order of magnitude of those obtained in previous work about healthy subjects in controlled conditions and/or amputees, which lets us claim that this technique can be used by reasonably any subject, and in DLA situations. Use of previously trained models is not fully assessed here, but more recent work indicates it is a promising way ahead....
Complexes of transition metal Ni(II) with amino acids present in egg albumin have been synthesized. The complex is analyzed on the basis of spectroscopic methods of UV,IR,NMR Spectroscopy. The amino acid- metal Complex is decomposed at higher temperature to obtain metal carbon nano tubes. These metal carbon nano tubes are characterized using scanning probe instruments like AFM and STM....
Background: Image analysis is an essential component in many biological experiments that study gene expression, cell cycle progression, and protein localization. A protocol for tracking the expression of individual C. elegans genes was developed that collects image samples of a developing embryo by 3-D time lapse microscopy. In this protocol, a program called StarryNite performs the automatic recognition of fluorescently labeled cells and traces their lineage. However, due to the amount of noise present in the data and due to the challenges introduced by increasing number of cells in later stages of development, this program is not error free. In the current version, the error correction (i.e., editing) is performed manually using a graphical interface tool named AceTree, which is specifically developed for this task. For a single experiment, this manual annotation task takes several hours.\r\n\r\nResults: In this paper, we reduce the time required to correct errors made by StarryNite. We target one of the most frequent error types (movements annotated as divisions) and train a support vector machine (SVM) classifier\r\nto decide whether a division call made by StarryNite is correct or not. We show, via cross-validation experiments on several benchmark data sets, that the SVM successfully identifies this type of error significantly. A new version of StarryNite that includes the trained SVM classifier is available at http://starrynite.sourceforge.net.\r\n\r\nConclusions: We demonstrate the utility of a machine learning approach to error annotation for StarryNite. In the process, we also provide some general methodologies for developing and validating a classifier with respect to a given pattern recognition task....
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