Thermal interface tailoring in composite materials

The thermal transport in heterogeneous materials systems, such as in composites, is essentially controlled by the phonon scattering phenomena at the materials interface due to the interface materials property mismatch. Such phenomena are also prevalent in joints or component interfaces. The thermal property mismatch at the materials interface, in the molecular scale, is primarily dictated by the phonon density of state across the interface. In this paper, the interface materials configuration for tailoring the thermal properties of composite materials with nano constituents is presented. The materials modeling using both the finite element analysis (FEM) and molecular dynamics (MD) simulations is performed to identify the effect of materials constituent scale as well as the nano constituent surface functionalization on the interface thermal transport phenomena. It is observed that the effect of surface functionalization towards establishing covalent bonding between the nano constituent surface the matrix (such as polymers) is extremely important in enhancing the interface thermal conductance.     

Tribology Analysis of Cobalt Particulate Filled Al 7075 Alloy for Gear Materials: a Comparative Study

Silicon - The present study aims at developing a theoretical model for sliding wear analysis of the cobalt metal powder reinforced Aluminum (Al7075) alloy composites and perform an experimental run...     

Preliminary Evaluations on Development of Recycled Porcelain Reinforced LM-26/Al-Si10Cu3Mg1 Alloy for Piston Materials

Silicon - The current work deals with an endeavor to synthesis, metal composites by taking aluminum alloy (i.e. LM 26) as matrix material and porcelain (powder form) as reinforcement. The metal...     

Ligand-Induced GPR110 Activation Facilitates Axon Growth after Injury

Recovery from axonal injury is extremely difficult, especially for adult neurons. Here, we demonstrate that the activation of G-protein coupled receptor 110 (GPR110, ADGRF1) is a mechanism to stimulate axon growth after injury. N-docosahexaenoylethanolamine (synaptamide), an endogenous ligand of GPR110 that promotes neurite outgrowth and synaptogenesis in developing neurons, and a synthetic GPR110 ligand stimulated neurite growth in axotomized cortical neurons and in retinal explant cultures. Intravitreal injection of GPR110 ligands following optic nerve crush injury promoted axon extension in adult wild-type, but not in gpr110 knockout, mice. In vitro axotomy or in vivo optic nerve injury rapidly induced the neuronal expression of gpr110. Activating the developmental mechanism of neurite outgrowth by specifically targeting GPR110 that is upregulated upon injury may provide a novel strategy for stimulating axon growth after nerve injury in adults.     

Physico-mechanical and Surface Wear Assessment of Magnesium Oxide Filled Ceramic Composites for Hip Implant Application

Silicon - In the present study, applicability of ceramic composites as ceramic-on-ceramic hip prostheses is explored. Hence, ceramic composites containing zirconium oxide, silicon nitride, chromium...     

Evaluation of the Role of p53 Tumour Suppressor Posttranslational Modifications and TTC5 Cofactor in Lung Cancer

Mutations in the p53 tumor suppressor are found in over 50% of cancers. p53 function is controlled through posttranslational modifications and cofactor interactions. In this study, we investigated the posttranslationally modified p53, including p53 acetylated at lysine 382 (K382), p53 phosphorylated at serine 46 (S46), and the p53 cofactor TTC5/STRAP (Tetratricopeptide repeat domain 5/ Stress-responsive activator of p300-TTC5) proteins in lung cancer. Immunohistochemical (IHC) analysis of lung cancer tissues from 250 patients was carried out and the results were correlated with clinicopathological features. Significant associations between total or modified p53 with a higher grade of the tumour and shorter overall survival (OS) probability were detected, suggesting that mutant and/or modified p53 acts as an oncoprotein in these patients. Acetylated at K382 p53 was predominantly nuclear in some samples and cytoplasmic in others. The localization of the K382 acetylated p53 was significantly associated with the gender and grade of the disease. The TTC5 protein levels were significantly associated with the grade, tumor size, and node involvement in a complex manner. SIRT1 expression was evaluated in 50 lung cancer patients and significant positive correlation was found with p53 S46 intensity, whereas negative TTC5 staining was associated with SIRT1 expression. Furthermore, p53 protein levels showed positive association with poor OS, whereas TTC5 protein levels showed positive association with better OS outcome. Overall, our results indicate that an analysis of p53 modified versions together with TTC5 expression, upon testing on a larger sample size of patients, could serve as useful prognostic factors or drug targets for lung cancer treatment.     

ANN techniques in microwave engineering

In the last two decades, artificial neural network (ANN) technology has leaped forward and is now being applied in different areas such as speech recognition, control, telecommunication, remote sensing, pattern recognition, etc. ANN application to the field of microwaves is very recent. This article reviews the development of neural network techniques, introduces some basic concepts involved in it, and gives a comprehensive survey of neural network application to different branches of microwave engineering     

Epileptic EEG detection using neural networks and post-classification

Electroencephalogram (EEG) has established itself as an important means of identifying and analyzing epileptic seizure activity in humans. In most cases, identification of the epileptic EEG signal is done manually by skilled professionals, who are small in number. In this paper, we try to automate the detection process. We use wavelet transform for feature extraction and obtain statistical parameters from the decomposed wavelet coefficients. A feed-forward backpropagating artificial neural network (ANN) is used for the classification. We use genetic algorithm for choosing the training set and also implement a post-classification stage using harmonic weights to increase the accuracy. Average specificity of 99.19%, sensitivity of 91.29% and selectivity of 91.14% are obtained.     

Parallel Mining of Neuronal Spike Streams on Graphics Processing Units

Multi-electrode arrays (MEAs) provide dynamic and spatial perspectives into brain function by capturing the temporal behavior of spikes recorded from cultures and living tissue. Understanding the firing patterns of neurons implicit in these spike trains is crucial to gaining insight into cellular activity. We present a solution involving a massively parallel graphics processing unit (GPU) to mine spike train datasets. We focus on mining frequent episodes of firing patterns that capture coordinated events even in the presence of intervening background events. We present two algorithmic strategies—hybrid mining and two-pass elimination—to map the finite state machine-based counting algorithms onto GPUs. These strategies explore different computation-to-core mapping schemes and illustrate innovative parallel algorithm design patterns for temporal data mining. We also provide a multi-GPU mining framework, which exhibits additional performance enhancement. Together, these contributions move us towards a real-time solution to neuronal data mining.     

An optimization algorithm based on the method of feasible directions

The theory and implementation of an optimization algorithm code based on the method of feasible directions are presented. Although the method of feasible directions was developed during the 1960's, the present implementation of the algorithm includes several modifications to improve its robustness. In particular, the search direction is generated by solving a quadratic program which uses an interior method based on a variation of Karmarkar's algorithm. The constraint thickness parameter is dynamically adjusted to yield usable-feasible directions. The theory is discussed with emphasis on the important and often overlooked role played by the various parameters guiding the iterations within the program. Also discussed is a robust approach for handling infeasible starting points. The code was validated by solving a variety of structural optimization test problems that have known solutions (obtained by other optimization codes). A variety of problems from different infeasible starting points has been solved successfully. It is observed that this code is robust and accurate. Further research is required to improve its numerical efficiency while retaining its robustness.