Formation of nanocrystals based on decomposition in the amorphous Zr41.2Ti13.8Cu12.5Ni10Be22.5 alloy

Primary crystallization and decomposition in the bulk amorphous alloy Ar41.2Ti13.8Cu12.5Ni10Be22.5 have been studied by small angle neutron scattering (SANS), transmission electron microscopy (TEM), and differential scanning calorimetry (DSC). SANS data of samples annealed isothermally at 623 K exhibit an interference peak centered at q=0.46 nm(^-1) after an incubation time of about 100 min. TEM and DSC investigations confirm that the respective periodic variation in the scattering length density is due to the formation of nanocrystals embedded in the amorphous matrix. These observations suggest that during the incubation time a chemical decomposition process occurs and the related periodic composition fluctuations give rise to the observed periodic arrangement of the nanocrystals

Bio Inspired Lightweight Composite Material Design for 3D Printing

Lightweight material design is an indispensable subject in product design. The lightweight material design has high strength to weight ratio which becomes a huge attraction and an area of exploration for the researchers as its application is wide and increasing even in every day-to-day product. Lightweight composite material design is achieved by selection of the cellular structure and its optimization. Cellular structure is used as it has wide multifunctional properties in addition to the lightweight characteristics. Applications of light weight cellular structures are wide and is witnessed in all industries from aerospace to automotive, construction to product design. In this thesis, the one-step and two-step approaches for design and prediction of cellular structure\u27s performance are presented for developing lightweight cellular composites reinforced by discontinuous fibers. The topology designs of a 2D honeycomb hexagon model, a 2D cuttlefish model, and a 3D octahedron model, inspired by bio material, are presented. Computer modeling based on finite element analysis was conducted on the periodic representative volume elements identified from the cellular structural models to characterize the designed cellular composites performance and properties. Additive manufacturing technique (3D printing) was used for prototyping the design, and experimental tests were carried out for validating the design methodology

Mechanical Regulation in Cell Division and in Neurotransmitter Release

During their lifecycle, cells must produce forces which play important roles in several subcellular processes. Force-producing components are organized into macromolecular assemblies of proteins that are often dynamic, and are constructed or disassembled in response to various signals. The forces themselves may directly be involved in subcellular mechanics, or they may influence mechanosensing proteins either within or outside these structures. These proteins play different roles: they may ensure the stability of the force-producing structure, or they may send signals to a coupled process. The generation and sensing of subcellular forces is an active research topic, and this thesis focusses on the roles of these forces in two key areas: cell division and neurotransmitter release. The first part of the thesis deals with the effect of force on cell wall growth regulation during division in the fission yeast Schizosaccharomyces pombe, a cigar-shaped, unicellular organism. During cytokinesis, the last stage of cell division in which the cell physically divides into two, a tense cytokinetic ring anchored to the cellular membrane assembles and constricts, accompanied by the inward centripetal growth of new cell wall, called septum, in the wake of the inward-moving membrane. The contour of the septum hole maintains its circularity as it reduces in size—an indication of regulated growth. To characterize the cell wall growth process, we performed image analysis on contours of the leading edge of the septum obtained via fluorescence microscopy in the labs of our collaborators. We quantified the deviations from circularity using the edge roughness. The roughness was spatially correlated, suggestive of regulated growth. We hypothesized that the cell wall growers are mechanosensitive and respond to the force exerted by the ring. A mathematical model based on this hypothesis then showed that this leads to corrections of roughness in a curvature-dependent fashion. Thus, one of the roles of ring tension is to communicate with the mechanosensitive septum growth processes and coordinate growth to ensure the daughter cells have a functional cell wall. The second part of the thesis deals with how ring tension is produced and sustained, using experimentally measured ultrastructure of the cytokinetic ring itself. Recent super-resolution experiments have revealed that several key proteins of the fission yeast constricting ring are organized into membrane-anchored complexes called nodes. The force producing protein myosin-II in these nodes exerts pulling forces on polymeric actin filaments that are synthesized from polymerizers residing in the nodes. How these forces are marshalled to generate ring tension, and how such an organization maintains its stability is unclear. Using a mathematical model with coarse-grained representations of actin and myosin, we showed that such a node-based organization reproduces previously measured ring tension values. The model explains the origin of experimentally observed bidirectional motion of the nodes in the ring, and showed that turnover of the nodes rescues the ring from inherent contractile instabilities that would be expected when a force-producing structure is made up of small object that effectively attract one another. Finally, the third part of the thesis deals with the role of forces produced by SNARE proteins at synapses between two neurons during neurotransmission. A key step here is synaptic release, where inside a neuron, membrane-bound compartments called vesicles filled with neurotransmitter fuse with the membrane of the neuron forming a transient fusion pore, and release their contents to the outside of the cell. These neurotransmitter molecules are sensed by another neuron that is physically separate from the neuron in question and this neuron propagates the signal henceforth. Thus, regulation of neurotransmitter release is a key step in neurotransmission. A fusion machinery consisting of several proteins facilitates membrane fusion, and pore nucleation requires the formation of a SNARE protein complex in this machinery, whose role during pore dilation is unclear. Using electrophysiological measurements, our collaborators experimentally measured the statistics of the size of single fusion pores in vitro, and observed that average pore sizes increased with the number of SNARE proteins. Using mathematical modeling, we showed that this effect was due to an entropic crowding force that expands the pore and increases with the number of SNAREs, and counteracts the energy barrier to fusion pore expansion

Localized anomaly detection via hierarchical integrated activity discovery

2014 Spring.Includes bibliographical references.With the increasing number and variety of camera installations, unsupervised methods that learn typical activities have become popular for anomaly detection. In this thesis, we consider recent methods based on temporal probabilistic models and improve them in multiple ways. Our contributions are the following: (i) we integrate the low level processing and the temporal activity modeling, showing how this feedback improves the overall quality of the captured information, (ii) we show how the same approach can be taken to do hierarchical multi-camera processing, (iii) we use spatial analysis of the anomalies both to perform local anomaly detection and to frame automatically the detected anomalies. We illustrate the approach on both traffic data and videos coming from a metro station. We also investigate the application of topic models in Brain Computing Interfaces for Mental Task classification. We observe a classification accuracy of up to 68% for four Mental Tasks on individual subjects

Experimental investigation into the effect of magnetic fuel reforming on diesel combustion and emissions running on wheat germ and pine oil

© 2019 Elsevier B.V. All rights reserved.The present study aims to explore the effect of fuel ionisation on engine performance, emission and combustion characteristics of a twin cylinder compression ignition (CI) engine running on biofuel. Wheat germ oil (WGO) and pine oil (PO) have been identified as diesel fuel surrogates with high and low viscosities, respectively. High viscosity biofuels result in incomplete combustion due to poor atomisation and evaporation which ultimately leads to insufficient air-fuel mixing to form a combustible mixture. Consequently, engines running on this type of fuel suffer from lower brake thermal efficiency (BTE) and higher soot emission. In contrast, low viscosity biofuels exhibit superior combustion characteristics however they have a low cetane number which causes longer ignition delay and therefore higher NO emission. To overcome the limitations of both fuels, a fuel ionisation filter (FIF) with a permanent magnet is installed upstream of the fuel pump which electrochemically ionises the fuel molecules and aids in quick dispersion of the ions. The engine used in this investigation is a twin cylinder tractor engine that runs at a constant speed of 1500 rpm. The engine was initially run on diesel to warm-up before switching to WGO and PO, this was mainly due to poor cold start performance characteristics of both fuels. At 100% load, BTE for WGO is reduced by 4% compared to diesel and improved by 7% with FIF. In contrast, BTE for PO is 4% higher compared to diesel, however, FIF has minimal effect on BTE when running on PO. Although, smoke, HC and CO emissions were higher for WGO compared to diesel, they were lower with FIF due to improved combustion. These emissions were consistently lower for PO due to superior combustion performance, mainly attributed to low viscosity of the fuel. However, NO emission for PO (1610 ppm) is higher compared to diesel (1580 ppm) at 100% load and reduced with FIF (1415 ppm). NO emission is reduced by approximately 12% for PO+FIF compared to PO. The results suggest that FIF has the potential to improve diesel combustion performance and reduce NO emission produced by CI engines running on high and low viscosity biofuels, respectively.Peer reviewe

Marginal View on Moral literature - P. Velsamy and Raj Gauthaman

This article focuses on the marginal view on ‘Moral Literature’ which is called Sangam Maruviya literature. The development of critical analysis after the nineties became the starting point for re-meaning all literature. Virtues are taught to people in their day to day life. Even if there is no education provided for a person, some kind of virtues work spontaneously in his/her thoughts. In more theoretical terms, each individual body functions as a collection of virtues. In this case, there are 18 books in Moral Literature. Virtues are being taught in educational institutions that civilized the society and studies are also being conducted to re-read them. There is not enough attention and interest towards its field of study and most of the studies is conducted with the same ideas and it feels like a kind of stagnation. In order to change them and to view literature in a new way it is necessary to carry out studies by following the research ideas undertaken with efforts. Considering this, the article examines Velsamy and Raj gauthaman’s view of virtues in a comparative way

Performance Comparison of Hybrid CNN-SVM and CNN-XGBoost models in Concrete Crack Detection

Detection of cracks mainly has been a sort of essential step in visual inspection involved in construction engineering as it is the commonly used building material and cracks in them is an early sign of de-basement. It is hard to find cracks by a visual check for the massive structures. So, the development of crack detecting systems generally has been a critical issue. The utilization of contextual image processing in crack detection is constrained, as image data usually taken under real-world situations vary widely and also includes the complex modelling of cracks and the extraction of handcrafted features. Therefore the intent of this study is to address the above problem using two-hybrid machine learning models and classify the concrete digital images into having cracks or non-cracks. The Convolutional Neural Network is used in this study to extract features from concrete pictures and use the extracted features as inputs for other machine learning models, namely Support Vector Machines (SVMs) and Extreme Gradient Boosting (XGBoost). The proposed method is evaluated on a collection of 40000 real concrete images, and the experimental results show that application of XGBoost classifier to CNN extracted image features include an advantage over SVM approach in accuracy and achieve a relatively better performance than a few existing methods