Surface Treatment of Polyester Fabric with Atmospheric Pressure Plasma

Polyethylene Terephthalate (PET) fabric, a recycled synthetic fiber, has been frequently studied to innovate increased usage in both the clothing and medical industry. Some include ways to dye the fabric so that it can be commercially used for the purpose of environmental conservation from frequent discard of nonrecyclable fabric. Some biomedical applications involve the application of plasma treatment to reduce bacteria adhesion and improve anti-bacterial properties on the fabric. However, neither has been successful due to a lack of understanding of the surface modification of PET fabric to enable such properties. The hypothesis is that hydrophobicity is an issue in this study. The goal is to modify the surface of PET cloth to obtain a hydrophilic property through atmospheric-pressure plasma surface modification. Dielectric barrier discharge (DBD) plasma irradiation is a technique involving the electrical discharge between two electrodes separated by an insulating barrier. At a constant peak voltage, the smoothly flowing argon gas is turned into plasma, and the plasma is applied to the PET cloth surface. New functional groups are made or altered and attached to the surface layer which changes the character of the membrane but not its bulk properties. This study analyzes and reports on changes in surface hydrophobicity. This process tested three parameters followed by the water contact angle, XPS, and FTIR analysis. PET fabric successfully gains a hydrophilic property through plasma treatment along with consistency in the results of surface modification from FTIR and XPS. However slight differences in results still do appear which must be further analyzed

Development of a Computational Elbow Joint Model to Analyze the Effects of Synovial Fluid on Articular Cartilage during Joint Motion

While significant advances have been made in the development of computational elbow joint models, fluid-structure interaction (FSI) in the elbow joint has not yet been explored. The objective of this study is to develop a computational elbow joint model to simulate the effects of synovial fluid on articular cartilage during flexion, extension, pronation, and supination. The model was developed with anatomically accurate 3D bone geometries; articular cartilage geometries that were derived from the 3D bone geometries; ligaments defined as linear springs; muscles embedded as joint non-linear stiffness; and a fluid domain that encompassed the joint articulations with a homogenous, incompressible, Newtonian synovial fluid. Two FSI simulations with varying joint velocities were conducted for elbow flexion, extension, pronation, and supination each. Peak von Mises stress of 0.0073 MPa on proximal ulna articular cartilage and peak von Mises stress of 0.0085 MPa on proximal radius articular cartilage were recorded during flexion-extension and pronation-supination, respectively. Synovial fluid flow was found to be predominantly laminar for the slower joint velocity and turbulent for the faster joint velocity for all elbow joint motions. This model not only establishes a validated approach to developing FSI simulations in the elbow joint, but also presents information on crucial in vivo parameters such as articular cartilage stresses and synovial fluid flow patterns during joint motion

First person – Venkatram Yellapragada

Peer reviewe

Hierarchical Deep Learning Architecture For 10K Objects Classification

Evolution of visual object recognition architectures based on Convolutional Neural Networks & Convolutional Deep Belief Networks paradigms has revolutionized artificial Vision Science. These architectures extract & learn the real world hierarchical visual features utilizing supervised & unsupervised learning approaches respectively. Both the approaches yet cannot scale up realistically to provide recognition for a very large number of objects as high as 10K. We propose a two level hierarchical deep learning architecture inspired by divide & conquer principle that decomposes the large scale recognition architecture into root & leaf level model architectures. Each of the root & leaf level models is trained exclusively to provide superior results than possible by any 1-level deep learning architecture prevalent today. The proposed architecture classifies objects in two steps. In the first step the root level model classifies the object in a high level category. In the second step, the leaf level recognition model for the recognized high level category is selected among all the leaf models. This leaf level model is presented with the same input object image which classifies it in a specific category. Also we propose a blend of leaf level models trained with either supervised or unsupervised learning approaches. Unsupervised learning is suitable whenever labelled data is scarce for the specific leaf level models. Currently the training of leaf level models is in progress; where we have trained 25 out of the total 47 leaf level models as of now. We have trained the leaf models with the best case top-5 error rate of 3.2% on the validation data set for the particular leaf models. Also we demonstrate that the validation error of the leaf level models saturates towards the above mentioned accuracy as the number of epochs are increased to more than sixty.Comment: As appeared in proceedings for CS & IT 2015 - Second International Conference on Computer Science & Engineering (CSEN 2015

Optimization Of Command Execution Testing Procedures

Current Command Execution Testing (CET) procedures are conducted manually and can be arduous and impractical for frequent testing Inefficiency of an integral testing procedure can lead to delays within the other teams involved in the development of the cube satellit

Assessment of surgical outcome in emergency gastrointestinal surgeries using P-POSSUM score

Background: The physiological and operative severity score for the enumeration of mortality and morbidity (POSSUM) and its modification, Portsmouth-POSSUM (P-POSSUM), are considered as methods of risk scoring. Application of this scoring system helps in assessing the quality of the health care provided and surgical outcome. Its utilization in our country where the level of healthcare and resources differ is limited. Hence, a prospective study to assess the outcome of emergency GI surgeries using P-POSSUM scoring system in a teaching hospital at district level was taken up.Methods: 80 cases which underwent emergency GI surgeries were studied. Using P-POSSUM equation, predicted mortality and morbidity rates were calculated and compared with the actual outcome. Statistical significance was calculated using chi square test.Results: An observed to expected ratio of 0.71 and 0.60 was obtained for mortality and morbidity respectively. No significant difference was noted between expected to observed mortality and morbidity rates with P=0.23 and P=0.09 for mortality and morbidity respectively, suggesting a reasonably good quality of outcome. P-POSSUM over predicted mortality and morbidity in low risk groups while it accurately predicted the outcome in high risk groups.Conclusions: The quality of surgical care provided and surgical outcome are comparable to other health care systems, with observed to expected mortality and morbidity ratio being nearly same. P-POSSUM can be used as a tool for outcome audits

Conditional Generation from Unconditional Diffusion Models using Denoiser Representations

Denoising diffusion models have gained popularity as a generative modeling technique for producing high-quality and diverse images. Applying these models to downstream tasks requires conditioning, which can take the form of text, class labels, or other forms of guidance. However, providing conditioning information to these models can be challenging, particularly when annotations are scarce or imprecise. In this paper, we propose adapting pre-trained unconditional diffusion models to new conditions using the learned internal representations of the denoiser network. We demonstrate the effectiveness of our approach on various conditional generation tasks, including attribute-conditioned generation and mask-conditioned generation. Additionally, we show that augmenting the Tiny ImageNet training set with synthetic images generated by our approach improves the classification accuracy of ResNet baselines by up to 8%. Our approach provides a powerful and flexible way to adapt diffusion models to new conditions and generate high-quality augmented data for various conditional generation tasks