1,086 research outputs found
Study of detonation interactions inside a 2-D ejector using detonation transmission tubing
Study of detonation interactions inside a two-dimensional ejector using detonation transmission tubing was reported. The main objective of the ejector assembly in the study is to make the flow-field as close to 2-D as possible. Optical-grade Perspex sheets with a thickness of 10 mm were used on both sides of the nozzle to allow visualization of the flow. Wall pressure measurements were conducted at the locations. The NONEL tube was flush with the entrance of the nozzle. The signal to begin pressure measurements and image acquisition was obtained through a Kulite XT-190 transducer. The detonation was initiate by an electronic blasting machine, DynoStart 2, with a capacitance of 0.2μF and an output voltage of 2500 V. High-speed shadowgraphy was employed to visualize the flow. The results show that the effects of 3-D flow at the initial stage of the detonation affect the incident shock front and the reflected shock wave system at the nozzle entrance
Cryptographic Interweaving of Messages
During the past several decades, the information and communication technology sector has advanced significantly, enabling extensive information interchange over the internet, including message sharing and electronic transactions. These days, the main issue is how to transmit information securely. From ancient times, there has been interest in the field of cryptography research. A masterwork of cryptography is Muni Kumudendu's original work, Siribhoovalaya. His study served as the basis for the method suggested in this publication. Several messages can be sent using different keys utilising a single matrix. Encryption uses a variety of matrix traversal techniques, making it challenging for cryptanalysis to map the plaintext and ciphertext
Performance Comparison of Radial Basis Function Networks and Probabilistic Neural Networks for Telugu Character Recognition
The research on recognition of hand written scanned images of documents has witnessed several problems, some of which include recognition of almost similar characters. Therefore it received attention from the fields of image processing and pattern recognition. The system of pattern recognition comprises a two step process. The first stage is the feature extraction and the second stage is the classification. In this paper, the authors propose two classification methods, both of which are based on artificial neural networks as a means to recognize hand written characters of Telugu, a language spoken by more than 100 million people of south India(Negi et al. ,2001). In this model, the authors used Radial Basis Function (RBF) networks and Probabilistic Neural Networks (PNN) for classification. These classifiers were further evaluated using performance metrics such as accuracy, sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV) and F measure. This paper is a comparison of results obtained with both the methods. The values of F measure are quite satisfactory and this is a good indication of the suitability of the methods for classification of characters. The values of F-Measure for both the methods approach the value of 1, which is a good indication and out of the two, RBF is a better method than PNN
Evaluation of the wound healing effect of Jasminum grandiflorum by wound contraction studies and wound breaking strength
Background: Wound is a common process leading to inflammation and healing process. Jasminum grandiflorum is a plant widely grown in Karnataka and is mention in ancient medical systems to promote healing. So the study was taken up to confirm the anti-inflammatory effect of the plant.Methods: Excision and incision wound models in albino rats were used to study the wound healing effect. It was mainly studied by the degree of epithelisation at various intervals and compared with control group. The strength of the wound was assessed by its wound breaking strength at the end.Results: It was found that jasmine extract increased the degree of epethelisation and thus promoting wound healing. It was also found that the wound breaking strength was more in the drug treated group compared to normal.Conclusions: Jasminum grandiflorum leaf extract is known to have wound healing property.
Evaluation of wound healing effect of Jasminum grandiflorum in albino rats by histopathological studies
Background: Wound healing is an important process in regeneration of the lost tissue, it involves various steps. Varieties of substances are known to interact in the healing process, some of the plant products are found to be beneficial. Jasmine leaves are mentioned to have healing effect in ancient literature. Hence the study was carried out to evaluate the wound healing effect by assessing the histopathological parameters.Methods: The study was done in the albino rats which were divided into various groups. The histopathological parameters were studied at regular intervals.Results: It was found that jasmine paste improved the wound healing process at all the stages.Conclusion: Jasminum grandiflorum has wound healing effect in albino rats
NIT COVID-19 at WNUT-2020 Task 2: Deep Learning Model RoBERTa for Identify Informative COVID-19 English Tweets
This paper presents the model submitted by the NIT_COVID-19 team for
identified informative COVID-19 English tweets at WNUT-2020 Task2. This shared
task addresses the problem of automatically identifying whether an English
tweet related to informative (novel coronavirus) or not. These informative
tweets provide information about recovered, confirmed, suspected, and death
cases as well as the location or travel history of the cases. The proposed
approach includes pre-processing techniques and pre-trained RoBERTa with
suitable hyperparameters for English coronavirus tweet classification. The
performance achieved by the proposed model for shared task WNUT 2020 Task2 is
89.14% in the F1-score metric.Comment: 5 pages, one figures, conferenc
Randomised prospective comparative analysis of functional outcome of osteosynthesis of intra-articular distal humerus fracture using triceps reflecting and transolecrenon approach
Background: Intra articular fractures of distal humerus is one of the demanding injuries to manage due to its complex anatomy. Open reduction internal fixation is able to achieve painless, stable and mobile joint. This study is aimed at comparing functional outcome of patients treated with triceps reflecting and olecranon osteotomy approach.Methods: A hospital based randomized comparative study of 40 patients who diagnosed with distal humerus intraarticular fracture admitted in our hospital from April 2017 to March 2019. Triceps reflecting approach (group A) was used in 20 patients and olecranon osteotomy approach (group B) in 20 patients. Elbow range of movements and Mayo elbow performance score (MEPS) was used to compare outcome.Results: The mean elbow range of motion is 95.8±13.5 degree at 1 year follow up in group A and 94.5±9.3 degree in 1 year follow up at 1 year follow up in group B. The mean MEP score at end of 1 year in group A was 93.8±2.9 and in group B was 91.5±3.2 shows excellent results but there was no statistically significant difference between MEP scores of two groups. We observed 6 patients developed extension lag less than 10 degree in group A which was clinically insignificant to patients and 7 patients developed hardware prominence in group B.Conclusions: Triceps reflecting Bryan Morrey approach is equally effective as olecranon osteotomy approach in treatment of distal humerus intra articular fracture with less complication and operative time
Identifying Implementation Bugs in Machine Learning based Image Classifiers using Metamorphic Testing
We have recently witnessed tremendous success of Machine Learning (ML) in
practical applications. Computer vision, speech recognition and language
translation have all seen a near human level performance. We expect, in the
near future, most business applications will have some form of ML. However,
testing such applications is extremely challenging and would be very expensive
if we follow today's methodologies. In this work, we present an articulation of
the challenges in testing ML based applications. We then present our solution
approach, based on the concept of Metamorphic Testing, which aims to identify
implementation bugs in ML based image classifiers. We have developed
metamorphic relations for an application based on Support Vector Machine and a
Deep Learning based application. Empirical validation showed that our approach
was able to catch 71% of the implementation bugs in the ML applications.Comment: Published at 27th ACM SIGSOFT International Symposium on Software
Testing and Analysis (ISSTA 2018
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