Preparation and Characterisation of Polystyrene Grafted Sago Starch

Styrene grafting onto sago starch was carried out by using eerie ammonium nitrate (CAN) as a redox initiator. The parameters affecting the grafting reaction were investigated and the optimum conditions obtained are as follows: temperature, 50°C; nitric acid concentration, 0.01 M; amount of styrene, 0.35 mol; amount of CAN, 16.8 x 10-4 mol and reaction period, 2h. Percentages of grafting and grafting efficiency under the optimum condition were 53.92% and 73.21%, respectively. Reactions in the presence of nitrogen gas resulted in higher percentages of grafting and grafting efficiency. FTIR spectra analysis of the grafted chain and polystyrene was identical indicating that styrene was successfully grafted onto sago starch. TGA thermograms, DSC curves and SEM photographs of sago starch-g poly(styrene) and the original polymers (sago starch and polystyrene) were different which suggested that styrene was grafted onto sago starch. The bio-degradability study using a-amylase showed that the rate of degradation of gelatinised sago starch was higher than that of sago starch-gpoly( styrene). The highest rate of degradation of sago starch-gpoly(styrene) was obtained at 50 ppm of a-amylase concentration. Viscosity measurements showed that the intrinsic viscosity and the average molecular weight (Mv) increased with the increase in the percentage of grafted polystyrene. The Mv of the various percentages of grafted polystyrene were in the order of 104. The results obtained from the swelling of sago starch-gpoly(styrene) in polar and non polar solvents showed that the percentage of swelling at equilibrium and the swelling rate coefficient decreased in the following order: DMSO > water > acetone > cyclohexanone = CHCh > toluene = CCl4. Diffusions of the solvents onto the polymers were found to be of a Fickian only for DMSO

Advances in characterisation, calibration and data processing speed of optical coherence tomography systems

This thesis describes advances in the characterisation, calibration and data processing of optical coherence tomography (OCT) systems. Femtosecond (fs) laser inscription was used for producing OCT-phantoms. Transparent materials are generally inert to infra-red radiations, but with fs lasers material modification occurs via non-linear processes when the highly focused light source interacts with the materials. This modification is confined to the focal volume and is highly reproducible. In order to select the best inscription parameters, combination of different inscription parameters were tested, using three fs laser systems, with different operating properties, on a variety of materials. This facilitated the understanding of the key characteristics of the produced structures with the aim of producing viable OCT-phantoms. Finally, OCT-phantoms were successfully designed and fabricated in fused silica. The use of these phantoms to characterise many properties (resolution, distortion, sensitivity decay, scan linearity) of an OCT system was demonstrated. Quantitative methods were developed to support the characterisation of an OCT system collecting images from phantoms and also to improve the quality of the OCT images. Characterisation methods include the measurement of the spatially variant resolution (point spread function (PSF) and modulation transfer function (MTF)), sensitivity and distortion. Processing of OCT data is a computer intensive process. Standard central processing unit (CPU) based processing might take several minutes to a few hours to process acquired data, thus data processing is a significant bottleneck. An alternative choice is to use expensive hardware-based processing such as field programmable gate arrays (FPGAs). However, recently graphics processing unit (GPU) based data processing methods have been developed to minimize this data processing and rendering time. These processing techniques include standard-processing methods which includes a set of algorithms to process the raw data (interference) obtained by the detector and generate A-scans. The work presented here describes accelerated data processing and post processing techniques for OCT systems. The GPU based processing developed, during the PhD, was later implemented into a custom built Fourier domain optical coherence tomography (FD-OCT) system. This system currently processes and renders data in real time. Processing throughput of this system is currently limited by the camera capture rate. OCTphantoms have been heavily used for the qualitative characterization and adjustment/ fine tuning of the operating conditions of OCT system. Currently, investigations are under way to characterize OCT systems using our phantoms. The work presented in this thesis demonstrate several novel techniques of fabricating OCT-phantoms and accelerating OCT data processing using GPUs. In the process of developing phantoms and quantitative methods, a thorough understanding and practical knowledge of OCT and fs laser processing systems was developed. This understanding leads to several novel pieces of research that are not only relevant to OCT but have broader importance. For example, extensive understanding of the properties of fs inscribed structures will be useful in other photonic application such as making of phase mask, wave guides and microfluidic channels. Acceleration of data processing with GPUs is also useful in other fields

Feature Selection in UNSW-NB15 and KDDCUP’99 datasets

Machine learning and data mining techniques have been widely used in order to improve network intrusion detection in recent years. These techniques make it possible to automate anomaly detection in network traffics. One of the major problems that researchers are facing is the lack of published data available for research purposes. The KDD’99 dataset was used by researchers for over a decade even though this dataset was suffering from some reported shortcomings and it was criticized by few researchers. In 2009, Tavallaee M. et al. proposed a new dataset (NSL-KDD) extracted from the KDD’99 dataset in order to improve the dataset where it can be used for carrying out research in anomaly detection. The UNSW-NB15 dataset is the latest published dataset which was created in 2015 for research purposes in intrusion detection. This research is analysing the features included in the UNSW-NB15 dataset by employing machine learning techniques and exploring significant features (curse of high dimensionality) by which intrusion detection can be improved in network systems. Therefore, the existing irrelevant and redundant features are omitted from the dataset resulting not only faster training and testing process but also less resource consumption while maintaining high detection rates. A subset of features is proposed in this study and the findings are compared with the previous work in relation to features selection in the KDD’99 dataset

A Study on Azhal Keel Vayu (அழல் கீல் வாயு)

AIM AND OBJECTIVES: The disease “Azhal Keel Vayu” is a major ailment of the elderly. This produces pain and discomfort to the patients. The purpose of author’s work is to elucidate a good medicine from ancient Siddha literatures and to create hope and faith in their treatment. Their being a preliminary endeavour by the author, as if it would be a helping hand to the sufferers. With this view this dissertation subject was undertaken. 1. To prove the efficacy of our Siddha Medicine to the world. 2. To study the clinical cause of the disease “Azhal Keel Vayu” with keen observation on the Aetiology, Pathology, Diagnosis, Prognosis, Complications and the Treatment by making use of Siddha aspect. 3. To expose the unique diagnostic methods mentioned by Siddhars, to know the disease “Azhal Keel Vayu” alters the normal condition under the topic Mukkutram, Poripulangal, Ezhu Udal Kattukkal and Envagai thervugal. 4. To know the extent of correlation of Aetiology, Classification, Signs and Symptoms of Azhal Keel Vayu in Siddha aspect with Osteo arthritis in Modern medicine. 5. To have an idea about the incidence of the disease with age, sex, socio-economic status and climatic conditions. 6. To have a detailed clinical investigations. 7. To have a clinical trial on Azhal Keel Vayu with the medicines named NANNARIVER CHOORNAM as internal medicine and SANGANKUPPI VER ENNAI as external medicine. 8. To evaluate the Bio-chemical and Pharmacological effects of trial medicine. 9. To use modern parameters to confirm the diagnosis and prognosis of the disease. 10. To insist Thokkanam (Massage) and Asanas along with medicines to achieve the good results, which are the salient features of Sirappu Maruthuvam. SUMMARY: 1. Fifty Five cases of Azhal Keelvayu, diagnosed clinically. Out of them Thirty Cases were admitted in the in-patient PG Sirappu Maruthuvam Ward, Govt. Siddha Medical College Hospital, Palayamkottai were observed for clinical diagnosis, lab diagnosis and treatment by the trial medicines. Out of them Twenty Cases were selected for study. Twenty Five Cases were treated as out patients. 2. Clinical diagnosis of Azhal Keel vayu was done on the basis of clinical features described in the siddha text books. 3. Laboratory diagnosis of Azhal Keelvayu was done by modern methods of examination in the Govt. Siddha Medical College Hospital, Palayamkottai. 4. The various siddha aspects of examination of the disease were carried out and recorded in a proforma. 5. The trial medicines choosed for both internal and external treatment and the management of Azhal Keelavayu • Nannariver Choornam as per the severity of the complaints, the dosage was given 1 gm three times a day with white sugar for fifteen days and above. • Sangan kuppi ver ennai (Externally). 6. Before starting the treatment, careful detailed history was carried out and recorded from the twenty selected patients. 7. During the period of treatment, all the patients were put under strict pathiyam-a specific dietary regimen. 8. The observation made during the clinical study shows that the main drug Nannariver Choornam (Internally) is clinically effective. It has moderate analgesic action and significant anti inflammatory action. 9. The action of Sangan kuppi ver ennai (Externally) over the affected joint was also clinically effective. It has Significant anti inflammatory action. 10. A periodical laboratory investigation were made for all the case for blood, urine and motion test etc., along with radiological reports. 11. Since Azhal Keel vayu is a chronic disease, it required minimum treatment for twenty days, treated both internally and externally to minimize the severe pain, tenderness and Swelling, but also slight disappearance of the crepitation. CONCLUSION: All the twenty patients, selected for this Study were treated with Nannariver Choornam (Internal 1gm tds with white sugar) and Sangan kuppi ver ennai (Externally). Clinical results show improvement in large number of the cases that is 50%. During the meantime of treatment, under admission all the Azhal Keel vayu patients were instructed and guided to follow the following asanas. a) Komugaasana - The Cow head Posture b) Padmasana - The Lotus Posture c) Vajraasana - The Adamant posture It was sure that no one had any remission up to 6 months. If any further recurrence or no satisfied improvement, the individuals were instructed to follow up treatment both internally and externally. It is very pleasurable to say here, the author highlights the trial medicines are found effective just relieved from pain and tenderness, severe morning stiffness, severe crepitation, arresting of marked swelling and so on. It was noted that the internal drug Nannariver Choornam was free from adverse side effects, i.e. no cases were reported either nausea or vomiting and the external application Sangan kuppi ver ennai was not irritant, i.e. no cases were reported itching or inflammation or eruption wherever massaged. Meanwhile it gave good soothing effect to the affected part

Learning End-to-End Goal-Oriented Dialog with Multiple Answers

In a dialog, there can be multiple valid next utterances at any point. The present end-to-end neural methods for dialog do not take this into account. They learn with the assumption that at any time there is only one correct next utterance. In this work, we focus on this problem in the goal-oriented dialog setting where there are different paths to reach a goal. We propose a new method, that uses a combination of supervised learning and reinforcement learning approaches to address this issue. We also propose a new and more effective testbed, permuted-bAbI dialog tasks, by introducing multiple valid next utterances to the original-bAbI dialog tasks, which allows evaluation of goal-oriented dialog systems in a more realistic setting. We show that there is a significant drop in performance of existing end-to-end neural methods from 81.5% per-dialog accuracy on original-bAbI dialog tasks to 30.3% on permuted-bAbI dialog tasks. We also show that our proposed method improves the performance and achieves 47.3% per-dialog accuracy on permuted-bAbI dialog tasks.Comment: EMNLP 2018. permuted-bAbI dialog tasks are available at - https://github.com/IBM/permuted-bAbI-dialog-task

Mapping the binding site of ligands to proteins using chemical exchange parameters by NMR Spectroscopy

Ph.DDOCTOR OF PHILOSOPH

A Deterministic Model for Analyzing the Dynamics of Ant System Algorithm and Performance Amelioration through a New Pheromone Deposition Approach

Ant Colony Optimization (ACO) is a metaheuristic for solving difficult discrete optimization problems. This paper presents a deterministic model based on differential equation to analyze the dynamics of basic Ant System algorithm. Traditionally, the deposition of pheromone on different parts of the tour of a particular ant is always kept unvarying. Thus the pheromone concentration remains uniform throughout the entire path of an ant. This article introduces an exponentially increasing pheromone deposition approach by artificial ants to improve the performance of basic Ant System algorithm. The idea here is to introduce an additional attracting force to guide the ants towards destination more easily by constructing an artificial potential field identified by increasing pheromone concentration towards the goal. Apart from carrying out analysis of Ant System dynamics with both traditional and the newly proposed deposition rules, the paper presents an exhaustive set of experiments performed to find out suitable parameter ranges for best performance of Ant System with the proposed deposition approach. Simulations reveal that the proposed deposition rule outperforms the traditional one by a large extent both in terms of solution quality and algorithm convergence. Thus, the contributions of the article can be presented as follows: i) it introduces differential equation and explores a novel method of analyzing the dynamics of ant system algorithms, ii) it initiates an exponentially increasing pheromone deposition approach by artificial ants to improve the performance of algorithm in terms of solution quality and convergence time, iii) exhaustive experimentation performed facilitates the discovery of an algebraic relationship between the parameter set of the algorithm and feature of the problem environment.Comment: 4th IEEE International Conference on Information and Automation for Sustainability, 200

On End-to-End Learning of Neural Goal-Oriented Dialog Systems

Goal-oriented dialog systems assist users to complete tasks such as restaurant reservations and flight ticket booking. Deep neural networks have opened up the possibility of end-to-end learning of the entire goal-oriented dialog system directly from data. End-to-end learning enables automatic adaptation of the different parts of the dialog system accounting for how changes in one part affect the others. Since the entire dialog system is learned directly from the data, the design of the dialog system need not make any assumptions about the domain. This makes it possible to build dialog systems for new domains with different training data, without much domain-specific hand-crafting of the dialog system. With deep neural networks which can potentially capture the complexity of human dialog in natural language, learning neural goal-oriented dialog systems end-to-end holds the promise of bringing dialog systems into our everyday lives. In this thesis, we identify some of the challenges in end-to-end learning of neural goal-oriented dialog systems and propose methods to address them. We look at four challenges: 1) The challenge posed by the presence of a large number of named entities in goal-oriented dialog tasks. We propose a method to build neural embeddings for named entities on the fly and store them in a key-value table with neural embeddings as keys and the actual named entities as values. The proposed method allows for comparison and retrieval, using neural embeddings as well as actual named entities, which leads to significant improvement in performance, especially in the presence of out-of-vocabulary named entities. 2) The challenge of performing supervised learning of goal-oriented dialog systems with multiple valid next utterances. We propose a method to learn to use different parts of the neural network to encode different predictions of the next utterances with learning of one not interfering with the learning of the others. Our experiments show considerable improvement in the generalization performance. 3) The challenge of handling new user behaviors during deployment of a trained dialog system. We propose a method that learns to anticipate failures and efficiently transfers dialogs to human agents in order to make sure the overall task success of the users remains high. Our experiments show that using our proposed method it is possible to achieve very high user task success while minimally using human agents. 4) The challenge of requiring large amounts of training data for each new dialog task of interest. We show that by selectively learning from a related task's data that is already available, we can improve the performance on a new task of interest that has only a limited amount of training data.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169752/1/rjana_1.pd

Energy efficient control for compressed air with step modulation technique

The compressed air system is not only an energy intensive utility but also one of the least energy efficient. Over a period of time, both performance of compressors and compressed air system reduces drastically. The causes are many such as poor maintenance, wear and tear of equipment. The thesis presents the Energy efficient control for compressed air by using step modulating technique, which would optimize the energy consumption for the compressed air. The proposed technique has been tested on actual industrial consumer site. By adding the VSD “Variable Speed Drive” to the conventional control compressed air and by regulation the prime mover speeds on unloading cycle with step modulation technique it is discovered that the electrical energy consumption of the compressed air system has been optimized. This will give a prominent saving to the consumer in term of utility bill. From the analysis results, it showed that the saving of 22% can be achieved

Scientometric study of Research literature output by Madras Medical College during 1989 -2018

Madras Medical College is the one of well-known premier medical institution, situated in Chennai. The data was collected using PubMed database during 1989-2018, there are 646 Publications were found. Analyzed for year wise growth shows 53(8.20%) Publications found during 1989-1993 and Highest 283(43.81%) Publication found during 2014-2018 Authorship pattern shows single author have contributed 35(5.42%), multi author have contributed 611 articles, the mean relative growth rate is 0.0835 and mean doubling time is 14.65. Prolific contributed authors rank 1 occupied by Anand Chockalingam contributed 14(2.16%), 2nd by N, Deivanayagam contributed 13(2.01%),3rd by Ottilingam Somasundaram contributed 12(1.86%). The mean degree of collaboration is 0.930. the prolific contributed journals by authors rank 1 occupied by The Journal of the Association of Physicians of India have published 46 research papers, rank2 occupied by Indian Journal of Dermatology, Venereology and Leprology have published 26 research papers, rank 3 occupied by Indian Journal of Psychiatry have published 20 research papers. The most contributions of research publications by affiliated department 1st. Department of Dermatology, Contributed 70(9.87%), 2nd Institute of Mental Health. Have contributed 41(5.78%) research publications, 3rd Institute of Nephrology. have contributed 40 (5.64%) research publications. The author preferred Language for publication is found to be English, now a days peoples are facing various health problems the postgraduates and faculties should publish their research papers in a Indexed Journals to share their knowledge to the fellow researchers and the funding agencies should encourage the researcher to do their research on current trend on health and diseases