Tsunami Warning System

The effects of a tsunami on a coastline can be devastating. In an attempt to mitigate the damages caused by tsunamis, and to provide coastal communities with evacuation alerts, it is essential to have early warning systems installed offshore. Tsunami early warning systems can detect the formation of tsunami waves long before they reach shore, providing coastal communities with information about the strength of an incoming tsunami. Since providing accurate information about oceanic wave patterns is absolutely necessary to forecast the magnitude of a tsunami as it forms, the focus of this research is to develop a novel approach which will predict the height and velocity of a tsunami long before it makes landfall. The research comprises of a mathematical study of how the objects under water appear would appear to any observer outside. Also included in the research is study of water surface regeneration techniques. The investigators believe that the proposed approach will provide unprecedented detail and accuracy to help forecast the magnitude of forming tsunami waves. This research will lay the groundwork for the next tsunami early warning system, which will continue to save lives in coastal communities around the world

Approximate dynamic programming for large scale systems

Sequential decision making under uncertainty is at the heart of a wide variety of practical problems. These problems can be cast as dynamic programs and the optimal value function can be computed by solving Bellman's equation. However, this approach is limited in its applicability. As the number of state variables increases, the state space size grows exponentially, a phenomenon known as the curse of dimensionality, rendering the standard dynamic programming approach impractical. An effective way of addressing curse of dimensionality is through parameterized value function approximation. Such an approximation is determined by relatively small number of parameters and serves as an estimate of the optimal value function. But in order for this approach to be effective, we need Approximate Dynamic Programming (ADP) algorithms that can deliver `good' approximation to the optimal value function and such an approximation can then be used to derive policies for effective decision-making. From a practical standpoint, in order to assess the effectiveness of such an approximation, there is also a need for methods that give a sense for the suboptimality of a policy. This thesis is an attempt to address both these issues. First, we introduce a new ADP algorithm based on linear programming, to compute value function approximations. LP approaches to approximate DP have typically relied on a natural `projection' of a well studied linear program for exact dynamic programming. Such programs restrict attention to approximations that are lower bounds to the optimal cost-to-go function. Our program -- the `smoothed approximate linear program' -- is distinct from such approaches and relaxes the restriction to lower bounding approximations in an appropriate fashion while remaining computationally tractable. The resulting program enjoys strong approximation guarantees and is shown to perform well in numerical experiments with the game of Tetris and queueing network control problem. Next, we consider optimal stopping problems with applications to pricing of high-dimensional American options. We introduce the pathwise optimization (PO) method: a new convex optimization procedure to produce upper and lower bounds on the optimal value (the `price') of high-dimensional optimal stopping problems. The PO method builds on a dual characterization of optimal stopping problems as optimization problems over the space of martingales, which we dub the martingale duality approach. We demonstrate via numerical experiments that the PO method produces upper bounds and lower bounds (via suboptimal exercise policies) of a quality comparable with state-of-the-art approaches. Further, we develop an approximation theory relevant to martingale duality approaches in general and the PO method in particular. Finally, we consider a broad class of MDPs and introduce a new tractable method for computing bounds by consider information relaxation and introducing penalty. The method delivers tight bounds by identifying the best penalty function among a parameterized class of penalty functions. We implement our method on a high-dimensional financial application, namely, optimal execution and demonstrate the practical value of the method vis-a-vis competing methods available in the literature. In addition, we provide theory to show that bounds generated by our method are provably tighter than some of the other available approaches

Plant Classification based on Leaves using Artificial Neural Network

Manual methods to examine leaf for plant classification can be tedious, therefore, automation is desired. Existing methods try distinctive approaches to accomplish this task. Nowadays, Convolution Neural Networks (CNN) are widely used for such application which achieves higher accuracy. However, CNN's are computationally expensive and require extensive dataset for training. Other existing methods are far less resource expensive but they also have their shortcomings for example, some features cannot be processed accurately with automation, some necessary differentiators are left out. To overcome this, we have proposed a simple Artificial Neural Network (ANN) for automatic classification of plants based on their leaf features. Experimental results show that the proposed algorithm able to achieve an accuracy of 96% by incorporating only a single hidden layer of ANN. Hence, our approach is fairly computationally efficient compared to existing CNN based method

Incidence of maternal near-miss events in a tertiary care hospital of Gujarat, India

Background: Maternal near miss is said to have occurred when women presented with life threatening complication during pregnancy, child birth and within 42 days after delivery, but survive by chance or good institutional care. For identifying near-miss cases five-factor scoring system was used. In 2009 WHO working group has standardized the criteria for selecting these cases. Because maternal mortality is a rare event, it is important to study maternal near-miss as a complement to evaluate and improve the quality of obstetric care. Thus, the study was conducted with the aim of assessing the incidence and causes of maternal near-miss.Methods: A facility-based cross-sectional study was conducted in the department of obstetrics and gynecology in GMERS medical college and hospital, Sola, Ahmedabad from January 1, 2019 to August 31, 2019. All maternal near-miss cases admitted to the hospital during the study period were recruited. World Health Organization criteria were used to identify maternal near-miss cases. The number of maternal near-miss cases over one year per 1000 live births occurring during the same year was calculated to determine the incidence of maternal near-miss. Underlying and contributing causes of maternal near-miss were documented from each participant’s record.Results: During the period of study, 3235 deliveries were done at the institution while 16 cases of near-miss were identified. The prevalence of near-miss case in this study was 0.5%. Near-miss per 1000 delivery was 5%. Maternal death to near miss ratio was 1:2.67. The leading causes of maternal near miss were hypertensive disorders (62%) and haemorrhage (32%) The morbidity was high in un-booked cases.Conclusions: Maternal near miss is good alternative indicator of health care system. Efforts made toward improvement in the management of life-threatening obstetric complications could reduce the occurrence of maternal near-miss problems that occur during hospitalization

A comparative study of non-descent vaginal hysterectomy and total laparoscopic hysterectomy

Background: Hysterectomy is the second most common operation performed by the gynecologists, next only to caesarean section. Objective of the study was to compare fall in blood haemoglobin level, duration of operation, intra- and post-op complications between non-descent vaginal hysterectomy and total laparoscopic hysterectomy and establish the better method for hysterectomy in non-descent uterus.Methods: A retrospective comparative study of 90 hysterectomies was done from a period of May 2018 - April 2019 at GMERS Medical College and Hospital Sola, with 45 cases in group of non-descent vaginal hysterectomy (NDVH) and 45 in group of total laparoscopic hysterectomy (TLH). Demographic characteristics, co-morbid conditions, indications for surgery, operative time, intra- operative blood loss, post-operative analgesia requirements, post-operative hospital stay and post-operative complications were compared between both groups. Those patients having malignancy as diagnosed by Pap smear or by D and C were excluded from the studyResults: The most common age in both groups was 41-50 years. Adenomyotic uterus was the most common indication for surgery in both groups. The mean operative time in NDVH group was 45 min while it was 80 min in TLH group. p<0.001 suggested significant difference when operative time were compared between both groups. Both groups were similar in post-operative analgesia requirement and post-operative hospital stay. Post-operative complications were similar in both groups.Conclusions: In which way to approach the uterus shall depend upon skill of the surgeon, size and pathological nature of uterus, technology available in the hospital and preference of patient as well as surgeon

A Search for the Production of Technicolor Particles at the D-Zero Detector

A search for the Technicolor processes p{bar p} {yields} {rho}{sub T}{sup {+-}} {yields} W{sup {+-}}{pi}{sub T}{sup 0} {yields} {mu}{nu}b{bar b} and p{bar p} {yields} {rho}{sub T}{sup 0} {yields} W{sup {+-}}{pi}{sub T}{sup {+-}} {yields} {mu}{nu}b{bar c} is conducted at the D0 detector. Selection requirements are individually optimized for each of twenty mass hypotheses by means of a random grid search. No excess is seen in a 291 pb{sup -1} data set and 95% confidence level upper limits are set on the Technicolor production cross section. The mass combinations M{sub {rho}} = 195 GeV/c{sup 2}, M{sub {pi}} = 100 GeV/c{sup 2} and M{sub {rho}} = 200 GeV/c{sup 2}, M{sub {pi}} = 105 GeV/c{sup 2} are excluded for the choice of the Technicolor scale parameter M{sub V} = 500 GeV

A Combined Approach For Private Indexing Mechanism

Private indexing is a set of approaches for analyzing research data that are similar or resemble similar ones. This is used in the database to keep track of the keys and their values. The main subject of this research is private indexing in record linkage to secure the data. Because unique personal identification numbers or social security numbers are not accessible in most countries or databases, data linkage is limited to attributes such as date of birth and names to distinguish between the number of records and the real-life entities they represent. For security reasons, the encryption of these identifiers is required. Privacy-preserving record linkage, frequently used to link private data within several databases from different companies, prevents sensitive information from being exposed to other companies. This research used a combined method to evaluate the data, using classic and new indexing methods. A combined approach is more secure than typical standard indexing in terms of privacy. Multibit tree indexing, which groups comparable data in many ways, creates a scalable tree-like structure that is both space and time flexible, as it avoids the need for redundant block structures. Because the record pair numbers to compare are the Cartesian product of both the file record numbers, the work required grows with the number of records to compare in the files. The evaluation findings of this research showed that combined method is scalable in terms of the number of databases to be linked, the database size, and the time required

Modeling and Control of Formations of Nonholonomic Mobile Robots

This paper addresses the control of a team of nonholonomic mobile robots navigating in a terrain with obstacles while maintaining a desired formation and changing formations when required, using graph theory.We model the team as a triple, (g, r, H), consisting of a group element that describes the gross position of the lead robot, a set of shape variables g that describe the relative positions of robots, and a control graph H that describes the behaviors of the robots in the formation. Our framework enables the representation and enumeration of possible control graphs and the coordination of transitions between any two formations

Approximate Dynamic Programming via a Smoothed Linear Program

We present a novel linear program for the approximation of the dynamic programming cost-to-go function in high-dimensional stochastic control problems. LP approaches to approximate DP have typically relied on a natural “projection” of a well-studied linear program for exact dynamic programming. Such programs restrict attention to approximations that are lower bounds to the optimal cost-to-go function. Our program—the “smoothed approximate linear program”—is distinct from such approaches and relaxes the restriction to lower bounding approximations in an appropriate fashion while remaining computationally tractable. Doing so appears to have several advantages: First, we demonstrate bounds on the quality of approximation to the optimal cost-to-go function afforded by our approach. These bounds are, in general, no worse than those available for extant LP approaches and for specific problem instances can be shown to be arbitrarily stronger. Second, experiments with our approach on a pair of challenging problems (the game of Tetris and a queueing network control problem) show that the approach outperforms the existing LP approach (which has previously been shown to be competitive with several ADP algorithms) by a substantial margin