Longitudinally Jointed Edge-wise Compression Honeycomb Composite Sandwich Coupon Testing and FE Analysis: Three Methods of Strain Measurement, and Comparison

Three means (i.e., typical foil strain gages, fiber optic sensors, and a digital image correlation (DIC) system) were implemented to measure strains on the back and front surfaces of a longitudinally jointed curved test article subjected to edge-wise compression testing, at NASA Goddard Space Flight Center, according to ASTM C364. The Pre-test finite element analysis (FEA) was conducted to assess ultimate failure load and predict strain distribution pattern throughout the test coupon. The predicted strain pattern contours were then utilized as guidelines for installing the strain measurement instrumentations. The strain gages and fiber optic sensors were bonded on the specimen at locations with nearly the same strain values, as close as possible to each other, so that, comparisons between the measured strains by strain gages and fiber optic sensors, as well as the DIC system are justified. The test article was loaded to failure (at approximately 38 kips), at the strain value of approximately 10,000mu epsilon As a part of this study, the validity of the measured strains by fiber optic sensors is examined against the strain gage and DIC data, and also will be compared with FEA predictions

Political Economy of Ethnic Conflict

In this dissertation, I investigate the socioeconomic causes of consequences of ethnic conflict, and evaluate interventions that can reduce social animosity and misperceptions about outgroups. In particular, I focus on conflict between Hindus and Muslims in India. In recent years, online misinformation has emerged as a major contributor to misperceptions and animosity towards Muslims in India. In Chapter 1, I investigate if we can inoculate people against misinformation and mitigate its impact on people’s beliefs, attitudes, and behavior? We conduct a large field experiment in India with an intervention providing weekly digests containing a compilation of fact-checks of viral misinformation. In these digests, we also incorporate narrative explainers to give details and context of issues that are politically salient and consistent target of false stories. Specifically, we address misperceptions about Muslims increasingly fuelled by online misinformation. We find that familiarity with fact-checks increases people’s ability to correctly identify misinformation by eleven percentage points. However, belief in true news also decreases by four percentage points. We estimate a structural model to disentangle the two mechanisms of impact—truth discernment, which is the ability to correctly distinguish between false and true news; and skepticism, which changes the overall credulity for both false and true news. The impact is driven by an increase in both truth discernment and skepticism. Whereas skepticism increases immediately, it takes several weeks to become better at discerning truth. Finally, our intervention reduces misperceptions about Muslims, as well as leads to changes in policy attitudes and behavior. Treated individuals are less likely to support discriminatory policies and are more likely to pay for efforts to counter the harassment of inter-faith couples. In Chapter 2, I investigate the economic impacts of conflict and social animus by estimating the causal impact of ethnic violence on economic growth in India. For causal identification, I use shift-share instruments to isolate exogenous national shocks to violence from endogenous local shocks. On average, a riot reduces state GDP growth rate by 0.14 percentage points. To investigate mechanism, I estimate the dynamics of impact using the synthetic control method and compare it to theoretical predictions from a shock to social capital versus physical capital. This shows that the negative impact of violence is likely driven by a negative shock to social capital from higher animosity and discrimination among communities exposed to violence. This impact of violence on growth creates a vicious cycle when one also considers the effect in the opposite direction – lower growth leading to more violence. The multiplier due to this vicious cycle magnifies the impact of external growth shocks by 40 percent in equilibrium. Overall, the results highlight the importance of strong institutions to manage conflict for the long-term prosperity of societies. In Chapter 3, I investigate the historical origins of ethnic violence in India by comparing violence in regions that were directly ruled by British, versus those that were indirectly ruled through native kings who had significant autonomy. I find that regions that are directly ruled have more violence in post-independence period. I then use direct British rule as an instrument for ethnic violence to estimate the impact of violence and residential segregation

Automatic Classification and Color Changing of Saree Components Using Deep Learning Techniques

Sarees are integral to Indian culture and serve as daily attire for most women on the subcontinent. Despite their popularity, there exists a gap in research regarding the automatic segmentation of sarees and the independent color modification of distinct components. Existing methods rely on labor-intensive manual adjustments through commercial applications, impeding productivity and resulting in avoidable expenses. This paper presents a tool that smartly coordinates different deep-learning techniques to modify the color patterns found on different parts of a saree. MODNet is applied for background removal and custom-trained Mask R-CNN models are utilized to precisely segment the saree body and border. The subsequent application of a color-changing algorithm in the HSV color space facilitates independent color modification for the saree border and body. The methodology proposed in this paper can be extended to any kind of clothing such as shirts, trousers, kurtas, kimonos, etc. An accuracy of 93.01% was achieved for the saree border segmentation, and an accuracy of 89.23% was achieved for the saree body segmentation when tested on a set of 50 test images

Posterior Reversible Encephalopathy Syndrome: An Unusual Complication of Benzodiazepine Poisoning: A Case Report

Posterior Reversible Encephalopathy Syndrome (PRES), also known as Reversible Posterior Leukoencephalopathy Syndrome, presents with rapid onset symptoms, including headache, seizures, altered consciousness, and visual disturbance. It is seen most frequently in settings of acute hypertension and is usually related to eclampsia. Only a few cases in the literature described PRES syndrome following benzodiazepines. We present a young male with benzodiazepine poisoning brought to the hospital in a deep coma, hypoxia, acidosis, and shock. Diagnosis of PRES was made on history, clinical examination, and radiologic findings of symmetric bilateral hyperintensities on T2 weighted Magnetic Resonance Imaging (MRIs) representing vasogenic edema

Trenchless Mechanized Inspection and Retrofitting Strategy for Buried Sewerage Systems

767-775The conventional retrofitting methods of buried sewer pipes require heavy machinery, intensive manpower, and a longer time for rehabilitation. Such methods may also damage the nearby infrastructures and landscapes. The present study explores an integrated trenchless solution for damage identification and mechanized retrofitting of domestic buried sewerage pipelines of diameter ranging from 75 to 300 mm. A front-mounted camera of the retrofitting system assesses the damage inside the sewer pipes. The retrofitting of the damaged part of buried pipe is achieved by impregnation of Glass Fiber Reinforced Polymer (GFRP) composite sheet with 100:16 epoxy and hardener ratio. The wrapping of the GFRP sheets on damaged part is done by inflation and deflation technique with a cylindrical rubber bladder connected by a flexible shaft. The retrofitted sewer pipe can be resumed after 3–4 hours of applying the impregnated GFRP composite with above retrofitting strategy