The Effect of Preferential Weld Corrosion Study in CO2 Capture Plant at PFK

This paper discusses on the leakages issue due to severe localized corrosion attack along the pipeline (made from ASTM 106 B carbon steel) in the CO2 capture plant of PETRONAS Fertilizer Kedah (PFK) which its cause is still uncertain. The suspected cause was corrosion occurred to the high residual stresses trapped in the weldment region of the pipelines as the pipelines did not undergo post-welding heat treatment to release the stresses. The pattern of the corrosion suggested that Preferential Weld Corrosion (PWC) could have taken place in the pipelines. In order to investigate the relation of PWC and the leakages issue in PFK, the contribution of self corrosion rate of the Parent Metal, HAZ and Weld regions under the various conditions that includes saturated CO2, different concentration of caustic, KS-1, and under the temperature of 50oC will be studied. The methodology selected consists of EIS and LPR tests plus a basic metallographic observation between the three regions; Parent Metal, HAZ and Weld. It was found that when the corrosion rate is the highest under the condition of saturated CO2, 500ppm caustic, 250ppm KS-1 with 50oC and the Weld region has the highest corrosion rate in comparison with Parent Metal and HAZ regions for all conditions. It is therefore, concluded that the Weldment region (consists of HAZ and Weld) is prone to corrosion attack in comparison to Parent Metal region that was unaffected by the welding process. In the pursuit of a better answer to the issue, it is recommended that a research is done on the behavior of the different regions of the affected pipeline and to study further on the possible corrosive mixture of CO2, caustic and KS-1. Meanwhile, stress relieve heat treatment is suggested to be conducted on the welded pipelines to mitigate the corrosion problem

Entrepreneurship and Risk Aversion

Evans and Jovanovic (1989) find that wealth is an important determinant of business startups due to liquidity constraints. However, Cressy (2000) argues that if risk aversion is a negative function of wealth, Evans and Jovanovic's empirical results could be spurious and the positive effect of wealth could be due to the omission of risk aversion in the regression equation. In other words, according to Cressy, one's wealth does not have any e®ect on business startups once the degree of risk aversion is accounted for. This paper attempts to inves-tigate the validity of Cressy's conjecture. We empirically examine the e®ect of wealth on the transition into self-employment, while allowing for the effect of risk aversion. Our empirical findings show that Evans and Jovanovic's (1989) results are robust, i.e., wealth has a positive e®ect on business startups even allowing for the confounding e®ects of risk aversion.Business Startup, Self-employment, Liquidity Constraints

Obesity and Risk Knowledge

Obesity is an epidemic health problem in many developed countries, and it is an emerging public health concern in developing, transitional, and newly-developed countries. The purpose of this research is to investigate the relationship between individuals' knowledge concerning the health risks of obesity and their tendency to be obese (as measured by the \body mass index"). Instead of assuming that obesity is a pure physiological problem as in previous studies, we allow an individual's cost/bene¯t evaluation to play a role. Based on survey data from Taiwan, we investigate the relationship with the quantile regression technique. The results suggest that such a relationship does exist and it is di®erent for males and females.Obesity, Overweight, Risk Knowledge, Quantile Regression

ABC-CNN: An Attention Based Convolutional Neural Network for Visual Question Answering

We propose a novel attention based deep learning architecture for visual question answering task (VQA). Given an image and an image related natural language question, VQA generates the natural language answer for the question. Generating the correct answers requires the model's attention to focus on the regions corresponding to the question, because different questions inquire about the attributes of different image regions. We introduce an attention based configurable convolutional neural network (ABC-CNN) to learn such question-guided attention. ABC-CNN determines an attention map for an image-question pair by convolving the image feature map with configurable convolutional kernels derived from the question's semantics. We evaluate the ABC-CNN architecture on three benchmark VQA datasets: Toronto COCO-QA, DAQUAR, and VQA dataset. ABC-CNN model achieves significant improvements over state-of-the-art methods on these datasets. The question-guided attention generated by ABC-CNN is also shown to reflect the regions that are highly relevant to the questions