Assignment 10

Steve Jobs inspired me in many different ways. One way is that even though he realized college was not for him, he still went to classes that interested him. He was eager to learn and determined not to let anything stop him. Another reason is that when he was fired from his job, he was understandably mad, but he took that has another learning lesson and improved himself. He became CEO of Apple because he did not give up and was eager to learn. More importantly he was not afraid to fail at anything he did, which is something everybody needs to live, but very few people do. People who are not afraid to fail usually turn out to be really successful people or businessman.

Assignment 9

How can knowledge management support design-driven innovation?

Knowledge management is a huge part of design-driven innovation. Knowing how consumers act will help companies build products designed for them thus making the company more profitable. As stated in the article many of the most successful brands create breakthrough ideas inspired by a deep understanding of consumers' lives and use the principles of design to innovate and build value. This is where knowledge management comes in it can help companies have a greater understanding of certain process and how they can be performed better.

Assignment 8

Liver cell dysplasia (LCD) is considered a lesion, whose characterization and differentiation from hepatocellular carcinoma and from the reactive changes seen in cirrhosis has been controversial. We studied 12 cases of LCD (large cell type) with image analysis techniques (IA) and compared the findings with those of HCC, and a spectrum of non-neoplastic hepatic lesions including normal liver and cirrhosis. A minimum of 200 Feulgen-stained nuclei were measured from each lesion with the CAS 200 image analysis system. The data were collected with the aid of CellSheet software. Thirty-four variables were measured, including geometric, textural, and photometric nuclear features and DNA ploidy. The data were analyzed with multivariate statistics and a backpropagation neural network (NN). Stepwise statistical analysis selected 22 variables that were statistically significant in the three groups with P values <.05. Various NN architectures were developed using these variables. The best NN architecture included a sigmoidal transfer function, 14 input, 16 hidden, and 3 output neurons. It trained to completion after 8,887 runs using 90% of the lesions. This NN yielded a 100% cross-validation rate for unknown cases. These data support the concept of LCD (large cell type) as a lesion that can be objectively distinguished from HCC and non-neoplastic liver.