In the international businesses human resource elements acquired in different countries might have different values in varied industries due to the different quality of education and experiences in the original countries. Using selection models to evaluate expected values in earnings equation of human resource elements such as education and experiences etc. acquired in sending countries, system equations are expanded to examine also the values of science and engineering degrees in technology jobs with selectivity bias correction. This paper used the US census survey data of 2015 on earnings, academic degrees, occupations etc. The US has long maintained the policy of accepting more STEM workers than any other countries and helped maintaining own technological leadership. Assuming per capita GDP gap between the sending country and the US downgrades immigrant human resource quality, it rarely affects occupational selection but depresses earnings on average by two or more years’ worth of education. Immigrant quality index in the sense of GDP gap appears to be a valid tool to assess the expected earnings of the worker with. Engineering degrees increase significantly the probability of selecting not only engineering jobs but also general management jobs, as well as increasing the expected earning additionally over nine years’worth of education. Getting a technology job is additionally worth about four years of education. Economics and business degrees are worth additionally almost six years of education but humanities degrees depress expected earnings. Since years after immigration does not very fast enhance earnings capacity, education level and English language ability might be more useful criteria to expect better future earnings by.

In international businesses human resource elements acquired in different countries might have different values in varied industries due to different quality of education and experiences in original countries. Using existing models to evaluate expected values of human resource elements such as education and experience setc. acquired in sending countries they are expanded to examine also the values of science and engineering degrees in technology jobs with selectivity bias correction. This paper used the US census survey data of 2015 on earnings, academic degrees etc. to contrast qualitative effects with quantitative effects of human resource elements compared to those in the native and/or white group.

본 연구는 터널이 갖는 다양한 특성 가운데 운전자의 심리적 안정성과 쾌적성 등 경관을 구성하는 설계요인과 운전자의 감성적 요인간의 관계를 파악하여, 터널경관에 대한 평가방법을 제시하고, 이를 위하여 터널경관을 구성하는 물리적인 구성요소와 운전자의 감성인지 변화와의 관계를 파악하여 경관적 환경을 조성할 수 있는 방안을 제시하는데 연구의 목적이 있다. 터널경관에 대한 감성인지특성을 파악하기 위한 LISREL 모형을 구축한 결과 터널경관 영향요인은 터널경관 구성요소 조화에 의한 감성 이미지인 것으로 분석되었다. 이러한 감성이미지는 설계요인과 개인속성에 의하여 설명며, 설계요인의 영향이 개인속성의 영향보다 크게 나타났다. 개인 속성과 설계요인 모두의 관계는 (+)의 관계로 나타났고 "안정성"과 "변화성"간에는 서로 밀접한 상관관계가 있는 것으로 분석되었다. 이때, "안전성" 요인이 "변화성" 요인보다 경관인지도에 좀 더 영향을 미치는 것으로 분석되어, 경관 평가 시 운전자는 기본적으로 "안전성"을 중시하나 "변화성"에 대하여도 요구하고 있는 것으로 분석되었다.

Humans are well-known for being adept at using intuition and expertise in many situations. However, human experts are still susceptible to errors in judgment or execution, and failure to recognize the limits of knowledge. This would happen especially in semi-structured situations, in multi-disciplinary settings, under time or other stress, under uncertainty, or when knowledge is outdated Human errors are caused by cognitive biases, attentional slips/memory lapses, cultural motivations, and missing knowledge. The purpose of this research is to study errors of human experts committed in judgment and the general idea of critiquing systems as corresponding plan. Compared to expert systems, critiquing systems are narrowly focused programs useful in limited situations for collaborating with and supporting experts in their task activities. It supports an expert by detecting the human's errors by deploying various strategies that stimulate humans to improve their performance. A variety of types of critiquing systems has spread through numerous application areas.