<Articles>Learning as Translation in Our Own Lives: Interpreting Lost in Translation

特集I : 京都大学大学院教育学研究科 ユニバーシティ・カレッジ・ロンドン教育研究所 国際合同授業 （2022年2月5日-7日, オンライン）“Thinking about Education through Film”, International Collaborative Course, Graduate School of Education, Kyoto University and UCL IOE (February 5-7, 2022, Online)I want to explore the meaning of ‘learning as translation’ by interpreting the film Lost in Translation, drawing on Stanley Cavell's idea. Especially in this article, I pay attention to the feeling of lostness appearing in the protagonist of the movie, Charlotte. She is in Tokyo for the first time, accompanying her photographer-husband who is there for work. At first glance, her wandering mind and confusion seem to be a response to linguistic and cultural differences. A closer look reveals, however, problems in her sense of identity, with her lack of career, and with family, rather than just in her exposure to this unfamiliar Japanese culture. She is confused about her relationship with her husband and about what she has done after college. In particular, when it comes to Charlotte's previous learning experiences, if we pay close attention to the college she attended, her major, and her career problems, we can rethink the meaning of learning in life. In this paper, I am going to divide charlotte's learning experiences into two types: one is learning as initiation into a course of study (and, that is, into a particular curricular content), and the other is learning as translation of the meaning in the course of one's own life. After examining these two possibilities of learning, I suggest that the learning that matters most involves a transformation of the self through translation, appropriate to the particular context of one's life

An investigation of the user satisfaction of customer relationship management program

The thesis investigates user satisfaction for Microsoft Dynamics CRM 2011 by conduction surveys to graduate level students. The training manual was developed to guide the way to follow instructions to create an order and an invoice

Exploring Racially Informed Factors and Assessing Their Impacts on the Working Conditions and Burnout among Bicultural Asian Human Service Workers

The United States has undergone a significant increase in cultural diversity, with Asians being the fastest-growing immigrant group. Their population has almost doubled from 11.9 million in 2000 to 22.4 million in 2019, marking an 88% increase in less than two decades. Presently, Asians make up 6% of the total U.S. population and are estimated to grow to 46 million by 2060, representing over 10% of the U.S. population. Asians are often considered a model minority due to their higher educational and health status compared to other minority groups. However, they are still perceived as perpetual foreigners regardless of their length of stay and generational status in the country. During the pandemic, they became the target of pandemic-related racism that was supported by a political agenda. Amidst unprecedentedly heightened racism and collective trauma in the Asian community, bicultural and bilingual Asian human service workers play a critical role in providing culturally and linguistically aligned health and social services. However, these dedicated workers have not received much attention. Therefore, this research, based on the Asian Critical Race Theory, investigates how the racial positioning and racial realities of Asians in the United States relate to the working conditions of bicultural and bilingual Asian human service workers. This study uses a sequential exploratory mixed-method approach to explore the connection between racially informed factors and the working conditions and burnout of workers in the health and social service fields. The study applies the Job Demands and Job Resources Model to understand this link. The findings of this study support the need to better support a diverse and resilient workforce in the health and social service fields to achieve racial equity for an ever-growing Asian population

Sesame oil increases plasma γ-tocopherol and inhibits γ-tocopherol metabolism in humans

In rats, sesame lignans increase plasma γ-tocopherol concentrations and inhibit γ-tocopherol degradation to its metabolite γ-carboxyethylhydroxychroman (γ-CEHC). To test if sesame lignan consumption inhibits γ- tocopherol metabolism in humans, muffins prepared with either corn oil (control) or sesame oil and an equimolar mixture of deuterium labeled d6-α- and d2-γ- tocopheryl acetates were administered to male (n=5) and female (n=5) volunteers. Tocopherol and CEHC concentrations were followed for 72 h. Sesame lignan consumption significantly increased plasma d2-γ-TOH concentrations (p<0.05). In men, sesame lignans increased plasma d2-γ-tocopherol areas under the curve (AUC; sesame oil: 34.3 ± 4.6; corn oil: 28.9 ± 3.3 μmol/L·h, p<0.01) and reduced d2-γ-CEHC AUCs (p<0.05). In men, differences in urinary d2-γ-CEHC AUCs did not reach statistical significance (AUCs for 24 h, corn oil: 11.2 ± 3.0 μmol/g creatinine·h; vs sesame oil: 5.0 ± 1.5). In women, sesame lignan consumption did not alter plasma tocopherol CEHC concentrations but reduced urinary d2-γ-CEHC excretion (AUCs for 24 h, corn oil: 19.3 ± 4.9 μmol/g creatinine·h; and sesame oil: 7.7 ± 2.0, p < 0.05). These data suggest that sesame lignans alter γ-tocopherol metabolism differently in men and women. Further research is needed to assess the mechanism involved in these differences

Human Tracking Using Particle Filter with Reliable Appearance Model

In this paper, we present a human tracking algorithm that can work robustly in complex environments such that serious occlusion, various appearances and abrupt motion changes occur in the scenario. Our tracking framework is well known particle filter based on Condensation algorithm. In the observation model of the particle filter, we establish RAM(Reliable Appearance Model) which exhibits high discriminative performance in particular for human tracking. The RAM is to describe a target as features from local descriptors. In order to extract practical features from a larger number of local descriptors for robust tracking, the features were employed by boosting algorithm. The components of the features are utilized color and shape based-models. Experimental results demonstrate that our approach tracks the target accurately and reliably when position and scale are changing as well as occurrence of occlusion.SICE Annual Conference 2013 - International conference on Instrumentation, Control, Information Technology and System Integration, September 14-17, 2013, Nagoya University, Nagoya, Japa

Human Tracking Using Particle Filter with Reliable Appearance Model

In this paper, we present a human tracking algorithm that can work robustly in complex environments such that serious occlusion, various appearances and abrupt motion changes occur in the scenario. Our tracking framework is well known particle filter based on Condensation algorithm. In the observation model of the particle filter, we establish RAM(Reliable Appearance Model) which exhibits high discriminative performance in particular for human tracking. The RAM is to describe a target as features from local descriptors. In order to extract practical features from a larger number of local descriptors for robust tracking, the features were employed by boosting algorithm. The components of the features are utilized color and shape based-models. Experimental results demonstrate that our approach tracks the target accurately and reliably when position and scale are changing as well as occurrence of occlusion.SICE Annual Conference 2013 - International conference on Instrumentation, Control, Information Technology and System Integration, September 14-17, 2013, Nagoya University, Nagoya, Japa