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1. Background Systematic management of information about lecture courses is essential for good educational practice. In the case of translation practice courses, materials used in the courses and actions taken by students in practice processes constitute an important part of information for diagnosing students activities, promoting reflective learning, observing students' achievements and evaluating course performance for further improvement. To keep track of this information in translation practice courses, two issues should be solved: (1) Individual instances of relevant students' actions should be categorised in such a way that they can be systematically examined and compared. In order to achieve this, we need systematic languages; (2) Individual instances of relevant students' actions should be recorded by using the systematic languages and the records should be provided in such a way that students and teachers can collaboratively examine the actions to diagnose what have been done and to promote reflective learning. 2. Action categories or metalanguages The first issue above is concerned with defining relevant categories --- or "metalanguages" --- to talk about translations and actions involved in translation processes (cf. EMT 2017), while the second is concerned with providing a supportive platform. We have been developing an online platform for translator education, MNH-TT (Minna no Hon'yaku for Translator Training; Translator training for/by/of all) (Hartley et al. 2016; Kageura, et al. 2017). The system has the following main features: 1. It facilitates project-based translator training; 2. It supports learners by providing basic categories such as roles and tasks in the project. 3. It also incorporates some of the well-established categories such as translation issue categories (Castagnoli, et al., 2006; Fujita, et al., 2017) and dialogue acts for communications among project participants (Allen & Core 1997). 4. It incorporates visualisation mechanisms of action logs recorded in accordance with the categories explained in 2 and 3. It is therefore natural to extend these features and incorporate a wider range of categories that systematically capture students' actions in project-based translation practices to MNH-TT, record actions according to these categories and provide the recorded logs through visualisation. We decided to incorporate the following sets of categories or metalanguages that are being developed to describe the translation process, in addition to the above categories: - Categories to characterise source document (SD) properties and elements (Miyata & Miyauchi 2022); - Categories to describe translation strategies (Yamamoto & Yamada 2022); - Categories to describe effects of revisions (Miyata & Miyauchi 2022). These categories together cover actions in the core translation process and provide relevant metalanguages that enable to classify action instances. 3. The status of categories in MNH-TT The sets of categories to capture records of actions in translation practice courses introduced above are divided into two types in MNH-TT: 1. Implicit categories or metalanguages: these consist of a set of roles project participant takes, a set of tasks that participants are supposed to carry out, a set of dialogue act types, and a set of data types. 2. Explicit categories or metalanguages: these include the sets of categories to express SD properties and elements, translation strategies, issue types and effects of revisions. Implicit categories are not necessarily implicit; they operate in defining the basic environment within which project participants carry out translation-related tasks. Explicit categories, on the other hand, are used when students --- project participants --- are making core translation-related actions, and function as explicitating these actions. In MNH-TT, implicit categories are used at the level of system manipulation, while the explicit categories are deployed in the translation processes. Here, how explicit categories are assigned to action instances need to be clarified. Reflecting the fact that being able to talk about translation-related actions and decisions (translator competence), in addition to being able to translate (translation competence), has been increasingly recognised as a part of essential competences for translators (cf. EMT 2017), MNH-TT presumes a model of translation practice course in which knowledge-based scaffolding of what are to be done in the practices should be provided. As such, MNH-TT deploys explicit categories as guiding individual actions and promoting consciousness of taking actions. For instance, issue categories are provided as a list at the process of translation revisions and reviews, and learners are supposed to choose a particular issue category that explains their revisions. Thus the records of action instances systematised in accordance with the given categories are accumulated in MNH-TT. In contrast, acts in courses corresponding to implicit categories are accumulated through the operations that define basic setups of classes and projects, e.g. who to assign as project participants in what role, etc. 4. Deploying category-based records through visualisations The records of operations and actions accumulated according to relevant categories are then made available through MNH-TT for diagnosing lecture courses and for reflective learning as basic statistics with visualisations. Corresponding to the nature of categories, the statistics are divided into two types: 1. Statistics for lecture course management: This provides such statistics as the number of projects set up in the course, of missions, of documents, the number of tasks and roles students take, etc. This information is mainly for managing the lecture course. 2. Statistics for students actions in translation practices: This provides such statistics as the number of strategies students used in their translation, the number of revisions and issue categories, the correlation between the types of issues and effects of revisions, etc. This information is used for reflective learning. MNH-TT provides easy-to-understand visualisations for these statistics. In the presentation, we will elaborate on the visualisations as well.

KITECH and ODS performed a study of internal and external noise prediction of the KHST test train. The object of this study was 3 kind of cars; trailer car(TT2), motorized car(TM1) and power car(TP1) and the predicted noise was calculated for the two different driving speeds in free field and tunnel conditions. Data of carbody design and noise sources were delivered from each manufactures. Some of noise sources which were not available in project team, were chosen by experiences of ODS. Internal noise level of each car were predicted for two cases i.e, at 300 km/h and 350 km/h. In addition sound transmission path and dominant noise sources were also investigated of each section of car, which is circular shell typed part of whole carbody. In case of TT2, the dominating sound transmission path is floor in terms or structure-borne noise and air-borne noise. The main noise sources are structure-borne noise from the yaw-damper and air-borne noise from the wheel/rail contact, whereas the dominating sound transmission path of TM1 are floor and sidewall below the window in terms of structure-borne noise. The main noise sources of TM1 are structure-borne noise from motor/gear unit and the yaw-damper in the free field, and air-borne noise from the wheel/rail contact and structure-borne noise from motor/gear unit in the tunnel. Through the external noise prediction for the KHST test train formation, the noise form the wheel/rail contact is estimated as one of the major sources. In addition, the noise specification of sub-component was proposed for managing each sub-surpplier to reach the KHST noise requirement. The specification provide the sound power of machinery part and transmission loss of component of carbody structure. The predicted noise level in each case exceeded the required limit. Through this study, the noise characteristics of the test train were investigated by simulation, and then the actual test will be performed in near future. Both measured and calculated data will be compared and further work for noise reduction will be continued.

KITECH and ODS performed a study of internal and external noise prediction of the Korean high speed prototype test train(HSR 350X). The object of this study was 3 kinds of cars, trailer car(TT2), motorized car(TMI ) and power car(TPI) and the predicted noise was for the two different driving speeds in free field and tunnel conditions. Data of carbody design and noise sources were delivered from manufactures. Some of noise sources which were not available in the project team, were chosen by experiences of ODS. Internal noise level of each car was predicted for two cases i.e, at 300 km/h and 350 km/h. In addition sound transmission path and dominant noise sources were also investigated for each section of the car, which is circular shell typed part of whole carbody. In case of TT2, the dominating sound transmission path is the (floor in terms of structure-borne noise and air-borne noise. The main noise sources are structure-borne noise from the yaw-damper and air-borne noise from the wheel/rail contact, whereas the dominating sound transmission path of TMI are floor and sidewall below the window in terms of structure-borne noise. The main noise sources of TMI are structure-borne noise from motor/gear unit and the yaw-damper in the free field, and air-borne noise from the wheel/rail contact and structure-borne noise from motor/gear unit in the tunnel. Through the external noise prediction for the KHST test train formation, the noise form the wheel/rail contact is estimated as one of the major sources. In addition, the noise specification of sub-component was proposed for managing each sub-surpplier to reach the KHST noise requirement. The specification provide the sound power of machinery part and transmission loss of component of carbody structure. The predicted noise level in each case exceeded the required limit. Through this study, the noise characteristics of the test train were investigated by simulation, and then the actual test will be performed in near future. Both measured and calculated data will be compared and further work for noise reduction will be continued.