Since 1992, various numerical codes, such as TOUGH-FLAC and ROCMAS, have been developed and validated to dispose of Spent Nuclear Fuel (SNF) safely through a series of DEvelopment of COupled models and their VALidation against EXperiments (DECOVALEX) projects. These codes have been developed using different approaches, such as general two-phase flow and Richards’ flow which is an approximated approach neglecting gas pressure change, to implement the same multiphysics behaviors. However, the quantitative analysis for numerical results, which originated from different fundamental approaches, has not been conducted accurately. As a result, improper utilization of the approach to analyze certain conditions occurring such as dramatic gas pressure change may result in erroneous outcomes and systemic problem pertaining to TH analysis. In this study, the quantitative analysis of the two approaches, in terms of TH behavior, was conducted by comparing them with a 1D simulation of the CTF1 experiment carried out by laboratory experiment. The results calculated by different approaches show agreement in terms of TH behaviors and material properties change until 120°C. The results verify the applicability of Richards’ flow approach in a high temperature environment above the current thermal criteria, set as 100°C, and gas pressure change does not have a significant impact until 120°C. Therefore, although further studies for applicability of Richards’ flow are needed to suggest the appropriate temperature range, these quantitative analyses may contribute to the performance assessment of a compact repository using the high-temperature bentonite concept, which is currently gaining attention.

This study investigates how sociolinguistic factors such as gender and language proficiency affect speech rhythm by examining the spoken data of 40 Korean learners of English. Prosodic rhythm, which has been perceived to differentiate various regional varieties of English from General American English, is applied to L2 speech. By using the Normalized Vocalic Pairwise Variability Index (nPVI-V; Low, Grabe & Nolan 2000), L2 speech rhythm is quantified and results indicate that nPVI-V scores are higher for advanced learners of English (indicating more stress-timed speech) than for non-advanced speakers. The effect of gender on rhythm metrics is also tested. Results show that L2 male speakers are more likely to be syllable-timed, whereas L2 female speakers more stress-timed. This may reflect an aspect of gender paradox: women are more apt to adopt the new language than men. Overall, the results of the present study disclose how sociolinguistic factors such as gender and language proficiency interact with rhythm metrics in L2 speech.

Initial situation and Problem Statement The world faces extraordinary challenges relating to the environment and society. Rapidly increasing demand is colliding with declining resources, the awareness of the need for environmental and social sustainability has grown (Martin & Schouten, 2014, p. 20). Due to these facts collaborative consumption has emerged and has disrupted various established industries all over the world. People’s attitude towards ownership and the way societies consume are changing (Chen, 2009, p. 926). Today, consumers are willing to pay for using or accessing a product rather than buying or owning it (Chen, 2009, p. 926). Gradually, consumers are turning their backs on the traditional consumer-oriented paradigm and over-consumption and are progressively looking for ways which downshift or simplify their lives (Albinsson, Wolf, & Kopf, 2010, p. 414). Collaborative consumption is driven by a variety of factors including the global recession, anti-hyper-consumerism, cost-consciousness as well as awareness of the need for a waste-reducing and sustainable living (Gansky, 2010, p. 16). The main drivers are technological advances, such as the internet and social media, which allow new ways of accessing and sharing. By using information technology, products and services can be reused, distributed and shared at the right time and location to the right customer (Gansky, 2010, p. 16). The most successful field of collaborative consumption is the one of tangible assets, such as clothing, which are not used to their full potential by their owners and can be therefore temporarily shared (Botsman & Rogers, 2010, p. XVI). Within the fashion industry many collaborative consumption concepts are developing and thriving (Pedersen & Netter, 2015, p. 259). Generation Y is interested in collaborative consumption and can identify with this phenomenon as it fits their increasingly ideological lifestyles (Höflehner, 2015). According to a study published by Mindshare in November 2015, 44 % of persons of Generation Y in Austria are aware of the prominent online collaborative consumption platforms and one out of ten has already participated in a fashion-sharing activity (Mindshare, 2015). In 2014, 8.5 billion euros were spent on clothing (Statista, 2016), whereas almost 60 % consisted of fast fashion items (Ökosoziales Forum, 2013). On average, a person has 90 clothing items in their wardrobe, and more than half, is not used at all any more. Moreover, the average item is worn for about a month before its usage drops significantly (Threadflip, 2014) or the item gets disposed of in spite of still being in good condition (Shephard & Pookulangara, 2014, p. 11). Online collaborative consumption for fashion can help expand products’ life-cycles, as unneeded or unwanted fashion items can be easily rented, lent, given or sold to other consumers by using various online platforms available (Fletcher & Grose, 2012, p. 88). However, hardly any research was found that specifically focus on online collaborative consumption for Gen Y and examine the influencing factors Research Aim and Research Methods The aim of this study was to find out about the determinants influencing the intention to participate in online collaborative consumption for fashion items among Gen Y taking as an example young adults in Austria. Additionally, the factors, which can increase participation should be identified. In order to find out the influencing determinants, the Unified Theory of Acceptance and Use of Technology 2 of Venkatesh et al. (2012) will serve as the basis for the applied research construct. The following research questions are to be answered in this study: RQ: Which factors have an influence on consumer's intention to participate in online collaborative consumption for fashion among Generation Y in Austria? SubQ: Which measures help Generation Y in Austria to be more motivated to participate in online collaborative consumption for fashion?. In order to be able to provide answers to the research questions a quantitative study in the form of an online questionnaire (n= 219) among respondents of Gen Y in Austria was carried out. A number of research hypotheses have been developed in order to identify a possible influence of several variables on the intention to participate in online collaborative consumption for fashion items, mainly focusing on the Theory of Planned Behaviour (Ajzen, 1991) and on the Theory of Acceptance and Use of Technology 2 of Venkatesh et al. (2012). Theoretical Background Theory of Planned Behaviour by Ajzen (1991) The Theory of Planned Behaviour (TPB) was presented by Ajzen in 1991 as an extension of the Theory of Reasoned Action (TRA) by Ajzen and Fishbein, introduced in 1975 (Teo & Lee, 2010, p. 60). Both models are part of the multiattribute models. The theory of reasoned action tries to offer an explanation for a performed behaviour. The idea behind the theory is that behaviour is performed due to an intention to perform that behaviour. The intention is influenced by the subjective norm and the attitude towards the behaviour. The TRA thereby attempts to predict the possibility of the occurrence of a specific behaviour (Schwenkert, 2006, p. 27). Although it can be said that no behaviour can be predicted with a 100% certainty there are several studies that unveil that the intention to perform a behaviour makes a significant contribution to the actual performance. The intention is the motivational factor behind the behaviour. It indicates “how hard people are willing to try, of how much of an effort they are planning to exert, in order to perform the behaviour” (Ajzen, 1991, p. 180). The main variables of the model are the Subjective Norm and the Attitude towards the Behaviour. Subjective Norm (SN) can be described as the assumption of the individual that people important to the individual are expecting the behaviour to be performed. In other words, that people close to the individual think that the behaviour should be performed by him or her (Schwenkert, 2006, pp. 27-28; Teo & Lee, 2010, p. 61). This belief somehow conveys a sense of unconscious social pressure for the individual – the pressure to perform as the norm demands. Some researchers have found that the social norm does not have as much influence on the intention to perform a specific behaviour as originally assumed (Li, Mizerski, Lee & Liu, 2009, p. 233). It has been found that the culture also tends to have an impact on the influence of the Subjective Norm. People living in an individualistic culture have a tendency to be not as influenced by social pressure as people living in a collectivistic culture (Li et al., 2009, p. 234). This may result in the fact that in collectivistic cultures the opinion of the group is highly important and therefore often followed. In individualistic cultures people are not that influenced by others which may explain those differences in the impact of the Subjective Norm. The TRA involves another variable – the Attitude towards the Behaviour (AtB). As the phrase already suggests, it does not describe the attitude towards an object, but the attitude towards the behaviour itself (Schwenkert, 2006, p. 27). The individual has a positive or negative feeling about the behaviour which influences the intention to perform it, is influenced. The attitude towards a specific behaviour is connected with one’s inner beliefs about the consequences of executing it. Therefore, also the possible consequences and outcomes of the behaviour are evaluated. If they are seen or predicted as positive the possibility of the intention to exert the behaviour is high, and so is the possibility of an actual performance (Li et al., 2009, p. 233; Teo & Lee, 2010, p. 61). As the Theory of Planned Behaviour (TPB) is an extension of the TRA, there has been added another variable – the Perceived Behavioural Control (PBC). The PBC points out the “perceived ease or difficulty of performing the behaviour” (Li et al., 2009, p. 234). By adding this variable Ajzen tried to include the element of uncertainty and to increase the ability to predict the behaviour. Studies show that the accuracy of prediction is higher than with the TRA but still the cognitive processes are in the foreground (Kroeber-Riel & Gröppel-Klein, 2013, p. 236). Unified Theory of Acceptance and Use of Technology by Venkatesh et al. (2012) The original Unified Theory of Acceptance and Use of Technology (UTAUT) was constructed by Venkatesh et al. in 2003 after reviewing eight existing popular models for IT adoption. The eight models revised were The Model of PC Utilisation, Innovation Diffusion Theory, Social Cognitive Theory (SCT), Motivational Model (MM), Theory of Reasoned Action (TRA), Technology of Acceptance Model (TAM and TAM2), Theory of Planned Behaviour (TPB) and the Combined TAM/TPB (Venkatesh, Morris, Davis, & Davis, 2003, pp. 428-433). The UTAUT was mainly created for IT corporate use. The extension, the UTAUT2, was developed in 2012 by Venkatesh et al. to make it applicable in consumer contexts and it is used to study new technology applications (Venkatesh, Thong, & Xu, 2012, p. 158). The UTAUT2 consists of seven variables that have an influence on Behavioural Intention. Behavioural Intention is a determinant for adoption of the Use Behaviour. Variables that refer to an individual’s difference, namely age, gender and experience, are used to moderate various relationships within the model. Performance Expectancy is the extent to which consumers gain benefits in their performance when using a technology. From reviewing the eight prior models, Venkatesh et al. (2003) have developed the construct Performance Expectancy, which pertains to Perceived Usefulness (TAM/TAM2), Outcome Expectations (SCT) and Extrinsic Motivation (MM), which refers to the behaviour that is driven by external rewards. According to Venkatesh et al. (2003) it is the strongest predictor of Behavioural Intention (Venkatesh, Morris, Davis, & Davis, 2003, p. 447). Effort Expectancy is the “degree of ease associated with consumers’ use of technology” (Venkatesh, Thong, & Xu, 2012, p. 159). In general, the easier a technology it is to use in the adoption phase, the more positive the attitude towards said technology (ibid.). Social Influence is defined as the degree to which consumers think it is important that others, such as family and friends, believe they should use a certain technology. This construct refers to the Subjective Norm in TRA and TBP. Prior research suggests that individuals, who are not well informed about the technology in question, are more likely to intend to use the technology, if other, well-informed, peers use said technology (Venkatesh, Morris, Davis, & Davis, 2003, p. 453). Facilitating Conditions refer to the extent to which consumers “believe that an organisational and technical infrastructure exists to support use of the system” (Venkatesh, Morris, Davis, & Davis, 2003, p. 453). Hedonic Motivation, or intrinsic motivation, refers as the enjoyment or pleasure derived from using a technology. It is shown to be an important factor in determining technology acceptance and use in consumer contexts (ibid.). Price Value is an important value in a consumer use setting as the consumer usually pays for the technology application or the use of it. The Price Value is positive and adds to explaining the Behavioural Intention to use when the benefits of using the technology perceive to outweigh the costs (ibid.). Finally, Habit is the degree to which people tend to behave automatically because of learning (Limayem, Hirt, & Cheung, 2007, p. 718). Conclusions Summary The empirical study has shown that the factors Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Price Value and Sustainability have an influence on the Behavioural Intention to participate in online collaborative consumption for fashion. Firstly, it has been hypothesized that Performance Expectancy has an influence on the intention to use online collaborative consumption for fashion. As it turns out, the results showed no significant difference. Therefore, consumers might find other ways of acquiring clothing more appropriate and better suited. The second examined factor is Effort Expectancy, defined as how easy it is for consumers to use the technology. The results display that this factor shows a significant, positive influence on the intention to participate. This is in accordance with results of Venkatesh et al. (2012, p. 159), stating that the easier a technology is to use, the more likely is its adoption. The third factor, Social Influence, explains whether consumers feel it is important that other people, such as family and friends, think they should use a technology. As it turns out, this variable is positively and highly significantly related to the adoption of online collaborative consumption for fashion. Prior research proposes that persons, who are less informed about the technology, are more likely to use it if other influential persons use said technology (Venkatesh, Morris, Davis, & Davis, 2003, p. 453). Next, Facilitating Conditions were investigated as a potential factor influencing the intention to participate in online collaborative consumption for fashion. It was hypothesized that Facilitating Conditions, such as the access to a technological device with access to the Internet, have an influence on the adoption of online collaborative consumption. According to the results of the study, this variable has a highly significantly correlation to the outcome variable Behavioural Intention. The results also show that Hedonic Motivation is significantly and positively related to the Behavioural Intention. Thus, utilitarian and hedonic components of online consumption need to be taken in consideration, as has been shown by previous research in the context of traditional online shopping (Childers, Carr, Peck, & Carson, 2001, p. 533). The sixth investigated factor was Price Value, which is considered positive when the benefits of using the technology perceive to outweigh the costs. In this research, Price Value is a positive and highly significant predictor for the intention to use online collaborative consumption for fashion. Therefore, the outcomes of the study show that Price Value influences the intention to participate in online collaborative consumption for fashion. The next two factors of the research model, Trust and Sustainability, played a tremendous role when discussing collaborative consumption and even are considered main principles of collaborative consumption in the literature. As expected, Sustainability showed a highly significant and positive impact on Behavioural Intention. As mentioned before, this predictor accounted for 28 % of the total contribution of the research model. Trust did not significantly influence the adoption of online collaborative consumption for fashion. This result might be based on the fact that respondents feel insecure when interacting with strangers on the Internet in order to sell, buy or rent fashion items. Managerial Implications Several implications for retailers could be deduced from the results: According to this study, especially the factors Hedonic Motivation and Sustainability account each for 28 % of explaining the contribution to Behavioural Intention to use online collaborative consumption for fashion. Considering these insights, platforms dedicated to online collaborative consumption for fashion might put emphasis on these factors and increase their level of enjoyment and social interaction. For instance, platforms could boost these factors by including the usage of gamification. This way, the level of fun and pleasure for persons can be enhanced which in turn may lead to the adoption of online collaborative consumption behaviour. Additionally, in order to increase the social factor, online forums and discussion groups might not only help to express users’ experiences, reviews or interests, but also generate general social interaction, which is considered as very important for the participation in collaborative consumption. Furthermore, the study has shown that two thirds of the sample would welcome the possibility of fast fashion retailers offering online collaborative consumption activities. Since there are still a limited number of persons aware of the phenomenon of collaborative consumption, the alternative of peer-to-peer exchange in collaboration with a prominent fast fashion retail chain would be a perfect alternative. Instead of being tied to relatively unknown platforms dedicated to collaborative consumption for fashion, persons would most certainly be more aware and more willing to participate in such practices, if a well-known, established retailer would allow collaborative activities. Moreover, the aspect of sustainability seemed to be a crucial factor for the intention to use online collaborative consumption platforms for fashion. For this reason, platforms should communicate this aspect and publicise the fact that using collaborative consumption activities is an environmentally friendly, To conclude with, more than half of respondents would need more information about the topic in order to participate. Existing online platforms should invest in effective communication strategies or inexpensive guerrilla campaigns in order to attract more users. As the results of this study show, if persons are better informed and more aware of this phenomenon and its advantages, the better are the chances for adoption of collaborative consumption for fashion.

In this study, an experimental study of Impulse Thermography was carried out on a concrete specimen with in-placed artificial defects at different depths and dimensions. Then, all the data were processed by Pulse Phase Thermography technique by performing Fast Fourier Transformation. The results were compared with the absolute contrast method.

본 연구에서는 조선왕조실록의 문헌 기록 호우사상의 특성을 파악하고 구조적으로 간단한 강우모형인 구형펄스모형을 이용하여 평가하였다. 전체적으로 '큰비'에서와 같이 강우에 대한 언급만이 있는 경우가 '큰물', '홍수', '폭우'와 같이 홍수유출 및 이에 따른 피해가 설명되어 있는 경우보다 강우의 재현기간이 작게 나타나는 것을 파악할 수 있었다. 기본적으로 기록된 호우사상은 모두 극치사상이 아닌 것으로 파악되었다. 일관되게 어느 정도 이상의 재현기간을 갖고