Paul Wrigley;Paul Wood;Daniel Robertson;Jason Joannou;Sam O'Neill;Richard Hall
Nuclear Engineering and Technology
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v.56
no.1
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pp.222-232
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2024
New modular factory-built methodologies implemented in the construction and industrial plant industries may bring down costs for modular reactors. A factory-built environment brings about benefits such as; improved equipment, tools, quality, shift patterns, training, continuous improvement learning, environmental control, standardisation, parallel working, the use of commercial off shelf equipment and much of the commissioning can be completed before leaving the factory. All these benefits combine to reduce build schedules, increase certainty, reduce risk and make financing easier and cheaper.Currently, the construction and industrial chemical plant industries have implemented successful modular design and construction techniques. Therefore, the objectives of this paper are to understand and analyse the state of the art research in these industries through a systematic literature review. The research can then be assessed and applied to modular reactors.The literature review highlighted analysis methods that may prove to be useful. These include; modularisation decision tools, stakeholder analysis, schedule, supply chain, logistics, module design tools and construction site planning. Applicable research was highlighted for further work exploration for designers to assess, develop and efficiently design their modular reactors.
Journal of Information Technology Applications and Management
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v.30
no.5
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pp.41-57
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2023
This paper aims to analyse the factors influencing the implementation of smart factories and their performance after implementation, using the grounded theory analysis method based on interview data. The research subjects were 21 companies that were selected by the Smart Manufacturing Innovation Promotion Group under the SME Technology Information Promotion Agency in 2020-2021 as the best case smart factory implementation companies, and introduced the intermediate stage 1 or above. A total of 87 concepts were generated as a result of the analysis. We were able to classify them into 16 detailed categories, and finally derived six broad categories. These six categories are "motivation for adoption", "adoption context", "adoption level", "technology adoption", "usage effect" and "management effect". As a result of the overall structure analysis, it was found that the adoption level of smart factory is determined by the adoption motivation, the IT technology experience affects the adoption level, the adoption level determines the usage and usage satisfaction, internal and external training affects the usage and usage satisfaction, and the performance or results obtained by the usage and usage are reduced defect rate, improved delivery rate and improved productivity. This study was able to derive detailed variables of environmental factors and technical characteristics that affect the adoption of smart factories, and explore the effects on the usage effects and management effects according to the level of adoption. Through this study, it is possible to suggest the direction of adoption according to the characteristics of SMEs that want to adopt smart factories.
Background: Workers are often exposed to hazardous heat due to their work environment, leading to various injuries. As a result of climate change, heat-related injuries (HRIs) are becoming more problematic. This study aims to identify critical contributing factors to the severity of occupational HRIs. Methods: This study analyzed historical injury reports from the Occupational Safety and Health Administration (OSHA). Contributing factors to the severity of HRIs were identified using text mining and model-free machine learning methods. The Multinomial Logit Model (MNL) was applied to explore the relationship between impact factors and the severity of HRIs. Results: The results indicated a higher risk of fatal HRIs among middle-aged, older, and male workers, particularly in the construction, service, manufacturing, and agriculture industries. In addition, a higher heat index, collapses, heart attacks, and fall accidents increased the severity of HRIs, while symptoms such as dehydration, dizziness, cramps, faintness, and vomiting reduced the likelihood of fatal HRIs. Conclusions: The severity of HRIs was significantly influenced by factors like workers' age, gender, industry type, heat index , symptoms, and secondary injuries. The findings underscore the need for tailored preventive strategies and training across different worker groups to mitigate HRIs risks.
This study evaluates the direct tensile strength of ultra-high-performance concrete (UHPC) using tests. A total of 45 dogbone-shaped specimens are tested, with the test variables being the fiber volume fraction and notch length. The test results showed that the material properties of UHPC were largely dependent on the fiber volume fraction and compressive strength. When steel fibers with more than 1% fiber volume fraction are mixed in the manufacturing of UHPC, the tensile strength can be more than twice that of plain UHPC. In addition, the incorporation of steel fibers enabled the significant improvement of the initial cracking strength. However, the effect of the notch length on the tensile behavior was insignificant. An assessment of the direct tensile strength is conducted using machine-learning algorithms (ML). For evaluation of the direct tensile strength of UHPC using ML, a total of 98 test data, including 53 data from other research works and 45 data from this experimental program, were collected. In total, 67 data with a 70% confidence interval on a normal distribution curve were selected, with 47 data among 67 used for ML training and 20 data used for ML testing. As a result, the machine-learning algorithm with a steel fiber volume fraction predicted that the tensile strength has an average of 0.98 and the lowest values of regression evaluation metrics among analytical and ML-based models. It is considered that an ML-based model can help to predict a more accurate tensile strength of UHPC.
Asia-Pacific Journal of Business Venturing and Entrepreneurship
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v.11
no.3
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pp.1-15
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2016
This study has analyzed predication capabilities leveraging multi-variate model, logistic regression model, and artificial neural network model based on financial information of medium-small sized companies list in KOSDAQ. 83 delisted companies from 2009 to 2012 and 83 normal companies, i.e. 166 firms in total were sampled for the analysis. Modelling with training data was mobilized for 100 companies inlcuding 50 delisted ones and 50 normal ones at random out of the 166 companies. The rest of samples, 66 companies, were used to verify accuracies of the models. Each model was designed by carrying out T-test with 79 financial ratios for the last 5 years and identifying 9 significant variables. T-test has shown that financial profitability variables were major variables to predict a financial risk at an early stage, and financial stability variables and financial cashflow variables were identified as additional significant variables at a later stage of insolvency. When predication capabilities of the models were compared, for training data, a logistic regression model exhibited the highest accuracy while for test data, the artificial neural networks model provided the most accurate results. There are differences between the previous researches and this study as follows. Firstly, this study considered a time-series aspect in light of the fact that failure proceeds gradually. Secondly, while previous studies constructed a multivariate discriminant model ignoring normality, this study has reviewed the regularity of the independent variables, and performed comparisons with the other models. Policy implications of this study is that the reliability for the disclosure documents is important because the simptoms of firm's fail woule be shown on financial statements according to this paper. Therefore institutional arragements for restraing moral laxity from accounting firms or its workers should be strengthened.
Kim, Chung Hwan;Moon, Inyoung;Park, Shinhee;Kim, Ji Hyeon
대한공업교육학회지
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v.45
no.2
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pp.21-45
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2020
The purpose of this study was to verify how the vocational education for training industrial workforce as a national human resource development (NHRD) system has undergone a transition process in relation to the national economic, industrial, and socio-cultural contexts. First, how vocational education as an industrial workforce training system has changed in accordance with Korea's economic environment, industrial development, and social changes; second, what are the main factors that influenced the role and importance of vocational education; and third, vocational education as a system for training industrial workforce and training workforce in science and engineering were analyzed differently from the perspective of the NHRD model. To this end, domestic and international academic journal papers, research reports, and thesis were investigated and classified by period, and major changes in vocational education were analyzed in relation to economic, industrial, and social issues and policies by period. As a result of the research, first, as the industry advanced, the level of vocational education increased and the scope expanded. Second, vocational education tended to shrink gradually after the manufacturing industry base, and especially secondary vocational education tended to decline after the national industry focused on light industry. Third, since the 1970s, the diversification of the NHRD and jobs has resulted in wage gaps depending on the level of education, which has increased the preference for university education and avoided secondary vocational education. In addition, a NHRD model focusing on training science and engineering workforce was proposed to compare the existing NHRD model focusing on overall vocational education, and it was revealed that the NHRD needs to be subdivided into various fields or levels to derive a model and examine changes. From the results of the study, vocational education in Korea, especially in secondary vocational education, has declined due to large impacts on socio-cultural perception due to economic growth, enthusiasm for education, and external shocks such as the financial crisis, and the long-term effort to change this perception is suggested to overcome the crisis of vocational education.
With increasing interest in 3D printing, the interest in the 3D modelling training that should precede the 3D printing is increasing. However, the existing 3D modelling software is developed mostly by foreign brands. Thus, the interfaces are all in English. 3D modelling software training for Korean novices who are not familiar with these terms has constraints. This study aims to explore what to consider when developing a Korean model for 3D modelling educational software for 3D printing in the face of such reality. For this goal, after having novices with no experience in 3D modeling to perform a house building task using either 12D Design or Tinker CAD, we conducted a survey. It was found in the result that more users favored Tinker CAD over 123D Design, and the errors involved while working with the Tinker CAD were less than those with the 123D Design, and the ratio of people who completed the task with the Tinker CAD was higher than that with the 123D Design. In general discussion, an introductory level educational 3D modeling software development is proposed which utilize characteristics of Tinker CAD (easy modelling is possible by three-dimensional figures) and web-based method. Also, a beginner/intermediate level educational 3D modeling software development is proposed which utilize characteristics of 123D Design (with finer measurement manipulations and figure alignment) and Windows-based method.
This paper examined the relationship between innovation and firm performance by integrating industry competitive context and firm-level knowledge constructs. After controlling for firm site, age, and the presence of a union, cross-section analysis of survey data from 1,419 medium sized manufacturing firms yielded following findings. The first was the expected positive relationship between firm-level innovation and firm knowledge and also industry dynamism, as measured by the intensity of industry-level R&D. This results indicate that industries with greater aggregate levels of R&D intensity are home to higher rates of firm-level innovative activity and managers must increase their numbers of technical staff and the level of training. But the interaction between firm knowledge and industry dynamism was non-significant. Second, innovation was not significantly related to firm performance, as measured by revenue growth. This relationship was not moderated by industry dynamism and firm level knowledge. In high and low technology sectors, the relationship between innovation and performance was non-significant, consistent with the full-sample analysis. The results suggest that the effects of firm-level knowledge assets and investments in training don't work in different ways in different industry settings. This research used three control variables to analyze innovation and firm performance. Firm age was negatively associated with firm performance and did not significantly predict innovation. Firm size was positively associated with innovation and performance in the low-technology sector. The presence of a labor union was not a significant with respect to innovation.
The Ministry of Education has been quick to apply National Competency Standars (NCS) to industrial sites and educational·training institutions in order to resolve discrepancies between practical affairs in industrial sites and vocational education, training & requirements. Full implementation of NCS-based curriculum in vocational education of vocational high schools has been mandatory since 2018. This research used a region-stratified sample of 350 from teachers in 'machinery' and 'machine·metal' majors in mechanical departments of vocational high schools to investigate the awareness of practical courses for metal machining among the teachers. The research results are as follows. First, a majority of the respondents indicated the availability of turning process, milling process, computer integrated manufacturing and measuring courses in mechanical departments. Second, capabilities required by the industry are considered most in selecting practical courses and competence units. Third, positive changes with the introduction of practical courses in the school education are students' practical skills improvement and satisfaction of industrial requirements. Fourth, negative changes with the introduction of practical courses in the school education are too difficult learning modules used in practical courses for students and students' difficulty in learning because of the difference between equipment in schools and industrial equipment in learning modules. Fifth, teachers' satisfaction with practical courses classes and overall conditions is above the average, and their satisfaction with the level of practical courses and bookbinding or purchase of rearranged textbooks of practical courses is below the average. Therefore, application conditions of above-mentioned representative 4 practical courses should be examined and taken care of for consistent improvement to stabilize NCS-based educational courses in mechanical departments.
The purpose of this study was to investigate the employment possibility of the humanities college graduates to science and engineering field occupations, and to identify the occupational characteristics related to employability perceived by workers. To do this, basic statistical analysis, correlation analysis, and hierarchical multiple regression analysis were conducted using the data surveyed on 2,600 workers in the science and engineering field in the 'Research on Korean Occupational Index for Career and Employment Service(2017)'. The main results are as follows. First, the employment possibility of the humanities college graduates to science and engineering field was low, except for some occupations in the information communication, manufacturing and processing fields. Second, the occupational characteristics affecting the employment possibilities of the humanities college graduates to science and engineering field are as follows: low importance of the final education, low importance of the major, low importance of qualification, high importance of vocational training, easy to return after the career break, high level of gender equality, high level of pleasant work environment, high employment retention, easy to self-employment or start-up, and increasing number of jobs. Based on the results of this study, to support employment of humanities college graduate from the occupational aspect, it is necessary to find out some detailed jobs or to develop convergence occupations. At this time, it is possible to utilize the occupational characteristics factors that increase the employment possibility of humanities college graduates to science and engineering occupations.
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