基于工作过程的数控编程与操作课程学习情境设计

现代制造企业对高职院校学生的职业技能要求越来越高,开展课程教学改革,实施理实一体化教学非常重要。数控编程与加工课程是数控技术专业核心课程,开展基于工作过程的数控编程与操作课程学习情境设计,实施理实一体化教学,有利于提高学生职业技能的培养。     

Learning to bid in sequential Dutch auctions

We propose an agent-based computational model to investigate sequential Dutch auctions with particular emphasis on markets for perishable goods and we take as an example wholesale fish markets. Buyers in these markets sell the fish they purchase on a retail market. The paper provides an original model of boundedly rational behavior for wholesale buyers׳ behavior incorporating learning to improve profits, conjectures as to the bids that will be made and fictitious learning. We analyze the dynamics of the aggregate price under different market conditions in order to explain the emergence of market price patterns such as the well-known declining price paradox and the empirically observed fact that the very last transactions in the day may be at a higher price. The proposed behavioral model provides alternative explanations for market price dynamics to those which depend on standard hypotheses such as diminishing marginal profits. Furthermore, agents learn the option value of having the possibility of bidding in later rounds. When confronted with random buyers, such as occasional participants or new entrants, they learn to bid in the optimal way without being conscious of the strategies of the other buyers. When faced with other buyers who are also learning their behavior still displays some of the characteristics learned in the simpler case even though the problem is not analytically tractable.     

A gradient boosting approach to the Kaggle load forecasting competition

We describe and analyse the approach used by Team TinTin (Souhaib Ben Taieb and Rob J Hyndman) in the Load Forecasting track of the Kaggle Global Energy Forecasting Competition 2012. The competition involved a hierarchical load forecasting problem for a US utility with 20 geographical zones. The data available consisted of the hourly loads for the 20 zones and hourly temperatures from 11 weather stations, for four and a half years. For each zone, the hourly electricity loads for nine different weeks needed to be predicted without having the locations of either the zones or stations. We used separate models for each hourly period, with component-wise gradient boosting for estimating each model using univariate penalised regression splines as base learners. The models allow for the electricity demand changing with the time-of-year, day-of-week, time-of-day, and on public holidays, with the main predictors being current and past temperatures, and past demand. Team TinTin ranked fifth out of 105 participating teams.     

Converging scientific fields and new technological paradigms as main drivers of the division of scientific labour in drug discovery process: the effects on strategic management of the R&D corporate change

The thesis of this study is that the convergence of genetics, genomics and proteomics spurs new technological paradigms in medicine, which are generating a R&D corporate change: division of scientific labour of the drug discovery process by strategic alliances among firms in order to reinforce the integrative capabilities in different biomedical research fields and collective and cumulative learning between in-house R&D and external sources of innovation. This study shows, by key a case study of pharmaceutical companies, as scientific and technological paradigms in medicine are main drivers of industrial and R&D corporate change to enhance and accelerate the discovery process of ground-breaking drugs for more and more personalised healthcare.     

Organizational learning in exporting: A bibliometric analysis and critical review of the empirical research

In the latest decades, empirical research on organizational learning in the export context has recorded an increasing trend. On the other hand, in spite of the wealth of research on the subject, no effort has yet been made to offer an integrative review to investigate the improvement of this body of knowledge. To fill this gap, this study endeavors to synthesize the stream of empirical research on organizational learning in the field of exporting published between 1997 and 2017. Initially, the relevant studies were bibliometrically analyzed on the basis of the data gathered from the Web of Science database. The bibliometric data analysis indicates the most influential publication outlets, most influential institutions, most influential scholars, and most influential articles in relation to organizational learning in the export literature. Additionally, each article was content-analyzed in terms of scope of research, research methodology, and empirical issues. The content analysis reveals that albeit significant progress, organizational learning research within the context of exporting is still at the introduction stage and certain weaknesses in scope, research designs, and conceptual understanding attract criticism.     

Learning about the real-world in MICE education: The case of exhibition learning from Thailand

Abstract

The use of experiential learning in tourism and hospitality education is well-documented in literature. Experiential learning studies in this field may include, for example, internship experiences, field trip perceptions, conferences, and social events. However, there is still insufficient literature to understand students’ learning and their real-world experience in MICE education, especially in the exhibition sector. This study, therefore, addresses this gap by reporting the experiential learning of graduate students in an event course with the objectives to investigate student perceptions on academic learning experiences and the development of work-related skills by carrying out the exhibition project. Students are challenged to perform a complicated task as a real exhibition organizer, and to deal with other stakeholders of the exhibition industry (e.g., exhibition venue, exhibitors, contractors, and visitors). The experiential learning method is discussed through the Plan-Do-Check-Act (PDCA) process. The results indicate that students not only gained in-depth learning about the exhibition industry, but also developed important work skills (e.g., teamwork, planning, and coordinating skills). Moreover, classroom learning, industry visits, and real-world experience are found to be the important factors contributing to exhibition learning. The current study contributes to the limited exhibition learning literature and provides event educators new insights into the teaching and learning of exhibition-based projects in regard to how students plan, learn and carry out the exhibition event through the case of Thailand. Other similar courses may apply the learning processes and results of this study to develop effective experiential learning in MICE education.     

The Moderating Role of Strategic Integration in Performance Improvement Through Purchasing Social Responsibility in Thailand

This research aims to understand the performance of purchasing social responsibility (PSR) through moderating effect of purchasing strategic integration. The results show that PSR directly influences purchasing performance, while the relationship between PSR and purchasing performance is partially mediated by organizational learning. Moreover, strategic integration negatively moderates the relationship between PSR and purchasing performance. This study suggests that the adoption of PSR affects the operations of both buyers and suppliers in a supply chain that further encourage organizational learning and increases purchasing efficiency. The results also show that organizations may realize this effect of PSR practices but may focus on other purchasing practices, which affects purchasing performance. Copyright © 2018 ASAC. Published by John Wiley & Sons, Ltd.     

Far from the madding crowd: collective wisdom in prediction markets

We investigate market selection and bet pricing in a repeated prediction market model. We derive the conditions for long-run survival of more than one agent (the crowd) and quantify the information content of prevailing prices in the case of fractional Kelly traders with heterogeneous beliefs. It turns out that, apart some non-generic situations, prices do not converge, neither almost surely nor on average, to true probabilities, nor are they always nearer to the truth than the beliefs of all surviving agents. This implies that, in general, prediction market prices are not maximum likelihood estimators of the true probabilities. However, when more than one agent survives, the average price emerging from a prediction market approximates the true probability with lower information loss than any individual belief.     

A new multiscale decomposition ensemble approach for forecasting exchange rates

Due to the high complexity and strong nonlinearity nature of foreign exchange rates, how to forecast foreign exchange rate accurately is regarded as a challenging research topic. Therefore, developing highly accurate forecasting method is of great significance to investors and policy makers. A new multiscale decomposition ensemble approach to forecast foreign exchange rates is proposed in this paper. In the approach, the variational mode decomposition (VMD) method is utilized to divide foreign exchange rates into a finite number of subcomponents; the support vector neural network (SVNN) technique is used to model and forecast each subcomponent respectively; another SVNN technique is utilized to integrate the forecasting results of each subcomponent to generate the final forecast results. To verify the superiority of the proposed approach, four major exchange rates were chosen for model comparison and evaluation. The experimental results indicate that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach outperforms some other benchmarks in terms of forecasting accuracy and statistical tests. This demonstrates that our proposed VMD-SVNN-SVNN multiscale decomposition ensemble approach is promising for forecasting foreign exchange rates.