Physica A Statistical Mechanics and its Applications

International Journal of Project Management , Volume 28, Issue 3,April 2010, Pages 285-295Paul Bowen, Peter Edwards, Keith Cattell, Ian JayShow preview | Related articles | Related reference work articlesPurchase85Dynamics of R&D networked relationships and mergers and acquisitions in the smart card field Original ResearchArticleResearch Policy , Volume 38, Issue 9, November 2009,Pages 1453-1467 Zouhaïer M’ChirguiClose preview | Related articles | Related reference work articlesAbstract | Figures/Tables | ReferencesAbstractThis paper analyzes how the structure and the evolution of inter-firmagreements have shaped the development of the smart card industry. The aimis to establish a closer connection between the evolution of inter-firmagreements in the smart card industry and the patterns of change of technologyand demand in this new high-tech industry. Based on a proprietary databasecovering both collaborative agreements and mergers and acquisitions (M&As)occurring in this industry over the period 1992–2006, we find that the evolutionof technology and market demand shapes the dynamics of R&D networks andPurchaseM&As are likely to change the industry structure. We also find that a small group of producers – first-movers – still control the industry and technological trajectories. Their position arises not for oligopolistic reasons of marketstructure, but for technological and organizational reasons.Article Outline1. Introduction2. Theoretical background3. The smart card industry: delineating the boundaries and identifying the actors3.1. Defining the smart card3.2. The differentiated market(s)3.3. The actors3.4. The smart card oligopoly: a dual market structure4. Research methods4.1. Methodology4.2. SCIFA database5. Trends in inter-firm agreements and emergence of networks in the smart cardindustry6. The structure of the network6.1. Network evolution6.2. Major players and centrality7. ConclusionAcknowledgementsReferences86The role of industrial maintenance in the maquiladoraindustry: An empirical analysis Original Research ArticleInternational Journal of Production Economics, Volume 114,Issue 1, July 2008, Pages 298-307Shad DowlatshahiClose preview | Related articles | Related reference work articlesPurchaseAbstract | Figures/Tables | ReferencesAbstractThis study explored the role of industrial maintenance in the maquiladora industry. The maquiladora industry is a manufacturing system that utilizes the Mexican workforce and foreign investment and technology on the border region between the United States and Mexico. The issues related to industrial maintenance were studied through a survey instrument and 11 in-depth and extensive field interviews with experts of eight maquiladora industries in El Paso, TX and Juarez, Mexico. Based on an 86% response rate (with 131 usable questionnaires) and four major survey questions, statistical analyses were performed. The survey questions included: collaboration between the maintenance and other functional areas, likely sources of maintenance problems (equipment, personnel, and management), major common losses of maintenance problems, and the role of ISO certification in maintenance. Finally, additional insights and assessment of the results were provided.Article Outline1. Introduction1.1. Review of literature2. Evolution of and various approaches to maintenance3. Historical, operational characteristics and the importance of the maquiladora industry4. Research design4.1. Data collection4.2. The interviews with maquiladora managers5. Analyses of results5.1. Statistical analysis for question 15.2. Statistical analysis for question 25.3. Statistical analysis for question 3 5.4. Statistical Analysis for question 46.Conclusions and assessmentReferences87 A variable P value rolling Grey forecasting model forTaiwan semiconductor industry production OriginalResearch ArticleTechnological Forecasting and Social Change, Volume 72,Issue 5, June 2005, Pages 623-640Shih-Chi Chang, Hsien-Che Lai, Hsiao-Cheng YuClose preview | Related articles |Related reference work articlesAbstract | Figures/Tables | ReferencesAbstractThe semiconductor industry plays an important role in Taiwan's economy. In thispaper, we constructed a rolling Grey forecasting model (RGM) to predictTaiwan's annual semiconductor production. The univariate Grey forecastingmodel (GM) makes forecast of a time series of data without considering possible correlation with any leading indicators. Interestingly, within the RGM there is aconstant, P value, which was customarily set to 0.5. We hypothesized thatmaking the P value a variable of time could generate more accurate forecasts. Itwas expected that the annual semiconductor production in Taiwan should beclosely tied with U.S. demand. Hence, we let the P value be determined by theyearly percent change in real gross domestic product (GDP) by U.S.manufacturing industry. This variable P value RGM generated better forecaststhan the fixed P value RGM. Nevertheless, the yearly percent change in realGDP by U.S. manufacturing industry is reported after a year ends. It cannotserve as a leading indicator for the same year's U.S. demand. We found out thatthe correlation between the yearly survey of anticipated industrial productiongrowth rates in Taiwan and the yearly percent changes in real GDP by U.S. manufacturing industry has a correlation coefficient of 0.96. Therefore, we usedPurchasethe former to determine the P value in the RGM, which generated very accurate forecasts. Article Outline1.Introduction2. The semiconductor industry in Taiwan3. Rolling GM (1,1)4. Forecast Taiwan semiconductor production with RGM (1,1)5. Forecast Taiwan semiconductor production with variable P value RGM (1,1)6. ConclusionsAppendix A. AppendixA.1. 1998 Production forecast for the semiconductor industry under different PvaluesA.2. 1999 Production forecast for the semiconductor industry under different PvaluesA.3. 2000 Production forecast for the semiconductor industry under different PvaluesA.4. 2001 Production forecast for the semiconductor industry under different PvaluesA.5. 2002 Production forecast for the semiconductor industry under different PvaluesReferencesVitae88 Energy demand estimation of South Korea using artificial neural network Original Research ArticleEnergy Policy , Volume 37, Issue 10, October 2009, Pages4049-4054Zong Woo Geem, William E. Roper Close preview | Related articles | Related reference work articlesAbstract | Figures/Tables | ReferencesPurchaseAbstractBecause South Korea's industries depend heavily on imported energy sources (fifth largest importer of oil and second largest importer of liquefied natural gas in the world), the accurate estimating of its energy demand is critical in energy policy-making. This research proposes an artificial neural network model (a structure with feed-forward multilayer perceptron, error back-propagation algorithm, momentum process, and scaled data) to efficiently estimate the energy demand for South Korea. The model has four independent variables, such as gross domestic product (GDP), population, import, and export amounts. The data are obtained from diverse local and international sources. The proposed model better estimated energy demand than a linear regression model (a structure with multiple linear variables and least square method) or an exponential model (a structure with mixed integer variables, branch and bound method, and Broyden–Fletcher–Goldfarb–Shanno (BFGS) method) in terms of root mean squared error (RMSE). The model also forecasted better than the other two models in terms of RMSE without any over-fitting problem. Further testing with four scenarios based upon reliable source data showed unanticipated results. Instead of growing permanently, the energy demands peaked at certain points, and then decreased gradually. This trend is quite different from the results by regression or exponential model.Article Outline1. Introduction2. Artificial neural network model3. Case study of South Korea4. Results of linear regression model5. Results of exponential model6. Results of ANN model7. Validation of the ANN model8.Future estimation with different scenarios9. ConclusionsReferences89 Catching up through developing innovation capability: evidence from China's telecom-equipmentindustry Original Research ArticleTechnovation , Volume 26, Issue 3, March 2006,Pages359-368 Peilei FanShow preview | Related articles | Related reference work articlesPurchase90 Optimization of material and production to develop fluoroelastomer inflatable seals for sodium cooled fastbreeder reactor Original Research ArticleNuclear Engineering and Design , In Press, Corrected Proof, Available online 16 February 2011N.K. Sinha, Baldev RajShow preview | Related articles | Related reference work articlesPurchase Research highlights► Production of thin fluoroelastomer profiles by cold feed extrusion and continuous cure involving microwave and hot air heating. ► Use of peroxide curing in air during production . ► Use offluoroelastomers based on advanced polymer architecture (APA) for the production of profiles. ► Use of the profiles in inflatable seals for critical application of Prototype Fast Breeder Reactor. ► Tailoring of material formulation by synchronized optimization of material and production technologies to ensure that the produced seal ensures significant gains in terms of performance and safety in reactor under synergistic influences of temperature, radiation, air and sodium aerosol.91 The dynamic transfer batch-size decision for thin film transistor –liquid crystal display array manufacturing by artificialneural-network Original Research ArticleComputers & Industrial。