陳煥文

Bio: 陳煥文 is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 77 citations.

Tech Mining: Exploiting New Technologies for Competitive Advantage

Abstract: List of Figures. Preface. Acknowledgments. Acronyms & Shorthands-Glossary. PART I. UNDERSTAND TECH MINING. Chapter 1. Technological Innovation and the Need for Tech Mining. 1.1 Why Innovation is Significant. 1.2 Innovation Processes. 1.3 Innovation Institutions and Their Interests. 1.4 Innovators and Their Interests. 1.5 Technological Innovation in an Information Age. 1.6 Information About Emerging Technologies. Chapter 1 Take-Home Messages. Chapter Resources. Chapter 2. How Tech Mining Works. 2.1 What is Tech Mining? 2.2 Why Do Tech Mining? 2.3 What Is Tech Mining's Ancestry? 2.4 How to Conduct the Tech Mining Process? 2.5 Who Does Tech Mining? 2.6 Where Is Tech Mining Most Needed? Chapter 2 Take-Home Messages. Chapter Resources. Chapter 3. What Tech Mining Can Do for You. 3.1 Tech Mining Basics. 3.2 Tech Mining Analyses. 3.3 Putting Tech Mining Information to Good Use. 3.4 Managing and Measuring Tech Mining. Chapter 3 Take-Home Messages. Chapter 4. Example Results: Fuel Cells Tech Mining. 4.1 Overview of Fuel Cells. 4.2 Tech Mining Analyses. 4.3 Tech Mining Results. 4.4 Tech Mining Information Processes. 4.5 Tech Mining Information Products. Chapter 4 Take-Home Messages. Chapter Resources. Chapter 5. What to Watch For in Tech Mining. 5.1 Better Basics. 5.2 Research Profiling and Other Perspectives on the Data. 5.3 More Informative Products. 5.4 Knowledge Discovery. 5.5 Knowledge Management. 5.6 New Tech Mining Markets. 5.7 Dangers. Chapter 5 Take-Home Messages. Chapter Resources. PART II. DOING TECH MINING. Chapter 6. Finding the Right Sources. 6.1 R&D Activity. 6.2 R&D Output Databases. 6.3 Determining the Best Sources. 6.4 Arranging Access to Databases. Chapter 6 Take-Home Messages. Chapter Resources. Chapter 7. Forming the Right Query. 7.1 An Iterative Process. 7.2 Queries Based on Substantive Terms. 7.3 Nominal Queries. 7.4 Tactics and Strategies for Query Design. 7.5 Changing the Query. Chapter 7 Take-Home Messages. Chapter 8. Getting the Data. 8.1 Accessing Databases. 8.2 Search and Retrieval from a Database. 8.3 What to Do, and Not to Do. Chapter 8 Take-Home Messages. Chapter 9. Basic Analyses. 9.1 In the Beginning. 9.2 What You Can Do with the Data. 9.3 Relations Among Documents and Terms Occurring in Their Information Fields. 9.4 Relationships. 9.5 Helpful Basic Analyses. Chapter 9 Take-Home Messages. Chapter 10. Advanced Analyses. 10.1 Why Perform Advanced Analyses? 10.2 Data Representation. 10.3 Analytical Families. Chapter 10 Take-Home Messages. Chapter Resources. Chapter 11. Trend Analyses. 11.1 Perspective. 11.2 An Example Time Series Description and Forecast. 11.3 Multiple Forecasts. 11.4 Research Fronts. 11.5 Novelty. Chapter 11 Take-Home Messages. Chapter Resources. Chapter 12. Patent Analyses. 12.1 Why patent Analyses? 12.2 Getting Started. 12.3 The 'What' and 'Why' of patent Analysis. 12.4 Tech Mining Patent Analysis Case Illustration: Fuel Cells. 12.5 Patent Citation Analysis. 12.6 For Whom? 12.7 TRIZ. 12.8 Reflections. Chapter 12 Take-Home Messages. Chapter Resources. Chapter 13. Generating and Presenting Innovation Indicators. 13.1 Expert Opinion in Tech Mining. 13.2 Innovation Indicators. 13.3 Information Representation and Packaging. 13.4 Examples of Putting Tech Mining Information Representation to Use. 13.5 Summing Up. Chapter Resources. Chapter 14. Managing the Tech Mining Process. 14.1 Tough Challenges. 14.2 Tech Mining Communities. 14.3 Process Management. 14.4 Enhancing the Prospects of Tech Mining Utilization. 14.5 Institutionalizing the Tech Mining Function. 14.6 The Learning Curve. Chapter 14 Take-Home Messages. Chapter 15. Measuring Tech Mining Results. 15.1 Why Measure? 15.2 What to Measure. 15.3 How to Measure. 15.4 Enabling Measurement. 15.5 Effective Measurement. 15.6 Using Measurements to Bolster Tech Mining. Chapter 15 Take-Home Messages. Chapter Resources. Chapter 16. Examples Process: Tech Mining on Fuel Cells. 16.1 Introduction. 16.2 First Step: Issue Identification. 16.3 Second Step: Selection of Information Sources. 16.4 Third Step: Search Refinement and Data Retrieval. 16.5 Fourth Step: Data Cleaning. 16.6 Fifth Step: Basic Analyses. 16.7 Sixth Step: Advanced Analyses. 16.8 Seventh Step: Representation. 16.9 Eight Step: Interpretation. 16.10 Ninth Step: Utilization. 16.11 What Can We Learn. Chapter 6 Take-Home Messages. Chapter Resources. Appendix A: Selected Publication and patent Databases. Appendix B: Text Mining Software. Appendix C: What You Can Do Without Tech Mining Software. Appendix D: Statistics and Distributions for Analyzing Text Entities. References. Index.

Internalization and cytotoxicity of graphene oxide and carboxyl graphene nanoplatelets in the human hepatocellular carcinoma cell line Hep G2

Abstract: Graphene and graphene derivative nanoplatelets represent a new generation of nanomaterials with unique physico-chemical properties and high potential for use in composite materials and biomedical devices. To date little is known about the impact graphene nanomaterials may have on human health in the case of accidental or intentional exposure. The objective of this study was to assess the cytotoxic potential of graphene nanoplatelets with different surface chemistry towards a human hepatoma cell line, Hep G2, and identify the underlying toxicity targets. Graphene oxide (GO) and carboxyl graphene (CXYG) nanoplatelet suspensions were obtained in water and culture medium. Size frequency distribution of the suspensions was determined by means of dynamic light scattering. Height, lateral dimension and shape of the nanoplatelets were determined using atomic force and electron microscopy. Cytotoxicity of GO and CXYG nanoplatelets was assessed in Hep G2 cells using a battery of assays covering different modes of action including alterations of metabolic activity, plasma membrane integrity and lysosomal function. Induction of oxidative stress was assessed by measuring intracellular reactive oxygen species levels. Interaction with the plasma membrane, internalization and intracellular fate of GO and CXYG nanoplatelets was studied by scanning and transmission electron microscopy. Supplementing culture medium with serum was essential to obtain stable GO and CXYG suspensions. Both graphene derivatives had high affinity for the plasma membrane and caused structural damage of the latter at concentrations as low as 4 μg/ml. The nanoplatelets penetrated through the membrane into the cytosol, where they were concentrated and enclosed in vesicles. GO and CXYG accumulation in the cytosol was accompanied by an increase in intracellular reactive oxygen species (ROS) levels, alterations in cellular ultrastructure and changes in metabolic activity. GO and CXYG nanoplatelets caused dose- and time-dependent cytotoxicity in Hep G2 cells with plasma membrane damage and induction of oxidative stress being important modes of toxicity. Both graphene derivatives were internalized by Hep G2, a non-phagocytotic cell line. Moreover, they exerted no toxicity when applied at very low concentrations (< 4 μg/ml). GO and CXYG nanoplatelets may therefore represent an attractive material for biomedical applications.

Dynamic relationship between technological innovations, financial development, renewable energy, and ecological footprint: fresh insights based on the STIRPAT model for Asia Pacific Economic Cooperation countries.

Abstract: This article seeks to analyze the impact of technological innovations, financial development, renewable energy consumption, economic growth, and population on the ecological footprint in Asia Pacific Economic Cooperation (APEC) countries by utilizing the balanced longitudinal data set during the period from 1990 to 2017. This study creates a new technological innovation index through principle component analysis including three important indicators that represent the technology and employs a consistent environmental framework identified as Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model. The second generation panel estimation technique is employed to calculate robust and reliable outcomes. After confirming the cross-sectional dependency among series, panel unit root tests confirm that all variables are stationary at their first integrated order. Furthermore, Westerlund cointegration test confirms the presence of long-run association among variables. The outcomes explore that financial development and renewable energy utilization significantly accelerate the environmental quality by 0.0927% and 0.4274%, respectively. While, the increase in technological innovation activities, economic growth, and population size has a detrimental effect on environmental quality in the long run by 0.099%, 0.517%, and 0.458%, respectively. Moreover, the results of panel Dumitrescu and Hurlin (D-H) non-causality test discovered the bidirectional causality relationship between financial development, technological innovations, renewable energy consumption, economic growth, and population size with the ecological footprint. These empirical findings provide some vital policy implications for central authority and policymakers to overcome the detrimental impact on environmental quality in the APEC region.

Relatedness, external linkages and regional innovation in Europe

Abstract: Relatedness, external linkages and regional innovation in Europe. Regional Studies. This paper analyses if the generation of new knowledge benefits from the combination of similar or dissimilar pieces of existing technologies, in terms of their technological content (related versus unrelated variety), for the case of European regions. Specifically, it analyses the relevance of variety in the case of local knowledge as well as in the case of the knowledge coming from other regions. At the local level, it shows that while related variety is conducive to regional innovation, unrelated variety does play a role, too, when it comes to radical innovations. Conversely, it also shows that external knowledge flows have a higher impact, the higher the similarity between these flows and the extant local knowledge base.

Has the importance of intangibles really grown? And if so, why?

Abstract: Intangibles are ideas or knowledge about the natural (physical and biological) and socio‐cultural worlds that enable people to better accomplish their goals, both in primitive societies and in modern economies. Intangibles include basic research and technology improvements, as well as knowledge to better organise exchange and production, and over time become inextricably embedded in improved tangible assets. Accounting intangibles are legally excludable subsets of economic intangibles, which in turn are the subsets of cultural intangibles that can be used to create tradable goods or services. Because economic intangibles are cumulative, synergistic, and frequently inseparable from other tangible assets and/or economic intangibles not owned by any single entity, it is usually futile to estimate a separate accounting value for individual intangibles. However, the income that intangibles together generate provides useful inputs for equity valuation, and voluntary non‐financial disclosures could be i...