On the study of catalytic membrane reactor for water detritiation: Membrane characterization.

Tritium waste recycling is a real economic and ecological issue. Generally under the non-valuable Q2Oform (Q = H, D or T), waste can be converted into fuel Q2for a fusion machine (e.g. JET, ITER) by isotopeexchange reaction Q2O + H2= H2O + Q2. Such a reaction is carried out over Ni-based catalyst bed packed ina thin wall hydrogen permselective membrane tube. This catalytic membrane reactor can achieve higherconversion ratios than conventional fixed bed reactors by selective removal of reaction product Q2bythe membrane according to Le Chatelier’s Law. This paper presents some preliminary permeation tests performed on a catalytic membrane reactor.Permeabilities of pure hydrogen and deuterium as well as those of binary mixtures of hydrogen, deu-terium and nitrogen have been estimated by measuring permeation fluxes at temperatures ranging from573 to 673 K, and pressure differences up to 1.5 bar. Pure component global fluxes were linked to perme-ation coefficient by means of Sieverts’ law. The thin membrane (150 �m), made of Pd–Ag alloy (23 wt.%Ag),showed good permeability and infinite selectivity toward protium and deuterium. Lower permeabilityvalues were obtained with mixtures containing non permeable gases highlighting the existence of gasphase resistance. The sensitivity of this concentration polarization phenomenon to the composition andthe flow rate of the inlet was evaluated and fitted by a two-dimensional model

Influence of chemical composition on biochemical methane potential of fruit and vegetable waste

This study investigates the influence of chemical composition on the biochemical methane potential (BMP) of twelve different batches of fruit and vegetable waste (FVW) with different compositions collected over one year. BMP ranged from 288 to 516 LN CH4 kg VS-1, with significant statistical differences between means, which was explained by variations in the chemical composition over time. BMP was most strongly correlated to lipid content and high calorific values. Multiple linear regression was performed to develop statistical models to more rapidly predict methane potential. Models were analysed that considered chemical compounds and that considered only high calorific value as a single parameter. The best BMP prediction was obtained using the statistical model that included lipid, protein, cellulose, lignin, and high calorific value (HCV), with R² of 92.5%; lignin was negatively correlated to methane production. Because HCV and lipids are strongly correlated, and because HCV can be determined more rapidly than overall chemical composition, HCV may be useful for predicting BMP.Postprint (author's final draft

Développement d'un modèle de transferts couplés pour l'aide à la conception et à la conduite des systèmes de purification du sodium des réacteurs à neutrons rapides

Les pièges froids sont des systèmes de purification du fluide caloporteur sodium indispensables au bon fonctionnement des réacteurs à neutrons rapides. Ils permettent de contrôler la teneur en impuretés du sodium, notamment celles de l oxygène et de l hydrogène. Le piégeage de ces impuretés est basé sur leur cristallisation sous forme d oxyde et d hydrure de sodium, sur garnissage et sur parois froides. Appréhender le remplissage de ces systèmes de purification permettra d orienter les choix technologiques en termes de conception et de conduite. L objectif est de développer un outil d aide à la conception et à la simulation des pièges froids. Le modèle de cristallisation intègre le couplage des différents phénomènes mis en jeu lors de la purification du sodium, à savoir l hydrodynamique, transfert thermique et transfert de matière.Operating a Sodium Fast Reactor (SFR) in reliable and safe conditions requires to master the quality of the sodium fluid coolant, regarding oxygen and hydrogen impurities contents. A cold trap is a purification unit in SFR, designed for maintaining oxygen and hydrogen contents within acceptable limits. The purification of these impurities is based on crystallization of sodium hydride on cold walls and sodium oxide or hydride on wire mesh packing. Indeed, as oxygen and hydrogen solubilities are nearly nil at temperatures close to the sodium fusion point, i.e. 97.8C, on line sodium purification can be performed by crystallization of sodium oxide and hydride from liquid sodium flows. However, the management of cold trap performances is necessary to prevent from unforeseen maintenance operations, which could induce shut-down of the reactor. It is thus essential to understand how a cold trap fills up with impurities crystallization in order to optimize the design of this system and to overcome any problems during nominal operation. The objective is to develop a design and simulation tool for cold traps able to predict the location and the amount of the impurities deposited. Crystallization model involve phenomena coupling in a porous medium with hydrodynamics, heat and mass transfer, distinguishing nucleation and growth phases for each impurity. It enables to understand how thermo hydraulic conditions and growing impurities interact on each other. This analysis will adapt operating and management conditions in order to optimize purification requirements.TOULOUSE-INP (315552154) / SudocSudocFranceF

Sub-clinical detection of gut microbial biomarkers of obesity and type 2 diabetes

Background: Obesity and type 2 diabetes (T2D) are linked both with host genetics and with environmental factors, including dysbioses of the gut microbiota. However, it is unclear whether these microbial changes precede disease onset. Twin cohorts present a unique genetically-controlled opportunity to study the relationships between lifestyle factors and the microbiome. In particular, we hypothesized that family-independent changes in microbial composition and metabolic function during the sub-clinical state of T2D could be either causal or early biomarkers of progression. Methods: We collected fecal samples and clinical metadata from 20 monozygotic Korean twins at up to two time points, resulting in 36 stool shotgun metagenomes. While the participants were neither obese nor diabetic, they spanned the entire range of healthy to near-clinical values and thus enabled the study of microbial associations during sub-clinical disease while accounting for genetic background. Results: We found changes both in composition and in function of the sub-clinical gut microbiome, including a decrease in Akkermansia muciniphila suggesting a role prior to the onset of disease, and functional changes reflecting a response to oxidative stress comparable to that previously observed in chronic T2D and inflammatory bowel diseases. Finally, our unique study design allowed us to examine the strain similarity between twins, and we found that twins demonstrate strain-level differences in composition despite species-level similarities. Conclusions: These changes in the microbiome might be used for the early diagnosis of an inflamed gut and T2D prior to clinical onset of the disease and will help to advance toward microbial interventions

Sub-clinical detection of gut microbial biomarkers of obesity and type 2 diabetes

Background: Obesity and type 2 diabetes (T2D) are linked both with host genetics and with environmental factors, including dysbioses of the gut microbiota. However, it is unclear whether these microbial changes precede disease onset. Twin cohorts present a unique genetically-controlled opportunity to study the relationships between lifestyle factors and the microbiome. In particular, we hypothesized that family-independent changes in microbial composition and metabolic function during the sub-clinical state of T2D could be either causal or early biomarkers of progression. Methods: We collected fecal samples and clinical metadata from 20 monozygotic Korean twins at up to two time points, resulting in 36 stool shotgun metagenomes. While the participants were neither obese nor diabetic, they spanned the entire range of healthy to near-clinical values and thus enabled the study of microbial associations during sub-clinical disease while accounting for genetic background. Results: We found changes both in composition and in function of the sub-clinical gut microbiome, including a decrease in Akkermansia muciniphila suggesting a role prior to the onset of disease, and functional changes reflecting a response to oxidative stress comparable to that previously observed in chronic T2D and inflammatory bowel diseases. Finally, our unique study design allowed us to examine the strain similarity between twins, and we found that twins demonstrate strain-level differences in composition despite species-level similarities. Conclusions: These changes in the microbiome might be used for the early diagnosis of an inflamed gut and T2D prior to clinical onset of the disease and will help to advance toward microbial interventions. Electronic supplementary material The online version of this article (doi:10.1186/s13073-016-0271-6) contains supplementary material, which is available to authorized users

On the study of catalytic membrane reactor for water detritiation: Modeling approach

In the framework of tritium recovery from tritiated water, efficiency of packed bed membrane reactors have been successfully demonstrated. Thanks to protium isotope swamping, tritium bonded water can be recovered under the valuable Q2 form (Q = H, D or T) by means of isotope exchange reactions occurring on catalyst surface. The use of permselective Pd-based membrane allows withdrawal of reactions products all along the reactor, and thus limits reverse reaction rate to the benefit of the direct one (shift effect). The reactions kinetics, which are still little known or unknown, are generally assumed to be largely greater than the permeation ones so that thermodynamic equilibriums of isotope exchange reactions are generally assumed. This paper proposes a new phenomenological 2D model to represent catalytic membrane reactor behavior with the determination of gas effluents compositions. A good agreement was obtained between the simulation results and experimental measurements performed on a dedicated facility. Furthermore, the gas composition estimation permits to interpret unexpected behavior of the catalytic membrane reactor. In the next future, further sensitivity analysis will be performed to determine the limits of the model and a kinetics study will be conducted to assess the thermodynamic equilibrium of reaction

Development and validation of a risk model for prediction of hazardous alcohol consumption in general practice attendees : the PredictAL study

Background: Little is known about the risk of progression to hazardous alcohol use in people currently drinking at safe limits. We aimed to develop a prediction model (predictAL) for the development of hazardous drinking in safe drinkers. Methods: A prospective cohort study of adult general practice attendees in six European countries and Chile followed up over 6 months. We recruited 10,045 attendees between April 2003 to February 2005. 6193 European and 2462 Chilean attendees recorded AUDIT scores below 8 in men and 5 in women at recruitment and were used in modelling risk. 38 risk factors were measured to construct a risk model for the development of hazardous drinking using stepwise logistic regression. The model was corrected for over fitting and tested in an external population. The main outcome was hazardous drinking defined by an AUDIT score >= 8 in men and >= 5 in women. Results: 69.0% of attendees were recruited, of whom 89.5% participated again after six months. The risk factors in the final predictAL model were sex, age, country, baseline AUDIT score, panic syndrome and lifetime alcohol problem. The predictAL model's average c-index across all six European countries was 0.839 (95% CI 0.805, 0.873). The Hedge's g effect size for the difference in log odds of predicted probability between safe drinkers in Europe who subsequently developed hazardous alcohol use and those who did not was 1.38 (95% CI 1.25, 1.51). External validation of the algorithm in Chilean safe drinkers resulted in a c-index of 0.781 (95% CI 0.717, 0.846) and Hedge's g of 0.68 (95% CI 0.57, 0.78). Conclusions: The predictAL risk model for development of hazardous consumption in safe drinkers compares favourably with risk algorithms for disorders in other medical settings and can be a useful first step in prevention of alcohol misuse

Influence of chemical composition on biochemical methane potential of fruit and vegetable waste

This study investigates the influence of chemical composition on the biochemical methane potential (BMP) of twelve different batches of fruit and vegetable waste (FVW) with different compositions collected over one year. BMP ranged from 288 to 516 LN CH4 kg VS−1, with significant statistical differences between means, which was explained by variations in the chemical composition over time. BMP was most strongly correlated to lipid content and high calorific values. Multiple linear regression was performed to develop statistical models to more rapidly predict methane potential. Models were analysed that considered chemical compounds and that considered only high calorific value as a single parameter. The best BMP prediction was obtained using the statistical model that included lipid, protein, cellulose, lignin, and high calorific value (HCV), with R2 of 92.5%; lignin was negatively correlated to methane production. Because HCV and lipids are strongly correlated, and because HCV can be determined more rapidly than overall chemical composition, HCV may be useful for predicting BMP.info:eu-repo/semantics/submittedVersio

The impact of cluster connectedness on firm innovation: R&D effort and outcomes in the textile industry

This is an Author's Accepted Manuscript of an article published in "The impact of cluster connectedness on firm innovation: R&D effort and outcomes in the textile industry" version of the article as published in the Entrepreneurship and Regional Development, 2012 september,[copyright Taylor & Francis], available online at: http://www.tandfonline.com/10.1080/08985626.2012.710260"[EN] Recent research into the clustering effect on firms has moved away from a simplistic view to a more complex approach. More realistic and complex causal relationships are now considered when analysing these territorial networks. Specifically, this paper attempts to analyse how cluster connect- edness moderates the relationship of a firm's innovation effort and the results obtained from this effort. We want to question the commonly accepted direct and positive impact of R&D effort, and moreover, we suggest the existence of a saturation effect and that the level of cluster's inter-connectedness in the cluster moderates this effect. We have developed our empirical study focusing on the Spanish textile industrial cluster. This is a complex manufacturing industry that uses relatively low-technology manufacturing and R&D. Our findings suggest that the degree to which a firm is involved with, or connected to, other firms in the cluster can moderate the effect of the R&D effort on its innovation results. More generally, we aim to contribute to the discussion on the degree to which firms should be involved in the cluster network in order to operate efficiently and gain the maximum competitive advantages. Our findings have implications both in recent cluster and network literature as well for institutional policy.Molina Morales, FX.; Expósito Langa, M. (2012). The impact of cluster connectedness on firm innovation: R&D effort and outcomes in the textile industry. Entrepreneurship and Regional Development. 24(7-8):685-704. doi:10.1080/08985626.2012.710260S685704247-8Agarwal, R., Audretsch, D., & Sarkar, M. B. (2007). The process of creative construction: knowledge spillovers, entrepreneurship, and economic growth. Strategic Entrepreneurship Journal, 1(3-4), 263-286. doi:10.1002/sej.36Aharonson, B. S., Baum, J. A. C., & Feldman, M. P. (2007). Desperately seeking spillovers? Increasing returns, industrial organization and the location of new entrants in geographic and technological space. Industrial and Corporate Change, 16(1), 89-130. doi:10.1093/icc/dtl034Albino, V., Carbonara, N., & Giannoccaro, I. (2006). Innovation in industrial districts: An agent-based simulation model. International Journal of Production Economics, 104(1), 30-45. doi:10.1016/j.ijpe.2004.12.023Audretsch, D. B., & Lehmann, E. E. (2005). Does the Knowledge Spillover Theory of Entrepreneurship hold for regions? Research Policy, 34(8), 1191-1202. doi:10.1016/j.respol.2005.03.012Bell, G. G. (2005). Clusters, networks, and firm innovativeness. Strategic Management Journal, 26(3), 287-295. doi:10.1002/smj.448Bell, M., & Albu, M. (1999). Knowledge Systems and Technological Dynamism in Industrial Clusters in Developing Countries. World Development, 27(9), 1715-1734. doi:10.1016/s0305-750x(99)00073-xBelussi, F., & Arcangeli, F. (1998). A typology of networks: flexible and evolutionary firms. Research Policy, 27(4), 415-428. doi:10.1016/s0048-7333(98)00074-2Cantwell, J., & Piscitello, L. (2005). Recent Location of Foreign-owned Research and Development Activities by Large Multinational Corporations in the European Regions: The Role of Spillovers and Externalities. Regional Studies, 39(1), 1-16. doi:10.1080/0034340052000320824Boschma, R. A., & ter Wal, A. L. J. (2007). Knowledge Networks and Innovative Performance in an Industrial District: The Case of a Footwear District in the South of Italy. Industry & Innovation, 14(2), 177-199. doi:10.1080/13662710701253441Brass, D. J. (1984). Being in the Right Place: A Structural Analysis of Individual Influence in an Organization. Administrative Science Quarterly, 29(4), 518. doi:10.2307/2392937Breschi, S. (2001). Knowledge Spillovers and Local Innovation Systems: A Critical Survey. Industrial and Corporate Change, 10(4), 975-1005. doi:10.1093/icc/10.4.975CALANTONE, R. (1997). New product activities and performance: The moderating role of environmental hostility. Journal of Product Innovation Management, 14(3), 179-189. doi:10.1016/s0737-6782(97)00004-0Chell, E., & Baines, S. (2000). Networking, entrepreneurship and microbusiness behaviour. Entrepreneurship & Regional Development, 12(3), 195-215. doi:10.1080/089856200413464Chung, S. (Andy), Singh, H., & Lee, K. (2000). Complementarity, status similarity and social capital as drivers of alliance formation. Strategic Management Journal, 21(1), 1-22. doi:10.1002/(sici)1097-0266(200001)21:13.0.co;2-pCockburn, I. M., & Henderson, R. M. (2003). Absorptive Capacity, Coauthoring Behavior, and the Organization of Research in Drug Discovery. The Journal of Industrial Economics, 46(2), 157-182. doi:10.1111/1467-6451.00067Cohen, W. M., & Levinthal, D. A. (1989). Innovation and Learning: The Two Faces of R & D. The Economic Journal, 99(397), 569. doi:10.2307/2233763Cohen, W. M., & Levinthal, D. A. (1990). Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly, 35(1), 128. doi:10.2307/2393553Coleman, J. S. (1988). Social Capital in the Creation of Human Capital. American Journal of Sociology, 94, S95-S120. doi:10.1086/228943Coombs, J. E., Deeds, D. L., & Duane Ireland, R. (2009). Placing the choice between exploration and exploitation in context: a study of geography and new product development. Strategic Entrepreneurship Journal, 3(3), 261-279. doi:10.1002/sej.74Crestanello, P., & Tattara, G. (2011). Industrial Clusters and the Governance of the Global Value Chain: The Romania–Veneto Network in Footwear and Clothing. Regional Studies, 45(2), 187-203. doi:10.1080/00343401003596299Dierickx, I., & Cool, K. (1989). Asset Stock Accumulation and Sustainability of Competitive Advantage. Management Science, 35(12), 1504-1511. doi:10.1287/mnsc.35.12.1504Dyer, J. H., & Singh, H. (1998). The Relational View: Cooperative Strategy and Sources of Interorganizational Competitive Advantage. The Academy of Management Review, 23(4), 660. doi:10.2307/259056Eraydin, A., & Armatli-Köroğlu, B. (2005). Innovation, networking and the new industrial clusters: the characteristics of networks and local innovation capabilities in the Turkish industrial clusters. Entrepreneurship & Regional Development, 17(4), 237-266. doi:10.1080/08985620500202632Evenson, R. E., & Kislev, Y. (1973). Research and Productivity in Wheat and Maize. Journal of Political Economy, 81(6), 1309-1329. doi:10.1086/260129Expósito-Langa, M., Molina-Morales, F. X., & Capó-Vicedo, J. (2011). New Product Development and Absorptive Capacity in Industrial Districts: A Multidimensional Approach. Regional Studies, 45(3), 319-331. doi:10.1080/00343400903241535Foss, N. J. (1996). Higher-order industrial Capabilities and competitive advantage. Journal of Industry Studies, 3(1), 1-20. doi:10.1080/13662719600000001George, G., Robley Wood, D., & Khan, R. (2001). Networking strategy of boards: implications for small and medium-sized enterprises. Entrepreneurship & Regional Development, 13(3), 269-285. doi:10.1080/08985620110058115Giuliani, E. 2005. The structure of cluster knowledge networks: Uneven and selective, not pervasive and collective. DRUID Working Paper no. 05-11Giuliani, E., & Bell, M. (2005). The micro-determinants of meso-level learning and innovation: evidence from a Chilean wine cluster. Research Policy, 34(1), 47-68. doi:10.1016/j.respol.2004.10.008Glasmeier, A. (1991). Technological discontinuities and flexible production networks: The case of Switzerland and the world watch industry. Research Policy, 20(5), 469-485. doi:10.1016/0048-7333(91)90070-7Grant, R. M. (1996). Prospering in Dynamically-Competitive Environments: Organizational Capability as Knowledge Integration. Organization Science, 7(4), 375-387. doi:10.1287/orsc.7.4.375Guerrieri, P., & Pietrobelli, C. (2004). Industrial districts’ evolution and technological regimes: Italy and Taiwan. Technovation, 24(11), 899-914. doi:10.1016/s0166-4972(03)00048-8Huggins, R., & Johnston, A. (2010). Knowledge flow and inter-firm networks: The influence of network resources, spatial proximity and firm size. Entrepreneurship & Regional Development, 22(5), 457-484. doi:10.1080/08985620903171350Ibarra, H. (1992). Homophily and Differential Returns: Sex Differences in Network Structure and Access in an Advertising Firm. Administrative Science Quarterly, 37(3), 422. doi:10.2307/2393451Lane, P. J., & Lubatkin, M. (1998). Relative absorptive capacity and interorganizational learning. Strategic Management Journal, 19(5), 461-477. doi:10.1002/(sici)1097-0266(199805)19:53.0.co;2-lLechner, C., Frankenberger, K., & Floyd, S. W. (2010). Task Contingencies in the Curvilinear Relationships Between Intergroup Networks and Initiative Performance. Academy of Management Journal, 53(4), 865-889. doi:10.5465/amj.2010.52814620Levin, D. Z., & Cross, R. (2004). The Strength of Weak Ties You Can Trust: The Mediating Role of Trust in Effective Knowledge Transfer. Management Science, 50(11), 1477-1490. doi:10.1287/mnsc.1030.0136Madill, J. J., Haines, G. H., & Riding, A. L. (2004). Networks and linkages among firms and organizations in the Ottawa-region technology cluster. Entrepreneurship & Regional Development, 16(5), 351-368. doi:10.1080/0898562042000188414Maskell, P. (1998). Low-Tech Competitive Advantages and the Role Of Proximity. European Urban and Regional Studies, 5(2), 99-118. doi:10.1177/096977649800500201Maskell, P. (2001). Towards a Knowledge-based Theory of the Geographical Cluster. Industrial and Corporate Change, 10(4), 921-943. doi:10.1093/icc/10.4.921McEvily, B., & Marcus, A. (2005). Embedded ties and the acquisition of competitive capabilities. Strategic Management Journal, 26(11), 1033-1055. doi:10.1002/smj.484McEvily, B., & Zaheer, A. (1999). Bridging ties: a source of firm heterogeneity in competitive capabilities. Strategic Management Journal, 20(12), 1133-1156. doi:10.1002/(sici)1097-0266(199912)20:123.0.co;2-7Xavier Molina-Morales, F., & Teresa Martínez-Fernández, M. (2006). Industrial districts: something more than a neighbourhood. Entrepreneurship & Regional Development, 18(6), 503-524. doi:10.1080/08985620600884750Molina-Morales, F. X., & Martínez-Fernández, M. T. (2009). Too much love in the neighborhood can hurt: how an excess of intensity and trust in relationships may produce negative effects on firms. Strategic Management Journal, 30(9), 1013-1023. doi:10.1002/smj.766Morrison, A. (2008). Gatekeepers of Knowledgewithin Industrial Districts: Who They Are, How They Interact. Regional Studies, 42(6), 817-835. doi:10.1080/00343400701654178Morrison, A., & Rabellotti, R. (2009). Knowledge and Information Networks in an Italian Wine Cluster. European Planning Studies, 17(7), 983-1006. doi:10.1080/09654310902949265Mowery, D. C., Oxley, J. E., & Silverman, B. S. (1996). Strategic alliances and interfirm knowledge transfer. Strategic Management Journal, 17(S2), 77-91. doi:10.1002/smj.4250171108Nahapiet, J., & Ghoshal, S. (1998). Social Capital, Intellectual Capital, and the Organizational Advantage. The Academy of Management Review, 23(2), 242. doi:10.2307/259373O’Connor, G. C. (1998). Market Learning and Radical Innovation: A Cross Case Comparison of Eight Radical Innovation Projects. Journal of Product Innovation Management, 15(2), 151-166. doi:10.1111/1540-5885.1520151Oba, B., & Semerciöz, F. (2005). Antecedents of trust in industrial districts: an empirical analysis of inter-firm relations in a Turkish industrial district. Entrepreneurship & Regional Development, 17(3), 163-182. doi:10.1080/08985620500102964Parrilli, M. D. (2009). Collective efficiency, policy inducement and social embeddedness: Drivers for the development of industrial districts. Entrepreneurship & Regional Development, 21(1), 1-24. doi:10.1080/08985620801886513Podolny, J. M., & Baron, J. N. (1997). Resources and Relationships: Social Networks and Mobility in the Workplace. American Sociological Review, 62(5), 673. doi:10.2307/2657354Porter, M. E. (1990). The Competitive Advantage of Nations. doi:10.1007/978-1-349-11336-1Pouder, R., & St. John, C. H. (1996). Hot Spots and Blind Spots: Geographical Clusters of Firms and Innovation. Academy of Management Review, 21(4), 1192-1225. doi:10.5465/amr.1996.9704071867Torre, A., & Rallet, A. (2005). Proximity and Localization. Regional Studies, 39(1), 47-59. doi:10.1080/0034340052000320842Rosenkopf, L., & Almeida, P. (2003). Overcoming Local Search Through Alliances and Mobility. Management Science, 49(6), 751-766. doi:10.1287/mnsc.49.6.751.16026Rosenthal, S. S., & Strange, W. C. (2003). Geography, Industrial Organization, and Agglomeration. Review of Economics and Statistics, 85(2), 377-393. doi:10.1162/003465303765299882Rowley, T., Behrens, D., & Krackhardt, D. (2000). Redundant governance structures: an analysis of structural and relational embeddedness in the steel and semiconductor industries. Strategic Management Journal, 21(3), 369-386. doi:10.1002/(sici)1097-0266(200003)21:33.0.co;2-mRusso, M. (1985). Technical change and the industrial district: The role of interfirm relations in the growth and transformation of ceramic tile production in Italy. Research Policy, 14(6), 329-343. doi:10.1016/0048-7333(85)90003-4Sammarra, A., & Belussi, F. (2006). Evolution and relocation in fashion-led Italian districts: evidence from two case-studies. Entrepreneurship & Regional Development, 18(6), 543-562. doi:10.1080/08985620600884685Simmie, J. (2004). Innovation and Clustering in the Globalised International Economy. Urban Studies, 41(5-6), 1095-1112. doi:10.1080/00420980410001675823Sparrowe, R. T., Liden, R. C., Wayne, S. J., & Kraimer, M. L. (2001). SOCIAL NETWORKS AND THE PERFORMANCE OF INDIVIDUALS AND GROUPS. Academy of Management Journal, 44(2), 316-325. doi:10.2307/3069458STABER, U. (2007). Contextualizing Research on Social Capital in Regional Clusters. International Journal of Urban and Regional Research, 31(3), 505-521. doi:10.1111/j.1468-2427.2007.00742.xStock, G. N., Greis, N. P., & Fischer, W. A. (2001). Absorptive capacity and new product development. The Journal of High Technology Management Research, 12(1), 77-91. doi:10.1016/s1047-8310(00)00040-7Tallman, S., Jenkins, M., Henry, N., & Pinch, S. (2004). Knowledge, Clusters, and Competitive Advantage. The Academy of Management Review, 29(2), 258. doi:10.2307/20159032Thompson, P., & Fox-Kean, M. (2005). Patent Citations and the Geography of Knowledge Spillovers: A Reassessment. American Economic Review, 95(1), 450-460. doi:10.1257/0002828053828509Tsai, W. (2001). KNOWLEDGE TRANSFER IN INTRAORGANIZATIONAL NETWORKS: EFFECTS OF NETWORK POSITION AND ABSORPTIVE CAPACITY ON BUSINESS UNIT INNOVATION AND PERFORMANCE. Academy of Management Journal, 44(5), 996-1004. doi:10.2307/3069443Tsai, W., & Ghoshal, S. (1998). SOCIAL CAPITAL AND VALUE CREATION: THE ROLE OF INTRAFIRM NETWORKS. Academy of Management Journal, 41(4), 464-476. doi:10.2307/257085Tushman, M., & Nadler, D. (1986). Organizing for Innovation. California Management Review, 28(3), 74-92. doi:10.2307/41165203Uzzi, B. (1997). Social Structure and Competition in Interfirm Networks: The Paradox of Embeddedness. Administrative Science Quarterly, 42(1), 35. doi:10.2307/2393808Varaldo, R., & Ferrucci, L. (1996). The evolutionary nature of the firm within industrial districts. European Planning Studies, 4(1), 27-34. doi:10.1080/09654319608720327Waxell, A., & Malmberg, A. (2007). What is global and what is local in knowledge-generating interaction? The case of the biotech cluster in Uppsala, Sweden. Entrepreneurship & Regional Development, 19(2), 137-159. doi:10.1080/08985620601061184Yli-Renko, H., Autio, E., & Sapienza, H. J. (2001). Social capital, knowledge acquisition, and knowledge exploitation in young technology-based firms. Strategic Management Journal, 22(6-7), 587-613. doi:10.1002/smj.183ZUCKER, L. G., DARBY, M. R., & ARMSTRONG, J. (1998). GEOGRAPHICALLY LOCALIZED KNOWLEDGE: SPILLOVERS OR MARKETS? Economic Inquiry, 36(1), 65-86. doi:10.1111/j.1465-7295.1998.tb01696.