Knowledge, Promotional Events, and the Contribution of Clustering to Innovation

This is an author's accepted manuscript of an article published in: “Journal of Promotion Management"; Volume 21, Issue 4, 2015; copyright Taylor & Francis; available online at: http://dx.doi.org/10.1080/10496491.2015.1051407This research provides insight on the mechanisms through which knowledge acquired through promotional events and through spatial co-location simultaneously sustains firm s innovation. Applying the concept of temporary clusters to promotional events, we simultaneously test how internal resources mediating effect and promotional events moderating role affect innovation in clusters. Regression analyses with nonparametric bootstrapping and a large sample of Spanish clusters confirm the synergies derived from the combination of internal resources, local relationships, and complementary foreign events. Although extra-cluster linkages increase this effect, synergies creation requires attendance to international exhibitions or conventions. Valuable implications for practitioners and policy makers are discussed.Belso Martínez, JA.; Mas Verdú, F.; Roig Tierno, H. (2015). Knowledge, Promotional Events, and the Contribution of Clustering to Innovation. Journal of Promotion Management. 21(4):504-515. doi:10.1080/10496491.2015.1051407S504515214Asheim, B. T., & Isaksen, A. (2002). The Journal of Technology Transfer, 27(1), 77-86. doi:10.1023/a:1013100704794Autant-Bernard, C., Billand, P., Frachisse, D., & Massard, N. (2007). Social distance versus spatial distance in R&D cooperation: Empirical evidence from European collaboration choices in micro and nanotechnologies. Papers in Regional Science, 86(3), 495-519. doi:10.1111/j.1435-5957.2007.00132.xBathelt, H., Malmberg, A., & Maskell, P. (2004). Clusters and knowledge: local buzz, global pipelines and the process of knowledge creation. Progress in Human Geography, 28(1), 31-56. doi:10.1191/0309132504ph469oaBathelt, H., & Schuldt, N. (2008). Between Luminaires and Meat Grinders: International Trade Fairs as Temporary Clusters. Regional Studies, 42(6), 853-868. doi:10.1080/00343400701543298Boschma, R. (2005). Proximity and Innovation: A Critical Assessment. Regional Studies, 39(1), 61-74. doi:10.1080/0034340052000320887Cohen, W. M., & Levinthal, D. A. (1990). Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly, 35(1), 128. doi:10.2307/2393553Geigenmüller, A. (2010). The role of virtual trade fairs in relationship value creation. Journal of Business & Industrial Marketing, 25(4), 284-292. doi:10.1108/08858621011038243Giner, J. M., & María, M. J. S. (2002). ‘Territorial systems of small firms in Spain: an analysis of productive and organizational characteristics in industrial districts’. Entrepreneurship & Regional Development, 14(3), 211-228. doi:10.1080/08985620210136009Giuliani, 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.2004.10.008Hervas-Oliver, J.-L., & Albors-Garrigos, J. (2008). The role of the firm’s internal and relational capabilities in clusters: when distance and embeddedness are not enough to explain innovation. Journal of Economic Geography, 9(2), 263-283. doi:10.1093/jeg/lbn033Lagendijk, A., & Lorentzen, A. (2007). Proximity, Knowledge and Innovation in Peripheral Regions. On the Intersection between Geographical and Organizational Proximity. European Planning Studies, 15(4), 457-466. doi:10.1080/09654310601133260Ling-yee, L. (2006). Relationship learning at trade shows: Its antecedents and consequences. Industrial Marketing Management, 35(2), 166-177. doi:10.1016/j.indmarman.2005.03.006Lorentzen, A. (2007). The Geography of Knowledge Sourcing—A Case Study of Polish Manufacturing Enterprises. European Planning Studies, 15(4), 467-486. doi:10.1080/09654310601133252Markusen, A. (1996). Sticky Places in Slippery Space: A Typology of Industrial Districts. Economic Geography, 72(3), 293. doi:10.2307/144402McCann, B. T., & Folta, T. B. (2011). Performance differentials within geographic clusters. Journal of Business Venturing, 26(1), 104-123. doi:10.1016/j.jbusvent.2009.04.004Palumbo, F., & Herbig, P. A. (2002). Trade Shows and Fairs. Journal of Promotion Management, 8(1), 93-108. doi:10.1300/j057v08n01_09Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing Moderated Mediation Hypotheses: Theory, Methods, and Prescriptions. Multivariate Behavioral Research, 42(1), 185-227. doi:10.1080/00273170701341316Ramírez-Pasillas, M. (2008). Resituating Proximity and Knowledge Cross-fertilization in Clusters by Means of International Trade Fairs. European Planning Studies, 16(5), 643-663. doi:10.1080/09654310802049158Ramírez-Pasillas, M. (2010). International trade fairs as amplifiers of permanent and temporary proximities in clusters. Entrepreneurship & Regional Development, 22(2), 155-187. doi:10.1080/08985620902815106Rinallo, D., & Golfetto, F. (2011). Exploring the Knowledge Strategies of Temporary Cluster Organizers: A Longitudinal Study of the EU Fabric Industry Trade Shows (1986-2006). Economic Geography, 87(4), 453-476. doi:10.1111/j.1944-8287.2011.01127.xRychen, F., & Zimmermann, J.-B. (2008). Clusters in the Global Knowledge-based Economy: Knowledge Gatekeepers and Temporary Proximity. Regional Studies, 42(6), 767-776. doi:10.1080/00343400802088300Tafesse, W., & Korneliussen, T. (2012). Managing Trade Show Campaigns: Why Managerial Responsibilities Matter? Journal of Promotion Management, 18(2), 236-253. doi:10.1080/10496491.2012.668434Tanner, J. F., & Chonko, L. B. (2002). Using Trade Shows Throughout the Product Life Cycle. Journal of Promotion Management, 8(1), 109-125. doi:10.1300/j057v08n01_10Torre, A. (2008). On the Role Played by Temporary Geographical Proximity in Knowledge Transmission. Regional Studies, 42(6), 869-889. doi:10.1080/00343400801922814Wolfe, D. A., & Gertler, M. S. (2004). Clusters from the Inside and Out: Local Dynamics and Global Linkages. Urban Studies, 41(5-6), 1071-1093. doi:10.1080/00420980410001675832Wood, E. H. (2009). Evaluating Event Marketing: Experience or Outcome? Journal of Promotion Management, 15(1-2), 247-268. doi:10.1080/1049649090289258

Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location

[EN] A large volume of the water produced for public supply is lost in the systems between sources and consumers. An important-in many cases the greatest-fraction of these losses are physical losses, mainly related to leaks and bursts in pipes and in consumer connections. Fast detection and location of bursts plays an important role in the design of operation strategies for water loss control, since this helps reduce the volume lost from the instant the event occurs until its effective repair (run time). The transient pressure signals caused by bursts contain important information about their location and magnitude, and stamp on any of these events a specific "hydraulic signature". The present work proposes and evaluates three methods to disaggregate transient signals, which are used afterwards to train artificial neural networks (ANNs) to identify burst locations and calculate the leaked flow. In addition, a clustering process is also used to group similar signals, and then train specific ANNs for each group, thus improving both the computational efficiency and the location accuracy. The proposed methods are applied to two real distribution networks, and the results show good accuracy in burst location and characterization.Manzi, D.; Brentan, BM.; Meirelles, G.; Izquierdo Sebastián, J.; Luvizotto Jr., E. (2019). Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location. Water. 11(11):1-13. https://doi.org/10.3390/w11112279S1131111Creaco, E., & Walski, T. (2017). Economic Analysis of Pressure Control for Leakage and Pipe Burst Reduction. Journal of Water Resources Planning and Management, 143(12), 04017074. doi:10.1061/(asce)wr.1943-5452.0000846Campisano, A., Creaco, E., & Modica, C. (2010). RTC of Valves for Leakage Reduction in Water Supply Networks. Journal of Water Resources Planning and Management, 136(1), 138-141. doi:10.1061/(asce)0733-9496(2010)136:1(138)Campisano, A., Modica, C., Reitano, S., Ugarelli, R., & Bagherian, S. (2016). Field-Oriented Methodology for Real-Time Pressure Control to Reduce Leakage in Water Distribution Networks. Journal of Water Resources Planning and Management, 142(12), 04016057. doi:10.1061/(asce)wr.1943-5452.0000697Vítkovský, J. P., Simpson, A. R., & Lambert, M. F. (2000). Leak Detection and Calibration Using Transients and Genetic Algorithms. Journal of Water Resources Planning and Management, 126(4), 262-265. doi:10.1061/(asce)0733-9496(2000)126:4(262)Pérez, R., Puig, V., Pascual, J., Quevedo, J., Landeros, E., & Peralta, A. (2011). Methodology for leakage isolation using pressure sensitivity analysis in water distribution networks. Control Engineering Practice, 19(10), 1157-1167. doi:10.1016/j.conengprac.2011.06.004Jung, D., & Kim, J. (2017). Robust Meter Network for Water Distribution Pipe Burst Detection. Water, 9(11), 820. doi:10.3390/w9110820Colombo, A. F., Lee, P., & Karney, B. W. (2009). A selective literature review of transient-based leak detection methods. Journal of Hydro-environment Research, 2(4), 212-227. doi:10.1016/j.jher.2009.02.003Choi, D., Kim, S.-W., Choi, M.-A., & Geem, Z. (2016). Adaptive Kalman Filter Based on Adjustable Sampling Interval in Burst Detection for Water Distribution System. Water, 8(4), 142. doi:10.3390/w8040142Christodoulou, S. E., Kourti, E., & Agathokleous, A. (2016). Waterloss Detection in Water Distribution Networks using Wavelet Change-Point Detection. Water Resources Management, 31(3), 979-994. doi:10.1007/s11269-016-1558-5Guo, X., Yang, K., & Guo, Y. (2012). Leak detection in pipelines by exclusively frequency domain method. Science China Technological Sciences, 55(3), 743-752. doi:10.1007/s11431-011-4707-3Holloway, M. B., & Hanif Chaudhry, M. (1985). Stability and accuracy of waterhammer analysis. Advances in Water Resources, 8(3), 121-128. doi:10.1016/0309-1708(85)90052-1Sanz, G., Pérez, R., Kapelan, Z., & Savic, D. (2016). Leak Detection and Localization through Demand Components Calibration. Journal of Water Resources Planning and Management, 142(2), 04015057. doi:10.1061/(asce)wr.1943-5452.0000592Zhang, Q., Wu, Z. Y., Zhao, M., Qi, J., Huang, Y., & Zhao, H. (2016). Leakage Zone Identification in Large-Scale Water Distribution Systems Using Multiclass Support Vector Machines. Journal of Water Resources Planning and Management, 142(11), 04016042. doi:10.1061/(asce)wr.1943-5452.0000661Mounce, S. R., & Machell, J. (2006). Burst detection using hydraulic data from water distribution systems with artificial neural networks. Urban Water Journal, 3(1), 21-31. doi:10.1080/15730620600578538Covas, D., Ramos, H., & de Almeida, A. B. (2005). Standing Wave Difference Method for Leak Detection in Pipeline Systems. Journal of Hydraulic Engineering, 131(12), 1106-1116. doi:10.1061/(asce)0733-9429(2005)131:12(1106)Liggett, J. A., & Chen, L. (1994). Inverse Transient Analysis in Pipe Networks. Journal of Hydraulic Engineering, 120(8), 934-955. doi:10.1061/(asce)0733-9429(1994)120:8(934)Caputo, A. C., & Pelagagge, P. M. (2002). An inverse approach for piping networks monitoring. Journal of Loss Prevention in the Process Industries, 15(6), 497-505. doi:10.1016/s0950-4230(02)00036-0Van Zyl, J. E. (2014). Theoretical Modeling of Pressure and Leakage in Water Distribution Systems. Procedia Engineering, 89, 273-277. doi:10.1016/j.proeng.2014.11.187Izquierdo, J., & Iglesias, P. . (2004). Mathematical modelling of hydraulic transients in complex systems. Mathematical and Computer Modelling, 39(4-5), 529-540. doi:10.1016/s0895-7177(04)90524-9Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007). Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 15(2), 107-144. doi:10.1007/s10618-007-0064-zNavarrete-López, C., Herrera, M., Brentan, B., Luvizotto, E., & Izquierdo, J. (2019). Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework. Water, 11(2), 246. doi:10.3390/w11020246Meirelles, G., Manzi, D., Brentan, B., Goulart, T., & Luvizotto, E. (2017). Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks. Water Resources Management, 31(13), 4339-4351. doi:10.1007/s11269-017-1750-2Adamowski, J., & Chan, H. F. (2011). A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407(1-4), 28-40. doi:10.1016/j.jhydrol.2011.06.013Brentan, B., Meirelles, G., Luvizotto, E., & Izquierdo, J. (2018). Hybrid SOM+ k -Means clustering to improve planning, operation and management in water distribution systems. Environmental Modelling & Software, 106, 77-88. doi:10.1016/j.envsoft.2018.02.013Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics - Theory and Methods, 3(1), 1-27. doi:10.1080/0361092740882710

On Weakly Associative Lattices and Near Lattices

The main aim of this article is to introduce formally two generalizations of lattices, namely weakly associative lattices and near lattices, which can be obtained from the former by certain weakening of the usual well-known axioms. We show selected propositions devoted to weakly associative lattices and near lattices from Chapter 6 of [15], dealing also with alternative versions of classical axiomatizations. Some of the results were proven in the Mizar [1], [2] system with the help of Prover9 [14] proof assistant.Damian Sawicki - Institute of Informatics, University of Białystok, PolandAdam Grabowski - Institute of Informatics, University of Białystok, PolandGrzegorz Bancerek, Czesław Bylinski, Adam Grabowski, Artur Korniłowicz, Roman Matuszewski, Adam Naumowicz, Karol Pak, and Josef Urban. Mizar: State-of-the-art and beyond. In Manfred Kerber, Jacques Carette, Cezary Kaliszyk, Florian Rabe, and Volker Sorge, editors, Intelligent Computer Mathematics, volume 9150 of Lecture Notes in Computer Science, pages 261–279. Springer International Publishing, 2015. ISBN 978-3-319-20614-1. doi:10.1007/978-3-319-20615-8_17.Grzegorz Bancerek, Czesław Bylinski, Adam Grabowski, Artur Korniłowicz, Roman Matuszewski, Adam Naumowicz, and Karol Pak. The role of the Mizar Mathematical Library for interactive proof development in Mizar. Journal of Automated Reasoning, 61(1):9–32, 2018. doi:10.1007/s10817-017-9440-6.Garrett Birkhoff. Lattice Theory. Providence, Rhode Island, New York, 1967.B.A. Davey and H.A. Priestley. Introduction to Lattices and Order. Cambridge University Press, 2002.Ervin Fried and George Grätzer. Some examples of weakly associative lattices. Colloquium Mathematicum, 27:215–221, 1973. doi:10.4064/cm-27-2-215-221.Adam Grabowski. Mechanizing complemented lattices within Mizar system. Journal of Automated Reasoning, 55:211–221, 2015. doi:10.1007/s10817-015-9333-5.Adam Grabowski and Markus Moschner. Managing heterogeneous theories within a mathematical knowledge repository. In Andrea Asperti, Grzegorz Bancerek, and Andrzej Trybulec, editors, Mathematical Knowledge Management Proceedings, volume 3119 of Lecture Notes in Computer Science, pages 116–129. Springer, 2004. doi:10.1007/978-3-540-27818-4_9. 3rd International Conference on Mathematical Knowledge Management, Bialowieza, Poland, Sep. 19–21, 2004.Adam Grabowski and Damian Sawicki. On two alternative axiomatizations of lattices by McKenzie and Sholander. Formalized Mathematics, 26(2):193–198, 2018. doi:10.2478/forma-2018-0017.Adam Grabowski and Christoph Schwarzweller. Translating mathematical vernacular into knowledge repositories. In Michael Kohlhase, editor, Mathematical Knowledge Management, volume 3863 of Lecture Notes in Computer Science, pages 49–64. Springer, 2006. doi:https://doi.org/10.1007/11618027 4. 4th International Conference on Mathematical Knowledge Management, Bremen, Germany, MKM 2005, July 15–17, 2005, Revised Selected Papers.Adam Grabowski, Artur Korniłowicz, and Christoph Schwarzweller. Equality in computer proof-assistants. In Ganzha, Maria and Maciaszek, Leszek and Paprzycki, Marcin, editor, Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, volume 5 of ACSIS-Annals of Computer Science and Information Systems, pages 45–54. IEEE, 2015. doi:10.15439/2015F229.George Grätzer. General Lattice Theory. Academic Press, New York, 1978.George Grätzer. Lattice Theory: Foundation. Birkhäuser, 2011.Dominik Kulesza and Adam Grabowski. Formalization of quasilattices. Formalized Mathematics, 28(2):217–225, 2020. doi:10.2478/forma-2020-0019.William McCune. Prover9 and Mace4. 2005–2010.William McCune and Ranganathan Padmanabhan. Automated Deduction in Equational Logic and Cubic Curves. Springer-Verlag, Berlin, 1996.Ranganathan Padmanabhan and Sergiu Rudeanu. Axioms for Lattices and Boolean Algebras. World Scientific Publishers, 2008.Piotr Rudnicki and Josef Urban. Escape to ATP for Mizar. In First International Workshop on Proof eXchange for Theorem Proving-PxTP 2011, 2011.Stanisław Zukowski. Introduction to lattice theory. Formalized Mathematics, 1(1):215–222, 1990.292778

Formalization of Quasilattices

The main aim of this article is to introduce formally one of the generalizations of lattices, namely quasilattices, which can be obtained from the axiomatization of the former class by certain weakening of ordinary absorption laws. We show propositions QLT-1 to QLT-7 from [15], presenting also some short variants of corresponding axiom systems. Some of the results were proven in the Mizar [1], [2] system with the help of Prover9 [14] proof assistant.Dominik Kulesza - Institute of Informatics, University of Białystok, PolandAdam Grabowski - Institute of Informatics, University of Białystok, PolandGrzegorz Bancerek, Czesław Byliński, Adam Grabowski, Artur Korniłowicz, Roman Matuszewski, Adam Naumowicz, Karol Pąk, and Josef Urban. Mizar: State-of-the-art and beyond. In Manfred Kerber, Jacques Carette, Cezary Kaliszyk, Florian Rabe, and Volker Sorge, editors, Intelligent Computer Mathematics, volume 9150 of Lecture Notes in Computer Science, pages 261–279. Springer International Publishing, 2015. ISBN 978-3-319-20614-1. doi:10.1007/978-3-319-20615-8_17.Grzegorz Bancerek, Czesław Byliński, Adam Grabowski, Artur Korniłowicz, Roman Matuszewski, Adam Naumowicz, and Karol Pąk. The role of the Mizar Mathematical Library for interactive proof development in Mizar. Journal of Automated Reasoning, 61(1):9–32, 2018. doi:10.1007/s10817-017-9440-6.Garrett Birkhoff. Lattice Theory. Providence, Rhode Island, New York, 1967.B.A. Davey and H.A. Priestley. Introduction to Lattices and Order. Cambridge University Press, 2002.G. Gierz, K.H. Hofmann, K. Keimel, J.D. Lawson, M. Mislove, and D.S. Scott. A Compendium of Continuous Lattices. Springer-Verlag, Berlin, Heidelberg, New York, 1980.Adam Grabowski. Mechanizing complemented lattices within Mizar system. Journal of Automated Reasoning, 55:211–221, 2015. doi:10.1007/s10817-015-9333-5.Adam Grabowski and Robert Milewski. Boolean posets, posets under inclusion and products of relational structures. Formalized Mathematics, 6(1):117–121, 1997.Adam Grabowski and Markus Moschner. Managing heterogeneous theories within a mathematical knowledge repository. In Andrea Asperti, Grzegorz Bancerek, and Andrzej Trybulec, editors, Mathematical Knowledge Management Proceedings, volume 3119 of Lecture Notes in Computer Science, pages 116–129. Springer, 2004. doi:10.1007/978-3-540-27818-4_9. 3rd International Conference on Mathematical Knowledge Management, Bialowieza, Poland, Sep. 19–21, 2004.Adam Grabowski and Damian Sawicki. On two alternative axiomatizations of lattices by McKenzie and Sholander. Formalized Mathematics, 26(2):193–198, 2018. doi:10.2478/forma-2018-0017.Adam Grabowski and Christoph Schwarzweller. Translating mathematical vernacular into knowledge repositories. In Michael Kohlhase, editor, Mathematical Knowledge Management, volume 3863 of Lecture Notes in Computer Science, pages 49–64. Springer, 2006. doi:https://doi.org/10.1007/11618027_4. 4th International Conference on Mathematical Knowledge Management, Bremen, Germany, MKM 2005, July 15–17, 2005, Revised Selected Papers.Adam Grabowski, Artur Korniłowicz, and Christoph Schwarzweller. Equality in computer proof-assistants. In Ganzha, Maria and Maciaszek, Leszek and Paprzycki, Marcin, editor, Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, volume 5 of ACSIS-Annals of Computer Science and Information Systems, pages 45–54. IEEE, 2015. doi:10.15439/2015F229.George Grätzer. General Lattice Theory. Academic Press, New York, 1978.George Grätzer. Lattice Theory: Foundation. Birkhäuser, 2011.William McCune. Prover9 and Mace4. 2005–2010.William McCune and Ranganathan Padmanabhan. Automated Deduction in Equational Logic and Cubic Curves. Springer-Verlag, Berlin, 1996.Ranganathan Padmanabhan and Sergiu Rudeanu. Axioms for Lattices and Boolean Algebras. World Scientific Publishers, 2008.Piotr Rudnicki and Josef Urban. Escape to ATP for Mizar. In First International Workshop on Proof eXchange for Theorem Proving-PxTP 2011, 2011.Stanisław Żukowski. Introduction to lattice theory. Formalized Mathematics, 1(1):215–222, 1990.28221722

Formalizing Two Generalized Approximation Operators

Rough sets, developed by Pawlak [15], are important tool to describe situation of incomplete or partially unknown information. In this article we give the formal characterization of two closely related rough approximations, along the lines proposed in a paper by Gomolińska [2]. We continue the formalization of rough sets in Mizar [1] started in [6].Adam Grabowski - Institute of Informatics, University of Białystok, PolandMichał Sielwiesiuk - Institute of Informatics, University of Białystok, PolandGrzegorz Bancerek, Czesław Byliński, Adam Grabowski, Artur Korniłowicz, Roman Matuszewski, Adam Naumowicz, Karol Pąk, and Josef Urban. Mizar: State-of-the-art and beyond. In Manfred Kerber, Jacques Carette, Cezary Kaliszyk, Florian Rabe, and Volker Sorge, editors, Intelligent Computer Mathematics, volume 9150 of Lecture Notes in Computer Science, pages 261–279. Springer International Publishing, 2015. ISBN 978-3-319-20614-1. doi:10.1007/978-3-319-20615-8_17.Anna Gomolińska. A comparative study of some generalized rough approximations. Fundamenta Informaticae, 51:103–119, 2002.Adam Grabowski. Automated discovery of properties of rough sets. Fundamenta Informaticae, 128:65–79, 2013. doi:10.3233/FI-2013-933.Adam Grabowski. Lattice theory for rough sets – a case study with Mizar. Fundamenta Informaticae, 147(2–3):223–240, 2016. doi:10.3233/FI-2016-1406.Adam Grabowski. Formalization of generalized almost distributive lattices. Formalized Mathematics, 22(3):257–267, 2014. doi:10.2478/forma-2014-0026.Adam Grabowski. Basic properties of rough sets and rough membership function. Formalized Mathematics, 12(1):21–28, 2004.Adam Grabowski. Relational formal characterization of rough sets. Formalized Mathematics, 21(1):55–64, 2013. doi:10.2478/forma-2013-0006.Adam Grabowski. Binary relations-based rough sets – an automated approach. Formalized Mathematics, 24(2):143–155, 2016. doi:10.1515/forma-2016-0011.Adam Grabowski and Magdalena Jastrzębska. A note on a formal approach to rough operators. In Marcin S. Szczuka and Marzena Kryszkiewicz et al., editors, Rough Sets and Current Trends in Computing – 7th International Conference, RSCTC 2010, Warsaw, Poland, June 28-30, 2010. Proceedings, volume 6086 of Lecture Notes in Computer Science, pages 307–316. Springer, 2010. doi:10.1007/978-3-642-13529-3_33.Adam Grabowski and Magdalena Jastrzębska. Rough set theory from a math-assistant perspective. In Rough Sets and Intelligent Systems Paradigms, International Conference, RSEISP 2007, Warsaw, Poland, June 28–30, 2007, Proceedings, pages 152–161, 2007. doi:10.1007/978-3-540-73451-2_17.Adam Grabowski and Christoph Schwarzweller. On duplication in mathematical repositories. In Serge Autexier, Jacques Calmet, David Delahaye, Patrick D. F. Ion, Laurence Rideau, Renaud Rioboo, and Alan P. Sexton, editors, Intelligent Computer Mathematics, 10th International Conference, AISC 2010, 17th Symposium, Calculemus 2010, and 9th International Conference, MKM 2010, Paris, France, July 5–10, 2010. Proceedings, volume 6167 of Lecture Notes in Computer Science, pages 300–314. Springer, 2010. doi:10.1007/978-3-642-14128-7_26.Adam Grabowski and Christoph Schwarzweller. Rough Concept Analysis - theory development in the Mizar system. In Asperti, Andrea and Bancerek, Grzegorz and Trybulec, Andrzej, editor, Mathematical Knowledge Management, Third International Conference, MKM 2004, Bialowieza, Poland, September 19–21, 2004, Proceedings, volume 3119 of Lecture Notes in Computer Science, pages 130–144, 2004. doi:10.1007/978-3-540-27818-4_10. 3rd International Conference on Mathematical Knowledge Management, Bialowieza, Poland, Sep. 19-21, 2004.Jouni Järvinen. Lattice theory for rough sets. Transactions of Rough Sets, VI, Lecture Notes in Computer Science, 4374:400–498, 2007.Eliza Niewiadomska and Adam Grabowski. Introduction to formal preference spaces. Formalized Mathematics, 21(3):223–233, 2013. doi:10.2478/forma-2013-0024.Zdzisław Pawlak. Rough sets. International Journal of Parallel Programming, 11:341–356, 1982. doi:10.1007/BF01001956.Y.Y. Yao. Two views of the theory of rough sets in finite universes. International Journal of Approximate Reasoning, 15(4):291–317, 1996. doi:10.1016/S0888-613X(96)00071-0.William Zhu. Generalized rough sets based on relations. Information Sciences, 177: 4997–5011, 2007.26218319

Strategic Knowledge Measurement and Management

Knowledge and intellectual capital are now recognized as vital resources for organizational survival and competitive advantage. A vast array of knowledge measures has evolved, spanning many disciplines. This chapter reviews knowledge measures focusing on groups of individuals (such as teams, business and organizations), as they reflect the stock or flow of knowledge, as well as enabling processes that enhance knowledge stocks and flows. The chapter emphasizes the importance of organizational value chains, pivotal talent pools and the link between knowledge and competitive success, in understanding the significance of today’s knowledge measures, and opportunities for future research and practice to enhance them

KM Maturity Factors Affecting High Performance in Universities

This paper aims to measure Knowledge Management Maturity (KMM) in the universities to determine the impact of knowledge management on high performance. This study was applied on Al-Quds Open University in Gaza strip, Palestine. Asian productivity organization model was applied to measure KMM. Second dimension which assess high performance was developed by the authors. The controlled sample was (306). Several statistical tools were used for data analysis and hypotheses testing, including reliability Correlation using Cronbach’s alpha, “ANOVA”, Simple Linear Regression and Step Wise Regression.The overall findings of the current study suggest that KMM is suitable for measuring high performance. KMM assessment shows that maturity level is in level three. Findings also support the main hypothesis and it is sub- hypotheses. The most important factors effecting high performance are: Processes, KM leadership, People, KM Outcomes and Learning and Innovation. Furthermore the current study is unique by the virtue of its nature, scope and way of implied investigation, as it is the first comparative study in the universities of Palestine explores the status of KMM using the Asian productivity Model

Organizational Excellence in Palestinian Universities of Gaza Strip

The research aims to identify the organizational excellence in Palestinian universities of Gaza Strip, from the perspective of senior management. The questionnaires were distributed the top senior management in the Palestinian universities, and the study population was (344) employees in senior management in Palestinian universities. A stratified random sample were selected from of employees in the Palestinian universities consist of (182) employees at return rate of (69.2%). SPSS program for analyzing and processing the data was used. The study reached the following results: the senior management agrees largely on the importance of the axis of "Leadership Excellence" and "Excellence service sectors". The senior management agrees moderately about the importance of the axis of the “Knowledge excellence". The study showed that there is a weakness in the employment of scientific research to serve the community, there is weakness in the follow-up of the universities management for the performance of their graduates in the institutions in which they work. Senior management agrees on the importance of the "Organizational Excellence" moderately. The recommendations of study includes: the need to develop principles and fair criteria for the selection of the best candidates for the university and university leaders based on specialization, competence, experience, skills, integrity and not on the basis of favoritism