OEM Providers, Product Quality Assurance, EMS Worldwide, Finished Goods

* Barr, P. (1998). Adapting to unfamiliar environmental events: A look at the evolution of interpretation and its role in strategic change. Organization Science, 9, 644–669.��
* Bartunek, J. M., & Moch, M. K. (1987). First-order, second-order, and third-order change and organization development interventions: A cognitive approach. The Journal of Applied Behavioral Science, 23(4), 483–500.��
* Benedettini, O., Neely, A., & Swink, M. (2015). Why do servitized firms fail? A risk-based explanation. International Journal of Operations & Production Management, 35(6), 946–979.��
* Berger, P., & Luckmann, T. (1967). The social construction of reality. New York: Anchor Books.��
* Brummans, B., Putnam, L., Gray, B., Hanke, R., Lewicki, R. J., & Wiethoff, C. (2008). Making sense of intractable multiparty conflict: A study of framing in four environmental disputes. Communication Monographs, 75(1), 25–51.��
* Burton-Jones, A., & Straub, D. W. (2006). Re-conceptualizing system usage: An approach and empirical tests. Information Systems Research, 17(3), 228–246.��
* Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An overview of business intelligence technology. Communications of the ACM, 54(8), 88.��
* Choo, W. C. (2002). Sensemaking, knowledge creation, and decision making: Organizational knowing as emergent strategy. In C. W. Choo & N. Bontis (Eds.), Strategic management of intellectual capital and organizational knowledge (pp. 79–89). Oxford: Oxford University Press.��
* Davern, M., Shaft, M., & Te’eni, D. (2012). Cognition matters: Enduring questions in cognitive IS research. Journal of the Association for Information Systems, 13, 273–314.��
* Davidson, A. (2002). Technology frames and framing: A socio-cognitive investigation of requirements determination. MIS Quarterly, 26(4), 329–358.��
* Davidson, E. (2006). A technological frames perspective on information technology and organizational change. The Journal of Applied Behavioral Science, 42(1), 23–39.��
* Galbraith, J. R. (2002). Organizing to deliver solutions. Organizational Dynamics, 31(2), 194–207.��
* Garreau, L., Mouricou, P., & Grimand, A. (2015). Drawing on the map: An exploration of strategic sensemaking/giving practices using visual representations. British Journal of Management, 26(4), 689–712.��
* Gartner Press. (2014). Predicts 2014: Don’t try to prevent the digital revolution, exploit IT now. Gartner, Inc.��
* Gebauer, H., Fleisch, E., & Friedli, T. (2005). Overcoming the service paradox in manufacturing companies. European Management Journal, 23(1), 14–26.��
* Gephart, R. (2004). Sensemaking and the new media at work. American Behavioral Scientist, 48, 479–495.��
* Gibson, J. J. (1986). The ecological approach to visual perception L. Hillsdale, NJ: Lawrence Erlbaum.��
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* Heisterber, R., & Verma, A. (2014). Creating business agility. Hoboken, NJ: Wiley.��
* Homburg, C., Fassnacht, M., & Guenther, C. (2003). The role of soft factors in implementing a service-oriented strategy in industrial marketing companies. Journal of Business to Business Marketing, 10(2), 23–51.��
* Jarzabkowski, P., & Kaplan, S. (2015). Strategy tools-in-use: A framework for understanding “technologies of rationality” in practice. Strategic Management Journal, 36(4), 537–558.��
* Kaptelinin, V., & Nardi, B. A. (2006). Acting with technology: Activity theory and interaction design. Cambridge, MA: MIT Press.�
* Kindström, D., Kowalkowski, C., & Nordin, F. (2012). Visualizing the value of service- based offerings: Empirical findings from the manufacturing industry. Journal of Business & Industrial Marketing, 27(7), 538–546.��
* Kowalkowski, C., & Brehmer, P. O. (2008). Technology as a driver for changing customer-provider interfaces. Management Research News, 31(10), 746–757.��
* Kowalkowski, C., Brehmer, P. O., & Kindström, D. (2009). Managing industrial service offerings: Requirements on content and processes. International Journal of Services Technology and Management, 11(1), 42.��
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Smart Cities / Regions / For Development / Community Building / Learning / Network Mobilization / Innovation / Market Integration

1. Austin, Texas Collaboration
2. Los Angeles, California Insight
3. San Diego, California Team Basics
4. Denver, Colorado Value Propositions
5. Houston, Texas Software Design
6. Chicago, Illinois Community Building
7. New York, New York SuperRadiance
8. London, United Kingdom Joy
9. Bangkok, Thailand Honor
10. Seoul Korea Good Faith
11. Italy / Brasil / Hong Kong Integrity
12. Berlin, Germany Mindfulness
13. Stockholm, Sweden ComplexSystems
14. Detroit, Michigan Multi Objective Optimization
15. Miami, Florida Family Science
16. Tampa, Florida Quantum Chemistry
17. Charlotte, NC Decision Support
18. Richmond, Virginia Sustainability
19. Boston, Massachusetts Learning and Improvement
20. Phoenix, AZ Intelligent Systems
21. Seattle, WA Smart City as a Service
22. Portland, OR Rational Unified Process
23. Vancouver, BC Quantum Optimization
24. Toronto, Ontario Health
25. Madrid, Spain Quality
26. Mumbai, India Apple Development
27. Kansas City, MO Microsoft Development
28. St. Petersburg, FL Edge Computing
29. Nashville, Tennessee SaaS
30. Netherlands, Israel, Australia
31. Atlanta, Georgia, Oracle
32. Tokyo, Japan Tesla Elon Musk
33. Paris, France Embedded Systems
34. Cape Town, South Africa Spiritual and Economic Linkages
35. Dubai, UAE Cognitive Computing
36. Minneapolis, MN Rural and Urban Linkages
37. Zurich, Germany Engineering, Development, Operations
38. Nordic Countries - Cognitive Computing, Learning Reasoning Optimization

Feedback and Engagement:
Getting to the heart of the matter for holistic transformation. .

Smart Cities / Regions / For Development / Community Building / Learning / Network Mobilization / Innovation / Market Integration

1. Austin, Texas Collaboration
2. Los Angeles, California Insight
3. San Diego, California Team Basics
4. Denver, Colorado Value Propositions
5. Houston, Texas Software Design
6. Chicago, Illinois Community Building
7. New York, New York SuperRadiance
8. London, United Kingdom Joy
9. Bangkok, Thailand Honor
10. Seoul Korea Good Faith
11. Italy / Brasil / Hong Kong Integrity
12. Berlin, Germany Mindfulness
13. Stockholm, Sweden ComplexSystems
14. Detroit, Michigan Multi Objective Optimization
15. Miami, Florida Family Science
16. Tampa, Florida Quantum Chemistry
17. Charlotte, NC Decision Support
18. Richmond, Virginia Sustainability
19. Boston, Massachusetts Learning and Improvement
20. Phoenix, AZ Intelligent Systems
21. Seattle, WA Smart City as a Service
22. Portland, OR Rational Unified Process
23. Vancouver, BC Quantum Optimization
24. Toronto, Ontario Health
25. Madrid, Spain Quality
26. Mumbai, India Apple Development
27. Kansas City, MO Microsoft Development
28. St. Petersberg, FL Edge Computing
29. Nashville, Tennesse SaaS
30. Netherlands, Israel, Australia
31. Atlanta, Georgia, Oracle
32. Tokyo, Japan Tesla Elon Musk
33. Paris, France Embedded Systems
34. Cape Town, South Africa Spiritual and Economic Linkages
35. Dubai, UAE Cognitive Computing
36. Minneapolis, MN Rural and Urban Linkages

Feedback and Engagement:
Integrating Market and State with People and Community, .

Chu, J.-H., Feng, K.-T., & Chang, T.-S. (2014). Energy-efficient cell selection and resource allocation in LTE-A heterogeneous networks. In 2014 IEEE 25th annual international symposium on personal, indoor, and mobile radio communication (PIMRC), 2014: IEEE, pp. 976–980.��
�Guvenc, I. (2011). Capacity and fairness analysis of heterogeneous networks with range expansion and interference coordination. IEEE Communications Letters, 15(10), 1084–1087.��
* Okino, K., Nakayama, T., Yamazaki, C., Sato, H., & Kusano, Y. (2011). Pico cell range expansion with interference mitigation toward LTE-Advanced heterogeneous networks. In 2011 IEEE international conference on communications workshops (ICC), 2011: IEEE, pp. 1–5.��
* Tefft, J. R., & Kirsch, N. J. (2013). A proximity-based Q-learning reward function for femtocell networks. In 2013 IEEE 78th vehicular technology conference (VTC Fall), 2013: IEEE, pp. 1–5.� �
* Saad, H., Mohamed, A., & ElBatt, T. (2012). Distributed cooperative Q-learning for power allocation in cognitive femtocell networks. In 2012 IEEE vehicular technology conference (VTC Fall), 2012: IEEE, pp. 1–5.�
* Wen, B., Gao, Z., Huang, L., Tang, Y., & Cai, H. (2014). A Q-learning-based downlink resource scheduling method for capacity optimization in LTE femtocells. In 2014 9th international conference on computer science & education, 2014: IEEE, pp. 625–628.��
* Galindo-Serrano, A., & Giupponi, L. (2010). Distributed Q-learning for interference control in OFDMA-based femtocell networks. In 2010 IEEE 71st vehicular technology conference, 2010: IEEE, pp. 1–5.��
* Guo, D., Tang, L., Zhang, X., & Liang, Y.-C. (2020). Joint optimization of handover control and power allocation based on multi-agent deep reinforcement learning. IEEE Transactions on Vehicular Technology, 69(11), 13124–13138.��
* Alnwaimi, G., Vahid, S., & Moessner, K. (2014). Dynamic heterogeneous learning games for opportunistic access in LTE-based macro/femtocell deployments. IEEE Transactions on Wireless Communications, 14(4), 2294–2308.��
* Onireti, O., et al. (2015). A cell outage management framework for dense heterogeneous networks. IEEE Transactions on Vehicular Technology, 65(4), 2097–2113.�
* Behjati, M., & Cosmas, J. (2013). Self-organizing network interference coordination for future LTE-advanced networks. In 2013 IEEE international symposium on broadband multimedia systems and broadcasting (BMSB), 2013: IEEE, pp. 1–5.�
* Aguilar-Garcia, A., et al. (2015). Location-aware self-organizing methods in femtocell networks. Computer Networks, 93, 125–140.��
* Kudo, T., & Ohtsuki, T. (2013). Cell range expansion using distributed Q-learning in heterogeneous networks. Eurasip journal on wireless communications and networking, 2013(1), 1–10.��
* Gomez, C. A., Shami, A., & Wang, X. (2018). Machine learning aided scheme for load balancing in dense IoT networks. Sensors, 18(11), 3779.��
* Ye, Q., Rong, B., Chen, Y., Al-Shalash, M., Caramanis, C., & Andrews, J. G. (2013). User association for load balancing in heterogeneous cellular networks. IEEE Transactions on Wireless Communications, 12(6), 2706–2716.��
* Jiang, H., Pan, Z., Liu, N., You, X., & Deng, T. (2016). Gibbs-sampling-based CRE bias optimization algorithm for ultradense networks. IEEE Transactions on Vehicular Technology, 66(2), 1334–1350.��
* Park, J.-B., & Kim, K. S. (2017). Load-balancing scheme with small-cell cooperation for clustered heterogeneous cellular networks. IEEE Transactions on Vehicular Technology, 67(1), 633–649.��
* Afshang, M., & Dhillon, H. S. (2018). Poisson cluster process based analysis of HetNets with correlated user and base station locations. IEEE Transactions on Wireless Communications, 17(4), 2417–2431.��
* Musleh, S., Ismail, M., & Nordin, R. (2017). Load balancing models based on reinforcement learning for self-optimized macro-femto LTE-advanced heterogeneous network. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1), 47–54.��
* Jaber, M., Imran, M., Tafazolli, R., & Tukmanov, A. (2015). An adaptive backhaul-aware cell range extension approach. In 2015 IEEE international conference on communication workshop (ICCW), 2015: IEEE, pp. 74–79.�
* Hamidouche, K., Saad, W., Debbah, M., Song, J. B., & Hong, C. S. (2017). The 5G cellular backhaul management dilemma: To cache or to serve. IEEE Transactions on Wireless Communications, 16(8), 4866–4879.��
* Samarakoon, S., Bennis, M., Saad, W., & Latva-aho, M. (2013). Backhaul-aware interference management in the uplink of wireless small cell networks. IEEE Transactions on Wireless Communications, 12(11), 5813–5825.
* Team Basics: Edge, Security, Cloud, Data. Holistic Development with Community.

1�Aprodu, M., Naie, D.: Enriques diagrams and the log-canonical threshold for curves. Preprint, arXiv:math/07070783.�2�Casas-Alvero E.: Infinitely near imposed singularities and singularities of polar curves. Math. Ann. 287, 429–454 (1990)�� 3�Ein, L.: Multiplier ideals, vanishing theorems and applications. Algebraic geometry—Santa Cruz, pp. 203–219 (1995)�4�Ein L., Lazarsfeld R., Smith K.E., Varolin D.: Jumping coefficients of multiplier ideals. Duke Math. J. 123(3), 469–506 (2004)�� 5�Enriques F., Chisini O.: Lezioni Sulla Teoria Geometrica Delle Equazioni e Delle Funzioni Algebriche. N. Zanichelli, Bologna (1915)�� 6�Evain L.: La fonction de Hilbert de la réunion de 4h gros points génériques de ℙ� de même multiplicité. J. Algebraic Geom. 8, 787–796 (1999)�� 7�Favre Ch., Jonsson M.: Valuations and multiplier ideals. J. Am. Math. Soc. 18(3), 655–684 (2005)�� 8�Howald J.A.: Multiplier ideals of monomial ideals. Trans. Am. Math. Soc. 353, 2665–2671 (2001)�� 9�Järviletho, T.: Jumping numbers of a simple complete ideal in a two-dimensional regular local ring. Ph.D. Thesis, University of Helsinky (2007)�10�Lazarsfeld, R.: Positivity in algebraic geometry. A Series of Modern Surveys in Mathematics. Springer, Berlin (2004)�11�Naie D.: Irregularity of cyclic multiple planes after Zariski. L’enseignement mathématique 53, 265–305 (2008)�� 12�Semple J.G., Kneebone G.T.: Algebraic Curves. Oxford University Press, London-New York (1959)�� 13�Smith, K.E., Thompson, H.M.: Irrelevant exceptional divisors for curves on a smooth surface. Preprint, arXiv:math/0611765�14�Tucker, K.: Jumping Numbers on algebraic surfaces with rational singularities. Preprint, arXiv:math/081.0734�15�Wall, C.T.C.: Singular points of plane curves. London Mathematical Society Student Texts, vol. 63. Cambridge University Press, Cambridge (2004)

Smart Cities / Regions / For Development / Community Building / Learning / Network Mobilization / Innovation / Market Integration

1. Austin, Texas
2. Los Angeles, California
3. San Diego, California
4. Denver, Colorado
5. Houston, Texas
6. Chicago, Illinois
7. New York, New York
8. London, United Kingdom
9. Bangkok, Thailand
10. Seoul Korea
11. Italy / Brasil / Hong Kong
12. Berlin, Germany
13. Stockholm, Sweden
14. Detroit, Michigan
15. Miami, Florida
16. Tampa, Florida
17. Charlotte, NC
18. Richmond, Virginia
19. Boston, Massachusetts
20. Phoenix, AZ
21. Seattle, WA
22. Portland, OR
23. Vancouver, BC
24. Toronto, Ontario
25. Madrid, Spain
26. Mumbai, India
27. Kansas City, MO
28. St. Petersberg, FL
29. Nashville, Tennesse
30. Netherlands, Israel, Australia
31. Atlanta, Georgia,
32. Tokyo, Japan
33. Paris, France
34. Cape Town, South Africa
35. Dubai, UAE
36. Minneapolis, MN

Integrating Market and State with People and Community, with Wisdom and Motivation.

Chu, J.-H., Feng, K.-T., & Chang, T.-S. (2014). Energy-efficient cell selection and resource allocation in LTE-A heterogeneous networks. In 2014 IEEE 25th annual international symposium on personal, indoor, and mobile radio communication (PIMRC), 2014: IEEE, pp. 976–980.��
�Guvenc, I. (2011). Capacity and fairness analysis of heterogeneous networks with range expansion and interference coordination. IEEE Communications Letters, 15(10), 1084–1087.��
* Okino, K., Nakayama, T., Yamazaki, C., Sato, H., & Kusano, Y. (2011). Pico cell range expansion with interference mitigation toward LTE-Advanced heterogeneous networks. In 2011 IEEE international conference on communications workshops (ICC), 2011: IEEE, pp. 1–5.��
* Tefft, J. R., & Kirsch, N. J. (2013). A proximity-based Q-learning reward function for femtocell networks. In 2013 IEEE 78th vehicular technology conference (VTC Fall), 2013: IEEE, pp. 1–5.� �
* Saad, H., Mohamed, A., & ElBatt, T. (2012). Distributed cooperative Q-learning for power allocation in cognitive femtocell networks. In 2012 IEEE vehicular technology conference (VTC Fall), 2012: IEEE, pp. 1–5.�
* Wen, B., Gao, Z., Huang, L., Tang, Y., & Cai, H. (2014). A Q-learning-based downlink resource scheduling method for capacity optimization in LTE femtocells. In 2014 9th international conference on computer science & education, 2014: IEEE, pp. 625–628.��
* Galindo-Serrano, A., & Giupponi, L. (2010). Distributed Q-learning for interference control in OFDMA-based femtocell networks. In 2010 IEEE 71st vehicular technology conference, 2010: IEEE, pp. 1–5.��
* Guo, D., Tang, L., Zhang, X., & Liang, Y.-C. (2020). Joint optimization of handover control and power allocation based on multi-agent deep reinforcement learning. IEEE Transactions on Vehicular Technology, 69(11), 13124–13138.��
* Alnwaimi, G., Vahid, S., & Moessner, K. (2014). Dynamic heterogeneous learning games for opportunistic access in LTE-based macro/femtocell deployments. IEEE Transactions on Wireless Communications, 14(4), 2294–2308.��
* Onireti, O., et al. (2015). A cell outage management framework for dense heterogeneous networks. IEEE Transactions on Vehicular Technology, 65(4), 2097–2113.�
* Behjati, M., & Cosmas, J. (2013). Self-organizing network interference coordination for future LTE-advanced networks. In 2013 IEEE international symposium on broadband multimedia systems and broadcasting (BMSB), 2013: IEEE, pp. 1–5.�
* Aguilar-Garcia, A., et al. (2015). Location-aware self-organizing methods in femtocell networks. Computer Networks, 93, 125–140.��
* Kudo, T., & Ohtsuki, T. (2013). Cell range expansion using distributed Q-learning in heterogeneous networks. Eurasip journal on wireless communications and networking, 2013(1), 1–10.��
* Gomez, C. A., Shami, A., & Wang, X. (2018). Machine learning aided scheme for load balancing in dense IoT networks. Sensors, 18(11), 3779.��
* Ye, Q., Rong, B., Chen, Y., Al-Shalash, M., Caramanis, C., & Andrews, J. G. (2013). User association for load balancing in heterogeneous cellular networks. IEEE Transactions on Wireless Communications, 12(6), 2706–2716.��
* Jiang, H., Pan, Z., Liu, N., You, X., & Deng, T. (2016). Gibbs-sampling-based CRE bias optimization algorithm for ultradense networks. IEEE Transactions on Vehicular Technology, 66(2), 1334–1350.��
* Park, J.-B., & Kim, K. S. (2017). Load-balancing scheme with small-cell cooperation for clustered heterogeneous cellular networks. IEEE Transactions on Vehicular Technology, 67(1), 633–649.��
* Afshang, M., & Dhillon, H. S. (2018). Poisson cluster process based analysis of HetNets with correlated user and base station locations. IEEE Transactions on Wireless Communications, 17(4), 2417–2431.��
* Musleh, S., Ismail, M., & Nordin, R. (2017). Load balancing models based on reinforcement learning for self-optimized macro-femto LTE-advanced heterogeneous network. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 9(1), 47–54.��
* Jaber, M., Imran, M., Tafazolli, R., & Tukmanov, A. (2015). An adaptive backhaul-aware cell range extension approach. In 2015 IEEE international conference on communication workshop (ICCW), 2015: IEEE, pp. 74–79.�
* Hamidouche, K., Saad, W., Debbah, M., Song, J. B., & Hong, C. S. (2017). The 5G cellular backhaul management dilemma: To cache or to serve. IEEE Transactions on Wireless Communications, 16(8), 4866–4879.��
* Samarakoon, S., Bennis, M., Saad, W., & Latva-aho, M. (2013). Backhaul-aware interference management in the uplink of wireless small cell networks. IEEE Transactions on Wireless Communications, 12(11), 5813–5825.
* Team Basics: Edge, Security, Cloud, Data. Holistic Development with Community.

1�Aprodu, M., Naie, D.: Enriques diagrams and the log-canonical threshold for curves. Preprint, arXiv:math/07070783.�2�Casas-Alvero E.: Infinitely near imposed singularities and singularities of polar curves. Math. Ann. 287, 429–454 (1990)�� 3�Ein, L.: Multiplier ideals, vanishing theorems and applications. Algebraic geometry—Santa Cruz, pp. 203–219 (1995)�4�Ein L., Lazarsfeld R., Smith K.E., Varolin D.: Jumping coefficients of multiplier ideals. Duke Math. J. 123(3), 469–506 (2004)�� 5�Enriques F., Chisini O.: Lezioni Sulla Teoria Geometrica Delle Equazioni e Delle Funzioni Algebriche. N. Zanichelli, Bologna (1915)�� 6�Evain L.: La fonction de Hilbert de la réunion de 4h gros points génériques de ℙ� de même multiplicité. J. Algebraic Geom. 8, 787–796 (1999)�� 7�Favre Ch., Jonsson M.: Valuations and multiplier ideals. J. Am. Math. Soc. 18(3), 655–684 (2005)�� 8�Howald J.A.: Multiplier ideals of monomial ideals. Trans. Am. Math. Soc. 353, 2665–2671 (2001)�� 9�Järviletho, T.: Jumping numbers of a simple complete ideal in a two-dimensional regular local ring. Ph.D. Thesis, University of Helsinky (2007)�10�Lazarsfeld, R.: Positivity in algebraic geometry. A Series of Modern Surveys in Mathematics. Springer, Berlin (2004)�11�Naie D.: Irregularity of cyclic multiple planes after Zariski. L’enseignement mathématique 53, 265–305 (2008)�� 12�Semple J.G., Kneebone G.T.: Algebraic Curves. Oxford University Press, London-New York (1959)�� 13�Smith, K.E., Thompson, H.M.: Irrelevant exceptional divisors for curves on a smooth surface. Preprint, arXiv:math/0611765�14�Tucker, K.: Jumping Numbers on algebraic surfaces with rational singularities. Preprint, arXiv:math/081.0734�15�Wall, C.T.C.: Singular points of plane curves. London Mathematical Society Student Texts, vol. 63. Cambridge University Press, Cambridge (2004)