A Description of Multiscale Modeling for the Head-Disk Interface Focusing on Bottom-Level Lubricant and Carbon Overcoat Models

The challenges in designing future head disk interface (HDI) demand efficient theoretical modeling tools with flexibility in investigating various combinations of perfluoropolyether (PFPE) and carbon overcoat (COC) materials. For broad range of time and length scales, we developed multiscale/multiphysical modeling approach, which can bring paradigm-shifting improvements in advanced HDI design. In this paper, we introduce our multiscale modeling methodology with an effective strategic framework for the HDI system. Our multiscale methodology in this paper adopts a bottom to top approach beginning with the high-resolution modeling, which describes the intramolecular/intermolecular PFPE-COC degrees of freedom governing the functional oligomeric molecular conformations on the carbon surfaces. By introducing methodology for integrating atomistic/molecular/mesoscale levels via coarse-graining procedures, we investigated static and dynamic properties of PFPE-COC combinations with various molecular architectures. By bridging the atomistic and molecular scales, we are able to systematically incorporate first-principle physics into molecular models, thereby demonstrating a pathway for designing materials based on molecular architecture. We also discussed future materials (e.g., graphene for COC, star-like PFPEs) and systems (e.g., heat-assisted magnetic recording (HAMR)) with higher scale modeling methodology, which enables the incorporation of molecular/mesoscale information into the continuum scale models

A Case-Mix System for Adults with Developmental Disabilities

Effective management of publicly funded services matches the provision of needed services with cost-efficient payment methods. Payment systems that recognize differences in care needs (eg, case-mix systems) allow for greater proportions of available funds to be directed to providers supporting individuals with more needs. We describe a new way to allocate funds spent on adults with intellectual disabilities (ID) as part of a system-wide Medicaid payment reform initiative in Arkansas. Analyses were based on population-level data for persons living at home, collected using the interRAI ID assessment system, which were linked to paid service claims. We used automatic interactions detection to sort individuals into unique groups and provide a standardized relative measure of the cost of the services provided to each group. The final case-mix system has 33 distinct final groups and explains 26% of the variance in costs, which is similar to other systems in health and social services sectors. The results indicate that this system could be the foundation for a future case-mix approach to reimbursement and stand the test of “fairness” when examined by stakeholders, including parents, advocates, providers, and political entities

Effect of Randomness on Quantum Data Buses of Heisenberg Spin Chains

A strongly coupled spin chain can mediate long-distance effective couplings or entanglement between remote qubits, and can be used as a quantum data bus. We study how the fidelity of a spin-1/2 Heisenberg chain as a spin bus is affected by static random exchange couplings and magnetic fields. We find that, while non-uniform exchange couplings preserve the isotropy of the qubit effective couplings, they cause the energy levels, the eigenstates, and the magnitude of the couplings to vary locally. On the other hand, random local magnetic fields lead to an avoided level crossing for the bus ground state manifold, and cause the effective qubit couplings to be anisotropic. Interestingly, the total magnetic moment of the ground state of an odd-size bus may not be parallel to the average magnetic field. Its alignment depends on both the direction of the average field and the field distribution, in contrast with the ground state of a single spin which always aligns with the applied magnetic field to minimize the Zeeman energy. Lastly, we calculate sensitivities of the spin bus to such local variations, which are potentially useful for evaluating decoherence when dynamical fluctuations in the exchange coupling or magnetic field are considered

Scaling near Quantum Chaos Border in Interacting Fermi Systems

The emergence of quantum chaos for interacting Fermi systems is investigated by numerical calculation of the level spacing distribution $P(s)$ as function of interaction strength $U$ and the excitation energy $\epsilon$ above the Fermi level. As $U$ increases, $P(s)$ undergoes a transition from Poissonian (nonchaotic) to Wigner-Dyson (chaotic) statistics and the transition is described by a single scaling parameter given by $Z = (U \epsilon^{\alpha}-u_0) \epsilon^{1/2\nu}$, where $u_0$ is a constant. While the exponent $\alpha$, which determines the global change of the chaos border, is indecisive within a broad range of $0.9 \sim 2.0$, finite value of $\nu$, which comes from the increase of the Fock space size with $\epsilon$, suggests that the transition becomes sharp as $\epsilon$ increases.Comment: 4 pages, 4 figures, to appear in Phys. Rev. E (Rapid Communication

General Localization Lengths for Two Interacting Particles in a Disordered Chain

The propagation of an interacting particle pair in a disordered chain is characterized by a set of localization lengths which we define. The localization lengths are computed by a new decimation algorithm and provide a more comprehensive picture of the two-particle propagation. We find that the interaction delocalizes predominantly the center-of-mass motion of the pair and use our approach to propose a consistent interpretation of the discrepancies between previous numerical results.Comment: 4 pages, 2 epsi figure

Quantum computing of quantum chaos and imperfection effects

We study numerically the imperfection effects in the quantum computing of the kicked rotator model in the regime of quantum chaos. It is shown that there are two types of physical characteristics: for one of them the quantum computation errors grow exponentially with the number of qubits in the computer while for the other the growth is polynomial. Certain similarity between classical and quantum computing errors is also discussed.Comment: revtex, 4 pages, 4 figure

Low energy transition in spectral statistics of 2D interactingfermions

We study the level spacing statistics $P(s)$ and eigenstate properties of spinless fermions with Coulomb interaction on a two dimensional lattice at constant filling factor and various disorder strength. In the limit of large lattice size, $P(s)$ undergoes a transition from the Poisson to the Wigner-Dyson distribution at a critical total energy independent of the number of fermions. This implies the emergence of quantum ergodicity induced by interaction and delocalization in the Hilbert space at zero temperature.Comment: revtex, 5 pages, 4 figures; new data for eigenfunctions are adde

Universal non-adiabatic control of small-gap superconducting qubits

Resonant transverse driving of a two-level system as viewed in the rotating frame couples two degenerate states at the Rabi frequency, an amazing equivalence that emerges in quantum mechanics. While spectacularly successful at controlling natural and artificial quantum systems, certain limitations may arise (e.g., the achievable gate speed) due to non-idealities like the counter-rotating term. Here, we explore a complementary approach to quantum control based on non-resonant, non-adiabatic driving of a longitudinal parameter in the presence of a fixed transverse coupling. We introduce a superconducting composite qubit (CQB), formed from two capacitively coupled transmon qubits, which features a small avoided crossing -- smaller than the environmental temperature -- between two energy levels. We control this low-frequency CQB using solely baseband pulses, non-adiabatic transitions, and coherent Landau-Zener interference to achieve fast, high-fidelity, single-qubit operations with Clifford fidelities exceeding $99.7\%$. We also perform coupled qubit operations between two low-frequency CQBs. This work demonstrates that universal non-adiabatic control of low-frequency qubits is feasible using solely baseband pulses

Customer relationship management: digital transformation and sustainable business model innovation

[EN] The point of departure for this study is the understanding of customer relationship management (CRM) as a set of technological solutions key for efficient business management, the benefits of which, highlighted by previous works, are presented and defined here as crucial for entrepreneurial success. Of particular interest for this purpose are the existing studies on sustainability, which provide a viable research model to assess and validate the potential effect of each CRM component (sales, marketing, and services) on the three dimensions of sustainability (economic, environmental, and social). Upon confirmation of our hypotheses, the subsequent validation of such model should bring a better understanding of the way in which CRM-related benefits may increase the positive impact of its components on each dimension of sustainability. CRM can hence be considered a sort of Green IT, oriented toward digital transformation and sustainable business model innovation. Indeed, this research model may be the basis for a more specific methodology to measure the impact and benefits of applying CRM, understood, as we will contend, both in terms of sustainable business models and innovation.Gil Gómez, H.; Guerola-Navarro, V.; Oltra Badenes, RF.; Lozano Quilis, JA. (2020). Customer relationship management: digital transformation and sustainable business model innovation. Economic Research-Ekonomska Istra ivanja. 33(1):2733-2750. https://doi.org/10.1080/1331677X.2019.1676283S27332750331Abson, D. J., Fischer, J., Leventon, J., Newig, J., Schomerus, T., Vilsmaier, U., … Lang, D. J. (2016). Leverage points for sustainability transformation. Ambio, 46(1), 30-39. doi:10.1007/s13280-016-0800-yAlegre, J., Sengupta, K., & Lapiedra, R. (2011). Knowledge management and innovation performance in a high-tech SMEs industry. International Small Business Journal: Researching Entrepreneurship, 31(4), 454-470. doi:10.1177/0266242611417472Arsić, S., Banjević, K., Nastasić, A., Rošulj, D., & Arsić, M. (2018). Family Business Owner as a Central Figure in Customer Relationship Management. Sustainability, 11(1), 77. doi:10.3390/su11010077Bose, R. (2002). Customer relationship management: key components for IT success. Industrial Management & Data Systems, 102(2), 89-97. doi:10.1108/02635570210419636Bradshaw, D., & Brash, C. (2001). Managing customer relationships in the e‐business world: how to personalise computer relationships for increased profitability. International Journal of Retail & Distribution Management, 29(12), 520-530. doi:10.1108/09590550110696969Cantner, U., Joel, K., & Schmidt, T. (2009). The use of knowledge management by German innovators. Journal of Knowledge Management, 13(4), 187-203. doi:10.1108/13673270910971923Chen, A. J. W., Boudreau, M., & Watson, R. T. (2008). Information systems and ecological sustainability. Journal of Systems and Information Technology, 10(3), 186-201. doi:10.1108/13287260810916907Chen, I. J., & Popovich, K. (2003). Understanding customer relationship management (CRM). Business Process Management Journal, 9(5), 672-688. doi:10.1108/14637150310496758Christofi, M., Leonidou, E., & Vrontis, D. (2015). Cause-related marketing, product innovation and extraordinary sustainable leadership: the root towards sustainability. Global Business and Economics Review, 17(1), 93. doi:10.1504/gber.2015.066533Chua, A. Y. ., & Banerjee, S. (2013). Customer knowledge management via social media: the case of Starbucks. Journal of Knowledge Management, 17(2), 237-249. doi:10.1108/13673271311315196Claycomb, C., Dröge, C., & Germain, R. (1999). The Effect of Just‐in‐Time with Customers on Organizational Design and Performance. The International Journal of Logistics Management, 10(1), 37-58. doi:10.1108/09574099910805923Cohen, W. M., & Levinthal, D. A. (1990). Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly, 35(1), 128. doi:10.2307/2393553Stefanou, C. J., Sarmaniotis, C., & Stafyla, A. (2003). CRM and customer‐centric knowledge management: an empirical research. Business Process Management Journal, 9(5), 617-634. doi:10.1108/14637150310496721Damanpour, F. (1996). Organizational Complexity and Innovation: Developing and Testing Multiple Contingency Models. Management Science, 42(5), 693-716. doi:10.1287/mnsc.42.5.693Damanpour, F., & Evan, W. M. (1984). Organizational Innovation and Performance: The Problem of «Organizational Lag». Administrative Science Quarterly, 29(3), 392. doi:10.2307/2393031Dervitsiotis, K. N. (2010). Developing full-spectrum innovation capability for survival and success in the global economy. Total Quality Management & Business Excellence, 21(2), 159-170. doi:10.1080/14783360903549865Dewhurst, F., Martínez Lorente, A. R., & Dale, B. G. (1999). Total quality management and information technologies: an exploration of the issues. International Journal of Quality & Reliability Management, 16(4), 392-406. doi:10.1108/02656719910249333Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: what are they? Strategic Management Journal, 21(10-11), 1105-1121. doi:10.1002/1097-0266(200010/11)21:10/113.0.co;2-eEpiphany.com. (2001). CRM benefits. Retrieved from http://www.ephany.com/market/crm_benefits.html2001European Commission. (2013). One trillion euro to invest in Europe’s future—The EU’s budget framework 2014–2020. Retrieved from https://ec.europa.eu/regional_policy/en/newsroom/news/2013/11/one-trillion-euro-to-invest-in-europe-s-future-the-eu-s-budget-framework-2014-2020Fidel, P., Schlesinger, W., & Cervera, A. (2015). Collaborating to innovate: Effects on customer knowledge management and performance. Journal of Business Research, 68(7), 1426-1428. doi:10.1016/j.jbusres.2015.01.026FIDEL, P., SCHLESINGER, W., & EMILO, E. (2018). EFFECTS OF CUSTOMER KNOWLEDGE MANAGEMENT AND CUSTOMER ORIENTATION ON INNOVATION CAPACITY AND MARKETING RESULTS IN SMEs: THE MEDIATING ROLE OF INNOVATION ORIENTATION. International Journal of Innovation Management, 22(07), 1850055. doi:10.1142/s136391961850055xGallego, J., Rubalcaba, L., & Hipp, C. (2012). Organizational innovation in small European firms: A multidimensional approach. International Small Business Journal: Researching Entrepreneurship, 31(5), 563-579. doi:10.1177/0266242611430100Garbarino, E., & Johnson, M. S. (1999). The Different Roles of Satisfaction, Trust, and Commitment in Customer Relationships. Journal of Marketing, 63(2), 70-87. doi:10.1177/002224299906300205Gholami, H., Zameri Mat Saman, M., Mardani, A., Streimikiene, D., Sharif, S., & Zakuan, N. (2018). Proposed Analytic Framework for Student Relationship Management based on a Systematic Review of CRM Systems Literature. Sustainability, 10(4), 1237. doi:10.3390/su10041237Gibbert, M., Leibold, M., & Probst, G. (2002). Five Styles of Customer Knowledge Management, and How Smart Companies Use Them To Create Value. European Management Journal, 20(5), 459-469. doi:10.1016/s0263-2373(02)00101-9Gopalakrishnan, S., & Damanpour, F. (1997). A review of innovation research in economics, sociology and technology management. Omega, 25(1), 15-28. doi:10.1016/s0305-0483(96)00043-6Grawe, S. J., Chen, H., & Daugherty, P. J. (2009). The relationship between strategic orientation, service innovation, and performance. International Journal of Physical Distribution & Logistics Management, 39(4), 282-300. doi:10.1108/09600030910962249Huang, E. Y., & Lin, C. (2005). Customer‐oriented financial service personalization. Industrial Management & Data Systems, 105(1), 26-44. doi:10.1108/02635570510575171Huang, P.-S., & Shih, L.-H. (2008). Effective environmental management through environmental knowledge management. International Journal of Environmental Science & Technology, 6(1), 35-50. doi:10.1007/bf03326058Hult, G. T. M., & Ketchen, D. J. (2001). Does market orientation matter?: a test of the relationship between positional advantage and performance. Strategic Management Journal, 22(9), 899-906. doi:10.1002/smj.197Hult, G. T. M., Hurley, R. F., & Knight, G. A. (2004). Innovativeness: Its antecedents and impact on business performance. Industrial Marketing Management, 33(5), 429-438. doi:10.1016/j.indmarman.2003.08.015Karimi, J., Somers, T. M., & Gupta, Y. P. (2001). Impact of Information Technology Management Practices on Customer Service. Journal of Management Information Systems, 17(4), 125-158. doi:10.1080/07421222.2001.11045661King, S. F., & Burgess, T. F. (2008). Understanding success and failure in customer relationship management. Industrial Marketing Management, 37(4), 421-431. doi:10.1016/j.indmarman.2007.02.005Krizanova, A., Gajanova, L., & Nadanyiova, M. (2018). Design of a CRM Level and Performance Measurement Model. Sustainability, 10(7), 2567. doi:10.3390/su10072567Liao, S., Fei, W.-C., & Chen, C.-C. (2007). Knowledge sharing, absorptive capacity, and innovation capability: an empirical study of Taiwan’s knowledge-intensive industries. Journal of Information Science, 33(3), 340-359. doi:10.1177/0165551506070739Lin, R., Che, R., & Ting, C. (2012). Turning knowledge management into innovation in the high‐tech industry. Industrial Management & Data Systems, 112(1), 42-63. doi:10.1108/02635571211193635Lopez-Nicolas, C., & Molina-Castillo, F. J. (2008). Customer Knowledge Management and E-commerce: The role of customer perceived risk. International Journal of Information Management, 28(2), 102-113. doi:10.1016/j.ijinfomgt.2007.09.001March, J. G. (1991). Exploration and Exploitation in Organizational Learning. Organization Science, 2(1), 71-87. doi:10.1287/orsc.2.1.71Massey, A. P., Montoya-Weiss, M. M., & Holcom, K. (2001). Re-engineering the customer relationship: leveraging knowledge assets at IBM. Decision Support Systems, 32(2), 155-170. doi:10.1016/s0167-9236(01)00108-7Mckenzie, C. R. M., & Liersch, M. J. (2011). Misunderstanding Savings Growth: Implications for Retirement Savings Behavior. Journal of Marketing Research, 48(SPL), S1-S13. doi:10.1509/jmkr.48.spl.s1Menguc, B. (2006). Creating a Firm-Level Dynamic Capability through Capitalizing on Market Orientation and Innovativeness. Journal of the Academy of Marketing Science, 34(1), 63-73. doi:10.1177/0092070305281090Mishra, D., Akman, I., & Mishra, A. (2014). Theory of Reasoned Action application for Green Information Technology acceptance. Computers in Human Behavior, 36, 29-40. doi:10.1016/j.chb.2014.03.030Molla, A. & Abareshi, A. (2011). Green IT adoption: A motivational perspective. In Proceedings of the 15th Pacific Asia Conference on Information Systems, Brisbane, QLD, Australia, 7–11 July; p. 137.Introductory Chapter: Framework for Energy and Environmental Management in Industry. (s. f.). Applied Industrial Energy and Environmental Management, 1-22. doi:10.1002/9780470714379.chNgo, L. V., & O’Cass, A. (2012). In Search of Innovation and Customer-related Performance Superiority: The Role of Market Orientation, Marketing Capability, and Innovation Capability Interactions. Journal of Product Innovation Management, 29(5), 861-877. doi:10.1111/j.1540-5885.2012.00939.xPanayides, P. (2006). Enhancing innovation capability through relationship management and implications for performance. European Journal of Innovation Management, 9(4), 466-483. doi:10.1108/14601060610707876Pohludka, M., Stverkova, H., & Ślusarczyk, B. (2018). Implementation and Unification of the ERP System in a Global Company as a Strategic Decision for Sustainable Entrepreneurship. Sustainability, 10(8), 2916. doi:10.3390/su10082916Prahalad, C. K., & Ramaswamy, V. (2004). Co‐creating unique value with customers. Strategy & Leadership, 32(3), 4-9. doi:10.1108/10878570410699249Rao, H., & Drazin, R. (2002). OVERCOMING RESOURCE CONSTRAINTS ON PRODUCT INNOVATION BY RECRUITING TALENT FROM RIVALS: A STUDY OF THE MUTUAL FUND INDUSTRY, 1986-94. Academy of Management Journal, 45(3), 491-507. doi:10.2307/3069377Robson, P. J. A., & Bennett, R. J. (2000). Small Business Economics, 15(3), 193-208. doi:10.1023/a:1008129012953Rollins, M. & Halinen, A. (2005). Customer knowledge management competence: Towards a theoretical framework. In Proceedings of the 38th Annual Hawaii International Conference on System Sciences, Hawaii (pp. 1–10). IEEE.Romano, A. C. Jr (2000). Customer relations management in information systems research. In H. M. Chung (Ed.), Proceedings of the Americas Conference in Information Systems (AMCIS), Long Beach, California, 10–13 August, pp. 811–819.Romano Jr, N. C., & Fjermestad, J. (2001). Electronic Commerce Customer Relationship Management: An Assessment of Research. International Journal of Electronic Commerce, 6(2), 61-113. doi:10.1080/10864415.2001.11044232Sahay, B. S., & Ranjan, J. (2008). Real time business intelligence in supply chain analytics. Information Management & Computer Security, 16(1), 28-48. doi:10.1108/09685220810862733Scullin, S., Allora, J., Lloyd, G. O. & Fjermestad, J. (2002). Electronic customer relationship management: Benefits, considerations, pitfalls and trends. In Proceedings of the IS One World Conference, Las Vegas, Nevada, April, pp. 3–5.Shane, S. A., & Ulrich, K. T. (2004). 50th Anniversary Article: Technological Innovation, Product Development, and Entrepreneurship inManagement Science. Management Science, 50(2), 133-144. doi:10.1287/mnsc.1040.0204Sims, D. (2000). A new ROI for new economy CRM and just why doesn’t high-tech get it? crmguru.com, April 2000.Sin, L. Y. M., Tse, A. C. B., & Yim, F. H. K. (2005). CRM: conceptualization and scale development. European Journal of Marketing, 39(11/12), 1264-1290. doi:10.1108/03090560510623253Tushman, M. L. (1997). Winning through innovation. Strategy & Leadership, 25(4), 14-19. doi:10.1108/eb054591Verhoef, P. C., & Donkers, B. (2001). Predicting customer potential value an application in the insurance industry. Decision Support Systems, 32(2), 189-199. doi:10.1016/s0167-9236(01)00110-5Vorhies, D. W., & Harker, M. (2000). The Capabilities and Perfor Mance Advantages of Market‐Driven Firms: An Empirical Investigation. Australian Journal of Management, 25(2), 145-171. doi:10.1177/031289620002500203Waltner, C. (2001). CRM makes on-line shopping personal. InformationWeek, January 29, 2001.Weerawardena, J. (2003). Exploring the role of market learning capability in competitive strategy. European Journal of Marketing, 37(3/4), 407-429. doi:10.1108/03090560310459023Whatis.com. (2001). DIY BI: A guide to self-service business intelligence implementation. Retrieved from http://whatis.techtarget.com/whatis_definition_page/0,4152,211901,00.htmlZhu, Z., & Nakata, C. (2007). Reexamining the Link Between Customer Orientation and Business Performance: The Role of Information Systems. Journal of Marketing Theory and Practice, 15(3), 187-203. doi:10.2753/mtp1069-667915030