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Traditional adaptive analysis based on a coarse mesh, using finite element method (FEM) analysis, produces the original solution. Then post-processing the result and figuring out the regions should be refined and these regions refined once. Finally, this new mesh is used to get the solution of first refinement. After several iterations of above procedures, we can achieve the last result that is closer to the true solution, which takes time, making adaptive scheme inpractical to engineering application. The paper aims to discuss these issues.

This paper based on FEM proposes a multi-level refinement strategy with a refinement strategy and an indicator. The proposed indicator uses value of the maximum difference of strain energy density among the elements that associated with one node, and divides all nodes into several categories based on the value. A multi-level refinement strategy is proposed according to which category the node belongs to refine different elements to different times rather than whether refine or not.

Multi-level refinement strategy takes full use of the numerical calculation, resulting in the whole adaptive analysis that only need to iterate twice while other schemes must iterate more times. Using much less times of numerical calculation and approaches, more accurate solution, making adaptive analysis more practical to engineering.

Multi-level refinement strategy takes full use of the numerical calculation, resulting in the whole adaptive analysis only need iterate twice while other schemes must iterate more times. using much less times of numerical calculation and approaches more accurate solution, making adaptive analysis more practical to engineering.

The purpose of this paper is to present an efficient algorithm based on a multi‐level adaptive mesh refinement strategy for the solution of ill‐posed inverse heat conduction problems arising in pool boiling using few temperature observations.

The stable solution of the inverse problem is obtained by applying the conjugate gradient method for the normal equation method together with a discrepancy stopping rule. The resulting three‐dimensional direct, adjoin and sensitivity problems are solved numerically by a space‐time finite element method. A multi‐level computational approach, which uses an a posteriori error estimator to adaptively refine the meshes on different levels, is proposed to speed up the entire inverse solution procedure.

This systematic approach can efficiently solve the large‐scale inverse problem considered without losing necessary detail in the estimated quantities. It is shown that the choice of different termination parameters in the discrepancy stopping conditions for each level is crucial for obtaining a good overall estimation quality. The proposed algorithm has also been applied to real experimental data in pool boiling. It shows high computational efficiency and good estimation quality.

The high efficiency of the approach presented in the paper allows the fast processing of experimental data at many operating conditions along the entire boiling curve, which has been considered previously as computationally intractable. The present study is the authors' first step towards a systematic approach to consider an adaptive mesh refinement for the solution of large‐scale inverse boiling problems.

This paper gives a bibliographical review of the finite element and boundary element parallel processing techniques from the theoretical and application points of view. Topics include: theory – domain decomposition/partitioning, load balancing, parallel solvers/algorithms, parallel mesh generation, adaptive methods, and visualization/graphics; applications – structural mechanics problems, dynamic problems, material/geometrical non‐linear problems, contact problems, fracture mechanics, field problems, coupled problems, sensitivity and optimization, and other problems; hardware and software environments – hardware environments, programming techniques, and software development and presentations. The bibliography at the end of this paper contains 850 references to papers, conference proceedings and theses/dissertations dealing with presented subjects that were published between 1996 and 2002.

The real challenge in the solution of contact problems is the lack of an optimal adaptive scheme. As the contact zone is a priori unknown, successive refinement and iterative method are necessary to obtain a high-accuracy solution. The purpose of this paper is to provide an optimal adaptive scheme based on second-generation finite element wavelets for the solution of non-linear variational inequality of the contact problem.

To generate an elementary multi-resolution mesh, the authors used hierarchical bases (HB) composed of Lagrange finite element interpolation functions. These HB functions are customized using second-generation wavelet techniques for a fast convergence rate. At each step of the algorithm, the active set method along with mesh adaptation is used for solving the constrained minimization problem of contact case. Wavelet coefficients-based error indicators are used, and computation is focused on mesh zones with a high error indication. The authors take advantage of the wavelet transform to develop a parameter-free adaptive scheme to generate an appropriate and optimal mesh.

Adaptive wavelet Galerkin scheme (AWGS), a newly developed method for multi-scale mesh adaptivity in this work, is a combination of the second-generation wavelet transform and finite element method and significantly improves the accuracy of the results without approximating an additional problem of error estimation equations. A comparative study is performed taking a solution on a highly refined mesh and results are generated using AWGS.

The proposed adaptive technique can be utilized in the simulation of mechanical and biomechanical structures where multiple bodies come into contact with each other. The algorithm of the method is easy to implement and found to be successful in producing a sufficiently accurate solution with relatively less number of mesh nodes.

Although many error estimation techniques have been developed over the past several years to solve contact problems adaptively, because of boundary non-linearity development, a reliable error estimator needs further investigation. The present study attempts to resolve this problem without having to recompute the entire solution on a new mesh.

The process approach to multi-level organizational behavior is based on the assumption that multi-level organizational behavior is processual in nature. This article defines group and organizational processes and their representation as process frameworks. Both functional and inclusional classes of levels exist, each of which has at least five categories of levels. All ten categories are special cases of process frameworks. This article provides examples of each category level, which it uses to illustrate new models of organizational work, extended models of interdependence, a new typology of theories based on their levels of processes, and a new tool for survey research called knobby analyses. After explaining the basic idea of knobby analysis, the article briefly describes the processual theory of the organizational hologram, the use of linear programming, and causal-chain analysis to provide multi-level explanations of employee opinion data. These ideas are embodied in conducting a strategic organizational diagnosis, which is the first stage of organizational design. Organizational design encompasses multiple stages, each of which itself involves multiple, multi-level phenomena and analyses. The basic point is that the processual nature of multi-level organizational phenomena gives more hope for improvements in theory building and their application if one uses the process approach rather than a variable approach.

Jelinek has developed a multi-level model for conceptualizing the contextual influences through which intellectual property (IP) is “understood, interpreted and made sense of” by key parties to IP “deals.” This commentary reflects upon that model through a historical examination of industry–university relationships in one case – specifically, IBM. Since the late 1920s, IBM has encouraged multifaceted relationships with universities. From the start, IBM sought relationships with academia not only because of the market potential represented by university campuses, but also because Thomas Watson Sr. viewed academic customers as potential research collaborators, a novel idea at the time that later proved instrumental in the development of the corporation's successful research enterprise. IBM's university relationships have continued to evolve over time, reflecting shifts in the corporation's business strategy, and changes in larger macroeconomic structures. The case of IBM reveals complex interactions among governmental, corporate, and academic actors and their policies at different points in time, providing support for Jelinek's multi-level approach to framing IP dynamics, and suggesting possible refinements of the model for the future.

This study aims to present Lean Six Sigma (LSS) deployment and sustainment strategies for the healthcare sector from a multi-level perspective. The objective is to present LSS implementation insights to enable policymakers, practitioners and academicians to test and develop an LSS framework for healthcare sectors.

The strategies identified are the result of a multi-method research design involving literature review, action research (AR) and Delphi study. Further, the AR portion of the study involved more than 10 years of projects focused on the deployment of LSS in the healthcare sector.

The strategies include a holistic view from the multi-level perspective, considering the Top Management Level, Middle Management Level and Operational Level. The authors ascertained 27 strategies across the three levels of organizational structure for the effective deployment of LSS. Further, the authors present a customized LSS “pocket guide” from the healthcare perspective for quick reference.

The strategies delineated in this study are based on the Indian healthcare section only; thus, further research in additional geographic contexts is needed. Also, further research is necessary to provide additional empirical validation of the effects of the identified strategies on LSS program outcomes and to verify that the strategies operate at the proposed organizational levels. Future research should also focus on identifying the interrelationships between strategies within and across levels, developing a “road map” for LSS implementation in hospitals and designing the LSS curriculum for medical schools and other medical training programs.

Observations of this study can contribute to developing a holistic framework for successful LSS implementation in the healthcare sector for academicians, practitioners and policymakers. This, in turn, ensures an enhanced value proposition, improved quality of life and reduced healthcare operational costs. Thus, it ensures a win-win situation among all the stakeholders of the healthcare sector.

The strategies put forth will enable the LSS researchers, academicians and, more particularly, practitioners to delve deeper into specific enablers and safeguard the LSS deployment from backlash. The research has two significant benefits. Firstly, it enhances the understanding of LSS from the healthcare perspective. Secondly, it provides direction for future studies with specific components for hospitals’ LSS framework, which can be further tested, refined and improved.

Recognizing the impact of innovation on organizational performance, scholars from a number of disciplines have sought to identify the conditions that make innovation possible. Although these studies have served to identify a number of key variables, the relationship between these variables and innovation is complex. In this chapter, we argue that the apparent complexity of these relationships may be attributed to cross-level differences in the requirements for innovation and the existence of complex interactions among the phenomena operating at a given level of analysis. The implications of this multi-level perspective for understanding how innovation occurs in organizational settings are discussed.

This study aims to examine the applicability of Hartmann et al.’s (2018) service ecosystem framework to the day-to-day management of the modern sales force. The authors provide a review of the framework, acknowledging its strengths, while also indicating areas for advancement. The authors conclude with recommendations to the framework and indicate opportunities where future research could advance sales theory.

A review of the theoretical underpinnings of the service ecosystem framework is weighed against the established roles and responsibilities of the modern sales force in the literature.

The ability of the framework to capture the multi-level, multi-actor and dynamic aspects of sales represents an improvement in the conceptualization of selling is critical. Suggestions around the refinement for meso-level sales interactions and a more pliant application of service dominant-logic are offered.

The suggested extensions of the framework continue the advancement of novel theorization for the field of sales. Priorities for future research include consideration of ethical implications of the framework and formulations of new management strategies reflective of the broad and dynamic properties of the ecosystem conceptualization.

This paper provides managerial guidelines and implications tied specifically to the thick and thin crossing points and how they may impact employee decision-making.

To the best of the authors’ knowledge, this study is the first to pointedly examine the service ecosystem framework with respect to established principles of managing a modern sales force.

To evaluate various hedge programs designed to minimize the risk of an extreme monthly gas bill subject to a pre‐determined hedge program budget.Design/methodology/approach – Historical data were collected on natural gas spot and futures prices. Also, theoretical options prices were calculated. These data were then applied to derive the risk associated with extreme bills under different hedge strategies.Findings – In every instance, having a price cap hedge program is better for core customers of a utility company than not having a hedge program.Research limitations/implications – The better hedge performance is based on historical data. It may not apply to future scenarios. Also, the theoretical options prices may need refinements.Practical implications – Any utility company should seriously consider a price cap hedge program to protect its core customers. The exact program design will likely change but the basic principles and methods described in this paper are directly applicable.Originality/value – This paper provide/guidelines for a utility company to design its hedge programs for the benefits of core customers. Currently, there is no such guideline available and there is no study evaluating these hedge programs. This paper provides a first attempt.