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Partitioned analysis is an increasingly popular approach for modeling complex systems with behaviors governed by multiple, interdependent physical phenomena. Yielding accurate representations of reality from partitioned models depends on the availability of all necessary constituent models representing relevant physical phenomena. However, there are many engineering problems where one or more of the constituents may be unavailable because of lack of knowledge regarding the underlying principles governing the behavior or the inability to experimentally observe the constituent behavior in an isolated manner through separate-effect experiments. This study aims to enable partitioned analysis in such situations with an incomplete representation of the full system by inferring the behavior of the missing constituent.

This paper presents a statistical method for inverse analysis infer missing constituent physics. The feasibility of the method is demonstrated using a physics-based visco-plastic self-consistent (VPSC) model that represents the mechanics of slip and twinning behavior in 5182 aluminum alloy. However, a constituent model to carry out thermal analysis representing the dependence of hardening parameters on temperature is unavailable. Using integral-effect experimental data, the proposed approach is used to infer an empirical constituent model, which is then coupled with VPSC to obtain an experimentally augmented partitioned model representing the thermo-mechanical properties of 5182 aluminum alloy.

Results demonstrate the capability of the method to enable model predictions dependent upon relevant operational conditions. The VPSC model is coupled with the empirical constituent, and the newly enabled thermal-dependent predictions are compared with experimental data.

The method developed in this paper enables the empirical inference of a functional representation of input parameter values in lieu of a missing constituent model. Through this approach, development of partitioned models in the presence of uncertainty regarding a constituent model is made possible.

Highly anisotropic zirconium is a material used in the cladding of nuclear fuel rods, ensuring containment of the radioactive material within. The complex material structure of anisotropic zirconium requires model developers to replicate not only the macro-scale stresses but also the meso-scale material behavior as the crystal structure evolves; leading to strongly coupled multi-scale plasticity models. Such strongly coupled models can be achieved through partitioned analysis techniques, which couple independently developed constituent models through an iterative exchange of inputs and outputs. Throughout this iterative process, biases, and uncertainties inherent within constituent model predictions are inevitably transferred between constituents either compensating for each other or accumulating during iterations. The paper aims to discuss these issues.

A finite element model at the macro-scale is coupled in an iterative manner with a meso-scale viscoplastic self-consistent model, where the former supplies the stress input and latter represents the changing material properties. The authors present a systematic framework for experiment-based validation taking advantage of both separate-effect experiments conducted within each constituent’s domain to calibrate the constituents in their respective scales and integral-effect experiments executed within the coupled domain to test the validity of the coupled system.

This framework developed is shown to improve predictive capability of a multi-scale plasticity model of highly anisotropic zirconium.

For multi-scale models to be implemented to support high-consequence decisions, such as the containment of radioactive material, this transfer of biases and uncertainties must be evaluated to ensure accuracy of the predictions of the coupled model. This framework takes advantage of the transparency of partitioned analysis to reduce the accumulation of errors and uncertainties.

Numerical models are being increasingly relied upon to evaluate wind turbine performance by simulating phenomena that are infeasible to measure experimentally. These numerical models, however, require a large number of input parameters that often need to be calibrated against available experiments. Owing to the unavoidable scarcity of experiments and inherent uncertainties in measurements, this calibration process may yield non-unique solutions, i.e. multiple sets of parameters may reproduce the available experiments with similar fidelity. The purpose of this paper is to study the trade-off between fidelity to measurements and the robustness of this fidelity to uncertainty in calibrated input parameters.

Here, fidelity is defined as the ability of the model to reproduce measurements and robustness is defined as the allowable variation in the input parameters with which the model maintains a predefined level of threshold fidelity. These two vital attributes of model predictiveness are evaluated in the development of a simplified finite element beam model of the CX-100 wind turbine blade.

Findings of this study show that calibrating the input parameters of a numerical model with the sole objective of improving fidelity to available measurements degrades the robustness of model predictions at both tested and untested settings. A more optimal model may be obtained by calibration methods considering both fidelity and robustness. Multi-criteria Decision Making further confirms the conclusion that the optimal model performance is achieved by maintaining a balance between fidelity and robustness during calibration.

Current methods for model calibration focus solely on fidelity while the authors focus on the trade-off between fidelity and robustness.

Although accounting professors around the globe have addressed various social aspects of accounting, very rarely does that research address the concerns of students. This is despite the fact that students are the focus of the educational mission of most universities. In an effort to address this gap, this chapter extends the field of social accounting to an issue critical to students: the cost of accounting textbooks in the United States. Textbook cost is drawing increasing attention from public interest groups and government regulators as costs are growing at a more rapid rate than many other costs, and constitute a significant portion of the total cost of obtaining a higher education degree. For accounting students, these costs are exacerbated by the fact that accounting textbooks are among the most expensive of any major, and they are being revised with increasing frequency – which eliminates students’ ability to buy less expensive used books – often with little or no discernible benefit to students. We argue that in some subfields of accounting – especially managerial/cost and introductory courses – topics are relatively stable, and that frequent textbook revisions are unnecessarily costly for our students, many of whom, along with their families, are making significant financial sacrifices to earn their degrees. In this study, we provide background on the textbook pricing issue, include data from a survey of accounting faculty demonstrating that they consider the revisions too frequent, document the increasing frequency of accounting textbook revisions over recent decades, analyze content in a leading accounting textbook, and discuss options for reducing the cost of accounting textbooks, including following student activists’ lead in advocating for open-source, free textbooks.

Evaluation is the process by which we estimate how things should go, explore how things are going, and determine how things went in terms of course redesign. In this chapter, we examine formative and summative methods for assessing student learning and establishing teacher effectiveness and course quality. Evaluation is a subjective, value-laden process. To introduce the rigor needed to make it meaningful, evaluation should be multifaceted, planned in advance, made transparent to learners, and employ valid and reliable methods. Moving courses online presents both opportunities and challenges for evaluation. We explore ways to implement assessment to make full use of the advantages of technology while mitigating the problems associated with online delivery.

Following the rapid shift to online learning due to COVID-19, this paper aims to compare the relative efficacy of face-to-face and online university teaching methods.

A scoping review was conducted to examine the learning outcomes within and between online and face-to-face (F2F) university teaching programmes.

Although previous research has supported a “no significant difference” position, the review of 91 comparative studies during 2000–2020 identified 37 (41%) which found online teaching was associated with better learning outcomes, 17 (18%) which favoured F2F and 37 (41%) reporting no significant difference. Purpose-developed online content which supports “student-led” enquiry and cognitive challenge were cited as factors supporting better learning outcomes.

This study adopts a pre-defined methodology in reviewing literature which ensures rigour in identifying relevant studies. The large sample of studies (n = 91) supported the comparison of discrete learning modes although high variability in key concepts and outcome variables made it difficult to directly compare some studies. A lack of methodological rigour was observed in some studies.

As a result of COVID-19, online university teaching has become the “new normal” but also re-focussed questions regarding its efficacy. The weight of evidence from this review is that online learning is at least as effective and often better than, F2F modalities in supporting learning outcomes, albeit these differences are often modest. The findings raise questions about the presumed benefits of F2F learning and complicate the case for a return to physical classrooms during the pandemic and beyond.

Academic libraries are experiencing numerous changes in their services due to high demands for digital resources and changes in users’ information needs and expectations. Many academic library users give preferences to Google, Google Scholar and other search engines on the internet when they search for information. As reference transactions are decreasing in many academic institutions, this paper aims to investigate the continuing need for mediated reference services in the technology-driven environment in academic libraries.

The authors have conducted a literature review to document and analyze the current trends in reference services in academic libraries. They have examined the relevant published literature through a series of reflective questions to determine whether the demise of mediated reference services is imminent in academic libraries. While this literature review is by no means an exhaustive one, the authors have provided a fairly comprehensive representation of articles to synthesize an overview of the history, evolution, and current trends of reference services in academic libraries.

This paper clearly demonstrates the importance of human-mediated reference services in academic libraries. It reinforces the need for skilled, knowledgeable professional librarians to provide effective and efficient reference services in a digital environment.

This paper provides a comprehensive overview of current trends in reference services in academic libraries and analyzes the merits and demerits of these trends to establish the need for mediated reference services in academic libraries. The arguments used in this paper will be useful for library and informational professionals as validation for the need to hire skilled, knowledgeable reference librarians to provide reference services in a digital environment.

This paper critically looks at the current trends and practices in reference services through the published literature to determine the future need for mediated reference services in academic libraries. It offers important insights to demonstrate why professional librarians’ skills, knowledge and expertise are essential to provide efficient reference services in the digital age.

Adding to the existing research on criminal justice officers’ attitudes towards and experiences with civil liability, this study compared the attitudes and experiences of law enforcement officers and correctional officers. Logistic regression analyses were employed to examine the affects of rank, education, experience and criminal justice occupation on officers’ attitudes towards and experiences with civil liability. Statistically significant differences by occupation, years of service, rank, and education were found. Implications and future research suggestions are discussed.

Outlines the major developments in methodology and analysis applied to location by geographers in recent years and reviews the literature on this topic – assessing the principal contributions and suggesting some possible lines for follow up by marketing analysts. Concerns itself with retail location, transportation and manufacturers spatial behaviour. Examines the range of geographical considerations of location and special attention is given to retail studies, transportation and the spatial behaviour of manufacturers. Summarises that published work on the locational and spatial aspects of product or customer distribution is rare.