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This chapter discusses the steps taken to access and use the ACS five-year data. The format of the data is discussed pointing out the fact that there is no requirement that an ACS five-year variable holds data for the same field year to year. The development of a cross-reference table is discussed allowing the data to be accessed by a common label.

This project set out to explore information scholars’ perceptions of the influence of their keyword selections and the implications of their linguistic choices on possibilities for and perceptions of the field of Information Science. We trialed a narrative methodological approach to investigate the multiple stories told with specific keywords, how they relate to larger discourses within the field and the impact they have on the lives of information researchers.

This paper draws on Arthur Frank’s narrative analysis to consider keywords as stories, which shape one’s sense of professional identity and belonging. The analysis, which is informed by insights from multi-disciplinary scholars of keywords, employs data from a keywords-oriented workshop with Information School faculty and students, as well as an online questionnaire sent to heads of Information Schools.

We did not find a singular definitive story of information science scholars’ experiences with keywords. Rather we identify tensions surrounding common and contested understandings of discipline, canon and information, engaging the complexity of interdisciplinary, international, intellectual and moral claims of the field. This research offers insight into the experiential factors that shape scholars’ engagement with keywords and the tensions they can create.

A wealth of bibliometric analyses of keywords focuses on finding the “right” words to describe the scholarship you seek or the work you want others to discover. However, this study offers information researchers a novel approach, creating space to acknowledge the generative tensions of keywords, beyond the extractive logic of search and retrieval.

Physics-informed neural networks (PINNs) have become a new tendency in flow simulation, because of their self-advantage of integrating both physical and monitored information of fields in solving the Navier–Stokes equation and its variants. In view of the strengths of PINN, this study aims to investigate the impact of spatially embedded data distribution on the flow field results around the train in the crosswind environment reconstructed by PINN.

PINN can integrate data residuals with physical residuals into the loss function to train its parameters, allowing it to approximate the solution of the governing equations. In addition, with the aid of labelled training data, PINN can also incorporate the real site information of the flow field in model training. In light of this, the PINN model is adopted to reconstruct a two-dimensional time-averaged flow field around a train under crosswinds in the spatial domain with the aid of sparse flow field data, and the prediction results are compared with the reference results obtained from numerical modelling.

The prediction results from PINN results demonstrated a low discrepancy with those obtained from numerical simulations. The results of this study indicate that a threshold of the spatial embedded data density exists, in both the near wall and far wall areas on the train’s leeward side, as well as the near train surface area. In other words, a negative effect on the PINN reconstruction accuracy will emerge if the spatial embedded data density exceeds or slips below the threshold. Also, the optimum arrangement of the spatial embedded data in reconstructing the flow field of the train in crosswinds is obtained in this work.

In this work, a strategy of reconstructing the time-averaged flow field of the train under crosswind conditions is proposed based on the physics-informed data-driven method, which enhances the scope of neural network applications. In addition, for the flow field reconstruction, the effect of spatial embedded data arrangement in PINN is compared to improve its accuracy.

This paper aims to examine the uniform diffracted fields from a perfectly magnetic conductive (PMC) surface with the extended theory of boundary diffraction wave (BDW) approach.

Miyamoto and Wolf’s symbolic expression of the vector potential was used in the extended theory of BDW integral. This vector potential is applied to the problem, and the nonuniform field expression found was made uniform. Here, the expression is made uniform, using the detour parameter with the help of the asymptotic correlation of the Fresnel function. The BDW theory for the PMC surface extended the diffracted fields, and the uniform diffracted fields were calculated.

The field expressions obtained were interpreted with the graphs numerically for different aperture radii and observation distances. It has been shown that the BDW is continuous behind the diffracting aperture. There does not exist any discontinuity at the geometrically light-to-shadow transition boundary, as is required by the theory.

The results were graphically compared with diffracted fields for other surfaces. As far as we know, the uniform diffracted fields from the circular aperture on a PMC surface were calculated for the first time with the extended theory of the BDW approach.

Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine. However, it remains a daunting task to predict the nonlinear and transient in-cylinder flow motion because they are highly complex which change both in space and time. Recently, machine learning methods have demonstrated great promises to infer relatively simple temporal flow field development. This paper aims to feature a physics-guided machine learning approach to realize high accuracy and generalization prediction for complex swirl-induced flow field motions.

To achieve high-fidelity time-series prediction of unsteady engine flow fields, this work features an automated machine learning framework with the following objectives: (1) The spatiotemporal physical constraint of the flow field structure is transferred to machine learning structure. (2) The ML inputs and targets are efficiently designed that ensure high model convergence with limited sets of experiments. (3) The prediction results are optimized by ensemble learning mechanism within the automated machine learning framework.

The proposed data-driven framework is proven effective in different time periods and different extent of unsteadiness of the flow dynamics, and the predicted flow fields are highly similar to the target field under various complex flow patterns. Among the described framework designs, the utilization of spatial flow field structure is the featured improvement to the time-series flow field prediction process.

The proposed flow field prediction framework could be generalized to different crank angle periods, cycles and swirl ratio conditions, which could greatly promote real-time flow control and reduce experiments on in-cylinder flow field measurement and diagnostics.

The purpose of this study is to analyze the linear induction motor (LIM) thermal field when the motor passes through the discontinuous area of the secondary plate in rail transit operation.

To study the thermal field when the LIM passes through the discontinuous area of the secondary plate, the two-dimensional (2D) electromagnetic field model of the motor is first established to calculate and analyze the electromagnetic field of the LIM, and then the motor thermal field is calculated according to the calculation results of the motor electromagnetic field. Finally, the heat source (the motor loss) and the heat dissipation coefficient are added to the thermal field calculation model to calculate the motor thermal field.

This study found that the discontinuity of the secondary board would lead to changes in the electromagnetic field of the linear induction motor to change, which would reduce the motor efficiency and increase the loss, resulting in a sudden increase in the temperature field of the motor.

The LIM temperature field calculation when the LIM passes through the discontinuous area of the secondary board could provide an early warning for the potential risks of the LIM in rail transit applications.

This Introduction gives a historical and theoretical overview of this volume on Fields of Knowledge: Science, Politics and Publics in the Neoliberal Age, which showcases original research in political sociology of science targeting the changes in scientific and technological policy and practice associated with the rise of neoliberal thought and policies since the 1970s. We argue that an existing family of field theoretic frameworks and empirical field analyses provides a particularly useful set of ideas and approaches for the meso-level understanding of these historical changes in ways that complement as well as challenge other theory traditions in sociology of science, broadly defined. The collected papers exhibit a dual focus on sciences’ interfield relations, connecting science and science policy to political, economic, educational, and other fields and on the institutional logics of scientific fields that pattern expert discourses, practices, and knowledge and shape relations of the scientific field to the rest of the world. By reconceptualizing the central problem for political sociology of science as a problem of field- and inter-field dynamics, and by critically engaging other theory traditions whose assumptions are in some ways undermined by the contemporary history of neoliberalism, we believe these papers collectively chart an important theoretical agenda for future research in the sociology of science.

Comparative education research in Japan is strongly oriented toward emphasizing fieldwork, unlike Western methodologies that aim for theorization. For this reason, it is sometimes regarded as peripheral research without a theorizing orientation or as a counterstrategy to Western research. This study examines why Japanese comparative education research emphasizes fieldwork, focusing on discussions at the Japanese Society of Comparative Education from the 1990s to the present, and considers whether the discussion far from aimed at theorizing. It can be said that Japanese comparative educational research, while characterized by a field-oriented orientation, has been trying to analyze the subject with sincerity through more in-depth fieldwork and is aware of the back and forth between theorizing and differentiation. Furthermore, recently, an international, agenda-based approach and the concept of transboundary fieldwork based on triangulation and Border Studies as a new way of looking at the field itself have also emerged. Therefore, it can be said that Japanese comparative educational research, while characterized by a field-oriented orientation, is increasingly aiming for a multilayered and relative analysis of the field, which is an argument autonomously derived from a focus on the field rather than being a strategy or a challenge to Western universalization-oriented methodologies.

Hexahedral meshing is one of the most important steps in performing an accurate simulation using the finite element analysis (FEA). However, the current hexahedral meshing method in the industry is a nonautomatic and inefficient method, i.e. manually decomposing the model into suitable blocks and obtaining the hexahedral mesh from these blocks by mapping or sweeping algorithms. The purpose of this paper is to propose an almost automatic decomposition algorithm based on the 3D frame field and model features to replace the traditional time-consuming and laborious manual decomposition method.

The proposed algorithm is based on the 3D frame field and features, where features are used to construct feature-cutting surfaces and the 3D frame field is used to construct singular-cutting surfaces. The feature-cutting surfaces constructed from concave features first reduce the complexity of the model and decompose it into some coarse blocks. Then, an improved 3D frame field algorithm is performed on these coarse blocks to extract the singular structure and construct singular-cutting surfaces to further decompose the coarse blocks. In most modeling examples, the proposed algorithm uses both types of cutting surfaces to decompose models fully automatically. In a few examples with special requirements for hexahedral meshes, the algorithm requires manual input of some user-defined cutting surfaces and constructs different singular-cutting surfaces to ensure the effectiveness of the decomposition.

Benefiting from the feature decomposition and the 3D frame field algorithm, the output blocks of the proposed algorithm have no inner singular structure and are suitable for the mapping or sweeping algorithm. The introduction of internal constraints makes 3D frame field generation more robust in this paper, and it can automatically correct some invalid 3–5 singular structures. In a few examples with special requirements, the proposed algorithm successfully generates valid blocks even though the singular structure of the model is modified by user-defined cutting surfaces.

The proposed algorithm takes the advantage of feature decomposition and the 3D frame field to generate suitable blocks for a mapping or sweeping algorithm, which saves a lot of simulation time and requires less experience. The user-defined cutting surfaces enable the creation of special hexahedral meshes, which was difficult with previous algorithms. An improved 3D frame field generation method is proposed to correct some invalid singular structures and improve the robustness of the previous methods.

This chapter revisits social space and field theory, constructs foundational to the work of Kurt Lewin but largely abandoned by his followers. It describes the “mystification” surrounding these concepts in the work of Lewin and the sociologist, Pierre Bourdieu. It then attempts to demystify social space and field theory by looking at their roots in the idea of “relational thinking” – an idea set forth by the philosopher Ernst Cassirer, who had a powerful influence on both Lewin and Bourdieu. Finally, it suggests how these concepts can generate innovative thinking about organizational change and development.