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This paper aims to propose a view similarity-based shape complexity metric to guide part selection for additive manufacturing (AM) and advance the goals of design for AM. The metric helps to improve the selection process by objectively screening a large number of parts and identifying the parts most suited for AM and enabling experts to prioritize parts from a smaller set based on relevant subjective/contextual factors.

The methodology involves calculating a part’s shape complexity based on the concept of view similarity, that is, the similarity of different views of the outer shape and internal cross-sectional geometry. The combined shape complexity metric (weighted sum of the external shape and internal structure complexity) has been used to rank various three dimensional (3D) models. The metric has been tested for its sensitivity to various input parameters and thresholds are suggested for effective results. The proposed metric’s applicability for part selection has also been investigated and compared with the existing metric-based part selection.

The proposed shape complexity metric can distinguish the parts of different shapes, sizes and parts with minor design variations. The method is also efficient regarding the amount of data and computation required to facilitate the part selection. The proposed method can detect differences in the mass properties of a 3D model without evaluating the modified parameters. The proposed metric is effective in initial screening of a large number of parts in new product development and for redesign using AM.

The proposed metric is sensitive to input parameters, such as the number of viewpoints, design orientation, image resolution and different lattice structures. To address this issue, this study suggests thresholds for each input parameter for optimum results.

This paper evaluates shape complexity using view similarity to rank parts for prototyping or redesigning with AM.

This study aims to compute 3D model similarity by extracting and comparing shape features from the neutral files.

In this work, the clear text encoding document STEP (Standard for The Exchange of Product model data) of 3D models was analysed, and the models were characterized by two-depth trees consisting of both surface and shell nodes. All surfaces in the STEP files can be subdivided into three kinds, namely, free, analytical and loop surfaces. Surface similarity is defined by the variation coefficients of distances between data points on two surfaces, and subsequently, the shell similarity and 3D model similarity are determined using an optimal algorithm for bipartite graph matching.

This approach is used to experimentally verify the effectiveness of the 3D model similarity algorithm.

The novelty of this study research lies in the computation of 3D model similarity by comparison of all surfaces. In addition, the study makes several key observations: surfaces reflect the most information concerning the functions and attributes of a 3D model and so the similarity between surfaces generates more comprehensive content (both external and internal); semantic-based 3D retrieval can be obtained under the premise of comparison of surface semantics; and more accurate similarity of 3D models can be obtained using the optimal algorithm of bipartite graph matching for all surfaces.

The objective of this study was to investigate the effect of appearance, lifestyle and status similarity on interaction intensity, satisfaction with a banker and repurchase intention. Also examined was the moderating effect of client knowledge in the enhancement of customer satisfaction with a banker.

A total of 800 questionnaires using the snowball sampling technique were performed to distribute the questionnaires to bank customers at different ethnic community centers in New Zealand. A total of 377 useable questionnaires were collected for further analysis.

The findings indicated that the three types of similarity affect interaction intensity differently. Lifestyle similarity was found to positively influence interaction intensity. The similarity constructs of appearance and status were found to have an insignificant relationship with interaction intensity. The findings show that appearance similarity and interaction intensity are able to enhance customer satisfaction with a banker. Customer satisfaction with a banker has a significant relationship with repurchase intention. Client knowledge influences the degree of interaction intensity and satisfaction with a banker.

The findings of this study help bankers to understand the importance of their similarities with a customer and to design recruitment strategies and training sections to improve customer satisfaction.

This study contributes to the body of knowledge by incorporating interaction intensity, similarity and satisfaction with a bank into the repurchase intention model.

This study aims to deal with the case adaptation problem associated with continuous data by providing a non-zero base solution for knowledge users in solving a given situation.

Firstly, the neighbourhood transformation of the initial case base and the view similarity between the problem and the existing cases will be examined. Multiple cases with perspective similarity or above a predefined threshold will be used as the adaption cases. Secondly, on the decision rule set of the decision space, the deterministic decision model of the corresponding distance between the problem and the set of lower approximate objects under each choice class of the adaptation set is applied to extract the decision rule set of the case condition space. Finally, the solution elements of the problem will be reconstructed using the rule set and the values of the problem's conditional elements.

The findings suggest that the classic knowledge matching approach reveals the user with the most similar knowledge/cases but relatively low satisfaction. This also revealed a non-zero adaptation based on human–computer interaction, which has the difficulties of solid subjectivity and low adaptation efficiency.

In this study the multi-case inductive adaptation of the problem to be solved is carried out by analyzing and extracting the law of the effect of the centralized conditions on the decision-making of the adaptation. The adaption process is more rigorous with less subjective influence better reliability and higher application value. The approach described in this research can directly change the original data set which is more beneficial to enhancing problem-solving accuracy while broadening the application area of the adaptation mechanism.

The examination of the calculation cases confirms the innovation of this study in comparison to the traditional method of matching cases with tacit knowledge extrapolation.

The algorithm models established in this study develop theoretical directions for a multi-case induction adaptation study of tacit knowledge.

This study designs a multi-case induction adaptation scheme by combining NRS and CBR for implicitly knowledgeable exogenous cases. A game-theoretic combinatorial assignment method is applied to calculate the case view and the view similarity based on the threshold screening.

Traditional case-adaptation methods have poor accuracy, low efficiency and limited applicability, which cannot meet the needs of knowledge users. To address the shortcomings of the existing research in the industry, this paper proposes a case-adaptation optimization algorithm to support the effective application of tacit knowledge resources.

The attribute simplification algorithm based on the forward search strategy in the neighborhood decision information system is implemented to realize the vertical dimensionality reduction of the case base, and the fuzzy C-mean (FCM) clustering algorithm based on the simulated annealing genetic algorithm (SAGA) is implemented to compress the case base horizontally with multiple decision classes. Then, the subspace K-nearest neighbors (KNN) algorithm is used to induce the decision rules for the set of adapted cases to complete the optimization of the adaptation model.

The findings suggest the rapid enrichment of data, information and tacit knowledge in the field of practice has led to low efficiency and low utilization of knowledge dissemination, and this algorithm can effectively alleviate the problems of users falling into “knowledge disorientation” in the era of the knowledge economy.

This study provides a model with case knowledge that meets users’ needs, thereby effectively improving the application of the tacit knowledge in the explicit case base and the problem-solving efficiency of knowledge users.

The adaptation model can serve as a stable and efficient prediction model to make predictions for the effects of the many logistics and e-commerce enterprises' plans.

This study designs a multi-decision class case-adaptation optimization study based on forward attribute selection strategy-neighborhood rough sets (FASS-NRS) and simulated annealing genetic algorithm-fuzzy C-means (SAGA-FCM) for tacit knowledgeable exogenous cases. By effectively organizing and adjusting tacit knowledge resources, knowledge service organizations can maintain their competitive advantages. The algorithm models established in this study develop theoretical directions for a multi-decision class case-adaptation optimization study of tacit knowledge.

In robot programming by demonstration (PbD) of small parts assembly tasks, the accuracy of parts poses estimated by vision-based techniques in demonstration stage is far from enough to ensure a successful execution. This paper aims to develop an inference method to improve the accuracy of poses and assembly relations between parts by integrating visual observation with computer-aided design (CAD) model.

In this paper, the authors propose a spatial information inference method called probabilistic assembly graph with optional CAD model, shorted as PAGC*, to achieve this task. Then an assembly relation extraction method from CAD model is designed, where different assembly relation descriptions in CAD model are summarized into two fundamental relations that are colinear and coplanar. The relation similarity, distance similarity and rotation similarity are adopted as the similar part matching criterions between the CAD model and the observation. The knowledge of part in CAD is used to correct that of the corresponding part in observation. The likelihood maximization estimation is used to infer the accurate poses and assembly relations based on the probabilistic assembly graph.

In the experiments, both simulated data and real-world data are applied to evaluate the performance of the PAGC* model. The experimental results show the superiority of PAGC* in accuracy compared with assembly graph (AG) and probabilistic assembly graph without CAD model (PAG).

The paper provides a new approach to get the accurate pose of each part in demonstration stage of the robot PbD system. By integrating information from visual observation with prior knowledge from CAD model, PAGC* ensures the success in execution stage of the PbD system.

The purpose of this paper is to clarify the diversity of professional perspectives on police culture in an international context.

In a first step the authors developed a standardized instrument of 45 occupational features for comparative analysis of police professional views. This set was inductively created from 3,441 descriptors of the police profession from a highly diverse sample of 166 police officers across eight European countries. Using this standardized instrument, Q-methodological interviews with another 100 police officers in six European countries were conducted.

The authors identified five perspectives on the police profession suggesting disparities in officers’ outlooks and understanding of their occupation. Yet, the findings also outline considerable overlaps in specific features considered important or unimportant across perspectives.

The study emphasizes that police culture needs to be described beyond the logic of distinct dimensions in well-established typologies. Considering specific features of the police profession determines which aspects police officers agree on across organizational and national contexts and which aspects are unique.

The feature-based approach provides concrete pointers for the planning and implementation of (inter)national and inter-organizational collaborations as well as organizational change.

This study suggests an alternative approach to investigate police culture. It further offers a new perspective on police culture that transcends context-specific boundaries.

Projects have high stakes in how they are categorized. The final place of a project within a classification scheme depends on the inclusion or exclusion of certain classification criteria. So far, many researchers and organizations have used a variety classification criteria to construct different project classification schemes. However, most of these classification criteria have been taken for granted and the process of selecting them to categorize projects still remains a black box. The purpose of this paper is to open the black box of classification process and explain how it is reflected in picking the classification criteria.

Drawing on insights from cognitive psychology’s literature, the authors examine the main views of classification process to provide insight into the unknown or implicit reasons that one might have to pick particular attributes as project classification criteria.

The authors argue that classification occurs in the eye of the beholder; it is not only the project’s features per se but also the classifier’s “goals, ideal and preference” or “knowledge of causal relations” that are reflected in the classification criteria.

By elaborating the classification process, the authors brought the project context into the big picture of classification and provide a more rational, and coherent picture of how project classification works. This contributes to a theoretical blind spot, raised by prior researchers, related to the selection of project classification criteria.

Understanding classification processes will reduce the ambiguities, inconsistencies and multiple interpretations of project categories and help practitioners increase their projects’ visibility and legitimacy within an already established classification scheme. These implications help organizations in addressing some of the main obstacles to using categorization in project management practice.

The review of prior work in the category research literature and the insights from this paper will provide project management scholars with a useful toolbox for future research on project classification, which has long been understudied.

Frank Lloyd Wright's famous house Fallingwater has been the subject of enduring scholarly debate centred on the allegedly clear parallels between its form and that of its surrounding natural setting. Despite these claims being repeated many times, no quantitative approach has ever been used to test this argument. In response, this paper uses a quantitative method, fractal analysis, to measure the relationship between the architecture of Fallingwater and of its natural surroundings.

Using fractal dimension analysis, a computational method that mathematically measures the characteristic visual complexity of an object, this paper mathematically measures and tests the similarity between the visual properties of Fallingwater and its natural setting. Twenty analogues of the natural surroundings of Fallingwater are measured and the results compared to those developed for the properties of eight views of the house.

Although individual results suggest various levels of visual similarity or difference, the complete set of results do not support the claim that the form of Frank Lloyd Wright's Fallingwater exhibits clear visual similarities to the surrounding landscape.

In addition to testing a prominent theory about Wright's building for the first time, the paper demonstrates a rare application of fractal analysis to interpreting relations between architecture and nature.

In the extant research on online knowledge communities (OKCs), little attention has been paid to the influence of membership fluidity on the coevolution of the social and knowledge systems. This article aims to fill this gap.

Based on the attraction-selection-attrition (ASA) framework, this paper constructs a simulation model to study the coevolution of these two systems under different levels of membership fluidity.

By analyzing the evolution of these systems with the vector autoregression (VAR) method, we find that social and knowledge systems become more orderly as the coevolution progresses. Furthermore, in communities with low membership fluidity, the microlevel of the social system (i.e. users) drives the coevolution, whereas in communities with high membership fluidity, the microlevel of the knowledge system (i.e. users' views) drives the coevolution.

This paper extends the application of the ASA framework and enriches the literature on membership fluidity of online communities and the literature on driving factors for coevolution of the social and knowledge systems in OKCs. On a practical level, our work suggests that community administrators should adopt different strategies for different membership fluidity to efficiently promote the coevolution of the social and knowledge systems in OKCs.