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A common pipeline of apparel design and simulation is adjusting 2D apparel patterns, putting them onto a virtual human model and performing 3D physically based simulation. However, manually adjusting 2D apparel patterns and performing simulations require repetitive adjustments and trials in order to achieve satisfactory results. To support future made-to-fit apparel design and manufacturing, efficient tools for fast custom design purposes are desired. The purpose of this paper is to propose a method to automatically adjust 2D apparel patterns and rapidly generate acustom apparel style for a given human model.

The authors first pre-define a set of constraints using feature points, feature lines and ease allowance for existing apparels and human models. The authors formulate the apparel fitting to a human model, as a process of optimization using these predefined constraints. Then, the authors iteratively solve the problem by minimizing the total fitting metric.

The authors observed that through reusing existing apparel styles, the process of designing apparels can be greatly simplified. The authors used a new fitting function to measure the geometric fitting of corresponding feature points/lines between apparels and a human model. Then, the optimized 2D patterns are automatically obtained by minimizing the matching function. The authors’ experiments show that the authors’ approach can increase the reusability of existing apparel styles and improve apparel design efficiency.

There are some limitations. First, in order to achieve interactive performance, the authors’ current 3D simulation does not detect collision within or between adjacent apparel surfaces. Second, the authors’ did not consider multiple layer apparels. It is non-trivial to define ease allowance between multiple layers.

The authors use a set of constraints such as ease allowance, feature points, feature lines, etc. for existing apparels and human models. The authors define a few new fitting functions using these pre-specified constraints. During physics-driven simulation, the authors iteratively minimize these fitting functions.

The purpose of this study is to reveal the role of individual–social–technology fit in online social network (OSN) value development. The social software features (e.g. communication and interaction), social features (e.g. privacy and trust) and individual features (e.g. sense of belonging and self-disclosure) are considered fitting forms to describe the OSN value. Implications and suggestions are addressed.

The literature review on social software, the social and individual characteristics and the research gap with respect to OSN value is presented. The research arguments are then hypothesized, and research model used to describe the proposed role is examined empirically. The research targeted mobile phone users as the subjects, and the extent of the activities of these users on OSN for both work and studies. A salient investigation explores the moderation effect of gender. The research results are obtained, and the findings are revealed on the basis of 468 social software users.

The significant effect of individual–social–technology fit on OSN value development is presented through the satisfaction of both participation and sharing information, and knowledge about this fit is verified. The interplay of social software, social and individual features contributes significantly to individual–social–technology fit development, implying that OSN value development is not a single issue. OSN value development should be considered concurrently with technological, personal and social issues.

The empirical study confirms that fitness analysis produces a systematic outcome, in which all elements (e.g. social, technology and individual) are required to cooperate with one another to maximize the OSN value. An individual adopts online channels to communicate with others; thus, the benefits may be a multidimensional issue instead of only a single information service issue. They also consider building an equal social relationship to be important, as it enables diverse propositions, maintains acceptable privacy and behaves on faith to enhance the fit of technology features and individual features to value development. The subjects also likely accepted the fact that emotion generation is important for the advantage of fit of technology features and social features, thereby likely benefitting OSN value development.

The OSN does not only add new values to the society but also brings new effects on social development, especially in terms of social cognition from virtual community formation, development and creation. Although existing studies in the literature present the important aspects and antecedents linked significantly to OSN value development, these studies also insufficiently discuss the effect of fit of these facets on OSN value development. This exploratory study mainly aims to propose and examine the individual–social–technology fit model through an empirical investigation. The main argument of the study is that when a positive and healthy virtual society is developed through social software, the individual and social characteristics, as well as the social software features, should be defined with a suitable fit to promote the social networking value.

This first part of a comprehensive six‐part series of articles on integral attachment using snap‐fit features familiarizes the reader with the key terms relating to the subject. Every area of study and practice must have associated with it a language to express objects, actions, and ideas. To understand any subject, understanding the language is essential. Developing clear, concise, unambiguous definitions of key terms is a tedious but necessary and critical first step to promoting understanding by allowing effective and efficient communication. These terms and definitions have been carefully compiled and thoughtfully refined from a broad industrial base, published literature, and university‐based research. They are the beginning of a lexicon for the embryonic but promising technology of integral attachment using snap‐fit features.

Previous research in the context of feature fit has examined the effects of congruence (i.e. more specifically, the extent to which a new feature and the product are similar in the hedonic-utilitarian benefits they provide to consumers). The purpose of this paper is to examine a second dimension of feature fit: complementarity (i.e. the extent to which a new feature is related and contributing to the main functionality of the product).

The role of feature fit is examined in two experimental studies (n=593) in the context of feature additions, and also for feature deletions.

The results showed that complementarity adds value to a product as an additional dimension of feature fit beyond congruence, complementarity matters more for a hedonic than for a utilitarian product, and complementarity can compensate for lack of congruence.

For a product developer, adding new features to a product offers an array of choices in terms of what feature(s) to include. Although having a large pool of potential features to choose from is attractive it can also prove problematic, as products may become overly complex and features do not fit well together. The results demonstrate the importance of both congruence and complementarity as predictors of feature fit when features are added to or deleted from products.

The third part of a comprehensive six‐part series on a promising and growing approach to mechanical attachment amenable to automation. Integral snap‐fit attachment design has traditionally focused almost exclusively on the individual features that actually accomplish locking between parts of an assembly (e.g. cantilever hooks, bayonet‐fingers, compressive hooks, traps, and others). The placement and orientation of features that facilitate or enhance engagement or eliminate unwanted translation, rotation or vibration, i.e. locating features and enhancements, are rarely considered. Here, describes integral features classified as locks, locators or enhancements. More importantly, presents a systematic six‐step approach or methodology to guide designers at the higher, attachment or conceptual design level (as opposed to lower, feature or detail design level).

Mass customization (MC) is one of the changes for the US apparel industry that will provide a competitive advantage and offer products needed by consumers. However, as the points of customization and their extent of customization characterize business models including the process strategies to achieve a successful MC strategy, it is imperative to define the points of customization. The purpose of this research is to investigate and introduce the critical points of customization and their extent for apparel.

An apparel industry survey together with case studies was used to collect information to test and support the hypothesis developed through the comprehensive literature review.

Five critical points of apparel customization are defined. These points and their extents of customization are compared, analyzed and validated. It is suggested that the success and the capability of apparel MC will depend on how effectively a company can combine the defined points of customization and their extent of customization in pre‐production, production and post‐production of the apparel product.

The research findings of points of customization is for an apparel product. However, the concepts may be applied to products in other industries.

The post‐production customization point using current technologies is a popular practice for mass customization. However, to achieve a higher level or extent of customization such as design point of customization, it is essential to use advanced product development, manufacturing and processing technologies.

The points of apparel customization and their extent of customization have not been studied before for the apparel MC domain. These points and extent of customization provide new insights into the mass customized apparel manufacturing and apparel MC business models.

The purpose of this paper is to assess the impact of seven variables that emerge from forensic research on facial-composite construction and naming using contemporary police systems: EvoFIT, Feature and Sketch.

The paper involves regression- and meta-analyses on composite-naming data from 23 studies that have followed procedures used by police practitioners for forensic face construction. The corpus for analyses contains 6,464 individual naming responses from 1,069 participants in 41 experimental conditions.

The analyses reveal that composites constructed from the holistic EvoFIT system were over four-times more identifiable than composites from “Feature” (E-FIT and PRO-fit) and Sketch systems; Sketch was somewhat more effective than Feature systems. EvoFIT was more effective when internal features were created before rather than after selecting hair and the other (blurred) external features. Adding questions about the global appearance of the face (as part of the holistic-cognitive interview (H-CI)) gives a valuable improvement in naming over the standard face-recall cognitive interview (CI) for all three system types tested. The analysis also confirmed that composites were considerably less effective when constructed from a long (one to two days) compared with a short (0-3.5 hours) retention interval.

Variables were assessed that are of importance to forensic practitioners who construct composites with witnesses and victims of crime.

Using a large corpus of forensically-relevant data, the main result is that EvoFIT using the internal-features method of construction is superior; an H-CI administered prior to face construction is also advantageous (cf. face-recall CI) for EvoFIT as well as for two further contrasting production systems.

Organizational psychologists and human resource management (HRM) practitioners often have to select the “right fit” candidate by manually scouting data from various sources including job portals and social media. Given the constant pressure to lower the recruitment costs and the time taken to extend an offer to the right talent, the HR function has to inevitably adopt data analytics and machine learning for employee selection. This paper aims to propose the “Quality of Hire” concept for employee selection using the person-environment (P-E) fit theory and machine learning.

The authors demonstrate the aforementioned concept using a clustering algorithm, namely, partition around mediod (PAM). Based on a curated data set published by the IBM, the authors examine the dimensions of different P-E fits and determine how these dimensions can lead to selection of the “right fit” candidate by evaluating the outcome of PAM.

The authors propose a multi-level fit model rooted in the P-E theory, which can improve the quality of hire for an organization.

Theoretically, the authors contribute in the domain of quality of hire using a multi-level fit approach based on the P-E theory. Methodologically, the authors contribute in expanding the HR analytics landscape by implementing PAM algorithm in employee selection.

The proposed work is expected to present a useful case on the application of machine learning for practitioners in organizational psychology, HRM and data science.

Every geometric model corresponding to a unique feature whose errors of parameters uncorrelated, so the linearization technique can be successfully applied. The solution of a linear least square problem can be applied straightforwardly. This method has advantages especially in calibrate the redundant robot because it’s relatively small. The parameters of kinematics are unique and determined by this algorithm.

In this paper, a geometric identification method has been studied to estimate the parameters in the Denavit–Hartenberg (DH) model of the robot. Through studying the robot’s geometric features, specific trajectories are designed for calibrating the DH parameters. On the basis of these geometric features, several fitting methods have been deduced so that the important geometric parameters of robots, such as the actual rotation centers and rotate axes, can be found.

By measuring the corresponding motion trajectory at the end-effector, the trajectory feature can be identified by using curve fitting methods, and the trajectory feature will reflect back to the actual value of the DH parameters.

This method is especially suitable for rigid serial-link robots especially for redundant robots because of its specific calibration trajectory and geometric features. Besides, this method uses geometric features to calibrate the robot which is relatively small especially for the redundant robot comparing to the numerical algorithm.

This fifth part of a comprehensive six‐part series of articles presents a systematic procedure for formalising the generation of alternative concepts for a particular design employing integral snap‐fit attachments. With the procedure, the alternatives generated are representative of the entire pertinent design space, since they include alternative attachment interface geometries, assembly procedures, attachment features, and constraint options for a particular application. The procedure is easy to use, effective and efficient, and results in a number of alternatives which are sufficient to represent the entire pertinent design space, but not so large as to preclude selection of a best concept using an optimisation method.