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Although the literature recognizes that coopetition plays a significant role in the success of international construction joint ventures (ICJVs), the impacts of coopetition on the performance outcomes of ICJVs remain largely unknown. This study extends this line of research by theorizing coopetition from three dimensions, i.e. coopetition intensity, coopetition balance and coopetition structure, and examining the relationships between coopetition and ICJV performance outcomes from both the contingency and configuration perspectives.

The hypotheses were tested using survey data from a sample of 188 ICJVs. Structural equation modelling was employed for the contingency approach to estimate the relationships between the three dimensions of coopetition and performance. For the configuration approach, cluster analysis was utilized to identify coopetition patterns. Subsequently, an analysis of variance was employed to analyse the relationships between these coopetition patterns and performance.

The contingency results indicate that while coopetition intensity is positively related to all types of performance, coopetition balance is only positively related to project performance and partner performance. Moreover, coopetition structure is only related to partner performance and socioenvironmental performance. The configuration approach identifies six patterns of coopetition, manifesting different levels of project, partner and socioenvironmental performance.

These findings, therefore, contribute to the ICJV literature by extending the understanding of how coopetition dimensions individually and jointly influence ICJV performance.

Educational data mining (EDM) discovers significant patterns from educational data and thus can help understand the relations between learners and their educational settings. However, most previous data mining techniques focus on prediction of learning performance of learners without integrating learning patterns identification techniques.

This study proposes a new framework for identifying learning patterns and predicting learning performance. Two modules, the learning patterns identification module and the deep learning prediction models (DNN), are integrated into this framework to identify the difference of learning performance and predicting learning performance from profiles of students.

Experimental results from survey data indicate that the proposed identifying learning patterns module could facilitate identifying valuable difference (change) patterns from student’s profiles. The proposed learning performance prediction module which adapts DNN also performs better than traditional machine techniques in prediction performance metrics.

To our best knowledge, the framework is the only educational system in the literature for identifying learning patterns and predicting learning performance.

This study aims to develop a novel design method to make personalized masks for the effective prevention of pandemic respiratory infectious disease.

The changes in facial shape during speaking were analyzed using a three-dimensional (3D) scanning technique. In total, 13 anthropometric items were measured, and mask patterns were generated using a parametric pattern design method. Three sizing methods were proposed to reflect not only static but also dynamic body dimensions on the mask patterns.

A significant increase or decrease was observed in 10 out of 13 measurement items. Based on this, four items were selected to be used in the mask pattern design. The nose and cheek areas of a mask were fixed to protect the respiratory tract against viruses. The lower jaw area was deformed to improve the fit.

This study is expected to provide fundamental data to understand the changes in facial shape during movement. In addition, it is expected that the development of individualized personal protective equipment with movement adaptability will facilitate an effective response to various pandemic respiratory diseases.

In order to develop a personal protective equipment (PPE) that has a good fit and can protect against pandemic respiratory infectious diseases, morphological analysis was attempted using 3D facial data. It would be possible to design various products and equipment to be worn on the face by using the method proposed in this study.

A comprehensive apparel CAD system was developed to perform automatic garment pattern drafting and the prediction of the final drape shape of designed garment putting on the human body. Three dimensional apparel CAD system starts with a flat garment pattern drafting system. A computerized pattern design script language has been created based on the traditional patterner’s principles to develop an automatic draft system of performing basic garment pattern drafting as well as grading rule generation. A pattern modification system was also developed considering functions required in apparel CAD such as auxiliary pattern generation, seam line creation, and dart manipulation to generate engineering patterns which can be used in the three dimensional garment shape prediction system presented later in part II of this paper.

The purpose of this article is to link the associative learning process of the human brain to the relationship and emergence of really significant ideas on the global horizon.

First, learning is explored from the viewpoint of the brain/mind, with a focus on the creation of patterns and their relationships to our personal frames of reference. Second, the associations of three really significant ideas are explored, and a pattern of patterns is surfaced.

The paper finds that in concert with the functioning of the brain, significant ideas emerge in relationship with other ideas that have personal historical significance, i.e. external patterns from the environment are detected, recognized, made sense of and have meaning in relationship with our internal patterns of significance.

The paper creates an appreciation of the role of patterns in thinking and learning.

Researchers in the past have used Fourier transformation method to determine the in‐plane displacement components from moiré fringes generated by a pair of overlapping circular gratings. In this approach it is necessary to assume that the transmittance is sinusoidal. The purpose of this paper is to propose a graphical method for determining the 2D displacement components from the moiré patterns more easily instead of the complex Fourier transformation method.

The moiré patterns were spatially transformed from Cartesian‐to‐polar coordinate system. The morphological grayscale dilation operation was used to eliminate the residual gratings in the transformed pattern while preserving the moiré fringes. The center line of the moiré fringe was fitted with a sine curve and the in‐plane displacement values were determined directly from the peak‐to‐valley height and the position of the peak in the fitted curve.

Experimental results showed that the proposed moiré pattern analysis method is able to give in‐plane displacement accuracies of 0.002 mm in the x‐direction and 0.01 in the y‐direction without the need for complex computation.

Resolution of the proposed method is limited only by the resolution of the imaging system.

The proposed graphical method for determining 2D displacement components from the moiré patterns can be applied to low‐frequency circular gratings whose transmittance is not sinusoidal.

The graphical analysis method is novel and allows the displacements components to be determined more easily.

Apparel pattern making creates a set of pattern pieces of fabric which are sewn into the desired garment. The pattern pieces are developed through fashion analysis, pattern design and pattern drafting. Seeks to build an object‐oriented model of the apparel pattern‐making process through these subprocesses. Defines the model in terms of a requirements specification and subsequently uses it in the development of a computerized pattern‐making system. Uses object behaviour analysis, which is derived from object‐oriented technology, as the method for defining the model.

Pattern marking is the allocation of garment patterns on the cloth roll minimizing the fabric loss, and thus very important for the cost reduction in garment manufacturing industries. But automatic marking is very difficult because it is a non‐deterministic in polynomial time problem. Most previous pattern marking methods needed collision detection routine to lay out patterns without interfering each other, which was the bottle neck of nesting speed. In this study, rectilinear polygon approximation technique was used to reduce the overall calculation time because the garment patterns are usually in non‐convex shape that can effectively be approximated by rectangles. Additionally, we adapted stochastic simulated annealing to search the optimal pattern marking.

The development of a parametric garment pattern design system that utilizes anthropometric data for consumer-oriented garment pattern design.

Action list and interactive user interface were developed to design flat garment patterns. Three-dimensional drape simulation was also implemented to verify the fit of patterns.

Patterns generated by the parametric design system developed in this study could be modified easily by providing appropriate anthropometric data regardless of their complexities.

Parametric pattern design system can reduce considerable amount of time and cost by replacing the trial-and-error based grading processes.

Parametric pattern design system can generate customized garment patterns quickly and easily. Therefore, it is expected to contribute to the production of sustainable fashion and textile by reducing the loss of time and resource.

A versatile and comprehensive action list structure was implemented to manage the drawing actions of the user. Various numerical analysis methods were also used to maintain the geometrical validity of patterns.

Mining sequential patterns in large databases has become an important data mining task with broad applications, such as business analysis, web mining, security, and bio‐sequences analysis. The purpose of this paper is to propose the notion of condensed frequent sequential pattern base (SP base) with guaranteed maximal error bound.

A subset of frequent sequential patterns is computed, and then used to approximate the supports of arbitrary frequent sequential patterns with guaranteed maximal error bound, because in many applications it is sufficient to generate only frequent sequential patterns with support frequency in close‐enough approximation instead of in full precision.

The concept of condensed frequent SP base is introduced, and an efficient algorithm for mining condensed SP bases is developed.

A condensed frequent SP base can significantly reduce the set of sequential patterns that need to be mined, stored, and analyzed, while providing guaranteed error bound for frequencies of sequential patterns not in the base.

A much smaller base of patterns can help users to comprehend the mining results. Computing a much smaller pattern base also leads to better efficiency.

The paper shows that by adopting a novel pruning technology, the algorithm out‐performs the previous work by one order of magnitude.