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A matrix is a Q0-matrix if for every k∈{1,2,…,n}, the sum of all k×k principal minors is nonnegative. In this paper, we study some necessary and sufficient conditions for a digraph to have Q0-completion. Later on we discuss the relationship between Q and Q0-matrix completion problem. Finally, a classification of the digraphs of order up to four is done based on Q0-completion.

Low response rates in web surveys and the use of different devices in entering web survey responses are the two main challenges to response quality of web surveys. The purpose of this study is to compare the effects of using interviewers to recruit participants in computer-assisted self-administered interviews (CASI) vs computer-assisted personal interviews (CAPI) and smartphones vs computers on participation rate and web survey response quality.

Two field experiments using two similar media use studies on US college students were conducted to compare response quality in different survey modes and response devices.

Response quality of computer entry was better than smartphone entry in both studies for open-ended and closed-ended question formats. Device effect was only significant on overall completion rate when interviewers were present.

Survey researchers are given guidance how to conduct online surveys using different devices and choice of question format to maximize survey response quality. The benefits and limitations of using an interviewer to recruit participants and smartphones as web survey response devices are discussed.

It shows how computer-assisted self-interviews and smartphones can improve response quality and participation for underprivileged groups.

This is the first study to compare response quality in different question formats between CASI, e-mailed delivered online surveys and CAPI. It demonstrates the importance of human factor in creating sense of obligation to improve response quality.

In today’s world, the recommender systems are very valuable systems for the online users, as the World Wide Web is loaded with plenty of available information causing the online users to spend more time and money. The recommender systems suggest some possible and relevant recommendation to the online users by applying the recommendation filtering techniques to the available source of information. The recommendation filtering techniques take the input data denoted as the matrix representation which is generally very sparse and high dimensional data in nature. Hence, the sparse data matrix is completed by filling the unknown or missing entries by using many matrix completion techniques. One of the most popular techniques used is the matrix factorization (MF) which aims to decompose the sparse data matrix into two new and small dimensional data matrix and whose dot product completes the matrix by filling the logical values. However, the MF technique failed to retain the loss of original information when it tried to decompose the matrix, and the error rate is relatively high which clearly shows the loss of such valuable information.

To alleviate the problem of data loss and data sparsity, the new algorithm from formal concept analysis (FCA), a mathematical model, is proposed for matrix completion which aims at filling the unknown or missing entries without loss of valuable information to a greater extent. The proposed matrix completion algorithm uses the clustering technique where the users who have commonly rated the items and have not commonly rated the items are captured into two classes. The matrix completion algorithm fills the mean cluster value of the unknown entries which well completes the matrix without actually decomposing the matrix.

The experiment was conducted on the available public data set, MovieLens, whose result shows the prediction error rate is minimal, and the comparison with the existing algorithms is also studied. Thus, the application of FCA in recommender systems proves minimum or no data loss and improvement in the prediction accuracy of rating score.

The proposed matrix completion algorithm using FCA performs good recommendation which will be more useful for today’s online users in making decision with regard to the online purchasing of products.

This paper presents the new technique of matrix completion adopting the vital properties from FCA which is applied in the recommender systems. Hence, the proposed algorithm performs well when compared to other existing algorithms in terms of prediction accuracy.

The missing travel time data for roads is a common problem encountered by traffic management departments. Tensor decomposition, as one of the most widely used method for completing missing traffic data, plays a significant role in the intelligent transportation system (ITS). However, existing methods of tensor decomposition focus on the global data structure, resulting in relatively low accuracy in fibrosis missing scenarios. Therefore, this paper aims to propose a novel tensor decomposition model which further considers the local spatiotemporal similarity for fibrosis missing to improve travel time completion accuracy.

The proposed model can aggregate road sections with similar physical attributes by spatial clustering, and then it calculates the temporal association of road sections by the dynamic longest common subsequence. A similarity relationship matrix in the temporal dimension is constructed and incorporated into the tensor completion model, which can enhance the local spatiotemporal relationship of the missing parts of the fibrosis type.

The experiment shows that this method is superior and robust. Compared with other baseline models, this method has the smallest error and maintains good completion results despite high missing rates.

This model has higher accuracy for the fibrosis missing and performs good convergence effects in the case of the high missing rate.

The purpose of this paper is to present a novel method for fabric defect detection.

The method based on joint low-rank and spare matrix recovery, since patterned fabric is manufactured by a set of predefined symmetry rules, and it can be seen as the superposition of sparse defective regions and low-rank defect-free regions. A robust principal component analysis model with a noise term is designed to handle fabric images with diverse patterns robustly. The authors also estimate a defect prior and use it to guide the matrix recovery process for accurate extraction of various fabric defects.

Experiments on plain and twill, dot-, box- and star-patterned fabric images with various defects demonstrate that the method is more efficient and robust than previous methods.

The authors present a RPCA-based model for fabric defects detection, and show how to incorporate defect prior to improve the detection results. The authors also show that more robust detection and less running time can be obtained by introducing a noise term into the model.

In this “Info-plosion” era, recommendation systems (or recommenders) play a significant role in finding interesting items in the surge of online digital activity and e-commerce. The purpose of this paper is to model users' preference evolution to recommend potential items which users may be interested in.

A novel recommendation system, namely evolution-learning recommendation (ELR), is developed to precisely predict user interest for making recommendations. Differing from prior related methods, the authors integrate the matrix factorization (MF) and recurrent neural network (RNN) to effectively describe the variation of user preferences over time.

A novel cumulative factorization technique is proposed to efficiently decompose a rating matrix for discovering latent user preferences. Compared to traditional MF-based methods, the cumulative MF could reduce the utilization of computation resources. Furthermore, the authors depict the significance of long- and short-term effects in the memory cell of RNN for evolution patterns. With the context awareness, a learning model, V-LSTM, is developed to dynamically capture the evolution pattern of user interests. By using a well-trained learning model, the authors predict future user preferences and recommend related items.

Based on the relations among users and items for recommendation, the authors introduce a novel concept, virtual communication, to effectively learn and estimate the correlation among users and items. By incorporating the discovered latent features of users and items in an evolved manner, the proposed ELR model could promote “right” things to “right” users at the “right” time. In addition, several extensive experiments are performed on real datasets and are discussed. Empirical results show that ELR significantly outperforms the prior recommendation models. The proposed ELR exhibits great generalization and robustness in real datasets, including e-commerce, industrial retail and streaming service, with all discussed metrics.

Expert systems (ES) are designed to support and replicate management tasks and decision making characterized by experience and expertise. These commodities are becoming increasingly limited as organizations flatten their management structure. Effective identification and evaluation of domains appropriate for ES‐based solutions are critical to their successful development and implementation. Presents a comprehensive model for ES domain identification and evaluation that includes an emphasis on total quality management (TQM) and can be used as a project management tool. The TQM matrix evaluation model proposed facilitates qualitative and quantitative assessment of ES domains and can provide for dynamic evaluation, feedback and continuous quality management over the entire project life cycle. Tests the TQM matrix evaluation model by applying it to a real business problem and presents and discusses the results.

Over the years, there have been many words written on the subject of labour turnover. Many models have been put forward to understand the phenomenon and to provide a basis for its diagnosis and analysis. A variety of measures of labour turnover have been developed to assist in this analysis. Standard measures have concerned stability, survival and the propensity to leave relating to a wide range of factors either individual, organisational or societal. Most of these measures have been developed so that the user, usually the personnel specialist, can better appreciate the phenomenon and then improve the chances of diagnostic success in reducing labour wastage.

This article seeks to propose a technique called total quality function deployment (TQFD) and to appraises its practicality by applying it to a traditional pump‐manufacturing environment.

The deficiencies of QFD techniques were studied. The theory of the TQFD technique was developed with the objective of overcoming these deficiencies. Two pump‐manufacturing companies were involved, while examining the practicality of TQFD technique.

A traditional pump‐manufacturing environment lacks knowledge of implanting techniques like QFD, which challenges the researchers to test‐implement, the techniques like TQFD. Yet the successful development of TQFD documents during the reported research project indicates the practical feasibility of TQFD implementation in pump‐manufacturing companies.

The practicality of TQFD was tested by involving two pump‐manufacturing companies located in the city of Coimbatore in India. These two companies may not represent the global pump‐manufacturing environment. However, the pump‐manufacturing trend is uniformly showing sluggish growth at global level. Hence, the inferences drawn may considerably represent traditional pump‐manufacturing environment at the global level.

Since TQFD documents are successfully developed, engineers can be trained to become TQFD coordinators. These engineers will transfer the voice of customers into the practical field by following the implementation procedure presented in the paper.

So far no researchers have reported the theory and practice of TQFD. Pump manufacturing companies lose their market share because of the failure to translate customers' vague languages into technical languages through the involvement of all personnel and holistic exploitation of resources of the organisation. TQFD is a very valuable technique to meet this imperative.

The development of a new class of engineering materials is the current demand for aircraft and automobile companies. In this context metal, composite materials have a widespread application in different areas of manufacturing sectors.

In this paper, an attempt is made to develop the aluminium-based nano metal matrix composite reinforced with graphene nanoparticles (GNP) by using the stir casting method. Different weight percentage (0.4%, 0.8% and 1.2% by weight) of GNPs are used to fabricate metal matrix composites (MMCs). The developed nanocomposites were further validated by density calculation and optical microstructures to discuss the distribution of GNPs. The tensile test was conducted to determine the strength of the developed MMCs and also supported by fractographic analysis. In addition to it, the Rockwell hardness test and impact test (toughness) with fracture analysis were also conducted to strengthen the present work.

The results reveal the uniform distribution of GNPs into the matrix material. The yield strength and ultimate tensile strength obtained a maximum value of 155.67 MPa and 170.28 MPa, respectively. The hardness value (HRB) is significantly increased and 84 HRB was obtained for the sample with AA1100/0.4% GNP, while maximum hardness value (94 HRB) was obtained for the sample AA1100/1.2% GNP. The maximum value of toughness 14.3 Jules/cm² is recorded for base alloy AA1100 while increasing the reinforcement percentage, it decreases up to 9.7 Jules/cm² for AA1100/1.2% GNP.

Graphene nanoparticles are used to develop nanocomposites, which is one of the suitable alternatives for heavy engineering materials such as steels and cast irons. It has improved microstructural and mechanical properties which makes it preferable for many engineering and structural applications.