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The purpose of this paper is to develop a computational technique to couple finite element and meshfree methods for locking‐free analysis of shear deformable beams and plates, and to impose the boundary conditions directly when the matching field approach is adopted in the meshfree region.

Matching field approach eliminates shear‐locking which may occur due to inconsistencies in the approximations of the transverse displacement and rotation fields in shear‐deformable beams and plates. Continuous blending method is modified in order to be able to satisfy the constraint conditions of the matching field strategy.

For both transverse displacement and rotation fields, the developed technique produces approximation functions that satisfy the Kronecker delta property at the required nodes of the meshfree region when the matching field approach is adopted.

This approach allows for direct assembly of the stiffness matrices that are built for separate finite element and meshfree regions when the matching field approach is adopted. The boundary conditions can be directly applied, and the reaction forces can also be calculated directly from the structural stiffness matrix by using the developed technique.

To achieve a full understanding of the role of marketing from plan to profit requires a knowledge of the basic building blocks. This textbook introduces the key concepts in the art or science of marketing to practising managers. Understanding your customers and consumers, the 4 Ps (Product, Place, Price and Promotion) provides the basic tools for effective marketing. Deploying your resources and informing your managerial decision making is dealt with in Unit VII introducing marketing intelligence, competition, budgeting and organisational issues. The logical conclusion of this effort is achieving sales and the particular techniques involved are explored in the final section.

This study aims to propose that providers should tailor recovery responses to consumers’ emotional states to improve evaluations and behavioral intentions.

A multi-study approach comprising field and lab data was used. The field study, conducted on the Boston public transport network during a weather crisis, sought to determine how a provider should deliver their recovery response to match the consumer’s affective state. In the lab studies, the importance of tailoring a recovery message to the consumer’s state is experimentally demonstrated while controlling for factors such as consumer brand involvement.

This study finds that an emotion-focused recovery emphasizing empathy should be given to those in an avoidance affective state (i.e. focused on the avoidance of negative outcomes) such as worry. A problem-focused recovery, in which the focus is on the process that led to the failure and the steps that will be taken to correct it, should be provided to those in an approach state (i.e. concerned with advancement and accomplishment) such as anger. This study also finds this effect is more salient under low involvement conditions.

Future research should examine how nonverbal behavior during recovery can be tailored to a consumer’s state.

Service providers are encouraged to tailor recovery messages to consumers’ affective states.

To the best of the authors’ knowledge, this study is the first to examine matching recovery messages to affective states, an important contribution as service failures can elicit a wide variety of affective states that influence how consumers react to recovery messages.

The peer-to-peer (P2P) accommodation-sharing market has developed rapidly on the strength of information technology in recent years. Matching providers and customers in an information technology (IT)-enabled platform is a key determinant of both parties' experiences and the healthy development of the platform. However, previous research has not sufficiently explained the mechanism of provider–customer matching in accommodation sharing, especially at the psychological level. Based on field cognitive style theory, this study examines how the match and mismatch affect customers' online and offline satisfaction and whether a significant difference exists between online and offline satisfaction under different matching patterns.

The authors test the proposed theoretical model using 122 provider–customer dyad data collected through a field study.

The results suggest that customers' online and offline satisfaction under match is significantly higher than that under mismatch. In addition, customers' online satisfaction is significantly higher than their offline satisfaction under mismatch, but there is no significant difference between the two under match. The perceived price fairness also plays a moderating role in the case of mismatch.

In summary, these findings provide a novel understanding about the matching patterns and their outcomes in the accommodation-sharing context and expand the contents and applications of field cognitive style theory and matching theory. This study will help these IT-enabled platforms to provide personalized matching services at the psychological level, thereby enhancing user experience and corporate competitiveness. 10; 10;

Over the past decade, the cost of product development has increased drastically, and this is due to the inability of most enterprises to locate suitable and optimal collaborators for knowledge sharing. Nevertheless, knowledge sharing is a mechanism that helps people find the best collaborators with relevant knowledge. Hence, a new approach for locating optimal collaborators with relevant knowledge is needed, which could help enterprise in reducing cost and time in a knowledge-sharing environment. The paper aims to discuss these issues.

One unique challenge in the domain of knowledge sharing is that collaborators do not possess the same number of events resident in the knowledge available for sharing. In this paper, the authors present a new approach for locating optimal collaborators in knowledge-sharing environment using the combinatorial algorithm (CA-KSE).

The proposed pattern-matching approach implemented in Java is considered efficient for solving the issue peculiar to collaboration in knowledge-sharing domain. The authors benchmarked the proposed approach with its semi-global pairwise alignment and global alignment counterparts through scores comparison and the receiver operating characteristic curve. The results obtained from the comparisons showed that CA-KSE is a perfect test having an area under curve of 0.9659, compared to the other approaches.

The paper has proposed an efficient algorithm, which is considered better than related methods, for matching several collaborators (more than two) in KS environment. The method could be deployed in medical field for gene analysis, software organizations for distributed development and academics for knowledge sharing.

One sign of strength of this approach, compared to most sequence alignment approaches that can only match two collaborators at a time, is that it can match several collaborators at a faster rate.

Search trees are a set of paths with branches or choices that enable a system to carry out the most sensible search approach at each stage of a search. A new design for subject access to online catalogs enlists search trees to identify the characteristics of end‐user queries for subjects, control system responses, and determine appropriate subject‐searching approaches in response to the subject queries users entered. The purpose of this article is to identify characteristics of the most difficult user queries and recommend enhancements to the new subject‐searching design to enable it to produce useful retrievals in response to the wide variety of queries users pose to online catalogs. Online catalogs governed by search trees are more effective than the users themselves in selecting subject‐searching approaches that would produce useful information for the subjects users seek. The enhanced search trees presented and tested in this article enlist subject‐searching approaches that are not typical of the functionality of operational online catalogs. Design and development is required to upgrade existing online catalogs with search trees and new subject‐searching functionality to be successful in responding with useful retrievals to the most difficult user queries.

The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.

A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.

1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.

NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.

A series of seven empirical studies conducted in Sheffield lend support to the notion that learners do spontaneously display styles of information processing behaviour originally identified by Pask and Scott, that even versatile postgraduate students are susceptible to the effects of matching and mismatching of teaching and learning styles, and that the arena in which these learning styles may be observed extends beyond learning to information seeking activity including database searching. Much research is still to be done to resolve the enigma of learning styles. However, arguably Pask’s time has come in the sense that current computing software and educational infrastructure now allow with relative ease the testing of the potential of Pask’s constructs using large samples of students, and the realisation of this potential in the development and delivery of mainstream teaching and learning resources.