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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.

The purpose of this paper is to propose an agent‐based condition monitoring system for use in industrial applications. An intelligent maintenance agent is described that is able to autonomously perform necessary actions and/or aid a human in the decision‐making process. An example is presented as a case‐study from manufacturing of industrial robots.

The paper is mainly based on a case‐study performed at a large multi‐national company aiming to explore the usefulness of case‐based experience reuse in production.

This paper presents a concept of case‐based experience reuse in production. A maintenance agent using a case‐based reasoning (CBR) approach to collect, preserve and reuse available experience in the form of sound recordings exemplifies this concept. Sound from normal and faulty robot gearboxes are recorded during the production end test and stored in a case library together with their diagnosis results. Given an unclassified sound signal, relevant cases are retrieved to aid a human in the decision‐making process. The maintenance agent demonstrated good performance by making right judgments in 91 per cent of all the tests, which is better than an inexperienced technician.

Experienced staffs acquire their experience during many years of practice and sometimes also through expensive mistakes. The acquired experience is difficult to preserve and transfer and it often gets lost if the corresponding personnel leave their job due to retirements, etc. The proposed CBR approach to collect, preserve and reuse the available experience enables a large potential for time and cost savings, predictability and reduced risk in the daily work. The paper exemplifies experience reuse for quality improvement in production using a number of methods and techniques from artificial intelligence.

The main focus of this paper is to show how to perform efficient experience reuse in modern production industry to improve quality of products. Two approaches are used: a case‐study describing an example of experience reuse in production using a fault diagnosis system recognizing and diagnosing audible faults on industrial robots and an efficient approach on how to package such a system using the agent paradigm and agent architecture.

The purpose of this study is to construct a gray relational model based on information diffusion to avoid rank reversal when the available decision information is insufficient, or the decision objects vary.

Considering that the sample dependence of the ideal sequence selection in gray relational decision-making is based on case sampling, which causes the phenomenon of rank reversal, this study designs an ideal point diffusion method based on the development trend and distribution skewness of the sample information. In this method, a gray relational model for sample classification is constructed using a virtual-ideal sequence. Subsequently, an optimization model is established to obtain the criteria weights and classification radius values that minimize the deviation between the comprehensive relational degree of the classification object and the critical value.

The rank-reversal problem in gray relational models could drive decision-makers away from using this method. The results of this study demonstrate that the proposed gray relational model based on information diffusion and virtual-ideal sequencing can effectively avoid rank reversal. The method is applied to classify 31 brownfield redevelopment projects based on available interval gray information. The case analysis verifies the rationality and feasibility of the model.

This study proposes a robust method for ideal point choice when the decision information is limited or dynamic. This method can reduce the influence of ideal sequence changes in gray relational models on decision-making results considerably better than other approaches.

As we know, the quality of processes is technically depicted by variation, a product or process with the best quality must naturally require the variation as less as possible. The variation is usually reduced with many ways, say, by adjusting parameters settings under robust design with many turns expensive experiements. So ones are trying to reach the robusiness by detecting cheap and simple methods. In this paper, a both practical and simple technique for quality improvement, namely reducing the variation, by data classification is studied. First, all possible system factors are included, which may dominate the variation law. And then we make use of the past observations and their classification as well as boxplot charts to find out the internal rule between the variation and the system factor. Next, adjust the location of the system factor according to the rule so that the variation could, to some extent, be lessened. Finally, two typical quality improvement cases based on data classification are presented.

Successful implementation of a building information modeling (BIM) for building operation and maintenance (O&M) requires purposeful, early-design identification of end-user-specific model exchange requirements. This paper aims to provide a semantic data-rich classification system for model objects to convey facilities management (FM) requirements in BIM guidelines in support of efficient FM-BIM data workflows.

A modularized, repeatable and technical solution for semantic requirements of BIM exchange objects was developed through ontology-based data mapping of the industry foundation classes. The proposed solution further contextualizes syntax per the buildingSMART Data Dictionary schema and provides an implementation agreement to address the quality issues of discipline BIMs and establish consistent modeling and naming conventions to facilitate automated BIM data workflow.

The level of semantics (LOS) development framework and the results of LOS implementation focusing on a building mechanical system case project are presented and discussed to showcase the increased efficiency resulting from its implementation throughout the BIM data management workflows.

This study represents a pioneering effort to create and implement the LOS schema as a modularized solution in support of automatic BIM data creation, adjustment, verification and transition across the design, construction and O&M workflows of a large owner organization in the Midwest USA.

Three adjustment modes are alternatives for mixed-model assembly lines (MMALs) to improve their production plans according to constantly changing customer requirements. The purpose of this paper is to deal with the decision-making problem between these modes by proposing a novel multi-classification method. This method recommends appropriate adjustment modes for the assembly lines faced with different customer orders through machine learning from historical data.

The decision-making method uses the classification model composed of an input layer, two intermediate layers and an output layer. The input layer describes the assembly line in a knowledge-intensive manner by presenting the impact degrees of production parameters on line performances. The first intermediate layer provides the support vector data description (SVDD) of each adjustment mode through historical data training. The second intermediate layer employs the Dempster–Shafer (D–S) theory to combine the posterior classification possibilities generated from different SVDDs. The output layer gives the adjustment mode with the maximum posterior possibility as the classification result according to Bayesian decision theory.

The proposed method achieves higher classification accuracies than the support vector machine methods and the traditional SVDD method in the numerical test consisting of data sets from the machine-learning repository and the case study of a diesel engine assembly line.

This research recommends appropriate adjustment modes for MMALs in response to customer demand changes. According to the suggested adjustment mode, the managers can improve the line performance more effectively by using the well-designed optimization methods for a specific scope.

The adjustment mode decision belongs to the multi-classification problem featured with limited historical data. Although traditional SVDD methods can solve these problems by providing the posterior possibility of each classification result, they might have poor classification accuracies owing to the conflicts and uncertainties of these possibilities. This paper develops a novel classification model that integrates the SVDD method with the D–S theory. By handling the conflicts and uncertainties appropriately, this model achieves higher classification accuracies than traditional methods.

The purpose of this paper is to present brief YouTube life stories to learn about how extensive users experience YouTube use and manage (or fail to manage) their use. It also explores the consequences of different types of extensive use.

In this paper, a biographical approach was used. Nine students who used YouTube for two or more hours every day were guided to tell life stories of their introduction to YouTube, subsequent use and critical events associated with YouTube use. Thematic analysis distinguished between non-problematic, compulsive and addicted users. Three single case life stories illustrate the experiences of users in each category.

These extensive YouTube users tell similar stories of informal learning from early interaction with the platform. For some, extensive YouTube use became problematic; for others, it remained functional. Similar to other social platforms, users unable to regulate use became compulsive users and some users can become addicted. While the symptoms of YouTube addiction are similar to other online addictions, compulsive YouTube use is driven more by algorithm-generated content chaining than overt social interaction.

The paper introduces life stories as a way to present case studies of social media use. The distinction between extensive, but functional, and problematic YouTube use illustrates how extensive social media use is not necessarily dysfunctional. User education for self-regulation of YouTube use is recommended.

The purpose of this paper is to find out wheter the cognitive style of the manager affect as his view of the viable strategy for a firm.

This study uses data from 70 managers in the spa industry.

Concludes that managers' cognitive style and particularly their way of taking in information (sensing or intuition) have effect on strategies they tend to prefer. Intuitive managers tend to view the prospector or the analyzer strategy as the most viable future alternative for a firm. The analyzer or the defender strategy is preferred by the sensing managers.

For managers, the results of this study emphasize the importance of knowing oneself and especially one's way of perception and understanding its suggested effect on strategic decision making. This paper also attempts to inspire researchers to include the cognitive style in studying the effects of the managers and top management team on firms' strategy.

The purpose of this exploratory study is to examine if and how victim credibility affects investigative decision-making and case outcomes in domestic violence and sexual assault reports through the use of the US Department of Justice's Gender Bias Principles (GBPs).

The authors conducted a content analysis of 370 DVSA police reports from one agency in the western US. Multivariate regression models were estimated to examine the relationships among victim credibility and investigative activities, victim cooperation and case clearance.

Victim credibility significantly predicts specific investigative actions and case clearance, but not victim cooperation. Multiple aspects of DVSA investigations significantly impact victim cooperation as well as case clearance, regardless of victim credibility issues. The GBPs are an effective framework for disaggregating investigative activities and identifying specific areas for improvement in policing response to DVSA.

Further study is needed to determine the temporal ordering of officer assessment of victim credibility and investigative activities, the stability of such assessments during investigations, and if credibility problems noted in police reports are valid indicators of myth acceptance among officers or represent a downstream orientation of information requested by prosecutors. Victim service referral as a part of policing response is vastly under-researched given referral's strong effects on victim cooperation and case clearance. Crime-specific differences exist in many cases, yet not in others, suggesting separate and combined DVSA analyses are warranted.

A more complex analysis of investigative actions offers a targeted approach to officer training and administrative rule-making that may be more efficient and effective than current generalized approaches.

The study is the first to empirically test the utility of the GBP framework, as well as individual aspects of DVSA investigations, and from a gender-based crime rather than crime-specific approach.

This paper seeks to identify empirically the factors underlying the decision to adopt online banking in Poland.

The sample used in this study is based on 3,519 interactive questionnaires completed by Polish internet users. The dichotomous decision of whether to adopt internet banking services was linked, via Binomial Logistic Regression, to numerous explanatory variables.

Generally, the behaviour of Polish internet users and that of consumers in more developed countries exhibit similar traits. One of the dominant relationships that has been observed in our study is the link between the decision to open an online account and the perceived level of security of internet transactions. Experience with the medium of internet and certain demographic variables also proved to be robust predictors of the adoption status. Moreover, this inquiry documents that advertising appears to be efficacious and that online banking interacts with consumption of other products offered by banks. These findings imply that financial institutions can encourage customers to use this cost‐effective distribution channel through carefully‐planned actions.

The results presented in this paper can be of assistance to banks that either operate in Poland or intend to design a pan‐European strategy. Useful insights are also provided with regard to market segmentation, security and strategies fostering the acceptance of online banking.

The analysis is based on a large sample and broadens our understanding of the attitudes towards innovative financial products by considering factors rarely discussed in prior literature.