Search results

In order to solve the problem of inaccurate calculation of index weights, subjectivity and uncertainty of index assessment in the risk assessment process, this study aims to propose a scientific and reasonable centralized traffic control (CTC) system risk assessment method.

First, system-theoretic process analysis (STPA) is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis. Then, to enhance the accuracy of weight calculation, the fuzzy analytical hierarchy process (FAHP), fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and entropy weight method are employed to calculate the subjective weight, relative weight and objective weight of each index. These three types of weights are combined using game theory to obtain the combined weight for each index. To reduce subjectivity and uncertainty in the assessment process, the backward cloud generator method is utilized to obtain the numerical character (NC) of the cloud model for each index. The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system. This cloud model is used to obtain the CTC system's comprehensive risk assessment. The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud. Finally, this process yields the risk assessment results for the CTC system.

The cloud model can handle the subjectivity and fuzziness in the risk assessment process well. The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.

This study provides a cloud model-based method for risk assessment of CTC systems, which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment, achieving effective risk assessment of CTC systems. It can provide a reference and theoretical basis for risk management of the CTC system.

The determination of the measurement uncertainty is relevant for all measurement processes. In production engineering, the measurement uncertainty needs to be known to avoid erroneous decisions. However, its determination is associated to high effort due to the expertise and expenditure that is needed for modelling measurement processes. Once a measurement model is developed, it cannot necessarily be used for any other measurement process. In order to make an existing model useable for other measurement processes and thus to reduce the effort for the determination of the measurement uncertainty, a procedure for the migration of measurement models has to be developed.

This paper presents an approach to migrate measurement models from an old process to a new “similar” process. In this approach, the authors first define “similarity” of two processes mathematically and then use it to give a first estimate of the measurement uncertainty of the similar measurement process and develop different learning strategies. A trained machine-learning model is then migrated to a similar measurement process without having to perform an equal size of experiments.Similarity assessment and model migration

The authors’ findings show that the proposed similarity assessment and model migration strategy can be used for reducing the effort for measurement uncertainty determination. They show that their method can be applied to a real pair of similar measurement processes, i.e. two computed tomography scans. It can be shown that, when applying the proposed method, a valid estimation of uncertainty and valid model even when using less data, i.e. less effort, can be built.

The proposed strategy can be applied to any two measurement processes showing a particular “similarity” and thus reduces the effort in estimating measurement uncertainties and finding valid measurement models.

Supply chain management (SCM)-embedded valuable resources, such as capital, raw-materials, products, partners, customers and finished inventories, where the evaluation of environmental texture and flexibilities are needed to perceive sustainability. The present study aims to identify and evaluate the directory of green and agile (G-A) attributes based on decision support framework (DSF) for identifying dominating measures in SCM.

DSF is developed by exploiting generalized interval valued trapezoidal fuzzy numbers (GIVTFNs). Two technical approaches, i.e. degree of similarity approach (DSA) and distance approach (DA) under the extent boundaries of GIVTFNs, are implicated for data analytics and for recognizing constructive G-A measures based on comparative study for robust decision. A fuzzy-based performance indicator, i.e. fuzzy performance important index (FPII), is presented to enumerate the weak and strong G-A characteristics to manage knowledge risks in allied business environment.

The modeling is illustrated from the insights of decision-makers for augmenting business value based on cognitive identification of measures, where the best performance score is identified by the “sustainable packaging” under the traits of green supply chain management (GSCM). “The use of Web-based applications” under the traits of agile supply chain management (ASCM) and “Outsourcing flexibility” under traits of ASCM is found as the second and third most significant performance characteristics for business sustainability. Additionally, the “Reutilization (recycling) and reprocessing” under GSCM in manufacturing and “Responsiveness and speed toward customers needs” under ASCM are found difficult in attainment.

The G-A evaluation will assist in attaining performance excellence in day-to-day operations and overall functioning. The outcomes will help executives to plan strategic objectives and attaining success.

To reinforce the capabilities of SCM, wide extent of G-A dimensions are presented, concept of FPII is reported to manage knowledge risks based on identification of strong attributes and two technical approaches, i.e. DSA and DA under GIVTFNs are presented for attaining robust decision and directing managerial decision-making process.

Nowadays, online platforms which provide products or services try to implement their homegrown communities to facilitate users' social interactions. Reviewers' activities in these communities can reflect their interests. Based on the theory of homophily, the authors aim to explore the impacts of the reviewer preference similarity and opinion similarity on the rate of product diffusion.

First, the authors construct reviewer similarity network based on their common interests and propose typical network metrics to measure reviewer preference similarity. Second, the authors measure reviewer opinion similarity with natural language processing. Finally, based on a panel data from an online video platform in China, both the fixed-effect and random-effect panel data models are constructed.

The authors find that reviewer preference similarity has a positive effect on the product diffusion, whereas reviewer opinion similarity has a negative effect on the diffusion. Furthermore, temporal distance moderates the relationship between reviewer similarity and the product diffusion. As a double-edged sword, review preference similarity hinders product diffusion in the initial phase, whereas benefits it in the later phase. Reviewer opinion similarity is always detrimental to product diffusion, especially in the initial phase.

This paper extends the understanding of homophily from the micro peer level to the group level by constructing reviewers' similarity network and highlights the important role of reviewer preference similarity and opinion similarity in product diffusion. The results also provide important insights for managers to design and implement diversity strategies for better product adoption in the community context.

This study aims to determine the barriers hindering the incorporation of blockchain technology (BCT) in two key service industries – hotel and health care – as well as to assess their readiness for implementing BCT after overcoming the barriers.

The barriers of this study are determined through two phases: a review of prior literature and obtaining expert opinions, which are then analyzed to identify specific barriers that are impeding the incorporation of BCT. Moreover, to generate a blockchain implementation reluctance index (BIRI), this study presents an interval-valued intuitionistic fuzzy set (IVIFS) that uses graph theory and matrix approach (GTMA). The permanent function in the GTMA approach is computed using the PERMAN algorithm. Finally, to compare the readiness of the hotel and health-care industries to adopt BCT, the BIRI values are plotted and evaluated.

The barriers identified by this study are listed under five major headings, namely, financial, operational, behavioral, technical and legal. This study revealed that the operational and technical barriers of BCT are critically hindering its widespread integration in hotel and health-care industries. Furthermore, on comparing the BIRI values of both industries, the result suggested that the hotel industry needs to work more on these barriers to effectively incorporate BCT. Besides the comparison, the BIRI values clearly indicate that both industries have to put a lot of effort into the mitigation of the barriers found by this study to successfully integrate BCT.

The experts’ opinions are used to evaluate the identified barriers, which raises the chance that the opinions are prejudiced based on the experts’ perspectives and ideologies. The sensitivity of decision-maker loads toward preference outcomes is not analyzed in this manuscript. Therefore, any recent sensitivity analysis may be considered a prospective field for future research. This study applies a multicriteria decision-making (MCDM) approach, IVIFS–GTMA, which limits the evaluation of the influence caused by individual barriers on the integration of BCT in the hotel and health-care industries. Henceforth, in future investigations, alternative MCDM methods may be used to analyze individual barriers.

According to the findings, if the hotel or health-care industry aims to incorporate BCT in its supply chain operations, it is recommended to emphasize more on the operational barriers along with the technical and behavioral barriers. The barriers mentioned in this manuscript can be used as guidance for developers in their development activities, such as scalability concerns, establishment costs, the 51% attack and the inefficient nature of BCT. Furthermore, they may address the potential users’ negative perceptions about security, privacy, trust and risk avoidance through creatively developed blockchain solutions to promote BCT implementation.

To the best of the author’s knowledge, this is the first study that identifies barriers toward BCT incorporation in the major service industries, i.e. hotel and health care. Moreover, this is the first study that compares the preparedness of the hotel and health-care industries to determine the industry that requires more work to implement BCT.

Nowadays, artificial intelligence (AI) technology has demonstrated extensive applications in the field of art design. Attribute editing is an important means to realize clothing style and color design via computer language, which aims to edit and control the garment image based on the specified target attributes while preserving other details from the original image. The current image attribute editing model often generates images containing missing or redundant attributes. To address the problem, this paper aims for a novel design method utilizing the Fashion-attribute generative adversarial network (AttGAN) model was proposed for image attribute editing specifically tailored to women’s blouses.

The proposed design method primarily focuses on optimizing the feature extraction network and loss function. To enhance the feature extraction capability of the model, an increase in the number of layers in the feature extraction network was implemented, and the structure similarity index measure (SSIM) loss function was employed to ensure the independent attributes of the original image were consistent. The characteristic-preserving virtual try-on network (CP_VTON) dataset was used for train-ing to enable the editing of sleeve length and color specifically for women’s blouse.

The experimental results demonstrate that the optimization model’s generated outputs have significantly reduced problems related to missing attributes or visual redundancy. Through a comparative analysis of the numerical changes in the SSIM and peak signal-to-noise ratio (PSNR) before and after the model refinement, it was observed that the improved SSIM increased substantially by 27.4%, and the PSNR increased by 2.8%, serving as empirical evidence of the effectiveness of incorporating the SSIM loss function.

The proposed algorithm provides a promising tool for precise image editing of women’s blouses based on the GAN. This introduces a new approach to eliminate semantic expression errors in image editing, thereby contributing to the development of AI in clothing design.

The purpose of this study is to explore whether the level of language content matching (LCM) between the chair and the CEO varies with their firm's financial performance.

This study examines a sample of 119 Australian firms and 476 annual letters to shareholders produced by the firms' chairs and CEOs over a four-year period. Chair–CEO LCM is measured by calculating the similarity score between the chair's and CEO's written text to shareholders within each firm year, while firm profitability is measured by return on assets. Univariate analysis of variance (ANOVA) tests as well as three multivariate linear models are used to examine the research question.

The results show that the profitability of the firm is significantly associated with the level of chair–CEO LCM. When a firm is profitable, there is a lower level of chair–CEO LCM than when the firm is unprofitable and that profitability is related to a lower level of chair–CEO LCM. Firm size is positively and significantly related to the level of chair–CEO LCM. These findings are supportive of the view that the written communications of the chair and CEO are the outcome of strategic considerations and depend on a firm's specific economic situation.

Future studies may consider alternative approaches to measure textual similarity.

LCM may provide insights into management techniques that may be used to explain firm performance and provide a signal to external stakeholders, such as shareholders and fund managers.

This study provides new insights into the letters written by the chair and the CEO to explain or justify their firm's financial performance. Rather than focus on a single letter, this study examines the level of LCM between the shareholder letters of two different people in a firm (the chair and CEO) and finds that the extent of chair–CEO LCM is varying with firm performance and size. The findings of this study suggest that LCM is an important dimension of the communications of a firm's chair and CEO.

Some individuals with autism may engage in violent behaviour. Research into autism specific factors (ASFs) that may contribute to violence is limited. The purpose of this paper is to explore the feasibility of use of an ASF framework which was developed to inform risk formulation and treatment needs.

Retrospective identification and evaluation of ASFs in offenders with autism (N = 13) at childhood, time of index offence and in current risk behaviours. The ASFs described in the framework were repetitive behaviours and circumscribed interests; poor social understanding and empathy; sensitivity to sensory stimulation; and disruption to rigid behavioural routines or difficulties coping with change.

There was evidence for all four ASFs being consistently present in childhood, contributing to violent behaviour at the index offence and current risk behaviours in at least one case. Poor social understanding and empathy was often present at both childhood and index offence in all cases. Of the seven cases with offence paralleling behaviour, five had at least one ASF present at each timepoint.

This proof-of-concept study provides initial support that ASFs play a role in offending behaviour of individuals with autism. Clinicians working in specialist and generic forensic services, with an in-depth knowledge of autism, could use the ASF framework to enhance risk assessments and facilitate more targeted treatment planning for offenders with autism in specialist and general forensic services.

To the best of the authors’ knowledge, this study is the first to test the application of the ASF framework in an autistic offending population.

The role of academic libraries in dissemination of information and facilitating research of academic community is well-established world over. However, a significant role of libraries in ensuring fair use of information to promote an ethical research culture at the higher education institutes is also flourishing. This study aims to explore the perceived causes of unethical use of literature by university scholars and deterrents to plagiarism in universities of Pakistan.

The design of the study was qualitative, and interviews were conducted on nine university library heads in Pakistan who were officially administrating anti-plagiarism (Turnitin) software service at their universities and were providing “similarity index certification” of PhD and MPhil research work through the software. They were purposively selected for the interviews because of their direct interaction with the researchers and faculty on matters of information searching as well as its ethical use. The study is based on PhD thesis work of the first author.

The findings of the study depicted that conceptual unawareness of plagiarism, lack of information skills, socio-cultural factors and weak policies were the prime reasons for unethical research practices among graduate scholars. Participants were positive that a role of university library in combating plagiarism through provision of anti-plagiarism software and guidance could prove more beneficial for both the researchers and libraries.

Multiple studies have been conducted, in and out of south Asian region, to gather the views of the students and faculty on the matters of academic misconduct. However, no study was found presenting the views of the other stakeholders in promotion of academic integrity. To the best of the authors’ knowledge, this study is first of its kind in South Asian context and provided observation-based views of a group of university library heads who are also administrating anti-plagiarism service in their universities on the causes and deterrents to plagiarism.

This research outlines the technological structure of the entire Japanese manufacturing and service industry using the patent information from research and development (R&D) activities to set R&D goals.

By analyzing the technological development capability of individual companies, the direction of the companies' R&D activities and current state of technological fusion between them can be understood. A group of companies participating in a particular product/service market must have the same technological development capabilities. As a result, the ratio of patent applications by a company to the total number of applications in a technical field will be similar across companies. This study uses the inter-company correlation coefficient of the ratio of patent applications by technical field as an index of technological development capability. A total of 167 major companies covering the major industries of Japan were analyzed. The analysis period was 15 years from 2004 to 2018, and the technical fields were rearranged to 42 fields with reference to the International Patent Classification (IPC)-Technology Concordance used by the World Intellectual Property Organization (WIPO). Considering the fluctuation in patent application opportunities, the number of patent applications was collected for at least three years for the analysis of patent applications by technical field, company and industry.

Examining the entire Japanese industry, the research found that chemicals, ceramics, non-ferrous metals and electrical/electronic equipment act as intermediaries between the respective groups and are linked to the transportation equipment, electrical/electronic equipment and information and communication services industries that are currently driving the Japanese economy. However, the technical connections between these groups are relatively loose. Over the last 15 years, the propagation structure of technical knowledge information has not changed. The progress of technological fusion remains within the scope of commerce and is conditioned by commerce.

Studies focusing on the technological development capability between companies and the technological structure of the Japanese manufacturing and service industries are almost non-existent since 2000 when Japan's economic growth slowed. The analytical methods presented in this research can be applied to individual companies to gain an understanding of technical positions of companies and can be useful for planning a technical environment, business or R&D strategy.