Search results

The present study focusses on algorithmic aspects related to deformation dependent loads in non‐linear static finite element analysis. If the deformation dependency is considered only on the right hand side, a considerable increase in the number of iterations follows. It may also cause failure of convergence in the proximity of critical points. If in turn the deformation dependent loading is included within the consistent linearization, an additional left hand side term emerges, the so‐called load stiffness matrix. In this paper several numerical test cases are used to show and quantify the influence of the two different approaches on the iteration process. Consideration of the complete load stiffness matrix may result in a cumbersome coding effort, different for each load case, and in certain cases its derivation is even not practicable at all. Therefore also several formulations for approximated load stiffness matrices are presented. It is shown that these simplifications not only reduce the additional effort for linearization and implementation, but also keep the iterative costs relatively small and still allow the calculation of the entire equilibrium path.

The purpose of this paper is to analyse algorithms for fluid‐structure interaction (FSI) from a purely algorithmic point of view.

First of all a 1D model problem is selected, for which both the fluid and structural behavior are represented through a minimum number of parameters. Different coupling algorithm and time integration schemes are then applied to the simplified model problem and their properties are discussed depending on the values assumed by the parameters. Both exact and approximate time integration schemes are considered in the same framework so to allow an assessment of the different sources of error.

The properties of staggered coupling schemes are confirmed. An insight on the convergence behavior of iterative coupling schemes is provided. A technique to improve such convergence is then discussed.

All the results are proved for a given family of time integration schemes. The technique proposed can be applied to other families of time integration techniques, but some of the analytical results need to be reworked under this assumption.

The problems that are commonly encountered in FSI can be justified by simple arguments. It can also be shown that the limit at which trivial iterative schemes experience convergence difficulties is very close to that at which staggered schemes become unstable.

All the results shown are based on simple mathematics. The problems are presented so to be independent of the particular choice for the solution of the fluid flow.

COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The main purpose of this work is, COVID-19 has occurred in more than 150 countries and causes a huge impact on the health of many people. The COVID-19 diagnosis is required to detect at the beginning stage and special attention should be given to them. The fastest way to detect the COVID-19 infected patients is detecting through radiology and radiography images. The few early studies describe the particular abnormalities of the infected patients in the chest radiograms. Even though some of the challenges occur in concluding the viral infection traces in X-ray images, the convolutional neural network (CNN) can determine the patterns of data between the normal and infected X-rays that increase the detection rate. Therefore, the researchers are focusing on developing a deep learning-based detection model.

The main intention of this proposal is to develop the enhanced lung segmentation and classification of diagnosing the COVID-19. The main processes of the proposed model are image pre-processing, lung segmentation and deep classification. Initially, the image enhancement is performed by contrast enhancement and filtering approaches. Once the image is pre-processed, the optimal lung segmentation is done by the adaptive fuzzy-based region growing (AFRG) technique, in which the constant function for fusion is optimized by the modified deer hunting optimization algorithm (M-DHOA). Further, a well-performing deep learning algorithm termed adaptive CNN (A-CNN) is adopted for performing the classification, in which the hidden neurons are tuned by the proposed DHOA to enhance the detection accuracy. The simulation results illustrate that the proposed model has more possibilities to increase the COVID-19 testing methods on the publicly available data sets.

From the experimental analysis, the accuracy of the proposed M-DHOA–CNN was 5.84%, 5.23%, 6.25% and 8.33% superior to recurrent neural network, neural networks, support vector machine and K-nearest neighbor, respectively. Thus, the segmentation and classification performance of the developed COVID-19 diagnosis by AFRG and A-CNN has outperformed the existing techniques.

This paper adopts the latest optimization algorithm called M-DHOA to improve the performance of lung segmentation and classification in COVID-19 diagnosis using adaptive K-means with region growing fusion and A-CNN. To the best of the authors’ knowledge, this is the first work that uses M-DHOA for improved segmentation and classification steps for increasing the convergence rate of diagnosis.

Relationship management evolves with dynamic and complex environments of megaprojects. However, studies on the longitudinal measurement of relationship management performance for each stakeholder in dynamic and complex project environments are lacking. The purpose of this research is to propose an NK-network evolution model to evaluate stakeholder performance on relationship management in the development of megaprojects.

The model input includes the stakeholder-associated issues and stakeholders' relational strategies, the co-effects of which determine the internal effects of relationship management in megaprojects. The model processing simulates the stakeholder performance of relationship management under the dynamic and complex nature of megaprojects. The NK model shows the dynamic stakeholder interactions on relationship management, whereas the network model presents the complex stakeholder structures of the relationships between stakeholders and relevant issues. The model output is the evolution graph to reveal the weak stakeholder performance on relationship management in the timeline of the project duration.

The research finding reveals that all stakeholders experience the plunge of stakeholder performance of relationship management at the decision-making moment of the planning stage. Construction, environmental and pressure groups may experience the hardship of relationship management at the start of the construction stage. The government is likely to suffer difficulties in relationship management in the late construction stage. Local industry groups would face challenges in relationship management in the middle of the construction stage and handover stage.

The research provides a useful approach to measuring weak moments of relationship management for each stakeholder in various project phases, considering the dynamic and complex environments of megaprojects. The proposed model extends the current knowledge body on how to make project stakeholder analysis by modelling dynamic and complex environments of megaprojects, with bridging the knowledge domains of evolution modeling techniques and network methods.

As the developing nations grow and experience rapid institutional transformation, research has begun to investigate the roles of culture, cognition and institutional context on entrepreneurship and innovation. This chapter aims to advance the entrepreneurial cognition literature by juxtaposing entrepreneurial effectuation, domain-specific expertise and ambiguity. By conducting a qualitative study of Chinese high-tech domestic and returnee entrepreneurs, the authors propose a spectrum between causation and effectuation and argue that the entrepreneur’s perceived level of ambiguity may better explain differing logic orientations among entrepreneurs, contributing to our understanding of entrepreneurial cognition. The authors theorize that (1) individual actors and the level of institutional development jointly comprise the entrepreneur’s logic orientation; (2) the level of perceived ambiguity mediates the strategy adopted by high-tech entrepreneurs; (3) the entrepreneur’s logic orientation can be regarded as a continual spectrum from effectuation to causation. Finally, the logic orientation concept is applied to the context of cross-border mergers and acquisitions (M&A) from a process perspective and the implications and fit of logic orientation with the stages of cross-border M&A are discussed.

The concept and practice of e-services has become essential in business transactions. Yet there are still many organizations that have not developed e-services optimally. This is especially relevant in the context of Indonesian Airline companies. Therefore, many airline customers in Indonesia are still in doubt about it, or even do not use it. To fill this gap, this study attempts to develop a model for e-services adoption and empirically examines the factors influencing the airlines customers in Indonesia in using e-services offered by the Indonesian airline companies. Taking six Indonesian airline companies as a case example, the study investigated the antecedents of e-services usage of Indonesian airlines. This study further examined the impacts of motivation on customers in using e-services in the Indonesian context. Another important aim of this study was to investigate how ages, experiences and geographical areas moderate effects of e-services usage.

The study adopts a positivist research paradigm with a two-phase sequential mixed method design involving qualitative and quantitative approaches. An initial research model was first developed based on an extensive literature review, by combining acceptance and use of information technology theories, expectancy theory and the inter-organizational system motivation models. A qualitative field study via semi-structured interviews was then conducted to explore the present state among 15 respondents. The results of the interviews were analysed using content analysis yielding the final model of e-services usage. Eighteen antecedent factors hypotheses and three moderating factors hypotheses and 52-item questionnaire were developed. A focus group discussion of five respondents and a pilot study of 59 respondents resulted in final version of the questionnaire.

In the second phase, the main survey was conducted nationally to collect the research data among Indonesian airline customers who had already used Indonesian airline e-services. A total of 819 valid questionnaires were obtained. The data was then analysed using a partial least square (PLS) based structural equation modelling (SEM) technique to produce the contributions of links in the e-services model (22% of all the variances in e-services usage, 37.8% in intention to use, 46.6% in motivation, 39.2% in outcome expectancy, and 37.7% in effort expectancy). Meanwhile, path coefficients and t-values demonstrated various different influences of antecedent factors towards e-services usage. Additionally, a multi-group analysis based on PLS is employed with mixed results. In the final findings, 14 hypotheses were supported and 7 hypotheses were not supported.

The major findings of this study have confirmed that motivation has the strongest contribution in e-services usage. In addition, motivation affects e-services usage both directly and indirectly through intention-to-use. This study provides contributions to the existing knowledge of e-services models, and practical applications of IT usage. Most importantly, an understanding of antecedents of e-services adoption will provide guidelines for stakeholders in developing better e-services and strategies in order to promote and encourage more customers to use e-services. Finally, the accomplishment of this study can be expanded through possible adaptations in other industries and other geographical contexts.

The COVID-19 pandemic situation is increasing day by day and has affected the lifestyle and economy worldwide. Due to the absence of specific treatment, the only way to control a pandemic is by stopping its spread. Early identification of affected persons is urgently in demand. Diagnostic methods applied in hospitals are time-consuming, which delay the identification of positive patients. This study aims to develop machine learning-based diagnosis model which can predict positive cases and helps in decision-making.

In this research, the authors have developed a diagnosis model to check coronavirus positivity based on an artificial neural network. The authors have trained the model with clinically assessed symptoms, patient-reported symptoms, other medical histories and exposure data of the person. The authors have explored filter-based feature selection methods such as Chi2, ANOVA F-score and Mutual Information for improving performance of a classification model. Metrics used to evaluate performance of the model are accuracy, precision, sensitivity and F1-score.

The authors got highest classification performance with model trained with features ranked according to ANOVA FS method. Highest scores for accuracy, sensitivity, precision and F1-score of predictions are 0.93, 0.99, 0.94 and 0.93, respectively. The study reveals that most relevant predictors for COVID-19 diagnosis are sob severity, cough severity, sob presence, cough presence, fatigue and number of days since symptom onset.

Treatment for COVID-19 is not available to date. The best way to control this pandemic is the isolation of positive persons. It is very much necessary to identify positive persons at an early stage. RT-PCR test used to check COVID-19 positivity is the time-consuming, expensive and laborious method. Current diagnosis methods used in hospital demand more medical resources with increasing cases of coronavirus that introduce shortage of resources. The developed model provides solution to the problem cheaper and faster decreases the immediate need for medical resources and helps in decision-making.

In neo‐classical economic theory labour is a commodity and the ultimate value of the employer's services is determined by the sales value of the product of these services: the cost of supply reflects both the disutility of work for the recruit and his equalisation of net advantages between jobs. For modern labour economists the assumption that entrepreneurs require identical inputs of labour and the new recruits will therefore possess similar skills (the conditions of free competition) is an unrealistic one. Hence segmental labour market theory has grown out of the need to explain differences between shared needs and commonalities within each group of consumers (employers) on the one hand and suppliers (employees) on the other. In this way it has been possible to carry on assuming the existence of perfect competition on both sides of the market within the boundaries of labour markets thus defined.

In Hong Kong higher education, students' learning outcomes are increasingly treated as evidence to inform course and teaching improvement. Therefore, outcome-based teaching and learning (OBTL) has been encouraged by the University Grants Committee (UGC) since 2007. OBTL has gradually been implemented by Hong Kong higher education institutions (HEIs) to enhance student learning outcomes. Relating OBTL to the social cohesion/regulation matrix, this chapter aims at analyzing how OBTL is being implemented by the HEIs in Hong Kong. Given the high institutional autonomy and academic freedom afforded to individual HEIs, each university has established its own systematic framework for integrating outcome-based approaches into its teaching, learning, and assessment. Unlike other higher education systems in Asia with strong government supervision, the government in Hong Kong acts as an enabler and facilitator, leaving the UGC to invite international experts as an independent audit body to assure the quality of student learning. As a result, this chapter chooses the eight UGC-funded universities to investigate how they engage their faculty members in OBTL, and what the enabling and hindering factors are. Based upon the social cohesion/regulation matrix, the Hong Kong higher education system is featured by the individualist way of promoting OBTL. Nonetheless, while universities are empowered with institutional autonomy to decide upon teaching, and student learning matters, their strong orientation with OBTL means they cannot simply do whatever they like. Adopting a robust quality assurance mechanism in evaluating university performance through University Accountability Agreements, the institutional autonomy that universities enjoy rests heavily upon their performance in teaching and student learning, which is assessed through rigorous international benchmarking via the Quality Assurance Audit conducted by the UGC and research performance through the Research Assessment Exercise. This chapter discusses the unique university governance of Hong Kong through the critical review of OBTL being adopted in teaching and learning in Hong Kong universities.