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.

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.

This paper aims to report empirical research investigating how school‐based management (SBM) and paradigm shift (PS) in education are closely related to teachers' student‐centered teaching and students' active learning in a sample of Hong Kong secondary schools.

It is a cross‐sectional survey research involving 31 secondary schools, 1,119 teachers and 7,063 students with seven sets of questionnaires: three for students, three for teachers and one for principals.

The results of analysis indicate the following findings. The greater tendency towards SBM of a school associates with the greater extent of PS from the site‐bounded paradigm towards the triplization paradigm in education. Both the measures of SBM and PS in education are closed related to teachers' student‐centered teaching (in terms of facilitating student learning, facilitating student thinking and facilitating student self‐reflection and assessment) and students' active learning (in terms of positive learning attitudes, application of various learning methods, learning effectiveness, multiple thinking in learning and satisfaction in learning). The profiles of “high SBM and high‐PS” schools are much more preferable than “low SBM and low‐PS” schools in terms of various measures of teachers' teaching and students' learning.

Even though SBM and PS in education are strongly emphasized in ongoing educational reforms in different parts of the world, there is lack of empirical study to show how they are related to teachers' teaching and students' learning in practice. The findings of the research contribute to filling this research gap and advancing theoretical and practical understanding in such a frontier area.

The purpose of the study is to explore how financial leverage influences profitability of 1,503 listed manufacturing firms in China.

The sample of the study is composed of the listed manufacturing firms in China. For the manufacturing firms, the annual financial information from 2008 to 2016 is obtained from the ORBIS database. In this study, initially a simultaneous equation approach is used to control for potential endogeneity. Then, additional regression analyses are conducted with panel data over the period of 2008-2016 using OLS, Fixed-effects, First-difference, Random-effects and Arellano and Bond’s (1991) two-step Generalized Method of Moments (GMM) methods.

The results reveal that the impact of leverage on profitability is inverted U-shaped. In this inverted U-shaped relationship, the positive impact of financial leverage on profitability could be attributed to tax shield, whereas the negative impact might be because of bankruptcy cost, financial distress, severe agency problems and information asymmetry that the listed Chinese firms suffer from because of some institutional characteristics of China.

First, this study focuses on only listed manufacturing firms in China. Second, ownership types are not taken into account in this study.

First, the Chinese government should direct its efforts toward developing the bond markets and promoting alternative privately owned loan creditors to state-owned banks. Parallel to this, the transformation process toward market economy should be accelerated to facilitate the privatization of state-owned enterprises (SOEs). In addition to this, development of the bond market and privatization of SOEs will also mitigate the agency conflict between creditors and managers and between shareholders and managers.

To the best of the author’s knowledge, this is the first study which investigates the impact of capital structure on profitability of the listed firms in China.

Concurrency is a desirable property that enhances workflow efficiency. The purpose of this paper is to propose six polynomial-time algorithms that collectively maximize control flow concurrency for Business Process Model and Notation (BPMN) workflow models. The proposed algorithms perform model-level transformations on a BPMN model during the design phase of the model, thereby improving the workflow model’s execution efficiency.

The approach is similar to source code optimization, which solely works with syntactic means. The first step makes implicit synchronizations of interdependent concurrent control flows explicit by adding parallel gateways. After that, every control flow can proceed asynchronously. The next step then generates an equivalent sequence of execution hierarchies for every control flow such that they collectively provide maximum concurrency for the control flow. As a whole, the proposed algorithms add a valuable feature to a BPMN modeling tool to maximize control flow concurrency.

In addition, this paper introduces the concept of control flow independence, which is a user-determined semantic property of BPMN models that cannot be obtained by any syntactic means. But, if control flow independence holds in a BPMN model, the model’s determinism is guaranteed. As a result, the proposed algorithms output a model that can be proved to be equivalent to the original model.

This paper adds value to BPMN modeling tools by providing polynomial-time algorithms that collectively maximize control flow concurrency in a BPMN model during the design phase of the model. As a result, the model’s execution efficiency will increase. Similar to source code optimization, these algorithms perform model-level transformations on a BPMN model through syntactic means; and the transformations performed to each control flow are guaranteed to be equivalent to the control flow. Furthermore, a case study on a real-life new employee preparation process is provided to demonstrate the proposed algorithms’ usefulness on increasing the process’s execution efficiency.

Globalization and the evolution of a knowledge‐based economy have caused dramatic changes to the character and functions of education in most countries around the world. In order to enhance the overall competitiveness of individual nation‐states in the global market environment, comprehensive education reforms have been launched in different parts of the globe to strengthen manpower training. Realizing the fact that there is only one resource in Singapore – human capital – the Singapore government therefore has tried to maximize the potential of its citizens in the further advancement of its economic modernization. In order to make its citizens more creative and innovative, the Singapore government openly acknowledges the importance of allowing more autonomy for schools in charting their own courses of development. By introducing a policy of decentralization, the Singapore government hopes that schools could have more autonomy and flexibility to develop their strengths and thereby individual schools can evolve with their own unique features. One way to promote quality education is the introduction of the “school excellence model” (SEM) to engage schools in self‐improvement and self‐assessment exercises. This paper sets out in this policy context to examine and study the newly proposed SEM, with particular reference to examining and studying the philosophy and principles, major features and detailed procedures of this quality assurance model. More specifically, this paper will also analyze this model in light of the global trends of educational decentralization and marketization, reflecting upon the changing role of the Singapore government in educational governance.