The purpose of this study is to investigate the aerosol dynamics of the particle coagulation process using a newly developed weighted fraction Monte Carlo (WFMC) method.
Abstract
Purpose
The purpose of this study is to investigate the aerosol dynamics of the particle coagulation process using a newly developed weighted fraction Monte Carlo (WFMC) method.
Design/methodology/approach
The weighted numerical particles are adopted in a similar manner to the multi-Monte Carlo (MMC) method, with the addition of a new fraction function (α). Probabilistic removal is also introduced to maintain a constant number scheme.
Findings
Three typical cases with constant kernel, free-molecular coagulation kernel and different initial distributions for particle coagulation are simulated and validated. The results show an excellent agreement between the Monte Carlo (MC) method and the corresponding analytical solutions or sectional method results. Further numerical results show that the critical stochastic error in the newly proposed WFMC method is significantly reduced when compared with the traditional MMC method for higher-order moments with only a slight increase in computational cost. The particle size distribution is also found to extend for the larger size regime with the WFMC method, which is traditionally insufficient in the classical direct simulation MC and MMC methods. The effects of different fraction functions on the weight function are also investigated.
Originality Value
Stochastic error is inevitable in MC simulations of aerosol dynamics. To minimize this critical stochastic error, many algorithms, such as MMC method, have been proposed. However, the weight of the numerical particles is not adjustable. This newly developed algorithm with an adjustable weight of the numerical particles can provide improved stochastic error reduction.
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The purpose of this paper is to study the soot formation and evolution by using this newly developed Lagrangian particle tracking with weighted fraction Monte Carlo (LPT-WFMC…
Abstract
Purpose
The purpose of this paper is to study the soot formation and evolution by using this newly developed Lagrangian particle tracking with weighted fraction Monte Carlo (LPT-WFMC) method.
Design/methodology/approach
The weighted soot particles are used in this MC framework and is tracked using Lagrangian approach. A detailed soot model based on the LPT-WFMC method is used to study the soot formation and evolution in ethylene laminar premixed flames.
Findings
The LPT-WFMC method is validated by both experimental and numerical results of the direct simulation Monte Carlo (DSMC) and Multi-Monte Carlo (MMC) methods. Compared with DSMC and MMC methods, the stochastic error analysis shows this new LPT-WFMC method could further extend the particle size distributions (PSDs) and improve the accuracy for predicting soot PSDs at larger particle size regime.
Originality/value
Compared with conventional weighted particle schemes, the weight distributions in LPT-WFMC method are adjustable by adopting different fraction functions. As a result, the number of numerical soot particles in each size interval could be also adjustable. The stochastic error of PSDs in larger particle size regime can also be minimized by increasing the number of numerical soot particles at larger size interval.
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Swati Bankar and Kasturi Shukla
Artificial Intelligence (AI) is one of the newest technology that is quickly advancing and can be utilised to improve human resource competence in the age of rapid digital…
Abstract
Artificial Intelligence (AI) is one of the newest technology that is quickly advancing and can be utilised to improve human resource competence in the age of rapid digital transformation. The present competitive scenario demands accurate data that need to be collected and analysed for organisational growth.
Purpose: The research examines the applications and usage of AI in performance management and further analyses the future of PM from the perspectives of AI.
Methodology: The study is conceptual and relies on secondary data from research papers, publications, HR blogs, survey reports and other sources. Employee performance and attitudes were monitored using digital technologies, big data analytics and AI. The quality of employee performance continues to increase with the integration of AI, enabling predictive analytics to increase employee performance.
Research Implication: In employee performance appraisal, a digital performance management system leads to openness and honesty with time, effort and sincerity. It is based on the performance management system’s practical usefulness.
Theoretical Implication: The study’s findings provide HR managers, academics, IT professionals and practitioners with an understanding of how AI may be used for performance management and its consequences on their operations. In addition, the connection between the HR devolution theory on performance management and AI is discussed.
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Kangqi Jiang, Xin Xie, Yu Xiao and Badar Nadeem Ashraf
The main purpose of this study is to examine the effect of corporate digital transformation on bond credit spreads. Additionally, it also explores the two potential channels…
Abstract
Purpose
The main purpose of this study is to examine the effect of corporate digital transformation on bond credit spreads. Additionally, it also explores the two potential channels, information asymmetry and default risk, through which digital transformation can influence bond credit spreads.
Design/methodology/approach
We use the bond issuance data of Chinese listed companies over the period 2008–2020. Corporate digital transformation of these companies is measured with textual analysis of the management discussion and analysis part of annual reports. We employ a panel regression model to estimate the effect of digital transformation on bond credit spreads.
Findings
We find robust evidence that companies with higher digital transformation experience lower bond credit spreads. We further observe that credit spread reduction is higher for firms that are smaller, non-state-owned, have lower credit ratings and have less analyst coverage. We also find evidence that digital transformation reduces credit spreads by reducing the information asymmetry between firms and investors with enhanced information transformation mechanisms and lowering corporate default risk by strengthening operating efficiency.
Originality/value
To the best of our knowledge, this study is the first attempt to understand the impact of corporate digital transformation on bond credit spreads. Our findings help to understand the effect of digital transformation on firms’ credit worthiness and access to capital.
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Hua Pang, Enhui Zhou and Yi Xiao
In light of the stimulus-organism-response (SOR) theoretical paradigm, this paper explores how information relevance and media richness affect social network exhaustion and…
Abstract
Purpose
In light of the stimulus-organism-response (SOR) theoretical paradigm, this paper explores how information relevance and media richness affect social network exhaustion and, moreover, how social network exhaustion ultimately leads to health anxiety and COVID-19-related stress.
Design/methodology/approach
The conceptual model is explicitly analyzed and estimated by using data from 309 individuals of different ages in mainland China. Confirmatory factor analysis (CFA) and structural equation modeling (SEM) were utilized to validate the proposed hypotheses through the use of online data.
Findings
The findings suggest that information relevance is negatively associated with social network exhaustion. In addition, social network exhaustion is a significant predictor of health anxiety and stress. Furthermore, information relevance and media richness can indirectly influence health anxiety and stress through the mediating effect of social network exhaustion.
Research limitations/implications
Theoretically, this paper verifies the causes and consequences of social network exhaustion during COVID-19, thus making a significant contribution to the theoretical construction and refinement of this emerging research area. Practically, the conceptual research model in this paper may provide inspiration for more investigators and scholars who are inclined to further explore the different dimensions of social network exhaustion by utilizing other variables.
Originality/value
Although social network exhaustion and its adverse consequences have become prevalent, relatively few empirical studies have addressed the deleterious effects of social network exhaustion on mobile social media users’ psychosocial well-being and mental health during the prolonged COVID-19. These findings have important theoretical and practical implications for the rational development and construction of mobile social technologies to cultivate proper health awareness and mindset during the ongoing worldwide COVID-19 epidemic.
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Xiaofang Ma, Wenming Wang, Gaoguang Zhou and Jun Chen
This study aims to take advantage of the unprecedented anti-corruption campaign launched in China in December 2012 and examine the effect of improved public governance on…
Abstract
Purpose
This study aims to take advantage of the unprecedented anti-corruption campaign launched in China in December 2012 and examine the effect of improved public governance on tunneling.
Design/methodology/approach
This study uses a sample of Shanghai and Shenzhen Stock Exchange listed companies from 2010 to 2014 and conduct regression analyses to investigate the effect of improved public governance attributed to the anti-corruption campaign on tunneling.
Findings
This study finds that the level of tunneling decreased significantly after the anti-corruption campaign, suggesting that increased public governance effectively curbs tunneling. Cross-sectional results show that this mitigating effect is more pronounced for non-SOE firms, especially non-SOE firms with political connections, firms audited by non-Big 8 auditors, firms with a large divergence between control rights and cash flow rights and firms located in areas with lower marketization.
Practical implications
This study highlights the importance of anti-corruption initiatives in improving public governance and in turn reducing tunneling. This study provides important implications for many other emerging economies to improve public governance.
Originality/value
This study contributes to the literature on the role of public governance in constraining corporate agency problems and advances the understanding of the economic consequences of China's anti-corruption campaign in the context of tunneling.
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Zhou Jiang, Zuoli Xiao, Yipeng Shi and Shiyi Chen
The knowledge about the heat transfer and flow field in the ribbed internal passage is particularly important in industrial and engineering applications. The purpose of this paper…
Abstract
Purpose
The knowledge about the heat transfer and flow field in the ribbed internal passage is particularly important in industrial and engineering applications. The purpose of this paper is to identify and analyze the performance of the constrained large-eddy simulation (CLES) method in predicting the fully developed turbulent flow and heat transfer in a stationary periodic square duct with two-side ribbed walls.
Design/methodology/approach
The rib height-to-duct hydraulic diameter ratio is 0.1 and the rib pitch-to-height ratio is 9. The bulk Reynolds number is set to 30,000, and the bulk Mach number of the flow is chosen as 0.1 in order to keep the flow almost incompressible. The CLES calculated results are thoroughly assessed in comparison with the detached-eddy simulation (DES) and traditional large-eddy simulation (LES) methods in the light of the experimentally measured data.
Findings
It is manifested that the CLES approach can predict both aerodynamic and thermodynamic quantities more accurately than the DES and traditional LES methods.
Originality/value
This is the first time for the CLES method to be applied to simulation of heat and fluid flow in this widely used geometry.
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Chunxiao Yin, Libo Liu and Kristijian Mirkovski
The purpose of this paper is to focus on investigating the impact of crowd participation on degree of project success, which is defined as the total amount of funds a project can…
Abstract
Purpose
The purpose of this paper is to focus on investigating the impact of crowd participation on degree of project success, which is defined as the total amount of funds a project can obtain after it reaches its initial funding goal threshold.
Design/methodology/approach
Drawing on the theory of crowd capital, this study develops six hypotheses about the impact of crowd capability of a fundraiser (i.e. project updates, goal setting, reward levels and social media usage) and crowd participation (i.e. namely, funds pledge and on-site communication) on degree of project success. The hypotheses are tested using data sets of successful projects collected from two popular crowdfunding websites.
Findings
This study finds that funds pledge has an inverse U-shaped relationship with degree of project success. Project updates, reward levels and on-site communication positively influence degree of project success, while funding goal negatively affects degree of project success.
Research limitations/implications
This study contributes to prior literature by investigating the degree of project success determinants using the perspectives of both fundraisers and crowds, which provides a more comprehensive understanding of what makes a crowdfunded project a success.
Practical implications
The empirical results of this study provide fundraisers with guidelines about how to access more funds after achieving the initial funding goals.
Originality/value
This work is one of the first to investigate the degree of project success and its determinants from the perspectives of both fundraisers and crowds.
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Zhihuai Xiao, Jiang Guo, Hongtao Zeng, Pan Zhou and Shuqing Wang
The purpose of this paper is to develop a new hybridized controller based on fuzzy reasoning and neural network (NN) for hydropower generator unit (HGU).
Abstract
Purpose
The purpose of this paper is to develop a new hybridized controller based on fuzzy reasoning and neural network (NN) for hydropower generator unit (HGU).
Design/methodology/approach
The approach contains fuzzy neural networks controller (FNNC), RBF network identification (RBFNI) and HGU system. FNNC may give control value to control HGU via fuzzy NN reasoning and computing according to HGU rotate speed error and error varying rate. RBFNI is used to identify the character of HGU system and predict its output. FNNC may adjust parameters and member function according to the identifying and predictive outcome of RBFNI.
Findings
Sees that the hybridized control system is feasible and stable, and the controlling performance of the hybridized system is superior to conventional fuzzy controller.
Research limitations/implications
The theoretical proof of stability of the proposed scheme still remains to be studied. Accessibility and availability of membership functions and control rules is also a limitation applied.
Practical implications
The main advantage of the proposed method is that FNNC has reasoning, learning, and optimizing capability which can control effectively HGU. This will be useful for control engineers to control complex industrial plants.
Originality/value
The paper proposes new combined approach to optimal control of HGU using FNNC, and it is aimed at operational researches and engineers, especially those who dealt with HUG controller.