Warot Moungsouy, Thanawat Tawanbunjerd, Nutcha Liamsomboon and Worapan Kusakunniran
This paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face…
Abstract
Purpose
This paper proposes a solution for recognizing human faces under mask-wearing. The lower part of human face is occluded and could not be used in the learning process of face recognition. So, the proposed solution is developed to recognize human faces on any available facial components which could be varied depending on wearing or not wearing a mask.
Design/methodology/approach
The proposed solution is developed based on the FaceNet framework, aiming to modify the existing facial recognition model to improve the performance of both scenarios of mask-wearing and without mask-wearing. Then, simulated masked-face images are computed on top of the original face images, to be used in the learning process of face recognition. In addition, feature heatmaps are also drawn out to visualize majority of parts of facial images that are significant in recognizing faces under mask-wearing.
Findings
The proposed method is validated using several scenarios of experiments. The result shows an outstanding accuracy of 99.2% on a scenario of mask-wearing faces. The feature heatmaps also show that non-occluded components including eyes and nose become more significant for recognizing human faces, when compared with the lower part of human faces which could be occluded under masks.
Originality/value
The convolutional neural network based solution is tuned up for recognizing human faces under a scenario of mask-wearing. The simulated masks on original face images are augmented for training the face recognition model. The heatmaps are then computed to prove that features generated from the top half of face images are correctly chosen for the face recognition.
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Yamin Xie, Zhichao Li, Wenjing Ouyang and Hongxia Wang
Political factors play a crucial role in China's initial public offering (IPO) market due to its distinctive institutional context (i.e. “economic decentralization” and “political…
Abstract
Purpose
Political factors play a crucial role in China's initial public offering (IPO) market due to its distinctive institutional context (i.e. “economic decentralization” and “political centralization”). Given the significant level of IPO underpricing in China, we examine the impact of local political uncertainty (measured by prefecture-level city official turnover rate) on IPO underpricing.
Design/methodology/approach
Using 2,259 IPOs of A-share listed companies from 2001 to 2019, we employ a structural equation model (SEM) to examine the channel (voluntarily lower the issuance price vs aftermarket trading) through which political uncertainty affects IPO underpricing. We check the robustness of the results using bootstrap tests, adopting alternative proxies for political uncertainty and IPO underpricing and employing subsample analysis.
Findings
Local official turnover-induced political uncertainty increases IPO underpricing by IPO firms voluntarily reducing the issuance price rather than by affecting investor sentiment in aftermarket trading. These relations are stronger in firms with pre-IPO political connections. The effect of political uncertainty on IPO underpricing is also contingent upon the industry and the growth phase of an IPO firm, more pronounced in politically sensitive industries and firms listed on the growth enterprise market board.
Originality/value
Local government officials in China usually have a short tenure and Chinese firms witness significantly severe IPO underpricing. By introducing the SEM model in studying China IPO underpricing, this study identifies the channel through which local government official turnover to political uncertainty on IPO underpricing.
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Lihua Chen, Yi Lu and Rui Zhao
The purpose of this paper is make a significant contribution to the supply chain knowledge system through research on modern supply chain system in China, providing guidance for…
Abstract
Purpose
The purpose of this paper is make a significant contribution to the supply chain knowledge system through research on modern supply chain system in China, providing guidance for theoretical research such as methodology of dynamic resource allocation and application of innovative small- and middle-sized service system in the supply chain.
Design/methodology/approach
The paper uses structural analysis of Chinese competitive advantage, and it applies comparative analysis of supply chain models in China, the USA and Japan through the factor disintegration of trading environment.
Findings
China’s supply chain model has virtual scale and virtual capabilities. The relationship with suppliers is more dynamic. The requirements for resolving uncertainty are higher. Business transfer is more frequent.
Research limitations/implications
Researchers are encouraged to propose the specific supply chain models in China further with the game theory, auction theory, etc.
Practical implications
It provides advice for government policy making and gives Chinese enterprises guidance to improve operation management.
Originality/value
This paper specifically analyzes characteristics of China supply chain and gives enlightenment for supply chain innovation.
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En-Ze Rui, Guang-Zhi Zeng, Yi-Qing Ni, Zheng-Wei Chen and Shuo Hao
Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural…
Abstract
Purpose
Current methods for flow field reconstruction mainly rely on data-driven algorithms which require an immense amount of experimental or field-measured data. Physics-informed neural network (PINN), which was proposed to encode physical laws into neural networks, is a less data-demanding approach for flow field reconstruction. However, when the fluid physics is complex, it is tricky to obtain accurate solutions under the PINN framework. This study aims to propose a physics-based data-driven approach for time-averaged flow field reconstruction which can overcome the hurdles of the above methods.
Design/methodology/approach
A multifidelity strategy leveraging PINN and a nonlinear information fusion (NIF) algorithm is proposed. Plentiful low-fidelity data are generated from the predictions of a PINN which is constructed purely using Reynold-averaged Navier–Stokes equations, while sparse high-fidelity data are obtained by field or experimental measurements. The NIF algorithm is performed to elicit a multifidelity model, which blends the nonlinear cross-correlation information between low- and high-fidelity data.
Findings
Two experimental cases are used to verify the capability and efficacy of the proposed strategy through comparison with other widely used strategies. It is revealed that the missing flow information within the whole computational domain can be favorably recovered by the proposed multifidelity strategy with use of sparse measurement/experimental data. The elicited multifidelity model inherits the underlying physics inherent in low-fidelity PINN predictions and rectifies the low-fidelity predictions over the whole computational domain. The proposed strategy is much superior to other contrastive strategies in terms of the accuracy of reconstruction.
Originality/value
In this study, a physics-informed data-driven strategy for time-averaged flow field reconstruction is proposed which extends the applicability of the PINN framework. In addition, embedding physical laws when training the multifidelity model leads to less data demand for model development compared to purely data-driven methods for flow field reconstruction.
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Muzammil Hussain, Trong Tuan Luu and Timothy Marjoribanks
Healthcare is a service industry where fulfilling the needs of patients (customers) is challenging. Various factors, including cost, system complexity, staffing behaviours and…
Abstract
Purpose
Healthcare is a service industry where fulfilling the needs of patients (customers) is challenging. Various factors, including cost, system complexity, staffing behaviours and technological advances, play vital roles. Drawing upon social exchange theory, this study seeks to determine how paternalistic leadership (authoritarianism, benevolence and morality) influences employee service innovative behaviour and counterproductive work behaviour via perceived supervisor support in the healthcare sector. Additionally, the study investigates the role of the public service motivation of individuals as a moderating factor in this relationship.
Design/methodology/approach
A pilot study and a main study were conducted to test the hypotheses. We collected data from healthcare professionals in Pakistan’s large public, private and semi-government hospitals. We applied bootstrapping with 5,000 replications and structural equation modelling to analyse the data.
Findings
Results indicate that authoritarianism was negatively associated with service innovative behaviour, whereas benevolent and moral behaviours were positively associated with service innovative behaviour via perceived supervisor support (mediation). Our findings shed light on the moderating role of public service motivation.
Originality/value
This empirical quantitative study has several theoretical and practical implications. Findings of our study provide evidence that a paternalistic leadership style can influence both positive (service innovative behaviour) and negative (counterproductive working behaviour) working behaviours simultaneously via perceived supervisor support at an individual level in the service (healthcare) industry. This study also highlights the moderating role of public service motivation as an individual motivation factor.
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Muhammad Yusuf Shaharudin, Zulkhairi Mohamad and Asmah Husaini
The wake of the novel coronavirus (COVID-19) pandemic had caused substantial disruptions to the usual delivery of healthcare services. This is because of restrictive orders that…
Abstract
The wake of the novel coronavirus (COVID-19) pandemic had caused substantial disruptions to the usual delivery of healthcare services. This is because of restrictive orders that were put in place to curb the spread of the infection. Palliative care services in Brunei also face challenges to deliver effective services during this period. However, the impact of advanced illnesses on patients' health and end-of-life care are issues that cannot be planned, postponed or cancelled. Hence, the palliative care team needs to continue to deliver effective palliative care services. As Brunei faced its second pandemic wave in August 2021, crucial adaptations were made to ensure palliative care service was not disrupted. This reflective case study aims to discuss the adaptations made in providing palliative care during this era of disruptions.