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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.

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.

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.

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.

Based on social cognitive theory, this study aims to explore the psychological mechanism behind consumer verification behavior following tourism e-commerce live-streaming.

Based on grounded theory, data were collected through 20 semi-structured in-depth interviews and analyzed.

This study identified that companies commonly use reminder messages and secondary promotions to facilitate the verification of tourism live-streaming products. Throughout this process, consumers undergo various psychologies related to verification. Specifically, they experience four positive verification psychologies: fear of missing out, anticipated emotions, status self-esteem and promotional perception. They also encounter two negative verification psychologies: psychological reactance and invasiveness. In addition, environmental factors such as the type of tourism live-streaming products and tourism destinations, along with individual trait factors like cognitive miserliness, tourism experience, autonomy, regulatory mode and impulsiveness, play significant roles in shaping verification behavior. These factors collectively influence the formation of verification behavior.

This study can provide recommendations for tourism companies to conduct marketing events following live-streaming. It is one of the earlier comprehensive studies discussing how to promote verification behavior following tourism e-commerce live-streaming. It helps to understand the psychological mechanism underlying the formation of verification behavior.

Unconventional machining processes, particularly ultrasonic vibration cutting (UVC), can overcome such technical bottlenecks. However, the precise mechanism through which UVC affects the in-service functional performance of advanced aerospace materials remains obscure. This limits their industrial application and requires a deeper understanding.

The surface integrity and in-service functional performance of advanced aerospace materials are important guarantees for safety and stability in the aerospace industry. For advanced aerospace materials, which are difficult-to-machine, conventional machining processes cannot meet the requirements of high in-service functional performance owing to rapid tool wear, low processing efficiency and high cutting forces and temperatures in the cutting area during machining.

To address this literature gap, this study is focused on the quantitative evaluation of the in-service functional performance (fatigue performance, wear resistance and corrosion resistance) of advanced aerospace materials. First, the characteristics and usage background of advanced aerospace materials are elaborated in detail. Second, the improved effect of UVC on in-service functional performance is summarized. We have also explored the unique advantages of UVC during the processing of advanced aerospace materials. Finally, in response to some of the limitations of UVC, future development directions are proposed, including improvements in ultrasound systems, upgrades in ultrasound processing objects and theoretical breakthroughs in in-service functional performance.

This study provides insights into the optimization of machining processes to improve the in-service functional performance of advanced aviation materials, particularly the use of UVC and its unique process advantages.

Nurses' turnover intention has become a major issue in developing countries with high power distance cultures. Therefore, the authors attempt to investigate how turnover intention among nurses' can be reduced through paternalistic leadership (PL). The authors further investigate the mediating role of job satisfaction between the associations of benevolent, moral and authoritarian dimensions of PL with turnover intention. Finally, the authors examined perceived organizational support (POS) as a conditional variable between job satisfaction and turnover intention.

The authors collected data from 374 nurses working in public and private hospitals of high power distance culture using a questionnaire-based survey on convenience basis.

Structural equation modeling confirms that benevolent and moral dimensions of PL positively affect nurses' job satisfaction which helps them reduce their turnover intention. While the authoritarian dimension of PL negatively affects job satisfaction to further enhance their turnover intention. In addition, the authors noted POS as a conditional variable to trigger the negative effect of job satisfaction on turnover intention.

The authors used a cross-sectional design to collect responses and ensured the absence of common method variance through Harman's Single factor test.

This study identified the mechanism (job satisfaction and POS) through which benevolent, moral and authoritative dimensions of PL predict turnover intention among nurses working in high power distance culture.

護士有離職意向，在擁有高權力距離文化的發展中國家，已成為一個重大的問題。因此，我們擬探討如何可以透過採用家長式領導、把護士離職的意欲減低，繼而研究工作滿足感，在離職意向與家長式領導中仁慈、道德和獨裁這三個層面的關係中所起的中介作用。最後，我們就組織支持感，作為是工作滿足感與離職意向之間的一個條件變數，進行了研究。

本研究透過採用在便利的基礎上進行的問卷調查，從374名在高權力距離文化的公營和私營醫院內工作的護士取得數據，進行分析。

結構方程模型證實了家長式領導中的仁慈和道德這兩個層面，會對可減低護士離職意欲的工作滿足感，產生積極的影響。家長式領導中的獨裁層面、則會對護士的工作滿足程度產生負面的影響，繼而增強其離職意欲。而且，我們確認了組織支持感是一個會增強工作滿足感與離職意向之間負相聯的條件變數。

我們以橫斷面的設計法來收集回應，並透過採用哈曼 (Harman) 的單因素檢定法，來確保共同方法變異不會存在。

本研究確定了一個 (工作滿足感與組織支持感) 機制，透過這機制，家長式領導中的仁慈、道德和獨裁這三個層面可預測於高權力距離文化工作的護士的離職意向。

Financial literacy is a crucial element of financial decision-making, exerting significant influence on the behaviour of individual investors, while making budgetary, house financing, stock investing and retirement planning decisions. So, the purpose of this research is to determine the relationship between financial literacy and behavioural biases of individual investors in Pakistan.

In this research paper, a sample of 300 observations was obtained through questionnaires from individual investors residing in Lahore and invested in Pakistan Stock Exchange. The data obtained, was passed through Cronbach’s Alpha and Exploratory Factor Analysis (EFA). The hypothesis developed for the research was tested by Pearson’s Chi-square and Ordinal Regression Analysis.

The hypothesis testing of the research concluded that there is a negative association between financial literacy and behavioural biases of individual investors. So, it means; with an increase in level of financial literacy, the likelihood of investor facing behavioural biases reduces. It also appeared that male respondents have more financial literacy than female respondents

Previous studies in the field of finance, identified different factors causing the financial behaviour of individual investor of Pakistan, and also focused on level of financial literacy in Pakistan, but these studies have not emphasized the crucial relationship between financial literacy and behavioural biases of individual investors. Thus, the unique empirical analysis developed in this paper has accentuated the financial literacy as a factor that mitigates behavioural biases of individual investor.

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.

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.

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.

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.

The purpose of this study is to introduce new tools to develop a more precise and focused bibliometric analysis on the field of digitalization in healthcare management. Furthermore, this study aims to provide an overview of the existing resources in healthcare management and education and other developing interdisciplinary fields.

This work uses bibliometric analysis to conduct a comprehensive review to map the use of the unified theory of acceptance and use of technology (UTAUT) and the unified theory of acceptance and use of technology 2 (UTAUT2) research models in healthcare academic studies. Bibliometric studies are considered an important tool to evaluate research studies and to gain a comprehensive view of the state of the art.

Although UTAUT dates to 2003, our bibliometric analysis reveals that only since 2016 has the model, together with UTAUT2 (2012), had relevant application in the literature. Nonetheless, studies have shown that UTAUT and UTAUT2 are particularly suitable for understanding the reasons that underlie the adoption and non-adoption choices of eHealth services. Further, this study highlights the lack of a multidisciplinary approach in the implementation of eHealth services. Equally significant is the fact that many studies have focused on the acceptance and the adoption of eHealth services by end users, whereas very few have focused on the level of acceptance of healthcare professionals.

To the best of the authors’ knowledge, this is the first study to conduct a bibliometric analysis of technology acceptance and adoption by using advanced tools that were conceived specifically for this purpose. In addition, the examination was not limited to a certain era and aimed to give a worldwide overview of eHealth service acceptance and adoption.