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This paper reports on the impact of China's Community of Shared Destiny Policy (CCSDP) on ethnicity, and the development and trade benefits in Africa, whilst proposing suggestions for improvements.

A mixed-research (desktop-based and online survey-based) approach was employed.

Trade and foreign direct investment alone can account for 11.8% of changes in the peaceful coexistence of China and some selected African countries, and cause changes to the mutual prosperity of China and African nations, to the tune of 6.3%. Therefore, the activation of mutual prosperity among these nations is not necessarily trade and foreign direct investment. The CCSDP is effective and has increased economic development for ethnic groups (50%), although with some negative concerns.

Inadequate/small sample size for the study.

Chinese investment in Africa has had a transformative impact, driving economic growth, improving infrastructure, and fostering regional integration. The share of trade between China and Africa in the continent's overall external trade has increased dramatically. Overall, the CCSDP should be kept in place, but with some modifications to improve its effectiveness and mitigate its negative effects. Finally, as China's engagement with Africa evolves, it is vital that partnerships are founded on mutual understanding, respect, and benefit, and that policies reflect the different needs and ambitions of African communities.

A chaotic system is a nonlinear deterministic system that displays complex, noisy‐like and unpredictable behavior. The interest in chaotic systems lies mostly upon their complex, unpredictable behavior, and extreme sensitivity to initial conditions as well as parameter variations. Based on wavelet neural network's (WNN) online approximation ability, the purpose of this paper is to propose an adaptive Gaussian wavelet neural control (AGWNC) system to control a chaotic system.

The proposed AGWNC system is composed of a wavelet neural controller and a compensation tangent controller. The wavelet neural controller utilizes a Gaussian WNN to mimic an ideal controller, and the compensation tangent controller is designed to compensate the approximation error between the ideal and the wavelet neural controllers. The controller parameters of the proposed AGWNC can online tune in the Lyapunov sense, thus the uniformly ultimately bounded stability of closed‐loop system can be guaranteed.

The proposed AGWNC system is applied to a chaotic system. Simulation results are used to demonstrate the effectiveness and performance of the proposed AGWNC scheme. Simulation results show that not only the favorable control performance can be achieved but also the control efforts without any chattering phenomena. Moreover, all controller parameters can be online tuning by the derived adaptive laws based on the Lyapunov function.

The proposed AGWNC approach is interesting for the design of an intelligent control scheme. The main contributions of this paper are: the overall closed‐loop control system is globally stable in uniform ultimate boundedness; the tracking error can be asymptotically attenuated to a desired small level around zero by appropriate chosen parameters and learning rates; and the AGWNC system can achieve favorable tracking performance.

The research aims to measure the effectiveness of collaborative learning exchanges transpired through digital tools and technologies (DT&Ts) employed by the mentor universities during the COVID-19 pandemic by conducting an empirical study on undergraduate students in Indian higher educational institutions (HEIs) under the mentorship program based on the corporate social responsibility (CSR) initiative. The pandemic scenario, its impact on the mentor university's social responsibility and the way DT&Ts can assist are investigated in this article.

The interactions with experts and students were conducted to explore the DT&Ts for learning exchanges. Next, structural equation modeling (SEM) was performed to validate the model and perform regression analysis. The quantitative data collection was made through questionnaires during the second deadly wave of COVID-19 that hit India.

The independent variables (IVs) such as the IT infrastructure support (IT_IS), virtual collaborative tools (VCTs) and future-oriented technologies (FOTs) have a significant impact on the CSR learning outcomes (CSR_LOs) of undergraduate students under the mentorship program. However, IV research instruments for innovation could not make a significant effect.

The IVs IT_IS, VCTs and FOTs influence the CSR_LOs, while RII does not have an influential impact.

As the online learning environment is expected to stay at least in a blended form, adequate CSR funding in infrastructure is necessitated to harness the full potential of this important resource, technology. The results of this empirical investigation affirm that IT_IS, VOTs and FOTs significantly impact CSR_LOs during the crisis. The study findings would encourage the mendtor universities and their stakeholders, including the mentee universities, to evolve and create an ecosystem for effective management of these resources to attain positive outcomes. The study findings can guide the mentor universities in managing uncertainties like pandemics and effectively using the earlier-mentioned critical resources for social responsibility. This research also allows the development of future applications adnd models in mentor-mentee universities for social responsibility, post-pandemic transformation and resilience.

The DT&Ts came to the immediate rescue during the pandemic and positively affected collaborative CSR_LOs by the mentor universities, but they have not evolved to a level where offline learning can be replaced entirely. Hence, it can be inferred that a hybrid model is preferable. The study also improves the understanding of how DT&Ts are being harnessed to aid collaborative learning in fulfilling the mentors' CSR in fatal emergencies. The purpose is to equip the education system through mentorship so that universities can sustain, innovate and grow even in trying times. Also, it discusses the dynamics of various DT&Ts for creating a sustainable learning environment and utilizing them to make the teaching prolific and influential.

There is a scarcity of literature regarding the learning outcomes realized through CSR initiatives and collaboration between mentor-mentee institutions. There is a need to understand how these knowledge exchanges continued despite the physical restrictions during the pandemic. In this direction, this study helps to understand how the DT&Ts played a critical role in continuing learning and keeping abreast in a knowledge society from the perspective of resource-based view (RBV) in these precarious situations.

In 2001, the US moved to regulate internal control reporting by management and auditors. While some jurisdictions have followed the lead of the US, many others have not. An important question, therefore, is the relevance of internal control to stakeholders. The more specific issue of the benefits of US-style regulation of internal control reporting is also topical. We review studies on the determinants of internal control quality and its economic consequences for stakeholders including investors, creditors, managers, auditors and financial analysts. We extend previous reviews by focusing on US studies published since 2013 as well as all non-US studies investigating IC quality including countries regulating IC disclosure as well as unregulated settings and both developed and developing economies. In doing so, we identify research questions where evidence remains mixed and new directions in which there are research opportunities.

Three main insights arise from our analysis. First, evidence on the economic consequences of internal control quality suggests that the quality of internal control can have a significant effect on decision making by users of financial information. Second, the results of research on the empirical association between ownership structure, certain board characteristics and internal control quality is generally mixed. Empirical evidence concerning the association between audit committee characteristics and internal control quality generally supports a positive and significant association. Finally, while studies in non-US jurisdictions are increasing, opportunities remain to explore the determinants and consequences of internal control in other jurisdictions. Our review provides evidence for policy makers of whether there are benefits from requiring management and auditors to report on internal control over financial reporting.

The fundamental change in accounting rules for equity-based compensation (EBC) instituted by SFAS 123, SFAS 123r, and IFRS 2 has allowed for new insights related to a variety of research questions. This paper discusses the empirical evidence generated in the wake of the new regulation and categorizes it into two broad streams. The first stream encompasses research on the changed use of EBC and the incentives provided. The second stream addresses how firms account for EBC, including the underreporting phenomenon and how it was affected by the mandatory recognition of EBC expenses. I discuss where research delivers unanimous findings versus contradictory results. Using these insights, I make recommendations for further research opportunities in the area of EBC.

This paper aims to research the effect of different concentrations for Nd(NO₃)₃ and Ce(NO₃)₃ on the microstructures and corrosion resistance of Ni-W-P composite coatings through electroless plating method.

Scanning electron microscope, attached energy dispersive spectroscopy system and X-ray diffraction were used in this work. Meanwhile, the immersion test and electrochemical tests were used to characterize the corrosion behavior of the coating.

The coatings prepared at 1.00 g·L⁻¹ Nd(NO₃)₃ exhibit a dense structure and high phosphorus content (12.38 wt.%). In addition, compared to the addition of Ce(NO₃)₃, when Nd(NO₃)₃ was introduced at a concentration of 1.00 g·L⁻¹, the minimum corrosion rate of the coating was 1.209 g·m⁻²·h⁻¹, with a noble E_corr (−0.29 V) and lower I_corr (8.29 × 10⁻⁴ A·cm⁻²).

The effects of rare earths on the deposition and corrosion resistance mechanisms of Ni-W-P composite coatings were explored, with the rare earth elements promoting the deposition of nickel and tungsten atoms. Simultaneously, the amorphization of the coating increases, which excellently enhances the corrosion resistance of the coating.

Physics-informed neural networks (PINNs) have become a new tendency in flow simulation, because of their self-advantage of integrating both physical and monitored information of fields in solving the Navier–Stokes equation and its variants. In view of the strengths of PINN, this study aims to investigate the impact of spatially embedded data distribution on the flow field results around the train in the crosswind environment reconstructed by PINN.

PINN can integrate data residuals with physical residuals into the loss function to train its parameters, allowing it to approximate the solution of the governing equations. In addition, with the aid of labelled training data, PINN can also incorporate the real site information of the flow field in model training. In light of this, the PINN model is adopted to reconstruct a two-dimensional time-averaged flow field around a train under crosswinds in the spatial domain with the aid of sparse flow field data, and the prediction results are compared with the reference results obtained from numerical modelling.

The prediction results from PINN results demonstrated a low discrepancy with those obtained from numerical simulations. The results of this study indicate that a threshold of the spatial embedded data density exists, in both the near wall and far wall areas on the train’s leeward side, as well as the near train surface area. In other words, a negative effect on the PINN reconstruction accuracy will emerge if the spatial embedded data density exceeds or slips below the threshold. Also, the optimum arrangement of the spatial embedded data in reconstructing the flow field of the train in crosswinds is obtained in this work.

In this work, a strategy of reconstructing the time-averaged flow field of the train under crosswind conditions is proposed based on the physics-informed data-driven method, which enhances the scope of neural network applications. In addition, for the flow field reconstruction, the effect of spatial embedded data arrangement in PINN is compared to improve its accuracy.

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.

This study examines the relationship between board capital, including human and social capital, and corporate innovation. We propose two hypotheses: that a board with a higher level of human and social capital, respectively, is expected to have a higher level of innovation. To test these hypotheses, we use data from different sources, including SEC EDGARD-10k, Noah Stoffman, and S&P 500 Capital IQ for US public firms from all industries from 2000 to 2018. Four different innovation measurements are used to proxy for innovation: R&D, patents, citations, and number of new products. We use directors' level of education and industry experience to proxy for board human capital. The directors' social networks and interlocking ties are used to proxy for board social capital. We use fixed effect regressions to test the hypotheses and two-stage least square (2SLS) regressions to address endogeneity issues. We find that boards with higher levels of human capital are highly associated with corporate innovation in terms of citations. The findings imply that firms should hire directors with higher education and industry experience if they wish to increase their innovation.

Family dinner on Lunar New Year's Eve is the most important and most ritualized feast for families in China. It is the time for the entire family to reunite. Families gather together to reflect their past and talk about the future. Through the lens of consumer culture theories, this study explores how Chinese consumers construct family identity.

Based on constant comparative analysis of primary data including in-depth interviews and participant observation, and secondary data including historical archives, cultural tracing, documentary reports and essays, the authors deconstruct the consumption rituals of family dinner on Chinese Lunar New Year's Eve. The authors focus on four aspects, including participants, place, time and related activities, and analyze Chinese consumers' ritual experiences.

The authors’ findings show how young consumers construct and strengthen individual self-identity, relational identity and family identity in various ways through consumption and ritual practices during Chinese Lunar New Year celebration.

The study of family dinner on Lunar New Year's Eve helps the authors understand contemporary consumer culture in three aspects. First, it helps the authors understand the relationship between consumption and culture. Second, the study shows the changes and continuities of consumption rituals. Third, the research highlights the experience of “home” among contemporary Chinese consumers.