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This study aims to investigate the impacts of bank capital requirements on the performance and risk of the emerging economy, i.e. Bangladeshi banking sector.

The study applies an unbalanced panel data which comprises 30 banks yielding a total of 413 bank-year observations over the period 2000 to 2015.

Using generalized methods of moments, the empirical results of this research reveal that bank capital is positively and significantly impressive on bank performance, whereas negatively and significantly impact on risk. The study also finds the inverse relationship between risk and performance in both the performance and risk equations. The results also indicate that there is a persistence of performance and risk from one year to the next year.

This is the unique investigation on Bangladeshi bank industry that considers the simultaneous effect of bank capital requirements on risk and performance. Therefore, it is predicted that the empirical evidence of this research shows policy implications to the regulatory authority of Bangladeshi banking industry to determine relevant policies.

The authors aimed to identify the opinions of young adult consumers regarding food processing companies’ (FPCs) credibility in terms of food safety (FS).

The authors surveyed Generation Z (GenZ) consumers. The authors assessed the reliability of the research questionnaire using Cronbach’s alpha statistics. The authors used descriptive statistics and one-way ANOVA analysis of variance in the data analysis to determine intergroup variability. The authors performed statistical analyses using IBM SPSS Statistics. 27.

The most valued determinants for consumers were competence and skills, and the most valued family members’ opinions on FS, followed by experts’ opinions. FS concerns are more associated with FPCs than with farmers. The ethics of conduct and moral responsibility play an important role in assessing the FPCs’ credibility.

The questionnaire did not focus on specific food industries, such as fruit and vegetables, fish, meat, dairy, etc. In the future, a similar survey on producers’ credibility should consider the issue of FS risks associated with the specifics of a particular industry.

The authors proposed a set of factors that may determine young adult consumers’ perception of the FPCs’ credibility, which they may use for research within other consumer groups.

This study aims to introduce the DoraHand, and the basic capability and performance have been verified in this paper. Besides the idea of sharing modular design and sensor design, the authors want to deliver an affordable and practical dexterous hand to the research area to contribute to the robotic manipulation area.

This paper introduced the DoraHand, a novel scalable and practical modular dexterous hand, which, adopting modular finger and palm design, fully actuated joint and tactile sensors, can improve the dexterity for robotic manipulation and lower the complexity of maintenance. A series of experiments are delivered to verify the performance of the hand and sensor module.

The parameters of the DoraHand are verified and suitable for the research of robotics manipulation area, the sensing capability has been tested with the static experiment and the slip prediction algorithm. And, the advantage of modular design and extensible interface have been verified by the real application.

The authors continue improving the DoraHand and extend it to more different applications. The authors want to make the DoraHand as a basic research platform in the robotic manipulation area.

The DoraHand has been sent to more than ten different research institutes for different research applications. The authors continue working on this hand for better performance, easier usage and more affordability.

This kind of dexterous hand can help researchers get rid of complex physical issues and pay more attention to the algorithm part; it can help to make robotic manipulation work more popular.

The key design in the DoraHand is the modular finger and sensing module. With the special design in mechanical and electrical parts, the authors build reliable hardware and can support the diversity requirement in the robotic manipulation area. The hand with tactile sensing capability can be used in more research and applications with its extensibility.

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.

Transportation infrastructure asset management has long been an active but challenging problem for agencies, which urges to maintain a good state of their assets but faces budgetary limitations. Managing a network of transportation infrastructure assets, especially when the number is large, is a multifaceted challenge. This paper aims to develop a life-cycle cost analysis (LCCA) based transportation infrastructure asset management analytical framework to study the impacts of a few key parameters/factors on deterioration and life-cycle cost. Using the bridge as an example infrastructure type, the framework incorporates an optimization model for optimizing maintenance, repair, rehabilitation (MR&R) and replacement decisions in a finite planning horizon.

The analytical framework is further developed through a series of model variations, scenario and sensitivity analysis, simulation processes and numerical experiments to show the impacts of various parameters/factors and draw managerial insights. One notable analysis is to explicitly model the epistemic uncertainties of infrastructure deterioration models, which have been overlooked in previous research. The proposed methodology can be adapted to different types of assets for solving general asset management and capital planning problems.

The experiments and case studies revealed several findings. First, the authors showed the importance of the deterioration model parameter (i.e. Markov transition probability). Inaccurate information of p will lead to suboptimal solutions and results in excessive total cost. Second, both agency cost and user cost of a single facility will have significant impacts on the system cost and correlation between them also influences the system cost. Third, the optimal budget can be found and the system cost is tolerant to budge variations within a certain range. Four, the model minimizes the total cost by optimizing the allocation of funds to bridges weighing the trade-off between user and agency costs.

On the path forward to develop the next generation of bridge management systems methodologies, the authors make an exploration of incorporating the epistemic uncertainties of the stochastic deterioration models into bridge MR&R capital planning and decision-making. The authors propose an optimization approach that does not only incorporate the inherent stochasticity of bridge deterioration but also considers the epistemic uncertainties and variances of the model parameters of Markovian transition probabilities due to data errors or modeling processes.

This paper aims to review the research on accounting professionalisation in China to develop insights into how the research is developing, offer a critique of the research to date and outline future research directions and opportunities.

This paper adopts a methodological approach of systematic literature review, as suggested by Tranfield et al. (2003) and Denyer and Tranfield (2009), to identify, select and analyse the extant literature on the Chinese public accounting profession. In total, 68 academic works were included in the review process.

This paper finds that the extant literature has produced fruitful insights into the processes and underlying motivation of accounting professionalisation in China, demonstrating that the Chinese experience has differed, to a large extent, from the hitherto mainly Anglo-American-dominated understandings of accounting professionalisation. However, due to the lack of common theoretical vernacular and an agreed upon focus, the extant literature illustrates a fragmented and contradictory picture, making attempts to accumulate prior knowledge in the field increasingly difficult.

This paper focusses only on research published in English. Consequently, the scope of review has been limited as some works published in languages other than English may be excluded.

This paper provides one of the pioneering exercises to systematically review the research on accounting professionalisation in China. It explores significant issues arising from the analysis and provides several suggestions for furthering the research effort in this field.

This study investigates the relationships among digital transformation, technological innovation, industry–university–research collaborations and labor income share in manufacturing firms.

The relationships are tested using an empirical method, constructing regression models, by collecting 1,240 manufacturing firms and 9,029 items listed on the A-share market in China from 2013 to 2020.

The results indicate that digital transformation has a positive effect on manufacturing companies’ labor income share. Technological innovation can mediate the effect of digital transformation on labor income share. Industry–university–research cooperation can positively moderate the promotion effect of digital transformation on labor income share but cannot moderate the mediating effect of technological innovation. Heterogeneity analysis also found that firms without service-based transformation and nonstate-owned firms are better able to increase their labor income share through digital transformation.

This study provides a new path to increase the labor income share of enterprises to achieve common prosperity, which is important for manufacturing enterprises to better transform and upgrade to achieve high-quality development.

The purpose of this paper is to explain the difference and connection between the network big data analysis technology and the psychological empirical research method.

This study analyzed the data from laboratory setting first, then the online sales data from Taobao.com to explore how the influential factors, such as online reviews (positive vs negative mainly), risk perception (higher vs lower) and product types (experiencing vs searching), interacted on the online purchase intention or online purchase behavior.

Compared with traditional research methods, such as questionnaire and behavioral experiment, network big data analysis has significant advantages in terms of sample size, data objectivity, timeliness and ecological validity.

Future study may consider the strategy of using complementary methods and combining both data-driven and theory-driven approaches in research design to provide suggestions for the development of e-commence in the era of big data.

Fabrics with photothermal conversion functions were developed based on the introduction of shape stable composite phase change materials (CPCMs).

Acidified single-walled carbon nanotubes (SWCNTs) were selected as support material to prepare CPCMs with n-octadecane to improve the thermal conductivity and shape stability. The CPCMs were finished onto the surface of cotton fabric through the coating and screen-printing method. The chemical properties of CPCMs were characterized by Fourier transform infrared spectrometer, XRD and differential scanning calorimetry (DSC). The shape stability and thermal conductivity were also evaluated. In addition, the photothermal conversion and temperature-regulating performance of the finished fabrics were analyzed.

When the addition amount of acidified SWCNTs are 14% to the mass of n-octadecane, the best shape stability of CPCMs is obtained. DSC analysis shows that the latent heat energy storage of CPCMs is as high as 183.1 J/g. The thermal conductivity is increased by 84.4% compared with that of n-octadecane. The temperature-regulating fabrics coated with CPCMs have good photothermal conversion properties.

CPCMs with high latent heat properties are applied to the fabric surface through screen printing technology, which not only gives the fabric the photothermal conversion performance but also reflects the design of personalized patterns.

CPCMs and polydimethylsiloxane (PDMS) are mixed to make printing paste and printed cotton fabric with temperature-regulating functional is developed.

SWCNTs and n-octadecane are composited to prepare CPCMs with excellent thermal properties, which can be mixed with PDMS to make printing paste without adding other pastes. The fabric is screen-printed to obtain a personalized pattern and can be given a thermoregulatory function.