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The purpose of this paper is to present a full three-dimensional (3D) computational fluid dynamics (CFD) analysis of a rectangular asymmetric 3D diffuser utilizing seven turbulence models frequently used in engineering to assess the predictive capabilities of the turbulence models for separated flows in internal flows.

The structured computational grids are generated by means of the mesh generation tool IGG software package. The computational grids are imported into the commercial CFD code Fluent. The performance of the different turbulence models adopted has been systematically assessed by comparing the numerical results with the available experimental and direct numerical simulation/large eddy simulations data.

The comparisons show that the Reynolds stress model (RSM) evidently performs better than the other turbulence models for predicting wall pressure, velocity, and vorticity fields. Moreover, only the RSM can predict the separation bubble region around the top right corner, which is consistent with the experiment. It is found that the RSM can well predict Prandtl’s secondary flow of the second kind for considering turbulence anisotropy, whereas the other models cannot.

The paper utilizes seven turbulence models frequently used in engineering in detailed numerical investigations of a real 3D diffuser to expand the scope of application for various turbulence models. The studies are valuable for the proper use of the turbulence models, allowing the designers to understand the numerical results further and contributing to the modification of the turbulence models for 3D flows.

This paper aims to explore the pathways of carbon transfer in 200 US corporations along with the motivations that drive such transfers. The particular focus is on each firm’s embeddedness in the global value chain (GVC) and the influence of environmental law, operational costs and corporate social responsibility (CSR). The insights gleaned bridge a gap in the literature surrounding GVCs and corporate carbon transfer.

The methodology comprised a two-step research approach. First, the authors used a two-sided fixed regression to analyse the relationship between each firm’s embeddedness in the GVC and its carbon transfers. The sample consisted of 217 US firms. Next, the authors examined the influence of environmental law, operational costs and CSR on carbon transfers using a quantitative comparison analysis. These results were interpreted through the theoretical frameworks of the GVC and legitimacy theory.

The empirical results indicate positive relationships between carbon transfers and GVC embeddedness in terms of both a firm’s position and its degree. From the quantitative comparison, the authors find that the pressure of environmental law and operational costs motivate these transfers through the value chain. Furthermore, CSR does not help to mitigate transfers.

The findings offer insights for policymakers, industry and academia to understand that, with globalised production and greater value creation, transferring carbon to different parts of the GVC – largely to developing countries – will only become more common. The underdeveloped nature of environmental technology in these countries means that global emissions will likely rise instead of fall, further exacerbating global warming. Transferring carbon is not conducive to a sustainable global economy. Hence, firms should be closely regulated and given economic incentives to reduce emissions, not simply shunt them off to the developing world.

Carbon transfer is a major obstacle to effectively reducing carbon emissions. The responsibilities of carbon transfer via GVCs are difficult to define despite firms being a major consideration in such transfers. Understanding how and why corporations engage in carbon transfers can facilitate global cooperation among communities. This knowledge could pave the way to establishing a global carbon transfer monitoring network aimed at preventing corporate carbon transfer and, instead, encouraging emissions reduction.

This study extends the literature by investigating carbon transfers and the GVC at the firm level. The authors used two-step research approach including panel data and quantitative comparison analysis to address this important question. The authors are the primary study to explore the motivation and pathways by which firms transfer carbon through the GVC.

This paper aims to propose a novel model predictive control (MPC) with time varying weights to develop a lateral control law in an automatic carrier landing system (ACLS), which minimizes landing risk and improves flight quality.

First, a nonlinear mathematic model of an F/A-18 aircraft during lateral landing is established. Then the landing model is linearized in the form of state deviations on the equilibrium points. Second, landing risk windows are proposed and a high-dimensional landing risk model is addressed through a back propagation (BP) neural network. The trained samples are acquired based on a pilot behavior model. Third, time varying weights created from the lateral landing risk are introduced into the performance function of MPC. Optimal solution is solved quicker and some state deviations are focused on and eliminated. Fourth, the algebraic inequalities are substituted by the linear matrix inequalities (LMIs), which are easily calculated by the computers.

On a semi-physical platform, the proposed method compares with a traditional MPC algorithm and a modified MPC with an additional term. The test results indicate that the proposed algorithm brings about an excellent landing performance as well as an ability of eliminating landing risk.

The landing phase of a carrier-based aircraft is one of the most dangerous and complicated stages, and the algorithm proposed by this paper plays a vital role in the lateral landing.

This paper establishes a lateral landing risk model, which considers not only the current landing state but also the future touchdown point. This lateral landing risk is integrated into the time varying weights of the MPC algorithm so that the state deviations and landing risk can be both reduced in the rolling optimization.

The primary objective of this study is to unveil the relationships that interconnect ESG and three pillars disclosures with technological innovation while also investigating the moderating impact of product market competition. The paper seeks to identify the underlying mechanisms that facilitate technological innovation in sustainable management.

Using data from 8,738 Chinese firms from 2011 to 2019, this study employs quantitative analysis to examine the relationship between ESG disclosure and technological innovation and the moderating effect. Moreover, this study explores the heterogeneous impacts while considering factors such as property rights and firm size.

The findings reveal a positive correlation between ESG disclosure and technological innovation. The study also investigates the moderating role of product market competition and finds that increasing competition mitigates the positive effects of ESG disclosure on technological innovation. Additionally, the conclusions reveal that the relationship between ESG and three pillars disclosures and technological innovation, as well as the moderating role of product market competition, exhibits inconsistency across firms with different property rights and sizes.

This study offers a clear understanding of the relationship between ESG disclosures and technological innovation, and how it varies across businesses of different sizes and ownership structures. It also provides fresh perspectives on the influence of product market competition on this relationship, with implications for strategy development in corporations.

The purpose of this paper was to identify whether artificial intelligence (AI) products can possess human rights, how to define their rights and obligations and what ethical standards they should follow. In this study, the human rights ethical dilemma encountered in the application and development of AI technology has been focused on and analyzed in detail in the light of the existing research status of AI ethics.

In this study, first of all, the development and application of AI technology, as well as the concept and characteristics of human rights ethics, are introduced. Second, the human rights ethics of AI technology are introduced in detail, including the human rights endowment of AI machines, the fault liability of AI machines and the moral orientation of AI machines. Finally, the approaches to human rights ethics are proposed to ensure that AI technology serves human beings. Every link of its research, production and application should be strictly managed and supervised.

The results show that the research in this study can provide help for the related problems encountered in AI practice. Intelligent library integrates human rights protection organically so that readers or users can experience more intimate service in this system. It is a kind of library operation mode with more efficient and convenient characteristics, which is based on digital, networked and intelligent information science. It aims at using the greenest way and digital means to realize the reading and research of human rights protection literature in the literature analysis method.

Intelligent library is the future development mode of new libraries, which can realize broad interconnection and sharing. It is people-oriented and can make intelligent management and service and establish the importance of the principle of human rights protection and the specific idea of the principle. The development of science and technology brings not only convenience to people's social life but also questions to be thought. People should reduce its potential harm, so as to make AI technology continue to benefit humankind.

Defining and measuring competitiveness has been a major focus in the business and competition literature over the past decades. The paper aims to use data-driven principal component analysis (PCA) to measure firm competitiveness.

A “3Ps” (performance, potential, and process) firm competitiveness indicator system is structured for indicator selection. Data-driven PCA is proposed to measure competitiveness by reducing the dimensionality of indicators and assigning weights according to the endogenous structure of a dataset. To illustrate and validate the method, a case study applying to Chinese international construction companies (CICCs) was conducted.

In the case study, 4 principal components were derived from 11 indicators through PCA. The principal components were labeled as “performance” and “capability” under the two respective super-components of “profitability” and “solvency” of a company. Weights of 11 indicators were then generated and competitiveness of CICCs was finally calculated by composite indexes.

This study offers a systematic indicator framework for firm competitiveness. The study also provides an alternative approach to better solve the problem of firm competitiveness measurement that has long plagued researchers.

The data-driven PCA approach alleviates the difficulties of dimensionality and subjectivity in measuring firm competitiveness and offers an alternative choice for companies and researchers to evaluate business success in future studies.

The construction industry has been investigating “where Henry Ford is in the industry system.” Given that listed construction enterprises are the backbone of the promotion of the high-quality development of the industry, their research and innovation are of considerable importance. This study aims to comprehensively assess the research and development (R&D) status quo and trends within various types of construction enterprises in order to identify effective strategies to enhance R&D efficiency in the construction industry.

Based on the data won from annual reports and the CSMAR database for the period 2016–2020, this study examines 104 listed construction enterprises in China. By applying both the data envelopment analysis (DEA) method and the Malmquist productivity index, this research compares and analyzes the static and dynamic differences in R&D efficiency across different types of construction enterprises.

Results suggest that the magnitude of change in the Malmquist decomposition index of 104 listed construction enterprises gradually narrowed, but the comprehensive technological level remained relatively low. Although state-owned enterprises had an advantage in scale efficiency, meaning they could maximize output with given inputs, their technological progress efficiency, also known as the degree of technological innovation, was significantly lower than that of private enterprises. As one finding, state-owned enterprises in comparison with private enterprises experience significant R&D inefficiency. It represents the main cause of their low degree of technological innovation and efficiency.

This study assesses the R&D efficiency of listed construction enterprises in China from the perspective of different market segments, state-owned and private enterprises and suggests approaches to improve strategies for various corporate types. Thus, the study’s new findings contribute to addressing the challenge of low R&D levels in the construction industry in the fields of engineering, construction and architectural management.

One of the hurdles to the adoption of sustainable practices across supply chains is the lack of pan-chain performance measurements and their related information and organizational structures. The authors review the literature on performance measurement of sustainable supply chains with a focus on comprehensive measures that include multiple supply chain partners as well as different sustainability aspects. The purpose of this paper is to analyze the reviewed literature and propose some research questions.

The authors reviewed 140 journal articles, cases and reports that appeared since 1994.

The authors classify the reviewed literature according to seven sustainability dimensions (economical, environmental, social, reputable, valuable, equitable and sustainable) as well as the type of industry and methodology used. In addition the authors synthesize the available performance measurements into a comprehensive framework that incorporates different stages of the supply chain operations and decision-making processes.

The results of this study can be used by researchers to focus on research that may have more implications on supply chains. Practitioners can use the authors proposed performance measurement framework for developing practical and comprehensive measures for their respective industries.

The work is original in the way the authors integrate sustainability (seven dimensions) across the supply chain taking into account the type of operational decisions. The framework can be used by researchers and practitioners to develop practical sustainability performance measurement systems for supply chains.