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When solving the cogging torque of complex electromagnetic structures, such as consequent pole hybrid excitation synchronous (CPHES) machine, traditional methods have a huge computational complexity. The notable feature of CPHES machine is the symmetric range of field-strengthening and field-weakening, but this type of machine is destined to be equipped with a complex electromagnetic structure. The purpose of this paper is to propose a hybrid analysis method to quickly and accurately solve the cogging torque of complex 3D electromagnetic structure, which is applicable to CPHES machine with different magnetic pole shapings.

In this paper, a hybrid method for calculating the cogging torque of CPHES machine is proposed, which considers three commonly used pole shapings. Firstly, through magnetic field analysis, the complex 3D finite element analysis (FEA) is simplified to 2D field computing. Secondly, the discretization method is used to obtain the distribution of permeance and permeance differential along the circumference of the air-gap, taking into account the effect of slots. Finally, the cogging torque of the whole motor is obtained by using the idea of modular calculation and the symmetry of the rotor structure.

This method is applicable to different pole shapings. The experimental results show that the proposed method is consistent with 3D FEA and experimental measured results, and the average calculation time is reduced from 8 h to 4 min.

This paper proposes a new concept for calculating cogging torque, which is a hybrid calculation of dimension reduction and discretization modules. Based on magnetic field analysis, the 3D problem is simplified into a 2D issue, reducing computational complexity. Based on the symmetry of the machine structure, a modeling method for discretized analytical models is proposed to calculate the cogging torque of the machine.

The People's Republic of China (PRC) is the world's largest official creditor nation with its outstanding debt claims on the rest of the world totaling an estimated US$5.5 trillion in 2019 or more than 6% of global GDP. However, given Beijing’s very limited transparency in its disclosures when it comes to its external lending (and now its alleged culpability in the spread of the coronavirus) the accusation that the PRC has for years engaged in “debt-trap diplomacy” has come under renewed scrutiny. Specifically, the charge is that the Chinese government, which is the world's leading creditor lending via its multibillion dollar “Belt and Road Initiative” as well as various state-owned and controlled entities, lures developing countries, in particular, low-income countries, with easy money to fund often economically unviable projects. This is because China's ultimate goal is to get access to the borrowers' local markets and natural resources and indirectly controlling or outright seizing assets and resources, including extracting economic and political concessions when these countries fail to service their loans – which is often given at market or above-market interest rates and carry shorter maturities, thereby requiring regular refinancing. This paper reviews this claim and concludes that it seems exaggerated.

Engaging suppliers in joint innovation can be an effective means for buyer firms to overcome internal resource/capability limitations. The purpose of this research is to investigate the impacts of cultural and trust congruences between the supplier and buyer firms on joint innovation. In addition, we examine the relationship commitment as an antecedent of cultural and trust congruences.

The study constructs a theoretical model based on social exchange theory (SET) and examines it using data from Chinese manufacturing firms.

The results suggest that cultural and trust congruences between suppliers and buyers positively influence joint processes and product innovations. Furthermore, we find that while normative relationship commitments of supplier firms promote cultural and trust congruences with buyers, instrumental relationship commitments only positively affect trust congruence.

This study enhances our understanding of social exchanges by adopting a dyadic view to examine the interconnectedness between relationship commitment, cultural and trust congruences, and joint innovation. These findings also offer practical managerial implications for managing collaborative innovation projects.

The set-union knapsack problem is one of the most significant generalizations of the Non-deterministic Polynomial (NP)-hard 0-1 knapsack problem in combinatorial optimization, which has rich application scenarios. Although some researchers performed effective algorithms on normal-sized instances, the authors found these methods deteriorated rapidly as the scale became larger. Therefore, the authors design an efficient yet effective algorithm to solve this large-scale optimization problem, making it applicable to real-world cases under the era of big data.

The authors develop three targeted strategies and adjust them into the adaptive tabu search framework. Specifically, the dynamic item scoring tries to select proper items into the knapsack dynamically to enhance the intensification, while the age-guided perturbation places more emphasis on the diversification of the algorithm. The lightweight neighborhood updating simplifies the neighborhood operators to reduce the algorithm complexity distinctly as well as maintains potential solutions. The authors conduct comparative experiments against currently best solvers to show the performance of the proposed algorithm.

Statistical experiments show that the proposed algorithm can find 18 out of 24 better solutions than other algorithms. For the remaining six instances on which the competitor also achieves the same solutions, ours performs more stably due to its narrow gap between best and mean value. Besides, the convergence time is also verified efficiency against other algorithms.

The authors present the first implementation of heuristic algorithm for solving large-scale set-union knapsack problem and achieve the best results. Also, the authors provide the benchmarks on the website for the first time.

The COVID-19 pandemic has resulted in significant changes in consumer behaviour, which has had a cascading effect on consumer-centric logistics. As a result, this study conducts a focused literature review of pandemic-related consumer behaviour research to address two research questions: 1) what are the pandemic's direct effects on consumer consumption behaviour, with an emphasis on changes in their basic and psychological needs? and 2) what are the consequences of behavioural changes on consumer-centric logistics?

The scientific procedure and rationales for systematic literature review (SPAR-4-SLR) protocol and the theory, context, characteristics and methodology (TCCM) framework were adopted as a guideline to map, refine, evaluate and synthesise the literature. A total of 53 research articles were identified for further analysis.

Using Maslow's hierarchy of human needs as a theoretical guide, this review synthesises the COVID-19 pandemic's effects on consumer behaviour into four categories: abnormal buying behaviour, changes in consumer preferences, digitalisation of shopping behaviour and technology-related behaviour. Furthermore, four consumer-centric logistics propositions are proposed based on the four aspects of consumer behavioural changes.

This study outlines the significant behavioural changes in consumers in the face of the COVID-19 pandemic and how these changes impact consumer-centric logistics, with implications for managing consumers' involvement in logistics and pointing out future research directions.

This paper aims to propose a new autonomous driving controller to calibrate the absolute heading adaptively. Besides, the second purpose of this paper is to propose a new angle-track loop with a mass regulator to improve the adaptability of the autonomous driving system under different loads and road conditions.

In this paper, the error model of heading is built and a new autonomous driving controller with heading adaptive calibration is designed. The new controller calculates the average lateral error by the self-adjusting interval window and calibrates the absolute heading through the incremental proportional–integral–derivative (PID) controller. A window-size adjustment strategy, based on the current lateral error and the derivative of lateral error, is proposed to improve both the transient and the steady-state responses. An angle-tracking loop with mass regulator is proposed to improve the adaptability of autonomous steering system under different loads and road conditions.

The experiment results demonstrate that this method can compensate the heading installation error and restrain the off-track error from 13.8 to 1.30 cm. The standard error of new controller is smaller than fuzzy-PID calibration controller and the accuracy of autonomous driving system is improved.

The accuracy of heading calibrated by the new controller is not affected by external factors and the efficiency of calibration is improved. As the model parameters of steering system can be obtained manually, the new autonomous steering controller has more simple structure and is easy to implement. Mass regulator is adjusted according to the road conditions and the mass of harvester, which can improve the system adaptability.

For decades, financing constraints have been a major obstacle to corporate performance. Volumes have been written about the probable factors that can help firms alleviate such financial constraints. Nonetheless, empirical evidence concerning the various perspectives on how inventory control may influence financing constraints has been surprisingly scant. Using the resource- and region-based view as theoretical lenses, this study seeks to estimate the relationship between lean inventory, regional financial technology (fintech) and financing constraints.

Utilizing a large-scale sample of small- and medium-sized enterprises (SMEs) in China's manufacturing sector, the authors empirically test their hypotheses by using hierarchical linear regression models with multiple high-dimensional fixed effects.

Results indicate that firms with higher levels of inventory leanness and those located in more fintech-developed regions are less likely to encounter financing constraints. Furthermore, inventory leanness and regional fintech ecosystem development interact with each other to mitigate financing constraints. Moreover, inventory leanness significantly decreases firms' financing constraints when the regional fintech ecosystem is highly developed.

The present research contributes to the literature on the interface of supply chain management and financial management. It also provides managerial implications for policymakers and SME stakeholders.

Sailing toward sustainability is becoming the strategic focus of shipping firms. Drawing on organizational information processing theory (OIPT) and the theory of planned behavior (TPB), we investigated the impact of digital transformation (DT) on shipping firms’ sustainable management performance and the boundary conditions guiding this relationship.

The authors examined the hypotheses by employing hierarchical linear modeling on two-wave time-lagged data from 189 shipping firm employees in China.

The results suggest that a shipping firm’s DT is positively associated with its sustainable management performance and that the relationship is strengthened by having better cross-functional and customer coordination mechanisms. Furthermore, our three-way interaction analyses show that while injunctive norms in a shipping firm’s networks can strengthen the contingency roles of both cross-functional and customer coordination mechanisms, descriptive norms alone significantly influence customer coordination.

Drawing on organizational information processing and planned behavior theories, the present research provides new insights into leveraging DT for sailing toward sustainable success. Moreover, this study extends the current understandings of the boundary conditions of the relationship between DT and sustainable management performance by showing the two-way and three-way interaction effects of coordination mechanisms and subjective norms. The findings of the present research can be utilized as effective strategies for promoting sustainable management performance.

The purpose of this paper is to study the multi-phase flow behaviors in solid fluidization exploitation of natural gas hydrate (NGH) and its effect on the engineering safety.

In this paper, a multi-phase flow model considering the endothermic decomposition of hydrate is established and finite difference method is used to solve the mathematical model. The model is validated by reproducing the field test data of a well in Shenhu Sea area. Besides, optimization of design parameters is presented to ensure engineering safety during the solid fluidization exploitation of NGH in South China Sea.

To ensure the engineering safety during solid fluidization exploitation of marine NGH, taking the test well as an example, a drilling flow rate range of 40–50 L/s, drilling fluid density range of 1.2–1.23 g/cm3 and rate of penetration (ROP) range of 10–20 m/h should be recommended. Besides, pre-cooled drilling fluid is also helpful for inhibiting hydrate decomposition.

Systematic research on the effect of multiphase flow behaviors on the engineering safety is scare, especially for the solid fluidization exploitation of NGH in South China Sea. With the growing demand for energy, it is of great significance to ensure the engineering safety before the large-scale extraction of commercial gas from hydrate deposits. The result of this study can provide profound theoretical bases and valuable technical guidance for the commercial solid fluidization exploitation of NGH in South China Sea.

This study aims to extract knowledge of ancient Chinese scientific and technological documents bibliographic summaries (STDBS) and provide the knowledge graph (KG) comprehensively and systematically. By presenting the relationship among content, discipline, and author, this study focuses on providing services for knowledge discovery of ancient Chinese scientific and technological documents.

This study compiles ancient Chinese STDBS and designs a knowledge mining and graph visualization framework. The authors define the summaries' entities, attributes, and relationships for knowledge representation, use deep learning techniques such as BERT-BiLSTM-CRF models and rules for knowledge extraction, unify the representation of entities for knowledge fusion, and use Neo4j and other visualization techniques for KG construction and application. This study presents the generation, distribution, and evolution of ancient Chinese agricultural scientific and technological knowledge in visualization graphs.

The knowledge mining and graph visualization framework is feasible and effective. The BERT-BiLSTM-CRF model has domain adaptability and accuracy. The knowledge generation of ancient Chinese agricultural scientific and technological documents has distinctive time features. The knowledge distribution is uneven and concentrated, mainly concentrated on C1-Planting and cultivation, C2-Silkworm, and C3-Mulberry and water conservancy. The knowledge evolution is apparent, and differentiation and integration coexist.

This study is the first to visually present the knowledge connotation and association of ancient Chinese STDBS. It solves the problems of the lack of in-depth knowledge mining and connotation visualization of ancient Chinese STDBS.