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Manufacturing processes, such as laminations, may introduce uncertainties in the magnetic properties of materials used in electrical machines. This issue, together with magnetization errors, can cause serious deterioration in the performance of the machines. Hence, stochastic material models are required for the study of the influences of the material uncertainties. The purpose of this paper is to present a methodology to study the impact of magnetization pattern uncertainties in permanent magnet electric machines.

The impacts of material uncertainties on the performances of an interior permanent magnet (IPM) machine were analyzed using two different robustness metrics (worst-case analysis and statistical study). In addition, two different robust design formulations were applied to robust multi-objective machine design problems.

The computational analyses show that material uncertainties may result in deviations of the machine performances and cause nominal solutions to become non-robust.

In this paper, the authors present stochastic models for the quantification of uncertainties in both ferromagnetic and permanent magnet materials. A robust multi-objective evolutionary algorithm is demonstrated and successfully applied to the robust design optimization of an IPM machine considering manufacturing errors and operational condition changes.

Fabric has complicated anisotropic mechanical behavior because of the woven pattern and complex physical properties. However, most current fabric simulation models are not satisfied because the models are usually geometrical models with stiffness parameters.

In this paper, the authors present a modeling technique to simulate fabric with Riemann manifold. The proposed nonlinear model is formed with ridge wave-curved surface based on the Riemann zero curvature, and the authors develop a solution to conserve the surface area. It decomposes the m × n matrix constituting the fabric into several batches and processes the fabric dots in batches. In this model, the distance between any two adjacent particles of the fabric's is assumed to be equal, and the area of the curved surface is always constant, and the inclination and decay of the ridge wave-curved surface are also considered.

As the result, the simulated shape is lifelike. In time cost performance, the model improves the efficiency of the fabric styling and meets the requirements of real-time simulation.

The proposed nonlinear model is formed with ridge wave-curved surface based on the Riemann zero curvature, and the authors develop a solution to conserve the surface area.

Drawing on the resource-based view and institutional theory, this study explores how firms select export channels to realise the value of their product development capabilities (PDC) and improve export performance by aligning PDC, entrepreneurial orientation (EO), cultural-cognitive institutional distance (CCID) and channel selection.

This study adopted a quantitative design and used data collected from multiple respondents in 294 Chinese exporting ventures. Hypotheses were tested using logistic regression analysis and multiple regression analysis.

The results of the study suggest that PDC plays a vital role in export channel decisions. The results also show that there is a three-way interaction between PDC, EO and CCID regarding export channel selection. More importantly, this study suggests that firms using export channels that align with PDC, contingent on EO and CCID, generate superior export performance.

This study extends the export channel literature by looking at the different roles of important organisational capabilities (i.e. PDC and EO) on export channel selection. Further, it shows that firms need to align the exploitation of their PDC with the export channel selection, along with EO capabilities, and CCID to achieve better performance in the export market.

This study aims to the three major problems of low cleaning efficiency, high labor intensity and difficult to evaluate the cleaning effect for manual insulators cleaning in ultra high voltage (UHV) converter station, the purpose of this paper is to propose a basic configuration of UHV vertical insulator cleaning robot with multi-freedom-degree mechanical arm system on mobile airborne platform and its innovation cleaning operation motion planning.

The main factors affecting the insulators cleaning effect in the operation process have been analyzed. Because of the complex coupling relationship between the influencing factors and the insulators cleaning effect, it is difficult to establish its analytical mathematical model. Combining the non-linear mapping and approximation characteristics of back propagation (BP) neural network, the insulator cleaning effect evaluation can be abstracted as a non-linear approximation process from actual cleaning effect to ideal cleaning effect. An evaluation method of robot insulator cleaning effect based on BP neural network has been proposed.

Through the BP neural network training, the robot cleaning control parameters can be obtained and used in the robot online operation control, so that the better cleaning effect can be also obtained. Finally, a physical prototype of UHV vertical insulator cleaning robot has been developed, and the effectiveness and engineering practicability of the proposed robot configuration, cleaning effect evaluation method are all verified by simulation experiments and field operation experiments. At the same time, this method has the remarkable characteristics of sound versatility, strong adaptability, easy expansion and popularization.

An UHV vertical insulator cleaning robot operation system platform with multi-arm system on airborne platform has been proposed. Through the coordinated movement of the manipulator each joint, the manipulator can be positioned to the insulator strings, and the insulator can be cleaned by two pairs high-pressure nozzles located at the double manipulator. The influence factors of robot insulator cleaning effect have been analyzed. The BP neural network model of insulator cleaning effect evaluation has been established. The evaluation method of robot insulator cleaning effect based on BP neural network has also been proposed, and the corresponding evaluation result can be obtained through the network training. Through the system integration design, the robot physical prototype has been developed. For the evaluation of other operation effects of power system, the validity and engineering practicability of the robot mechanism, motion planning and the method for evaluating the effect of robot insulator cleaning have been verified by simulation and field operation experiments.

As global supply chains continue to develop, uncertainty grows and supply chains are frequently threatened with disruption. Although big data technology is being used to improve supply chain resilience, big data technology's role in human–machine collaboration is shifting between “supporters” and “substitutes.” However, big data technology's applicability in supply chain management is unclear. Choosing appropriate big data technology based on the enterprise's internal and external environments is important.

This study built a three-factor structural model of the factors “management support,” “big data technology adoption” and “supply chain resilience”. Big data technology adoption was divided into big data-assisted decision-making technology (ADT) and big data intelligent decision-making technology (IDT). A survey was conducted on more than 260 employees from supply chain departments in Chinese companies. The data were analyzed through structural equation modeling using Analyze of Moment Structures (AMOS) software.

The study's empirical results revealed that adopting both ADT and IDT improved supply chain resilience. The effects of both types of big data were significant in low-dynamic environments, but the effect of IDT on supply chain resilience was insignificant under high-dynamic environments. The authors also found that government support had an insignificantly effect on IDT adoption but significantly boosted ADT adoption, whereas management support factors promoted both ADT and IDT adoption.

By introducing two types of big data technology from the perspectives of the roles in human–machine collaborative decision-making, the research results provide a theoretical basis and management implications for enterprises to reduce the supply chain risk of enterprises.

This study analyzed the relationships among sub-factors of mindfulness and two anti-consumption lifestyles caused by personal and social/environmental concerns. This study also investigated the pursuit of authenticity mediating this relationship.

A survey based on self-administered questionnaires and structural equation modeling was used to analyze the collected data via Statistical Product Service Solutions (SPSS) 23 and Analysis of MOment Structure (AMOS) 23. Multiple mediation analysis was adopted to investigate the mediating role of authenticity dimensions via SPSS PROCESS macro.

The relationship between mindfulness and anti-consumption behavior was generally positive. Except for describing and non-judgment, most facets of mindfulness were positively related to anti-consumption patterns. Only the indirect effect of authentic living (AL) was significant in the impact of mindfulness on anti-consumption behavior.

To the authors' knowledge, this study is the first to examine the link between mindfulness and anti-consumption and potential mechanism of anti-consumption – authenticity seeking, extending knowledge about mindfulness and providing insights for environmentalists, public decision-makers, marketers and consumers.

This paper aims to presents a novel application of neural network (NN) pattern recognition to ore rock sorting using inductive electromagnetic (EM) sensors.

The impedance of a metallic rock can be measured with an inductive method based on Faraday’s law and eddy current theory. A virtual rock model is then created for the simulation of the EM measurements. An NN is trained to differentiate between waste and useful ore samples (containing high amount of minerals) based on the EM sensor signals produced by the rocks.

The NN solution showed high accuracy of rock classification and produced relatively robust results from signals with noise.

A pattern recognition NN was applied to classify low- and high-grade ore samples. It has the potential to determine the approximate amount of conductive materials inside ore rocks through multiple classes. This method can be used to improve the performance of EM-based ore sorting for mineral pre-concentration.

This study aims to explore the relationships among institutional pressures, commitment of resources and returns management. Returns management is regarded as a part of supply chain management. However, the research in returns management has received much less attention. To bridge the gap, this study concerns key concepts from two important schools of thought, i.e. institutional theory and the resource-based view, to build up the research model.

Retailers and maintenance providers in the 3C industry (computers, communication and consumer electronics) in Taiwan were surveyed, and the statistical methods of hierarchical and moderated regression were used to examine the relationships among institutional pressures, commitment of resources and returns management.

Institutional pressures, comprising non-market and market pressures, affect the implementation of returns management (product return practices and product recovery practices). Commitments of resources positively and significantly moderate the relationship between the pressures imposed by non-market and market actors and product return practices and product recovery practices.

This study investigates only the factors that drive returns management. Future research can examine the relationship between the antecedents and consequences of returns management. Furthermore, returns management may become increasingly critical for firms to develop and perform corporate social responsibility (CSR). Therefore, future research can investigate the relationship between CSR practices and returns management.

This research suggests that managers under institutional pressures should continually pay attention to the effects of external factors on returns management. Additionally, the results reveal that a commitment of resources can reinforce the relationship between the pressures imposed by non-market and market actors and the implementation of returns management. Under significant institutional pressures and resource constraints, managers may increase the effectiveness of returns management while attending to the concerns of non-market and market actors.

This study presents a model that considers three major explicative variables: institutional pressures, resources commitment and returns management. It is the first investigation to integrate three streams of literature on institutional theory, the resource-based view and returns management.

Process mining tools can help discover and improve the business processes of urban community services from historical service event records. However, for the community service domains with small datasets, the effects of process mining are generally limited due to process incompleteness and data noise. In this paper, a cross-domain knowledge transfer method is proposed to help service process discovery with small datasets by making use of rich knowledge in similar domains with large datasets.

First, ontology modeling is used to reduce the effects of cross-domain semantic ambiguity on knowledge transfer. Second, association rules (of the activities in the service processes) are extracted with Bayesian network. Third, applicable association rules are retrieved using an applicability assignment function. Further, the retrieved association rules in domains with large datasets are mapped to those with a small dataset using a linear programming method, with a heuristic miner being adopted to generate the process model.

The proposed method is verified based on the empirical data of 10 service domains from Beidaihe, China. Results show that process discovery performance of all 10 domains were improved with the overall robustness score, precision, recall and F1 score increased by 13%, 13%, 17% and 15%, respectively. For the domains with only small datasets, the cross-domain knowledge transfer method outperforms popular state-of-the art methods.

The limitations of sample sizes are greatly reduced. This scheme can be followed to establish business process management systems of community services with reasonable performance and limited sample sizes.

This study aims to evaluate the use of polyoxyethylene lauryl ether (PLE) as a dispersant in the preparation of novel pigment dispersion with enhanced dispersion ability, which can find application in the printing industry.

To obtain a good dispersion, PLE was used as a dispersant in pigments dispersion. The colloidal and rheological properties of the PLE-based dispersion, such as particle distribution, zeta potentials and apparent viscosity were evaluated.

The particle sizes of the pigment dispersions were within the range of 150 to 200 nm. The measurement of zeta potentials varied between −24 to −32 mV, revealing a strong surface charge interaction between pigments and PLE. Subsequently, its stability to high-speed centrifuge and freeze-thaw treatment was carefully investigated. To demonstrate the coverage of pigment particles by PLE, thermogravimetric analysis was carried out. Moreover, X-ray diffraction was used to disclose the combined impacts of PLE and ultrasonic power on the crystal structures of the pigments. Finally, the coloring performance and leveling properties of pigment dispersions on cotton substrates were evaluated by measuring their K/S values (color strength), rub and color fastness properties, which possessed good results.

The dispersant used is incompatible with strong oxidizing agents and strong bases. More so, modification to improve its dispersion properties can be studied.

The use of PLE as a dispersant could be readily used in pigment dispersion processes and other suitable applications. PLE could also be used as a co-surfactant in synergy with other surfactants or dispersants in the dispersion process.

The use of PLE in pigment dispersion as well as investigating its coloring properties on cotton fabric is novel and can find various applications in the dying, printing and coating industry.