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Confucian nurturing doctrine of xin (heart‐mind) comprehensively illustrates the importance of emerging state of feelings, which are the antecedents of organizational and individual behaviors. This introductory paper seeks to provide meaningful references for the management education and research trends like organizational justice and emotional intelligence.

Based on Confucian nurturing doctrine of xin (heart‐mind), this paper analyzes: the connotation of xin, qing (emotions/feelings) as the manifestation of xing (nature) by xin, and the cultivation of xin. Accordingly, the authors propose the holistic man model based on emotions.

The holistic man model based on emotions contains natural, social, moral, and transcendental dimensions. Self‐cultivation is significant for management practices and education.

As a conceptual essay, the statements in the paper need further argument and exploration.

A referential hypothetical model of human nature based on Confucian nurturing doctrine of xin can make people have transcendent aspiration in their daily life and focus on the unity of individuals' natural feelings and moral cultivation. In this case, people can live an integrated, meaningful and healthy life based on their emerging state of emotions, which are the antecedents of the transactional, instrumental, and procedural regulations.

This paper contributes to the notions of the holistic man based on emotions and emerging state of emotions by referring to Confucian nurturing doctrine of xin (heart‐mind).

Trajectory tracking error of robotic manipulator has limited its applications in trajectory tracking control systems. This paper aims to improve the trajectory tracking accuracy of robotic manipulator, so a linear-extended-state-observer (LESO)-based prescribed performance controller is proposed.

A prescribed performance function with the convergence rate, maximum overshoot and steady-state error is derived for the output error transformation, whose stability can guarantee trajectory tracking accuracy of the original robotic system. A LESO is designed to estimate and eliminate the total disturbance, which neither requires a detailed system model nor a heavy computation load. The stability of the system is proved via the Lyapunov theory.

Comparative experimental results show that the proposed controller can achieve better trajectory tracking accuracy than proportional-integral-differential control and linear active disturbance rejection control.

In the LESO-based prescribed performance control (PPC), the LESO was incorporated into the PPC design, it solved the problem of stabilizing the complex transformed system and avoided the costly offline identification of dynamic model and estimated and eliminated the total disturbance in real-time with light computational burden. LESO-based PPC further improved control accuracy on the basis of linear-active-disturbance-rejection-control. The new proposed method can reduce the trajectory tracking error of the robotic manipulators effectively on the basis of simplicity and stability.

With the analysis of the causes of corruption, this study aims to investigate specific anti-corruption measures that can be implemented to reform the political system and the social climate of China.

This study examines 97 severe corruption cases of high-ranking officials in China, which occurred between 2012 and 2015. As this insinuates that both institutional and social corruption are major problems in China, the analysis delves into multiple facts of corruption, including different types, four primary underlying causes, and suggestions regarding the implementation of three significant governmental shifts that focus on investigation, prevention tactics and legal regulations.

China’s corruption is not only individual-based but also it has developed into institutional corruption and social corruption. Besides human nature and instinct, the causes of corruption can be organised into four categories, namely, social customs, social transitions, institutional designs and institutional operations. For the removed high-ranking officials, the formation of interest chains was an important underlying cause behind their corruption.

This study makes a significant contribution to the literature because this study provides a well-rounded approach to a complex issue by highlighting the significance of democracy and the rule of law as ways to regulate human behaviour to combat future corruption.

The purpose of this study was to investigate the tribological properties of bulk Fe₂B with pre-oxidation treatment.

Bulk Fe₂B was oxidized in an electric box furnace with a soaking time of 9 min under 750°C in air. Then, the tribological experiments were carried out on an UMT-Tribolab tester.

The oxide layer was composed of Fe, Fe₂O₃, Fe₃O₄, B₂O₃ and H₃BO₃. The oxidative direction of bulk Fe₂B was perpendicular to the sample surface. But, the oxidative direction of Fe₂B crystals was irregular. At 0.1 m/s, the friction coefficient was the lowest. The effects of shortening the running-in period of friction and reducing the friction coefficient by pre-oxidation treatment at 0.1 m/s were remarkable. Nevertheless, the effect of pre-oxidation treatment was futile at 0.2 m/s. Wear mechanisms of oxidized Fe₂B mainly were adhesive and abrasive wear.

The effects of shortening the running-in period of friction and reducing the friction coefficient by pre-oxidation treatment were remarkable.

Although numerous signal modalities are available for emotion recognition, audio and visual modalities are the most common and predominant forms for human beings to express their emotional states in daily communication. Therefore, how to achieve automatic and accurate audiovisual emotion recognition is significantly important for developing engaging and empathetic human–computer interaction environment. However, two major challenges exist in the field of audiovisual emotion recognition: (1) how to effectively capture representations of each single modality and eliminate redundant features and (2) how to efficiently integrate information from these two modalities to generate discriminative representations.

A novel key-frame extraction-based attention fusion network (KE-AFN) is proposed for audiovisual emotion recognition. KE-AFN attempts to integrate key-frame extraction with multimodal interaction and fusion to enhance audiovisual representations and reduce redundant computation, filling the research gaps of existing approaches. Specifically, the local maximum–based content analysis is designed to extract key-frames from videos for the purpose of eliminating data redundancy. Two modules, including “Multi-head Attention-based Intra-modality Interaction Module” and “Multi-head Attention-based Cross-modality Interaction Module”, are proposed to mine and capture intra- and cross-modality interactions for further reducing data redundancy and producing more powerful multimodal representations.

Extensive experiments on two benchmark datasets (i.e. RAVDESS and CMU-MOSEI) demonstrate the effectiveness and rationality of KE-AFN. Specifically, (1) KE-AFN is superior to state-of-the-art baselines for audiovisual emotion recognition. (2) Exploring the supplementary and complementary information of different modalities can provide more emotional clues for better emotion recognition. (3) The proposed key-frame extraction strategy can enhance the performance by more than 2.79 per cent on accuracy. (4) Both exploring intra- and cross-modality interactions and employing attention-based audiovisual fusion can lead to better prediction performance.

The proposed KE-AFN can support the development of engaging and empathetic human–computer interaction environment.

This paper scrutinizes exergy loss and hydrothermal analysis of Linear Fresnel Reflector (LFR) unit by means of FLUENT. Several mirrors were used to guide the solar radiation inside the receiver, which has parabolic shape. Radiation model was used to simulate radiation mode.

Heat losses from receiver should be minimized to reach the optimized design. Outputs were summarized as contours of incident radiation, isotherm and streamline. Outputs were classified in terms of contours and plots to depict the influence of temperature of hot wall, wind velocity and configurations on performance of Linear Fresnel Reflector (LFR) based on thermal and exergy treatment. Four arrangements for LFR units are considered and all of them have same height.

Greatest Nu and E_x can be obtained for case D due to the highest heat loss from hot wall. Share of radiative heat flux relative to total heat flux is about 94% for case D. In case D when T_r = 0.388, As h_ext rises from 5 to 20, Nu_total enhances about 11.42% when T_r = 0.388. By selecting case D instead of case A, E_x rises about 16.14% for lowest T_r. Nu_total and E_x of case D augment by 3.65 and 6.23 times with rise of T_r when h_ext = 5. To evaluate the thermal performance (η_th) of system, absorber pipe was inserted below the parabolic reflector and 12 mirrors were used above the ground. The outputs revealed that η_th decreases about 14.31% and 2.54% with augment of T_in and Q if other factors are minimum.

This paper scrutinizes exergy loss and hydrothermal analysis of LFR unit by means of finite volume method. Several mirror used to guide the solar radiation inside the receiver, which has parabolic shape. DO model was used to simulate radiation mode. Heat losses from receiver should be minimized to reach the optimized design. Outputs were summarized as contours of incident radiation, isotherm and streamline.

This study aims to automate the process of converting grading patterns into parametric patterns using artificial intelligence and to objectively evaluate the fitness of the converted patterns.

The developed system consists of a user interface that defines input data by importing multi-size grading patterns, an artificial neural network that learns the relationship between human body size and pattern geometry, and a module that converts training results into parametric patterns. In order to evaluate the fitness of the generated pattern, an objective fitting evaluation method using drape simulation was developed.

The body sizes of the wearer were input to the converted parametric pattern to generate a customized pattern. Resulting pattern showed a better fit than the grading pattern on the off-average body model.

In this study, a method has been developed that enables the users with minimal pattern drafting knowledge to convert grading patterns into parametric patterns using artificial intelligence and drape simulation. The human body's symmetry and the physical properties of fabric were not considered.

The system developed in this study requires less data compared to existing methods that attempt to design clothing patterns with machine learning. In addition, it was possible to evaluate pattern fitness on various body models through drape simulation based fit evaluation process for the first time.

The purpose of this paper is to investigate three preparation processes and the photoluminescent (PL) properties of PbS/poly(p‐phenylene vinylene) (PPV) composite fibres.

By the combination of electrospinning technology, chemical vapour deposition and chemical liquid deposition, PbS/PPV composite nanofibres were prepared by three simple methods. The morphologies of the fibres and the PL properties were researched with the transmission electron microscopy (TEM) and the eclipse fluorescence spectrophotometer.

By different synthetic methods, the dispersion morphologies of PbS nanoparticles and the PL properties of their respective PbS/PPV composite fibres were different. Moreover, the effects of PbS nanoparticles on the luminescence quenching of the PPV were observed in all the synthesised PbS/PPV composite fibres, respectively.

The dispersion morphologies of PbS nanoparticles were not uniform enough.

A new method was used for preparing nanoparticles/polymer composite fibres.

The combination of electrospinning technology and chemical liquid deposition was used for the first time to fabricate the sulfide/polymer composite fibres. In addition, we hope that the results obtained here will provide some useful evidences for the interaction mechanism between IV‐VI group semiconductors and conjugated polymers, and the prepared composite fibres will have applications in the nano‐optoelectronic field.

Based on appraisal theory and social response theory, this study aims to explore the mechanism of AI failure types on consumer recovery expectation from the perspective of service failure assessment and validate the moderate role of anthropomorphism level.

Three scenario-based experiments were conducted to validate the research model. First, to test the effect of robot service failure types on customer recovery expectation; second, to further test the mediating role of perceived controllability, perceived stability and perceived severity; finally, to verify the moderating effect of anthropomorphic level.

Non-functional failures reduce consumer recovery expectation compared to functional failures; perceived controllability and perceived severity play a mediating role in the impact of service failure types on recovery expectation; the influence of service failure types on perceived controllability and perceived severity is moderated by the anthropomorphism level.

The findings enrich the influence mechanism and boundary conditions of service failure types, and have implications for online enterprise follow-up service recovery and improvement of anthropomorphic design.