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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 review aims to give a critical view of the wheel/rail high frequency vibration-induced vibration fatigue in railway bogie.

Vibration fatigue of railway bogie arising from the wheel/rail high frequency vibration has become the main concern of railway operators. Previous reviews usually focused on the formation mechanism of wheel/rail high frequency vibration. This paper thus gives a critical review of the vibration fatigue of railway bogie owing to the short-pitch irregularities-induced high frequency vibration, including a brief introduction of short-pitch irregularities, associated high frequency vibration in railway bogie, typical vibration fatigue failure cases of railway bogie and methodologies used for the assessment of vibration fatigue and research gaps.

The results showed that the resulting excitation frequencies of short-pitch irregularity vary substantially due to different track types and formation mechanisms. The axle box-mounted components are much more vulnerable to vibration fatigue compared with other components. The wheel polygonal wear and rail corrugation-induced high frequency vibration is the main driving force of fatigue failure, and the fatigue crack usually initiates from the defect of the weld seam. Vibration spectrum for attachments of railway bogie defined in the standard underestimates the vibration level arising from the short-pitch irregularities. The current investigations on vibration fatigue mainly focus on the methods to improve the accuracy of fatigue damage assessment, and a systematical design method for vibration fatigue remains a huge gap to improve the survival probability when the rail vehicle is subjected to vibration fatigue.

The research can facilitate the development of a new methodology to improve the fatigue life of railway vehicles when subjected to wheel/rail high frequency vibration.

This paper aims to improve the life of the printed circuit boards (PCB) used in computers based on modal analysis by increasing the natural frequency of the PCB assembly.

In this work, through experiments and numerical simulations, an attempt has been made to increase the fundamental natural frequency of the PCB assembly as high as practically achievable so as to minimize the impacts of dynamic loads acting on it. An optimization tool in the finite element software (ANSYS) was used to search the specified design space for the optimal support location of the six fastening screws.

It is observed that by changing the support locations based on the optimization results the fundamental natural frequency can be raised up to 51.1% and the same is validated experimentally.

Manufacturers of PCBs used in computers fix the support locations based on symmetric feature of the board not on the dynamic behavior of the assembly. This work might lead manufacturers to redesign the location of other surface mount components.

This work provides guidelines for PCB manufacturers to finalize their support locating points which will improve the dynamic characteristics of the PCB assembly during its functioning.

This study provides a novel method to improve the life of PCB based on support locations optimization which includes majority of the surface mount components that contributes to the total mass the PCB assembly.

The research on social media-based academic communication has made great progress with the development of the mobile Internet era, and while a large number of research results have emerged, clarifying the topology of the knowledge label network (KLN) in this field and showing the development of its knowledge labels and related concepts is one of the issues that must be faced. This study aims to discuss the aforementioned issue.

From a bibliometric perspective, 5,217 research papers in this field from CNKI from 2011 to 2021 are selected, and the title and abstract of each paper are subjected to subword processing and topic model analysis, and the extended labels are obtained by taking the merged set with the original keywords, so as to construct a conceptually expanded KLN. At the same time, appropriate time window slicing is performed to observe the temporal evolution of the network topology. Specifically, the basic network topological parameters and the complex modal structure are analyzed empirically to explore the evolution pattern and inner mechanism of the KLN in this domain. In addition, the ARIMA time series prediction model is used to further predict and compare the changing trend of network structure among different disciplines, so as to compare the differences among different disciplines.

The results show that the degree sequence distribution of the KLN is power-law distributed during the growth process, and it performs better in the mature stage of network development, and the network shows more stable scale-free characteristics. At the same time, the network has the characteristics of “short path and high clustering” throughout the time series, which is a typical small-world network. The KLN consists of a small number of hub nodes occupying the core position of the network, while a large number of label nodes are distributed at the periphery of the network and formed around these hub nodes, and its knowledge expansion pattern has a certain retrospective nature. More knowledge label nodes expand from the center to the periphery and have a gradual and stable trend. In addition, there are certain differences between different disciplines, and the research direction or topic of library and information science (LIS) is more refined and deeper than that of journalism and media and computer science. The LIS discipline has shown better development momentum in this field.

KLN is constructed by using extended labels and empirically analyzed by using network frontier conceptual motifs, which reflects the innovation of the study to a certain extent. In future research, the influence of larger-scale network motifs on the structural features and evolutionary mechanisms of KLNs will be further explored.

This work compares the performance of the three boundary element techniques for solving Helmholtz problems: dual reciprocity, multiple reciprocity and direct interpolation. All techniques transform domain integrals into boundary integrals, despite using different principles to reach this purpose.

Comparisons here performed include the solution of eigenvalue and response by frequency scanning, analyzing many features that are not comprehensively discussed in the literature, as follows: the type of boundary conditions, suitable number of degrees of freedom, modal content, number of primitives in the multiple reciprocity method (MRM) and the requirement of internal interpolation points in techniques that use radial basis functions as dual reciprocity and direct interpolation.

Among the other aspects, this work can conclude that the solution of the eigenvalue and response problems confirmed the reasonable accuracy of the dual reciprocity boundary element method (DRBEM) only for the calculation of the first natural frequencies. Concerning the direct interpolation boundary element method (DIBEM), its interpolation characteristic allows more accessibility for solving more elaborate problems. Despite requiring a greater number of interpolating internal points, the DIBEM has presented higher-quality results for the eigenvalue and response problems. The MRM results were satisfactory in terms of accuracy just for the low range of frequencies; however, the neglected higher-order primitives impact the accuracy of the dynamic response as a whole.

There are safe alternatives for solving engineering stationary dynamic problems using the boundary element method (BEM), but there are no suitable comparisons between these different techniques. This paper presents the particularities and detailed comparisons approaching the accuracy of the three important BEM techniques, aiming at response and frequency evaluation, which are not found in the specialized literature.

This study proposes a predictive neural network model reference decoupling control method for the coupling problem between the leg joints of hydraulic quadruped robots, and verifies its decoupling effect..

The machine–hydraulic cross-linking coupling is studied as the coupling behavior of the hydraulically driven quadruped robot, and the mechanical dynamics coupling force of the robot system is controlled as the disturbance force of the hydraulic system through the Jacobian matrix transformation. According to the principle of multivariable decoupling, a prediction-based neural network model reference decoupling control method is proposed; each module of the control algorithm is designed one by one, and the stability of the system is analyzed by the Lyapunov stability theorem.

The simulation and experimental research on the robot joint decoupling control method is carried out, and the prediction-based neural network model reference decoupling control method is compared with the decoupling control method without any decoupling control method. The results show that taking the coupling effect experiment between the hip joint and knee joint as an example, after using the predictive neural network model reference decoupling control method, the phase lag of the hip joint response line was reduced from 20.3° to 14.8°, the amplitude attenuation was reduced from 1.82% to 0.21%, the maximum error of the knee joint coupling line was reduced from 0.67 mm to 0.16 mm and the coupling effect between the hip joint and knee joint was reduced from 1.9% to 0.48%, achieving good decoupling.

The prediction-based neural network model reference decoupling control method proposed in this paper can use the neural network model to predict the next output of the system according to the input and output. Finally, the weights of the neural network are corrected online according to the predicted output and the given reference output, so that the optimization index of the neural network decoupling controller is extremely small, and the purpose of decoupling control is achieved.

Although there has been considerable growth in the higher education systems of Turkey and China in about the last two decades, there is still a room for development in enabling equity in all regions, increasing opportunities and resources regardless of socio-economic status or gender differences. Students coming from disadvantaged backgrounds do not have enough tools to change their fate for the better due to the accumulated barriers they face. Given this background, the chapter discusses how the barriers to equitable HE admissions relate to each other and which one has a greater negative impact over the Accumulated Conversion Barriers Modal we propose defined by personal, discriminatory, institutional, and geographical barriers. The perspectives of Turkish and Chinese HE stakeholders were examined through 21 in-depth interviews that were subjected to content analysis and interpreted in a comparative style using the lens of Capabilities Approach of Sen. We also offer policy suggestions to increase students’ conversion capacities for better outcomes to serve both the national and the international educational contexts owing to the adaptable nature of our modal to other countries experiencing similar issues in their higher education systems.

This paper aims to design an active shock absorber scheme for use in conjunction with a passive shock absorber to suppress the horizontal vibration of elevator cars in a smaller range and shorter time. The developed active shock absorber will also improve the safety and comfort of passengers driving in ultra-high-speed elevators.

A six-degree of freedom dynamic model is established according to the position and condition of the car. Then the active shock absorber and disturbance compensation-based adaptive control scheme are designed and simulated in MATLAB/Simulink. The results are analysed and compared with the traditional shock absorber.

The results show that, compared with traditional spring-based passive damping systems, the designed active shock absorber can reduce vibration displacement by 60%, peak acceleration by 50% and oscillation time by 2/3 and is more robust to different spring stiffness, damping coefficient and load.

The developed active shock absorber and its control algorithm can significantly reduce vibration amplitude and converged time. It can also adjust the damping strength according to the actual load of the elevator car, which is more suitable for high-speed elevators.

The purpose of this study is to describe the proposed alpha solar rotary mechanism (ASRM) and how it is used to accurately modify the solar array of the China Space Station (CSS) in orbit to maintain continuous tracking of the sun to provide power. It also highlights the need to evaluate the performance of the ASRM and predict potential failure modes in various extreme scenarios.

To evaluate the performance of the ASRM, a dynamic model was created and tested under normal and faulty conditions. In addition, a multidirectional stiffness test was conducted on the prototype to verify the accuracy of the ASRM's dynamic model. The high-precision ASRM model was then used to predict potential failure modes and damaged parts in various extreme scenarios.

The simulation results were in good agreement with the test results, with a maximum error of less than 8.85%. The high-precision ASRM's model was able to accurately predict potential failure modes and damaged parts in extreme scenarios, demonstrating the effectiveness of the proposed model and simulation evaluation test.

The proposed high-precision ASRM model and simulation evaluation test provide an effective way to evaluate the structural safety and optimize the design of the spacecraft. This information can be used to improve the performance and reliability of the CSS's solar array and ensure continuous power supply to the station.

Metaverse, that is extended reality (XR)-based technologies such as augmented reality (AR) and virtual reality (VR), are increasingly believed to facilitate fundamental human practice in the future. One of the vanguards of this development has been the consumption domain, where the multi-modal and multi-sensory technology-mediated immersion is expected to enrich consumers' experience. However, it remains unclear whether these expectations have been warranted in reality and whether, rather than enhancing the experience, metaverse technologies inhibit the functioning and experience, such as cognitive functioning and experience.

This study utilizes a 2 (VR: yes vs no) × 2 (AR: yes vs no) between-subjects laboratory experiment. A total of 159 student participants are randomly assigned to one condition — a brick-and-mortar store, a VR store, an AR store and an augmented virtuality (AV) store — to complete a typical shopping task. Four spatial attention indicators — visit shift, duration shift, visit variation and duration variation — are compared based on attention allocation data converted from head movements extracted from recorded videos during the experiments.

This study identifies three essential effects of XR technologies on consumers' spatial attention allocation: the inattention effect, acceleration effect and imbalance effect. Specifically, the inattention effect (the attentional visit shift from showcased products to the environmental periphery) appears when VR or AR technology is applied to virtualize the store and disappears when AR and VR are used together. The acceleration effect (the attentional duration shift from showcased products to the environmental periphery) exists in the VR store. Additionally, AR causes an imbalance effect (the attentional duration variation increases horizontally among the showcased products).

This study provides valuable empirical evidence of how VR and AR influence consumers' spatial bias in attention allocation, filling the research gap on cognitive function in the metaverse. This study also provides practical guidelines for retailers and XR designers and developers.