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This study argues that online user comments on social media platforms provide feedback and evaluation functions. These functions can provide services for the relevant departments of organizations or institutions to formulate corresponding public opinion response strategies.

This study considers Chinese universities’ public opinion events on the Weibo platform as the research object. It collects online comments on Chinese universities’ network public opinion governance strategy texts on Weibo, constructs the sentiment index based on sentiment analysis and evaluates the effectiveness of the network public opinion governance strategy adopted by university officials.

This study found the following: First, a complete information release process can effectively improve the effect of public opinion governance strategies. Second, the effect of network public opinion governance strategies was significantly influenced by the type of public opinion event. Finally, the effect of public opinion governance strategies is closely related to the severity of punishment for the subjects involved.

The theoretical contribution of this study lies in the application of image repair theory and strategies in the field of network public opinion governance, which further broadens the scope of the application of image repair theory and strategies.

This study expands online user comment research to network public opinion governance and provides a quantitative method for evaluating the effect of governance strategies.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-05-2022-0269

Through a theoretical lens of psychological ownership, this study aims to investigate how technology mindfulness may stimulate metaverse tourism users’ feelings of individual psychological ownership, aesthetic value and conversational value, which in turn fosters intention to engage in prosocial behaviors.

The study used a scenario-based survey that allowed U.S.-based participants to create their own avatars and imagine using their avatars to explore heritage sites in the metaverse. Structural equality modeling was applied for data analysis.

The results from 357 valid responses indicate that technology mindfulness arouses tourists’ individual psychological ownership, aesthetic value, conversational value and prosocial behavioral intentions. The moderating role of biospheric value orientation on willingness to donate and intention to volunteer is investigated.

The research sheds light on the significance of technology mindfulness, conversational value and psychological ownership perspectives in the metaverse, which have been previously overlooked. The authors used a scenario-based survey for mental stimulation due to current metaverse technology limitations.

The study is one of the first to explore the possibility of encouraging prosocial behaviors using metaverse-facilitated technology. The research offers guidelines to engage hospitality and tourism customers in the metaverse that can blend their virtual experiences into the real world.

This study represents one of the pioneering efforts to gain an in-depth understanding of the application of metaverse in triggering prosocial behavior toward heritage sites, explained via a technology mindfulness-driven model with a psychological ownership perspective.

This study aims to investigate the factors influencing the adoption intention of artificial intelligence (AI) by micro, small and medium enterprises (MSMEs) in Jordan.

The study adopts the technology–organization–environment (TOE) model. It examines the moderating effects of innovation culture, employee digital skill level and market competition on the relationships between the independent and dependent variables. A survey was utilized to collect data from 537 MSME owners or managers in Jordan and employed partial least squares structural equation modeling to test the hypotheses.

The results of the study support seven out of eight hypotheses. Business innovativeness, management support, perceived benefits and technological infrastructure have positive and significant effects on AI adoption intention, while perceived costs have no significant effect. However, the innovation culture, employee digital skill level and market competition were found to moderate the relationships between some of the independent variables and dependent variables.

The study provides valuable insights and recommendations for MSME owners, managers, employees, policymakers, educators and researchers interested in promoting and facilitating AI adoption by MSMEs in Jordan.

The current attempt extends the TOE framework by adding significant constructs representing the three contexts. Moreover, it is one of the few studies that analyzed the factors influencing the adoption intention of AI by MSMEs in Jordan, which are significant to the Jordanian economy and represent 99.5% of enterprises.

Light field images (LFIs) have gained popularity as a technology to increase the field of view (FoV) of plenoptic cameras since they can capture information about light rays with a large FoV. Wide FoV causes light field (LF) data to increase rapidly, which restricts the use of LF imaging in image processing, visual analysis and user interface. Effective LFI coding methods become of paramount importance. This paper aims to eliminate more redundancy by exploring sparsity and correlation in the angular domain of LFIs, as well as mitigate the loss of perceptual quality of LFIs caused by encoding.

This work proposes a new efficient LF coding framework. On the coding side, a new sampling scheme and a hierarchical prediction structure are used to eliminate redundancy in the LFI's angular and spatial domains. At the decoding side, high-quality dense LF is reconstructed using a view synthesis method based on the residual channel attention network (RCAN).

In three different LF datasets, our proposed coding framework not only reduces the transmitted bit rate but also maintains a higher view quality than the current more advanced methods.

(1) A new sampling scheme is designed to synthesize high-quality LFIs while better ensuring LF angular domain sparsity. (2) To further eliminate redundancy in the spatial domain, new ranking schemes and hierarchical prediction structures are designed. (3) A synthetic network based on RCAN and a novel loss function is designed to mitigate the perceptual quality loss due to the coding process.

To ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous massage path planning and stable interaction control.

First, back region extraction and acupoint recognition based on deep learning is proposed, which provides a basis for determining the working area and path points of the robot. Second, to realize the standard approach and movement trajectory of the expert massage, 3D reconstruction and path planning of the massage area are performed, and normal vectors are calculated to control the normal orientation of robot-end. Finally, to cope with the soft and hard changes of human tissue state and body movement, an adaptive force tracking control strategy is presented to compensate the uncertainty of environmental position and tissue hardness online.

Improved network model can accomplish the acupoint recognition task with a large accuracy and integrate the point cloud to generate massage trajectories adapted to the shape of the human body. Experimental results show that the adaptive force tracking control can obtain a relatively smooth force, and the error is basically within ± 0.2 N during the online experiment.

This paper incorporates deep learning, 3D reconstruction and impedance control, the robot can understand the shape features of the massage area and adapt its planning massage path to carry out a stable and safe force tracking control during dynamic robot–human contact.

This paper aims to establish a lattice Boltzmann (LB) method for solid-liquid phase transition (SLPT) from the pore scale to the representative elementary volume (REV) scale. By applying this method, detailed information about heat transfer and phase change processes within the pores can be obtained, while also enabling the calculation of larger-scale SLPT problems, such as shell-and-tube phase change heat storage systems.

Three-dimensional (3D) pore-scale enthalpy-based LB model is developed. The computational input parameters at the REV scale are derived from calculations at the pore scale, ensuring consistency between the two scales. The approaches to reconstruct the 3D porous structure and determine the REV of metal foam were discussed. The implementation of conjugate heat transfer between the solid matrix and the solid−liquid phase change material (SLPCM) for the proposed model is developed. A simple REV-scale LB model under the local thermal nonequilibrium condition is presented. The method of bridging the gap between the pore-scale and REV-scale enthalpy-based LB models by the REV is given.

This coupled method facilitates detailed simulations of flow, heat transfer and phase change within pores. The approach holds promise for multiscale calculations in latent heat storage devices with porous structures. The SLPT of the heat sinks for electronic device thermal control was simulated as a case, demonstrating the efficiency of the present models in designing and optimizing SLPT devices.

A coupled pore-scale and REV-scale LB method as a numerical tool for investigating phase change in porous materials was developed. This innovative approach allows for the capture of details within pores while addressing computations over a large domain. The LB method for simulating SLPT from the pore scale to the REV scale was given. The proposed method addresses the conjugate heat transfer between the SLPCM and the solid matrix in the enthalpy-based LB model.

An abundance of techniques has been presented so forth for waste classification but, they deliver inefficient results with low accuracy. Their achievement on various repositories is different and also, there is insufficiency of high-scale databases for training. The purpose of the study is to provide high security.

In this research, optimization-assisted federated learning (FL) is introduced for thermoplastic waste segregation and classification. The deep learning (DL) network trained by Archimedes Henry gas solubility optimization (AHGSO) is used for the classification of plastic and resin types. The deep quantum neural networks (DQNN) is used for first-level classification and the deep max-out network (DMN) is employed for second-level classification. This developed AHGSO is obtained by blending the features of Archimedes optimization algorithm (AOA) and Henry gas solubility optimization (HGSO). The entities included in this approach are nodes and servers. Local training is carried out depending on local data and updations to the server are performed. Then, the model is aggregated at the server. Thereafter, each node downloads the global model and the update training is executed depending on the downloaded global and the local model till it achieves the satisfied condition. Finally, local update and aggregation at the server is altered based on the average method. The Data tag suite (DATS_2022) dataset is used for multilevel thermoplastic waste segregation and classification.

By using the DQNN in first-level classification the designed optimization-assisted FL has gained an accuracy of 0.930, mean average precision (MAP) of 0.933, false positive rate (FPR) of 0.213, loss function of 0.211, mean square error (MSE) of 0.328 and root mean square error (RMSE) of 0.572. In the second level classification, by using DMN the accuracy, MAP, FPR, loss function, MSE and RMSE are 0.932, 0.935, 0.093, 0.068, 0.303 and 0.551.

The multilevel thermoplastic waste segregation and classification using the proposed model is accurate and improves the effectiveness of the classification.

The aim of this study was (1) to investigate the relationship between destination image (DI), destination personality (DP) and behavioral intention (BI) in the context of ski tourism and (2) especially the role of DP in the relationship between DI and BI among ski tourists.

We collected data using WJX.CN (N = 400) to test the hypothesized model. Confirmatory factor analysis (CFA) was used to examine the psychometric properties of the measurement model and partial least squares structural equation modeling (PLS-SEM) was used to test the hypotheses.

The results show that DI directly affects DP and partially affects BI, while DP directly affects ski tourists' BI. In addition, the indirect effect of DP between affective image and BI was significant, showing full mediation, and the indirect effect of DP between cognitive image and BI was significant, showing a partial mediation effect.

The findings enrich the ski tourism literature, contribute to the development of ski tourism in destination cities and the strategic marketing of ski resorts and provide recommendations for ski tourism researchers and marketers.

This research aims to develop a method for defining the identity of multilayered neighbourhoods by taking a case study in Nantes/France. It utilizes the urban identity concept to achieve this goal, which is defined by physical and identificatory relation to the neighbourhood.

The methodology includes historical periodical analysis, housing form and architectural stylistic definition, visualization and geographic information system (GIS) mapping. The research conducts spatial analysis to reveal the physical component of the urban identity of the neighbourhood and interviews (No = 50) with dwellers for the identificatory relation, asking about neighbourhood tangible/non-tangible elements. All these data are mapped through GIS.

The study found that the physical component is defined by three urban layers (identified as industrial, reconstruction and development, and post-industrial) and eleven housing typologies. As for the identificatory relation, the authors found that the interviewees mostly identified with their neighbourhood, whereas a minority did not. The most important form of identification with the neighbourhood was its atmosphere, and as reasons were given, the neighbourhood's positively evaluated quality, good location and social values.

It proposes the definition of the physical component through urban layers and housing typologies. The identificatory relation also considers the identification of the residents with the neighbourhood's tangible/non-tangible urban elements.

This study aims to obtain methods to identify and find the place of damage, which is one of the topics that has always been discussed in structural engineering. The cost of repairing and rehabilitating massive bridges and buildings is very high, highlighting the need to monitor the structures continuously. One way to track the structure's health is to check the cracks in the concrete. Meanwhile, the current methods of concrete crack detection have complex and heavy calculations.

This paper presents a new lightweight architecture based on deep learning for crack classification in concrete structures. The proposed architecture was identified and classified in less time and with higher accuracy than other traditional and valid architectures in crack detection. This paper used a standard dataset to detect two-class and multi-class cracks.

Results show that two images were recognized with 99.53% accuracy based on the proposed method, and multi-class images were classified with 91% accuracy. The low execution time of the proposed architecture compared to other valid architectures in deep learning on the same hardware platform. The use of Adam's optimizer in this research had better performance than other optimizers.

This paper presents a framework based on a lightweight convolutional neural network for nondestructive monitoring of structural health to optimize the calculation costs and reduce execution time in processing.