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As the two sides of the same coin, usefulness and usability have emerged as pivotal research themes in user experience field. This study compares cognitive effort and cognitive resource allocation strategy across documents varying perceived usefulness and then across documents with different objective usability (unimodal vs multimodal discourses).

A controlled user study of four identifying tasks related to public health epidemics was conducted to collect data, including document usefulness as perceived by participants, presentation modes of the document and gaze behaviors on each document.

Usefulness and modality discourse impact cognitive effort and resource allocation strategy in health information search. In useless health documents, spatial encoding resource spending increased significantly with multimodal discourse, and a spatial browsing strategy with an evident exploratory feature was applied; while in useful documents, including low-useful and high-useful, both spatial and information encoding resource spending increased significantly with multimodal discourse, and an information processing strategy with an evident comprehensive feature was applied. Notably, multimodal discourse failed to enhance decision-making effectiveness. Furthermore, in useful documents, the interaction effect of the presentation mode of useful information and multimodal discourse on cognitive effort followed an inverted U-shape pattern.

This paper sheds new light on the interaction effect of usefulness and usability on cognitive effort and resource allocation strategy, highlighting its significance in cognitive effort detecting for multimodal discourse and improving effectiveness and efficacy of health information identification by optimizing information presentation mode design.

Social media texts as a data source in depression research have emerged as a significant convergence between Information Management and Public Health in recent years. This paper aims to sort out the depression-related study conducted on the text on social media, with particular attention to the research theme and methods.

The authors finally selected research articles published in Web of Science, Wiley, ACM Digital Library, EBSCO, IEEE Xplore and JMIR databases, covering 57 articles.

(1) According to the coding results, Depression Prediction and Linguistic Characteristics and Information Behavior are the two most popular themes. The theme of Patient Needs has progressed over the past few years. Still, there is a lesser focus on Stigma and Antidepressants. (2) Researchers prefer quantitative methods such as machine learning and statistical analysis to qualitative ones. (4) According to the analysis of the data collection platforms, more researchers used comprehensive social media sites like Reddit and Facebook than depression-specific communities like Sunforum and Alonelylife.

The authors recommend employing machine learning and statistical analysis to explore factors related to Stigmatization and Antidepressants thoroughly. Additionally, conducting mixed-methods studies incorporating data from diverse sources would be valuable. Such approaches would provide insights beneficial to policymakers and pharmaceutical companies seeking a comprehensive understanding of depression.

This article signifies a pioneering effort in systematically gathering and examining the themes and methodologies within the intersection of health-related texts on social media and depression.

This paper adds risk perception and personalized human-computer interaction to the technology acceptance model, and further analyzes the impact of personalized unmanned ride hailing on users' behavior intention.

This study model was tested using a sample of 299 social media users from China and we apply structural equation modeling (SEM) to build the theoretical framework.

Our results show that perceived ease of use has a greater positive impact on behavior intention compared to perceived usefulness. In addition, we find that the impact of risk perception on behavior intention is manifested in a number of ways, including people’s risk perception of the new technology, people’s risk perception of data leakage, and so on. Finally, we find that users’ personalized human-computer interaction has a positive effect on their perceived ease of use, perceived usefulness, and behavior intention.

Our study contributes to illuminate the pivotal role of tailoring the human-computer interface to individual preferences and needs for ride-hailing platforms from the perspective of behavior intention.

This study aims to examine the ramifications of corporate environmental, social and governance (ESG) investing in zombie firms and considers how external funding support may moderate this relationship given the sustainable nature of ESG performance, which often incurs costs.

Panel regression analyses used data from China’s A-share listed companies from 2011 to 2019, resulting in a data set comprising 6,054 observations.

Despite firms’ additional financial burdens, corporate ESG investing emerges as a catalyst in resurrecting zombie firms by attracting investor attention. Further analysis underscores the significance of funding support from entities such as the government and banks in alleviating ESG cost pressures and enhancing the efficacy of corporate ESG investing. Notably, the positive impact of corporate ESG investing is most pronounced in non-heavily polluting and non-state-owned firms. The results of classification tests reveal that social (S) and governance (G) investing yield greater efficacy in revitalizing zombie firms compared to environmental (E) investing.

This research enriches the discourse on corporate ESG investing and offers insights for governing zombie firms and shaping government policies.

By extending the domain of ESG research to encompass zombie firms, this paper sheds light on the multifaceted role of corporate ESG investing. Furthermore, this study comprehensively evaluates the influence of external funding support on the positive outcomes of ESG investing, thereby contributing to the resolution of the longstanding debate on the relationship between ESG performance and corporate financial performance, particularly with regard to ESG costs and benefits.

This study aims to introduce a method for predicting the remaining useful life (RUL) of bearings based on parallel feature extraction. The proposed model provides prior knowledge and removes redundant handcrafted feature information, additionally, which focuses on the important features at different time scales.

Distinct from traditional parallel feature extraction methods, which can lead to information redundancy, a one-dimensional convolutional autoencoder is introduced to process selected indicators to remove redundancy and retain useful feature information. To fully capture the important degradation information within different stages in the feature sequences, a novel multi-scale attention feature fusion module is proposed to extract degradation features at different time scales. Considering the impact of degradation modes on RUL prediction, a dual-task prediction module based on no degradation mode labels is designed to obtain accurate RUL.

Comparative experiments and ablation studies on the PHM2012 bearing dataset verified the effectiveness of the proposed method. Furthermore, the rationality of the selected parameters is confirmed through model parameter analysis.

The novelty of the proposed method is that it not only provides prior knowledge but also further removes redundant information from prior knowledge. In addition, the distribution differences between the original features and their multi-scale convolution results are measured through Kullback–Leibler divergence as the attention scores, which allows the proposed method to focus on important information at different time scales.

Gas transmission pipelines are at constant risk of gas leakage or fire due to various atmospheric environments, corrosion on pipe metal surfaces and other external factors. This study aims to reduce the human and financial risks associated with gas transmission by regularly monitoring pipeline performance, controlling situations and preventing disasters.

Facility managers can monitor the status of gas transmission lines in real-time by integrating sensor information into a building information modeling (BIM) 3D model. Using the Monitoring Panel plugin, coded in C# programming language and operated through Navisworks software, the model provides up-to-date information on pipeline safety and performance.

By collecting project information on the BIM and installing critical sensors, this approach allows facility manager to observe the real-time safety status of gas pipelines. If any risks of gas leakage or accidents are identified by the sensors, the BIM model quickly shows the location of the incident, enabling facility managers to make the best decisions to reduce financial and life risks. This intelligent gas transmission pipeline approach changes traditional risk management and inspection methods, minimizing the risk of explosion and gas leakage in the environment.

This research distinguishes itself from related work by integrating sensor data into a BIM model for real-time monitoring and providing facility managers with up-to-date safety information. By leveraging intelligent gas transmission pipelines, the system enables quick identification and location of potential hazards, reducing financial and human risks associated with gas transmission.

Previous research suggests that emotional intelligence (EI) may benefit managers when resolving conflicts. However, past studies relied on self-reports of EI and conflict management styles, and a theoretical model explaining the mechanisms of the link between EI and conflict management outcomes for managers is still missing. This study aims to test a theoretical model proposing that during conflicts, managers with higher performance-based ability EI are perceived as warmer and more competent, which in turn contributes to higher conflict management effectiveness.

A total of 108 Executive MBA students with managerial experience completed a performance-based EI test designed for the workplace and engaged in a conflict management exercise during which they were videotaped. In the exercise, managers spontaneously responded to video-based vignettes in which “employees” addressed them regarding a work-related conflict (e.g. a disagreement regarding tasks and working hours). Independent observers (n = 262) rated the managers’ videotaped responses on items tapping warmth, competence and conflict management effectiveness.

Managers with higher performance-based EI (in particular, emotion regulation in oneself and emotion management in others) received higher observer ratings on warmth, competence and conflict management effectiveness. Warmth and competence fully mediated the link between EI and effectiveness.

These results demonstrate that managers’ performance-based EI translates into actual work-related behaviors and outcomes. Implications for training EI and effective conflict management are discussed.

Polyethylene terephthalate (PET) as a fiber molding polymer is widely used in aerospace, electrical and electronic, clothing and other fields. The purpose of this study is to improve the thermal insulation performance of polyethylene terephthalate (PET), the SiO₂ aerogel/PET composites slices and fibers were prepared, and the effects of the SiO₂ aerogel on the morphology, structure, crystallization property and thermal conductivity of the SiO₂ aerogel/PET composites slices and their fibers were systematically investigated.

The mass ratio of purified terephthalic acid and ethylene glycol was selected as 1:1.5, which was premixed with Sb₂O₃ and the corresponding mass of SiO₂ aerogel, and SiO₂ aerogel/PET composites were prepared by direct esterification and in-situ polymerization. The SiO₂ aerogel/PET composite fibers were prepared by melt-spinning method.

The results showed that the SiO₂ aerogel was uniformly dispersed in the PET matrix. The thermal insulation coefficient of PET was significantly reduced by the addition of SiO₂ aerogel, and the thermal conductivity of the 1.0 Wt.% SiO₂ aerogel/PET composites was reduced by 75.74 mW/(m · K) compared to the pure PET. The thermal conductivity of the 0.8 Wt.% SiO₂ aerogel/PET composite fiber was reduced by 46.06% compared to the pure PET fiber. The crystallinity and flame-retardant coefficient of the SiO₂ aerogel/PET composite fibers showed an increasing trend with the addition of SiO₂ aerogel.

The SiO₂ aerogel/PET composite slices and their fibers have good thermal insulation properties and exhibit good potential for application in the field of thermal insulation, such as warm clothes. In today’s society where the energy crisis is becoming increasingly serious, improving the thermal insulation performance of PET to reduce energy loss will be of great significance to alleviate the energy crisis.

In this study, SiO₂ aerogel/PET composite slices and their fibers were prepared by an in situ polymerization process, which solved the problem of difficult dispersion of nanoparticles in the matrix and the thermal conductivity of PET significantly reduced.

This paper aims to propose an active disturbance rejection control (ADRC)-based visual servoing strategy for regulating a wheeled mobile robot from varying initial poses to a desired pose at an exponential rate. It addresses challenges associated with non-holonomic constraints, uncertain depth information and unknown translational parameters in monocular vision systems.

The uncertain depth information in monocular vision and unknown camera-to-robot translational parameters are modeled as internal uncertainties of the visual servo system. An input-state scaling technique is used to decouple the system into two subsystems, controlled by angular and linear velocities, respectively. The angular velocity controller is designed to ensure strict exponential convergence, while the internal parametric and bounded uncertainties of the system are estimated and compensated for by an extended state observer and a switching linear velocity controller.

The separate design of the angular and linear velocity controllers effectively overcomes the non-holonomic constraints of the mobile robot, ensuring robust performance under diverse conditions. Furthermore, the ADRC-based strategy successfully handles uncertain depth information and unknown translational parameters. The convergence of the error system is rigorously proven using Lyapunov theory, and simulation results verify the effectiveness of the proposed scheme.

To the best of the authors’ knowledge, this study introduces, for the first time, a novel approach that combines ADRC with visual servoing for non-holonomic mobile robots. This approach enhances the adaptability and accuracy of the robot’s navigation in environments characterized by unknown system uncertainties. The proposed method demonstrates enhanced practical performance over conventional techniques by effectively managing the inherent uncertainties of the system.

This study aims to examine the determinants that influence housing prices in Dammam metropolitan area (DMA), Saudi Arabia, by using the analytic hierarchy process (AHP) model. The study considers determinants such as building age (BLD AG), building size (BLD SZ), building condition (BLD CN), access to parking (ACC PK), proximity to transport infrastructure (PRX TRS), proximity to green areas (PRX GA) and proximity to amenities (PRX AM).

The AHP decision model was used to assess the determinants of housing prices in DMA, using a pair-wise comparison matrix to determine the influence of the investigated factors on housing prices.

The study’s results revealed that building size (BLD SZ) was the most critical determinant affecting housing prices in DMA, with a weight of 0.32, trailed by proximity to transport infrastructure (PRX TRS), with a weight of 0.24 as the second most influential housing price determinant in DMA. The third most important determinant was proximity to amenities (PRX AM), with a weight of 0.18.

This study addresses a research gap by using the AHP model to assess the spatial determinants of housing prices in DMA, Saudi Arabia. Few studies have used this model in examining housing price factors, particularly in the context of Saudi Arabia. Consequently, the findings of this study provide unique insights for policymakers, housing developers and other stakeholders in understanding the importance of building size, proximity to transport infrastructure and proximity to amenities in influencing housing prices in DMA. By considering these determinants, stakeholders can make informed decisions to improve housing quality and prices in the region.