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Free knowledge sharing in the online health community has been widely documented. However, whether free knowledge sharing can help physicians accumulate popularity and further the accumulated popularity can help physicians attract patients remain unclear. To unveil these gaps, this study aims to examine how physicians' popularity are affected by their free knowledge sharing, how the relationship between free knowledge sharing and popularity is moderated by professional capital, and how the popularity finally impacts patients' attraction.

The authors collect a panel dataset from Hepatitis B within an online health community platform with 10,888 observations from April 2020 to August 2020. The authors develop a model that integrates free knowledge sharing, popularity, professional capital, and patients' attraction. The hierarchical regression model is used to for examining the impact of free knowledge sharing on physicians' popularity and further investigating the impact of popularity on patients' attraction.

The authors find that the quantity of articles acted as the heuristic cue and the quality of articles acted as the systematic cue have positive effect on physicians' popularity, and this effect is strengthened by physicians' professional capital. Furthermore, physicians' popularity positively influences their patients' attraction.

This study reveals the aggregation of physicians' popularity and patients' attraction within online health communities and provides practical implications for managers in online health communities.

Drawing upon social information processing theory, this study investigates the correlation between self-serving leadership and employee expediency. It also explores the mediating effect of self-interest motivation and the moderating effect of trait mindfulness.

A total of 147 part-time MBA students were enlisted to participate in a scenario experiment (Study 1), and 291 valid employee questionnaires were collected through a multiple-time point survey (Study 2). SPSS 23.0, MPLUS 8.0 and PROCESS programs were used to analyze the data and test the hypotheses.

Study 1 illustrated a positive correlation between self-serving leadership and employee expediency. It also identified self-interest motivation as a mediating factor in the correlation between self-serving leadership and expediency. Study 2 replicated the results obtained in Study 1 and expanded upon them by demonstrating that trait mindfulness moderates the association between self-serving leadership and self-interest motivation. Additionally, trait mindfulness moderates the indirect effect of self-serving leadership on expediency.

This research argues that organizations should take steps to prevent self-serving leadership in order to reduce employee expediency. Furthermore, it is advisable to provide ethics training to employees who exhibit high trait mindfulness, as they show increased sensitivity to self-serving leadership and are more likely to engage in unethical behavior.

This study expands the existing research on the ethical outcomes of self-serving leadership and contributes to a deeper understanding of the negative aspects of trait mindfulness.

To optimize train operations, dispatchers currently rely on experience for quick adjustments when delays occur. However, delay predictions often involve imprecise shifts based on known delay times. Real-time and accurate train delay predictions, facilitated by data-driven neural network models, can significantly reduce dispatcher stress and improve adjustment plans. Leveraging current train operation data, these models enable swift and precise predictions, addressing challenges posed by train delays in high-speed rail networks during unforeseen events.

This paper proposes CBLA-net, a neural network architecture for predicting late arrival times. It combines CNN, Bi-LSTM, and attention mechanisms to extract features, handle time series data, and enhance information utilization. Trained on operational data from the Beijing-Tianjin line, it predicts the late arrival time of a target train at the next station using multidimensional input data from the target and preceding trains.

This study evaluates our model's predictive performance using two data approaches: one considering full data and another focusing only on late arrivals. Results show precise and rapid predictions. Training with full data achieves a MAE of approximately 0.54 minutes and a RMSE of 0.65 minutes, surpassing the model trained solely on delay data (MAE: is about 1.02 min, RMSE: is about 1.52 min). Despite superior overall performance with full data, the model excels at predicting delays exceeding 15 minutes when trained exclusively on late arrivals. For enhanced adaptability to real-world train operations, training with full data is recommended.

This paper introduces a novel neural network model, CBLA-net, for predicting train delay times. It innovatively compares and analyzes the model's performance using both full data and delay data formats. Additionally, the evaluation of the network's predictive capabilities considers different scenarios, providing a comprehensive demonstration of the model's predictive performance.

To explore the economical and reasonable semi-rigid permeable base layer ratio, solve the problems caused by rainwater washing over the pavement base layer on the slope, improve its drainage function, improve the water stability and service life of the roadbed pavement and promote the application of semi-rigid permeable base layer materials in the construction of asphalt pavement in cold regions.

In this study, three semi-rigid base course materials were designed, the mechanical strength and drainage properties were tested and the effect and correlation of air voids on their performance indexes were analyzed.

It was found that increasing the cement content increased the strength but reduced the air voids and water permeability coefficient. The permeability performance of the sandless material was superior to the dense; the performance of the two sandless materials was basically the same when the cement content was 7%. Overall, the skeleton void (sand-containing) type gradation between the sandless and dense types is more suitable as permeable semi-rigid base material; its gradation is relatively continuous, with cement content? 4.5%, strength? 1.5 MPa, water permeability coefficient? 0.8 cm/s and voids of 18–20%.

The study of permeable semi-rigid base material with large air voids could help to solve the problems of water damage and freeze-thaw damage of the base layer of asphalt pavements in cold regions and ensure the comfort and durability of asphalt pavements while having good economic and social benefits.

This study aims to explore the impact of cultural practices on consumers’ buying behavior in sales promotional activities toward the fashion clothing brands based on the Theory of Black-Box Model under the conditions of COVID-19 at Pakistan.

The study has collected data through questionnaire-based survey from 600 consumers of fashion clothing brands using convenience sampling technique in Pakistan and analyzed through PLS-SEM to find results.

The results confirmed that cultural practices and sales promotional activities have positive significant relationship with the consumers’ buying behavior during the celebration of events, also under the conditions of COVID-19. The study also found that ethnocentrism and xenocentrism are positively associated with cultural practices while social factors, physical factors, product innovation and marketing innovation with sales promotional activities.

The outcomes provide interesting insights about consumers’ assessment toward the sale’s promotional activities and cultural practices of fashion clothing brands during the celebration of events and guide the marketing practitioners to develop the customers edifying and environment-oriented business strategies to boost up the buying behavior in crisis situations.

Although prior research has widely investigated the cultural practices, sales promotion and consumers’ buying behavior in various settings, but to the best of the authors’ knowledge, this is the first study in these domains.

This study aims to investigate the influences of Al₂O₃ mass fraction on the corrosive wear and electrochemical behaviors of FeAl–xAl₂O₃ coatings.

FeAl–xAl₂O₃ coatings were prepared on S355 steel by laser cladding to improve its corrosive wear and electrochemical properties.

The average coefficients of friction and wear rates of FeAl–xAl₂O₃ coatings are decreased with the Al₂O₃ mass fraction, and the Al₂O₃ plays a positive role in the corrosion wear resistance. Moreover, the charge transfer resistance of FeAl–xAl₂O₃ coatings is increased with the Al₂O₃ mass fraction, showing the FeAl–15%Al₂O₃ coating has the best corrosion resistance. The findings show the corrosion resistance of FeAl–15%Al₂O₃ coating is the highest among the three kinds of coatings.

Al₂O₃ was first added into FeAl coatings to further improve its corrosive wear and electrochemical properties by laser cladding.

This study aims to examine the moderating effect of sustainability marketing on brand loyalty of brands that advertise their sustainable development agenda goals. The study highlights the mediating effect of brand love having cognitive antecedents of brand authenticity, popularity and congruence with private and social self of the consumer.

A mall intercept survey was used to collect data from consumers who use brands that embrace sustainable marketing strategies. Only those brands were selected which are popular as well as advertise sustainable practices in their brand communication (mainstream and social media). The data was self-administered by trained research assistants, who gathered data from a sample of 350 respondents.

The findings revealed that the popularity and authenticity of a brand play an essential role in developing brand love and later influences brand loyalty behavior. A larger effect is seen on brand love when there is congruence of private and social self with the brand. The brand has even a stronger relationship with brand loyalty when moderated by sustainability marketing.

Brand love has the potential for long-term influences, only if sustainability marketing is used as a backbone. Brand managers should target an authenticity-seeking segment of consumers, who once convinced can lead to repeat business and brand loyalty and reduce dissonance. As sustainability marketing provides multiple benefits, genuine branding strategies should be devised that amalgamate into a single message spun around sustainability concerns and connecting the ethos of authenticity, popularity and self-expression. Future research may take into consideration more categories than this study on clothing, and consumer goods, adopting a mixed-methods approach. Moreover, a range of potential antecedents of brand love can be determined along with potential outcomes when aligned with external efforts such as sustainability, corporate social responsibility and international investment.

To the best of the authors’ knowledge, this is the first study investigating the moderating role of sustainability marketing on the relationship between brand love and brand loyalty and the mediating role of brand love between brand authenticity, popularity, social/private-self-expression and brand loyalty. It is also the first study documenting how sustainability marketing reinforces the brand loyalty for popular brands in developing countries like Pakistan. This study fills a research gap as it expands the existing literature on sustainability marketing and brand love that is generally focused on brand dimensions and not the brand communications and thus has not reached similar results.

This paper aims to present an unmarked method including entire two-dimensional (2D) and three-dimensional (3D) methods to recover absolute 3D humanoid robot poses from multiview images.

The method consists of two separate steps: estimating the 2D poses in multiview images and recovering the 3D poses from the multiview 2D heatmaps. The 2D one is conducted by High-Resolution Net with Epipolar (HRNet-Epipolar), and the Conditional Random Fields Humanoid Robot Pictorial Structure Model (CRF Robot Model) is proposed to recover 3D poses.

The performance of the algorithm is validated by experiments developed on data sets captured by four RGB cameras in Qualisys system. It illustrates that the algorithm has higher Mean Per Joint Position Error than Direct Linear Transformation and Recursive Pictorial Structure Model algorithms when estimating 14 joints of the humanoid robot.

A new unmarked method is proposed for 3D humanoid robot pose estimation. Experimental results show enhanced absolute accuracy, which holds important theoretical significance and application value for humanoid robot pose estimation and motion performance testing.

Targeting the common functions of the Zhejiang Library website, elderly individuals were invited to complete six experimental tasks on the improved website interfaces, and subjective data (PAD emotion scale and usability evaluation) and objective data (eye movement data) were recorded to verify the effects of graphic layout and navigation position on the information-seeking experience of elderly individuals.

This study analyzes the effect of the graphic layout and navigation position of the Zhejiang Library’s website interface on the emotional state, perceived usability and information-seeking time of elderly individuals, with the aim of providing guidance and suggestions for the elderly-oriented reform of the public library website.

The experimental results show that the graphic layout has a significant effect on the emotional state and perceived usability of elderly individuals, and the navigation position has a significant effect on the information-seeking time; the interaction between graphic layout and navigation position exerts a significant effect on the information-seeking time of elderly individuals. The eye movement data show that elderly individuals have a better information-seeking experience when the top navigation bar and image-text matched arrangement are used for the interface layout.

This study adopts a new approach combining subjective data and eye movement data to evaluate the effect of the public library website’s interface layout on the information-seeking experience for older people. The findings can provide a theoretical basis and methodological support for the elderly-oriented reform of public library websites. They can also provide scientific design suggestions for age-friendly interface layouts of other Internet products and service applications.

Traffic flow prediction has always been a top priority of intelligent transportation systems. There are many mature methods for short-term traffic flow prediction. However, the existing methods are often insufficient in capturing long-term spatial-temporal dependencies. To predict long-term dependencies more accurately, in this paper, a new and more effective traffic flow prediction model is proposed.

This paper proposes a new and more effective traffic flow prediction model, named channel attention-based spatial-temporal graph neural networks. A graph convolutional network is used to extract local spatial-temporal correlations, a channel attention mechanism is used to enhance the influence of nearby spatial-temporal dependencies on decision-making and a transformer mechanism is used to capture long-term dependencies.

The proposed model is applied to two common highway datasets: METR-LA collected in Los Angeles and PEMS-BAY collected in the California Bay Area. This model outperforms the other five in terms of performance on three performance metrics a popular model.

(1) Based on the spatial-temporal synchronization graph convolution module, a spatial-temporal channel attention module is designed to increase the influence of proximity dependence on decision-making by enhancing or suppressing different channels. (2) To better capture long-term dependencies, the transformer module is introduced.