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The outbreak of COVID-19 has become a major public health emergency worldwide. How to effectively guide public opinion and implement precise prevention and control is a hot topic in current research. Mining the spatiotemporal coupling between online public opinion and offline epidemics can provide decision support for the precise management and control of future emergencies.

This study focuses on analyzing the spatiotemporal coupling relationship between public opinion and the epidemic. First, based on Weibo information and confirmed case information, a field framework is constructed using field theory. Second, SnowNLP is used for sentiment mining and LDA is utilized for topic extraction to analyze the topic evolution and the sentiment evolution of public opinion in each coupling stage. Finally, the spatial model is used to explore the coupling relationship between public opinion and the epidemic in space.

The findings show that there is a certain coupling between online public opinion sentiment and offline epidemics, with a significant coupling relationship in the time dimension, while there is no remarkable coupling relationship in space. In addition, the core topics of public concern are different at different coupling stages.

This study deeply explores the spatiotemporal coupling relationship between online public opinion and offline epidemics, adding a new research perspective to related research. The result can help the government and relevant departments understand the dynamic development of epidemic events and achieve precise control while mastering the dynamics of online public opinion.

This paper aims to explore factors influencing suppliers' acceptance, integration challenges, expected benefits and support from customers when implementing a customer-introduced digital supply chain system.

The study investigates the perspective of suppliers using a mixed methodology approach that combines qualitative interviews with a large-scale quantitative survey conducted among 220 internationally located suppliers of an automotive-industrial firm.

As a result, the authors identified 11 factors that drive suppliers' acceptance of customer-introduced digital supply chain systems. These factors have been ranked based on their importance. The top three important factors identified were the digital system being provided at no cost to the suppliers, the system's ability to save time and the system offering benefits to the suppliers.

Further research can be conducted to validate the perspective of suppliers in other industries. Additionally, future studies can investigate the effectiveness of fulfilling these acceptance factors within an actual digital integration setup.

Companies can leverage these insights to accelerate their digital supply chain integration efforts. The insights on acceptance factors, challenges, benefits and support expected by suppliers can serve as a valuable guide for policy and decision makers within the industry.

To the best of the authors’ knowledge, this study is among the first to investigate the perspective of suppliers in the integration of a customer's digital supply chain. By including the supplier's perspective, this study makes a significant contribution to the academic literature about supply chain digitalisation.

The purpose of this study is to systematically investigate the novel phenomenon of rail corrugation on small radius curves with rail joints in mountainous city metros, characterized by the coexistence of short and long wavelengths (30–40 mm and 150–200 mm) on the low rail.

The finite element model of the wheel-rail system in the section with rail joint is constructed based on field surveys. The friction-coupled vibration characteristics of the wheel-rail system are studied from the perspective of friction self-excited vibration of the wheel-rail system and feedback vibration of the rail irregularity.

The rail corrugation with short wavelength is primarily induced by the friction self-excited vibration of wheel-rail system. In contrast, the rail corrugation with long wavelength is predominantly caused by the feedback vibration of rail joint irregularity. Additionally, the feedback vibration of corrugated irregularity accelerates the progression of corrugation depth without triggering the emergence of rail corrugation with new wavelength.

The research advances the understanding of the vibration inducement behind rail corrugation in mountainous city metros.

The purpose of this study is to evaluate the tea stem natural dye was extracted from tea stem waste and applied to dyeing silk fiber, after which the properties of dyed samples were tested and analyzed.

The dyeing process was optimized using the response surface methodology (RSM) approach. Dyeing temperature, pH and time were chosen as variables and the color difference value as a response. The properties of dyed samples were tested and analyzed.

The optimized dyeing process was as follows: dyeing temperature 70°C, pH 3.5 and time 110 min. The K/S and color difference value of silk fiber dyed with the optimal process dye enzymatic oxidation with laccase was 1.4 and 27.8, respectively. The silk fiber dyed has excellent color fastness, antioxidant and antibacterial property, which greatly increases the added value of the dyed products. Furthermore, the optimized dyeing process did not significantly affect the strength properties and handle of the silk fiber.

Researchers have not used statistical analysis to optimize the process of dyeing process of silk fiber by tea stem natural dye enzymatic oxidation with laccase using response surface methodology. Additionally, this dyeing process was a low-temperature dyeing process, which not only saves energy consumption and reduces silk fiber damage but also obtains superbly dyeing results and biological functional properties, achieve the effects of waste utilization and clean dyeing.

The accuracy and reliability of upper limb motion assessment have received great attention in the field of rehabilitation. Grasping test is widely carried out for motion assessment, which requires patients to grasp objects and move them to target place. The traditional assessments test the upper limb motion ability by therapists, which mainly relies on experience and lacks quantitative indicators. This paper aims to propose a deep learning method based on the vision system of our upper limb rehabilitation robot to recognize the motion trajectory of rehabilitation target objects automatically and quantitatively assess the upper limb motion in the grasping test.

To begin with, an SRF network is designed to recognize rehabilitation target objects grasped in assessment tests. Moreover, the upper limb motion trajectory is calculated through the motion of objects’ central positions. After that, a GAE network is designed to analyze the motion trajectory which reflects the motion of upper limb. Finally, based on the upper limb rehabilitation exoskeleton platform, the upper limb motion assessment tests are carried out to show the accuracy of both object recognition of SRF network and motion assessment of GAE network. The results including object recognition, trajectory calculation and deviation assessment are given with details.

The performance of the proposed networks is validated by experiments that are developed on the upper limb rehabilitation robot. It is implemented by recognizing rehabilitation target objects, calculating the motion trajectory and grading the upper limb motion performance. It illustrates that the networks, including both object recognition and trajectory evaluation, can grade the upper limb motion functionn accurately, where the accuracy is above 95.0% in different grasping tests.

A novel assessment method of upper limb motion is proposed and verified. According to the experimental results, the accuracy can be remarkably enhanced, and the stability of the results can be improved, which provide more quantitative indicators for further application of upper limb motion assessment.

This research explores support vector machine (SVM) with Gaussian radial basis function kernel (RBF) as the model and Analysis of Variance (ANOVA) for forecasting the invalidation re-examination decisions of China invention patents, it is beneficial to support patent monetization for corporate intellectual capital.

There were 8,666 China invention patents with their existing invalidation re-examination decisions during 2000∼2021 chosen to conduct classification model training and prediction for the accuracy of invalidation re-examination decisions through SVM with RBF. Statistical significance was performed by ANOVA to identify indicators for these invention patents selected in this research. These selected 8,666 China invention patents were divided into two groups based on their invalidation re-examination decisions during 2000∼2021 in Table 1, which Group 1 included 5,974 invention patents with all valid or partially valid claims, and Group 0 included 2,692 invention patents with all invalid claims. Thereafter, each group was further divided into sub-groups based on 13 major regions where the applicants filed invalidation re-examination. The training sets for Group 1, Group 0 and the sub-groups were selected based on the patent issued in January, February, April, May, July, August, October and November; while the prediction sets were selected from the invention patents issued in March, June, September and December.

The training and prediction accuracies were compared to the existing invalidation re-examination decisions. Accuracies of training sets were ranged from 100% in region 7 (Beijing) and region 9 (Shanghai) to 95.95% in region 1 (US), and the average accuracy of invalidation re-examination decisions was 98.95%. While the accuracies of prediction sets for Group 1 were ranged from 100.00% in region 7 (Beijing) to 90.78% in region 13 (Overseas-others), and the average accuracy of classification was 95.96%, this research’s outcomes confirmed the purpose of applying SVM with RBF to predict the patentability sustainability.

This research developed an empirical method through SVM with RBF to predict patentability sustainability which is crucial for corporate intellectual capital on patents. In particular, the investments on patents are huge, including the patent cultivation and maintenance, developments into products or services, patent litigations and dispute managements. Therefore, this research is beneficial not only for corporation, but also for research organisations to perform cost-effective and profitable patent strategies on intellectual capital.

Organic coatings are widely used for protecting metal equipment and structures from corrosion. Accurate detection and evaluation of the protective performance and service life of coatings are of great importance. This paper aims to review the research progress on performance evaluation and lifetime prediction of organic coatings.

First, the failure forms and aging testing methods of organic coatings are briefly introduced. Then, the technical status and the progress in the detection and evaluation of coating protective performance and the prediction of service life are mainly reviewed.

There are some key challenges and difficulties in this field, which are described in the end.

The progress is summarized from a variety of technical perspectives. Performance evaluation and lifetime prediction include both single-parameter and multi-parameter methods.

This paper aims to try to develop new, environmentally friendly and efficient lubricating additives; study the compatibility of carbon-based additives with different base oils [Polyalphaolefin (PAO)-3, PAO-20 and NPE-2]; and explore the lubrication mechanism.

Oleylamine modified carbon nanoparticles (CNPs-OA) were prepared and the dispersion stability of CNPs-OA in PAO-3, PAO-20 and NPE-2 base oils was investigated by transmission electron microscopy, Fourier transform infrared, thermogravimetric analysis, energy dispersive spectroscopy and X-ray photoelectron spectroscopy. Universal Mechanical Tester (UMT) platform was used to carry out experiments on the effects of different additive concentrations on the lubricating properties of base oil.

The mean friction coefficient of PAO-3, PAO-20 and NPE-2 reduced by 32.8%, 10.1% and 11.4% when the adding concentration of CNPs-OA was 1.5, 2.0 and 0.5 Wt.%, respectively. Generally, The CNPs-OA exhibited the best friction-reducing and anti-wear performance in PAO-3.

The agglomeration phenomenon of carbon nanoparticles as lubricating additive was improved by surface modification, and the lubricating effect of carbon nanoparticles in three synthetic aviation lubricating base oils was compared.

With the rapid growth of the gig economy worldwide, gig workers’ perceived algorithmic control has been proven to have a crucial impact on the service performance, well-being and mental health of gig workers. However, the literature suggests that gig workers’ perceived algorithmic control may be a double-edged sword. The purpose of this research is to explore how the perceived algorithmic control of gig workers can accelerate thriving at work.

Based on the model of proactive motivation and work design literature, a three-wave survey was employed, yielding 281 completed responses. The structural equation modeling method was used to test the theoretical hypothesis.

The results indicate that gig workers’ perceived algorithmic control has positive and indirect effects on thriving at work through the mediating role of job crafting. In addition, job autonomy can moderate the mediated relationship; specifically, when job autonomy is high, this mediated relationship will be stronger.

The health and well-being of gig workers is a concern around the world. The findings provide insights for service platform enterprises and gig workers.

Perceived algorithmic control is critical to mental health and positive work experiences during a gig worker’s service process. However, the current literature focuses more on the negative aspects of algorithmic control. This paper provides a comprehensive research agenda for how to accelerate thriving at work for gig workers.

The volume of passenger traffic at metro transfer stations serves as a pivotal metric for the orchestration of crowd flow management. Given the intricacies of crowd dynamics within these stations and the recurrent instances of substantial passenger influxes, a methodology predicated on stochastic processes and the principle of user equilibrium is introduced to facilitate real-time traffic flow estimation within transfer station streamlines.

The synthesis of stochastic process theory with streamline analysis engenders a probabilistic model of intra-station pedestrian traffic dynamics. Leveraging real-time passenger flow data procured from monitoring systems within the transfer station, a gradient descent optimization technique is employed to minimize the cost function, thereby deducing the dynamic distribution of categorized passenger flows. Subsequently, adhering to the tenets of user equilibrium, the Frank–Wolfe algorithm is implemented to allocate the intra-station categorized passenger flows across various streamlines, ascertaining the traffic volume for each.

Utilizing the Xiaozhai Station of the Xi’an Metro as a case study, the Anylogic simulation software is engaged to emulate the intra-station crowd dynamics, thereby substantiating the efficacy of the proposed passenger flow estimation model. The derived solutions are instrumental in formulating a crowd control strategy for Xiaozhai Station during the peak interval from 17:30 to 18:00 on a designated day, yielding crowd management interventions that offer insights for the orchestration of passenger flow and operational governance within metro stations.

The construction of an estimation methodology for the real-time streamline traffic flow augments the model’s dataset, supplanting estimated values derived from surveys or historical datasets with real-time computed traffic data, thereby enhancing the precision and immediacy of crowd flow management within metro stations.