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The purpose of this paper is to explore the use of inverse local context analysis (ILCA) to obtain data from limited accessible data sources.

The experimental results show that the method the authors proposed can obtain all retrieved documents from the limited accessible data source using the least number of queries.

The experimental results show that the method we proposed can obtain all retrieved documents from the limited accessible data source using the least number of queries.

To the best of the authors’ knowledge, this paper provides the first attempt to gather all the retrieved documents from limited accessible data source, and the efficiency and ease of implementation of the proposed solution make it feasible for practical applications. The method the authors proposed can also benefit the construction of web corpus.

The purpose of this paper is to reveal the temperature rise characteristics of the disc and pads under different load types.

Evolutions of the disc and pads temperature under a stable, gradual changing and sine-wave contact pressures widely used at present are analyzed numerically by using ANSYS software.

The results show that during the loading process, the temperature increases most rapidly under a stable contact pressure, most slowly under a gradual changing contact pressure; the disc temperature rise curves expose saw-shaped character, the closer it is to the friction surface, the more serious the fluctuations will be, the pads temperature rise curves are rather smooth; temperature gradient in the axial direction is higher than that in the other two directions under all of the three types of contact pressure and shows a sine-wave variation under a sine-wave contact pressure.

It indicates that a gradual changing contact pressure should be adopted preferentially in practical application. The simulation results of this work provide theoretical basis for load simulation.

With the popularity of the internet and the increasing numbers of netizens, tremendous information flows are generated daily by the intelligently interconnected individuals. The diffusion processes of different information are not independent, and they interact with and influence each other. Modeling and analyzing the interaction between correlated information play an important role in the understanding of the characteristics of information dissemination and better control of the information flows. This paper aims to model the correlated information diffusion process over the crowd intelligence networks.

This study extends the classic epidemic susceptible–infectious–recovered (SIR) model and proposes the SIR mixture model to describe the diffusion process of two correlated pieces of information. The whole crowd is divided into different groups with respect to their forwarding state of the correlated information, and the transition rate between different groups shows the property of each piece of information and the influences between them.

The stable state of the SIR mixture model is analyzed through the linearization of the model, and the stable condition can be obtained. Real data are used to validate the SIR mixture model, and the detailed diffusion process of correlated information can be inferred by the analysis of the parameters learned through fitting the real data into the SIR mixture model.

The proposed SIR mixture model can be used to model the diffusion of correlated information and analyze the propagation process.

The purpose of this study is to calculate the normal force of a two degree of freedom direct drive induction motor considering coupling effects based on an analytical model. Compared with the traditional single degree of freedom motor, normal force characteristics of two-degree-of-freedom direct drive induction motor (2DOFDDIM) is affected by coupling effect when the machine is in a helical motion. To theoretically explain the influence mechanism of coupling effect, this paper conducts a quantitative analysis of the influence of coupling effect on normal force based on the established analytical model of normal force considering coupling effect.

Firstly, the normal forces generated by 2DOFDDIM in linear motion, rotary motion and helical motion are investigated and compared to prove the effect of the coupling effect on the normal force. During this study, several coupling factors are established to modify the calculation equations of the normal force. Then, based on the multilayer theoretical method and Maxwell stress method, a novel normal force calculation model of 2DOFDDIM is established taking the coupling effect into account, which can easily calculate the normal force of 2DOFDDIM under different motions conditions. Finally, the calculation results are verified by the results of 3D finite element model, which proves the correctness of the established calculating model.

The coupling effect produced by the helical motion of 2DOFDDIM affects the normal force.

In this paper, the analytical model of the normal force of 2DOFDDIM considering the coupling effect is established, which provides a fast calculation for the design of the motor.

The study aims to find out the university students' health information service needs in post-COVID-19 age and then consider how to do the health information services well and promote it in further work.

This study uses Kano model to design a questionnaire about university students' health information service needs from academic library and investigates 243 students from seven universities in China. The data generated were analyzed using a statistical tool and presented in tables.

The results show the students' different requirements level of health information services from academic library as must-be requirements, one-dimensional requirements, attractive quality attributes and indifferent quality in post-COVID-19 age.

The questionnaire design and the scale of respondents have limitation, such as only focus on Chinese university, have not characteristically research on different group of students. It could optimize to cover more worldwide university students and refer to different groups of students' needs in further research.

The paper expands and deepens the theory about university library health information service and provides a practical reference and proposes some suggestions for academic libraries on how to carry out health information services and give the social health institutions inspiration on how to promote the health services, especially in post-COVID-19 age.

In the context of fire incidents within buildings, efficient scene perception by firefighting robots is particularly crucial. Although individual sensors can provide specific types of data, achieving deep data correlation among multiple sensors poses challenges. To address this issue, this study aims to explore a fusion approach integrating thermal imaging cameras and LiDAR sensors to enhance the perception capabilities of firefighting robots in fire environments.

Prior to sensor fusion, accurate calibration of the sensors is essential. This paper proposes an extrinsic calibration method based on rigid body transformation. The collected data is optimized using the Ceres optimization algorithm to obtain precise calibration parameters. Building upon this calibration, a sensor fusion method based on coordinate projection transformation is proposed, enabling real-time mapping between images and point clouds. In addition, the effectiveness of the proposed fusion device data collection is validated in experimental smoke-filled fire environments.

The average reprojection error obtained by the extrinsic calibration method based on rigid body transformation is 1.02 pixels, indicating good accuracy. The fused data combines the advantages of thermal imaging cameras and LiDAR, overcoming the limitations of individual sensors.

This paper introduces an extrinsic calibration method based on rigid body transformation, along with a sensor fusion approach based on coordinate projection transformation. The effectiveness of this fusion strategy is validated in simulated fire environments.

Considering the industrial site environment and installation requirements, the straight-beam ultrasound probe with easy installation and good coupling agent adaptability is adopted to replace the traditional water immersion focusing probe for film thickness measurement in cylindrical roller bearings. The straight-beam probe has a large echo receiving range, which will result in measurement regions overlapping and bring about large measurement errors. In this paper, an improved measurement method for film thickness of cylindrical roller bearing with the straight-beam probe is developed.

An improved method is proposed to enhance the spatial resolution of the straight-beam probe. By introducing a correction coefficient based on the percentage of the effective measurement area, the method improves the measuring accuracy successfully.

The experimental results demonstrate that the lubricant-film thickness can be measured to reasonable accuracy by this method and have a better agreement with the theoretical film thickness solutions.

This paper used analytical method and model that is helpful for the improvement of the spatial resolution, which has great influence on the measuring accuracy, is mainly determined by the echo reflection area size of the ultrasound transducer.

This study aims to discover the factors which influence user satisfaction levels and their continuous use intention (CUI) of academic library social media, and then considers how to promote and improve further work on library social media to reduce user churn and increase user satisfaction.

An updated DeLone and McLean information systems success (D&M ISS) model and the expectation confirmation model for information systems continuance (ECM-ISC) with new variables of emotions are used to examine the factors which influence user satisfaction levels and CUI of academic library social media through 445 questionnaires. Partial least squares structural equation modelling was used to analyse the data and presented in tables.

The results show that information quality, system quality and emotions affect user satisfaction and CUI, and reveal that emotions can affect that most obviously.

The WeChat public platform is mainly used in China, so the study only focuses on Chinese academic libraries. There are still limitations on the settings of observed variables which cannot cover all the causes of users’ positive and negative emotions. In addition, although the respondents of this questionnaire can represent academic library users, 445 samples are still fairly low in contrast to the great number of academic library WeChat public platform users.

This study integrates ECM-ISC and D&M ISS models, adds positive and negative emotions as new variables, to broaden the application scope of these models, and demonstrates the applicability of these two models in the fields of researching academic library social media, expanding and deepening related theories above. This also provides practical reference for academic libraries on how to improve user satisfaction and CUI of academic library social media and academic library WeChat public platforms, promoting the development of academic library social media.

Companies avoid taxes in a variety of ways and use different methods to do that, one of the most common being earnings management. The purpose of this paper is to investigate whether companies manipulate their financial data in order to reduce taxes paid.

We considered a sample of 63 listed Romanian companies for the period 2016–2021. The Beneish model was used for estimating earnings management, and the effective tax rate was used to measure tax avoidance. The analysis was carried out using regression analysis in Stata13 software.

The findings of the research indicate a negative and statistically significant association between effective tax rate and earnings management, implying that one of the main reasons why companies manipulate their earnings to reduce tax burden and avoid taxes. Moreover, our results show that return on assets (ROA) has a statistically significant negative influence on the effective tax rate. Furthermore, our analysis reveals that firm size, growth, and Big4 audit have no effect on effective tax rate.

Because it analyzes concrete cases using financial data and provides some recommendations for addressing the issue of tax avoidance, this work is useful in advancing both quantitative and qualitative research on this topic. This research is relevant for businesses, governments, regulators, audit professionals and investors.

The study, by analyzing concrete cases using reported financial data, contributes in filling the gap within the literature that results from a lack of scientific research on the relationship between tax avoidance and earnings management, and then it clarifies the nature of the causal connection between them. Moreover, it considers a combination of firm related variables including performance, size and also audit quality.

This study examines the impact of profitability, firm size and leverage on corporate tax avoidance in Bangladesh, an emerging South Asian economy.

A balanced panel data of 62 firms from Dhaka and Chittagong stock exchanges in Bangladesh from 2009 to 2020 were used to run the regression. This study employed the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) to examine the hypotheses.

The findings show that large firms positively impact corporate tax avoidance. Similarly, profitability and leverage are positively associated with tax avoidance, and the results are significant. Furthermore, the study conducts robustness tests that confirm the findings.

The use of cash effective tax rate (ETR) to investigate firms’ tax avoidance practices poses some limitations, and the results should be interpreted cautiously.

The current study may help policymakers better enhance tax collection from business firms. The findings could serve as a valuable input for effectively monitoring tax collection from large profit-earning firms.

To the authors' best knowledge, this is the first historical attempt in Bangladesh to use panel data to examine the relationship between the firm’s level characteristics and corporate tax avoidance. Panel data often provides greater flexibility with large data, simplifying calculation and statistical analysis.