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This paper aims to evaluate the influence of molybdenum disulfide (MoS₂) concentrations (5, 7.5, 10, 12.5 and 15 Wt.%) on the microstructure and tribological property of the self-lubrication cubic boron nitride (CBN) abrasive composites.

Three point bending method and rotating sliding test are used to evaluate the flexural strength and tribological property of self-lubricating CBN abrasive composites. Microstructure, wear morphology of the ball and scratch are supported by scanning electron microscopy, optical microscope and three-dimensional confocal microscopy, etc.

The MoS₂ concentration has a significant influence on the interface microstructure between CBN abrasives and matrix alloys, and thus, affects the flexural strength of CBN abrasive composites. The grain fracture modes of CBN abrasive composites are transformed from the transgranular fracture into intergranular fracture as the MoS₂ concentrations increase. Additionally, the friction coefficient of as-sintered samples decreases with the MoS₂ concentrations. The MoS₂ concentrations of 10 Wt.% are final determined to fabricate self-lubricating composites in basis of the mechanical and lubricating property.

The ball is fabricated under vacuum sintering process. The tribological property of self-lubricating CBN abrasive composites is evaluated in terms of the friction coefficient and morphologies of the ball and scratches after rotating sliding tests.

The purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation methods of the system.

A multi-channel data acquisition scheme based on PCI-E (rapid interconnection of peripheral components) was proposed. The flexible biosensor is integrated with the flexible data acquisition card with monitoring capability, and the embedded (device that can operate independently) chip STM32F103VET6 is used to realize the simultaneous processing of multi-channel human health parameters. The human health parameters were transferred to the upper computer LabVIEW by intelligent clothing through USB or wireless Bluetooth to complete the transmission and processing of clinical data, which facilitates the analysis of medical data.

The smart clothing provides a mobile medical cloud platform for wearable medical through cloud computing, which can continuously monitor the body's wrist movement, body temperature and perspiration for 24 h. The result shows that each channel is completely accurate to the top computer display, which can meet the expected requirements, and the wearable instant care system can be applied to healthcare.

The smart clothing in this study is based on the monitoring and diagnosis of textiles, and the electronic communication devices can cooperate and interact to form a wearable textile system that provides medical monitoring and prevention services to individuals in the fastest and most accurate way. Each channel of the system is precisely matched to the display screen of the host computer and meets the expected requirements. As a real-time human health protection platform technology, continuous monitoring of human vital signs can complete the application of human motion detection, medical health monitoring and human–computer interaction. Ultimately, such an intelligent garment will become an integral part of our everyday clothing.

By drawing on the research paradigm of collective action that occurs in physical space, the present study aims to explore the antecedent predictors of network social mobilization – feeling of injustice – and discuss the emotional mechanism of this prediction: mediating effect of anger and resentment.

Micro-blog postings about network social mobilization were collected to develop the dictionary of codes of fairness, anger and resentment. Then, according to the dictionary, postings on Sina Weibo were coded and analyzed.

The feeling of injustice predicted network social mobilization directly. The predictive value was 27% and 33%, respectively during two analyses. The feeling of injustice also predicted social mobilization indirectly via anger and resentment. In other words, anger and resentment account for the active mechanism in which the feeling of injustice predicts network social mobilization. Mediating effect value was 29.63% and 33.33% respectively.

This study is our first exploration to use python language to collect data from human natural language pointing on micro-blog, a large number of comments of netizen about certain topic were crawled, but a small portion of the comments could be coded into analyzable data, which results in a doubt of the reliability of the study. Therefore, we should put the established model under further testing.

In the cyberspace, this study confirms the mechanism of network social mobilization, expands and enriches the research on social mobilization and deepens the understanding of social mobilization.

This study provides an empirical evidence to understand the network social mobilization, and it gives us the clue to control the process of network social mobilization.

This study uses the Python language to write Web crawlers to obtain microblog data and analyze the microblog content for word segmentation and matching thesaurus. It has certain innovation.

As the supply chain is a highly integrated infrastructure in modern business, the risks in supply chain are also becoming highly contagious among the target company. This motivates researchers to continuously add new features to the datasets for the credit risk prediction (CRP). However, adding new features can easily lead to missing of the data.

Based on the gaps summarized from the literature in CRP, this study first introduces the approaches to the building of datasets and the framing of the algorithmic models. Then, this study tests the interpolation effects of the algorithmic model in three artificial datasets with different missing rates and compares its predictability before and after the interpolation in a real dataset with the missing data in irregular time-series.

The algorithmic model of the time-decayed long short-term memory (TD-LSTM) proposed in this study can monitor the missing data in irregular time-series by capturing more and better time-series information, and interpolating the missing data efficiently. Moreover, the algorithmic model of Deep Neural Network can be used in the CRP for the datasets with the missing data in irregular time-series after the interpolation by the TD-LSTM.

This study fully validates the TD-LSTM interpolation effects and demonstrates that the predictability of the dataset after interpolation is improved. Accurate and timely CRP can undoubtedly assist a target company in avoiding losses. Identifying credit risks and taking preventive measures ahead of time, especially in the case of public emergencies, can help the company minimize losses.

The purpose of this paper is to provide an imprinting perspective of the organizational culture evolution at a large state-owned heavy equipment manufacturer. It aims at exploring sensitive periods and the tension between persistence and decay of imprints.

It employs the case study approach. Both qualitative (interviews) and quantitative (survey) data were collected, from the directors, middle managements, and grass-roots staffs of Dong Fang Turbine Co. Ltd. Based on the set of four scenarios, both within-scenario analysis and cross-scenario analysis were conducted following the “replication logic.”

New survival threats are more possible to develop sensitive periods with new imprints than transition periods, and the authors suggest organizational culture can be divided into two categories as the institutional sensitive and the local community sensitive.

This study is not only an exploitation of imprinting theories, but also provides a different understanding of organizational evolution, especially in terms of imprints dynamic. Meanwhile, the case shows how institutional environment and local community has shaped differently the organizational culture.

In gearbox fault diagnosis, identifying the fault type and severity simultaneously, as well as the compound fault containing multiple faults, is necessary.

To diagnose multiple faults simultaneously, this paper proposes a multichannel and multi-task convolutional neural network (MCMT-CNN) model.

Experiments were conducted on a bearing dataset containing different fault types and severities and a gearbox compound fault dataset. The experimental results show that MCMT-CNN can effectively extract features of different tasks from vibration signals, with a diagnosis accuracy of more than 97%.

Vibration signals at different positions and in different directions are taken as the MC inputs to ensure the integrity of the fault features. Fault labels are established to retain and distinguish the unique features of different tasks. In MCMT-CNN, multiple task branches can connect and share all neurons in the hidden layer, thus enabling multiple tasks to share information.

The purpose of this paper is to analyse the impact of political connections and gender diversity on the performance of Iberian companies as a singular market and considering Portugal and Spain separately.

The authors used panel data methodology, specifically GMM system estimation model by Arellano and Bond (1991) for the period from 2015 to 2020.

Results show that the performance of listed Iberian companies is influenced by political connections, by gender diversity and that gender diversity has a mitigating effect on the effects of political connections in each country. The mitigating effect of women is evident in both Portugal and Spain, as they are more cautious and principled, which is valued by short-term investors interested in an immediate investment. However, considering the Iberian Peninsula as a whole, the results indicate that – in the long term – women's political relationships can benefit performance through a better reputation and image, which can lead to better social and economic results in the long term.

To the best of the authors’ knowledge, this paper is original and covers an important gap in the literature when considering political connections and women's impact on these connections as determinants of the performance of Iberian companies.

This study aims to characterize the behavior of blended concrete, including metakaolin (MK) and quarry dust (QD), as supplementary cementing materials. The study focuses on evaluating the effects of these materials on the fresh and hardened properties of concrete.

MK, a pozzolanic material, and QD, a fine aggregate by-product, are potentially sustainable alternatives for enhancing concrete performance and reducing environmental impact. The addition of different percentages of MK enhances the pozzolanic reaction, resulting in improved strength development. Furthermore, the optimum dosage of MK, mixed with QD, and mechanical properties like compressive, flexural and split tensile strength of concrete were evaluated to investigate the synergetic effect of MK and quarry dust for M20-grade concrete.

The results reveal the influence of metakaolin and QD on the overall performance of blended concrete. Cost analysis showed that the optimum mix can reduce the 7%–8% overall cost of the materials for M20-grade concrete. Energy analysis showed that the optimum mix can reduce 7%–8% energy consumption.

The effective utilization is determined with the help of the analytical hierarchy process method to find an optimal solution among the selected criteria. According to the AHP analysis, the optimum content of MK and quarry dust is 12% and 16%, respectively, performing best among all other trial mixes.

Heat transfer inside wavy fins is analyzed in this work. The paper aim to discuss this issue.

Six different types of wavy fins are considered. The fin equation for each fin type is solved using a high accurate finite difference method. Excellent agreement is obtained between the numerical solution under zero wave amplitude and the exact solution of the plain fin.

The following wavy fin types and conditions are found to produce larger heat transfer rate and its volumetric value than those for the plain fin and other wavy fins: short fins with parallel wavy profiles and large surface-wave frequency; long fins with symmetric wavy surface around the length axis, positive cross-sectional area gradient at the base, and large surface-wave frequency; and long fins with symmetric wavy profiles around the length axis, positive cross-sectional area gradient at the base, and small surface-wave frequency.

In addition, both fins with symmetric wavy surface around the width axis and parallel wavy surfaces along the width axis have same performance indicators. Also, these wavy fins possess higher fin efficiency than either that of the plain fin or those of the other types of wavy fins.

Finally, heat transfer enhancements in the studied wavy fins are increased by increases in the excess of the surface area, cross-sectional area gradient at the base, arc length and arc width relative to those of the plain fin.

This study investigates the nexus between the returns on oil prices (OP) and unemployment (UR) while taking into account the influences of two of the most representative measures of uncertainty, the Baker et al. (2016) and Caldara and Iacovello (2021) indexes of economic policy uncertainty (EP) and geopolitical risks (GP), in the relationship.

The authors use data on the US, Canada, France, Italy, Germany and Japan from January 2000 to February 2022 and the UK from January 2000 to December 2021. The authors then apply the continuous wavelet transform (CWT), wavelet coherence (WC), partial wavelet coherence (PWC) and multiple wavelet coherence (MWC) to examine the returns within a time and frequency framework.

The CWT tracks the movement and evolution of individual return series with evidence of high variances and heterogenous tendencies across frequencies that also align with critical events such as the GFC and COVID-19 pandemic. The WC reveals the presence of a bidirectional relationship between OP and UR across economies, showing that the two variables affect each other. The authors’ findings establish the predictive influence of oil price on unemployment in line with theory and also show that the variation in UR can impact the economy and alter the dynamics of OP. The authors employ the PWC and MWC to capture the impact of uncertainty indexes in the co-movement of oil price and unemployment in line with the theory of “investment under uncertainty”. Taking into account the common effects of EP and GP, PWC finds that uncertainty measures significantly drive the co-movement of oil prices and unemployment. This result is robust when the authors control for the influence of economic activity (proxied by the GDP) in the co-movement. Furthermore, the MWC reveals the combined intensity, strength and significance of both oil prices and the uncertainty measures in predicting unemployment across countries.

This study investigates the relationship between oil prices, uncertainty measures and unemployment under a time and frequency approach.

1. Wavelet approaches are used to examine the relationship between oil prices and unemployment in the G7.

2. We account for uncertainty measures in the dynamics of oil prices and unemployment.

3. We observe a bidirectional relationship between oil prices and unemployment.

4. Uncertainty measures significantly drive oil prices and unemployment co-movement.

5. Both oil prices and uncertainty measures significantly drive unemployment.

Wavelet approaches are used to examine the relationship between oil prices and unemployment in the G7.

We account for uncertainty measures in the dynamics of oil prices and unemployment.

We observe a bidirectional relationship between oil prices and unemployment.

Uncertainty measures significantly drive oil prices and unemployment co-movement.

Both oil prices and uncertainty measures significantly drive unemployment.