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As the crucial support component of the propeller power system, the reliability of the operation of submersible pumps is influenced by the lubrication performance of water-lubricated thrust bearings. When the water-lubricated thrust bearings are under start-stop or heavy load conditions, the effect of surface morphology is crucial as the mixed lubrication regime is encountered. This paper aims to develop one mixed lubrication model for the water-lubricated thrust bearings to predict the effects of surface skewness, kurtosis and roughness orientation on the loading carrying capacity and tribological behavior.

This paper developed one improved mixed lubrication model specifically for the water-lubricated thrust bearing system. In this model, the hydrodynamic model was improved by using the height of the rough surface and its probability density function, combined with the average flow model. The asperity contact model was improved by using the equation for the Pearson system of frequency curves to characterize the non-Gaussian aspect of surface roughness distribution.

According to the results, negative skewness, large kurtosis and lateral surface pattern can improve the tribological performance of water-lubricated thrust bearings. Optimizing the surface morphology is a reasonable design method that can improve the performance of water-lubricated thrust bearings.

In this paper, one mixed lubrication model specifically for the water-lubricated thrust bearing with the effect of surface roughness into consideration was developed. Based on the developed model, the effect of surface morphology on tribological behavior can be evaluated.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2023-0247/

This study aims to explore the dissemination and evolutionary path of online public opinion from a crisis management perspective. By clarifying the influencing factors and dynamic mechanisms of online public opinion dissemination, this study provides insights into attenuating the negative impact of online public opinion and creating a favorable ecological space for online public opinion.

This research employs bibliometric analysis and CiteSpace software to analyze 302 Chinese articles published from 2006 to 2023 in the China National Knowledge Infrastructure (CNKI) database and 276 English articles published from 1994 to 2023 in the Web of Science core set database. Through literature keyword clustering, co-citation analysis and burst terms analysis, this paper summarizes the core scientific research institutions, scholars, hot topics and evolutionary paths of online public opinion crisis management research from both Chinese and international academic communities.

The results show that the study of online public opinion crisis management in China and internationally is centered on the life cycle theory, which integrates knowledge from information, computer and system sciences. Although there are differences in political interaction and stage evolution, the overall evolutionary path is similar, and it develops dynamically in the “benign conflict” between the expansion of the research perspective and the gradual refinement of research granularity.

This study summarizes the research results of online public opinion crisis management from China and the international academic community and identifies current research hotspots and theoretical evolution paths. Future research can focus on deepening the basic theories of public opinion crisis management under the influence of frontier technologies, exploring the subjectivity and emotionality of web users using fine algorithms and promoting the international development of network public opinion crisis management theory through transnational comparison and international cooperation.

Building upon the cognition-affect-behavior (CAB) model and script theory, this research aims to enrich the existing literature on historic town tourism consumption by offering empirical evidence of how the cognitive and affective images of historic towns contributes to tourists' memorable tourism experiences (MTE) and revisit intention, while identifying the cognitive image dimensions that are relevant for evaluating historic towns.

An on-site survey was conducted with 486 local tourists who visited the historic towns in Chengdu. partial least squares-structural equation modeling (PLS-SEM) was utilized to assess both the measurement and structural models.

(1) Cognitive image emerged as a significant predictor of affective image; (2) Both cognitive image and affective image had a positive influence on MTE, in which cognitive image played a more dominant role in shaping MTE; (3) MTE was found to strongly predict revisit intention among tourists; (4) MTE and affective image mediated the relationship between cognitive image and revisit intention.

This research highlights the value of incorporating cognitive and affective constructs in predicting MTE, and the proposed integrated framework of the CAB model and script theory exhibits superior predictive power in understanding tourists' revisit intention.

This research provides empirical insights about how historic towns improve their marketing strategies as short day-trip destinations.

This research provides a novel insight on the applicability of an integrated model combining the CAB model and script theory in explaining the revisit behavior of local tourists within the context of historic towns.

This study aims to investigate the corrosion behavior of cold-rolled Fe₃₅Ni₂₀C_r12Mn_(28-x)Al_x high-entropy alloys (HEAs) using the potentiodynamic polarization technique in 1 M H₂SO₄ acid. Additionally, the influence of molybdenum (Mo) additions as inhibitors and the effect of variations in cold rolling reduction ratios and Al content on corrosion behavior are examined.

Two cold rolling reduction ratios, namely, 50% (R50) and 90% (R90), were examined for the cold-rolled Fe₃₅Ni₂₀Cr₁₂Mn₂₈Al₅ (Al₅) and Fe₃₅Ni₂₀Cr₁₂Mn₂₃Al₁₀ (Al₁₀) HEAs. Mo inhibitor additions were introduced at varying concentrations of 0.3, 0.6 and 0.9 Wt.%. The potentiodynamic polarization technique was used to evaluate the corrosion rates (CRs) under different experimental conditions.

The results indicate that the addition of 0.3 Wt.% Mo in 1 M H₂SO₄ yielded the lowest CR for both R50 and R90, irrespective of the Al content in the HEAs. However, the highest CR was observed at 0.6 Wt.% Mo addition. Furthermore, increasing the concentration of Al resulted in a corresponding rise in the CR. Comparatively, the CR decreased significantly when the cold rolling reduction ratio increased from R50 to R90.

This research provides valuable insights into the intricate relationship between Mo inhibitors, cold rolling reduction ratio, Al content and the resulting corrosion behavior of Fe₃₅Ni₂₀Cr₁₂Mn_(28-x)Al_x HEAs. The comprehensive analysis of corroded HEAs, including surface morphology, compositions and elemental distribution mapping, contributes to the understanding of the corrosion mechanisms and offers potential strategies for enhancing the corrosion behavior of HEAs.

The purpose of this study is to design and process the optimal V-shaped microstructure for 7075 aluminum alloy and reveal its wear resistance mechanism and performance.

The hydrodynamic pressure lubrication models of the nontextured, V-shaped, circular and square microtextures are established. The corresponding oil film pressure distributions are explored. The friction and wear experiments are conducted on a rotating device. The effects of the microstructure shapes and sizes on the wear mechanisms are investigated via the friction coefficients and surface morphologies.

In comparison, the V-shaped microtexture has the largest oil film carrying capacity and the lowest friction coefficient. The wear mechanism of the V-shaped microtexture is dominated by abrasive and adhesive wear. The V-shaped microtexture has excellent wear resistance under a side length of 300 µm, an interval of 300 µm and a depth of 20 µm.

This study is conductive to the design of wear-resistant surfaces for friction components.

We conduct a multidisciplinary systematic literature review on climate neutrality in the supply chain. While carbon neutrality has gained prominence, our study argues that achieving carbon neutrality alone is not enough to address climate change effectively, as non-CO₂ greenhouse gases (GHG) are potent contributors to global warming.

We used multiple databases, including EBSCO, ProQuest, Science Direct, Emerald and Google Scholar, to identify articles related to climate neutrality in the context of non-CO₂ gases. A total of 71 articles in environmental science, climate change, energy systems, agriculture and logistics are reviewed to provide insights into the climate neutrality of supply chains.

We find that, in addition to CO₂, other GHG such as methane, nitrous oxide, ozone and fluorinated gases also significantly contribute to climate change. Our literature review identified several key pillars for achieving net-zero GHG emissions, including end-use efficiency and electrification, clean electricity supply, clean fuel supply, “GHG capture, storage and utilization,” enhanced land sinks, reduced non-CO₂ emissions and improved feed and manure management.

We contribute to the literature on climate neutrality of supply chains by emphasizing the significance of non-CO₂ GHG along with CO₂ and highlighting the need for a comprehensive approach to climate neutrality in addressing climate change. This study advances the understanding of climate neutrality of supply chains and contributes to the discourse on effective climate change mitigation strategies. It provides clear future research directions.

This study aims to ensure reliable analysis of dynamic responses in asphalt pavement structures. It investigates noise reduction and data mining techniques for pavement dynamic response signals.

The paper conducts time-frequency analysis on signals of pavement dynamic response initially. It also uses two common noise reduction methods, namely, low-pass filtering and wavelet decomposition reconstruction, to evaluate their effectiveness in reducing noise in these signals. Furthermore, as these signals are generated in response to vehicle loading, they contain a substantial amount of data and are prone to environmental interference, potentially resulting in outliers. Hence, it becomes crucial to extract dynamic strain response features (e.g. peaks and peak intervals) in real-time and efficiently.

The study introduces an improved density-based spatial clustering of applications with Noise (DBSCAN) algorithm for identifying outliers in denoised data. The results demonstrate that low-pass filtering is highly effective in reducing noise in pavement dynamic response signals within specified frequency ranges. The improved DBSCAN algorithm effectively identifies outliers in these signals through testing. Furthermore, the peak detection process, using the enhanced findpeaks function, consistently achieves excellent performance in identifying peak values, even when complex multi-axle heavy-duty truck strain signals are present.

The authors identified a suitable frequency domain range for low-pass filtering in asphalt road dynamic response signals, revealing minimal amplitude loss and effective strain information reflection between road layers. Furthermore, the authors introduced the DBSCAN-based anomaly data detection method and enhancements to the Matlab findpeaks function, enabling the detection of anomalies in road sensor data and automated peak identification.

This paper aims to market analysis on the base many factors. Market analysis must be done correctly to increase the efficiency of smart grid technologies. On the other hand, it is not very possible for the company to make improvements for too many factors. The main reason for this is that businesses have constraints both financially and in terms of manpower. Therefore, a priority analysis is needed in which the most important factors affecting the effectiveness of the market analysis will be determined.

In this context, a new fuzzy decision-making model is generated. In this hybrid model, there are mainly two different parts. First, the indicators are weighted with quantum spherical fuzzy multi SWARA (M-SWARA) methodology. On the other side, smart grid technology investment projects are examined by quantum spherical fuzzy ELECTRE. Additionally, facial expressions of the experts are also considered in this process.

The main contribution of the study is that a new methodology with the name of M-SWARA is generated by making improvements to the classical SWARA. The findings indicate that data-driven decisions play the most critical role in the effectiveness of market environment analysis for smart technology investments. To achieve success in this process, large-scale data sets need to be collected and analyzed. In this context, if the technology is strong, this process can be sustained quickly and effectively.

It is also identified that personalized energy schedule with smart meters is the most essential smart grid technology investment alternative. Smart meters provide data on energy consumption in real time.

The purpose of this study is to investigate the link between corporate social responsibility (CSR) and the amount of trust customers have in Somali Islamic banks. Furthermore, the role of gender in CSR activities and Islamic bank clientele is evaluated.

Throughout February and March 2022, 410 clients of Islamic banks in Somalia were surveyed using a questionnaire. The partial least squares approach and the structural equation model are applied to examine the data.

Findings indicate that all variables of CSR activities, such as social product, social legal, social needs, social environment and social employees’ responsibility, are influential and significant predictors of trust in Islamic banks in Somalia. Gender inequalities moderate the relationship between social product, social needs, social environment, social employee and trust. Conversely, only social legal responsibility was unaffected by gender differences in Somalia regarding people’s trust in Islamic banks.

A sample from a developing country such as Somalia is useful for shedding light on the outcomes of consumers’ perceptions of and trust in businesses’ CSR in the developing world. Furthermore, this study contributes to knowledge regarding CSR and how it can help the Islamic banking industry. Its findings will be useful to policymakers and regulatory bodies in the banking industry in their efforts to improve CSR.

To the best of the authors’ knowledge, this study is the first empirical investigation of its kind about the understudied relationship among customer trust, CSR efforts and gender in Somalia context. Furthermore, it investigates how gender specifically moderates CSR in the Islamic banking sector in a developing country.

Purpose: This study analyses the mediating effect of parasocial interaction (PSI) in the link between hedonic motivation and impulsive buying intention (IBI) in fashion vlogging about sustainable cosmetics.

Need for the Study: Due to the mass popularity of YouTube, vlogging has led to an augmented level of PSI of vloggers with consumers, which strongly impacts a consumer’s behavioural consequences and persuades consumers to indulge in impulsive buying. Thus, marketers need to comprehend the changing behavioural patterns, including sustainable products, as this new communication medium serves the future of promotion and advertising.

Methodology: Online questionnaires were administered to 349 Gen Z female fashion vlog followers. Structural equation modelling and Hayes Process macros were employed to test the model relationships.

Findings: Results indicate that PI with the fashion vlogger partially mediates between hedonic motivation and impulse buying intention for sustainable cosmetic products. Fashion consciousness (FC) was also established as a significant moderator between all the model relationships.

Practical Implications: The findings of the study would be helpful for fashion brands in the content development of visual marketing communications, which would tap the female Gen Z consumer. Improving the PSI between the follower and the fashion vlogger can be easily enhanced by delivering the right content through the vlogger’s videos.