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The purpose of this paper is to develop a coupled lattice Boltzmann model for the simulation of the freezing process in unsaturated porous media.

In the developed model, the porous structure with complexity and disorder was generated by using a stochastic growth method, and then the Shan-Chen multiphase model and enthalpy-based phase change model were coupled by introducing a freezing interface force to describe the variation of phase interface. The pore size of porous media in freezing process was considered as an influential factor to phase transition temperature, and the variation of the interfacial force formed with phase change on the interface was described.

The larger porosity (0.2 and 0.8) will enlarge the unfrozen area from 42 mm to 70 mm, and the rest space of porous medium was occupied by the solid particles. The larger specific surface area (0.168 and 0.315) has a more fluctuated volume fraction distribution.

The concept of interfacial force was first introduced in the solid–liquid phase transition to describe the freezing process of frozen soil, enabling the formulation of a distribution equation based on enthalpy to depict the changes in the water film. The increased interfacial force serves to diminish ice formation and effectively absorb air during the freezing process. A greater surface area enhances the ability to counteract liquid migration.

Considering the greenwashing risk of symbolic environmental management, this study aims to distinguish the motivation for environmental investment growth (EIG) from the corporate cost stickiness and anti-stickiness perspectives.

This study analyzes the impact of substantive and symbolic environmental management on cost stickiness. Subsequently, competing hypotheses are proposed. Finally, empirical tests are conducted on Chinese A-share listed companies from 2010 to 2019.

EIG significantly improves enterprises’ cost stickiness. The cost of high EIG enterprises does not decrease significantly with a decline in income compared to other enterprises, which is consistent with the motivation for substantive environmental management. Enterprises with high asset specificity and optimistic management expectations show more obvious substantive environmental management. Government and public environmental concerns cause more pronounced substantive environmental management.

An evaluation of corporate environmental responsibility should take into account both what the company has disclosed and what it has actually done.

Governments and the public should have a comprehensive understanding of corporate environmental management. They need to strengthen their ability to recognize symbolic environmental management and support substantive environmental management.

Fundamental to the evaluation of corporate environmental responsibility, this study distinguishes the motivations for corporate EIG disclosures from the cost stickiness perspective to avoid the risk of greenwashing. Hypotheses on the impact of substantive and symbolic environmental management on cost stickiness are presented. This study verifies the substantive environmental management characteristics of listed Chinese companies.

This study aims to identify an antecedent that hinders knowledge sharing, namely, the perceived climate of Cha-xu. Based on the social exchange perspective, the authors propose a theoretical model that links the perceived climate of Cha-xu to employee knowledge sharing. This model focuses on the mediating role of two types of trust (vertical and horizontal trust) and the moderating role of task interdependence in influencing the mediation.

Using a sample of 509 Chinese employees, this study carried out a survey on an online platform. This study developed a structural equation model and tested the moderated mediation hypothesis by using Mplus 8.0.

The results showed that two types of trust act as mediators in the relationship between the perceived climate of Cha-xu and knowledge-sharing processes. The mediating effect of horizontal trust is stronger. Most significantly, findings show that this mediated relationship is contingent on the level of task interdependence.

This paper provides evidence for distinguishing vertical trust and horizontal trust in the field of knowledge management. From a managerial perspective, this study identifies traditional cultural factors for hindering knowledge-sharing processes within Chinese organizations.

Given the critical role of user-generated content (UGC) in e-commerce, exploring various aspects of UGC can aid in understanding user purchase intention and commodity recommendation. Therefore, this study investigates the impact of UGC on purchase decisions and proposes new recommendation models based on sentiment analysis, which are verified in Douban, one of the most popular UGC websites in China.

After verifying the relationship between various factors and product sales, this study proposes two models, collaborative filtering recommendation model based on sentiment (SCF) and hidden factors topics recommendation model based on sentiment (SHFT), by combining traditional collaborative filtering model (CF) and hidden factors topics model (HFT) with sentiment analysis.

The results indicate that sentiment significantly influences purchase intention. Furthermore, the proposed sentiment-based recommendation models outperform traditional CF and HFT in terms of mean absolute error (MAE) and root mean square error (RMSE). Moreover, the two models yield different outcomes for various product categories, providing actionable insights for organizers to implement more precise recommendation strategies.

The findings of this study advocate the incorporation of UGC sentimental factors into websites to heighten recommendation accuracy. Additionally, different recommendation strategies can be employed for different products types.

This study introduces a novel perspective to the recommendation algorithm field. It not only validates the impact of UGC sentiment on purchase intention but also evaluates the proposed models with real-world data. The study provides valuable insights for managerial decision-making aimed at enhancing recommendation systems.

The interaction between rock mass structural planes and dynamic stress levels is important to determine the stability of rock mass structures in underground geotechnical engineering. In this work, the authors aim to focus on the degradation effects of fracture geometric parameters and unloading stress paths on rock mechanical properties.

A three-dimensional Particle Flow Code (PFC3D) was used for a systematic numerical simulation of the strength failure and cracking behavior of granite specimens containing prefabricated cracks under conventional triaxial compression and triaxial unilateral unloading. The authors demonstrated the unique mechanical response of prefabricated fractured rock under two conditions. The crack initiation, propagation, and coalescence process of pre-fissured specimens were analyzed in detail.

The authors show that the prefabricated cracks and unilateral unloading conditions not only deteriorate the mechanical strength but also have significant differences in failure modes. The degrading effect of cracks on model strength increases linearly with the decrease of the dip angle. Under the condition of true triaxial unilateral unloading, the deterioration effect of peak strength of rock is very significant, and unloading plays a role in promoting the instability failure of rock after peak, making the rock earlier instability failure. Associating with the particle vector diagram and crack coalescence process, the authors find that model failure mode under unilateral loading conditions is obviously distinct from that in triaxial loading. The peak strain in the unloading direction increases sharply, resulting in a new shear slip.

This study is expected to improve the understanding of the strength failure and cracking behavior of fractured rock under unilateral unloading.

In the process of continuous casting and rolling of steel plate, due to the influence of rolling equipment and process, there are scratches, inclusions, patches, scabs and pitted surfaces on the surface of steel plate, which will not only affect the corrosion resistance, wear resistance and fatigue strength of steel plate but also may cause production accidents. Therefore, the detection of steel plate surface defect must be strengthened to ensure the production quality of steel plate and the smooth development of industrial construction.

(1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved Multi-Scale Retinex (MSR) enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

When applied to small dataset, the precision of the proposed method is 94.5% and the time is 23.7 ms. In order to compare with deep learning technology, after expanding the image dataset, the precision and detection time of this paper are 0.948 and 24.2 ms, respectively. The proposed method is superior to other traditional image processing and deep learning methods. And the field recognition precision is 91.7%.

In brief, the steel plate surface defect detection technology based on computer vision is effective, but the previous attempts and methods are not comprehensive and the accuracy and detection speed need to be improved. Therefore, a more practical and comprehensive technology is developed in this paper. The main contributions are as follows: (1) A steel plate surface defect detection technology based on small datasets is proposed, which can detect multiple surface defects and fill in the blank of scab defect detection. (2) A detection system based on intelligent recognition technology is built. The steel plate images are collected by the front-end monitoring device, then transmitted to the back-end monitoring center and processed by the embedded intelligent algorithms. (3) In order to reduce the impact of external light on the image, an improved MSR enhancement algorithm based on adaptive weight calculation is proposed, which lays the foundation for subsequent object segmentation and feature extraction. (4) According to the different factors such as the cause and shape, the texture and shape features are combined to classify different defects on the steel plate surface. The defect classification model is constructed and the classification results are recorded and stored, which has certain application value in the field of steel plate surface defect detection. (5) The practicability and effectiveness of the proposed method are verified by comparison with other methods, and the field running tests are conducted based on the equipment commissioning field of China Heavy Machinery Institute.

Research on the Internet of Things (IoT) has gained momentum in various industry contexts. However, the literature lacks broad empirical evidence on the factors that influence users' intention to adopt this cutting-edge technology, especially in the food and beverage industry (F&BI) – a significant yet unexplored setting. Therefore, the authors aim to extend the “Unified Theory of Acceptance and Use of Technology (UTAUT)” model by coupling it with perceived collaborative advantage, organizational inertia and perceived cost and explore the key determinants of IoT adoption for the digital transformation of the F&BI.

This study employs a cross-sectional quantitative approach, where a sample of 307 usable responses was drawn from the senior managers of the Australian F&BI.

The authors have found that performance expectancy, perceived collaborative advantage, effort expectancy, social influence and facilitating conditions have a strong positive influence on the behavioural intention to adopt IoT for the digital transformation of the F&BI. Furthermore, while high perceived costs and organizational inertia are often considered negative factors in adopting new technology, our results reveal the insignificant influence of these factors on the adoption of IoT, which is interesting. The findings also suggest that age and voluntariness significantly moderate most of the relationships, while gender is an insignificant moderator.

The study provides several novel insights into the existing body of knowledge by extending the UTAUT model with three variables and applying it in a unique context.

The traditional failure mode and effect analysis (FMEA) has some limitations, such as the neglect of relevant historical data, subjective use of rating numbering and the less rationality and accuracy of the Risk Priority Number. The current study proposes a machine learning–enhanced FMEA (ML-FMEA) method based on a popular machine learning tool, Waikato environment for knowledge analysis (WEKA).

This work uses the collected FMEA historical data to predict the probability of component/product failure risk by machine learning based on different commonly used classifiers. To compare the correct classification rate of ML-FMEA based on different classifiers, the 10-fold cross-validation is employed. Moreover, the prediction error is estimated by repeated experiments with different random seeds under varying initialization settings. Finally, the case of the submersible pump in Bhattacharjee et al. (2020) is utilized to test the performance of the proposed method.

The results show that ML-FMEA, based on most of the commonly used classifiers, outperforms the Bhattacharjee model. For example, the ML-FMEA based on Random Committee improves the correct classification rate from 77.47 to 90.09 per cent and area under the curve of receiver operating characteristic curve (ROC) from 80.9 to 91.8 per cent, respectively.

The proposed method not only enables the decision-maker to use the historical failure data and predict the probability of the risk of failure but also may pave a new way for the application of machine learning techniques in FMEA.

The high turnover rate among interns exacerbates the shortage of human resources in the hospitality industry. This study is based on previous concerns about the impact of occupational stigma and internship on turnover intention. This study aims to explore how the career adaptability of interns influences their perception of occupational stigma and occupational identity.

By using stratified sampling, semistructured interviews were conducted with 34 respondents who had academic and vocational education backgrounds. The data analysis was performed using the thematic analysis method.

This study demonstrates that a high level of career adaptability helps to reduce interns’ perception of occupational stigma and strengthen their occupational identity. Students from academic and vocational schools display different levels of career adaptability in terms of job matching and career promotion. In addition, the long-term influence on occupational identity is more significant from professional development potential compared to job adaptation.

This study provides valuable insights into the complex relationship between occupational stigma and occupational identity from the perspective of career adaptability. Moreover, it highlights the importance of job adaption, matching, promotion and professional development in retaining talent within the hospitality industry.

This study innovatively focuses on job matching and career promotion for coping with occupational stigma. It also considers interns’ educational backgrounds, facilitating further understanding of occupational identity under the influence of stigma. A fresh perspective on talent adaptation and retention in the hospitality industry is provided.

实习生的高流动率加剧了酒店业人力资源的短缺。本研究在职业污名与实习对离职倾向影响的研究基础上, 主要探讨了实习生的职业适应如何影响其职业污名感知和职业认同。

研究采用分层抽样的方法, 对34名拥有学术和职业教育背景的受访者进行半结构访谈, 并采用主题分析法进行数据分析。

研究发现, 良好的职业适应有助于减少实习生的职业污名感知, 并增强其职业认同。学术学校和职业学校的学生在工作匹配和职业晋升方面表现出不同程度的职业适应。此外, 职业发展潜力对职业认同的影响比工作适应更加长远。

本研究从职业适应的角度对职业污名与职业认同之间的复杂关系提供了有价值的见解。此外, 它还强调了工作适应、匹配、晋升和专业发展在酒店业人才保留方面的重要性。

本研究创新地关注了职业匹配与职业晋升对应对职业污名和提升职业认同的影响。研究还考虑了实习生教育背景的差异性, 进一步深化对污名影响下的职业认同的理解, 为酒店行业的人才适应和保留提供了一个新的研究视角。

La elevada tasa de rotación entre los trabajadores en prácticas agrava la escasez de recursos humanos en el sector de la hostelería. Este estudio se basa en preocupaciones previas sobre el impacto del estigma ocupacional y las prácticas en la intención de rotación. Explora principalmente cómo influye la adaptabilidad profesional de los becarios en su percepción del estigma ocupacional y la identidad ocupacional.

Mediante un muestreo estratificado, se realizaron entrevistas semiestructuradas a 34 encuestados con formación académica y profesional. El análisis de los datos se realizó mediante el método de análisis temático.

Este estudio demuestra que un alto nivel de adaptabilidad profesional contribuye a reducir la percepción de estigma ocupacional de los estudiantes en prácticas y a reforzar su identidad ocupacional. Los estudiantes de escuelas académicas y de formación profesional muestran diferentes niveles de adaptabilidad profesional en términos de adecuación al puesto de trabajo y promoción profesional. Además, la influencia a largo plazo sobre la identidad ocupacional es más significativa del potencial de desarrollo profesional en comparación con la adaptación laboral.

El estudio aporta información valiosa sobre la compleja relación entre el estigma ocupacional y la identidad ocupacional desde la perspectiva de la adaptabilidad profesional. Además, pone de relieve la importancia de la adaptación del puesto de trabajo, el emparejamiento, la promoción y el desarrollo profesional para retener el talento dentro de la industria de la hostelería.

Este estudio se centra de forma innovadora en la adecuación al puesto de trabajo y la promoción profesional para hacer frente al estigma laboral. También tiene en cuenta los antecedentes educativos de los becarios, lo que facilita una mayor comprensión de la identidad ocupacional bajo la influencia del estigma. Se aporta una nueva perspectiva sobre la adaptación y la retención del talento en la industria de la hostelería.

The existing Nonlinear Dynamic Vibration Absorbers (NLDVAs) have the disadvantages of complex structure, high cost, high installation space requirements and difficulty in miniaturization. And most of the NLDVAs have not been applied to reality. To address the above issues, a novel Triple-magnet Magnetic Dynamic Vibration Absorber (TMDVA) with tunable stiffness, only composed of triple cylindrical permanent magnets and an acrylic tube, is designed, modeled and tested in this paper.

(1) A novel TMDVA is designed. (2) Theoretical and experimental methods. (3) Equivalent dynamics model.

It is found that adjusting the magnet distance can effectively optimize the vibration reduction effect of the TMDVA under different resonance conditions. When the resonance frequency of the cantilever changes, the magnet distance of the TMDVA with a high vibration reduction effect shows an approximately linear relationship with the resonance frequency of the cantilever which is convenient for the design optimization of the TMDVA.

Both the simulation and experimental results prove that the TMDVA can effectively reduce the vibration of the cantilever even if the resonance frequency of the cantilever changes, which shows the strong robustness of the TMDVA. Given all that, the TMDVA has potential application value in the passive vibration reduction of engineering structures.