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This paper aims to discuss flow pattern transition (FPT) as an important factor in multiple-phase flow measurements. Several methods have been proposed to control FPT, but those methods fail to address the many issues in complex flow conditions that can affect flow patterns.

In this paper, a non-intrusive sensor instrumentation is applied to extract measurable data under different flow conditions. Using these data, a simple theoretical–mathematical method along with an orthogonal design is applied to FPT optimization. Orthogonal experiments are designed and carried out according to theoretical guidelines. Three selected process parameters – phase fraction, gas pressure in the initial independent process and liquid speed – are optimized for FPT results to produce a minimum FPT time.

The following results are obtained: the phase fraction in the initial independent process can lead to significant reductions in FPT time, gas pressure plays an important role and liquid speed has no apparent effect on FPT results. Under optimized conditions, FPT time can be shortened to 0.3-0.6 times by controlling the above three parameters compared with normal conditions.

The proposed method is simple, rapid and efficient for evaluating an FPT process and lays the foundation for further FPT applications.

This paper aims to study the influence of different seal structure parameters and working conditions on the air-oil two-phase flow characteristics and leakage characteristics of the seal cavity in the bearing cavity of the aero-engine spindle bearing tester.

In this paper, the VOF method and RNG k-ε turbulence model are used to explore the flow characteristics and leakage characteristics of the labyrinth seal cavity of an aero-engine spindle bearing tester under the condition of air-oil two-phase flow.

The distribution of the lubricating oil is related to the sealing clearance and the air-oil ratio. The amount of oil leakage increases with increasing of sealing chamber clearance, air-oil ratio and inlet velocity and decreases with increasing curvature and speed. The amount of air leakage increases with sealing clearance and inlet velocity.

In comparison to the pure air-phase flow field, the air-oil two-phase flow field can more accurately simulate the lubricating oil flow in the sealing chamber.

Given the scarcity of data during the early stages of the COVID-19 pandemic in China, the decision-making for non-pharmaceutical policies was mostly based on insufficient evidence. The purpose of this study is to assess the effectiveness of these policies, such as lockdown and government subsidies, on rural households and identify policy implications for China and other countries in dealing with pandemics.

The authors survey 2,408 rural households by telephone from 101 counties across 17 provinces in China during the first stage of the pandemic (March 2020). The authors use the ordered probit model and linear regression model to study the overall impact of policies and then use the quantile regression model and sub-sample regression method to study the heterogeneity of the effects of government policies.

The authors find that logistics disruption due to lockdown negatively affected rural households. Obstructed logistics is associated with a more significant loss for high-income households, while its impact on the loss expectation of low-income households is more severe. Breeding and other industries such as transport and sales suffer more from logistics than cultivation. The impact of logistics on intensive agricultural entities is more serious than that on professional farms. The government subsidy is more effective at reducing loss for low-income households. Lockdown and government subsidies have shown heterogeneous impacts on rural households.

The overall economic losses experienced by rural households in the early stages of the pandemic are controllable. The government policies of logistics and subsidies should target specific groups.

The authors evaluate the economic impacts of lockdown and government subsidies on rural households and show their heterogeneity among different groups. The authors further demonstrate the policy effectiveness in supporting rural households during the early stages of the pandemic and provide future policy guidance on major public health event.

This study aims to propose a comprehensive causal model to examine the relationships between customer experience and four key factors in brand building, i.e., brand loyalty, brand trust, brand affect and brand involvement. The dimensionality of customer experience in full-service hotel is also particularly examined in relation to brand building.

Three steps of data collection were used: interviews of 50 customers on their experiences of staying full-service hotels, a small survey of 176 hotel guests to establish the measurement scale of customer experience and a major survey of 732 hotel customers in ten major Chinese cities to test the model of brand loyalty.

Customers’ experiences with full-service hotels are proposed to be categorized into functional, affective and social. There is a chain effect from customer experience to brand trust and to brand affect and then to brand loyalty. The brand involvement does moderate relationships between customer experience and brand trust and brand affect but not brand loyalty.

For full-service hotels, social and functional experiences are critical in building brand loyalty, and therefore, they need to be the focal points in the enhancement of customer experience. Also, hoteliers are advised to develop emotional connections between the customers and the hotel brand – an effective way of building trust and affection.

According to the authors’ knowledge, this paper is one of the first few studies to link customer experience to brand loyalty with comprehensive causal effect analysis. This study also contributes to the knowledge of customer experience in the context of the full-service hotel sector.

The purpose of this paper is to determine the corrosion fatigue behavior and mechanism of 6005A aluminum alloy and welded joint.

Electron back-scattered diffraction (EBSD) were adopted to characterize the microstructure of 6005A aluminum alloy and welded joint. Through potentiodynamic polarization, electrochemical impedance spectroscopy (EIS) and corrosion fatigue experiments, the corrosion fatigue behavior and mechanism of 6005A aluminum alloy base metal and welded joint were studied.

The results show that the corrosion fatigue crack initiation of 6005A aluminum alloy base metal and welded joint is mainly caused by the preferential anodic dissolution and hydrogen concentration in the areas with inclusions and welding defects.

The research is an originality study on the corrosion fatigue behavior and mechanism of 6005A aluminum alloy and welded joint.

Food SMEs is the backbone of local and world economy. Even while food SMEs are aware of the potential advantages of implementing supply chain analytics (SCA), only a small number of companies use data-based decision-making. This is because of technophobia. In light of this, the purpose of this study is to investigate the factors that have an impact on SCA adoption which in turn influence the sustainable performance of firms.

The data were collected from 221 managers working in food-related SMEs in China by using a questionnaire-based survey. The framework of this study was validated using a rigorous statistical procedure using the technique, namely, partial least squares structural equation modelling.

The findings of this study suggest that all modified UTAUT components (i.e. performance expectancy, effort expectancy, social influence, facilitating conditions and technophobia) significantly influence SCA adoption. Moreover, the existing study highlights and confirms the significance of adopting SCA to improve sustainable performance.

This research is novel, as it extends and investigates the theoretical framework based on UTAUT theory in SCA context and its impact on sustainable organizational performance. In addition, the factor of technophobia is tested in SCA context. This study has several contributory managerial implications for food SMEs.

The authors hope to filter valuable information from online reviews, obtain objective and accurate information about the demands of auto consumers and help auto companies develop more reasonable production and marketing strategies for healthy and sustainable development. This paper aims to discuss the aforementioned objectives.

The authors collected review data from online automotive forums and generated a corpus after pre-processing. Then, the authors extracted consumer demands and topics using the LDA model. Finally, the authors used a trained Word2vec tool to extend the consumer demand topics.

Different types of vehicle consumers have the same demands, such as “Space,” “Power Performance,” and “Brand Comparison,” and distinct demands, such as “Appearance,” “Safety,” “Service,” and “New Energy Features”; consumers who buy new energy vehicles are still accustomed to comparing with the brands or models of fuel vehicles; new energy vehicles consumers pay more attention to services and service quality during the purchasing and using process.

The development time of new energy vehicles is relatively short, with some models being available for only one year or even six months. The smaller amount of available data may impact the applicability of topic models. The sample size, especially for new energy vehicles, needs to be increased to improve the general applicability of topic models further.

First, this measure helps online review websites improve their existing review publication mechanisms, enhance the overall quality of online review content, increase user traffic and promote the healthy development of online review websites. Second, this allows for timely adjustments in future product production and sales plans and further enhances automotive companies' ability to leverage online reviews for Internet marketing.

The authors have improved the accuracy and stability of the fused topic model, providing a scientific and efficient research tool for multi-dimensional topic mining of online reviews. With the help of research results, consumers can more easily understand the discussion topics and thus filter out valuable reference information. As a result, automotive companies may gain information about consumer demands and product quality feedback and thus quickly adjust production and marketing strategies to increase sales and market share.

Light Detection and Ranging (LiDAR) offers a fast and effective way to acquire DSM and extract ground objects such as building, trees and so on. However, it is difficult to extract sharp and precise building boundary from LiDAR data, because its ground sample distance (GSD) is often worse than that of high resolution image. Recently, fusion of LiDAR and high resolution image becomes a promising approach to extract precise boundary. To find the correct and precise boundary, the aim of this paper is to present a series of novel algorithms to improve the quality.

To find the correct and precise boundary, this paper presents a series of novel algorithms to improve the quality. At first, a progressive algorithm is presented to register LiDAR data and images; second, a modified adaptive TIN algorithm is presented to filter ground point, where a region growth method is applied in the adaptive TIN algorithm; third, a novel criterion based on the density, connectivity and distribution of point cluster is developed to distinguish trees point; fourth, a novel method based on the height difference between neighbor points is employed to extract coarse boundaries; at last, a knowledge based rule is put forward to identify correct building boundary from parallel edges.

Thorough experiments, it is conducted that: the registration results are accurate and reliable; filtered ground points has good quality, without missing or redundancy; all tree clusters bigger than one grid are detected, and points of walls and edges are eliminated with the new criterion; detected edges exactly locate at real building boundaries, and statistics show the detection correctness is 98 percent, and the detection completeness is 95 percent.

All results prove that precise boundary can be extracted with fusion of LiDAR and high resolution image.

Studies on mining text and generating intelligence on human resource documents are rare. This research aims to use artificial intelligence and machine learning techniques to facilitate the employee selection process through latent semantic analysis (LSA), bidirectional encoder representations from transformers (BERT) and support vector machines (SVM). The research also compares the performance of different machine learning, text vectorization and sampling approaches on the human resource (HR) resume data.

LSA and BERT are used to discover and understand the hidden patterns from a textual resume dataset, and SVM is applied to build the screening model and improve performance.

Based on the results of this study, LSA and BERT are proved useful in retrieving critical topics, and SVM can optimize the prediction model performance with the help of cross-validation and variable selection strategies.

The technique and its empirical conclusions provide a practical, theoretical basis and reference for HR research.

The novel methods proposed in the study can assist HR practitioners in designing and improving their existing recruitment process. The topic detection techniques used in the study provide HR practitioners insights to identify the skill set of a particular recruiting position.

To the best of the authors’ knowledge, this research is the first study that uses LSA, BERT, SVM and other machine learning models in human resource management and resume classification. Compared with the existing machine learning-based resume screening system, the proposed system can provide more interpretable insights for HR professionals to understand the recommendation results through the topics extracted from the resumes. The findings of this study can also help organizations to find a better and effective approach for resume screening and evaluation.

The purpose of this paper is to provide insights on the improvement of academic impact and social attention of Chinese collaboration articles from the perspective of altmetrics.

The authors retrieved articles which are from the Chinese Academy of Sciences (CAS) and indexed by Nature Index as sampled articles. With the methods of distribution analysis, comparative analysis and correlation analysis, authors compare the coverage differences of altmetric sources for CAS Chinese articles and CAS international articles, and analyze the correlation between the collaborative information and the altmetric indicators.

Results show that the coverage of altmetric sources for CAS international articles is greater than that for CAS Chinese articles. Mendeley and Twitter cover a higher percentage of collaborative articles than other sources studied. Collaborative information, such as number of collaborating countries, number of collaborating institutions, and number of collaborating authors, show moderate or low correlation with altmetric indicator counts. Mendeley readership has a moderate correlation with altmetric indicators like tweets, news outlets and blog posts.

International scientific collaboration at different levels improves attention, academic impact and social impact of articles. International collaboration and altmetrics indicators supplement each other. The results of this study can help us better understand the relationship between altmetrics indicators of articles and collaborative information of articles. It is of great significance to evaluate the influence of Chinese articles, as well as help to improve the academic impact and social attention of Chinese collaboration articles.

To the best of authors’ knowledge, few studies focus on the use of altmetrics to assess publications produced through Chinese academic collaboration. This study is one of a few attempts that include the number of collaborating countries, number of collaborating institutions, and number of collaborating authors of scientific collaboration into the discussion of altmetric indicators and figured out the relationship among them.