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This study aims to explore the relative importance of the subdimensions of total rewards satisfaction in predicting research and development (R&D) employee creativity. In addition, the study examines the indirect effects of the subdimensions of total rewards satisfaction on creativity via work engagement and the moderating role of challenge-related work stress in the first stage.

A two-wave design was used, in which total rewards satisfaction and challenge-related work stress were measured in the first wave. Work engagement and creativity were measured in the second wave. Dominance analysis and the latent moderated mediation model were used for the data analyses.

The analyses show that nonfinancial rewards satisfaction completely dominates indirect and direct financial rewards satisfaction when predicting creativity. Indirect financial rewards satisfaction completely dominates direct financial rewards satisfaction when predicting creativity. Work engagement mediates the relationships between the subdimensions of total rewards satisfaction and creativity. Challenge-related work stress moderates the relationships between the subdimensions of total rewards satisfaction and work engagement and the indirect effects of the subdimensions of total rewards satisfaction on creativity via work engagement.

The results imply that managers should set challenge demands for R&D employees and try to improve their total rewards satisfaction, especially their nonfinancial and indirect financial rewards satisfaction, for them to be more creative.

This empirical study contributes to the literature by comparing the relative importance of the different dimensions of total rewards satisfaction in predicting creativity. The study also clarifies how (through work engagement) and when (based on challenge-related work stress) the subdimensions of total rewards satisfaction are positively related to R&D employees' creativity.

Class imbalance learning, which exists in many domain problem datasets, is an important research topic in data mining and machine learning. One-class classification techniques, which aim to identify anomalies as the minority class from the normal data as the majority class, are one representative solution for class imbalanced datasets. Since one-class classifiers are trained using only normal data to create a decision boundary for later anomaly detection, the quality of the training set, i.e. the majority class, is one key factor that affects the performance of one-class classifiers.

In this paper, we focus on two data cleaning or preprocessing methods to address class imbalanced datasets. The first method examines whether performing instance selection to remove some noisy data from the majority class can improve the performance of one-class classifiers. The second method combines instance selection and missing value imputation, where the latter is used to handle incomplete datasets that contain missing values.

The experimental results are based on 44 class imbalanced datasets; three instance selection algorithms, including IB3, DROP3 and the GA, the CART decision tree for missing value imputation, and three one-class classifiers, which include OCSVM, IFOREST and LOF, show that if the instance selection algorithm is carefully chosen, performing this step could improve the quality of the training data, which makes one-class classifiers outperform the baselines without instance selection. Moreover, when class imbalanced datasets contain some missing values, combining missing value imputation and instance selection, regardless of which step is first performed, can maintain similar data quality as datasets without missing values.

The novelty of this paper is to investigate the effect of performing instance selection on the performance of one-class classifiers, which has never been done before. Moreover, this study is the first attempt to consider the scenario of missing values that exist in the training set for training one-class classifiers. In this case, performing missing value imputation and instance selection with different orders are compared.

This study investigates the effect of authentic leadership on service employees’ emotional labour strategies, surface acting and deep acting, from a human energy perspective.

A three-wave survey was conducted in a hotel chain in China, and 347 valid responses were obtained. Mplus software was used for structural equation modelling and bootstrapping analysis.

This study finds the following: authentic leadership predicts service employees’ emotional labour strategies; job insecurity mediates the influence of authentic leadership on surface acting but not on deep acting; relational energy mediates both surface and deep acting; and relational energy has more negative (positive) indirect effects than job insecurity.

The findings provide hospitality managers with insights into how to improve service employees’ capacity for emotional regulation. Hospitality managers should show more authenticity, pay attention to subordinates’ energy level and select and recruit candidates with positive energy traits. Hospitality organisations should encourage, select and train managers to behave as authentic leaders.

This study links authentic leadership with service employees’ emotional management in the hospitality industry. Moreover, it demonstrates the energising function of authentic leadership and introduces the new perspective of human energy to emotional labour research.

The purpose of this paper is to achieve the trajectory tracking measurement of a moving target based on double position sensitive detectors (PSDs).

In this paper, first, a double PSD-based measurement system including hardware system and software system is built up. Then, the working principle is studied to calculate parameters, and calibration experience is conducted. Finally, this double PSD-based measurement system is used to test angular displacement and axial displacement on the tool magazine and automatic tool changer.

In the experiment, the maximum position error of a space point based on double PSD measurement system is 0.8566 mm, and the average error is 0.4716 mm. These results show that the built double PSD-based measurement system of trajectory tracking of a moving target is reasonable.

Combining the characteristics of the PSD and principles of binocular visual measurement, a non-contact three-dimensional measuring system based on double PSDs is developed. The designed double-based measurement system is quite suitable for measurement of a fast-changing illuminant or in the case that the tracking accuracy is not tight.

The corrosion of buried steel pipelines is becoming more serious because of stress corrosion, stray current corrosion and other reasons. This paper aims to study the various alternating current (AC) interference densities on the stress corrosion cracking behaviors of X80 steel samples under cathodic protection (CP) in the simulated soil electrolyte environment by using an electrochemical method.

The change of corrosion rate and surface morphology of the X80 steel samples at various AC current densities from 0 to 150 A/m² or CP potential between −750 and −1,200 mV in the soil-simulating environment was revealed by the electrochemical methods and slow strain rate testing methods.

The results revealed that with the increase of interference density, the corrosion potential of the X80 steel samples shifted to the negative side, and the corrosion pitting was observed on the surface of the sample, this may cause a danger of energy leak. Moreover, the corrosion rate was found to follow a corresponding change with the stress–strain curve. Besides, with the introduction of the CP system, the corrosion rate of the X80 steel working electrode decreased at a low cathodic potential, while showed an opposite behavior at high cathodic potential. In this study, the correlation between AC stray current, cathodic potential and stress was established, which is beneficial to the protection of oil and gas pipeline.

Investigation results are of benefit to provide a new CP strategy under the interference of AC stray current corrosion and stress corrosion to reduce the corrosion rate of buried pipelines and improve the safety of pipeline transportation.

Cutting tool wear is known to affect tool life, surface quality, cutting forces and production time. Micro-milling of difficult-to-cut materials like Inconel 718 leads to significant flank wear on the cutting tool. To ensure the respect of final part specifications and to study cutting forces and tool catastrophic failure, flank wear (VB) has to be controlled. This paper aims to achieve flank wear prediction during micro-milling process, which fills the void of the commercial finite element software.

Based on tool geometry structure and DEFORM finite element simulation, flank wear of the micro tool during micro-milling process is obtained. Finally, experiments of micro-milling Inconel 718 validate the accuracy of the proposed method for predicting flank wear of the micro tool during micro-milling Inconel 718.

A new prediction method for flank wear of the micro tool during micro-milling Inconel 718 based on the assumption that the wear volume can be assumed as a cone-shaped body is proposed. Compared with the existing experiment techniques for predicting tool wear during micro-milling process, the proposed method is simple to operate and is cost-effective. The existing finite element investigations on micro tool wear prediction mainly focus on micro tool axial wear depth, which affects size accuracy of machined workpiece seriously.

The research can provide significant knowledge on the usage of finite element method in predicting tool wear condition during micro-milling process. In addition, the method presented in this paper can provide support for studying the effect of tool flank wear on cutting forces during micro-milling process.

The purpose of this paper is to provide an integrated semantic information retrieval (IR) solution based on an ontology-improved vector space model for situations where a digital collection is established or curated. It aims to create a retrieval approach which could return the results by meanings rather than by keywords.

In this paper, the authors propose a semantic term frequency algorithm to create a semantic vector space model (SeVSM) based on ontology. To support the calculation, a multi-branches tree model is created to represent the ontology and a set of algorithms is developed to operate it. Then, a semantic ontology-based IR system based on the SeVSM model is designed and developed to verify the effectiveness of the proposed model.

The experimental study using 30 queries from 15 different domains confirms the effectiveness of the SeVSM and the usability of the proposed system. The results demonstrate that the proposed model and system can be a significant exploration to enhance IR in specific domains, such as a digital library and e-commerce.

This research not only creates a semantic retrieval model, but also provides the application approach via designing and developing a semantic retrieval system based on the model. Comparing with most of the current related research, the proposed research studies the whole process of realizing a semantic retrieval.

This paper aims to predict tool wear and reveal the relationship between feed per tooth and tool wear in micro-milling Inconel 718 process.

To study and solve the tool wear problem in micro-milling of Inconel 718 micro components, in this paper, the investigation of micro-milling Inconel 718 process was implemented based on DEFORM finite element simulation, and tool wear depth of micro-milling cutter acted as output.

Different from the traditional macro milling process, diameter reduction percentage and average flank wear length decreased with the increase of feed per tooth; tool wear depth decreased when the feed per tooth was less than the minimum chip thickness.

At present, research on the prediction of tool wear in micro-milling of Inconel 718 has never been publicly reported. This study is significant to reveal the relationship between cutting parameters (feed per tooth) and tool wear in micro-milling Inconel 718.

The paper aims to study the wear and breakage characteristics of coated carbide cutting tools through micro-milling slot experiments on superalloy Inconel 718.

During the micro-milling process, the wear and breakage appearance on the rake face and flank face of the cutting tools, as well as the failure mechanism, have been studied. Furthermore, the wear and breakage characteristics of the micro-cutting tools have been compared with the traditional milling on Inconel 718.

The main failure forms of the micro tool when micro-milling Inconel 718 were tool tip breakage and coating shed on the rake and flank faces of the cutting tool and micro-crack blade. The main causes of tool wear were synthetic action of adhesive abrasion, diffusion wear and oxidation wear, while the causes of abrasive wear were not obvious.

The changing trend in tool wear during the micro-milling process and the main reasons of the tool wear are studied. The findings will facilitate slowing down the tool wear and prolonging the tool life during micro-milling Inconel718.

The results of this paper can help slow down the tool wear and realize high efficiency, high precision and economical processing of small workpiece or structure of the nickel-based superalloy.

This paper aims to investigate the causal inferences between mobile application adoption and changes in travelers’ purchasing behavior regarding services supported by the travel and tourism industry.

Using a quasi-experiment conducted by an airline, data sets from more than 10,000 travelers were collected, and hypotheses were tested using propensity score matching and difference-in-difference methods.

Mobile application adoption has a significant positive effect on the total purchasing frequency of services and a significant adverse effect on booking tickets in advance and purchasing frequency from self-owned websites. Besides, this finding also suggested that members or travelers who had high average purchases in the past tend to buy more air tickets on average after mobile application adoption, while the number of days to book tickets in advance and purchase auxiliary services declined after mobile application adoption. However, males purchased more auxiliary services via mobile applications.

The study is based on the causal effect of mobile application adoption on purchasing behavior. Nevertheless, the theoretical basis remains relatively weak. Furthermore, the underlying mechanisms that cause the changes in purchasing behavior via mobile applications need to be elucidated.

This study enriches the hospitality and tourism literature on mobile application adoptions, multichannel purchasing behavior and revenue management. First, a quasi-experimental design is used to verify a causal relationship between mobile applications’ adoption and travelers’ purchasing behavior in the travel and tourism industry. Second, this study adds to examining travelers’ multichannel purchasing behavior in the travel and tourism industry. Third, this work enriches the current literature that explores auxiliary services and revenue management in the travel industry.

Mobile application adoption significantly impacted the travel and tourism industry. Besides, To the best of the authors’ knowledge, this is one of the first empirical studies that examined changes in purchasing behavior due to mobile application adoption from the perspective of service type. The findings provide the first evidence of the impact of mobile application adoption on service purchasing in the travel industry.