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The objectives of this paper are to estimate the annual Malmquist TFP(total factor productivity) index of Korea and China’s road freight transport with DEA(data envelope analysis) and to decompose the index into technical efficiency change and technology change. In the process of the estimation, we used labor, capital, and fuel as input factors and ton-km of road freight transport as output factor. The panel data of Korea and China’s road freight transport industry from 1985 to 2004 are used. The results of the analysis show several points. First, there was no significant improvement in China’s TFP growth before 1997, but there was continuous growth in TFP since 1997 because of constantly increasing domestic freight transport demand. Second, there was downward trend in Korea’s TFP, especially there was a large reduction of productivity in 1998 because of the huge reduction of road freight transport demand during the period of the economic crisis. Third, the technology improvements play a significant role in the TFP growth and the technical efficiency had negative effects on the TFP growth of Korea. However, the technology improvements as well as the technical efficiency had positive effects on the TFP growth of China’s road freight transport industry.

Promoting customer–company identification (CCI) has become a crucial relationship marketing strategy for service firms. The purpose of this study is to examine how customers’ perceptions of service quality and corporate social responsibility (CSR) affect CCI over time. More importantly, a comparative analysis is conducted to compare the long-term effectiveness of service quality versus CSR in forming CCI.

A conceptual framework is developed and then empirically examined using latent growth curve modeling. The study data were collected from restaurant customers in Taiwan in four waves of 213 repeated measures.

The results of this study show that customers’ perceptions of both service quality and CSR affect CCI. Particularly, the results of this study indicate that compared with service quality, customers’ perceived CSR has a stronger effect in the promotion of CCI over time.

This study offers a new insight for service marketing practitioners who are planning and implementing strategies for enhancing CCI. The findings suggest that relationship investments are more effective over the long term when service firms shift their investment priority over time from achieving high service quality to increasing consumers’ belief in the firm’s commitment to CSR.

Though previous research has explored the various drivers of CCI, longitudinal examinations are surprisingly scarce in this context. Using latent growth curve modeling, this study examines how CCI antecedents influence changes in CCI over time. More importantly, this study reveals that CSR has a stronger long-term impact on CCI than service quality.

Enhancing consumer-company identification (CCI) is a useful means by which to build deeper, more committed relationships with consumers. The purpose of this paper is to examine how consumers’ perceptions about the company (service quality and corporate social responsibility) and construal of the self (independent self-construal and interdependent self-construal) affect their identification with a service firm. This study also investigates how consumers’ involvement with the service firm moderates the relationships between CCI and four specific drivers.

A conceptual model is developed and empirically tested through two field-based studies (restaurant services and financial services) and one follow-up laboratory experiment.

The results of this research show that service quality, corporate social responsibility and interdependent self-construal positively affect CCI and that independent self-construal has a negative effect on CCI. In particular, the greater the involvement of the consumer with the service firm, the more positive the effects of service quality and corporate social responsibility are on CCI.

This research provides new insight into services marketing management by suggesting that service firms can enhance CCI and, consequently, consumer loyalty by adopting different strategies for specific consumer segments.

This is the first empirical study to simultaneously incorporate both company characteristics and consumer personal factors into a framework, and provide an integrative understanding of what factors determine consumers’ identification with a service firm.

This paper aims to investigate an efficient approach to model the electromagnetic behaviors and predict stray-field loss inside the magnetic steel plate under 3D harmonic magnetization conditions so as to effectively prevent the structural components from local overheating and insulation damage in electromagnetic devices.

An experimental setup is applied to measure all the magnetic properties of magnetic steel plate under harmonic excitations with different frequencies and phase angles. The measurement and numerical simulation are carried out based on the updated TEAM Problem 21 Model B+ (P210-B+), under the 3D harmonic magnetization conditions. An improved method to evaluate the stray-field loss is proposed, and harmonic flux distribution in the structural components is analyzed.

The influence of the harmonic order and phase angle on the stray-field loss in magnetic steel components are noteworthy. Based on the engineering-oriented benchmark models, the variations of stray-field losses and magnetic field distribution inside the magnetic components under harmonic magnetization conditions are presented and analyzed in detail.

The capacity of the multi-function harmonic source, used in this work, was not large enough, which limits the magnetization level. Up to now, further improvements to increase the harmonic source capacity and investigations of the electromagnetic behaviors of magnetic steel components under multi-harmonic and DC-AC hybrid excitations are in progress.

To accurately predict the stray-field loss in magnetic steel plate, the improved method based on the combination of magnetic measurement and numerical simulation is proposed. The effects of the frequency and phase angle on the stray-field loss are analyzed.

This study aims to apply the affective events theory and psychological contract theory to investigate how job satisfaction and psychological safety mediate the effect of the behavioral integrity of supervisors on the organizational commitment of employees.

A questionnaire survey was conducted using purposive sampling. In total, 500 questionnaire copies were distributed, and 453 responses were collected, of which 441 were valid (valid response rate = 88.2%).

The behavioral integrity of supervisors has a direct negative effect on organizational commitment but significant positive effects on job satisfaction and psychological safety, and job satisfaction and psychological safety have significant positive effects on organizational commitment. Job satisfaction and psychological safety have significant mediating effects on the association between the behavioral integrity of supervisors and the organizational commitment of employees.

Leaders and top management should “practice what they preach,” integrate honesty into organizational culture through training and establish a code of conduct to ensure that employees uphold their commitments. Companies should establish appropriate disciplinary systems and norms related to work and other aspects of organizational culture; they should also establish fair, just and open assessment systems to minimize the gap between their employees’ actual and expected earnings.

This study is the first to simultaneously consider the mediating effects of job satisfaction and psychological safety on the association between behavioral integrity and organizational commitment.

The stock indexes are an important issue for investors, and in this paper good trading strategies will be aimed to be adopted according to the accurate prediction of the stock indexes to chase high returns.

To avoid the problem of insufficient financial data for daily stock indexes prediction during modeling, a data augmentation method based on time scale transformation (DATT) was introduced. After that, a new deep learning model which combined DATT and NGRU (DATT-nested gated recurrent units (NGRU)) was proposed for stock indexes prediction. The proposed models and their competitive models were used to test the stock indexes prediction and simulated trading in five stock markets of China and the United States.

The experimental results demonstrated that both NGRU and DATT-NGRU outperformed the other recurrent neural network (RNN) models in the daily stock indexes prediction.

A novel RNN with NGRU and data augmentation is proposed. It uses the nested structure to increase the depth of the deep learning model.

Customer satisfaction (CS) and customer-company identification (CCI) are two important relational constructs and play a complementary role in the service-profit chain. Drawing from the theory of relationship dynamics, the purpose of this paper is to define CS velocity and CCI velocity as the rate and direction of change in CS and CCI, respectively. A comparison of the relative effects of CCI velocity and CS velocity on customer loyalty is done through a latent growth curve modeling approach.

A conceptual model is developed and empirically tested based on four waves of a longitudinal survey of 213 restaurant customers.

The results show that both CCI velocity and CS velocity have positive effects on customer loyalty. More importantly, the effects of CCI velocity on customer loyalty over time are stronger than those of CS velocity. The moderation analysis further shows that the higher the frequency of visits to the service firm, the stronger the effects of relationship velocity on customer loyalty.

The results provide new insights for service marketing managers by suggesting that, to benefit the long-term effectiveness of relationship investments, service firms should shift the priority from increasing CS to engendering CCI.

This paper contributes to the theory of relationship dynamics by conceptualizing new constructs of CS velocity and CCI velocity and by empirically comparing their relative effects on customer loyalty over time.

This paper aims to develop a model that reflects the current transformer (CT) core materials nonlinearity. The model enables simulation and analysis of the CT excitation current that includes the inductive magnetizing current and the resistive excitation current.

A nonlinear CT model is established with the magnetizing current as the solution variable. This model presents the form of a nonlinear differential equation and can be solved discretely using the Runge–Kutta method.

By simulating variations in the excitation current for different primary currents, loads and core materials, the results demonstrate that enhancing the permeability of the B–H curve leads to a more significant improvement in the CT ratio error at low primary currents.

The proposed model has three obvious advantages over the previous models with the secondary current as the solution variable: (1) The differential equation is simpler and easier to solve. Previous models contain the time differential terms of the secondary current and excitation flux or the integral term of the flux, making the iterative solution complicated. The proposed model only contains the time differential of the magnetizing current. (2) The accuracy of the excitation current obtained by the proposed model is higher. Previous models calculate the excitation current by subtracting the secondary current from the converted primary current. Because these two currents are much greater than the excitation current, the error of calculating the small excitation current by subtracting two large numbers is greatly enlarged. (3) The proposed model can calculate the distorted waveform of the excitation current and error for any form of time-domain primary current, while previous models can only obtain the effective value.

The purpose of this paper is to mainly review the state-of-the-art developments in the field of hydrodynamics of offshore pipelines, identifying the key tools for analysis of pipeline free spans, their applications, their qualifying characteristics and capabilities and limitations.

These different analytical, numerical and semi-empirical tools available for predicting such hydrodynamic loads and their effects include VIVANA, PIPESIN, VIVSIM, SIMULATOR, FATFREE, amongst others. Inherent in these models are current effects, wave effects and/ or pipe–soil interactions.

Amongst these models, the most attention was given to the new VIVANA model because this model take into account the vortex-induced effects with respect to free-spanning pipelines (which have dominant effect in the span analysis in deep water) better than other semi-empirical models (such as Shear 7). Recent improvements in VIVANA include its ability to have arbitrary variation in speed and direction of current, as well as the ability for calculation of pure IL and combined IL-CF response. Improvements in fatigue assessments at free spans, i.e. pipe–soil interaction have been achieved through the combined frequency domain and non-linear time domain analysis methodology adopted. Semi-empirical models are still the de facto currently used in the design of free-spanning pipelines. However, there is need for further research on free-span hydrodynamic coefficients and on how in-line and cross-flow vibrations interact. Again, there is still the challenge due to VIV complexity in fully understanding the fluid structure interaction problem, as there is no consolidated procedure for its analysis. It has been observed that there is large scatter between the different codes adopted in the prediction of fatigue damage, as there lacks full-scale test data devoted to determination/validation of the coefficients used in the semi-empirical models. A case study of the preliminary design of a typical 48 in. pipeline has been presented in this study to demonstrate the use of the free-span analysis tool, DNV RP F105. Excel spreadsheet has been applied in the execution of formulas.

This review paper is the first of its kind to study the state-of-the-art development in pipeline free-span analysis models and demonstrate the use of analysis tool, DNV for MAFSL calculation. Hence, information obtained from this paper would be invaluable in assisting designers both in the industry and academia.

The purpose of this study is to develop luminescence sensors for the detection of nitroaromatic compounds (NACs) and metal ions to protect human health and prevent environmental pollution.

The composition and morphology of Eu-metal-organic frameworks (MOF) (1) were well characterized by powder X-ray diffraction, elemental analyses, Fourier-transform infrared spectroscopy, thermogravimetric analysis, X-ray photoelectron spectroscopy and scanning electron microscopy. The emission spectrum displays that 1 has significant characteristic emission bands of Eu(III) ions. The authors further investigated the fluorescence sensing performances of 1 to NACs and metal ions.

The results show that Eu-MOF (1) exhibits significant fluorescence quenching effect toward p-nitroaniline and Fe³⁺ ions with good stability and recyclability. This means that 1 can be used as a multifunctional sensing material for the detection of p-nitroaniline and Fe³⁺ ions.

The authors have successfully synthesized a fluorescence Eu-based sensing material under hydrothermal conditions. In addition, the fluorescence property and sensing performances for detecting NACs and metal ions were studied. The results suggest that 1 has highly selective fluorescence quenching toward p-nitroaniline and Fe³⁺ ions with not only high sensitivity and selectivity but also excellent stability and recyclability. Furthermore, this study has confirmed that the multifunctional MOF material is very useful in environment pollutants’ detection and monitoring.