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A time-varying grey Fourier model (TVGFM(1,1,N)) is proposed for the simulation of variable amplitude seasonal fluctuation time series, as the performance of traditional grey models can't catch the time-varying trend well.

The proposed model couples Fourier series and linear time-varying terms as the grey action, to describe the characteristics of variable amplitude and seasonality. The truncated Fourier order N is preselected from the alternative order set by Nyquist-Shannon sampling theorem and the principle of simplicity, then the optimal Fourier order is determined by hold-out method to improve the robustness of the proposed model. Initial value correction and the multiple transformation are also studied to improve the precision.

The new model has a broader applicability range as a result of the new grey action, attaining higher fitting and forecasting accuracy. The numerical experiment of a generated monthly time series indicates the proposed model can accurately fit the variable amplitude seasonal sequence, in which the mean absolute percentage error (MAPE) is only 0.01%, and the complex simulations based on Monte-Carlo method testify the validity of the proposed model. The results of monthly electricity consumption in China's primary industry, demonstrate the proposed model catches the time-varying trend and has good performances, where MAPE_F and MAPE_T are below 5%. Moreover, the proposed TVGFM(1,1,N) model is superior to the benchmark models, grey polynomial model (GMP(1,1,N)), grey Fourier model (GFM(1,1,N)), seasonal grey model (SGM(1,1)), seasonal ARIMA model seasonal autoregressive integrated moving average model (SARIMA) and support vector regression (SVR).

The parameter estimates and forecasting of the new proposed TVGFM are studied, and the good fitting and forecasting accuracy of time-varying amplitude seasonal fluctuation series are testified by numerical simulations and a case study.

To investigate the impact of tax enforcement on (a) debt aggressiveness (DEA) and (b) dynamic adjustment of capital structure in Chinese listed firms.

The authors estimate the target capital structure by employing the different models. This study uses data of Chinese A-share listed firms from year 1998 to 2015.

The study suggests that the greater the intensity of tax enforcement, the more radical the listed companies' debt policy. The macroeconomic status and nature of property rights have significant moderating effect on the positive relationship between tax enforcement and DEA of listed companies. Further, tax enforcement has a significant impact on the dynamic adjustment of capital structure.

Research conclusions are conducive to tax administration departments to understand the economic consequences of tax enforcement and further promote tax administration efficiency. Additionally, listed companies can rationally adjust their capital structure to strengthen tax enforcement.

This research helps extend the influencing factors of corporate debt decision-making and capital structure dynamic adjustment to the level of tax enforcement and provide new evidence on the effects of tax enforcement on corporate capital structure.

The purpose of this research is to examine the effect of temporal landmarks on positive illusions and the downstream implications of this effect on consumer preference for new products with functional risks.

Study 1 adopted a single factor (temporal landmarks: beginning vs ending) between-subjects design. Study 2 adopted a 2 (temporal landmarks: beginning vs. ending) × 2 (salience of the temporal landmark: salient vs not salient) between-subjects design. Study 3 used a single factor (temporal landmarks: beginning vs ending) between-subjects design.

Through three studies, we show that the ending temporal landmarks reduce positive illusions (Studies 1 and 2). The underlying process is enhanced perceptions of psychological resource depletion (Study 3). The authors further show that decreased positive illusions lead consumers to less prefer new products with functional risks (Study 3).

Existing studies on temporal landmarks have exclusively focused on the beginning landmarks and account for its effects from a motive perspective. In contrast, the authors take a look at the ending landmarks and identify perceptions of psychological resource depletion as the underlying process, which suggests a new angel understand how temporal landmarks influence individuals' cognitions and behavior.

This paper aims to effectively achieve endmembers and relative abundances simultaneously in hyperspectral image unmixing yield. Hyperspectral unmixing, which is an important step before image classification and recognition, is a challenging issue because of the limited resolution of image sensors and the complex diversity of nature. Unmixing can be performed using different methods, such as blind source separation and semi-supervised spectral unmixing. However, these methods have disadvantages such as inaccurate results or the need for the spectral library to be known a priori.

This paper proposes a novel method for hyperspectral unmixing called fuzzy c-means unmixing, which achieves endmembers and relative abundance through repeated iteration analysis at the same time.

Experimental results demonstrate that the proposed method can effectively implement hyperspectral unmixing with high accuracy.

The proposed method present an effective framework for the challenging field of hyperspectral image unmixing.

Drawing on job demands and resources theory and the challenge-hindrance stressor framework, this study aims to investigate the effect of team knowledge complexity on team information sharing and information searching and examine whether team learning goal orientation mediates these effects.

The authors conducted two studies. Study 1 used a field survey study conducted among 374 employees positioned in 68 new product teams. Study 2 used a three-wave online survey study conducted among 208 leaders to investigate the teams they managed.

The findings of the two studies reveal that team knowledge complexity has a positive direct effect on team information sharing and information searching. Furthermore, team learning goal orientation mediates these two relationships.

The findings indicate that team knowledge complexity is generally beneficial for the team information process. Therefore, instead of fearing an increase in the knowledge complexity of the projects, organizations should dare to present challenge demands to team members to enhance their engagement in information processing. Organizations could also pay attention to team member selection during team composition processes. For example, selecting team members with a high level of learning goal orientation is helpful in facilitating team information processing.

Although previous studies have found that knowledge complexity is beneficial for team output, less is known about how knowledge complexity influences team processes. This study clarifies the relationships between team knowledge complexity, information sharing and information searching and examines team learning goal orientation as a vital mediator.

Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assists in prescribing. However, in TCM prescription, there are the main (Jun) herb and the auxiliary (Chen, Zuo and Shi) herb collocations. In a prescription, the types of auxiliary herbs are often more than the main herb and the auxiliary herbs often appear in other prescriptions. This leads to different frequencies of different herbs in prescriptions, namely, imbalanced labels (herbs). As a result, the existing ML algorithms are biased, and it is difficult to predict the main herb with less frequency in the actual prediction and poor performance. In order to solve the impact of this problem, this paper proposes a framework for multi-label traditional Chinese medicine (ML-TCM) based on multi-label resampling.

In this work, a multi-label learning framework is proposed that adopts and compares the multi-label random resampling (MLROS), multi-label synthesized resampling (MLSMOTE) and multi-label synthesized resampling based on local label imbalance (MLSOL), three multi-label oversampling techniques to rebalance the TCM data.

The experimental results show that after resampling, the less frequent but important herbs can be predicted more accurately. The MLSOL method is shown to be the best with over 10% improvements on average because it balances the data by considering both features and labels when resampling.

The authors first systematically analyzed the label imbalance problem of different sampling methods in the field of TCM and provide a solution. And through the experimental results analysis, the authors proved the feasibility of this method, which can improve the performance by 10%−30% compared with the state-of-the-art methods.

China's marine economy occupies an important position within the national economy, and its contribution thereto is constantly improving. The overall operation of the marine economy shows positive developmental trends with potential for further growth. The purpose of this research is to analyse the prosperity of China's marine economy, reveal trends therein and forecast the likely turning point in its operation.

Based on the periodicity and fluctuation of China's marine economy development, China's marine economic prosperity indicator system is established from five perspectives. On this basis, China's marine economic operation prosperity index can be synthesised and calculated, then a dynamic factor model is constructed. Using the filtering method to calculate China's marine economic operational Stock–Watson index, Markov switching has been used to determine the trend to transition. Furthermore, China's current marine economic prosperity is evaluated through analysis of influencing factors and correlation analysis.

The analysis shows that, from 2017 to 2019, the operation of the marine economy is relatively stable, and the prosperity index supports this finding; meanwhile it also exposes problems in China's marine economy, such as an unbalanced industrial structure, low marine economic benefits and insufficient capacity for sustainable development.

Through the analysis of the prosperity of China's marine economy, the authors reveal the trends in China's marine economy and forecast its likely future turning point.

The purpose of this study is to analyze the intelligent fault diagnosis method of rolling bearing.

The vibration signal data of rolling bearing has long time series and strong noise interference, which brings great difficulties for the accurate diagnosis of bearing faults. To solve those problems, an intelligent fault diagnosis model based on stacked denoising autoencoder (SDAE) and convolutional neural network (CNN) is proposed in this paper. The SDAE is used to process the time series data with multiple dimensions and noise interference. Then the dimension-reduced samples can be put into CNN model, and the fault classification results can be obtained by convolution and pooling operations of hidden layers in CNN.

The effectiveness of the proposed method is validated through experimental verification and comparative experimental analysis. The results demonstrate that the proposed model can achieve an average classification accuracy of 96.5% under three noise levels, which is 3-13% higher than the traditional models and single deep-learning models.

The combined SDAE–CNN model proposed in this paper can denoise and reduce dimensions of raw vibration signal data, and extract the in-depth features in image samples of rolling bearing. Consequently, the proposed model has more accurate fault diagnosis results for the rolling bearing vibration signal data with long time series and noise interference.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2019-0496/

The research attempts to estimate how the use of WeChat, the most popular mobile social networking application in contemporary China, affects rural household income.

Our materials are 4,552 rural samples from the Chinese General Social Survey, and a treatment effect (TE) model is employed to address the endogeneity of WeChat usage.

The results prove that WeChat usage has a statistically significant and positive correlation with rural household income. This conclusion remains robust after using alternative variables to replace the explanatory and dependent variables. Our research provides two channels through which WeChat usage boosts rural household income, namely, it can promote their off-farm employment and participation in investment activities.

Theoretically, the study provides several micro-evidences for understanding the impact of mobile social networks on rural household welfare. Further, our findings may shed light on the importance of digital technology applications in rural poverty alleviation for developing countries.

The aim of this study was to investigate the themes related to the achievement of Sustainable Development Goal (SDG) 12 in relation to tourism, and specifically to explore how the emotional psyche affects tourists’ environmentally responsible behaviors.

Based on the value-belief-norm theory, a research framework was developed to examine the serial mediation effects of environmental emotions in predicting tourists’ environmentally responsible behaviors. A total of 741 responses was collected from an online survey. Data were analyzed by the partial least squares structural equation modeling.

Environmental concern does not directly predict tourists’ environmentally responsible behaviors. Instead, environmental awe and environmental worry serially mediate the relationship between environmental concern and tourists’ environmentally responsible behaviors.

This study extends the value-belief-norm theory by integrating environmental emotions and empirically tests the effect of multiple psyches on responsible consumption, contributing to the achievement of SDG 12 in UN Agenda 2030.

本研究的目的是探究与旅游相关的可持续发展目标12的实现, 特别是探讨环境情感如何影响旅游者的环境责任行为。

基于价值信念-规范理论, 构建了环境情感预测旅游者环境责任行为的链式中介模型。研究共收集741份有效样本, 并采用偏最小二乘结构方程模型进行分析。

环境关心并不能直接预测旅游者的环境责任行为。但是, 环境敬畏和环境忧虑在环境关心与环境责任行为之间起链式中介作用。

本研究将环境情感扩展到价值信念规范理论中, 并实证检验了环境敬畏和环境忧虑两种环境情感对旅游者的负责任消费行为的影响, 呼应了联合国2030年议程中的可持续发展目标12。

el objetivo de este estudio fue investigar los temas relacionados con el logro del Objetivo de Desarrollo Sostenible 12 en relación con el turismo, y específicamente explorar cómo la psique emocional afecta los comportamientos ambientalmente responsables de los turistas.

Basado en la teoría del valor-creencia-norma, se desarrolló un marco de investigación para examinar los efectos de mediación en serie de las emociones ambientales en la predicción de los comportamientos ambientalmente responsables de los turistas. Se recopiló un total de 741 respuestas de una encuesta en línea. Los datos se analizaron mediante el modelo de ecuaciones estructurales de mínimos cuadrados parciales.

la preocupación ambiental no predice directamente los comportamientos ambientalmente responsables de los turistas. En cambio, el temor ambiental y la preocupación ambiental median en serie la relación entre la preocupación ambiental y los comportamientos ambientalmente responsables de los turistas.

este estudio amplía la teoría del valor-creencia-norma al integrar las emociones ambientales y prueba empíricamente el efecto de múltiples psiques en el consumo responsable, contribuyendo al logro del ODS 12 en la Agenda 2030 de la ONU.