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The purpose of this paper is to examine how temporal leadership relates to employee innovative job performance through pro-social rule breaking for efficiency (PSRB_E) and vigor. As such, it draws from both motivational and affective perspectives to investigate the way in which leaders manage employees' time to boost employee innovation at work.

This study adopts a two-source survey with coworker dyads from multiple organizations in China. Two hundred and three focal employees rated temporal leadership, time pressure and vigor. Each focal employee was rated by a coworker on PSRB_E and innovative job performance. Structural equation modeling (SEM) and bootstrapping techniques are used to examine the hypothesized relationships with the R package for latent variable analysis (i.e. lavaan).

The results suggest that temporal leadership positively relates to employees' innovative job performance through the mediations of their PRSB_E and vigor, respectively.

This study is one of the first that sought to understand the effect of temporal leadership on employee innovative job performance. It also casts light upon the motivational and affective mechanisms underlying such a linkage.

This study is intended to investigate the relationship between supervisor bottom-line mentality (BLM) and employee workplace well-being. In addition, this study discusses the mediating roles of perception of organizational politics and job anxiety in this relationship.

Data were gathered from a two-wave survey of 301 full-time employees in southern China. The PROCESS macro in SPSS was applied to test the hypotheses.

Results showed that supervisor BLM was negatively related to employee workplace well-being. Moreover, perceptions of organizational politics and job anxiety played multiple mediating roles in the relationship between supervisor BLM and employee workplace well-being.

Drawing on the conservation of resource (COR) theory and cognitive-affective personality system (CAPS) theory, this study proposes a multiple mediation model to advance our understanding of how supervisor BLM affects employee workplace well-being.

Drawing on appraisal theory of emotion, this study aims to establish and test a moderated mediation model underlying the process of exploitative leadership by investigating the mediating role of job anxiety and the moderating role of psychological entitlement.

We collected two-wave questionnaire data from 355 supervisors with matched responses from 1,065 subordinates in Hubei province of China. Hierarchical multiple regression and PROCESS macro were applied to test our hypotheses.

The results indicated that supervisor perceived overqualification prompted supervisor job anxiety, which then led to raised exploitative leadership. Additionally, we found that supervisor psychological entitlement intensified the direct effect of supervisor perceived overqualification on supervisor job anxiety as well as the indirect effect of supervisor perceived overqualification on exploitative leadership through supervisor job anxiety.

Although researchers have documented a growing number of consequences of exploitative leadership, less focus has been placed on its antecedents. The study extends our understanding of why, how and when supervisors engage in exploitative behaviors.

The purpose of this paper is to propose a new video prediction-based methodology to solve the manufactural occlusion problem, which causes the loss of input images and uncertain controller parameters for the robot visual servo control.

This paper has put forward a method that can simultaneously generate images and controller parameter increments. Then, this paper also introduced target segmentation and designed a new comprehensive loss. Finally, this paper combines offline training to generate images and online training to generate controller parameter increments.

The data set experiments to prove that this method is better than the other four methods, and it can better restore the occluded situation of the human body in six manufactural scenarios. The simulation experiment proves that it can simultaneously generate image and controller parameter variations to improve the position accuracy of tracking under occlusions in manufacture.

The proposed method can effectively solve the occlusion problem in visual servo control.

As a distinctive form of communist heritage tourism, the ideology and government-led form of red tourism warrants an in-depth examination of how tourists consume and perceive it. This study aims to reveal tourists’ perception of red tourism through the lens of destination image.

This study collected 9,819 user-generated photographs within four types of red tourism destinations (RTDs) and used a computer visual and semiotic analysis approach to conduct photograph-based cognitive and affective attributes extraction. Network analysis further visualized the co-relations between cognitive images and affective images. ANOVA analysis compared the differences of the four types of destination images.

Ten dimensions of cognitive image and eight categories of affective image of red tourism were identified. It found that monuments, statues, memorial symbols were the distinctive cognitive features, and admiration was the most dominant emotion. Heterogeneity of destination images was identified among the four types of RTDs.

To the best of the authors’ knowledge, the study is one of the first to explore tourists’ consumption of red tourism through the lens of destination image, which reveals the inconsistencies between the officially projected images and tourists’ perceived images of red tourism. Using Plutchik’s model, it validates a series of positive and negative emotions contributing to the affective images of red tourism, which expands the findings of emotions within the extant red tourism research. Through combined applications of computer visual and semiotic analysis, ANOVA, network analysis and model visualization, the study provides an important methodological triangulation for photograph-based destination image studies.

红色旅游作为共产主义旅游的独特形式, 游客如何感知这种国家意识形态植入与政府主导型旅游值得深入研究。本研究旨在从目的地意象视角揭示游客红色旅游感知。

本研究收集四种类型的红色旅游地9819张用户生成照片, 利用计算机视觉-情感析法对照片进行认知和情感元素提取。复杂网络分析揭示了认知意象与情感意象之间的关联。方差分析比较了四种红色旅游地意象的差异。

本研究确定了红色旅游认知意象的十个维度和情感意象的八个类别。研究发现, 纪念碑、雕像、纪念符号是其独特的认知意象元素, 钦佩是其最主要的情感，四种类型红色旅游地意象存在差异性。

本文是同类研究中首次从目的地意象视角探索游客对红色旅游地感知, 揭示了红色旅游官方投射意象与游客感知意象之间的差异。利用Plutchik情感之轮模型, 验证了一系列积极和消极情绪构成红色旅游地情感意象, 拓展了红色旅游的情感发现。综合运用计算机视觉-情感分析、方差分析、网络分析和模型可视化等方法, 为基于照片的旅游目的地意象研究提供了一个重要方法。

Como forma distintiva del turismo del patrimonio comunista, la ideología y la forma gubernamental del turismo rojo justifican un examen en profundidad de cómo lo consumen y perciben los turistas. Este estudio pretende revelar la percepción que tienen los turistas del turismo rojo desde la perspectiva de la imagen del destino.

Este estudio recopiló 9.819 fotografías generadas por los usuarios dentro de cuatro tipos de destinos de turismo rojo, y utilizó un enfoque de análisis visual y semiótico por ordenador para llevar a cabo la extracción de atributos cognitivos y afectivos basados en fotografías. El análisis de redes visualizó además las correlaciones entre las imágenes cognitivas y las imágenes afectivas. El análisis ANOVA comparó las diferencias de los cuatro tipos de imágenes de destino.

Se identificaron diez dimensiones de imagen cognitiva y ocho categorías de imagen afectiva del turismo rojo. Se descubrió que los monumentos, las estatuas y los símbolos conmemorativos eran los rasgos cognitivos distintivos, y la admiración la emoción más dominante. Se identificó una heterogeneidad de imágenes de destino entre los cuatro tipos de destinos de turismo rojo.

El estudio es uno de los primeros en explorar el consumo de turismo rojo por parte de los turistas a través de la lente de la imagen del destino, lo que revela las incoherencias entre las imágenes proyectadas oficialmente y las imágenes percibidas por los turistas del turismo rojo. Utilizando el modelo de Plutchik, valida una serie de emociones positivas y negativas que contribuyen a las imágenes afectivas del turismo rojo, lo que amplía los hallazgos sobre las emociones dentro de la investigación existente sobre el turismo rojo. Mediante aplicaciones combinadas de análisis visual y semiótico por ordenador, ANOVA, análisis de redes y visualización de modelos, el estudio proporciona una importante triangulación metodológica para los estudios de la imagen del destino basados en fotografías.

The purpose of our study is to explore the “idiosyncratic volatility puzzle” in Chinese stock market from the perspective of investors' heterogeneous beliefs. To delve into the relationship between idiosyncratic volatility and investors' heterogeneous beliefs, and uncover the ability of heterogeneous beliefs, as well as to explain the “idiosyncratic volatility puzzle”, we construct our study as follows.

Our study adopts the unexpected trading volume as proxies of heterogeneity, the residual of Fama–French three-factor model as proxies of idiosyncratic volatility. Portfolio strategies and Fama–MacBeth regression are used to investigate the relationship between the two proxies and stock returns in Chinese A-share market.

Investors' heterogeneous beliefs, as an intermediary variable, are positively correlated with idiosyncratic volatility. Meanwhile, it could better demonstrate the negative correlation between the idiosyncratic volatility and future stock returns. It is one of the economic mechanisms linking idiosyncratic volatility to subsequent stock returns, which can account for 11.28% of the puzzle.

The findings indicate that idiosyncratic volatility is significantly and positively correlated with heterogeneous beliefs and that heterogeneous beliefs are effective intervening variables to explain the “idiosyncratic volatility puzzle”.

This paper aims to study the controlled release and synergistic effect of water-soluble polyvinyl alcohol (PVA) on phosphate corrosion inhibitor at the interface of thermal insulation cotton/carbon steel.

This study was carried out using a coating method, scanning electron microscopy, energy dispersive spectroscopy and AC impedance.

The single-phase phosphate particles were coated/adsorbed on the PVA film, which was formed on the fiber surface of corrosion inhibitor/PVA-impregnated rock wool sample. On the surface of Q235 steel, an effective protective film was formed by the corrosion inhibitor with partially dissolved PVA that can significantly increase the polarization resistance of corrosion reaction, and reduce the capacitive reactance of electric double layer. The rock wool impregnated with the phosphate corrosion inhibitor and 1.5 per cent PVA showed obvious controlled release and inhibition synergism.

The rock wool impregnated with the phosphate corrosion inhibitor and 1.5 per cent PVA showed the following advantages: the adsorption and release quantities of the corrosion inhibitor increased by 3.3 and 2.9 times, respectively; the release-adsorption equilibrium time increased from 2 to 6 h; and the corrosion inhibition efficiency increased from 61.55 per cent to 94.6 per cent.

To accurately forecast logistics freight volume plays a vital part in rational planning formulation for a country. The purpose of this paper is to contribute to developing a novel combination forecasting model to predict China’s logistics freight volume, in which an improved PSO-BP neural network is proposed to determine the combination weights.

Since BP neural network has the ability of learning, storing, and recalling information that given by individual forecasting models, it is effective in determining the combination weights of combination forecasting model. First, an improved PSO based on simulated annealing method and space-time adjustment strategy (SAPSO) is proposed to solve out the connection weights of BP neural network, which overcomes the problems of local optimum traps, low precision and poor convergence during BP neural network training process. Then, a novel combination forecast model based on SAPSO-BP neural network is established.

Simulation tests prove that the proposed SAPSO has better convergence performance and more stability. At the same time, combination forecasting models based on three types of BP neural networks are developed, which rank as SAPSO-BP, PSO-BP and BP in accordance with mean absolute percentage error (MAPE) and convergent speed. Also the proposed combination model based on SAPSO-BP shows its superiority, compared with some other combination weight assignment methods.

SAPSO-BP neural network is an original contribution to the combination weight assignment methods of combination forecasting model, which has better convergence performance and more stability.

The purpose of this study is to develop a novel region-based convolutional neural networks (R-CNN) approach that is more efficient while at least as accurate as existing R-CNN methods. In this way, the proposed method, namely R²-CNN, provides a more powerful tool for pedestrian extraction for person re-identification, which involve a huge number of images and pedestrian needs to be extracted efficiently to meet the real-time requirement.

The proposed R²-CNN is tested on two types of data sets. The first one the USC Pedestrian Detection data set, which consists of three sub-sets USC-A, UCS-B and USC-C, with respect to their characteristics. This data set is used to test the performance of R²-CNN in the pedestrian extraction task. The speed and performance of the investigated algorithms were collected. The second data set is the PASCAL VOC 2007 data set, which is a common benchmark data set for object detection. This data set was used to analyze characteristics of R²-CNN in the case of general object detection task.

This study proposes a novel R-CNN method that is both more efficient and more accurate than existing methods. The method, when used as an object detector, would facilitate the data preprocessing stage of person re-identification.

The study proposes a novel approach for object detection, which shows advantages in both efficiency and accuracy for pedestrian detection task. It contributes to both data preprocessing for person re-identification and the research on deep learning.

The purpose of this study was to investigate the reason of G105 coated drill-pipes suffering from washout after drilling for 70000–80000 m.

The microstructure, micromorphology and corrosion products near the washout were analyzed by metallurgical microscopy, SEM and EDS.

Results showed the metallographic microstructure of the material was typical tempered sorbite. No fatigue crack was observed. Drill-pipe washout was caused mainly by the inclusion of MnS in steel because of the excess S and by damaged coating, both of which induced pit nucleation and promoted the pitting corrosion process. The corrosion hole extended from the interior to the exterior, which resulted in the fracturing of the external drill-pipe surface under pressure.

This paper can give practical help to the selection of drill pipe materials in the future.