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This study aims to develop a sol-gel-based coating to provide a long-lasting corrosion protection on AZ31 Mg alloy. Silane-based sol-gel coatings have been successfully applied to Mg alloys for corrosion protection. However, the micro or nano defects formed during condensation and solidification will cause the coating failure during a long-lasting immersion in a saline solution. More durable corrosion-protective sol-gel coatings are needed.

A sol-gel-based coating was modified on AZ31 Mg alloy by levodopa (DOPA). The long-lasting corrosion protection mechanism was studied by multiple electrochemical testing methods and surface characterization techniques.

Long-term testing by electrochemical impedance spectroscopy in aqueous 0.1 M NaCl indicated that the modified DOPA@sol-gel coating exhibited significant corrosion protection performance (>14 days). In comparison, the DOPA-free sol-gel coating failed only after three days of testing. The improved corrosion protection is attributed to the self-polymerized DOPA filling to the micro or nano defects in the glassy cross-linked networks of the sol-gel coating, which greatly improves the compactness of the coating.

The method of this study is simple and easy to process, the raw materials are green and the protective effect is excellent, which is of significance for the study of magnesium alloy corrosion protection.

Incorporating dynamic spatial effects exhibits considerable potential in improving the accuracy of forecasting tourism demands. This study aims to propose an innovative deep learning model for capturing dynamic spatial effects.

A novel deep learning model founded on the transformer architecture, called the spatiotemporal transformer network, is presented. This model has three components: the temporal transformer, spatial transformer and spatiotemporal fusion modules. The dynamic temporal dependencies of each attraction are extracted efficiently by the temporal transformer module. The dynamic spatial correlations between attractions are extracted efficiently by the spatial transformer module. The extracted dynamic temporal and spatial features are fused in a learnable manner in the spatiotemporal fusion module. Convolutional operations are implemented to generate the final forecasts.

The results indicate that the proposed model performs better in forecasting accuracy than some popular benchmark models, demonstrating its significant forecasting performance. Incorporating dynamic spatiotemporal features is an effective strategy for improving forecasting. It can provide an important reference to related studies.

The proposed model leverages high-frequency data to achieve accurate predictions at the micro level by incorporating dynamic spatial effects. Destination managers should fully consider the dynamic spatial effects of attractions when planning and marketing to promote tourism resources.

This study incorporates dynamic spatial effects into tourism demand forecasting models by using a transformer neural network. It advances the development of methodologies in related fields.

纳入动态空间效应在提高旅游需求预测的准确性方面具有相当大的潜力。本研究提出了一种捕捉动态空间效应的创新型深度学习模型。

本研究提出了一种基于变压器架构的新型深度学习模型, 称为时空变压器网络。该模型由三个部分组成：时空转换器、空间转换器和时空融合模块。时空转换器模块可有效提取每个景点的动态时间依赖关系。空间转换器模块可有效提取景点之间的动态空间相关性。提取的动态时间和空间特征在时空融合模块中以可学习的方式进行融合。通过卷积运算生成最终预测结果。

结果表明, 与一些流行的基准模型相比, 所提出的模型在预测准确性方面表现更好, 证明了其显著的预测性能。纳入动态时空特征是改进预测的有效策略。它可为相关研究提供重要参考。

所提出的模型利用高频数据, 通过纳入动态空间效应, 在微观层面上实现了准确预测。旅游目的地管理者在规划和营销推广旅游资源时, 应充分考虑景点的动态空间效应。

本研究通过使用变压器神经网络, 将动态空间效应纳入旅游需求预测模型。它推动了相关领域方法论的发展。

La incorporación de efectos espaciales dinámicos ofrece un considerable potencial para mejorar la precisión de la previsión de la demanda turística. Este estudio propone un modelo innovador de aprendizaje profundo para capturar los efectos espaciales dinámicos.

Se presenta un novedoso modelo de aprendizaje profundo basado en la arquitectura transformadora, denominado red de transformador espaciotemporal. Este modelo tiene tres componentes: el transformador temporal, el transformador espacial y los módulos de fusión espaciotemporal. El módulo transformador temporal extrae de manera eficiente las dependencias temporales dinámicas de cada atracción. El módulo transformador espacial extrae eficientemente las correlaciones espaciales dinámicas entre las atracciones. Las características dinámicas temporales y espaciales extraídas se fusionan de manera que se puede aprender en el módulo de fusión espaciotemporal. Se aplican operaciones convolucionales para generar las previsiones finales.

Los resultados indican que el modelo propuesto obtiene mejores resultados en la precisión de las previsiones que algunos modelos de referencia conocidos, lo que demuestra su importante capacidad de previsión. La incorporación de características espaciotemporales dinámicas supone una estrategia eficaz para mejorar las previsiones. Esto puede proporcionar una referencia importante para estudios afines.

El modelo propuesto aprovecha los datos de alta frecuencia para lograr predicciones precisas a nivel micro incorporando efectos espaciales dinámicos. Los gestores de destinos deberían tener plenamente en cuenta los efectos espaciales dinámicos de las atracciones en la planificación y marketing para la promoción de los recursos turísticos.

Este estudio incorpora efectos espaciales dinámicos a los modelos de previsión de la demanda turística mediante el empleo de una red neuronal transformadora. Supone un avance en el desarrollo de metodologías en campos afines.

The purpose of this paper is an attempt to design a proper incentive coordination mechanism to deal with the channel conflicts between the traditional sales and online direct sales.

With respect to the problems of channel conflicts between the traditional sales and online direct sales, to optimize the sale system and get more profits, considering the influences of consumer network acceptance, the authors establish demand and profit function based on consumer's utility, respectively. What's more, we exploit the game theory to analyze the optional decisions of the supply chain under the incentive coordination condition and no incentive coordination condition, and then we discuss the supply chain's optimal pricing, demand, profit and compensation incentive levels with the different effect of consumer network acceptance.

The level of compensation incentive provided by the manufacturer is influenced by consumer network acceptance and product cost. The lower the consumer network acceptance, the better the compensation incentive coordination effect of manufacturers. Manufacturers, wholesalers, retailers and consumers are all important players in real supply chain relationships. When a manufacturer exists as a dominant role, it should pay full attention to the impact of consumer behavior on supply chain decisions.

The research can clarify the influence and mechanism of consumer behavior on supply chain channel conflict coordination, and deal with channel conflicts.

The proposed incentive coordination can effectively realize supply chain channel conflict resolution, and provide decision-making ideas and methods for manufacturers to develop the supply chain management of online direct sales channels.

This paper aims to compare the forecasting performance of different models with and without big data predictors from search engines and social media.

Using daily tourist arrival data to Mount Longhu, China in 2018 and 2019, the authors estimated ARMA, ARMAX, Markov-switching auto-regression (MSAR), lasso model, elastic net model and post-lasso and post-elastic net models to conduct one- to seven-days-ahead forecasting. Search engine data and social media data from WeChat, Douyin and Weibo were incorporated to improve forecasting accuracy.

Results show that search engine data can substantially reduce forecasting error, whereas social media data has very limited value. Compared to the ARMAX/MSAR model without big data predictors, the corresponding post-lasso model reduced forecasting error by 39.29% based on mean square percentage error, 33.95% based on root mean square percentage error, 46.96% based on root mean squared error and 45.67% based on mean absolute scaled error.

Results highlight the importance of incorporating big data predictors into daily demand forecasting for tourism attractions.

This study represents a pioneering attempt to apply the regularized regression (e.g. lasso model and elastic net) in tourism forecasting and to explore various daily big data indicators across platforms as predictors.

The purpose of this study was to clarify the applicable conditions for coatings with different element contents. Surface coating on existed tool material is an effective way to improve the cutting tool properties in extremely adverse machining conditions. These coatings are generally non-metering compounds. Each coating with a certain element ratio has a specific application condition, and the relationship between element contents and performance should be defined.

(Zr, Ti)N hard coatings with different element contents were deposited on cemented carbide. The ball-on-disk method reciprocating sliding wear tests against 40Cr hardened steel were carried out. This paper was focused on analysis of the friction and wear behaviors of these (Zr, Ti)N coatings with different element contents.

The results indicated that atomic ratio of Zr and Ti was the main factor that decided the friction coefficients. The friction coefficient and wear rate showed a gradual downward trend at 10 N with the increase of Zr content. The wear rate was the smallest at the atomic percentage of nitrogen 0.466. If the lattice distortion of the coatings gets too severe, the coatings would have a high wear rate.

The optimal application conditions of the coatings were defined, and this was important for customized design or choice of the coatings under different cutting parameters.

This paper aims to comprehensively clarify the research status of thermal transport of supercritical aviation kerosene, with particular interests in the effect of cracking on heat transfer.

A brief review of current research on supercritical aviation kerosene is presented in views of the surrogate model of hydrocarbon fuels, chemical cracking mechanism of hydrocarbon fuels, thermo-physical properties of hydrocarbon fuels, turbulence models, flow characteristics and thermal performances, which indicates that more efforts need to be directed into these topics. Therefore, supercritical thermal transport of n-decane is then computationally investigated in the condition of thermal pyrolysis, while the ASPEN HYSYS gives the properties of n-decane and pyrolysis products. In addition, the one-step chemical cracking mechanism and SST k-ω turbulence model are applied with relatively high precision.

The existing surrogate models of aviation kerosene are limited to a specific scope of application and their thermo-physical properties deviate from the experimental data. The turbulence models used to implement numerical simulation should be studied to further improve the prediction accuracy. The thermal-induced acceleration is driven by the drastic density change, which is caused by the production of small molecules. The wall temperature of the combustion chamber can be effectively reduced by this behavior, i.e. the phenomenon of heat transfer deterioration can be attenuated or suppressed by thermal pyrolysis.

The issues in numerical studies of supercritical aviation kerosene are clearly revealed, and the conjugation mechanism between thermal pyrolysis and convective heat transfer is initially presented.

This chapter presents and discusses various steps to ensure empirical reliability and theoretical validity in the construction of competence scales in graduate surveys. The development of a scale to assess demands of the teacher profession and related abilities in graduates for a German tracer study project serves as an example. Confirmatory factor analysis (CFA), principal component analysis (PCA) and Cronbach’s coefficient alpha are employed to test the reliability of the scale. Differing results illustrate how the method applied influences decisions in the process of developing a scale. Our findings show that multidimensionality can only be tested appropriately by CFA; PCA renders no feasible or similar results to CFA depending on the predetermination of the number of factors; Cronbach’s alpha produces misleading results as the prerequisite assumption of unidimensionality is violated by the data.

Inclination angle has been reported to have an enhancing effect on the thermal-hydraulic characteristics and entropy of some thermal systems. Therefore, this paper aims to numerically investigate the effects of inclination angle, volume concentration and Reynolds number on the thermal and hydraulic characteristics and entropy generation rates of water-based Al₂O₃ nanofluids through a smooth circular aluminum pipe in a turbulent flow.

A constant heat flux of 2,000 Watts is applied to the circular surface of the tube. Reynolds number is varied between 4,000 and 20,000 for different volume concentrations of alumina nanoparticles of 0.5%, 1.0% and 2.0% for tube inclination angles of ±90o, ±60o, ±45o, ±30o and 0o, respectively. The simulation is performed in an ANSYS Fluent environment using the realizable kinetic energy–epsilon turbulent model.

Results show that +45o tube orientation possesses the largest thermal deviations of 0.006% for 0.5% and 1.0% vol. concentrations for Reynolds numbers 4,000 and 12,000. −45o gives a maximum pressure deviation of −0.06% for the same condition. The heat transfer coefficient and pressure drop give maximum deviations of −0.35% and −0.39%, respectively, for 2.0% vol. concentration for Reynolds number of 20,000 and angle ±90o. A 95%–99.8% and 95%–98% increase in the heat transfer and total entropy generation rates, respectively, is observed for 2.0% volume concentration as tube orientation changes from the horizontal position upward or downward.

Research investigating the effect of inclination angle on thermal-hydraulic performance and entropy generation rates in-tube turbulent flow of nanofluid is very scarce in the literature.

This article aims to examine how a person’s thinking style, specifically holistic versus analytic, and a firm’s crisis apology with the remedial solution framed in “why” (vs “how”) terms can interactively impact consumers’ perceived efficacy of the firm to respond to the crisis and their impression or evaluation of the brand.

Hypotheses were tested through three experimental studies involving 308 participants recruited in China. Participants answered survey questions investigating the interactive effects from consumers’ thinking style (culture as a proxy in Study 1, measured in Study 2 or primed in Study 3) and a brand’s crisis apology with the remedial solution framed in “why” (vs “how”) terms on consumers’ perceived efficacy and evaluation of the firm.

The frame of the remedial solution resulting in a higher evaluation improvement depended on a consumer’s thinking style. For holistic thinkers, a “why” (vs “how”) framed remedial solution resulted in a higher evaluation improvement; however, for analytic thinkers, a “how” (vs “why”) framed remedial solution resulted in a higher evaluation improvement. Additionally, the results showed that a consumer’s perceived efficacy of the brand being able to successfully respond to the crisis mediated the interactive effects of the remedial solution framing and thinking styles on the evaluation improvement.

The findings provide evidence that framing of the remedial solution can be leveraged as a tool to reduce negative impact resulting from a brand crisis. Specifically, the results suggest that companies may do well to employ a “why” framed remedial solution, particularly in cases where consumers are likely to process information holistically. Conversely, a “how” framed remedial solution may be effective in situations where consumers are likely to process information analytically.

This research contributes to the literature, being among the first to consider how the remedial solution framing in a firm’s apology can enhance people’s evaluation of the brand and decrease the perceived negative impact resulting from the brand crisis.

The purpose of this paper is to analyze the historical pattern of environmental cost due to grain production in China and to provide further implications of technologies and policies for the transformation of China’s agricultural development toward sustainable intensification.

The data sets about grain production, arable land and chemical fertilizer use in China were collected from FAO, NBSC, and IFA. Greenhouse gas emissions were estimated using life cycle assessments. The policies concerning grain production and the environment were collected from the Ministry of Agriculture, and the State Council of China.

China has produced enough food to feed its growing population, but has neglected the resource-environmental costs of grain production since 1978. Consequently, China’s grain production is always accompanied with a high cost of resource and environment sustainability. However, from 2006 to 2015, the growth rate of grain production has surpassed that of chemical fertilizer consumption, resulting in improvement in nutrient use efficiency and decreasing trends of environmental cost for grain production. This could be partially attributed to technology innovations, such as Soil-Testing and Fertilizer-Recommendations (STFR), soil quality and crop management improvement, and so on, and policy supports (policies of STFR, soil quality improvement, and high-yield construction). This indicated that China’s grain production is starting to transform from high-input and high-output model to “less for more.”

This study is the first to determine the detailed, historical role of technological innovation and agri-environmental policy on the sustainability of grain production in China. The findings should have significant implications for technology and policy for the transformation of China’s agriculture development to sustainable intensification.