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This study aims to investigate the effect of supervisor bottom-line mentality (SBLM) on subordinate work well-being using self-determination theory. Furthermore, it examines the mediating role of subordinate harmonious work passion (HWP) and obsessive work passion (OWP), as well as the moderating role of subordinate family motivation on the indirect effect of SBLM on subordinate work well-being.

The authors conducted two studies, an experiment and a field study, to test the hypotheses. In Study 1, the authors conducted an experimental study using a sample of 127 undergraduate students to examine how family motivation moderates the relationship between SBLM and subordinate work passion. Concurrently, in Study 2, the authors conducted a time-lagged field study involving 261 corporate employees in China to validate the findings derived from Study 1, as well as test the entire conceptual model.

The authors find in Study 1 that family motivation moderates the effects of SBLM on subordinate HWP and OWP. Nevertheless, Study 2 uncovers a negative association between SBLM and subordinate work well-being, with HWP and OWP mediating this relationship. Besides, family motivation moderates the mediating effect of HWP on the relationship between SBLM and subordinate work well-being.

The main contribution of this study is that the negative effect of SBLM impacts subordinate work well-being, thereby building an accurate and fine-grained knowledge base of the detrimental effects of bottom-line mentality (BLM). Additionally, this study expands the frontiers of knowledge in this area by investigating the mediating mechanisms and boundary conditions of SBLM on subordinate work well-being, effectively addressing a theoretical gap in BLM research.

The purpose of this study was to fabricate silicone products that had different hardnesses and moduli, thus partially addressing the limitations of homogeneous materials whose deformation depends on altered structure or dimensions, and to provide new dimensions for the design of silicone soft structures.

A soft material three-dimensional printing platform with a dual-channel printing capability was designed and built. Using the material extrusion method, material screening was first performed using single-channel printing, followed by dual-channel-regulated printing experiments on products having different hardness and modulus values.

The proportion of additives has an effect on the accuracy of the printed product. Material screening revealed that Sylgard 527 and SE 1700 could be printed without additives. The hardness and mechanical properties of products are related to the percentage in their composition of hard and soft materials. The hardness of the products could be adjusted from 26A to 42A and the Young’s modulus from 0.875 to 2.378 Mpa.

Existing silicone products molded by casting or printing are mostly composed of a single material, whose uniform hardness and modulus cannot meet the demand for differentiated deformation in the structure. The existing multihardness silicone material printing method has the problems of long material mixing time and slow hardness switching and complicated multi-extrusion head switching. In this study, a simple, low-cost and responsive material extrusion-based hardness programmable preparation method for silicone materials is proposed.

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 to examine the performance of a fastener composite coating system, sherardized (SD) coating/zinc-aluminum (ZA) coating whether it has good performance in marine environment.

In this paper, SD coating was fabricated on fastener surface by solid-diffusion method. ZA coating was fabricated by thermal sintering method. Corrosion behaviours of the composite coating were investigated with potentiodynamic polarization curves, open circuit potential and electrochemical impedance spectroscopy methods.

Neutral salt spray (NSS) and deep sea exposure tests revealed that the composite coating had excellent corrosion resistance. Polarization curve tests showed that corrosion current density of the sample with composite coating was significantly decreased, indicating an effective corrosion protection of the composite coating. OCP measurement of the sample in NaCl solution demonstrated that the composite coating had the best cathodic protection effect. The good corrosion resistance of the composite coating was obtained by the synergy of SD and ZA coating.

SD/ZA coating can be used in marine environment to prolong the life of carbon steel fastener.

SD/ZA composite coating can reduce the risk and accident caused by failed fastener, avoid huge economic losses.

A new kind of composite coating was explored to protect the carbon steel fastener in marine environment. And the composite coating has the long-term anti-corrosion performance both in simulated and marine environment test.

Given the importance of spatial effects in improving the accuracy of hotel demand forecasting, this study aims to introduce price and online rating, two critical factors influencing hotel demand, as external variables into the model, and capture the spatial and temporal correlation of hotel demand within the region.

For high practical implications, the authors conduct the case study in Xiamen, China, where the hotel industry is prosperous. Based on the daily demand data of 118 hotels before and during the COVID-19 period (from January to June 2019 and from January to June 2021), the authors evaluate the prediction performance of the proposed innovative model, that is, a deep learning-based model, incorporating graph convolutional networks (GCN) and gated recurrent units.

The proposed model simultaneously predicts the daily demand of multiple hotels. It effectively captures the spatial-temporal characteristics of hotel demand. In addition, the features, price and online rating of competing hotels can further improve predictive performance. Meanwhile, the robustness of the model is verified by comparing the forecasting results for different periods (during and before the COVID-19 period).

From a long-term management perspective, long-term observation of market competitors’ rankings and price changes can facilitate timely adjustment of corresponding management measures, especially attention to extremely critical factors affecting forecast demand, such as price. While from a short-term operational perspective, short-term demand forecasting can greatly improve hotel operational efficiency, such as optimizing resource allocation and dynamically adjusting prices. The proposed model not only achieves short-term demand forecasting, but also greatly improves the forecasting accuracy by considering factors related to competitors in the same region.

The originalities of the study are as follows. First, this study represents a pioneering attempt to incorporate demand, price and online rating of other hotels into the forecasting model. Second, integrated deep learning models based on GCN and gated recurrent unit complement existing predictive models using historical data in a methodological sense.

The purpose of this paper is to study the influences of test conditions to the tribological behavior of LaF₃ nanoparticles as an additive to a polyalphaolefin (PAO).

An Optimol‐SRV4 oscillating friction and wear tester (SRV) were used to investigate the tribological properties of LaF₃ nanoparticles as an additive in a polyalphaolefin (PAO). The 3‐D morphologies and wear loss volume of the worn scar were measured using a surface profilometer. The chemical state and the intensity of La and F elements on worn surface after friction test was investigated with X‐ray photoelectron spectroscopy to interpret the possible mechanisms of friction‐reduction and anti‐wear with LaF₃ nanoparticles.

The experimental results show that LaF₃ nanoparticles added to PAO exhibit excellent load‐carrying capacity, anti‐wear and friction‐reduction properties. LaF₃ nanoparticles deposited on the worn surface under lower test temperature during the friction test, and higher applied load, higher test frequency and longer test duration are propitious to the deposition of LaF₃ nanoparticles accumulated on the rubbing surface. Under higher temperature, a complicated tribo‐chemical reaction occurred during the friction process, the tribo‐chemical reaction product of La₂O₃ deposit on worn surface, which also exhibits good lubricating performance.

This paper investigates the tribological properties of LaF₃ nanoparticles as green oil additive in poly‐alpha‐olefin (PAO) under variable temperature, applied load, sliding speed and sliding duration. The results could be very helpful for the further applications of LaF₃ nanoparticles additives in industry.

This paper aims to investigate the anti-icing performance of the nanosecond dielectric barrier discharge (NSDBD) plasma actuator.

With the Lagrangian approach and the Messinger model, two different ice shapes known as rime and glaze icing are predicted. The air heating in the boundary layer over a flat plate has been simulated using a phenomenological model of the NSDBD plasma. The NSDBD plasma actuators are planted in the leading edge anti-icing area of NACA0012 airfoil. Combining the unsteady Reynolds-averaged Navier–Stokes equations and the phenomenological model, the flow field around the airfoil is simulated and the effects of the peak voltage, the pulse repetition frequency and the direction arrangement of the NSDBD on anti-icing performance are numerically investigated, respectively.

The agreement between the numerical results and the experimental data indicates that the present method is accurate. The results show that there is hot air covering the anti-icing area. The increase of the peak voltage and pulse frequency improves the anti-icing performance, and the direction arrangement of NSDBD also influences the anti-icing performance.

A numerical strategy is developed combining the icing algorithm with the phenomenological model. The effects of three parameters of NSDBD on anti-icing performance are discussed. The predicted results show that the anti-icing method is effective and may be helpful for the design of the anti-icing system of the unmanned aerial vehicle.

In order to encourage the high-water-consumption (HWC) manufacturers to carry out water-saving transformation relying on self-strength or outsourcing to a water-saving service company (WSSC) during production processes, government subsidies are provided according to water-saving efforts (WSE) or investment cost. In this context, the authors derive the participant's equilibrium decisions and the manufacturer's water-saving strategy. Additionally, the effects of subsidies on WSE and stakeholders' profits are discussed.

Mathematical models including optimization model and Stackelberg game model are constructed under different subsidy schemes.

The study finds that (1) there exists a threshold related to the subsidy coefficient for the HWC manufacturer when choosing between self-saving and outsourcing-saving. (2) When the technological competitive advantage between WSSC and manufacturer is within a certain range, government's subsidy promotes HWC enterprises to choose outsourcing-saving. (3) Given a water-saving mode, subsidy on investment cost is more effective for the government to achieve more environmental performance.

First, subsidy endogeneity can be considered to explore the optimal interval for government subsidies to maximize social welfare. Second, in outsourcing-saving, other types of contract can be discussed. Another extension is about model uncertainties. Finally, other policies on improving water efficiency can be also examined.

The paper includes implication for HWC manufacturers to select the best water-saving mode under subsidy, and it allows policymakers to understand the efficiency of proposed subsidies.

Decisions on water-saving efforts, selection of water-saving modes and operational planning are also regarded as business strategies in the paper. Particularly, the influences of different government subsidies are also considered and compared.

The purpose of this paper is to improve the properties of Sn−9Zn solder, so as to meet the requirements of industrial applications.

The effects of Praseodymium on property and Sn whisker growth under aging treatment in Sn−9Zn lead‐free solder were investigated.

The results indicate that with the addition of rare earth Pr, the wettability and mechanical properties of Sn−9Zn solder were improved. The best wettability and comprehensive property of soldered joint is obtained when the content of Pr is 0.08 wt.%. After aging treatment at 150°C for 360 h, the mechanical properties of Sn−9Zn−0.08Pr decreased but are still obviously higher than that of Sn−9Zn. Moreover, when the content of Pr reached 0.1 wt.%, plenty of Sn−Pr compounds were found in the Sn−9Zn solder. The inevitable oxidation of Sn−Pr compounds would cause a high stress accumulated within PrSn3 phases, which would be served as driving force to induce the Sn whisker sprout and growth after aging treatment at 150°C for 120 h to 360 h. Compared with the results in Sn−9Zn−0.5Ga−0.7Pr solder that Sn whisker observed until the addition of Pr reached 0.7 wt.%, it could be inferred that the addition of Ga may react against the sprout of Sn whisker.

It is found that the addition of Pr can improve the properties of solder and avoid Sn whisker growth in the right range and proper conditions. The cost of the solder with added Pr is limited to RMB 2 yuan/kg so it can be widely used in industry.

The purpose of this paper is to propose a three-way group decision-making approach to address the selection of green supplier, by extending decision-theoretic rough set (DTRS) into hesitant fuzzy linguistic (HFL) environment, considering the flexible evaluation expression format of HFL term set (HFLTS) and the idea of minimum expected risk in DTRS.

Specifically, the authors first present the calculation method of the conditional probability and discuss the loss functions of DTRS with HFL element (HFLE), along with some associated properties being investigated in detail. Further, three-way group decisions rules can be deduced, followed by the cost of every green supplier candidate. Thus, based on these discussions, a novel green supplier selection DTRS model that combines multi-criteria group decision-making (MCGDM) and HFLTS is designed.

A numerical example of green supplier selection, the comparative analysis and associated discussions are conducted to illustrate the applicability and novelty of the presented model.

The selection of green supplier has played a critically strategic role in sustainable enterprise development due to continuous environmental concerns. This paper offers an insight for companies to select green supplier selection from the perspective of three-way group decisions.

This paper uses three-way decisions to address green supplier selection in the HFL context, which is considered as a MCGDM issue.