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The anti-friction and anti-wear properties of WS₂@GO composites on paraffin liquid were investigated with a four-ball tribometer.

A series of graphene oxide (GO) nano hybrid composites decorated with tungsten disulfide (WS₂) were prepared in-suit by hydrothermal strategy.

The results showed that compared to the virgin oil, friction coefficient and diameter of wear scare of lubricant oil containing W/G = 1:1 hybrid composite was reduced by 42.7% and 31.6%, respectively. At the microscopic, the excellent lubrication performance resulted from the tribo-chemical reaction on the sliding interface, which promotes the formation of tribo-film with a thickness of 8 nm. The carbonization compound, WO₃ and Fe₂O₃ in the tribo-film results from the tribo-chemical reactions at the sliding interface, which can improve the stability and strength of tribo-film. Thereby the metal surface was further protected from friction and wear.

A series of WS₂@GO composites were prepared in-suit by a hydrothermal strategy, and the tribo-film was analyzed by the transmission electron microscope and X-ray photoelectron spectrometer.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2024-0397

The accurate prediction of driving torque demand is essential for the development of motion controllers for mobile robots on complex terrains. This paper aims to propose a hybrid model of torque prediction, adaptive EC-GPR, for mobile robots to address the problem of estimating the required driving torque with unknown terrain disturbances.

An error compensation (EC) framework is used, and the preliminary prediction driving torque value is achieved using Gaussian process regression (GPR). The error is predicted using a continuous hidden Markov model to generate compensation for the prediction residual caused by terrain disturbances and uncertainties. As the final step, a gain coefficient is used to adaptively tune the significance of the compensation term through parameter resetting. The proposed model is verified on a sample set, including the driving torque of a mobile robot on three different sandy terrains with two driving modes.

The results show that the adaptive EC-GPR yields the highest prediction accuracy when compared with existing methods.

It is demonstrated that the proposed model can predict the driving torque accurately for mobile robots in an unconstructed environment without terrain identification.

Meteorological disasters pose a significant risk to people’s lives and safety, and accurate prediction of weather-related disaster losses is crucial for bolstering disaster prevention and mitigation capabilities and for addressing the challenges posed by climate change. Based on the uncertainty of meteorological disaster sequences, the damping accumulated autoregressive GM(1,1) model (DAARGM(1,1)) is proposed.

Firstly, the autoregressive terms of system characteristics are added to the damping-accumulated GM(1,1) model, and the partial autocorrelation function (PACF) is used to determine the order of the autoregressive terms. In addition, the optimal damping parameters are determined by the optimization algorithm.

The properties of the model were analyzed in terms of the stability of the model solution and the error of the restored value. By fitting and predicting the losses affected by meteorological disasters and comparing them with the results of four other grey models, the validity of the new model in fitting and prediction was verified.

The dynamic damping trend factor is introduced into the grey generation operator so that the grey model can flexibly adjust the accumulative order of the sequence. On the basis of the damping accumulated grey model, the autoregressive term of the system characteristics is introduced to take into account the influence of the previous data, which is more descriptive of the development trend of the time series itself and increases the effectiveness of the model.

This study investigates the impact of geopolitical risk (GPR) on foreign remittances (FRs) for the top remittance-receiving countries.

The sample includes Mexico, France, Egypt, China, the Philippines, India, Vietnam, Ukraine, Germany and Belgium for the annual period of 1998–2022 using the nonlinear panel autoregressive distributed lag (ARDL) model to determine the asymmetry in the relationship.

The results suggest that, in the short term, positive GPR shocks have a positive and significant impact on FRs received. On the other hand, the long-run results suggest that adverse GPR shocks negatively affect FRs received in the sampled countries. Additionally, the study confirms the asymmetric impact of GPR on top remittances received in countries.

The policymakers, migrants and recipients should consider the asymmetric nature of GPR while making decisions regarding policies and the transfer of remittances. This information can be used to create more effective policies for controlling and reducing the effects of GPR on overseas remittances, such as assisting migrant workers and developing methods to lessen the volatility of these flows.

Acknowledging the potential fluctuations and uncertainties associated with GPR is crucial to make informed choices regarding remittance-related matters.

This study aims to address the macroeconomic factors effect on the travel and leisure (T&L) industry throughout Europe within the context of the Russo-Ukrainian war that have started on 24 February 2022. Specifically, top tourist destinations are analysed, such as Spain, France, Italy and Portugal, as well as Europe in general.

This study adopts the panel regression approach based on the data that is provided on a daily basis, and it covers a period of nearly 14 months, starting on 24 February 2022 and ending on 15 April 2023.

The findings indicate that the European T&L sector is impacted by macroeconomic variables. Namely, the T&L sector is significantly impacted by interest rates, geopolitical risk, oil and gas, whereas inflation has a muted effect, indicating a comparatively lesser influence on the dynamics of the industry. This research contributes to existing literature by providing one of the first quantitative analyses of how macroeconomic factors impact the European T&L business in the context of a geopolitical conflict.

A study of the Russian–Ukrainian war may be limited by a number of research constraints. The continuing nature of the conflict, the lack of communication between the parties and potential political prejudice are some of these difficulties. Any research on the Russo-Ukrainian war should be done with these limits in mind.

Macroeconomic variables play a significant role on the T&L sector development; therefore, when designing resilience strategies, they need to be accounted for.

To the best of authors’ knowledge, this is one of the first studies to analyse how macroeconomic factors affected the European T&L business using a quantitative approach. The macroeconomic variables that were taken into account in this study included interest rates, inflation, oil and petrol prices, as well as the geopolitical risk index.

本研究探讨在 2022 年 2 月 24 日开始的俄乌战争对整个欧洲旅游和休闲 （T&L） 行业的宏观经济因素的影响。具体来说, 本文分析了著名旅游目的地, 例如西班牙, 法国, 义大利和葡萄牙, 以及整个欧洲。

研究采用基于每天提供的数据的面板回归方法, 涵盖近 14 个月, 从 2022 年 2 月 24 日开始, 到 2023 年 4 月 15 日结束。

我们的研究结果表明, 欧洲运输和物流行业受到宏观经济变数的影响。也就是说, 运输和物流行业受到利率、地缘政治风险、石油和天然气的严重影响, 而通货膨胀的影响则很温和。

对俄乌战争的研究可能受到许多研究限制的限制。冲突的持续性质、各方之间缺乏沟通以及潜在的政治偏见是其中一些困难。任何关于俄乌战争的研究都应该考虑到这些限制。

据我们所知, 这是首批使用定量方法分析宏观经济因素如何影响欧洲运输和物流业务的研究之一。本研究考虑的宏观经济变数包括利率、通货膨胀、石油和汽油价格, 以及地缘政治风险指数。

宏观经济变数在运输和物流行业发展中起着重要作用, 因此, 在设计弹性策略时, 需要考虑它们。

La presente investigación aborda el efecto de los factores macroeconómicos en la industria de viajes y ocio (VyO) en toda Europa en el contexto de la guerra ruso-ucraniana que comenzó el 24 de febrero de 2022. En concreto, se analizan los principales destinos turísticos, como España, Francia, Italia y Portugal, así como Europa en general.

El estudio adopta un enfoque de regresión de panel basado en datos diarios y cubre un período de casi 14 meses, del 24 de febrero de 2022 al 15 de abril de 2023.

Nuestros resultados indican que el sector europeo de VyO se ve afectado por variables macroeconómicas. En concreto, el sector se ve significativamente afectado por los tipos de interés, el riesgo geopolítico, el petróleo y el gas, mientras que la inflación tiene un efecto moderado.

Un estudio de la guerra ruso-ucraniana puede verse limitado por una serie de restricciones a la investigación. La persistencia del conflicto, la falta de comunicación entre las partes y los posibles prejuicios políticos son algunas de estas dificultades. Cualquier investigación sobre la guerra ruso-ucraniana debe hacerse teniendo en cuenta estos límites.

Las variables macroeconómicas desempeñan un papel importante en el desarrollo del sector de VyO, por lo que es necesario tenerlas en cuenta al diseñar estrategias de resiliencia.

Hasta donde sabemos, éste es uno de los primeros estudios que analiza cómo los factores macroeconómicos afectaron al negocio europeo de VyO utilizando un enfoque cuantitativo. Las variables macroeconómicas que se tuvieron en cuenta en este estudio fueron los tipos de interés, la inflación, los precios del petróleo y de la gasolina, así como el índice de riesgo geopolítico.

The interval number prediction of power generation can provide a reference for the rational planning of the power system. For the nonlinearity, uncertainty and complex trends of power generation in East China, a matrixed nonlinear grey Bernoulli model combined with the weighted conformable fractional accumulation generating operator (MWCFNGBM(1,1,tα)) is proposed.

First, the original sequence fluctuations are smoothed with the weighted conformable fractional accumulation generating operator. The time power term is introduced into the nonlinear grey Bernoulli model to enhance the flexibility and adaptability of predicting nonlinear and complex sequences. The model parameters are further matrixed so that the interval number sequences can be modeled directly. The improved MPA is chosen to optimize the nonlinear parameters through the algorithm comparison. Finally, the Cramer rule is used to derive the time recursive formula.

The validity and superiority of the MWCFNGBM(1,1,tα) is verified by the model comparison experiment. The total power generation in East China is predicted and analyzed from 2024 to 2027. The prediction shows that it will grow steadily over the next four years.

The trend of power generation in East China is complex in the short term. It is of research significance to use the grey model for short-term interval prediction of power generation. For the data characteristics of power generation, a grey interval number prediction model for power generation prediction is proposed.

The bridge expansion joint (BEJ) is a key device for accommodating spatial displacement at the beam end, and for providing vertical support for running trains passing over the gap between the main bridge and the approach bridge. For long-span railway bridges, it must also be coordinated with rail expansion joint (REJ), which is necessary to accommodate the expansion and contraction of, and reducing longitudinal stress in, the rails. The main aim of this study is to present analysis of recent developments in the research and application of BEJs in high-speed railway (HSR) long-span bridges in China, and to propose a performance-based integral design method for BEJs used with REJs, from both theoretical and engineering perspectives.

The study first presents a summary on the application and maintenance of BEJs in HSR long-span bridges in China representing an overview of their state of development. Results of a survey of typical BEJ faults were analyzed, and field testing was conducted on a railway cable-stayed bridge in order to obtain information on the major mechanical characteristics of its BEJ under train load. Based on the above, a performance-based integral design method for BEJs with maximum expansion range 1600 mm (±800 mm), was proposed, covering all stages from overall conceptual design to consideration of detailed structural design issues. The performance of the novel BEJ design thus derived was then verified via theoretical analysis under different scenarios, full-scale model testing, and field testing and commissioning.

Two major types of BEJs, deck-type and through-type, are used in HSR long-span bridges in China. Typical BEJ faults were found to mainly include skewness of steel sleepers at the bridge gap, abnormally large longitudinal frictional resistance, and flexural deformation of the scissor mechanisms. These faults influence BEJ functioning, and thus adversely affect track quality and train running performance at the beam end. Due to their simple and integral structure, deck-type BEJs with expansion range 1200 mm (± 600 mm) or less have been favored as a solution offering improved operational conditions, and have emerged as a standard design. However, when the expansion range exceeds the above-mentioned value, special design work becomes necessary. Therefore, based on engineering practice, a performance-based integral design method for BEJs used with REJs was proposed, taking into account four major categories of performance requirements, i.e., mechanical characteristics, train running quality, durability and insulation performance. Overall BEJ design must mainly consider component strength and the overall stiffness of BEJ; the latter factor in particular has a decisive influence on train running performance at the beam end. Detailed BEJ structural design must stress minimization of the frictional resistance of its sliding surface. The static and dynamic performance of the newly-designed BEJ with expansion range 1600 mm have been confirmed to be satisfactory, via numerical simulation, full-scale model testing, and field testing and commissioning.

This research provides a broad overview of the status of BEJs with large expansion range in HSR long-span bridges in China, along with novel insights into their design.

Accurate prediction of port cargo throughput within Free Trade Zones (FTZs) can optimize resource allocation, reduce environmental pollution, enhance economic benefits and promote sustainable transportation development.

This paper introduces a novel self-adaptive grey multivariate prediction modeling framework (FARDCGM(1,N)) to forecast port cargo throughput in China, addressing the challenges posed by mutations and time lag characteristics of time series data. The model explores policy-driven mechanisms and autoregressive time lag terms, incorporating policy dummy variables to capture deviations in system development trends. The inclusion of autoregressive time lag terms enhances the model’s ability to describe the evolving system complexity. Additionally, the fractional-order accumulative generation operation effectively captures data features, while the Grey Wolf Optimization algorithm determines optimal nonlinear parameters, enhancing the model’s robustness.

Verification using port cargo throughput forecasts for FTZs in Shanghai, Guangdong and Zhejiang provinces demonstrates the FARDCGM(1,N) model’s remarkable accuracy and stability. This innovative model proves to be an excellent forecasting tool for systematically analyzing port cargo throughput under external interventions and time lag effects.

A novel self-adaptive grey multivariate modeling framework, FARDCGM(1,N), is introduced for accurately predicting port cargo throughput, considering policy-driven impacts and autoregressive time-lag effects. The model incorporates the GWO algorithm for optimal parameter selection, enhancing adaptability to sudden changes. It explores the dual role of policy variables in influencing system trends and the impact of time lag on dynamic response rates, improving the model’s complexity handling.

Live streaming e-commerce, which integrates real-time video interaction with online shopping, has quickly become a popular sales channel. It not only allows for immediate feedback but also builds a sense of trust and connection between streamers and consumers. Drawing on the elaboration likelihood model (ELM), we investigate how central and peripheral route factors affect consumers’ trust building and purchase intentions. Additionally, we identify consumer involvement as a key moderator affecting the relationship between central route factors and trust in product as well as the relationship between peripheral route factors and trust in streamer.

To test the research model, we collected data from 423 consumers on TaoBao Live.

The findings show that information completeness, accuracy and currency positively affect trust in the product, while perceived physical characteristic similarity, streamer humor attractiveness and passion attractiveness positively affect trust in the streamer. Trust in the streamer positively influences trust in the product, which subsequently impacts purchase intention. Moreover, involvement moderates the effects of information accuracy, currency, perceived physical characteristic similarity and passion attractiveness on trust.

First, we examine the direct influence of product- and streamer-related cues on consumer trust and purchase intention through distinct pathways. Second, we adopt ELM to explain the process of consumer trust building by investigating how central and peripheral route factors influence purchase intention through consumer trust in live streaming settings. Third, we incorporate involvement as a crucial moderator, shedding light on the boundary conditions of trust building in live streaming e-commerce.

This article aims to unfold digital servitization by exploring the key resources and resource orchestration (i.e. resource configuration and interaction).

This article conducted an explorative two-stage research strategy of Chinese servitized manufacturers using a preliminary case study and fuzzy-set Qualitative Comparative Analysis (fsQCA) design. The data collection was conducted between 2016 and 2021.

This article identifies five key resources – radical, complex technological resources, complementary, specific market resources and digital resources – and their configurations – leveraging market opportunities, leveraging innovation integration and leveraging resource advantages – to facilitate servitization in the digital age. The findings underscore the interaction between technological and market resources as well as the role of digital resources in promoting the servitization journey.

This article contributes to the understanding of servitization in the digital context by examining the key resources and their interactions involved. It builds upon the configurational logic of servitization, expanding the existing framework in the digital context and highlighting the significance of technological and market resource orchestration and interaction in servitization research. Moreover, the paper contributes through its exploratory two-stage approach, going beyond a conceptual understanding of servitization by focusing on both the factors that enable servitization (WHAT) and the configurations that lead to servitization (HOW). Additionally, the article investigates the attributes of resources as lower-level components, addressing the need to explore the micro-level practice of resource realignment. By providing clarity on the configurations of servitization, the paper offers practical guidelines for practitioners on how to effectively utilize resources and benefit from digital servitization.