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The purpose of this paper is to develop a statistical-based model to forecast future domestic water demand in the context of climate change, population growth and technological development in Yellow River.

The model is developed through the analysis of the effects of climate variables and population on domestic water use in eight sub-basins of the Yellow River. The model is then used to forecast water demand under different environment change scenarios.

The model projected an increase in domestic water demand in the Yellow River basin in the range of 67.85 × 10⁸ to 62.20 × 10⁸ m³ in year 2020 and between 73.32 × 10⁸ and 89.27 × 10⁸ m³ in year 2030. The general circulation model Beijing Normal University-Earth System Model (BNU-ESM) predicted the highest increase in water demand in both 2020 and 2030, while Centre National de Recherches Meteorologiques Climate Model v.5 (CNRM-CM5) and Model for Interdisciplinary Research on Climate- Earth System (MIROC-ESM) projected the lowest increase in demand in 2020 and 2030, respectively. The fastest growth in water demand is found in the region where water demand is already very high, which may cause serious water shortage and conflicts among water users.

The simple regression-based domestic water demand model proposed in the study can be used for rapid evaluation of possible changes in domestic water demand due to environmental changes to aid in adaptation and mitigation planning.

Auxiliary power system is an indispensable part of the train; the auxiliary systems of both electric locomotives and EMUs mainly are powered by one of the two ways, which are either from auxiliary windings of traction transformers or from DC-link voltage of traction converters. Powered by DC-link voltage of traction converters, the auxiliary systems were maintained of uninterruptable power supply with energy from electric braking. Meanwhile, powered by traction transformers, the auxiliary systems were always out of power while passing the neutral section of power supply grid and control system is powered by battery at this time.

Uninterrupted power supply of auxiliary power system powered by auxiliary winding of traction transformer was studied. Failure reasons why previous solutions cannot be realized are analyzed. An uninterruptable power supply scheme for the auxiliary systems powered by auxiliary windings of traction transformers is proposed in this paper. The validity of the proposed scheme is verified by simulation and experimental results and on-site operation of an upgraded HXD3C type locomotive. This scheme is attractive for upgrading practical locomotives with the auxiliary systems powered by auxiliary windings of traction transformers.

This scheme regenerates braking power supplied to auxiliary windings of traction transformers while a locomotive runs in the neutral section of the power supply grid. Control objectives of uninterrupted power supply technology are proposed, which are no overvoltage, no overcurrent and uninterrupted power supply.

The control strategies of the scheme ensure both overvoltage free and inrush current free when a locomotive enters or leaves the neutral section. Furthermore, this scheme is cost low by employing updated control strategy of software and add both the two current sensors and two connection wires of hardware.

This paper aims to deepen the understanding of the relationship between global economic uncertainty and price volatility, specifically focusing on commodity, industrial materials and energy price indices as proxies for global inflation, analyzing data from 1997 to 2020.

The dynamic conditional correlation generalized autoregressive conditional heteroscedasticity model is used to study the dynamic relationship between variables over a while.

The results demonstrated a positive relationship between commodity prices and the global economic policy uncertainty (GEPU). Except for 1999–2000 and 2006–2008, the results of the energy price index model were very similar to those of the commodity price index. A predominant positive relationship is observed focusing on the connection between GEPU and the industrial material price index. The results of the pairwise Granger causality reveal a unidirectional relationship between the GEPU – the Global Commodity Price Index – and the GEPU – the Global Industrial Material Price Index. However, there is bidirectional causality between the GEPU – the Global Energy Price Index. In sum, changes in price indices can be driven by GEPU as a political factor indicating unfavorable economic conditions.

This paper provides a deeper understanding of the role of global uncertainty in the global inflation process. It fills the gap in the literature by empirically investigating the dynamic movements of global uncertainty and the three most important groups of prices.

In the past few years, millions of people started to acquire knowledge from the Massive Open Online Courses (MOOCs). MOOCs contain massive video courses produced by instructors, and learners all over the world can get access to these courses via the internet. However, faced with massive courses, learners often waste much time finding courses they like. This paper aims to explore the problem that how to make accurate personalized recommendations for MOOC users.

This paper proposes a multi-attribute weight algorithm based on collaborative filtering (CF) to select a recommendation set of courses for target MOOC users.

The recall of the proposed algorithm in this paper is higher than both the traditional CF and a CF-based algorithm – uncertain neighbors’ collaborative filtering recommendation algorithm. The higher the recall is, the more accurate the recommendation result is.

This paper reflects the target users’ preferences for the first time by calculating separately the weight of the attributes and the weight of attribute values of the courses.

The rapid development of 5G technology brings the expansion of the internet of things (IoT). A large number of devices in the IoT work independently, leading to difficulties in management. This study aims to optimize the member structure of the IoT so the members in it can work more efficiently.

In this paper, the authors consider from the perspective of crowd science, combining genetic algorithms and crowd intelligence together to optimize the total intelligence of the IoT. Computing, caching and communication capacity are used as the basis of the intelligence according to the related work, and the device correlation and distance factors are used to measure the improvement level of the intelligence. Finally, they use genetic algorithm to select a collaborative state for the IoT devices.

Experimental results demonstrate that the intelligence optimization method in this paper can improve the IoT intelligence level up to ten times than original level.

This paper is the first study that solves the problem of device collaboration in the IoT scenario based on the scientific background of crowd intelligence. The intelligence optimization method works well in the IoT scenario, and it also has potential in other scenarios of crowd network.

The Internet of Things (IoT) is used in the fresh agricultural product (FAP) supply chain, which can be coordinated through a revenue-sharing contract. The purpose of this paper is to make the three-level supply chain coordinate in IoT by considering the influence of FAP on market demand and costs of controlling freshness on the road.

A three-level FAP supply chain that comprises a manufacturer, distributor, and retailer in IoT is regarded as the research object. This study improves the revenue-sharing contract, determines the optimal solution when the supply chain achieves maximum profit in three types of decision-making situations, and develops the profit distribution model based on the improved revenue-sharing contract to coordinate the supply chain.

The improved revenue-sharing contract can coordinate the FAP supply chain that comprises a manufacturer, distributor, and retailer in IoT, as well as benefit all enterprises in the supply chain.

Resource utilization rate can be improved after coordinating the entire supply chain. Moreover, loss in the circulation process is reduced, and the circulation efficiency of FAPs is improved because of the application of IoT. The validity of the model is verified through a case analysis.

This study is different from other research in terms of the combination of supply chain coordination, FAPs, and radio frequency identification application in IoT.

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.

This study constructs the influence mechanism model of sustainable marketing activities (SMAs), event image, commemorative product perceived value and tourists’ behavioral intentions (TBIs) in the sports tourism context of the Beijing Winter Olympic Games. Additionally, the article discusses the role of event image and product perceived value in enhancing the SMAs’ effect on TBIs.

The research analyzed 315 valid questionnaires from tourists in the Chinese market by structural equation modeling.

The results indicate that SMAs positively impact sports tourism event image, tourists’ perceived commemorative product value and TBIs. Meanwhile, event image and product perceived value mediate the SMAs and TBIs relationship.

Considering SMAs as essential for sustainable development, this paper contributes to the strategic management discipline. Additionally, the research expands the analysis of event image and product perceived value in the brand theory and customer behavior research.

The article outlines the principal value of SMAs implementation in enhancing behavioral intentions. It also reveals that a favorable event image and good perceived value can enhance SMAs’ effectiveness toward positively influencing TBIs, especially purchase intentions. It provides a new vision for nonprofit organizations to prioritize SMAs’ implementation in marketing strategies.

It is pioneering work with a complex research framework for SMAs implementation in the sports tourism context.

The belt and road initiative (BRI) emanates from China and seeks to connect Europe, Asia and Africa through transport and telecommunications infrastructure. Despite the importance of Africa in the BRI network, very little research has been done on the BRI in Africa, and even less of this emanates from Africa itself. In particular, considering that the BRI investments in Africa are largely transport related, there is almost no research covering the area of logistics, which should be greatly affected by the infrastructure investments. This paper sought to establish the current state of logistics research related to the BRI in Africa.

A bibliometric analysis was conducted on documents extracted from the SCOPUS database.

The findings indicate that there is a lack of research in critical areas such as environmental, social and economic impact of BRI transport investments, governance, logistics performance and international cooperation. In particular, there is a massive gap in local knowledge regarding the BRI.

The study is limited to published research indexed in the SCOPUS database. Future research directions include empirical studies into BRI project initiation investigation, economic and environmental impacts, governance structures and policy intervention requirements and macro-level logistics impacts.

The study emphasises the importance publishing all the relevant information regarding BRI related projects in Africa to create transparency.

The study investigates the current research on the effect of China's BRI on transport and logistics in Africa through a bibliometric analysis. The investigation reveals that while there are huge investments in infrastructure, the actual effect on logistics of participating countries in Africa has not been interrogated.

This paper aims to contribute to the discussion on integrating humans and technology in customer service within the framework of Society 5.0, which emphasizes the growing role of artificial intelligence (AI). It examines how effectively new generative AI-based chatbots can handle customer emotions and explores their impact on determining the point at which a customer–machine interaction should be transferred to a human agent to prevent customer disengagement, referred to as the Switch Point (SP).

To evaluate the capabilities of new generative AI-based chatbots in managing emotions, ChatGPT-3.5, Gemini and Copilot are tested using the Trait Emotional Intelligence Questionnaire Short-Form (TEIQue-SF). A reference framework is developed to illustrate the shift in the Switch Point (SP).

Using the four-intelligence framework (mechanical, analytical, intuitive and empathetic), this study demonstrates that, despite advancements in AI’s ability to address emotions in customer service, even the most advanced chatbots—such as ChatGPT, Gemini and Copilot—still fall short of replicating the empathetic capabilities of human intelligence (HI). The concept of artificial emotional awareness (AEA) is introduced to characterize the intuitive intelligence of new generative AI chatbots in understanding customer emotions and triggering the SP. A complementary rather than replacement perspective of HI and AI is proposed, highlighting the impact of generative AI on the SP.

This study is exploratory in nature and requires further theoretical development and empirical validation.

The study has only an exploratory character with respect to the possible real impact of the introduction of the new generative AI-based chatbots on collaborative approaches to the integration of humans and technology in Society 5.0.

Customer Relationship Management managers can use the proposed framework as a guide to adopt a dynamic approach to HI–AI collaboration in AI-driven customer service.