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This study aims to investigate how environmentalism in photovoltaic (PV) substitution and nationalism in PV rivalry with the USA are associated with the trade-offs made by young consumers in Lanzhou when selecting Chinese brand portable solar power banks.

In this study, the choice-based conjoint survey was conducted to investigate mobile power bank consumers aged 18–28 in Lanzhou urban districts. A total of 2,004 valid questionnaires were collected and 1,813 sample was used in analyses. Logit and ordinary least squares regression models were run for empirical analyses.

The research results show that consumers tend to sacrifice certain levels of affordability for moderate technological capability, a reputable brand, better portability and advanced charging functions or sacrifice certain levels of technological capabilities for a moderate price. Consumers with stronger environmentalism in PV substitution tend to prioritize median price levels, larger battery capacity and better portability, while being less sensitive to brand and showing less preference for advanced charging functions. Consumers with stronger nationalism in PV rivalry tend to prioritize reasonably higher prices, bigger brands, enhanced portability, more solar panels and advanced charging functions.

This research sheds light on consumer trade-offs between price, brand, portability, technological capability and charging function. It also explores how environmentalism and nationalism sentiments are associated with consumer decision-making. These insights carry valuable policy implications for fostering product innovation, supporting brand-building initiatives for small and medium-size enterprises, promoting market competition and preventing the weaponization of consumer nationalism.

As an emerging solar power product, the portable solar power bank holds significant potential for widespread adoption as a means to drive energy transition. Within the current context, two notable sentiments have surfaced: environmentalism, which pertains to the adoption of PV technology as a substitute for conventional energy sources and nationalism, which manifests in the PV rivalry between China and the USA. This research aims to investigate consumer preference related to this emerging product, specifically focusing on its relationship with these two sentiments.

This paper aims to solve the problem of the inability to apply learning methods for robot motion skills based on dynamic movement primitives (DMPs) in tasks with explicit environmental constraints, while ensuring the reliability of the robot system.

The authors propose a novel DMP that takes into account environmental constraints to enhance the generality of the robot motion skill learning method. First, based on the real-time state of the robot and environmental constraints, the task space is divided into different regions and different control strategies are used in each region. Second, to ensure the effectiveness of the generalized skills (trajectories), the control barrier function is extended to DMP to enforce constraint conditions. Finally, a skill modeling and learning algorithm flow is proposed that takes into account environmental constraints within DMPs.

By designing numerical simulation and prototype demonstration experiments to study skill learning and generalization under constrained environments. The experimental results demonstrate that the proposed method is capable of generating motion skills that satisfy environmental constraints. It ensures that robots remain in a safe position throughout the execution of generation skills, thereby avoiding any adverse impact on the surrounding environment.

This paper explores further applications of generalized motion skill learning methods on robots, enhancing the efficiency of robot operations in constrained environments, particularly in non-point-constrained environments. The improved methods are applicable to different types of robots.

Drawing on goal setting theory (GST), this study explores the relationship between visionary leadership behavior (VLB) and employee performance, along with the mediating role of vision commitment and the moderating role of organization resource support.

We conducted two studies to test hypotheses. Study 1 and Study 2 collected data from 212 and 204 full-time employees and their leaders, respectively. The data were analyzed using hierarchical regression and bootstrapping technique.

We found that VLB is positively related to employee vision commitment and vision commitment mediates the effect of VLB on employee performance. Organization resource support moderates the effect of vision commitment on employee performance and the indirect effect of VLB on employee performance via vision commitment, such that the two effects are stronger when organization resource support is high.

First, this study reveals the mediating role of vision commitment in the relationships between VLB and employee performance. Second, this study explores the moderating role of organization resource support in the relationship between VLB and employee performance. Third, this study enriches the theoretical perspective of VLB research and expands the application scope of GST.

The purpose of this paper is to propose an new online path planning method for porcine belly cutting. With the proliferation in demand for the automatic systems of pork production, the development of efficient and robust meat cutting algorithms are hot issues. The uncertain and dynamic nature of the online porcine belly cutting imposes a challenge for the robot to identify and cut efficiently and accurately. Based on the above challenges, an online porcine belly cutting method using 3D laser point cloud is proposed.

The robotic cutting system is composed of an industrial robotic manipulator, customized tools, a laser sensor and a PC.

Analysis of experimental results shows that by comparing with machine vision, laser sensor-based robot cutting has more advantages, and it can handle different carcass sizes.

An image pyramid method is used for dimensionality reduction of the 3D laser point cloud. From a detailed analysis of the outward and inward cutting errors, the outward cutting error is the limiting condition for reducing the segments by segmentation algorithm.

The purpose of this study is to explore the potential contribution of the metaverse to improve training and development as a function of human resource development (HRD) perspective. The authors explore the benefits and challenges of the metaverse and introduce cases of companies using the metaverse in training.

A narrative literature review was conducted to collect information on the metaverse in training. The authors reviewed peer- and non-peer-reviewed articles, book chapters, white papers, corporate websites and blogs and business magazines.

A total of 75 articles were reviewed, including 14 cases, which were summarized to demonstrate how companies are applying metaverse technology in training contexts. For a more in-depth review, three cases were selected and summarized in terms of context, process and outcomes.

The metaverse is an emergent topic in HRD. It has the potential to revolutionize the functions of training and development through the combination of advanced technologies, including virtual reality, augmented reality and mixed reality. This article is the foundational attempt to provide a comprehensive summary of existing literature and case studies that highlight the potential of the metaverse in training within the context of HRD.

The main purpose of this paper is to explore whether the nature of innovation is accumulative or radical and to what extent past year accumulation of technology stock can predict future innovation. More importantly, the authors are concerned with whether a change of policy regime or a variance in the quality of technology will moderate the nature of innovation.

The authors examined a dataset of 3.6 million Chinese patents during 1985–2015 and constructed more than 5 million citation pairs across 8 sections and 128 classes to track knowledge spillover across technology fields. The authors used this citation dataset to calculate the technology innovation network. The authors constructed a measure of upstream invention, interacting the pre-existing technology innovation network with historical patent growth in each technology field, and estimated measure's impact on future innovation since 2005. The authors also constructed three sets of metrics – technology dependence, centrality and scientific value – to identify innovation quality and a policy dummy to consider the impact of policy on innovation.

Innovation growth is built upon past year accumulation and technology spillover. Innovation grows faster for technologies that are more central and grows more slowly for more valuable technologies. A pro-innovation and pro-intellectual property right (IPR) policy plays a positive and significant role in driving technical progress. The authors also found that for technologies that have faster access to new information or larger power to control knowledge flow, the upstream and downstream innovation linkage is stronger. However, this linkage is weaker for technologies that are more novel or general. On most occasions, the nature of innovation was less responsive to policy shock.

This paper contributes to the debate on the nature of innovation by determining whether upstream innovation has strong predictive power on future innovation. The authors develop the assumption used in the technology spillover literature by considering a time-variant, directional and asymmetric matrix to model technology diffusion. For the first time, the authors answer how the nature of innovation will vary depending on the technology network configurations and policy environment. In addition to contributing to the academic debate, the authors' study has important implications for economic growth and industrial or innovation management policies.

China has been going through a “museum boom” paralleling the domestic tourism boom since 2000; such growth changed the cultural landscape; museums became a vital characteristic of some Chinese cities for both residents and tourists. Encouraged by this growth, the more ambitious “All-for-one Museum (全域博物馆)” was proposed. The physical boundary between museums and living spaces is infinite ambiguity, challenging the idea of museums as “heterotopias.” This study aims to explore the musealization of urban spaces in the context of anthropology and museology, scrutinizing the cultural-political intentions and meanings of these developments, and seeks to ignite further investigation into the reconstruction of historical imaginaries for tourists and urban populations across related disciplines.

This paper examines two cases in Chinese metropolises, Beijing and Shanghai, to illustrate this development of musealization, that is, how the cities actively leverage museological values and methods to connect with their past. In the Beijing case, the authors explore how the local government is leading the effort to musealize the city; in the Shanghai case, they will see how tourists, especially dweller-tourists, navigate through a curated past story in the city and connect their own experience, memory and identity with the place.

The all-for-one museum creates a museal layer projected onto the bigger urban space, even though the authenticity of the “past” is challenged by the modernization development of the city. The authors also find out that for some tourists (especially dweller-tourists), an existential sense of authenticity plays a more significant role as they not only seek to sightsee the past of the city but also to take part in its creation.

This paper discusses two kinds of musealization in cosmopolitan cities of Beijing and Shanghai: top-down and bottom-up. It approaches questions about the musealization of urban spaces from the perspectives of anthropology and museology, and discusses musealization in the specific historical context of China’s modernization process.

As the demand for human–robot collaboration in manufacturing applications grows, the necessity for collision detection functions in robots becomes increasingly paramount for safety. Hence, this paper aims to improve the existing method to achieve efficient, accurate and sensitive robot collision detection.

The external torque is estimated by momentum observers based on the robot dynamics model. Because the state of the joints is more accessible to distinguish under the action of the suppression operator proposed in this paper, the mutated external torque caused by joint reversal can be accurately attenuated. Finally, time series analysis (TSA) methods can continuously generate dynamic thresholds based on external torques.

Compared with the collision detection method based only on TSA, the invalid time of the proposed method is less during joint reversal. Although the soft-collision detection accuracy of this method is lower than that of the symmetric threshold method, it is superior in terms of detection delay and has a higher hard-collision detection accuracy.

Owing to the mutated external torque caused by joint reversal, which seriously affects the stability of time series models, the collision detection method based only on TSA cannot detect continuously. The consequences are disastrous if the robot collides with people or the environment during joint reversal. After multiple experimental verifications, the proposed method still exhibits detection capabilities during joint reversal and can implement real-time collision detection. Therefore, it is suitable for various engineering applications.

By studying the green development efficiency (GDE) of 33 cities in the provinces of Jiangsu, Zhejiang, and Fujian in China, this study strives to conduct an analysis of the sustainable practices implemented in these developed regions, and derive valuable insights that can foster the promotion of green transformation.

First, the urban green development system (GDS) was decomposed into the economic benefit subsystem (EBS), social benefit subsystem (SBS), and pollution control subsystem (PCS). Then, a mixed network SBM model was proposed to evaluate the GDE during 20152020, with Moran’s I and Bootstrap truncated regression model subsequently applied to measure the spatial characteristics and driving factors of efficiency.

Subsystem efficiency presents a distribution trend of PCS > EBS > SBS. There is a particular spatial aggregation effect in EBS efficiency, whereas SBS and PCS efficiencies have no significant spatial autocorrelation. Furthermore, urbanization level contributes significantly to the efficiency of all subsystems; industrial structure, energy consumption, and technological innovation play a crucial role in EBS and SBS; external openness is a pivotal factor in SBS; and environmental regulation has a significant effect on PCS.

This study further decomposes the black box of GDS into subsystems including the economy, society, and environment. Additionally, by employing a mixed network SBM model and Bootstrap truncated regression model to investigate efficiency and its driving factors from the subsystem perspective, it endeavors to derive more detailed research conclusions and policy implications.

The purpose of this paper is to develop an index for measuring urban house price affordability that integrates spatial considerations and to explore the drivers of housing affordability using the post-least absolute shrinkage and selection operator (LASSO) approach and the ordinary least squares method of regression analysis.

The study is based on time-series data collected from 2005 to 2021 for 256 prefectural-level city districts in China. The new urban spatial house-to-price ratio introduced in this study adds the consideration of commuting costs due to spatial endowment compared to the traditional house-to-price ratio. And compared with the use of ordinary economic modelling methods, this study adopts the post-LASSO variable selection approach combined with the k-fold cross-test model to identify the most important drivers of housing affordability, thus better solving the problems of multicollinearity and overfitting.

Urban macroeconomics environment and government regulations have varying degrees of influence on housing affordability in cities. Among them, gross domestic product is the most important influence.

The paper provides important implications for policymakers, real estate professionals and researchers. For example, policymakers will be able to design policies that target the most influential factors of housing affordability in their region.

This study introduces a new urban spatial house price-to-income ratio, and it examines how macroeconomic indicators, government regulation, real estate market supply and urban infrastructure level have a significant impact on housing affordability. The problem of having too many variables in the decision-making process is minimized through the post-LASSO methodology, which varies the parameters of the model to allow for the ranking of the importance of the variables. As a result, this approach allows policymakers and stakeholders in the real estate market more flexibility in determining policy interventions. In addition, through the k-fold cross-validation methodology, the study ensures a high degree of accuracy and credibility when using drivers to predict housing affordability.