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In the current knowledge-based economy era, national innovation ability is crucial. Abundant information can be obtained through patent analysis, and such information can help in the formulation of policies and the making of R&D decisions; numerous researchers thus continue to make patent analyses. The quality of patents possessed by a country indicates the level of innovation and technology in the country, and this study aims to assess the quality of patents possessed by various countries.

In this study, the authors determined patent quality in various countries from the perspective of the reflective measurement model and used a novel method to construct a structural model of patent quality.

This study discovered that patent family, number of claims, number of international patent classifications, forward citations, nonpatent references and maintenance time are the structural factors that affect patent quality. Forward citations and the number of claims are particularly highly explained by patent quality, which is a latent construct.

The results of this study provide valuable information to the government and help in the assessment of patent quality in various countries. In addition, the assessment model proposed in this study can be used in the investigation of patent quality in academic research and can predict patent quality, which will be of interest to the government and industry.

Innovation plays a pivotal role in a national economy and in the research and development of science and technology. Because the elements, capability and development direction of innovation in various countries are dissimilar, national innovative capacity also varies by country. However, previous studies have predominantly measured national innovative capacity through empirical studies by using a single index of innovation output, ignoring that the forms of innovation are heterogeneous across countries and failing to examine the influence exerted by various innovation models on economic development. Thus, the purpose of this study is to fill this gap by using scientific driving force and technological driving force to present the influence of national innovative capacities on economic development.

This study used regression models to test the influence of different national innovative capacities (i.e. scientific and technological driving forces) on economic development and stability.

Using the data of 60 countries, this study determined that both scientific and technological driving forces influenced economic development; specifically, scientific driving force affected economic development through technological driving force. Moreover, both research paper quality and patent quality positively influenced economic stability, but patent quality was the mediator.

This study examined scientific output from both quantitative and qualitative perspectives to determine their influence on economic growth and particularly on economic stability, which lacks dedicated studies. This study strives to bridge this gap in the literature by asserting, from the concept of economic resilience, that high-quality science and technology can strengthen the stability of a country’s economy.

The objective of this study is to evaluate the overall technical efficiency, labor efficiency, capital efficiency and equipment efficiency of 30 Chinese construction sectors to foster sustainable economic growth in the construction industry.

This study employed the super-efficiency data envelopment analysis (SE-DEA) and artificial neural network model (ANN) to evaluate the industrial performance and improvement potential of the Chinese regional construction sectors from 2000 to 2017.

Results showed that the overall technical and capital efficiencies displayed relatively stable patterns. Equipment efficiency presented a relatively huge fluctuation during the sample period. Meanwhile, labor, capital and equipment efficiencies could potentially improve in the next five years. A spatial examination of efficiencies implied that the economic level was still a major factor in determining the efficiency performance of the regional construction industry. Beijing, Shanghai and Zhejiang were consistently the leading regions with the best performance in all efficiencies. Shandong and Hubei were critical regions with respect to their large reduction potential of labor, capital and equipment.

The study focused on the regional efficiency performance of the construction industry; however, it failed to further deeply discover the mechanism that captured the regional inefficiency. In addition, sample datasets used to predict might induce the accuracy of prediction results. Qualitative policy implications failed to regress the efficiency performance of the industrial policy variables. These limitations will be discussed in our further researches.

Enhancing the overall performance of the Chinese construction industry should focus on regions located in the western areas. In comparison with labor and capital efficiencies, equipment efficiency should be given priority by eliminating outdated equipment and developing high technology in the construction industry. In addition, the setting of the national reduction responsibility system should be stratified to account for regional variations.

The findings of this study can provide a systematic understanding for the current and future industry performance of the Chinese construction industry, which would help decision makers to customize appropriate strategies to improve the overall industrial performance with the consideration of regional differences.

Limited by the types of sensors, the state information available for musculoskeletal robots with highly redundant, nonlinear muscles is often incomplete, which makes the control face a bottleneck problem. The aim of this paper is to design a method to improve the motion performance of musculoskeletal robots in partially observable scenarios, and to leverage the ontology knowledge to enhance the algorithm’s adaptability to musculoskeletal robots that have undergone changes.

A memory and attention-based reinforcement learning method is proposed for musculoskeletal robots with prior knowledge of muscle synergies. First, to deal with partially observed states available to musculoskeletal robots, a memory and attention-based network architecture is proposed for inferring more sufficient and intrinsic states. Second, inspired by muscle synergy hypothesis in neuroscience, prior knowledge of a musculoskeletal robot’s muscle synergies is embedded in network structure and reward shaping.

Based on systematic validation, it is found that the proposed method demonstrates superiority over the traditional twin delayed deep deterministic policy gradients (TD3) algorithm. A musculoskeletal robot with highly redundant, nonlinear muscles is adopted to implement goal-directed tasks. In the case of 21-dimensional states, the learning efficiency and accuracy are significantly improved compared with the traditional TD3 algorithm; in the case of 13-dimensional states without velocities and information from the end effector, the traditional TD3 is unable to complete the reaching tasks, while the proposed method breaks through this bottleneck problem.

In this paper, a novel memory and attention-based reinforcement learning method with prior knowledge of muscle synergies is proposed for musculoskeletal robots to deal with partially observable scenarios. Compared with the existing methods, the proposed method effectively improves the performance. Furthermore, this paper promotes the fusion of neuroscience and robotics.

Optimization of light-emitting diodes’ (LEDs’) design together with long-term reliability is directly correlated with their photometric, electric and thermal characteristics. For a given thermal layout of the LED system, the maximum luminous flux occurs at an optimal electrical input power and can be determined using a photo-electro-thermal (PET) theory. The purpose of this study is to extend the application of the luminous flux equation in PET theory for low-power (LP) LEDs.

LP surface-mounted device LEDs were mounted on substrates of different thermal resistances. Three LEDs were attached to substrates which were flame-retardant fiberglass epoxy (FR4) and two aluminum-based metal core printed circuit boards (MCPCBs) with thermal conductivities of about 1.0 W/m.K, 2.0 W/m.K and 5.0 W/m.K, respectively. The conjunction of thermal transient tester and thermal and radiometric characterization of LEDs system was used to measure the thermal and optical parameters of the LEDs at a certain range of input current and temperature.

The validation of the extended application of the luminous flux equation was confirmed via a good agreement between the practical and theoretical results. The outcomes show that the optimum luminous flux is 25.51, 31.91 and 37.01 lm for the LEDs on the FR4 and the two MCPCBs, respectively. Accordingly, the stipulated maximum electrical input power in the LED datasheet (0.185 W) is shifted to 0.6284, 0.6963 and 0.8838 W between the three substrates.

Using a large number of LP LEDs is preferred than high-power (HP) LEDs for the same system power to augment the heat transfer and provide a higher luminous flux. The PET theory equations have been applied to HP LEDs using heatsinks with various thermal resistances. In this work, the PET theory luminous flux equation was extended to be used for Indium Gallium Aluminum Phosphide LP LEDs attached to the substrates with dissimilar thermal resistances.

The purpose of this paper is to review China’s past returns in a period over the last 40 years to public agricultural and rural investments to highlight the importance for future strategic investments in China’s agri-food system and in rural areas.

The paper synthesizes research findings from previous studies and reviews more recent trends. Based on the main findings, the authors provide forward-looking guidance for China’s investments agriculture and rural areas in the context of emerging global and domestic trends in agriculture, food security, and nutrition.

Public investments in the agricultural research and development (R&D), rural education, and rural infrastructure have been shown to have significant positive returns to agricultural growth as well as to reductions in poverty and regional inequality. Returns to overall agricultural GDP were highest for agricultural R&D, followed by education, roads, and telephones. Investment in education had the greatest returns to poverty reduction, as well as to nonfarm GDP and overall rural GDP. Investment in agricultural R&D had the second greatest returns in term of poverty reduction, and was also a close second in returns to nonfarm GDP and overall rural GDP following education. The rural infrastructure spending also saw significant returns to poverty reduction, largely through growth in agricultural and nonagricultural sectors. Investments in agriculture and rural areas will continue to be important, as China and the world face emerging challenges amidst a changing global landscape, particularly regarding climate change, rapid urbanization, nutritional imbalances, and food safety concerns. In addressing these emerging challenges, continued support for agricultural R&D and innovations can play a key role.

The paper highlights research findings on key investment areas that will be increasingly important for China’s agri-food system, and provides guidance in the context of emerging trends impacting food security and nutrition.

There are two major strategies for short video advertising which are KOL (key opinion leader) endorsement and in-feed advertising. The authors aim to research the effectiveness of these two strategies for heterogeneous sellers.

The study employed a data set of users from Douyin. Using an endogenous treatment model, the study empirically examines the two strategies' effectiveness in attracting product traffic for online retailors at a short video app Douyin (TikTok).

The results show that the performance of in-feed advertising is higher when the seller's product is of lower price and when the seller has smaller cumulative video exposure. In addition, KOL endorsement is effective regardless of the product price, but performs better when the seller has larger cumulative video exposure.

To the best of the authors’ knowledge, this study is one of the first to explore the interaction effects of two major advertising strategies, KOL endorsement and in-feed advertising on short video platforms. The findings provide important theoretical contributions and practical implications.

The purpose of the paper is to contribute to the extant sport marketing literature by positing fan engagement, team brand image and cumulative fan satisfaction with the team as factors influencing attitudinal and behavioural soccer (football) fan loyalty, with enduring involvement with the team as a moderator.

A convenience sample of Australian A-League soccer fans completed a paper-and-pencil, self-administered survey to evaluate their team on the focal constructs. A total of 207 participants were recruited from a major Australian east-coast university.

Using partial least squares-structural equation modelling (PLS-SEM), the study found that fan engagement influences both team brand image and cumulative fan satisfaction, while team brand image also influences cumulative fan satisfaction, and both of these constructs influence attitudinal loyalty and behavioural loyalty. The moderating role of enduring involvement was also found for two relationships: team brand image → attitudinal loyalty and team brand image → behavioural loyalty, along with a mediating role of attitudinal loyalty.

This study increases our understanding of the reasons why soccer fans are committed to and exhibit fan-related behaviours for a team, thus contributing to the sports-marketing literature on the relationships amongst fan engagement, team brand image, cumulative fan satisfaction, attitudinal loyalty and behavioural loyalty, along with the moderating role of enduring involvement. The findings also assist sports-marketing practitioners to formulate more effective, fan-centric marketing-communication strategies leading to a larger loyal fan base.

This study aims to propose a novel lightweight tendon-driven musculoskeletal arm (LTDM-arm) robot with a flexible series–parallel mixed skeletal joint structure and modularized artificial muscle system (MAMS). The proposed LTDM-arm exhibits human-like flexibility, safety and operational accuracy. In addition, to improve the safety and stability of the LTDM-arm, a control method is proposed to solve local artificial muscle overload accidents.

The proposed LTDM-arm comprises seven degrees of freedom skeletons, 15 MAMSs and various sensor systems (joint sensing, muscle tension sensing, visual sensing, etc.). It retains the morphology of a human skeleton (humerus, ulna and radius) and a simplified muscle configuration. This study proposes an input saturation control with full-state constraints to reduce local artificial muscle overload accidents caused by redundant muscle tension calculations.

3D circular trajectory experiments were conducted to verify the stability of the control method and the flexibility of the LTDM-arm. The results showed that the average error of the muscle length was approximately 0.35 mm (0.38%), which indicates that the proposed control scheme can make the output follow the target trajectory while ensuring constraint satisfaction.

The human arm is capable of performing compliant operations rapidly, flexibly and robustly in unstructured environments. Existing musculoskeletal arm robots lack simulations of the full morphology of the human arm and are insufficient in dexterity. However, the flexibility and safety features of the proposed LTDM-arm were consistent with that of the human arm. Therefore, this study offers a new approach for investigating the advantages of the musculoskeletal system and the concepts of muscle control.

The purpose of this paper is to improve the existing differential evolution (DE) mutation operator so as to accelerate its convergence.

A new general donor form for mutation operation in DE is presented, which defines a donor as a convex combination of the triplet of individuals selected for a mutation. Three new donor schemes from that form are deduced.

The three donor schemes were empirically compared with the original DE version and three existing variants of DE by using a suite of nine well‐known test functions, and were also demonstrated by a practical application case – training a neural network to approximate aerodynamic data. The obtained numerical simulation results suggested that these modifications to the mutation operator could improve the DE's convergence performance in both the convergence rate and the convergence reliability.

Further research is still needed for adequately explaining why it was possible to simultaneously improve both the convergence rate and the convergence reliability of DE to that extent despite the well‐known “No Free Lunch” theorem. Also further research is considered necessary for outlining more distinctively the particular class of problems, where the current observations can be generalized.

More complicated engineering problems could be solved sub‐optimally, whereas their real optimal solution may never be reached subject to the current computer capability.

Though DE has demonstrated a considerably better convergence performance than the other evolutionary algorithms (EAs), its convergence rate is still far from what is hoped for by scientists. On the one hand, a higher convergence rate is always expected for any optimization method used in seeking the global optimum of a non‐linear objective function. On the other hand, since all EAs, including DE, work with a population of solutions rather than a single solution, many evaluations of candidate solutions are required in the optimization process. If evaluation of candidate solutions is too time‐consuming, the overall optimization cost may become too expensive. One often has to limit the algorithm to operate within an acceptable time, which maybe is not enough to find the global optimum (optima), but enough to obtain a sub‐optimal solution. Therefore, it is continuously necessary to investigate the new strategies to improve the current DE algorithm.