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Owing to increased energy demands, China has become the world’s top CO2 emitter, with electricity generation accounting for the majority of emissions. Therefore, the Chinese Government aspires to achieve a low-carbon transformation of the electric industry by enhancing its green innovation capacity. However, little attention has been paid to the green development of electric technology. Thus, this paper aims to uncover the spatiotemporal evolution of electric technology in the context of China’s low-carbon transformation through patent analysis.

Using granted green invention patent data for China’s electric industry between 2000 and 2021, this paper conducted an exploratory, spatial autocorrelation and time-varying difference-in-differences (DID) analysis to reveal the landscape of electric technology.

Exploratory analysis shows that the average growth rate of electric technology is 8.1%, with spatial heterogeneity, as there is slower growth in the north and west and faster growth in the south and east. In addition, electric technology shows spatial clustering in local areas. Finally, the time-varying DID analysis provides positive evidence that low-carbon policies improve the green innovation capacity of electric technology.

The different effects of the low-carbon pilot policy (LCPC) on R&D subjects and the LCPC’s effectiveness in enhancing the value of patented technology were not revealed.

This paper reveals the spatiotemporal evolutionary characteristics of electric technology in mainland China. The results can help the Chinese Government clarify how to carry out innovative development in the electric industry as part of the low-carbon transformation and provide a theoretical basis and research direction for newcomers in this field.

The academic experience of top management team (TMT) has a positive impact on firms' innovation performance. However, existing studies predominantly focus on the educational qualifications and institutional prestige of TMT, failing to comprehensively evaluate whether TMT possess genuine academic experience and the role of academic competence. This article aims to examine whether TMT academic competence has a potential influence on firm innovation performance and to understand the mechanisms behind this relationship.

Using firm-level metrics of Chinese listed firms and TMT scholarly publication data spanning 2000–2021, this paper investigates whether TMT academic competence can promote firms' innovation performance and conducts a moderated mediating effect analysis.

(1) Academic competence of TMT can contribute positively to firms’ innovation performance; (2) university–industry collaboration partially mediates this relationship; (3) the mediating effect is enhanced by cognitive proximity and (4) distance proximity does not diminish the mediating effect.

Outcome of this study can assist academia in further understanding the impacts of TMT on firm innovation and aid government in promoting university–industry collaboration. Simultaneously, it can help firms adjust their TMT selection and training strategies to enhance innovation performance.

This article, as the first to construct an index of academic competence and to explore whether it has an impact on firms' innovation performance and its inherent mechanism, can provide a new research perspective for the study of the impact of TMT's characteristics on firms' innovation.

The most prominent example of scan matching algorithm is the Iterative Closest Point (ICP) algorithm. But the ICP algorithm and its variants excessively depend on the initial pose estimate between two scans. The purpose of this paper is to propose a scan matching algorithm, which is adaptable to big initial pose errors.

The environments are represented by flat units and upright units. The upright units are clustered to represent objects that the robot cannot cross over. The object cluster is further discretized to generate layered model consisting of cross-section ellipses. The layered model provides simplified features that facilitate an object recognition algorithm to discriminate among common objects in outdoor environments. A layered model graph is constructed with the recognized objects as nodes. Based on the similarity of sub-graphs in each scans, the layered model graph-based matching algorithm generates initial pose estimates and uses ICP to refine the scan matching results.

Experimental results indicate that the proposed algorithm can deal with bad initial pose estimates and increase the processing speed. Its computation time is short enough for real-time implementation in robotic applications in outdoor environments.

This paper proposes a bio-inspired scan matching algorithm for mobile robots based on layered model graph in outdoor environments.

In corridor environments, human-following robot encounter difficulties when the target turning around at the corridor intersections, as walls may cause complete occlusion. This paper aims to propose a collision-free following system for robot to track humans in corridors without a prior map.

In addition to following a target and avoiding collisions robustly, the proposed system calculates the positions of walls in the environment in real-time. This allows the system to maintain a stable tracking of the target even if it is obscured after turning. The proposed solution is integrated into a four-wheeled differential drive mobile robot to follow a target in a corridor environment in real-world.

The experimental results demonstrate that the robot equipped with the proposed system is capable of avoiding obstacles and following a human target robustly in the corridors. Moreover, the robot achieves a 90% success rate in maintaining a stable tracking of the target after the target turns around a corner with high speed.

This paper proposes a human target following system incorporating three novel features: a path planning method based on wall positions is introduced to ensure stable tracking of the target even when it is obscured due to target turns; improvements are made to the random sample consensus (RANSAC) algorithm, enhancing its accuracy in calculating wall positions. The system is integrated into a four-wheeled differential drive mobile robot effectively demonstrates its remarkable robustness and real-time performance.

An optimal solution method based on 2-norm is proposed in this study to solve the inverse kinematics multiple-solution problem caused by a high redundancy. The current research also presents a motion optimization based on the 2-Norm of high-redundant mobile humanoid robots, in which a kinematic model is designed through the entire modeling.

The current study designs a highly redundant humanoid mobile robot with a differential mobile platform. The high-redundancy mobile humanoid robot consists of three modular parts (differential driving platform with two degrees of freedom (DOF), namely, left and right arms with seven DOF, respectively) and has total of 14 DOFs. Given the high redundancy of humanoid mobile robot, a kinematic model is designed through the entire modeling and an optimal solution extraction method based on 2-norm is proposed to solve the inverse kinematics multiple solutions problem. That is, the 2-norm of the angle difference before and after rotation is used as the shortest stroke index to select the optimal solution. The optimal solution of the inverse kinematics equation in the step is obtained by solving the minimum value of the objective function of a step. Through the step-by-step cycle in the entire tracking process, the kinematic optimization of the highly redundant humanoid robot in the entire tracking process is realized.

Compared with the before and after motion optimizations based on the 2-norm algorithm of the robot, its motion after optimization shows minimal fluctuation, improved smoothness, limited energy consumption and short path during the entire mobile tracking and operating process.

In this paper, the whole kinematics model of the highly redundant humanoid mobile robot is established and its motion is optimized based on 2-norm, which provides a theoretical basis for the follow-up research of the service robot.

In this paper, the whole kinematics model of the highly redundant humanoid mobile robot is established and its motion is optimized based on 2-norm, which provides a theoretical basis for the follow-up research of the service robot.

In this paper, the whole kinematics model of the highly redundant humanoid mobile robot is established and its motion is optimized based on 2-norm, which provides a theoretical basis for the follow-up research of the service robot.

Motion optimization based on the 2-norm of a highly redundant humanoid mobile robot with the entire modeling is performed on the basis of the entire modeling. This motion optimization can make the highly redundant humanoid mobile robot’s motion path considerably short, minimize energy loss and shorten time. These researches provide a theoretical basis for the follow-up research of the service robot, including tracking and operating target, etc. Finally, the motion optimization algorithm is verified by the tracking and operating behaviors of the robot and an example.

The purpose of this paper is to examine the potential economic impacts of China’s insect-resistant GM maize and provide new evidence for decision making concerning its commercialization.

This study uses data drawn from the production trials of insect-resistant GM maize and expert interviews to determine the impacts of commercializing GM maize at farm level under three scenarios with varying severity of insect pest attacks in maize production. Economic impacts are simulated using a modified Global Trade Analysis Project model.

In farm terms, insect-resistant GM maize increases crop yield and reduces both pesticide and labor inputs. In national terms, China can increase its GDP by USD8.6 billion and maize self-sufficiency by about 2 percent given normal insect pest attacks if China commercializes GM maize. Additional beneficiaries include consumers and the livestock industry. Non-maize crops can also benefit from land saving through GM maize commercialization. Chemical is a sector with the decrease in its output because demand for pesticides will fall.

Although China has announced a roadmap for commercializing GM crops for use as feed and in processing after nearly two decades of producing GM cotton, no clear timetable for producing GM maize as feed has been established due to several concerns, including the potential for economic gains from GM maize. This study is the first to assess the economic impacts of commercializing China’s GM maize. The findings should have significant policy implications for the development and commercialization of GM crops in general and GM maize in particular.