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The 6D pose estimation is a crucial branch of robot vision. However, the authors find that due to the failure to make full use of the complementarity of the appearance and geometry information of the object, the failure to deeply explore the contributions of the features from different regions to the pose estimation, and the failure to take advantage of the invariance of the geometric structure of keypoints, the performances of the most existing methods are not satisfactory. This paper aims to design a high-precision 6D pose estimation method based on above insights.

First, a multi-scale cross-attention-based feature fusion module (MCFF) is designed to aggregate the appearance and geometry information by exploring the correlations between appearance features and geometry features in the various regions. Second, the authors build a multi-query regional-attention-based feature differentiation module (MRFD) to learn the contribution of each region to each keypoint. Finally, a geometric enhancement mechanism (GEM) is designed to use structure information to predict keypoints and optimize both pose and keypoints in the inference phase.

Experiments on several benchmarks and real robot show that the proposed method performs better than existing methods. Ablation studies illustrate the effectiveness of each module of the authors’ method.

A high-precision 6D pose estimation method is proposed by studying the relationship between the appearance and geometry from different object parts and the geometric invariance of the keypoints, which is of great significance for various robot applications.

This paper aims to investigate the suppression of end-point vibrations in industrial robot systems that exhibit joint flexibility and are subject to external disturbances.

The real-time position tracking error is effectively decomposed by using feedforward control based on a dynamic model. Various proportional-derivative controllers and adapted versions are used to compute real-time compensation torque for different position tracking errors. This approach aims to simultaneously achieve rapid response and stability in the control system, resulting in reduced end vibration in the industrial robot.

Experiments were conducted in torque compensation on a 6R industrial robot platform. Compared to the dynamic model calculate torque feedforward compensation method, the maximum reduction of the root mean square of the position error of each joint reached 77% and the minimum reduction was 36.2%. This enhancement improves the trajectory tracking accuracy and effectively suppresses the end-effector vibration.

An improved torque feedforward compensation method is proposed and verified. According to the experimental results, the method can effectively suppress vibration and further improve the trajectory tracking accuracy.

The purpose of this study is to value the thermal and hydraulic transport augmentation of turbulent fluid flow within the round-pipe axis fixed by a twisted-staggered concave/convex dimples tape.

This study meets the report’s novel design by axis-inserting a twisted plastic tape with staggered concave/convex dimples of varying diameters (4 and 6 mm) and depths (1, 1.4 and 1.8 mm). Introducing a realizable model integrated with an improved wall function and SIMPLE solving procedure evaluates the thermo-hydraulic transport as Reynolds number is feasible as 5,000, 10,000, 15,000 and 20,000. In addition, using the findings from the present experimental work validates the numerical methodology.

This paper reveals that the staggered concave/convex dimples on the axis-fixed plastic tape can significantly improve thermo-hydraulic transport within this outer-heated tube. Furthermore, the processed dimples can cause flow disturbance, which increases turbulent kinetic energy and accelerates fluid mixing around a twisted plastic tape, resulting in enhanced thermal convection. The six kinds of twisted tapes (C1−C6) result in the thermo-hydraulic performance index (η) of 1.18–1.32 at Re = 5000. Among all the cases, the dimples using 4 mm combined with 6 mm diameter and 1.4 mm height (C4) earn the highest, around 1.40 at Re = 5,000.

The conditions of constant hydraulic-thermal characteristics of working fluid (air), steady Newtonian fluid considered, and the ignored radiative heat transfer and gravity are the research limitations of the numerical simulation.

The given results can benefit from a round tube design of a thermal apparatus axis fixed by a twisted-staggered concave/convex dimples tape to augment the thermo-hydraulic transport.

Staggered concave/convex dimples on the surface of a twisted tape allow for impinging and swirling flow along the tape. These processed dimples can induce flow disturbance, which increases the turbulent kinetic energy and facilitates fluid mixing in a twisted tape. Furthermore, the hybrid-diameter dimples have enough flow channels for fluid separation-reattachment, and the thermo-hydraulic performance index has improved. This paper then presents a helpful passive approach for cooling a thermal device.

Green innovation in human-centric smart manufacturing (HSM-GI) has emerged as a new paradigm in innovation management for Industry 5.0. The evaluation analysis method is crucial for measuring the development progress and guiding continual improvements of HSM-GI. Since this process of HSM-GI can be regarded as complex and interactive, a holistic picture is often required to describe the interrelations of its antecedents and consequences. In this respect, this study aims to construct a causality network indicator system and proposes a synergy evaluation method for HSM-GI.

Firstly, based on the Driver force-State-Response (DSR) causal-effect framework, this study constructs a holistic indicator system to analyze the interactions between environmental and human concerns of HSM-GI. Secondly, owing to the imprecision of human cognition and synergy interaction in the evaluation process, a flexible hesitant fuzzy (HF) superiority-inferiority synergetic evaluation method is presented. This method quantifies the strengths of causal relationships and expresses the incentives and constraints attitudes of humans. Finally, the proposed framework is applied to six HSMs in the electronic technology industry.

The driving force and state of the HSM-GI system exhibit an upward trend, while the response continues to decline due to changing market demands. The order and synergy degree have shown an increasing trend during 2021–2023, particularly significant for BOE and Haier Smart Home. HSM-GI systems with higher scores mostly have functional coordination and a coherent synergy structure.

This study demonstrates the proposed approach’s applicability and assists policymakers in formulating targeted strategies for green innovation systems.

This study aims to assess the mediating and driving roles of knowledge cooperation in the effectiveness of G60 Sci-tech Innovation Corridor (G60 STIC) for regional collaborative innovation within the knowledge economy context. Furthermore, it focuses on whether knowledge cooperation is more effective than resource cooperation in terms of spatial spillover and its mediating effects on collaborative innovation.

This study employs multiple statistical and econometric approaches, including social cooperation network, Super-DEA, spatial difference-in-difference model (SDID) and mediating effect model, to measure the effectiveness of knowledge cooperation and resource cooperation paths within the framework of the G60 STIC on regional collaborative innovation in the Yangtze River Delta region (YRD) from 2002 to 2022.

First, the knowledge cooperation networks validate the strengthening of collaborative innovation is primarily centred on provincial cities and leading manufacturing locales, with smaller cities radiating outwards from these centres. The knowledge cooperation network was generally stronger than the resource cooperation network. Second, the G60 STIC significantly enhances collaborative innovation efficiency by intensifying knowledge, resource and interactive cooperation networks. Third, within the context of the knowledge economy, knowledge cooperation presents a stronger spillover and mediating effect in stimulating collaborative innovation than resource cooperation.

This study clarifies the existence of a knowledge cooperation network and its mediating role in stimulating the effectiveness of strategic, innovative platforms on collaborative innovation. This further verifies the stronger role of the knowledge cooperation than the resource cooperation, which serves as a vital element in promoting strategic innovative platforms to optimise collaborative innovation.

This paper takes the Shanghai-Shenzhen-Hong Kong Stock Connect as a quasi-natural experiment and investigates the impact of capital market liberalization on the corporate debt maturity structure. It also aims to provide some policy implications for corporate debt financing and further liberalization of the capital market in China.

Employing the exogenous event of Shanghai-Shenzhen-Hong Kong Stock Connect and using the data of Chinese A-share firms from 2010 to 2020, this study constructs a difference-in-differences model to examine the relationship between capital market liberalization and corporate debt maturity structure. To validate the results, this study performed several robustness tests, including the parallel test, the placebo test, the Heckman two-stage regression and the propensity score matching.

This paper finds that capital market liberalization has significantly increased the proportion of long-term debt of target firms. Further analyses suggest that the impact of capital market liberalization on the debt maturity structure is more pronounced for firms with lower management ownership and non-Big 4 audit. Channel tests show that capital market liberalization improves firms’ information environment and curbs self-interested management behavior.

This research provides empirical evidence for the consequences of capital market liberalization and enriches the literature on the determinants of corporate debt maturity structure. Further this study makes a reference for regulators and financial institutions to improve corporate financing through the governance role of capital market liberalization.

The Grey System Theory (GST) is an emerging area of research within artificial intelligence. Since its founding in 1982, it has seen a lot of multidisciplinary applications. In just a short period, it has garnered some considerable strengths. Based on the 1987–2021 data collected from the Web of Science (WoS), the current study reports the advancement of the GST.

Research papers utilizing the GST in the fields of economics and education were retrieved from the Web of Science (WoS) platform using a set of predetermined keywords. In the final stage of the process, the papers that underwent analysis were manually chosen, with selection criteria based on the information presented in the titles and abstracts.

The study identifies prominent authors, institutions, publications and journals closely associated with the subject. In terms of authors, two major clusters are identified around Liu SF and Wang ZX, while the institution with the highest number of publications is Nanjing University of Aeronautics and Astronautics. Moreover, significant keywords, trends and research directions have been extracted and analyzed. Additionally, the study highlights the regions where the theory holds substantial influence.

The study is subject to certain limitations stemming from factors such as the language employed in the chosen literature, the papers included within the Web of Science (WoS) database, the designation of works categorized as “articles” in the database, the specific selection of keywords and keyword combinations, and the meticulous manual process employed for paper selection. While the manual selection process itself is not inherently limiting, it demands a greater investment of time and meticulous attention, contributing to the overall limitations of the study.

The significance of the study extends not only to scholars and practitioners but also to readers who observe the development of emerging scientific disciplines.

The analysis of trends revealed a growing emphasis on the application of GST in diverse domains, including supply chain management, manufacturing and economic development. Notably, the emergence of COVID-19 as a new research focal point among GST scholars is evident. The heightened interest in COVID-19 can be attributed to its global impact across various academic disciplines. However, it is improbable that this interest will persist in the long term, as the pandemic is gradually brought under control.

This study aims to provide a comprehensive overview of thin-film temperature sensors (TTS), focusing on the interplay between material properties and fabrication techniques. It evaluates the current state of the art, addressing both low- and high-temperature sensors, and explores the potential applications across various fields. The study also identifies challenges and highlights emerging trends that may shape the future of this technology.

This study systematically examines existing literature on TTS, categorizing the materials and fabrication methods used. The study compares the performance metrics of different materials, addresses the challenges encountered in thin-film sensors and reviews the case studies to identify successful applications. Emerging trends and future directions are also analyzed.

This study finds that TTS are integral to various advanced technologies, particularly in high-performance and specialized applications. However, their development is constrained by challenges such as limited operational range, material degradation, fabrication complexities and long-term stability. The integration of nanostructured materials and the advancement of wireless, self-powered and multifunctional sensors are poised to drive significant advancements in this field.

This study offers a unique perspective by bridging the gap between material science and application engineering in TTS. By critically analyzing both established and emerging technologies, the study provides valuable insights into the current state of the field and proposes pathways for future innovation in terms of interdisciplinary approaches. The focus on emerging trends and multifunctional applications sets this review apart from existing literature.

The wheel wear of high-speed trains is the main concern in the operation process of the high-speed trains. Wheel reprofile usually adopts the thin flange wheel profiles for the economic wheel reprofile strategy; however, the wheel wear evolution law of the thin flange wheel profile is not clear. The purpose of this paper mainly researches the wheel wear evolution law of thin flange wheel profile and the influence factors of thin flange wheel.

To solve this problem, this paper analyzed the wheel-rail contact relationship and established a vehicle dynamic model of high-speed train. The Jendel wheel wear model was used to analyze the evolution of thin flange wheel wear and rail deviation affecting wheel wear under thin flange wheel profiles.

Due to the consistency of the tread area, the distribution of wheel-rail contact points for thin flange wheels in the tread area is close. As the wheel flange increases, wheel wear also increases. Thin flange wheel profiles reduce wheel wear to a certain extent. The maximum wheel wear depths with rail deviations of −0.4 mm, 0 mm and 0.4 mm are 0.9844 mm, 1.077 mm and 1.142 mm, respectively. The negative rail deviation suppresses wheel wear, but the positive rail deviation increases wheel wear.

The evolution of wheel wear for thin flange wheels after reprofiling is not clear. This paper analyzed the wear characteristics of thin flange wheel wear and rail deviation affecting wheel wear under thin flange wheels.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-10-2024-0401/