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The purpose of this paper aims to investigate the adaptive impedance control and its optimized PSO algorithm for force tracking of a dual-arm cooperative robot. Because the dual-arm robot is directly in contact with external environment, controlling the mutual force between robot and external environment is of great importance. Besides, a high compliance of the robot should be guaranteed.

An impedance control based on Particle Swarm Optimization (PSO) algorithm is designed to track the mutual force and achieve compliance control of the robot end.

The experimental results show that the impedance control coefficients can be automatically tuned converged by PSO algorithm.

The system can reach a steady state within 0.03 s with overshoot convergence, and the force fluctuation range at the steady state decreases to about ±0.08 N even under the force mutation condition.

Aluminum alloy is considered an ideal material in aerospace, automobile and other fields because of its lightweight, high specific strength and easy processing. However, low hardness and strength of the surface of aluminum alloys are the main factors that limit their applications. The purpose of this study is to obtain a composite coating with high hardness and lubricating properties by applying GO–PVA over MAO coating.

A pulsed bipolar power supply was used as power supply to prepare the micro-arc oxidation (MAO) coating on 6061 aluminum sample. Then a graphene oxide-polyvinyl alcohol (GO–PVA) composite coating was prepared on MAO coating for subsequent experiments. Samples were characterized by Fourier infrared spectroscopy, X-ray diffraction, Raman spectroscopy and thermogravimetric analysis. The friction test is carried out by the relative movement of the copper ball and the aluminum disk on the friction tester.

Results showed that the friction coefficient of MAO samples was reduced by 80% after treated with GO–PVA composite film.

This research has made a certain contribution to the surface hardness and tribological issues involved in the lightweight design of aluminum alloys.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2023-0427/

Despite the extensive body of research on absolute corporate social responsibility (CSR) performance, limited attention has been given to the distinct concepts of optimal and aggressive CSR engagement, as well as their associations with CSR awarding. This study aims to differentiate between optimal and aggressive CSR engagement and examine their relationship with CSR awarding while considering the moderating influence of board characteristics from the perspectives of stakeholder and agency theories.

This empirical analysis draws on an international dataset comprising 43,803 observations from nine sectors across 41 countries. We employ a least squares dummy variable regression approach that accounts for country, industry and year effects to conduct the analysis.

The results reveal that engagement in aggressive CSR activities beyond the optimal level leads to the generation of a social reputation through CSR awarding. However, the influence of board characteristics on this relationship is significant. Specifically, the presence of a dedicated CSR committee encourages CSR awarding in the context of aggressive CSR engagement. Conversely, board independence constrains the relationship between aggressive CSR engagement and CSR awarding. Notably, board gender diversity does not have a discernible impact on this connection.

Our evidence provides valuable insights to help firms seeking to enhance their social reputation through CSR activities better allocate their resources and avoid unnecessary financial commitments.

This study advances the current understanding by exploring the relationship between aggressive CSR engagement and the recognition of CSR awards. Furthermore, it scrutinises the factors that dictate when such aggressive CSR engagement translates into enhanced social reputation, as evidenced by the attainment of CSR awards.

With the growing concern of various sectors of society regarding environmental issues and the promotion of sustainable development, green technology innovation is generally considered to be conducive to the long-term development of enterprises. However, because of the existence of agency problems, managers may have shortsighted behaviors. Then how will managers' shortsighted behaviors affect enterprises' green technology innovation?

This paper uses machine learning-based text analysis methods to construct a manager myopia index based on the data from A-share listed companies on the Shanghai and Shenzhen Stock Exchanges from 2015 to 2020. We examine the impact of manager myopia on green technology innovation in companies.

Our study finds that manager myopia significantly inhibits green technology innovation in companies. However, when multiple large shareholders coexist and the proportion of institutional investors' holdings is high, it can alleviate the inhibitory effect of manager myopia on green innovation. Heterogeneity tests show that the impact of manager myopia on green technology innovation is relatively significant in non-state-owned and manufacturing companies, as well as in the electricity industry. Robustness tests demonstrate that our conclusions remain valid after using propensity score matching to eliminate endogeneity problems.

From the perspective of corporate governance, this paper incorporates managers' shortsightedness, multiple large shareholders and institutional investors' shareholding ratios into the same logical framework, analyzes their internal mechanisms, helps improve corporate governance, enhances green innovation capabilities and has strong implications for the implementation of national innovation-driven development strategies and the achievement of “carbon peak” and “carbon neutrality” targets.

Selective laser sintering (SLS) is an essential technology in the field of additive manufacturing. However, SLS technology is limited by the traditional point-laser sintering method and has reached the bottleneck of efficiency improvement. This study aims to develop an image-shaped laser sintering (ISLS) system based on a digital micromirror device (DMD) to address this problem. The ISLS system uses an image-shaped laser light source with a size of 16 mm × 25.6 mm instead of the traditional SLS point-laser light source.

The ISLS system achieves large-area image-shaped sintering of polymer powder materials by moving the laser light source continuously in the x-direction and updating the sintering pattern synchronously, as well as by overlapping the splicing of adjacent sintering areas in the y-direction. A low-cost composite powder suitable for the ISLS system was prepared using polyether sulfone (PES), pinewood and carbon black (CB) powders as raw materials. Large-sized samples were fabricated using composite powder, and the microstructure, dimensional accuracy, geometric deviation, density, mechanical properties and feasible feature sizes were evaluated.

The experimental results demonstrate that the ISLS system is feasible and can print large-sized parts with good dimensional accuracy, acceptable geometric deviations, specific small-scale features and certain density and mechanical properties.

This study has achieved the transition from traditional point sintering mode to image-shaped surface sintering mode. It has provided a new approach to enhance the system performance of traditional SLS.

At the moment, in terms of both research and commercial products, smart shoe technology and applications seem not to attract the same magnitude of attention compared to smart garments and other smart wearables such as wrist watches and wrist bands. The purpose of this study is to fill this knowledge gap by discussing issues regarding smart shoe sensing technologies, smart shoe sensor placements, factors that affect sensor placements and finally the areas of smart shoe applications.

Through a review of relevant literature, this study first and foremost attempts to explain what constitutes a smart shoe and subsequently discusses the current trends in smart shoe applications. Discussed in this study are relevant sensing technologies, sensor placement and areas of smart shoe applications.

This study outlined 13 important areas of smart shoe applications. It also uncovered that majority of smart shoe functionality are physical activity tracking, health rehabilitation and ambulation assistance for the blind. Also highlighted in this review are some of the bottlenecks of smart shoe development.

To the best of the authors’ knowledge, this is the first comprehensive review paper focused on smart shoe applications, and therefore serves as an apt reference for researchers within the field of smart footwear.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

This paper seeks to examine how leaders shape employee creativity by using interpersonal emotion management (IEM) strategies. Drawing on the social information processing (SIP) theory, the authors argue that psychological safety translates leader problem-focused IEM into employee creativity, an impact which is moderated by organizational justice.

Data were collected in two waves from 201 employees and their leaders in China. Regression analysis was used to test the hypotheses.

Leader problem-focused IEM is positively related to employee creativity, and this relationship is mediated by psychological safety. Organizational justice positively moderates the relationship between leader problem-focused IEM and psychological safety as well as the indirect relationship between leader problem-focused IEM and employee creativity via psychological safety.

This paper identifies a novel and useful predictor of employee creativity from the perspective of leader problem-focused IEM and provides practical insights for organizations regarding ways of improving employee creativity.

This study investigates how to motivate behavioral intentions toward green investment (BIGI) with the moderating effect of social media platforms usage (SMPU) among individual investors in Egypt.

The study used partial least squares structural equation modeling (PLS-SEM) to analyze the data and test hypotheses based on a sample of 550 individual investors with investment experience.

The results show that attitude, subjective norm (SN), and perceived behavioral control (PBC) have a significant relationship with investors' behavioral intention toward green investment. The moderating effect of (SMPU) supported the relationship between (SN), (PBC), and (BIGI), but (SMPU) does not support the relationship between attitude and (BIGI).

This study provides some implications for investment providers, service providers, and policymakers.

Despite the increasing global interest in climate change and its consequent opportunities and challenges for business, previous studies did not strongly emphasize green investment. So, based on the theory of planned behavior (TPB), this study sheds light on the motivational factors that may push investors' behavioral intentions toward green investment. With the increasing interest in digital transformation, the study also examined how digital platforms support (BIGI), especially in Egypt as a developing country.

This study's goal is to look at how visitors' experiences affect the indirect links between the destination's extrinsic motivations (DEMs) and tourists' intrinsic motives (TIMs), on the one hand, and the perceived destination image (PDI), on the other.

Using structural equation modeling, 613 tourists from different nationalities were used to test the five hypotheses.

The research results revealed that second-order destinations' extrinsic motivations directly impact TIM and PDI. It also showed that tourists' experiences as moderators reduce the direct effect of DEM on PDI for first-time visitors compared to repeat visitors. Moreover, it increases the direct effect of TIM on PDI for repeated visitors.

Destination managers can fix the problems that hurt their reputations and images by hiring police officers in tourist areas and cleaning tourist places. In the same way, destination managers and travel agencies should use AI tools to create social media marketing campaigns focusing on natural and historical monuments. Also, the marketing plans should stress the value for money (for example, lodging, food and attractions’ cost). Finally, destination marketers can make programs for repeat visitors, focusing on DEM and TIM.

This article tries to fill a gap in the research on PDI formation in emerging markets as a modern technique in destination marketing by using the push-intrinsic and pull-extrinsic theories. It also looks at how the tourists' experiences moderate the direct link between DEM, TIM and PDI. Lastly, this study examines how TIM affects a destination's image in emerging markets.