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Soft robots are known for their excellent safe interaction ability and promising in surgical applications for their lower risks of damaging the surrounding organs when operating than their rigid counterparts. To explore the potential of soft robots in cardiac surgery, this paper aims to propose an adaptive iterative learning controller for tracking the irregular motion of the beating heart.

In continuous beating heart surgery, providing a relatively stable operating environment for the operator is crucial. It is highly necessary to use position-tracking technology to keep the target and the surgical manipulator as static as possible. To address the position tracking and control challenges associated with dynamic targets, with a focus on tracking the motion of the heart, control design work has been carried out. Considering the lag error introduced by the material properties of the soft surgical robotic arm and system delays, a controller design incorporating iterative learning control with parameter estimation was used for position control. The stability of the controller was analyzed and proven through the construction of a Lyapunov function, taking into account the unique characteristics of the soft robotic system.

The tracking performance of both the proportional-derivative (PD) position controller and the adaptive iterative learning controller are conducted on the simulated heart platform. The results of these two methods are compared and analyzed. The designed adaptive iterative learning control algorithm for position control at the end effector of the soft robotic system has demonstrated improved control precision and stability compared with traditional PD controllers. It exhibits effective compensation for periodic lag caused by system delays and material characteristics.

Tracking the beating heart, which undergoes quasi-periodic and complex motion with varying accelerations, poses a significant challenge even for rigid mechanical arms that can be precisely controlled and makes tracking targets located at the surface of the heart with the soft robot fraught with considerable difficulties. This paper originally proposes an adaptive interactive learning control algorithm to cope with the dynamic object tracking problem. The algorithm has theoretically proved its convergence and experimentally validated its performance at the cable-driven soft robot test bed.

The purpose of this paper is to examine China’s stop-start adoption of fair value accounting (FVA) into its national accounting standards. The paper analyzes how FVA standards promoted by transnational organizations were eventually adopted in China despite its conservative accounting traditions.

The study uses archival records and an analytic framework adapted from the studies of social movements to identify the institutional factors that differ between China’ first unsuccessful attempt to adopt FVA and its second successful attempt.

Shared interests of elite national and international groups, creation of social infrastructure, marshaling of key resources, and specific actions to frame FVA standards are found to be crucial factors supporting FVA reform in China.

The study helps advance our understanding of dissemination of international accounting regulations in non-Western societies. The findings can help accounting standard setters to avoid costly failures.

The study provides a structured analysis of the propagation of global accounting regulations. It exposes the factors in the failure and success of FVA adoption in China.

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.

This paper aims to design a novel TiC/GTD222 nickel-based high-temperature alloy with excellent hot corrosion resistance by incorporating appropriate amounts of C, Al and Ti elements into GTD222 alloy.

The composite material was prepared using the selective laser melting (SLM) technology, followed by a hot isostatic pressing (HIP) treatment. Subsequently, the composite underwent a hot corrosion test in a 75% Na₂SO₄ + 25% NaCl mixed salt environment at 900 °C.

The HIP-SLMed TiC/GTD222 composite exhibits a relatively low weight loss rate. First, the addition of alloying elements facilitates the formation of multiple protective oxide films rich in Al, Ti and Cr. These oxide films play a crucial role in enhancing the material’s resistance to hot corrosion. Second, the HIP treatment results in a reduction of grain size in the composite and an increased number of grain boundaries, which further promote the formation of protective films.

The hot corrosion behavior of the TiC/GTD222 nickel-based composite material prepared through SLM and HIP processing has not been previously studied. This research provides a new approach for designing nickel-based superalloys with excellent hot corrosion resistance.

The purpose of this paper is to improve the bioactivity of variable gradient TC4 porous scaffolds prepared by selective laser melting (SLM) through the micro-arc oxidation (MAO) technique.

Variable gradient TC4 porous scaffolds were prepared by SLM, then treated with MAO at different oxidation voltages. The microstructure, thickness and composition of MAO coatings were characterized by scanning electron microscope (SEM), energy-dispersive spectroscopy (EDS) and X-ray diffraction. The bioactivity of the MAO coatings was tested by simulated body fluid (SBF) immersion test.

SEM and EDS results show that with the increase of oxidation voltage, the content of Ca and P elements and the thickness of the MAO coatings increases. The thickness of the coating inside the scaffold is smaller than that of the outside regions. SBF immersion experiments showed that MAO-treated TC4 porous scaffolds had highest bioactivity at 440 V.

The variable gradient porous scaffolds were treated with MAO in the electrolyte containing Ca and P elements for the first time. The effect of oxidation voltages on the different region of porous scaffolds was studied in detail.

The purposes of this paper are to (1) explore how personality traits pertaining to the dominance influence steadiness compliance model manifest themselves in terms of user interaction behavior on social media and (2) examine whether social interaction data on social media platforms can predict user personality.

Social interaction data was collected from 198 users of Sina Weibo, a popular social media platform in China. Their personality traits were also measured via questionnaire. Machine learning techniques were applied to predict the personality traits based on the social interaction data.

The results demonstrated that the proposed classifiers had high prediction accuracy, indicating that our approach is reliable and can be used with social interaction data on social media platforms to predict user personality. “Reposting,” “being reposted,” “commenting” and “being commented on” were found to be the key interaction features that reflected Weibo users' personalities, whereas “liking” was not found to be a key feature.

The findings of this study are expected to enrich personality prediction research based on social media data and to provide insights into the potential of employing social media data for the purpose of personality prediction in the context of the Weibo social media platform in China.

Lenders in online peer-to-peer (P2P) lending platforms are always non-experts and face severe information asymmetry. This paper aims to achieve the goals of gaining high returns with risk limitations or lowering risks with expected returns for P2P lenders.

This paper used data from a leading online P2P lending platform in America. First, the authors constructed a logistic regression-based credit scoring model and a linear regression-based profit scoring model to predict the default probabilities and profitability of loans. Second, based on the predictions of loan risk and loan return, the authors constructed linear programming model to form the optimal loan portfolio for lenders.

The research results show that compared to a logistic regression-based credit scoring method, the proposed new framework could make more returns for lenders with risks unchanged. Furthermore, compared to a linear regression-based profit scoring method, the proposed new framework could lower risks for lenders without lowering returns. In addition, comparisons with advanced machine learning techniques further validate its superiority.

Unlike previous studies that focus solely on predicting the default probability or profitability of loans, this study considers loan allocation in online P2P lending as an optimization research problem using a new framework based upon modern portfolio theory (MPT). This study may contribute theoretically to the extension of MPT in the specific context of online P2P lending and benefit lenders and platforms to develop more efficient investment tools.

This paper aims to investigate that how to mitigate the weaker party's risk perception in imbalanced supply chain relationships by framing contracts according to complexity and recurrence. The level of information technology (IT) integration is considered as the moderator influencing the effectuation of contract framing.

The authors conducted a questionnaire survey with 229 firms involved in imbalanced supply chains. Hierarchical regression analysis was used to test the hypotheses.

The authors found contractual complexity positively influenced performance and relational risk, while contractual recurrence negatively impacted performance and relational risk. This study further reveals the positive moderating effect of IT integration in influencing contractual complexity on relational risk and performance risk and the negative impact of IT integration in influencing contractual recurrence on relational risk and performance risk.

Overall, this study posits the coordinating role of contracts in reducing the weaker party's risk perception in imbalanced supply chain relationships.

The authors concluded by illustrating how to customize contracts based on the level of IT integration to maximize their role in reducing risk perception.

This study is embedded in imbalanced supply chain relationship, aiming to solve the problem of high-risk perception held by the weaker party, which is a salient threat to the sustainability of collaboration. Contract framing is proposed as an effective approach for mitigating risk perception, which should be carefully designed based on the level of IT integration of the relationship. The authors found that contractual complexity has a positive influence on performance and relational risk, but contractual recurrence has a negative impact on performance and relational risk. This study further reveals the moderating effect of IT integration on the effectuation of contractual framing.