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The accuracy and reliability of upper limb motion assessment have received great attention in the field of rehabilitation. Grasping test is widely carried out for motion assessment, which requires patients to grasp objects and move them to target place. The traditional assessments test the upper limb motion ability by therapists, which mainly relies on experience and lacks quantitative indicators. This paper aims to propose a deep learning method based on the vision system of our upper limb rehabilitation robot to recognize the motion trajectory of rehabilitation target objects automatically and quantitatively assess the upper limb motion in the grasping test.

To begin with, an SRF network is designed to recognize rehabilitation target objects grasped in assessment tests. Moreover, the upper limb motion trajectory is calculated through the motion of objects’ central positions. After that, a GAE network is designed to analyze the motion trajectory which reflects the motion of upper limb. Finally, based on the upper limb rehabilitation exoskeleton platform, the upper limb motion assessment tests are carried out to show the accuracy of both object recognition of SRF network and motion assessment of GAE network. The results including object recognition, trajectory calculation and deviation assessment are given with details.

The performance of the proposed networks is validated by experiments that are developed on the upper limb rehabilitation robot. It is implemented by recognizing rehabilitation target objects, calculating the motion trajectory and grading the upper limb motion performance. It illustrates that the networks, including both object recognition and trajectory evaluation, can grade the upper limb motion functionn accurately, where the accuracy is above 95.0% in different grasping tests.

A novel assessment method of upper limb motion is proposed and verified. According to the experimental results, the accuracy can be remarkably enhanced, and the stability of the results can be improved, which provide more quantitative indicators for further application of upper limb motion assessment.

Corporate digital transformation (CDT) has challenged traditional tax administration systems. This study examines the impact of CDT on tax avoidance behavior and tests whether tax authorities can identify this behavior.

Using data on listed companies on the Shanghai and Shenzhen Stock Exchanges from 2008 to 2020, this study applies the Heckman two-stage and cross-section models.

The results show that the higher the degree of CDT, the more aggressive the tax avoidance behavior. The CDT's impact on corporate tax avoidance is more significant under strong government tax efforts.

This study expands research on the economic consequences of CDT and the factors influencing corporate tax avoidance behavior. Moreover, it has important implications for governments to monitor tax avoidance behavior under the CDT, improve digital tax systems, and pay more attention to the tax administration of digital assets.

To examine the impact of manufacturer reputation, retailer reputation, and product price on consumers’ perceived quality and purchasing behavior regarding organic milk.

Employing a 2 × 2 experiment, data were collected from 1,259 consumers in 32 provinces in China.

When a low-reputation manufacturer sells products through a high-reputation retailer, it improves consumers’ perception of quality and positively influences their purchasing behavior. Interestingly, setting higher prices for products manufactured by low-reputation companies and selling them through high-reputation retailers did not significantly enhance consumers’ perceived quality and deter their purchasing behavior.

The analysis expands the framework for cue diagnosis. While the existing framework primarily focuses on the influence of cue-type combinations on perceived quality, it does not integrate purchasing behavior into the conceptual framework. This limitation hinders people understanding of the theoretical mechanisms underlying the use of cues in purchasing decisions. This paper address this by gradually introducing variables, such as retailer reputation and product price, into the baseline model, thereby extending this theory. In addition, this paper advances the marketing research literature within the business-to-business-to-consumer context by examining the additive effects of manufacturer reputation, retailer reputation, and product price on consumers’ perception of quality and purchasing behavior.

In this paper, we explore the impact of land management practices on post-disaster housing, to draw lessons from the case of reconstruction in Türkiye.

We conducted two qualitative case studies of two reconstruction experiences following the 2020 Elazığ and İzmir earthquakes. We analyzed 70 articles, technical reports and press releases and then used a set of policy analysis tools to examine five policy documents in depth. Finally, we wanted to understand how key officers interpreted these policy documents, so we closely analyzed the transcripts of eight semi-structured interviews.

Türkiye’s legal framework comprises five main policies that concentrate power in the central government and are not tailored to post-disaster reconstruction. This framework facilitates the construction of rubber-stamped apartment buildings, which disregard cultural and social contexts and practices. The current reconstruction policy neglects alternative options like cooperative housing, which could better respond to communal needs and expectations. It also often leads to the loss of land rights among affected residents.

We only analyzed decision-making processes in two case studies and based our study on a limited number of interviews. Therefore, it is difficult to generalize our results and apply them to other contexts. Further quantitative and qualitative work is necessary to conceptualize the links between land management and post-disaster housing reconstruction.

Our findings suggest a need for legislative frameworks specifically designed to address land management during post-disaster reconstruction. The concentration of power in central governments is problematic, thus it remains crucial to empower local authorities by reinforcing technical expertise and facilitating administrative autonomy.

This study offers unique insights into how power relations influence land management practices in post-disaster housing reconstruction. Examining the centralization of power and its impact on cultural and social practices identifies common forms of dispossession and points to key areas for policy improvement.