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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.

Due to the vital role of innovation for firms to respond to the change and achieve competitive advantage, the purpose of this study is to investigate the influence of knowledge-oriented leadership (KOL) on innovation performance via the mediating role of knowledge sharing (KS). This study also explores the moderating role of organizational justice in the relationship between KS and innovation performance.

Analysis of moment structures and structural equation modeling are applied to examine the relationship among the latent factors in the proposed research model using data collected from 335 participants in 121 manufacturing and service firms in Vietnam.

The findings revealed that KOL serves as a key precursor to foster innovation performance directly or indirectly through active and passive KS behaviors. In addition, the paper highlights the moderating role of organizational justice in strengthening the impact of KS activities on innovation performance.

By highlighting the important role of KOL for stimulating KS behaviors, this paper provides a valuable understanding and novel approach for firms to improve innovation performance. The research findings support the idea that building a climate of justice is crucial to enhance the effects of KS on innovation performance.

This study contributes to bridging the research gaps in the literature and advances the insights of how KOL directly and indirectly stimulates innovation performance via mediating roles of active and passive KS processes under the climate of justice.

This study aims to analyze and clarify the influence of knowledge-oriented leadership (KOL) on radical and incremental innovation via the mediating role of knowledge sharing (KS). This study also explores the relationship between KS and two aspects of innovation capability by examining the moderating role of market turbulence.

The research model was tested using a structural equation modeling design based on survey data collected from 335 participants in 132 manufacturing and service firms in Vietnam.

The findings revealed that KOL serves as a key precursor to foster radical and incremental innovation directly or indirectly through KS processes. In addition, it highlights the moderating role of market turbulence in strengthening the impact of KS activities on two forms of innovation capability.

By highlighting the important role KOL practice for stimulating KS behaviors, this paper provides a valuable understanding and novel approach for firms to improve radical and incremental. The research findings support the idea that market turbulence significantly contributes to increasing the effects of KS on innovation capability.

This study contributes to bridging research gaps in the the literature and advances the insights of how KOL directly and indirectly fosters innovation capability via the mediating role of KS processes under the market turbulence.

Global climate change characterized by an increase in temperature has become the focus of attention all over the world. China is a sensitive and significant area of global climate change. This paper specifically aims to examine the association between agricultural productivity and the climate change by using China’s provincial agricultural input–output data from 2000 to 2019 and the climatic data of the ground meteorological stations.

The authors used the three-stage spatial Durbin model (SDM) model and entropy method for analysis of collected data; further, the authors also empirically tested the climate change marginal effect on agricultural productivity by using ordinary least square and SDM approaches.

The results revealed that climate change has a significant negative effect on agricultural productivity, which showed significance in robustness tests, including index replacement, quantile regression and tail reduction. The results of this study also indicated that by subdividing the climatic factors, annual precipitation had no significant impact on the growth of agricultural productivity; further, other climatic variables, including wind speed and temperature, had a substantial adverse effect on agricultural productivity. The heterogeneity test showed that climatic changes ominously hinder agricultural productivity growth only in the western region of China, and in the eastern and central regions, climate change had no effect.

The findings of this study highlight the importance of various social connections of farm households in designing policies to improve their responses to climate change and expand land productivity in different regions. The study also provides a hypothetical approach to prioritize developing regions that need proper attention to improve crop productivity.

The paper explores the impact of climate change on agricultural productivity by using the climatic data of China. Empirical evidence previously missing in the body of knowledge will support governments and researchers to establish a mechanism to improve climate change mitigation tools in China.

Toward sustainable development, radical green innovation (RGI) is necessary. Despite extensive research on the factors influencing green innovation, few studies have been conducted on the precursors. Based on upper echelons (UE) theory, dynamic capability (DC) theory, “stimulus-organism-response” (SOR) theory, social information processing (SIP) theory and cognitive appraisal (CA) theory of emotion, the study explores how digital leadership (DL) affects RGI and investigates the mediating effects of green organizational identity (GOI) and the moderating effects of digital threat (DT) and technology for social good (TSG), as well as the multiple concurrent causalities that trigger high RGI.

The method of combining structural equation model (SEM) and fuzzy-set qualitative comparative analysis (fs QCA) is adopted in the study. Data from 233 questionnaires were collected at two different time points.

This study's findings indicate that the four dimensions of DL can positively influence RGI and GOI partially mediates between the four dimensions of DL and RGI. DT has a negative moderating effect between DL and GOI, while TSG is positively regulated between them, DT and TSG linkage moderates the partial mediating effect of GOI in DL and RGI. Further, fs QCA is used to analyze the causal complexity of DL dimensions and GOI to RGI and nine effective configuration paths are identified. It is found that the synergy of digital thinking ability (DTA), digital detection ability (DDA), digital social ability (DSA), digital reserve ability (DRA) and GOI is crucial to high RGI. Among them, GOI core appears the most times, indicating that GOI plays a vital role in improving enterprise RGI.

This study expands the literature on leadership and innovation by constructing a framework of “DL-GOI-RGI” and exploring the transmission of GOI and the boundary effect of DT and TSG. The study used fs QCA and SEM to better understand the statistical associations and the set relations between the conjunctions and conditions.

This paper aims to explore the influencing factors of public perceived efficacy of emergency infrastructure projects based on the triadic interactive determinism, and analyze the relationship among these factors.

Based on the triadic interactive determinism, we explored the factors influencing public perceived efficacy of emergency infrastructure project and empirically verified the relationship among these factors and perceived efficacy by using data drawn from a questionnaire survey of 491 residents near Leishenshan Hospital, Jiangxia District, Wuhan, China.

Prior experience, emotional response, personal expectation, public trust, context message and interactivity level, namely behavior, individual and environment, affect the perceived efficacy of public emergency infrastructure projects.

The results offer an insight into public perceived efficacy of emergency infrastructure project from the perspective of antecedents in a triadic reciprocal determinism, which provides a reference basis for the sustainable development of the emergency infrastructure projects. This study also suggests valuable practical implications to government departments to improve the quality of administrative decision-making effectively.

Although existing studies have found some influencing factors of public perceived efficacy in general infrastructure, there is still a lack of systematic carding and quantitative description of influencing factors of public perceived efficacy of emergency infrastructure projects. This study bridges this gap by exploring the determinants and their influencing relationship of public perceived efficacy especially for emergency infrastructure projects.

The purpose of this research is to address the complex multiobjective unrelated parallel machine scheduling problem with real-world constraints, including sequence-dependent setup times and periodic machine maintenance. The primary goal is to minimize total tardiness, earliness and total completion times simultaneously. This study aims to provide effective solution methods, including a Mixed-Integer Programming (MIP) model, an Epsilon-constraint method and the Nondominated Sorting Genetic Algorithm (NSGA-II), to offer valuable insights into solving large-sized instances of this challenging problem.

This study addresses a multiobjective unrelated parallel machine scheduling problem with sequence-dependent setup times and periodic machine maintenance activities. An MIP model is introduced to formulate the problem, and an Epsilon-constraint method is applied for a solution. To handle the NP-hard nature of the problem for larger instances, an NSGA-II is developed. The research involves the creation of 45 problem instances for computational experiments, which evaluate the performance of the algorithms in terms of proposed measures.

The research findings demonstrate the effectiveness of the proposed solution approaches for the multiobjective unrelated parallel machine scheduling problem. Computational experiments on 45 generated problem instances reveal that the NSGA-II algorithm outperforms the Epsilon-constraint method, particularly for larger instances. The algorithms successfully minimize total tardiness, earliness and total completion times, showcasing their practical applicability and efficiency in handling real-world scheduling scenarios.

This study contributes original value by addressing a complex multiobjective unrelated parallel machine scheduling problem with real-world constraints, including sequence-dependent setup times and periodic machine maintenance activities. The introduction of an MIP model, the application of the Epsilon-constraint method and the development of the NSGA-II algorithm offer innovative approaches to solving this NP-hard problem. The research provides valuable insights into efficient scheduling methods applicable in various industries, enhancing decision-making processes and operational efficiency.

Struck-by accidents (i.e. being hit by a falling object) are a leading cause of traumatic brain injuries in the construction industry. Despite the critical role of hard hats in minimizing such injuries, their overall design has not appreciably changed in decades. Therefore, this study aims to explore the potential benefits of modifying commercially available hard hat designs by incorporating a compliant cantilever and a sacrificial, energy-absorbing structure to enhance their protective capabilities against impacts.

This study involved conducting experimental impact tests to obtain the head acceleration attenuation using hard hats with a variety of compliant cantilever lattice insert designs. These lattice inserts were additively manufactured using three polymeric materials, including polylactide (PLA), acrylonitrile butadiene styrene, high-impact polystyrene and three porosity levels. A Hybrid III head/neck assembly was fitted with each hard hat design, and experimental drop tests were conducted using a 1.8-kg steel impactor dropped from 1.83 m. The maximum acceleration and head injury criterion (HIC) values were obtained for each test.

Analysis of variance revealed that HIC was significantly reduced for all lattices with 56% porosity (p < 0.023) compared to the control (unmodified) hard hat. The most effective insert was found to be a PLA insert with 56% porosity, which reduced the HIC value by 38% compared to the control (unmodified) hard hat, with a statistically significant p-value of 0.018.

The data present in this study reveals that simple and inexpensive modifications can be made to existing hard hat designs to reduce injury risk from overhead impacts.

This study examines the link between the knowledge creation process, ambidextrous innovation, and competitive advantage. Further, this study also tested the moderating role of organizational agility on the relationship between the knowledge creation process and ambidextrous innovation.

The empirical study’s data were collected by surveying 306 respondents employed in 140 Pakistani Small and Medium Enterprises (SMEs). The questionnaire was designed according to the study’s requirements and was based on theoretical knowledge and findings from previous research on the knowledge-creation process, ambidextrous innovation, and competitive advantage. All hypotheses were tested using a structured regression method.

The study indicates that the knowledge creation process significantly impacts a firm’s competitive advantage. Additionally, this study demonstrates that ambidextrous innovation can moderate the relationship between the knowledge-creation process and competitive advantage.

Future studies should examine mediating factors, such as organizational culture, leadership style, and industry characteristics, as well as moderating variables, such as environmental turbulence.

This study guides SME leaders on the importance of knowledge creation and ambidextrous innovation in achieving operational success and gaining a competitive advantage.

This study explores how the knowledge creation process directly and indirectly, enhances organizational capacity for competitive advantage through the mediating roles of ambidextrous innovation and the moderating role of organizational agility.

The purpose of this study is to solve the problem that existing researches ignore the long-term and staged nature of digital transformation, failing to conduct specific discussions for different stages. It responded the call by constructing a three-stage evolutionary model to analyze the impact of digital transformation at different stages on the sustainable performance of manufacturing enterprises. The moderating effect of core technology capabilities is also explored, guided by the theory of assimilation innovation.

Based on the panel data of Chinese listed manufacturing companies from 2012 to 2020, this study empirically investigate the impact of digital transformation (digital process, digital operation and digital ecology) on sustainability performance (economic performance and environmental performance).

The findings indicate that digital operations and digital ecology significantly improve economic performance and environmental performance. Furthermore, the core technological capacity of the enterprise serves to modify the positive correlation between digital transformation at each stage and sustainable performance to some extent. In other words, when an enterprise is equipped with the requisite technological capacity, the digital transformation at each stage accelerates both economic performance and environmental performance, which in turn is conducive to an improvement in the enterprise’s sustainable development performance.

The findings contribute to the theoretical framework of digital transformation and sustainable development in all stages of enterprises. Furthermore, they provide guidance for achieving sustainable development through the implementation of digital transformation and the enhancement of core technological capacity.