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The purpose of this paper is to study the control strategy of transition mode of the stopped-rotor (SR) aircraft under the condition of redundant control and complex aerodynamic characteristics.

This paper first proposes a transition strategy for the conversion between helicopter mode and fixed-wing mode. Then, aiming at the redundancy of the two control systems in the transition process, a control model is proposed, which greatly simplifies the control in conversion mode. Then, to facilitate the design of the control system, the Takagi-Sugeno model of the SR aircraft in transition mode is established. Finally, an explicit model tracking and tuning parameter stability augmentation control system is designed, so that the SR aircraft has a good stability during the transition process. Then, the outer loop control system of transition flight is designed.

The simulation results show that the control strategy proposed in this paper can realize the mode conversion well. It lays a solid foundation for the subsequent engineering flight test for the SR aircraft.

The work done in this paper provides ideas and methods for the flight control system design of SR aircraft in transition mode. The method of designing control model to solve the coordination of redundant control system is also applicable for other multimode aircraft, which provides a simple and convenient method for the multimode aircraft control.

This study aims to deal with the problem of trajectory tracking control for the quadrotor under external environmental disturbance and variable payloads.

In the field of unmanned aerial vehicle (UAV) control, external environmental disturbance and internal variable payloads as two major interference factors lead to control performance degradation or even instability, thus a trajectory tracking controller which innovatively combines sliding mode control technology and model-free control technique is proposed. The proposed controller is constructed with a learning rate-based sliding mode controller and an ultra-local model. Based on the proposed controller, the nonlinear system model of variable load quadrotor is locally estimated and the system’s uncertainties and disturbances can be compensated.

The simulation and actual test results demonstrate the satisfactory control performance and the robustness of the proposed controller compared with the PID and Backstepping controller under external environmental disturbance and variable payloads. Moreover, the proposed controller solves the trajectory tracking control problem not only when payloads change at the center of gravity but also when the position of load variation deviates from the center of gravity.

In both military and civilian domains, the quadrotor may encounter such situations that the payloads change, such as transporting goods, aerial refueling and so on. As a large internal interference factor, variable load tends to lead to unstable control. The research results provide theoretical guidance and technical support for trajectory tracking control of quadrotor under variable payloads.

The proposed controller combines learning rate-based sliding mode controller and model-free control technique to achieve a more efficient and accurate trajectory control of the quadrotor when considering system uncertainties and the load variation that happens in the unknown location.

Scientific knowledge is usually regarded as the basis for the management of natural environment and wildlife in ecotourism. However, recently, social construction approaches challenge the domination of natural science. This study aims to examine the effectiveness of the social construction paradigm in ecotourism management, through conducting a content analysis of social media comments on an accident caused by a monkey in a Chinese ecotourism area. The results show that people commented on the accident from five aspects. First, the public expressed their compassion and mourning for the deceased. Second, people thought that the death was casual and absurd, yet life is full of uncertainty and people should cherish the present. Third, people commented much on the deceased tourist’s company, which is a famous sugar brand well entrenched in many Chinese people’s childhood memories. Fourth, people constructed the monkey as Monkey King, Golden Monkey (another famous sugar brand in China) and as a criminal. Fifth, people also gave their opinions about possible causes of the accident, namely, it was caused by “the mandate of heaven,” company competition, conspiracies or poor management. This study only seriously considers the comments about the mandate of heaven. This explanation is consistent with the Chinese traditional construction of nature as “heaven,” which is believed to dominate the natural and human worlds. Most people, including the managers, accepted the accident and did not explore further about the reasons for the accident. In this case, such a social construction of nature does not aid effective ecotourism management.

The purpose of this paper is to design a glass handling robot and conduct a finite element analysis and structural optimization to solve the automation handling problem of large-scale glass production line and aiming at the phenomenon that the vibration of robot manipulator may result in breakage of glass products, especially the fragile chemical or medical glassware. Making modal analysis for the robot is to determine its natural frequencies and vibration modes and lay a foundation for the transient analysis to study the vibration shock response of the robot during its start-up and emergency stop operation.

First, a 3D model of the robot is established according to the requirements of the production field and a finite element model is built on the basis of the 3D model. Then the modal and transient analyses of the robot are carried out according to the fact that the maximum vibration impact of the robot usually appears at the start and emergency stop.

The structure of the robot is improved according to the results of finite element analysis. The dynamic analysis results show that the improved robot’s ability to resist deformation under the impact of vibration shock is enhanced, and the robot can operate smoothly and meet the requirements of design in industrial environments.

The research results avoided the damage caused by the vibration and improved the service life of the robot, providing a foundation for the structural design and mass production of the glass handling robot.

The purpose of this research is to determine how supply chain management (SCM) might be less affected by COVID-19 by using innovative technologies such as the Internet of Things (IoT), eco-friendly corporate practices and other digital advancements. It strongly emphasizes the use of technology to improve supply networks’ and Omani firms’ performance.

Using a mixed-methods research strategy, this study integrates both qualitative and quantitative approaches. It involves a survey and interviews with supply chain and IT managers from various industries in Oman to gather data and evaluate the impact of technology on SCM.

This study finds that IoT capabilities, smart technologies (STs) and green practices significantly mitigate COVID-19 impacts on SCM. The performance of the supply chain and the business are both improved by these technologies’ positive effects on integrating various supply chain elements, such as suppliers, internal processes and customer relations.

The main constraint of this study is its concentration on businesses in Oman, potentially restricting the applicability of its findings to broader contexts. Future studies could investigate similar frameworks across various geographic and industry settings.

The findings suggest that incorporating STs into SCM is crucial for enhancing operational efficiency and resilience against disruptions such as COVID-19. This offers valuable insights for managers and policymakers in adopting technology-driven strategies for SCM.

This study highlights the significant role of technology in sustaining supply chains during pandemics, thereby supporting economic stability and societal well-being. It underscores the importance of technological advancements in maintaining supply chain continuity in challenging times.

By empirically examining the effect of emerging technologies on enhancing SCM in the context of the COVID-19 pandemic, specifically in the Oman market, this research makes a unique contribution to the body of knowledge.

Organizations' technological management capabilities (TMC) have emerged as a powerful tool to enable manufacturing firms to deal with environmental issues. This empirical investigation aims to introduce and validate a novel conceptual framework that seeks to uncover the latent relationships among the selected constructs of this study. Organizational TMC could enhance green production (GP) and reinforce the green competitive advantage (GCA) among manufacturing firms. Therefore, this research investigates the role of TMC of firms such as artificial intelligence capability (AIC), big data analytics capability (BDAC) and Internet of things capability (IOTC) in reshaping green innovation (RGI), employee development (ED), GP and GCA.

The Partial Least Squares-Structural Equation Modeling was proposed to test and validate this research’s conceptual model using 463 valid responses from manufacturing under the China–Pakistan Economic Corridor (CPEC) umbrella.

Our statistical findings confirmed that TMCs such as AIC, BDAC and IOTC supported the GP and CGA. ED and RGI positively correlated to GP. The hypotheses testing results also confirmed the mediating role of ED, RGI and GP and the moderating role of green firm innovativeness capability (GFIC) in the underdeveloped context of the manufacturing industry under the CPEC.

Moreover, the statistical findings of this study extend the existing literature by validating the possible direct, indirect/mediation and indirect/moderation relationship between TMC and GCA.

The firms’ adoption and improvement of big data analytics capabilities to improve economic and environmental performance have recently increased. This makes it important to discover the underlying mechanism influencing the association between big data analytics (BDA) and economic and environmental performance, which is missing in the existing literature. The present study discovers the indirect effect of green innovation (GI) and the moderating role of corporate green image (CgI) on the impact of BDA capabilities, including big data management capability (MC) and big data talent capability (TC), on economic and environmental performance.

A time-lagged design was employed to collect data from 417 manufacturing firms, and study hypotheses were evaluated using Mplus.

The empirical outcomes indicate that both BDA capabilities of firms significantly influence green innovation (GI), which significantly mediates the relationship between BDA and economic and environmental performance. Our findings also revealed that CgI strengthened the effect of GI on economic and environmental performance. The empirical evidence provides important theoretical and practical repercussions for manufacturing SMEs and policymakers.

This study contributes to the literature on BDA by empirically exploring the effects of MC and TC on improving the EcP and EnP of manufacturing firms. It does so through the indirect impact of GIs and the moderating effect of CgI, thereby extending the Dynamic capabilities view (DCV) paradigm.

The current study examines the impact of big data analytics capabilities (BDAC) on supply chain performances of the manufacturing firms. Based on the underpinning of resource-based view (RBV) theory, the current study will highlight the significance of BDAC on green dynamic capabilities (GDC), supply chain agility (SCA) and green competitive advantage (GCA). Furthermore, the study examines the moderating effect of supply chain innovativeness (SCI) on the relationship between GCA and firm performance (FP).

Online survey method was employed for the data collection from the 331 managers employed in Pakistan Stock Exchange (PSX)-listed manufacturing firms. The hypothesized model was tested using partial least squares structural equation modeling (PLS-SEM) technique.

The study results indicate that BDAC has a positive influence on both GDC and SCA, leading to enhanced GCA. Furthermore, the results demonstrate that GCA significantly and positively impacts FP, and the relationship between them is positively moderated by SCI.

This study developed a novel theoretical perspective based on RBV theory and provided empirical evidence that manufacturing firms' performances are significantly influenced by BDAC, GDC and SCA. The study results provide valuable practical implications top management regarding the effectiveness of BDAC and SCA in the supply chain. The findings further highlight the significance of SCI strengthening relationship between GCA and FP.