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This study explores the factors that influence platform-to-platform cooperation (PPC) and designs a theoretical framework for platform research.

This multi-case study includes a combination of exploratory and explanatory case studies.

From the internal factor perspective, channel integration capability, technology-based order matching capability and service innovation capability positively affects the PPC. From the perspective of external factors, the impact of a new platform entry on the PPC depends on market power and complementarities between platforms in the supply and value chains. Diversity of demand also has a positive effect on the PPC, which is moderated by network externalities. It is worth noting that the incumbent platform prefers to diversify its services for collaborating platforms with a higher level of cooperation. In addition, the higher diversity of demand, the stronger the service innovation capability, which indirectly impacts cooperation positively.

The PPC has gained immense popularity in recent years. However, no scholars have investigated the factors influencing the PPC decisions, which warrants further exploration. This study sheds light on the factors and mechanisms that influence the PPC from both internal and external perspectives.

This study explores the influencing factors on organizational efficiency of the smart logistics ecological chain, and designs the corresponding theoretical framework to guide the practice of enterprises

A multi-case study method is adopted in this study. It includes four companies A, B, C and D in China as the case study objects, collects data through enterprise survey and uses the combination of open coding and spindle coding to process the data. By testing the reliability and validity, the theoretical framework is summarized.

First, organizational efficiency in smart logistics ecological chains is directly related to their service and technology innovation capability. Second, symbiotic relationships, information sharing and customer demand affect the efficiency of smart logistics multi-case ecological chains by influencing their service capacity; their technological innovation capability regulates the mechanism of influence. Third, technological innovation in smart logistics ecological chains positively impacts their service capabilities. Improving technological innovation capability can enhance logistics service capabilities.

According to the characteristics of smart logistics, the theoretical framework about organizational efficiency of smart logistics ecological chain is constructed, which fills the research gap and can provide interesting perspectives for the future research related to the smart logistics ecological chain. At the same time, the findings can also help enterprises to better build the smart logistics ecological chain in practice.

This study explores the influencing factors of smart supply chain innovation (SSCI) for going global companies and designs a theoretical framework.

This paper is a multi-case study that includes a combination of exploratory and explanatory case studies.

First, the authors find that SSCI is embodied in product development and supply chain empowerment, which represent exploitative innovation and explorative innovation, respectively. Meanwhile, supply chain empowerment has a positive impact on product development. Second, the going global policy affects the transformation of supply chain empowerment to SSCI practices. Third, in terms of exploitative innovation, personalized demand positively affects SSCI through product development. Finally, explorative innovation, including emerging technology application and supply chain ecologicalization, has a positive effect on supply chain empowerment and thereby affects SSCI.

Supply chain innovation in the context of a smart economy has gained great popularity. This study sheds light on the influencing factors and mechanisms of SSCI from the exploitative and explorative aspects of innovations.

This study explores the influencing factors of smart logistics ecological chain's (SLEC's) organizational collaboration and designs a corresponding conceptual framework.

The multi-case study is applied to this paper. Specifically, this study is a combination of exploratory and explanatory case studies.

The findings are threefold. First, empowerment capability and the information-sharing level are unique factors that affect SLEC's organizational collaboration. Second, greater empowerment capability stimulates the increase of information-sharing level. Third, emerging digital technology, personalized demand and peer competition affect the degree of SLEC's organizational collaboration through an intermediary variable – empowerment capability. Specifically, the emerging digital technology application and peer competition degrees have positive effects on empowerment capability, while the demand personalization degree negatively (positively) affects empowerment capability in the short (long) term.

As an important part of supply chain performance, organizational collaboration is receiving more attention. However, in the smart economy context, no theoretical framework exists for analyzing factors that affect the organizational collaboration degree of SLEC. This study fills this gap.

The review aims to facilitate a broader understanding of platform opening and cooperation and points out potential research directions for scholars.

This study searches Web of Science (WOS) database for relevant literature published between 2010 and 2021 and selects 86 papers for this review. The selected literature is categorized according to three dimensions: the strategic choice of platform opening and cooperation (before opening), the construction of an open platform (during opening) and the impact of platform opening and cooperation (after opening). Through comparative analysis, the authors identify research gaps and propose four future research agendas.

The study finds that the current studies are fragmented, and a research system with a theoretical foundation has not yet formed. In addition, with the development of platform operations, new topics such as platform ecosystems and open platform governance have emerged. In short, there is an urgent need for scholars to conduct exploratory research. To this end, the study proposes four future research agendas: strengthen basic research on platform opening and cooperation, deeply explore the dynamic evolution and cutting-edge models of platform opening and cooperation, analyze potential crises and impacts of platform openness and strengthen research on open platform governance.

This is the first systematic review on platform opening and cooperation. Through categorizing literature into three dimensions, this article clearly shows the research status and provides future research avenues.

Multi-robot laser-based simultaneous localization and mapping (SLAM) in large-scale environments is an essential but challenging issue in mobile robotics, especially in situations wherein no prior knowledge is available between robots. Moreover, the cumulative errors of every individual robot exert a serious negative effect on loop detection and map fusion. To address these problems, this paper aims to propose an efficient approach that combines laser and vision measurements.

A multi-robot visual laser-SLAM is developed to realize robust and efficient SLAM in large-scale environments; both vision and laser loop detections are integrated to detect robust loops. A method based on oriented brief (ORB) feature detection and bag of words (BoW) is developed, to ensure the robustness and computational effectiveness of the multi-robot SLAM system. A robust and efficient graph fusion algorithm is proposed to merge pose graphs from different robots.

The proposed method can detect loops more quickly and accurately than the laser-only SLAM, and it can fuse the submaps of each single robot to promote the efficiency, accuracy and robustness of the system.

Compared with the state of art of multi-robot SLAM approaches, the paper proposed a novel and more sophisticated approach. The vision-based and laser-based loops are integrated to realize a robust loop detection. The ORB features and BoW technologies are further utilized to gain real-time performance. Finally, random sample consensus and least-square methodologies are used to remove the outlier loops among robots.

The purpose of this paper is to use the wavelet neural network and genetic algorithm to study the effects of polyalphaolefin, TMP108 and OCP0016 on the kinematic viscosity, viscosity index and pour point of lubricating oil.

Wavelet neural network is used to train the known samples, test the unknown samples and compare the obtained results with those obtained with a traditional empirical formula.

It is found that the wavelet neural network prediction value is closer to the experimental value than the traditional empirical formula calculation value.

The results show that the wavelet neural network can be used to study the physical and chemical indexes of lubricating oil.

This paper aims to investigate the distributed formation control problem for a multi-quadrotor unmanned aerial vehicle system without linear velocity feedbacks.

A nonlinear controller is proposed based on the orthogonal group SE(3) to obviate singularities and ambiguities of the traditional parameterized attitude representations. A cascade structure is applied in the distributed controller design. The inner loop is responsible for attitude control, and the outer loop is responsible for translational dynamics. To ensure a linear-velocity-free characteristic, some auxiliary variables are introduced to construct virtual signals in distributed controller design. The stability analysis of the proposed distributed control method by the Lyapunov function is provided as well.

A group of four quadrotors with constant reference linear velocity and a group of six quadrotors with varying reference linear velocity are adopted to verify the effectiveness of the proposed strategy.

This is a new innovation for multi-robot formation control method to improve assembly automation.

Compared to quad-rotor unmanned aerial vehicle (UAV), the tilting dual-rotor UAV is more prone to instability during exercises and disturbances. The purpose of this paper is using an active balance tail to enhance the hovering stability and motion smoothness of tilting dual-rotor UAV.

A balance tail is proposed and integrated into the tilting dual-rotor UAV to enhance hovering stability and motion smoothness. By strategically moving, the balance tail generates additional force and moment, which can promote the rapid stability of the UAV. Subsequently, the control strategy of the UAV is designed, and the influence of the swing of the balance tail at different installation positions with different masses on the dual-rotor UAV is analyzed through simulation. The accompany motion law and the active control, which is based on cascade Proportion Integration Differentiation (PID) control to enhance the hovering stability and motion smoothness of the UAV, are proposed.

The results demonstrate that active control has obvious adjustment effectiveness when the UAV moves to the target position or makes an emergency stop compared with the results of balance tail no swing and accompany motion.

The balance tail offers a straightforward means to enhance the motion smoothness of tilting dual-rotor UAV, rendering it safer and more reliable for practical applications.

The novelty of this works comes from the application of an active balance tail to improve the stability and motion smoothness of dual-rotor UAV.

This study aims to introduce the fast reactive tree (FRT*) algorithm for enhancing replanning speed and reducing the overall cost of navigation in unknown dynamic environments.

FRT* comprises four key components: inverted tree build, convex hull construction, dead nodes inform activation and lazy-rewiring replanning. First, an initial path is found from the inverted tree where the valid structure is preserved to minimise re-exploration areas during the replanning phase. As the robot encounters environment changes, convex hulls are extracted to sparsely describe impacted areas. Next, the growth direction of the modified tree is biased by the inform activation of dead nodes to avoid unnecessary exploration. In the replanning phase, the tree structure is optimized using the proposed lazy-rewiring replanning to find a high-quality path with low computation burden.

A series of comprehensive simulation experiments demonstrate that the proposed FRT* algorithm can efficiently replan short-cost feasible paths in unknown dynamic environments. The differential wheeled mobile robot with varying reference linear velocities is used to validate the effectiveness and adaptability of the proposed strategy in real word scenarios. Furthermore, ablation studies are conducted to analyze the significance of the key components of FRT*.

The proposed FRT* algorithm introduces a novel approach to addressing the challenges of navigation in unknown dynamic environments. This capability allows mobile robots to safely and efficiently navigate through unknown and dynamic environments, making the method highly applicable to real-world scenarios.