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This paper examines the strategic management of people within enterprises as a driver of sustainable growth. As strategic people management (SPM) is founded on the Eastern knowledge workers’ perspective, we integrate SPM with the Confucian perspective to analyze the factors underlying the sustainable success of Chinese management. In so doing, we review the literature on sustainability, SPM, Chinese management, and the integration of Confucian cultural values. We utilize the qualitative case research method to examine 20 successful Chinese enterprises across five sectors. The results of the case analysis reveal three types of sustainable SPM associated with Confucian values: responsible people management, paradoxical people management, and humanistic people management. We propose a conception of sustainable SPM combined with Confucianism to be relevant in the Chinese business context, where a high degree of dynamism can be seen. The findings of this study could be extended through analyses conducted in other contexts with a high level of complexity, such as emerging markets, disruptive technology, unexpected crises, or any aggregated interactions of such contexts.

Simultaneous localization and mapping (SLAM), as one of the core prerequisite technologies for intelligent mobile robots, has attracted much attention in recent years. However, the traditional SLAM systems rely on the static environment assumption, which becomes unstable for the dynamic environment and further limits the real-world practical applications. To deal with the problem, this paper presents a dynamic-environment-robust visual SLAM system named YOLO-SLAM. In YOLO-SLAM, a lightweight object detection network named Darknet19-YOLOv3 is designed, which adopts a low-latency backbone to accelerate and generate essential semantic information for the SLAM system. Then, a new geometric constraint method is proposed to filter dynamic features in the detecting areas, where dynamic features can be distinguished by utilizing the depth difference with Random Sample Consensus (RANSAC). YOLO-SLAM composes the object detection approach and the geometric constraint method in a tightly coupled manner, which is able to effectively reduce the impact of dynamic objects. Experiments are conducted on the challenging dynamic sequences of TUM dataset and Bonn dataset to evaluate the performance of YOLO-SLAM. The results demonstrate that the RMSE index of absolute trajectory error can be significantly reduced to 98.13% compared with ORB-SLAM2 and 51.28% compared with DS-SLAM, indicating that YOLO-SLAM is able to effectively improve stability and accuracy in the highly dynamic environment.

Shape memory materials have been playing an important role in a wide range of bioengineering applications. At the same time, recent developments of graphene-based nanostructures, such as nanoribbons, have demonstrated that, due to the unique properties of graphene, they can manifest superior electronic, thermal, mechanical, and optical characteristics ideally suited for their potential usage for the next generation of diagnostic devices, drug delivery systems, and other biomedical applications. One of the most intriguing parts of these new developments lies in the fact that certain types of such graphene nanoribbons can exhibit shape memory effects. In this paper, we apply machine learning tools to build an interatomic potential from DFT calculations for highly ordered graphene oxide nanoribbons, a material that had demonstrated shape memory effects with a recovery strain up to 14.5% for 2D layers. The graphene oxide layer can shrink to a metastable phase with lower constant lattice through the application of an electric field, and returns to the initial phase through an external mechanical force. The deformation leads to an electronic rearrangement and induces magnetization around the oxygen atoms. DFT calculations show no magnetization for sufficiently narrow nanoribbons, while the machine learning model can predict the suppression of the metastable phase for the same narrower nanoribbons. We can improve the prediction accuracy by analyzing only the evolution of the metastable phase, where no magnetization is found according to DFT calculations. The model developed here allows also us to study the evolution of the phases for wider nanoribbons, that would be computationally inaccessible through a pure DFT approach. Moreover, we extend our analysis to realistic systems that include vacancies and boron or nitrogen impurities at the oxygen atomic positions. Finally, we provide a brief overview of the current and potential applications of the materials exhibiting shape memory effects in bioengineering and biomedical fields, focusing on data-driven approaches with machine learning interatomic potentials.

Planning an optimal path for a mobile robot is a complicated problem as it allows the mobile robots to navigate autonomously by following the safest and shortest path between starting and goal points. The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. A modification based on the age of the ant is introduced to standard ant colony optimization, called aging-based ant colony optimization (ABACO). The ABACO was implemented in association with grid-based modeling for the static and dynamic environments to solve the path planning problem. The simulations are run in the MATLAB environment to test the validity of the proposed algorithms. Simulations showed that the proposed path planning algorithms result in superior performance by finding the shortest and the most free-collision path under various static and dynamic scenarios. Furthermore, the superiority of the proposed algorithms was proved through comparisons with other traditional path planning algorithms with different static environments.

Stomatal movements control CO2 uptake for photosynthesis and water loss through transpiration, and therefore play a key role in plant productivity and water use efficiency. The predicted doubling of global water usage by 2030 mean that stomatal behaviour is central to current efforts to increase photosynthesis and crop yields, particularly under conditions of reduced water availability. In the field, slow stomatal responses to dynamic environmental conditions add a temporal dimension to gaseous fluxes between the leaf and atmosphere. Here, we review recent work on the rapidity of stomatal responses and present some of the possible anatomical and biochemical mechanisms that influence the rapidity of stomatal movements.

Increasingly chaotic business environments of today demand organizations to be more resilient. While the concept of resilience is widely discussed in disaster (e.g., Wildavsky, 1991) and crisis management literatures (e.g., Manyena, 2006), the literature on organizational resilience is developing disjointedly in organizational studies. The literature review suggests that some factors that are suggested in the literature as components of organizational resilience are sources contributing to the emergence of resilience in organizations. This study proposes an integrative framework for organizational resilience and introduces a new outcome concept of organizational evolvability, emphasizing the heightened sensitivity and increased wisdom of the post-event organization. In this model, sources of organizational resilience are categorized as perceptual stance, contextual integrity, strategic capacity and strategic acting, and organizational resilience leads to organizational evolvability as its outcome. The proposed organizational resilience framework attempts to provide a synthesis of the divergent literature on resilience and aims to strengthen organizational resilience research for richer theoretical and empirical progress.

PurposeAs global supply chains continue to develop, uncertainty grows and supply chains are frequently threatened with disruption. Although big data technology is being used to improve supply chain resilience, big data technology's role in human–machine collaboration is shifting between “supporters” and “substitutes.” However, big data technology's applicability in supply chain management is unclear. Choosing appropriate big data technology based on the enterprise's internal and external environments is important.Design/methodology/approachThis study built a three-factor structural model of the factors “management support,” “big data technology adoption” and “supply chain resilience”. Big data technology adoption was divided into big data-assisted decision-making technology (ADT) and big data intelligent decision-making technology (IDT). A survey was conducted on more than 260 employees from supply chain departments in Chinese companies. The data were analyzed through structural equation modeling using Analyze of Moment Structures (AMOS) software.FindingsThe study's empirical results revealed that adopting both ADT and IDT improved supply chain resilience. The effects of both types of big data were significant in low-dynamic environments, but the effect of IDT on supply chain resilience was insignificant under high-dynamic environments. The authors also found that government support had an insignificantly effect on IDT adoption but significantly boosted ADT adoption, whereas management support factors promoted both ADT and IDT adoption.Originality/valueBy introducing two types of big data technology from the perspectives of the roles in human–machine collaborative decision-making, the research results provide a theoretical basis and management implications for enterprises to reduce the supply chain risk of enterprises.

With the rapid advancements in artificial intelligence (AI), 5G technology, and robotics, multi-sensor fusion technologies have emerged as a critical solution for achieving high-precision localization in mobile robots operating within dynamic and unstructured environments. This study proposes a novel hybrid fusion framework that combines the Extended Kalman Filter (EKF) and Recurrent Neural Network (RNN) to address challenges such as sensor frequency asynchrony, drift accumulation, and measurement noise. The EKF provides real-time statistical estimation for initial data fusion, while the RNN effectively models temporal dependencies, further reducing errors and enhancing data accuracy. A complementary fusion mechanism integrating LiDAR (Light Detection and Ranging) data ensures robustness against noise and disturbances. The algorithm is validated through comprehensive simulations on the Gazebo platform, demonstrating a localization error within 8 cm under various noise levels and dynamic disturbances. The method also outperforms state-of-the-art algorithms, including Particle Filter (PF) and Graph SLAM, in both accuracy and computational efficiency, achieving an average runtime of 30.1 ms per frame, suitable for real-time applications. These results highlight the efficacy of the proposed EKF-RNN framework, which balances accuracy, robustness, and computational efficiency, offering significant contributions to autonomous robotic navigation.

Modern agriculture’s goal of improving crop resource acquisition efficiency relies on the intricate relationship between the root system and the soil. Root and rhizosphere traits play a critical role in the efficient use of nutrients and water, especially under dynamic environments. This review emphasizes a holistic perspective, challenging the conventional separation of nutrient and water uptake processes and the necessity for an integrated approach. Anticipating climate change-induced increase in the likelihood of extreme weather events that result in fluctuations in soil moisture and nutrient availability, the study explores the adaptive potential of root and rhizosphere traits to mitigate stress. We emphasize the significance of root and rhizosphere characteristics that enable crops to rapidly respond to varying resource availabilities (i.e. the presence of water and mobile nutrients in the root zone) and their accessibility (i.e. the possibility to transport resources to the root surface). These traits encompass for example root hairs, mucilage and extracellular polymeric substance (EPS) exudation, rhizosheath formation and the expression of nutrient and water transporters. Moreover, we recognize the challenge of balancing carbon investments, especially under stress, where optimized traits must consider carbon-efficient strategies. To advance our understanding, the review calls for well-designed field experiments, recognizing the limitations of controlled environments. Non-destructive methods such as mini rhizotron assessments and in-situ stable isotope techniques, in combination with destructive approaches such as root exudation analysis, are proposed for assessing root and rhizosphere traits. The integration of modeling, experimentation, and plant breeding is essential for developing resilient crop genotypes capable of adapting to evolving resource limitation.