338 research outputs found
A branch-and-cut algorithm for scheduling train platoons in urban rail networks
No full textWith the emerging of virtual coupling technologies, the concept of train platoon, where different vehicles can be flexibly and dynamically grouped or decoupled, has become a hot research topic. In this study, we investigate the scheduling of train platoons for urban rail networks with time-dependent demand to mitigate passenger inconvenience. We propose a mixed-integer linear programming (MILP) model that simultaneously optimizes the train-platoon (de)coupling strategies, arrival/departure times at each station, and the running orders of trains, while considering limited rolling stock resources at the depots and the safety of trains at cross-line zones. To tackle computational challenges in real-world instances, we develop a customized branch-and-cut solution algorithm, based on the analysis of mathematical properties of our MILP model, to generate (near-)optimal solutions more efficiently. In particular, we propose three sets of valid inequalities that are dynamically added to the model to strengthen the linear relaxation bounds at each node. We also design a customized branching rule in the search tree by imposing to branch on the key decision variables regarding the train orders at the cross-line zones. Real-world case studies based on the operational data of Beijing metro network are conducted to verify the effectiveness of our approach. The results demonstrate that our branch-and-cut-based approach evidently outperforms commercial solvers in terms of solution quality and computational efficiency. Compared to the current train schedule with fixed compositions in practice, our approach with flexible coupling strategies can reduce the passenger dissatisfaction by over 15%
Topology-Driven Attribute Recovery for Attribute Missing Graph Learning in Social Internet of Things
No full textWith the advancement of information technology, the Social Internet of Things (SIoT) has fostered the integration of physical devices and social networks, deepening the study of complex interaction patterns. Text Attribute Graphs (TAGs) capture both topological structures and semantic attributes, enhancing the analysis of complex interactions within the SIoT. However, existing graph learning methods are typically designed for complete attributed graphs, and the common issue of missing attributes in Attribute Missing Graphs (AMGs) increases the difficulty of analysis tasks. To address this, we propose the Topology-Driven Attribute Recovery (TDAR) framework, which leverages topological data for AMG learning. TDAR introduces an improved pre-filling method for initial attribute recovery using native graph topology. Additionally, it dynamically adjusts propagation weights and incorporates homogeneity strategies within the embedding space to suit AMGs' unique topological structures, effectively reducing noise during information propagation. Extensive experiments on public datasets demonstrate that TDAR significantly outperforms state-of-the-art methods in attribute reconstruction and downstream tasks, offering a robust solution to the challenges posed by AMGs. The code is available at https://github.com/limengran98/TDAR.This project is jointly supported by the Shenzhen Fundamental Research Program (No. JCYJ20240813151129038), the National Natural Science Foundation of China (Nos. 52172350, 51775565), the Guangdong Basic and Applied Research Foundation (No. 2022B1515120072), the Guangzhou Science and Technology Plan Project (No. 2024B01W0079), the Nansha Key R&D Program (No. 2022ZD014). (Corresponding author: Ronghui Zhang.)http://arxiv.org/abs/2501.1015
Adaptive Dual-Channel Event-Triggered Fuzzy Control for Autonomous Underwater Vehicles With Multiple Obstacles Environment
No full textThis article investigates the formation control of autonomous underwater vehicles (AUVs) suffering from unknown sea loads, unmoulded structure, limited communication and multiple static and moving obstacles. Given the challenge, a novel adaptive dual-channel event-triggered control scheme is proposed for formation tracking and obstacles avoidance. To economize the communication resources, the dual-channel event-triggered mechanism is designed in the sensor-to-controller and controller-to-actuator channels respectively. By adopting the approximation of fuzzy systems in the form of one-parameter integrated learning, the uncertainties consisted of the unmoulded structure and unknown sea loads are compressed together to be compensated online, which ensures a lower computational cost. To solve the multiple obstacles, the modified artificial potential field approach is employed, and the derived repulsive potential field can ensure that the multi-AUV formation can avoid obstacles smoothly regardless of static or moving obstacles. It is showed by the Lyapunov stability theorem that the tracking errors are guaranteed to be semi-globally uniformly ultimately bounded. Finally, three simulation examples illustrate the effectiveness and superiority of the proposed scheme.This work was supported in part by the National Natural
Science Foundation of China under Grant 52172350 and Grant 51775565,
in part by Guangdong Basic and Applied Research Foundation under Grant
2021B1515120032 and Grant 2022B1515120072, in part by Guangzhou
Science and Technology Plan Project under Grant 2024B01W0079, in part by
Nansha Key Research and Development Program under Grant 2022ZD014,
and in part by the Science and Technology Planning Project of Guangdong
Province under Grant 2023B1212060029. The Associate Editor for this article
was Z. Li. (Corresponding author: Ronghui Zhang.)https://ieeexplore.ieee.org/abstract/document/1051018
Building Chinese interdisciplinary research centers : the case of Tsinghua University
No full textInterdisciplinary research centers (hereinafter IRCs) are rapidly being established in top Chinese universities, and are becoming increasingly important to national development strategies. The case of Tsinghua University (hereinafter THU) is particularly important, as it is China’s leading research university, with 354 research institutes in regular operation. THU aims to solve leading-edge, complex, and dynamically-evolving problems. This research examines the processes underpinning the establishment and development of THU’s IRCs.
The literature review introduces the history of disciplines and interdisciplinarity, and the evolution of interdisciplinary research (hereinafter IDR) in Western and Chinese contexts. Also, Chinese interdisciplinary policies are discussed. Previous studies of IRCs have focused on several key concepts of the processes, including IDR programs, research collaboration, organizational attributes, organizational roles, barriers, and other challenges. Research methodologies have relied upon quantitative methods, case studies, and role theory. However, little has been revealed that can promote understanding of the unique processes of building Chinese IRCs to drive innovation.
This research uses qualitative methods and semi-structured interviews of 52 academic researchers working at six affiliated IRCs in THU. The multiple case studies apply an input-process-output model. A multi-level analysis, guided by new institutionalism, is used to gain an understanding of IRCs’ initiation, government-university/IRC-individuals’ interactions, and other key factors that influence IRCs.
This thesis contributes a new point of view for observing the developmental processes of Chinese IRCs, and assessing how they promote innovation. It demonstrates the key factors determining IRCs’ innovative success. THU’s IRC development can best be attributed to leadership, communication, and collaboration. This research has six main findings. First, a mixed top-down and bottom-up approach creates successful IRCs, which are isomorphic when established, but show heteromorphism in their outcomes. Second, charismatic leadership is a significant factor impacting IRCs’ development and innovation. Third, most researchers in substantive IRCs prefer face-to-face communication, while those in virtual IRCs generally prefer electronic communication. Fourth, interdisciplinary researchers are challenged by the need to build a common language with which to communicate with their fellow researchers form other disciplines. Fifth, researchers collaborate more with applied disciplines, and their major collaborative motivation is to gain mutual benefits based on by the complementation of effort. Sixth, center leaders collaborate with local governments to implement new collaborative and innovative strategies, leading to technology transfer and subsequent industrialization.
In summary, this study offers a roadmap of IRCs’ developmental processes. The external environment of government policies and internal charismatic leadership are two significant factors influencing IRCs’ IDR and innovation. Future research could explore the occasional failures of IRCs and their technological transformation, investigate trust among government, IRCs, and research teams, and explain interdisciplinary researchers’ academic identities.published_or_final_versionEducationDoctoralDoctor of Philosoph
Managing the COVID-19 Pandemic in China: Managing Trust and Accountability
No full textThis study focuses on the construction of public accountability and public trust during the Covid-19 Pandemic in Chin
Glycine max cultivar:Williams82 Transcriptome or Gene expression
No full textSoybean (Glycine max, cv Williams82) leaf petiole explants exposed to 25 ul/l ethylene for 0 to 72 h. Explants were prepared from 21 day-old greenhouse grown plants. Leaf abscission zones (LAZ) consisted of 2 mm of tissue collected below the leaf blade. The petioles (NAZ) consisted of approximately 3 to 4 mm of petiole tissue with the AZ removed. Explants and tissue were collected in February, March and April of 2013. Tissue and RNA were collected at USDA, Beltsville, MD (Mark L Tucker, Joonyup Kim and Ronghui Yang). Library construction and sequencing was completed at Univ of Cornell, Itheca, NY using a Illumina HiSeq 2000 (James J Giovannoni and Zhangjun Fei)
Digital nudges and gamification: how the China state promotes citizens to manage chronic diseases in neighborhoods
No full textAbstract Background Chronic diseases pose a growing threat to public health and have raised widespread social concern in China. In response, the Chinese government has rolled out chronic disease management campaigns at the community level to curb disease incidence. However, low public participation constrains their effectiveness. Although campaign-style mobilization has been widely studied, less is known about how digital nudges, particularly gamification, enhance citizen engagement in Chinese communities. Methods To address this gap, we conducted in-depth interviews with sixty Chinese stakeholders to examine how the Chinese state nudged individuals toward proactive chronic disease management using gamified mechanics. Results We identify four core gamification elements, including dramatization, level-up progression, rewards, and socialization, that function as digital nudges. These gamified interventions can significantly reduce participants’ cognitive burden, amplify their perceived benefits of self-care, enhanced individual agency, and foster supportive social networks. However, the sustainability of these gamified interventions is undermined by misaligned reward structures, standardized level-up progression systems, and unresolved intra-community conflicts. Conclusions To overcome these obstacles, we propose implementing culturally attuned reward mechanisms, dynamically tailored challenge pathways, and robust online community governance to foster enduring public engagement. By revealing China’s neighborhood-level digital nudge approaches, this research not only enriches global understanding of gamified chronic disease management but also provides transferable design principles for public health interventions worldwide
An integrated framework for assessing the dynamics of urban eco-resilience in China’s urban agglomerations
No full textUrban agglomerations in China are increasingly challenged by rapid urbanization, environmental degradation, and climate-induced vulnerabilities, making the assessment of urban eco-resilience both urgent and complex. Despite growing interest in resilience research, there remains a lack of comprehensive, data-driven frameworks that integrate ecological, socio-economic, and spatial dimensions to assess eco-resilience dynamics over time. This study aims to construct and apply an integrated analytical framework to evaluate the temporal evolution and spatial heterogeneity of urban eco-resilience across major Chinese urban agglomerations from 2000 to 2023. Employing a multi-dimensional resilience index system combined with a spatial Durbin model (SDM) and entropy-TOPSIS method, the research evaluates resilience performance in 19 key urban clusters, including the Yangtze River Delta, Pearl River Delta, and Beijing-Tianjin-Hebei regions. Empirical findings reveal five key insights: (1) eco-resilience shows an overall upward trend, with significant inter-regional disparities; (2) economic development and green infrastructure positively influence resilience levels, while land-use conflict and air pollution remain detrimental; (3) spatial spillover effects are significant, particularly in coastal regions; (4) innovation and policy efficiency amplify regional adaptive capacity; and (5) resilience gaps have widened between core and peripheral cities due to uneven resource allocation. The results underscore the need for differentiated, region-specific resilience strategies, and policy coordination mechanisms to enhance eco-resilience equity and sustainability across urban agglomerations. The study offers practical implications for urban planners and policymakers to strengthen ecological security through integrated and spatially targeted resilience policies
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