27 research outputs found

    Deep generative models for network data synthesis and monitoring

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    Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network. Although networks inherently have abundant amounts of monitoring data, its access and effective measurement is another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset without leaking commercial sensitive information. Second, it could be very expensive to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources in the network element that can be applied to support the measurement function are too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex structure. Various emerging optimization-based solutions (e.g., compressive sensing) or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet meet the current network requirements. The contributions made in this thesis significantly advance the state of the art in the domain of network measurement and monitoring techniques. Overall, we leverage cutting-edge machine learning technology, deep generative modeling, throughout the entire thesis. First, we design and realize APPSHOT , an efficient city-scale network traffic sharing with a conditional generative model, which only requires open-source contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system — GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time network telemetry system with latent GANs and spectral-temporal networks. Finally, we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through this research are summarized, and interesting topics are discussed for future work in this domain. All proposed solutions have been evaluated with real-world datasets and applied to support different applications in real systems

    GenDT: Mobile Network Drive Testing Made Efficient with Generative Modeling

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    An Empirical Study on the Incentive Mechanism for Public Active Involvement in Grass-Roots Social Governance Based on Stimulus-Organism-Response Theory

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    In the period of the normalization of COVID-19 prevention and control, Chinese grass-roots social governance, under the guidance of the dynamic zero-COVID policy, was unique, and the experience of actively mobilizing the public to be actively involved in grass-roots social governance, represented by epidemic prevention and control, has a profound internal logic. The Chinese government has long been committed to building a social governance community, and further empirical studies are needed to motivate the public to participate in grass-roots social governance in a sustainable manner. This study uses 428 members of the public who have experience in grass-roots social governance in 20 street offices in 11 cities, including Nanjing, Wuhan and Chengdu, as a valid sample to empirically test the incentive mechanism for the public’s active involvement in grass-roots social governance, from the perspective of Stimulus-Organism-Response Theory. The empirical results show that exogenously driven organizational institutional factors will eventually positively influence the incentive effect on the public’s active involvement in grass-roots social governance through the mediating effect of the individual’s endogenous drive. By adjusting organizational institutional factors to meet the public’s inner drive for acquisition, bond, comprehension, and defense, public motivation can be mobilized and the public can be motivated to be involved in grass-roots social governance in a sustainable manner. The results of the study reveal the incentive mechanism for the public’s active involvement in grass-roots social governance, analyze the internal logic of Chinese characteristics in motivating the public’s active involvement in grass-roots governance, and expand the scope of the application of Stimulus-Organism-Response Theory in studying the incentives for the public’s active involvement in grass-roots social governance, which is important for revealing the characteristic laws in a Chinese context with empirical research

    An Empirical Study on the Incentive Mechanism for Public Active Involvement in Grass-Roots Social Governance Based on Stimulus-Organism-Response Theory

    No full text
    In the period of the normalization of COVID-19 prevention and control, Chinese grass-roots social governance, under the guidance of the dynamic zero-COVID policy, was unique, and the experience of actively mobilizing the public to be actively involved in grass-roots social governance, represented by epidemic prevention and control, has a profound internal logic. The Chinese government has long been committed to building a social governance community, and further empirical studies are needed to motivate the public to participate in grass-roots social governance in a sustainable manner. This study uses 428 members of the public who have experience in grass-roots social governance in 20 street offices in 11 cities, including Nanjing, Wuhan and Chengdu, as a valid sample to empirically test the incentive mechanism for the public’s active involvement in grass-roots social governance, from the perspective of Stimulus-Organism-Response Theory. The empirical results show that exogenously driven organizational institutional factors will eventually positively influence the incentive effect on the public’s active involvement in grass-roots social governance through the mediating effect of the individual’s endogenous drive. By adjusting organizational institutional factors to meet the public’s inner drive for acquisition, bond, comprehension, and defense, public motivation can be mobilized and the public can be motivated to be involved in grass-roots social governance in a sustainable manner. The results of the study reveal the incentive mechanism for the public’s active involvement in grass-roots social governance, analyze the internal logic of Chinese characteristics in motivating the public’s active involvement in grass-roots governance, and expand the scope of the application of Stimulus-Organism-Response Theory in studying the incentives for the public’s active involvement in grass-roots social governance, which is important for revealing the characteristic laws in a Chinese context with empirical research

    Research on the Industrial Energy Eco-Efficiency Evolution Characteristics of the Yangtze River Economic Belt in the Temporal and Spatial Dimension, China

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    Based on the panel data of the 11 provinces along the Yangtze River Economic Belt from 1997 to 2015, the super slack-based model (Super-SBM) model is adopted to calculate the provincial-level eco-efficiency of industrial energy. While bringing in time series analysis and spatial differentiation feature analysis, the traditional and spatial Markov probability transition matrix is established. This study delves into the spatial-temporal dynamic evolution traits of the eco-efficiency of industrial energy along the Yangtze River Economic Belt. According to the results: the eco-efficiency of industrial energy of the Yangtze River Economic Belt manifests “single crest” evolution and distribution traits from left to right and top to bottom, indicating that the eco-efficiency of industrial energy of the Yangtze River Economic Belt is steadily improving gradually. However, the overall level is still low and there is still ample room for the improvement of the eco-efficiency of industrial energy. Furthermore, the eco-efficiency of industrial energy along the Yangtze River Economic Belt is elevating. The geographical spatial pattern plays a pivotal role in the spatial and temporal evolution of eco-efficiency of industrial energy, and the spatial agglomeration traits are noticeable

    Regulating the Charge Migration in CuInSe2/N‐Doped Carbon Nanorod Arrays via Interfacial Engineering for Boosting Photoelectrochemical Water Splitting

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    Abstract Regulating the charge migration and separation in photoactive materials is a great challenge for developing photoelectrochemical (PEC) applications. Herein, inspired by capacitors, well‐defined CuInSe2/N‐doped carbon (CISe/N‐C) nanorod arrays are synthesized by Cu/In‐metal organic frame‐derived method. Like the charge process of capacitor, the N‐doped carbon can capture the photogenerated electron of CISe, and the strong interfacial coupling between CISe and N‐doped carbon can modulate the charge migration and separation. The optimized the CISe/N‐C photoanode achieves a maximum photocurrent of 4.28 mA cm−2 at 1.23 V versus reversible hydrogen electrode (RHE) in neutral electrolyte solution under AM 1.5 G simulated sunlight (100 mW cm‐2), which is 8.4 times higher than that of the CuInSe2 photoanode (0.51 mA cm‐2). And a benefit of the strong electronic coupling between CISe and N‐doped carbon, the charge transfer rate is increased to 1.3–13 times higher than that of CISe in the range of 0.6–1.1 V versus RHE. The interfacial coupling effects on modulating the carrier transfer dynamics are investigated by Kelvin probe force microscopy analysis and density functional theory calculation. This work provides new insights into bulk phase carrier modulation to improve the performance of photoanode for PEC water splitting

    Assessing the Performance and Challenges of Low-Impact Development under Climate Change: A Bibliometric Review

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    Low-Impact Development (LID) represents a cogent strategy designed to conserve or reestablish antecedent hydrological states through an array of innovative mechanisms and methodologies. Since the dawn of the millennium, LID-centric research has demonstrated a persistent upward trajectory, mainly focusing on its capacity to mitigate climate change repercussions, particularly runoff and peak flows. However, a standardized rubric and toolkit for LID evaluation remain elusive. While numerous studies have documented the hydrological and water quality benefits of LID, the impacts of climate change on its effectiveness remain uncertain due to varying spatial and temporal climate patterns. This comprehensive review examined 1355 peer-reviewed articles in English, comprising both research articles and reviews, indexed in the Web of Science up until 2022. Findings from the bibliometric analysis revealed significant contributions and emergent trends in the field. Notably, there is an increasing emphasis on performance evaluation and efficiency of LID systems, and on understanding their impact on hydrology and water quality. However, this review identified the lack of a standardized LID evaluation framework and the uncertainty in LID effectiveness due to varying climate patterns. Furthermore, this study highlighted the urgent need for optimization of current hydrological models, advancement of LID optimization, modeling, monitoring, and performance, and stakeholder awareness about LID functionality. This review also underscored the potential future research trajectories, including the need to quantify LID’s effectiveness in urban flooding and water quality management and refining LID simulation models. Cumulatively, this review consolidates contemporaneous and prospective research breakthroughs in urban LID, serving as an indispensable compendium for academics and practitioners in the discipline

    Plasmonic Harvesting of Light Energy for Suzuki Coupling Reactions

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    The efficient use of solar energy has received wide interest due to increasing energy and environmental concerns. A potential means in chemistry is sunlight-driven catalytic reactions. We report here on the direct harvesting of visible-to-near-infrared light for chemical reactions by use of plasmonic Au–Pd nanostructures. The intimate integration of plasmonic Au nanorods with catalytic Pd nanoparticles through seeded growth enabled efficient light harvesting for catalytic reactions on the nanostructures. Upon plasmon excitation, catalytic reactions were induced and accelerated through both plasmonic photocatalysis and photothermal conversion. Under the illumination of an 809 nm laser at 1.68 W, the yield of the Suzuki coupling reaction was ∼2 times that obtained when the reaction was thermally heated to the same temperature. Moreover, the yield was also ∼2 times that obtained from Au–TiO<sub><i>x</i></sub>–Pd nanostructures under the same laser illumination, where a 25-nm-thick TiO<sub><i>x</i></sub> shell was introduced to prevent the photocatalysis process. This is a more direct comparison between the effect of joint plasmonic photocatalysis and photothermal conversion with that of sole photothermal conversion. The contribution of plasmonic photocatalysis became larger when the laser illumination was at the plasmon resonance wavelength. It increased when the power of the incident laser at the plasmon resonance was raised. Differently sized Au–Pd nanostructures were further designed and mixed together to make the mixture light-responsive over the visible to near-infrared region. In the presence of the mixture, the reactions were completed within 2 h under sunlight, while almost no reactions occurred in the dark
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