Update Speed Research Articles

Analyzing the Strategy for Maintaining Sales Popularity of Hongxing Erke

In 2021, the national epidemic in China will have reached a turning point before the country opens up. Hongxing Erke donated millions of yuan to the disaster area in Henan at this time. This article studied Hongxing Erke's fading out of the consumer's sight after the explosion. The purpose of this study was to explore how brands can maintain their popularity. This paper adopted the empirical research on Hongxing Erke and conducts further research with reference to brand-related papers. The conclusion of this study was that the brand needs to maintain brand popularity through perfect publicity, excellent update speed, and a special brand concept. If these conditions were not met, consumers would gradually forget the popular brand over time, and the brand would gradually lose its development opportunities. Brands need to skillfully use new media to increase popularity, greatly accelerate the update rate of products, deepen the brand concept, and ensure that their brand popularity can continue in all aspects.

Application research on digital twins of urban earthquake disasters

The digital twin of an earthquake disaster city constructed using digital twin technology can reflect the data of different stages of the earthquake disaster in the virtual space and can ensure high synchronisation and simulation of the two to effectively improve the safety of emergency rescue personnel and the scientific decision-making ability of decision makers. Oblique photogrammetry matched the Light Detection and Ranging (LiDAR) data to complete a high-precision three-dimensional real-scene model. The urban digital twin model was established as a module with the three-dimensional base model generated by the two-dimensional frame data with a fast update speed. Based on the shortest-fault-distance intensity-attenuation model, the seismic intensity was calculated using a known active fault and aftershock sequence, and the applicability of this method was verified. The elastic-plastic time history analysis method was used to calculate the seismic response of the building, and the seismic damage level was visualised using a three-dimensional model. The dynamic changes of earthquake disasters are monitored by sensors in real time and analysed in a digital twin model to provide an optimal scheme for earthquake emergency rescue and accurate evaluation results of earthquake disasters.

Research on the construction of weaponry indicator system and intelligent evaluation methods

To decrease subjective interference and improve the construction efficiency of the traditional weapon and equipment index system, an index system construction method based on target detection is proposed in combination with the equipment test video data. The three-level index system of combat effectiveness of a certain type of equipment is established, and various intelligent assessment methods are proposed. Firstly, an optimaized IPSO-BP network model is proposed, in which dynamic weights are set to improve the particle search network, and adaptive learning factors are introduced to optimize the update speed. Secondly, an improved DS evidence-parallel neural network assessment method is proposed, setting multiple parallel neural networks with different parameters, and improving the angle cosine to weaken the numerical nonlinear attributes in DS evidence fusion and increase the model's assessment operation stability. Thirdly, the three types of view features corresponding to the index item images are extracted to train the base classifiers. The integrated CNN network based multi-view feature integration assessment model is constructed and the improved residual network block is introduced to optimize the network gradient. Comparison with existing evaluation methods shows that the proposed methods achieve efficient and intelligent construction and evaluation of the indicator system and enrich the evaluation of indicator data.

Research on Dueling Double Deep Q Network Algorithm Based on Single-Step Momentum Update

Vehicle behavior decision control plays a crucial role in the development of autonomous driving. However, existing autonomous driving behavior decision control algorithms based on deep reinforcement learning face several challenges, such as low efficiency in updating target network data and a lack of effective balancing between old and new experiences. To address these issues, this paper proposes a dueling double deep Q network (dueling DDQN) algorithm based on a single-step momentum update mechanism. Firstly, a single-step momentum update mechanism is designed to significantly improve the update speed of target network parameters and achieve a balanced weighting of old and new experiences during the parameter update process. Subsequently, the network structures of dueling networks and DDQNs are integrated to enhance the understanding capability of autonomous vehicles concerning their current states. Finally, tests are conducted on the OpenAI Gym simulation platform to validate the effectiveness of the proposed algorithm. The results verified that the dueling DDQN algorithm with single-step momentum updates contributes to improving the convergence speed of autonomous driving car behavior decisions. Compared with the DQN and DDQN algorithms, the proposed algorithm achieved a success rate increase of 6.0 and 8.4 percentage points in the challenging three-lane highway Test Scenario 1, and a success rate increase of 16.7 and 2.9 percentage points in Test Scenario 2, respectively. These findings demonstrate a safer and more efficient performance in autonomous driving decision-making.

Marketing Strategy of China Heavy Duty VGV Based on 4P Theory

China National Heavy Duty Truck (CNHTC) is a famous brand in China. China National Heavy Duty Truck (CNHTC) is a well-known domestic automobile brand, which occupies a certain market share in the automobile market and is recognized and supported by many consumers. However, China National Heavy Duty Truck Corporation (CNHTC) mainly focuses on heavy-duty truck products, and the development of its VGV light-duty vehicle has not been very good in the past decade, which needs to readjust its marketing strategy. This study takes China National Heavy Duty Truck Corporation (CNHTC) VGV as the research object, and explores its marketing market environment, competitive environment, etc., to find out the current marketing status of the company, and the dilemmas that appear in marketing: slow product updating, solidified brand image, lack of effective supervision and flexibility in price positioning, lack of on-line channels, inadequate management of dealers, single means of promotion, and lack of innovation in advertisement. Targeted to put forward to speed up the product update speed, pay attention to brand building, pricing and product positioning, strengthen the effective supervision of product price, online and offline channels combined, reasonable planning product channel layout and other countermeasures.

Research on the Screening Method of Priority Pollutants with Integrated Environmental Socio-economic Indicators: Example of E-waste Dismantling

While benefiting mankind, electronic information technology has led to the elimination of many electrical and electronic products due to its rapid update and iteration speed. In addition, the normal use in daily life causes the wear and tear of electronic products, resulting in a large amount of electronic waste. With the increase in electronic products, the amount of electronic waste dismantling has also increased yearly. Therefore, it becomes an important to accurately screen the priority control pollutants generated in e-waste process. In view of the current situation of e-waste dismantling pollution and the needs of monitoring and evaluation in China, this study proposed a screening model that combined analysis at levels and systematic clustering methods and performed a comprehensive score of pollutants and cluster analysis on the basis of assigning and scoring the evaluation factors of e-waste dismantling, taking the most potentially dangerous class in the cluster results as the priority control pollutant.

A classification and extraction method of attribute hybrid big data based on Naive Bayes algorithm

In the identification of network text information, the existing technology is difficult to accurately extract and classify text information with high propagation speed and high update speed. In order to solve this problem, the research combines the Naive Bayes algorithm with the feature two-dimensional information gain weighting method, uses the feature weighting method to optimize the Naive Bayes algorithm, and calculates the dimension of different documents and data categories through a new feature operation method. The data gain between them can improve its classification performance, and the classification models are compared and analyzed in the actual Chinese and English databases. The research results show that the classification accuracy rates of the IGDC-DWNB model in the Sogou database, 20-newsgroup database, Fudan database and Ruster21578 database are 0.89, 0.89, 0.93, and 0.88, respectively, which are higher than other classification models in the same environment. It can be seen that the model designed in the research has higher classification accuracy, stronger overall performance, and stronger reliability and robustness in practical applications, which can provide a new development idea for big data classification technology.

Research on Marketing Strategy of China Heavy Duty VGV Based on 4P Theory

China National Heavy Duty Truck (CNHTC) is a famous brand in China. China National Heavy Duty Truck (CNHTC) is a well-known domestic automobile brand, which occupies a certain market share in the automobile market and is recognized and supported by many consumers. However, China National Heavy Duty Truck Corporation (CNHTC) mainly focuses on heavy-duty truck products, and the development of its VGV light-duty vehicle has not been very good in the past decade, which needs to readjust its marketing strategy. This study takes China National Heavy Duty Truck Corporation (CNHTC) VGV as the research object, and explores its marketing market environment, competitive environment, etc., to find out the current marketing status of the company, and the dilemmas that appear in marketing: slow product updating, solidified brand image, lack of effective supervision and flexibility in price positioning, lack of on-line channels, inadequate management of dealers, single means of promotion, and lack of innovation in advertisement. Targeted to put forward to speed up the product update speed, pay attention to brand building, pricing and product positioning, strengthen the effective supervision of product price, online and offline channels combined, reasonable planning product channel layout and other countermeasures.

Simultaneous determination of five indole/indazole amide-based synthetic cannabinoids in electronic cigarette oil by ultra performance liquid chromatography

Synthetic cannabinoids (SCs), which are considered some of the most widely abused new psychoactive substances available today, are much more potent than natural cannabis and display greater efficacy. New SCs can be developed by adding substituents such as halogen, alkyl, or alkoxy groups to one of the aromatic ring systems, or by changing the length of the alkyl chain. Following the emergence of the so-called first-generation SCs, further developments have led to eighth-generation indole/indazole amide-based SCs. Given that all SCs were listed as controlled substances on July 1, 2021, the technologies used to detect these substances must be quickly improved. Due to the sheer number of SCs, the chemical diversity and the fast update speed, it is challenging to determine and identify the new SCs. In recent years, several types of indole/indazole amide-based SCs have been seized, but systematic research on these compounds remains limited. Therefore, developing rapid, sensitive, and accurate quantitative methods to determine new SCs are of great importance. Compared with high performance liquid chromatography (HPLC), ultra performance liquid chromatography (UPLC) shows higher resolution, better separation efficiency, and faster analysis speeds; thus, it can meet the demand for the quantitative analysis of indole/indazole amide-based SCs in seized materials. In this study, a UPLC method was developed for the simultaneous determination of five indole/indazole amide-based SCs, including N-(1-amino-3,3-dimethyl-1-oxobutan-2-yl)-1-butyl-1H-indazole-3-carboxamide (ADB-BUTINACA), methyl 2-(1-(4-fluorobutyl)-1H-indole-3-carboxamido)-3,3-dimethylbutanoate (4F-MDMB-BUTICA), N-(1-methoxy-3,3-dimethyl-1-oxobutan-2-yl)-1-(5-fluoropentyl)-1H-indole-3-carboxamide (5F-MDMB-PICA), methyl 3,3-dimethyl-2-(1-(pent-4-en-1-yl)-1H-indazole-3-carboxamido)butanoate (MDMB-4en-PINACA), and N-(adamantan-1-yl)-1-(4-fluorobutyl)-1H-indazole-3-carboxamide (4F-ABUTINACA) in electronic cigarette oil; these SCs have been detected with increasing frequency in seized materials in recent years. The main factors influencing the separation and detection performance of the proposed method, including the mobile phase, elution gradient, column temperature, and detection wavelength, were optimized. The proposed method successfully quantified the five SCs in electronic cigarette oil via the external standard method. The samples were extracted using methanol, and the target analytes were separated on a Waters ACQUITY UPLC CSH C18 column (100 mm×2.1 mm, 1.7 μm) at column temperature of 35 ℃ and flow rate of 0.3 mL/min. The injection volume was 1 μL. The mobile phase consisted of acetonitrile and ultrapure water, and gradient elution was employed. The detection wavelengths were 290 and 302 nm. The five SCs were completely separated within 10 min under optimized conditions and showed good linear relationships between 1-100 mg/L, with correlation coefficients (r2) of up to 0.9999. The limits of detection (LOD) and quantification (LOQ) were 0.2 and 0.6 mg/L, respectively. Precision was determined using standard solutions of the five SCs at mass concentrations of 1, 10, and 100 mg/L. The intra-day precision (n=6) was <1.5%, and the inter-day precision (n=6) was <2.2%. Accuracy was determined by spiking electronic cigarette oil with low (2 mg/L), moderate (10 mg/L), and high (50 mg/L) levels of the five SCs, with six replicates per determination. The recoveries of the five SCs were 95.5%-101.9%, and their relative standard deviations (RSDs, n=6) were 0.2%-1.5%, with accuracies ranging from -4.5% to 1.9%. The proposed method showed good performance when applied to the analysis of real samples. It is accurate, rapid, sensitive, and effective for the determination of five indole/indazole amide-based SCs in electronic cigarette oil. Thus, it satisfies the requirements for practical determination and provides a reference for the determination of SCs with similar structures by UPLC.

DRnet: Dynamic Retraining for Malicious Traffic Small-Sample Incremental Learning

Deep learning has achieved good classification results in the field of traffic classification in recent years due to its good feature representation ability. However, the existing traffic classification technology cannot meet the requirements for the incremental learning of tasks in online scenarios. In addition, due to the high concealment and fast update speed of malicious traffic, the number of labeled samples that can be captured is scarce, and small samples cannot drive neural network training, resulting in poor performance of the classification model. Therefore, this paper proposes an incremental learning method for small-sample malicious traffic classification. The method uses the pruning strategy to find the redundant network structure and dynamically allocates redundant neurons for training based on the proposed measurement method according to the difficulty of the new class. This enables the network to perform incremental learning without excessively consuming storage and computing resources, and reasonable allocation improves the classification accuracy of new classes. At the same time, through the knowledge transfer method, the model can reduce the catastrophic forgetting of the old class, relieve the pressure of training large parameters with small-sample data, and improve the model classification performance. Experiments involving multiple datasets and settings show that our method is superior to the established baseline in terms of classification accuracy, consuming 50% less memory.

Characterizing Heterogeneous Traffic Flow at a Slope Bottleneck via Cellular Automaton Model

This paper considers the unique attributes of autonomous vehicles, the characteristics of expressways&#x2019; minor actual slope grade, and the speed update integer requirements of discrete cellular automata (CA). The continuous deceleration efficiency of the uphill is discretized by introducing variable parameters connected to the vehicle position and gradient, and the hybrid traffic flow CA model suitable for slope bottlenecks based on the principle of the safety distance is further improved. The simulation results show that the improved model can simulate the unique traffic flow phenomenon caused by the change in slope conditions. There is a critical mutation in the traffic flow phase on the slope road section. Secondly, according to the different main factors influencing traffic flow, free flow, terrain jams, and homogeneous congested flow coexist on the slope section. The traffic flow in the crowded terrain area is significantly negatively correlated with the length and longitudinal gradient of the slope, and increasing the longitudinal angle of the hill will extend the congested terrain area. In addition, the influence of the change in slope conditions on mixed traffic flow has a critical longitudinal gradient of 3% and a fixed critical slope length. In non-free flow areas, autonomous vehicles can significantly alleviate traffic congestion on upslopes.

Modelling wintertime sea-spray aerosols under Arctic haze conditions

Abstract. Anthropogenic and natural emissions contribute to enhanced concentrations of aerosols in the Arctic winter and early spring, with most attention being paid to anthropogenic aerosols that contribute to so-called Arctic haze. Less-well-studied wintertime sea-spray aerosols (SSAs) under Arctic haze conditions are the focus of this study, since they can make an important contribution to wintertime Arctic aerosol abundances. Analysis of field campaign data shows evidence for enhanced local sources of SSAs, including marine organics at Utqiaġvik (formerly known as Barrow) in northern Alaska, United States, during winter 2014. Models tend to underestimate sub-micron SSAs and overestimate super-micron SSAs in the Arctic during winter, including the base version of the Weather Research Forecast coupled with Chemistry (WRF-Chem) model used here, which includes a widely used SSA source function based on Gong et al. (1997). Quasi-hemispheric simulations for winter 2014 including updated wind speed and sea-surface temperature (SST) SSA emission dependencies and sources of marine sea-salt organics and sea-salt sulfate lead to significantly improved model performance compared to observations at remote Arctic sites, notably for coarse-mode sodium and chloride, which are reduced. The improved model also simulates more realistic contributions of SSAs to inorganic aerosols at different sites, ranging from 20 %–93 % in the observations. Two-thirds of the improved model performance is from the inclusion of the dependence on SSTs. The simulation of nitrate aerosols is also improved due to less heterogeneous uptake of nitric acid on SSAs in the coarse mode and related increases in fine-mode nitrate. This highlights the importance of interactions between natural SSAs and inorganic anthropogenic aerosols that contribute to Arctic haze. Simulation of organic aerosols and the fraction of sea-salt sulfate are also improved compared to observations. However, the model underestimates episodes with elevated observed concentrations of SSA components and sub-micron non-sea-salt sulfate at some Arctic sites, notably at Utqiaġvik. Possible reasons are explored in higher-resolution runs over northern Alaska for periods corresponding to the Utqiaġvik field campaign in January and February 2014. The addition of a local source of sea-salt marine organics, based on the campaign data, increases modelled organic aerosols over northern Alaska. However, comparison with previous available data suggests that local natural sources from open leads, as well as local anthropogenic sources, are underestimated in the model. Missing local anthropogenic sources may also explain the low modelled (sub-micron) non-sea-salt sulfate at Utqiaġvik. The introduction of a higher wind speed dependence for sub-micron SSA emissions, also based on Arctic data, reduces biases in modelled sub-micron SSAs, while sea-ice fractions, including open leads, are shown to be an important factor controlling modelled super-micron, rather than sub-micron, SSAs over the north coast of Alaska. The regional results presented here show that modelled SSAs are more sensitive to wind speed dependence but that realistic modelling of sea-ice distributions is needed for the simulation of local SSAs, including marine organics. This study supports findings from the Utqiaġvik field campaign that open leads are the primary source of fresh and aged SSAs, including marine organic aerosols, during wintertime at Utqiaġvik; these findings do not suggest an influence from blowing snow and frost flowers. To improve model simulations of Arctic wintertime aerosols, new field data on processes that influence wintertime SSA production, in particular for fine-mode aerosols, are needed as is improved understanding about possible local anthropogenic sources.

Single-exposure ultrasound-modulated optical tomography with a quaternary phase encoded mask.

Ultrasound-modulated optical tomography (UOT) is a deep-tissue imaging modality that provides optical contrast with acoustic resolution. Among existing implementations, camera-based UOT improves modulation depth through parallel detection but suffers from a low camera frame rate. The condition prohibits this technique from being applied to in vivo applications where speckles decorrelate on a time scale of 1 ms or less. To overcome this challenge, we developed single-exposure camera-based UOT by employing a quaternary phase encoded mask (QPEM). As a proof of concept, we demonstrated imaging of an absorptive target buried inside a dynamic scattering medium with a speckle correlation time as short as 0.49 ms, typical of living biological tissues. Benefiting from the QPEM-enabled single-exposure wavefront measurement (5.5 ms) and GPU-assisted wavefront reconstruction (0.97 ms), the point scanning and result update speed can reach up to 150 Hz. We envision that the QPEM-enabled single-exposure scheme paves the way for in vivo UOT imaging, which holds promise for a variety of medical and biological applications.

Directed Search Based on Improved Whale Optimization Algorithm for Test Case Prioritization

With the advent of the information age, the iterative speed of software update is gradually accelerating which makes software development severely limited by software testing. Test case prioritization is an effective way to accelerate software testing progress. With the introduction of heuristic algorithm to this task, the processing efficiency of test cases has been greatly improved. However, to overcome the shortcomings of slow convergence speed and easy fall into local optimum, the improved whale optimization algorithm is proposed for test case prioritization. Firstly, a model called n-dimensional directed search space is established for the swarm intelligence algorithm. Secondly, the enhanced whale optimization algorithm is applied to test case prioritization while the backtracking behavior is conducted for individuals when hitting the wall. In addition, a separate storage space for Pareto second optimization is also designed to filter the optimal solutions of the multi-objective tasks. Finally, both single-objective and multi-objective optimization experiments are carried out for open source projects and real-world projects, respectively. The results show that the improved whale optimization algorithm using n-dimensional directed search space is more conducive to the decisions of test case prioritization with fast convergence speed.

Improved particle swarm optimization algorithm with random mutation and perception

Since traditional particle swarm optimization(PSO) is prone to premature phenomenon when solving complex functions in high-dimensional space, a particle swarm optimization algorithm with random variation and dynamic perception factors in terms of the movement laws and dispersion characteristics of particles in space is proposed. In order to encourage individual particles to explore their own neighborhoods and reduce the premature phenomenon of particles due to over-reliance on individual optimality and global optimality, a random mutation factor with a questioning strategy for neighborhoods is added to the basic algorithm to improve the speed update. At the same time, a perception factor is added to the particle position update, so that the particle can dynamically and adaptively control the spatial distance between itself and other particles in the same dimension, so as to avoid falling into local optimum. The algorithm has obvious superiority and robustness in solving complex functions in high-dimensional space through test function experiments, algorithm comparison analysis experiments, random parameter influence experiments and algorithm complexity experiments.

Research progress on genome-guided precision oncology and development ideas of antitumor Chinese medicine

Genome-guided oncology refers to a new treatment concept that transcends histological classification and pathological ty-ping and uses drugs according to the genetic characteristics of tumors. New drug development technology and clinical trial design based on this concept provide new ideas for the clinical application of precision oncology. The multi-component and multi-target characteristics of Chinese medicine provide rich resources for the development of tumor-targeting drugs from natural products, and the design of the master protocol trial aiming at the characteristics of precision oncology supports the rapid clinical screening of effective tumor-targeting drugs. The emergence of the synthetic lethality strategy breaks through the bottleneck that the drug can only target the oncogene but cannot do anything to the tumor suppressor gene with the loss-of-function mutation in the past. With the rapid development of high-throughput sequencing technology, the cost of sequencing is also decreasing. For the development of tumor-targeting drugs, how to keep up with the update speed of target information is a difficult problem of concern. Based on the integration of innovative ideas and me-thods of precision oncology, network pharmacology, and synthetic lethality strategy on synthetic lethal interaction network of antitumor Chinese medicine compatibility formula design, and the combination of improvement of innovative clinical trial methods, such as master protocol trial, basket trial, and umbrella trial, unique advantages of Chinese medicine are expected to be exerted beyond the antibody-based drugs and small molecule-based drugs and corresponding targeted drugs are potentially developed for clinical application.

Bayesian Investor Belief Updating Speed and Market Underreaction to Earnings Announcements

AbstractBuilding on the Bayesian Theorem, we propose a multi‐period market microstructure model to understand how Bayesian investors underact new information and the duration of market underreaction. Applying the model to post‐earnings‐announcement drifts, our simulation and regression analyses show that the duration of the post‐announcement price adjustment process and the post‐announcement drifts can be explained by the new measure of belief updating speed that quantifies the uncertainties faced by Bayesian investors when incorporating new information into prices. Our study highlights the importance of incorporating the belief uncertainties of uninformed investors in explaining market underreaction in the Bayesian framework.

A color image contrast enhancement method based on improved PSO.

Image contrast enhancement uses the object intensity transformation function to maximize the amount of information to enhance an image. In this paper, the image enhancement problem is regarded as an optimization problem, and the particle swarm algorithm is used to obtain the optimal solution. First, an improved particle swarm optimization algorithm is proposed. In this algorithm, individual optimization, local optimization, and global optimization are used to adjust the particle's flight direction. In local optimization, the topology is used to induce comparison and communication between particles. The sparse penalty term in speed update formula is added to adjust the sparsity of the algorithm and the size of the solution space. Second, the three channels of the color images R, G, and B are represented by a quaternion matrix, and an improved particle swarm algorithm is used to optimize the transformation parameters. Finally, contrast and brightness elements are added to the fitness function. The fitness function is used to guide the particle swarm optimization algorithm to optimize the parameters in the transformation function. This paper verifies via two experiments. First, improved particle swarm algorithm is simulated and tested. By comparing the average values of the four algorithms under the three types of 6 test functions, the average value is increased by at least 15 times in the single-peak 2 test functions: in the multi-peak and multi-peak fixed-dimension 4 test functions, this paper can always search for the global optimal solution, and the average value is either the same or at least 1.3 times higher. Second, the proposed algorithm is compared with other evolutionary algorithms to optimize contrast enhancement, select images in two different data sets, and calculate various evaluation indicators of different algorithms under different images. The optimal value is the algorithm in this paper, and the performance indicators are at least a 5% increase and a minimum 15% increase in algorithm running time. Final results show that the effects the proposed algorithm have obvious advantages in both subjective and qualitative aspects.

Macro modeling of liquid crystal cell using machine learning method: reservoir computing approach

A macro model of liquid crystal cells including electrical and optical behaviors has been developed using a machine learning framework called reservoir computing and implemented into a circuit simulator. Assuming the arbitrary time steps given from the circuit simulator, we confirmed that our model in which the time-continuous reservoir update equation is discretized by a fourth-order Runge–Kutta method shows high prediction accuracy even at the different time steps from that in the training phase. The director distribution of liquid crystals, which is the microscopic state that realizes the specific macroscopic characteristic, capacitance, and transmittance, is not uniquely determined. Therefore, it is essential to utilize the reservoir’s ability to memorize history to improve prediction accuracy. We found it effective to adjust the parameters that control memory length and update speed according to the response time of each capacitance and transmittance.

Auxo: A Scalable and Efficient Graph Stream Summarization Structure

A graph stream refers to a continuous stream of edges, forming a huge and fast-evolving graph. The vast volume and high update speed of a graph stream bring stringent requirements for the data management structure, including sublinear space cost, computation-efficient operation support, and scalability of the structure. Existing designs summarize a graph stream by leveraging a hash-based compressed matrix and representing an edge using its fingerprint to achieve practical storage for a graph stream with a known upper bound of data volume. However, they fail to support the dynamically extending of graph streams. In this paper, we propose Auxo, a scalable structure to support space/time efficient summarization of dynamic graph streams. Auxo is built on a proposed novel prefix embedded tree (PET) which leverages binary logarithmic search and common binary prefixes embedding to provide an efficient and scalable tree structure. PET reduces the item insert/query time from O (| E |) to O ( log | E |) as well as reducing the total storage cost by a log | E | scale, where | E | is the size of the edge set in a graph stream. To further improve the memory utilization of PET during scaling, we propose a proportional PET structure that extends a higher level in a proportionally incremental style. We conduct comprehensive experiments on large-scale real-world datasets to evaluate the performance of this design. Results show that Auxo significantly reduces the insert and query time by one to two orders of magnitude compared to the state of the arts. Meanwhile, Auxo achieves efficiently and economically structure scaling with an average memory utilization of over 80%.