Street network patterns for mitigating urban heat islands in arid climates

This study explores the impact of street pattern measurements on urban heat islands(UHI)in the arid climate of Mashhad,Iran.The Landsat-8 top-of-the-atmosphere(TOA)brightness images from 2013 to 2021,average values of normalized difference vegetation index(NDvI)and land surface temperature(LST)were calculated.Street pattern measurements,including closeness-centrality,straightness,and street orientation,were employed to analyse the patterns in each district.The results indicated that districts with higher straightness and lower closeness-centrality exhibit,cooler surface temperatures.Strong correlations were observed between LST and NDVl,straightness,and local closeness-centrality.The research highlighted the importance of considering street network measurements in long-term urban planning and design to mitigate the UHI effect in arid regions.A moderate grid street pattern with a reasonable distribution of green spaces throughout the region is suggested to reduce surface temperatures sustainably.Street pattern indexes,such as straightness and local closeness-centrality,are identified as significant factors in urban design to mitigate UHl.These findings have implications for urban planners,who can use this information to create street network patterns with lower UHI effects by reducing local closeness-centrality and increasing straightness.

Crack Fault Diagnosis and Location Method for a Dual-Disk Hollow Shaft Rotor System Based on the Radial Basis Function Network and Pattern Recognition Neural Network

The crack fault is one of the most common faults in the rotor system,and researchers have paid close attention to its fault diagnosis.However,most studies focus on discussing the dynamic response characteristics caused by the crack rather than estimating the crack depth and position based on the obtained vibration signals.In this paper,a novel crack fault diagnosis and location method for a dual-disk hollow shaft rotor system based on the Radial basis function(RBF)network and Pattern recognition neural network(PRNN)is presented.Firstly,a rotor system model with a breathing crack suitable for a short-thick hollow shaft rotor is established based on the finite element method,where the crack's periodic opening and closing pattern and different degrees of crack depth are considered.Then,the dynamic response is obtained by the harmonic balance method.By adjusting the crack parameters,the dynamic characteristics related to the crack depth and position are analyzed through the amplitude-frequency responses and waterfall plots.The analysis results show that the first critical speed,first subcritical speed,first critical speed amplitude,and super-harmonic resonance peak at the first subcritical speed can be utilized for the crack fault diagnosis.Based on this,the RBF network and PRNN are adopted to determine the depth and approximate location of the crack respectively by taking the above dynamic characteristics as input.Test results show that the proposed method has high fault diagnosis accuracy.This research proposes a crack detection method adequate for the hollow shaft rotor system,where the crack depth and position are both unknown.

International Freight Forwarding Services Network in the Yangtze River Delta, 2005–2015: Patterns and Mechanisms

This study examined the spatio-temporal trajectories of the international freight forwarding service(IFFS) in the Yangtze River Delta(YRD) and explored the driving mechanisms of the service. Based on a bipartite network projection from an IFFS firm-city data source, we mapped three IFFS networks in the YRD in 2005, 2010, and 2015. A range of statistical indicators were used to explore changes in the spatial patterns of the three networks. The underlying influence of marketization, globalization, decentralization, and integration was then explored. It was found that the connections between Shanghai and other nodal cities formed the backbones of these networks. The effects of a city's administrative level and provincial administrative borders were generally obvious. We found several specific spatial patterns associated with IFFS. For example, the four non-administrative centers of Ningbo, Suzhou, Lianyungang, and Nantong were the most connected cities and played the role of gateway cities. Furthermore, remarkable regional equalities were found regarding a city's IFFS network provision, with notable examples in the weakly connected areas of northern Jiangsu and southwestern Zhejiang. Finally, an analysis of the driving mechanisms demonstrated that IFFS network changes were highly sensitive to the influences of marketization and globalization, while regional integration played a lesser role in driving changes in IFFS networks.

Geomorphologic patterns of dune networks in the Tengger Desert,China

Dune networks are widely distributed in the world＇s deserts,which include primary ridges and secondary ridges.However,they have not been sufficiently studied in a systematic manner and their origins and spatial and morphological characteristics remain unclear.To provide information on the geomorphology of dune networks,we analyze the software geomorphologic patterns of the dune networks in China＇s Tengger Desert using matrix and laboratory to process remote-sensing images.Based on analysis of image features and their layout in a topographic map,we identify two types of dune networks （square and rectangular dune networks） with different size and morphological structures in the Tengger Desert.Four important geomorphic pattern parameters,ridge length,spacing,orientation and defect density,are analyzed.The length of primary ridges of dune networks decreases from northwest of the desert to the southeast,resulting an increasing spacing and a transition from rectangular dune networks to square dune networks.Wind regime and sediment supply are responsible for the variation in pattern parameters.We use the spacing and defect density data to estimate the construction time of dune networks and found that the dune networks in the Tengger Desert formed since about 1.3 ka BP.

Multilevel Pattern Mining Architecture for Automatic Network Monitoring in Heterogeneous Wireless Communication Networks

The rapid development of network technology and its evolution toward heterogeneous networks has increased the demand to support automatic monitoring and the management of heterogeneous wireless communication networks.This paper presents a multilevel pattern mining architecture to support automatic network management by discovering interesting patterns from telecom network monitoring data.This architecture leverages and combines existing frequent itemset discovery over data streams,association rule deduction,frequent sequential pattern mining,and frequent temporal pattern mining techniques while also making use of distributed processing platforms to achieve high-volume throughput.

Fast Multi-Pattern Matching Algorithm on Compressed Network Traffic

Pattern matching is a fundamental approach to detect malicious behaviors and information over Internet, which has been gradually used in high-speed network traffic analysis. However, there is a performance bottleneck for multi-pattern matching on online compressed network traffic(CNT), this is because malicious and intrusion codes are often embedded into compressed network traffic. In this paper, we propose an online fast and multi-pattern matching algorithm on compressed network traffic(FMMCN). FMMCN employs two types of jumping, i.e. jumping during sliding window and a string jump scanning strategy to skip unnecessary compressed bytes. Moreover, FMMCN has the ability to efficiently process multiple large volume of networks such as HTTP traffic, vehicles traffic, and other Internet-based services. The experimental results show that FMMCN can ignore more than 89.5% of bytes, and its maximum speed reaches 176.470MB/s in a midrange switches device, which is faster than the current fastest algorithm ACCH by almost 73.15 MB/s.

Influence of Blurred Ways on Pattern Recognition of a Scale-Free Hopfield Neural Network

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Pattern recognition and prediction study of rock burst based on neural network

Many monitoring measures were used in the production field for predicting rockburst.However, predicting rock burst according to complicated observation data is alwaysa pressing problem in this research field.Though the critical value method gets extensiveapplication in practice, it stresses only on the superficial change of data and overlooks alot of features of rock burst and useful information that is concealed and hidden in the observationtime series.Pattern recognition extracts the feature value of time domain, frequencydomain and wavelet domain in observation time series to form Multi-Feature vectors,using Euclidean distance measure as the separable criterion between the same typeand different type to compress and transform feature vectors.It applies neural network asa tool to recognize the danger of rock burst, and uses feature vectors being compressedto carry out training and studying.It is proved by test samples that predicting precisionshould be prior to such traditional predicting methods as pattern recognition and critical indicatormethod.

Application of extension neural network to safety status pattern recognition of coalmines

In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of network,the rationale of recognition algorithm and the performance of proposed method were discussed in detail.The safety status pattern recognition problem of coalmines can be regard as a classification problem whose features are defined in a range,so using the ENN is most appropriate for this problem.The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers.To demonstrate the effectiveness of the proposed method,a real-world application on the geological safety status pattern recognition of coalmines was tested.Comparative experiments with existing method and other traditional ANN-based methods were conducted.The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coalmines accurately with shorter learning time and simpler structure.The experimental results also confirm that the proposed method has a better performance in recognition accuracy,generalization ability and fault-tolerant ability,which are very useful in recognizing the safety status pattern in the process of coal production.

A CASCADED MODEL OF NEURAL NETWORK FOR PATTERN RECOGNITION

A cascaded model of neural network and its learning algorithm suitable for opticalimplementation are proposed.Computer simulations have shown that this model may successfullybe applied to an error-tolerance pattern recognitions of multiple 3-D targets with arbitrary spatialorientations.

MOLTEN SALT PHASE DIAGRAMS CALCULATION USING ARTIFICIAL NEURAL NETWORK OR PATTERN RECOGNITION-BOND PARAMETERS

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Prediction of Enthalpies of Fusion for Divalent Rare Earth Halides Based on Modeling by Artificial Neural Networks and Pattern Recognition

The artificial neural network (ANN) and the pattern recognition were applied to study the correlation of enthalpies of fusion for divalent rare earth halides with their microstructural parameters,such as ionic radius and electronegativity. The model,represented by a back-propagation netal network, was trained with a 12 set of published data for divalent rare earth halides and then was used to predict the unknown ones. Also the criterion equations were ptesented to determine the enthalpies of fuSion for divalent rare earth halides using pattern recognition in mis work. The results from the model in ANN and criterion equations are in very good agreement with reference data.

The Structure and Pattern of Urban Network in the Lanzhou-Xining Urban Agglomeration

In this paper,we use factor analysis to evaluate the urban comprehensive quality of each city in the Lanzhou-Xining(Lan-Xi)urban agglomeration.The time distance was obtained by using GIS spatial analysis,and the structure and pattern of the spatial network were analyzed by using the gravity model and social network analysis method.The results show that:1)The scale effect of the Lan-Xi urban agglomeration is gradually emerging,and it is gradually forming the urban agglomeration with Lanzhou and Xining as the core,the Lan-Xi high-speed railway as the axis,and a high-dense connection.2)Lanzhou and Xining are at the core of the Lan-Xi urban agglomeration,which has a strong attraction and spreads to neighboring cities.3)In the network structure of the Lan-Xi urban agglomeration,Lanzhou,Baiyin,Gaolan,Yuzhong,Yongdeng,Dingxi,Lintao,Xining,Ledu,Huangzhong,Ping’an,Minhe and Datong are located in the network core position,which have the superiority position and lead to the entire regional communication enhancement and the regional integration development.4)This urban agglomeration has significant subgroups,eight tertiary subgroups and four secondary subgroup;the tertiary subgroups which compose secondary subgroup have a close connection and mutually influence each other.5)The Lanzhou Metropolitan Area and the Xining Metropolitan Area have an important impact on the surrounding cities,and the peripheral cities are basically controlled by the central city.The Dingxi subgroup,Lintao-Linxia subgroup,Gonghe subgroup have more structural holes than the subgroups within the Lanzhou Metropolitan Area and the Xining Metropolitan Area,so the peripheral cities of these subgroups have relatively less connection with surrounding cities.

Spatial patterns nitrogen transfer models of ectomycorrhizal networks in a Mongolian scotch pine plantation

Ectomycorrhizal(EM)networks provide a variety of services to plants and ecosystems include nutrient uptake and transfer,seedling survival,internal cycling of nutrients,plant competition,and so on.To deeply their structure and function in ecosystems,we investigated the spatial patterns and nitrogen(N)transfer of EM networks usingN labelling technique in a Mongolian scotch pine(Pinus sylvestris var.mongolica Litv.)plantation in Northeastern China.In August 2011,four plots(20 × 20 m)were set up in the plantation.125 ml 5 at.%0.15 mol/LNHNOsolution was injected into soil at the center of each plot.Before and 2,6,30 and 215 days after theN application,needles(current year)of each pine were sampled along four 12 m sampling lines.Needle total N andN concentrations were analyzed.We observed needle N andN concentrations increased significantly over time afterN application,up to 31 and0.42%,respectively.There was no correlation between needle N concentration andN/N ratio(R2=0.40,n=5,P=0.156),while excess needle N concentration and excess needleN/N ratio were positively correlated across different time intervals(R~2=0.89,n=4,P\0.05),but deceased with time interval lengthening.NeedleN/N ratio increased with time,but it was not correlated with distance.NeedleN/N ratio was negative with distance before and 6th day and 30th day,positive with distance at 2nd day,but the trend was considerably weaker,their slop were close to zero.These results demonstrated that EM networks were ubiquitous and uniformly distributed in the Mongolian scotch pine plantation and a random network.We found N transfer efficiency was very high,absorbed N by EM network was transferred as wide as possible,we observed N uptake of plant had strong bias forN andN,namely N fractionation.Understanding the structure and function of EM networks in ecosystems may lead to a deeper understanding of ecological stability and evolution,and thus provide new theoretical approaches to improve conservation practices for the management of the Earth’s ecosystems.

Analysis of Dual-Core Type City and Its Hinterland Contact Pattern under the Background of High-Speed Rail Networking—Urban Agglomeration in Zhejiang Province as an Example

Under the background of high-speed rail networking, this paper uses the passenger trains, the type and direction of the railway via Hang Yong and its extension road line, to construct adsorption and dependency index among cities, depict the contact pattern between the Hang Yong dual-core and its hinterland, measure the “net effect” that two center cities (Hangzhou, Ningbo) have on their hinterland, and estimate population agglomeration potential and future possible population flows of Zhejiang Province and the main sample cities. The result shows that, compared with Ningbo, Hangzhou has stronger radiation force to the vast majority of sample cities, and the sample cities affected more by Ningbo mainly concentrates in Ningbo-Taizhou-Wenzhou along;in addition, the sample cities such as Hangzhou, Ningbo and so on show better population agglomeration, then the population “scramble” phenomenon between cities has begun to appear.

A Fuzzy Neural Network for Fault Pattern Recognition

This paper combines fuzzy set theory with ART neural net-work , and demonstrates some important properties of the fuzzy ART neural net-work algorithm. The results from application on a ball bearing diagnosis indicate that a fuzzy ART neural net-work has an effect of fast stable recognition for fuzzy patterns.

Comprehensive Analysis of Caching Performance under Probabilistic Traffic Patterns for Content Centric Networking

The phenomenon of data explosion represents a severe challenge for the upcoming big data era.However,the current Internet architecture is insufficient for dealing with a huge amount of traffic owing to an increase in redundant content transmission and the end-point-based communication model.Information-centric networking(ICN)is a paradigm for the future Internet that can be utilized to resolve the data explosion problem.In this paper,we focus on content-centric networking(CCN),one of the key candidate ICN architectures.CCN has been studied in various network environments with the aim of relieving network and server burden,especially in name-based forwarding and in-network caching functionalities.This paper studies the effect of several caching strategies in the CCN domain from the perspective of network and server overhead.Thus,we comprehensively analyze the in-network caching performance of CCN under several popular cache replication methods(i.e.,cache placement).We evaluate the performance with respect to wellknown Internet traffic patterns that follow certain probabilistic distributions,such as the Zipf/Mandelbrot–Zipf distributions,and flashcrowds.For the experiments,we developed an OPNET-based CCN simulator with a realistic Internet-like topology.

Prediction of Composition of GaInAsSb Epilayers by MOCVD Using Pattern Recognition and Artificial Neural Network Method

he pattern recognition method and artificial neural network method to predict the composition of epilayer of GaInAsSb by MOCVD. It is concluded that a neural network with the composition of the vapor phase and growth temperature as training data can predict the composition of the epilayers. Satisfactory pattern recognition and artificial neural network classification results were obtained by using four technical parameters as characteristic features and the epilayers composition as classification criteria.

The Analysis of Frequency Reuse Irregular Patterns in Mobile Telephone Network

Some frequency reuse irregular patterns in radionetwork design are proposed,the characteristic and applica-tion measures of these patterns are analyzed.Then this paperaccounts that frequency reuse irregular patterns is a usefulway to impove spectrum efficiency and it is significative forartificial intelligence to be applied in this field.