Measurement and interpretation of connectivity of Chinese cities in world city network, 2010

This is an empirical paper that measures and interprets the position of Chinese cities in the world city network in 2010. Building on a specification of the world city network as a′interlocking network′in which business services firms play the crucial role in city network formation, information is gathered about the presence of global service firms in cities. This information is converted into data to provide the′service value′of a city for a firm′s provision of corporate services in a 526(cities)×175(firms) matrix. These data are then used as the input to the interlocking network model in order to measure cities′connectivity and its predominant geographical orientation. Here we focus on the position of some key Chinese cities in this regard, and discuss and interpret results in the context of the urban dimensions of the′opening up′of the Chinese economy.     

Tourism management in national parks: ?umava and Bayerischer Wald(Bavarian Forest) in the Czech-German borderland

Along the Czech-German border there are four national parks, two Czech and two German, arranged in cross-border ‘pairs'. This article focuses on the southern ‘pair' formed by the parks of ?umava and Bayerischer Wald(Bavarian Forest). The aim is to evaluate and compare tourism organization in their areas, taking into account selected aspects of management: the network of hiking trails with its related infrastructure, transport accessibility, a typology of tourist centers, as well as directions and destinations of tourist movements. Based on the results, it can be concluded that the availability of geographical space for tourists is much greater in the German than in the Czech national park, and the tourism infrastructure is clearly more extensive there, including the network of tourist trails. This is mainly due to the longer and fairly uninterrupted development of tourism in this area. ?umava National Park can be identified as a model in terms of how to adjust the directions of tourist movements and the layout of the tourist trail network to the needs of natural environment. On the basis of observations in both national parks, it is possible to indicate various solutions that, after appropriate adaptation, may bring benefits to other protected areas.     

UGC-YOLO: Underwater Environment Object Detection Based on YOLO with a Global Context Block

With the continuous development and utilization of marine resources,the underwater target detection has gradually become a popular research topic in the field of underwater robot operations and target detection.However,it is difficult to combine the environmental semantic information and the semantic information of targets at different scales by detection algorithms due to the complex underwater environment.In this paper,a cascade model based on the UGC-YOLO network structure with high detection...     

Underwater Target Detection Based on Reinforcement Learning and Ant Colony Optimization

Underwater optical imaging produces images with high resolution and abundant information and hence has outstanding advantages in short-distance underwater target detection.However,low-light and high-noise scenarios pose great challenges in un-derwater image and video analyses.To improve the accuracy and anti-noise performance of underwater target image edge detection,an underwater target edge detection method based on ant colony optimization and reinforcement learning is proposed in this paper.First,the reinforcement learning concept is integrated into artificial ants’movements,and a variable radius sensing strategy is pro-posed to calculate the transition probability of each pixel.These methods aim to avoid undetection and misdetection of some pixels in image edges.Second,a double-population ant colony strategy is proposed,where the search process takes into account global search and local search abilities.Experimental results show that the algorithm can effectively extract the contour information of underwater targets and keep the image texture well and also has ideal anti-interference performance.     

Determining critical support discharge of a riverhead and river network analysis: Case studies of Lhasa River and Nyangqu River

A riverhead is the demarcation point of continuous water channel and seasonal channel, which is characterized by a critical flow that can support a continuous water body. In this study, the critical support discharge(CSD) is defined as the critical steady flows required to form the origin of a stream. The CSD is used as the criterion to determine the beginning of the riverhead, which can be controlled by hydro-climate factors(e.g., annual precipitation, annual evaporation, or minimum stream flow in arid season). The CSD has a close correlation with the critical support/source area(CSA) that largely affects the density of the river network and the division of sub-watersheds. In general, river density may vary with regional meteorological and hydrological conditions that have to be considered in the analysis. In this paper, a new model referring to the relationship of CSA and CSD is proposed, which is based on the physical mechanism for the origin of riverheads. The feasibility of the model was verified using two watersheds(Duilongqu Basin of the Lhasa River and Beishuiqu Basin of the Nyangqu River) in Tibet Autonomous Region to calculate the CSA and extract river networks. A series of CSAs based on different CSDs in derived equation were tested by comparing the extracted river networks with the reference network obtained from a digitized map of river network at large scales. Comparison results of river networks derived from digital elevation model with real ones indicate that the CSD(equal to criterion of flow quantity(Q_c)) are 0.0028 m~3/s in Duilongqu and 0.0085 m~3/s in Beishuiqu. Results show that the Q_c can vary with hydro-climate conditions. The Q_c is high in humid region and low in arid region, and the optimal Q_c of 0.0085 m~3/s in Beishuiqu Basin(humid region) is higher than 0.0028 m~3/s in Duilongqu Basin(semi-arid region). The suggested method provides a new application approach that can be used to determine the Q_c of a riverhead in complex geographical regions, which can also reflect the effect of hydro-climate change on rivers supply in different regions.     

The recognition of ocean red tide with hyper-spectral-image based on EMD

A new technique is introduced in this paper regarding red tide recognition with remotely sensed hyper-spectral images based on empirical mode decomposition （EMD）, from an artificial red tide experiment in the East China Sea in 2002. A set of characteristic parameters that describe absorbing crest and reflecting crest of the red tide and its recognition methods are put forward based on general pictre data, with which the spectral information of certain non-dominant alga species of a red tide occurrence is analyzed for establishing the foundation to estimate the species. Comparative experiments have proved that the method is effective. Meanwhile, the transitional area between red-tide zone and non-red-tide zone can be detected with the information of thickness of algae influence, with which a red tide can be forecast.     

RESEARCH ON FRACTAL CHARACTERISTICS OF URBAN TRAFFIC NETWORK STRUCTURE BASED ON GIS

Traffic network is an importance aspect of researching controllable parameters of an urban spatial morpholo-gy. Based on GIS, traffic network structure complexity can be understood by using fractal geometry in which thelength-radius dimension describes change of network density, and ramification-radius dimension describes complexity andaccessibility of urban network. It is propitious to analyze urban traffic network and to understand dynamic change processof traffic network using expanding fractal-dimension quantification. Meanwhile the length-radius dimension and ramifica-tion-radius dimension could be regard as reference factor of quantitative describing urban traffic network.     

Using Statistical Learning Algorithms in Regional Landslide Susceptibility Zonation with Limited Landslide Field Data

Regional Landslide Susceptibility Zonation(LSZ) is always challenged by the available amount of field data, especially in southwestern China where large mountainous areas and limited field information coincide. Statistical learning algorithms are believed to be superior to traditional statistical algorithms for their data adaptability. The aim of the paper is to evaluate how statistical learning algorithms perform on regional LSZ with limited field data. The focus is on three statistical learning algorithms, Logistic Regression(LR), Artificial Neural Networks(ANN) and Support Vector Machine(SVM). Hanzhong city, a landslide prone area in southwestern China is taken as a study case. Nine environmental factors are selected as inputs. The accuracies of the resulting LSZ maps are evaluated through landslide density analysis(LDA), receiver operating characteristic(ROC) curves and Kappa index statistics. The dependence of the algorithm on the size of field samples is examined by varying the sizes of the training set. The SVM has proven to be the most accurate and the most stable algorithm at small training set sizes and on all known landslide sizes. The accuracy of SVM shows a steadilyincreasing trend and reaches a high level at a small size of the training set, while accuracies of LR and ANN algorithms show distinct fluctuations. The geomorphological interpretations confirm the strength of SVM on all landslide sizes. Our results show that the strengths of SVM in generalization capability and model robustness make it an appropriate and efficient tool for regional LSZ with limited landslide field samples.     

Array processing-a new method to detect and correct errors on array resistivity logging tool measurements

In recent years more and more multi-array logging tools, such as the array induction and the array lateralog, are applied in place of conventional logging tools resulting in increased resolution, better radial and vertical sounding capability and other features. Multi-array logging tools acquire several times more individual measurements than conventional logging tools. In addition to new information contained in these data, there is a certain redundancy among the measurements. The sum of the measurements actu- ally composes a large matrix. Providing the measurements are error-free, the elements of this matrix show certain consistencies. Taking advantage of these consistencies, an innovative method is developed to de- tect and correct errors in the array resistivity logging tool raw measurements, and evaluate the quality of the data. The method can be described in several steps. First, data consistency patterns are identified based on the physics of the measurements. Second, the measurements are compared against the consis- tency patterns for error and bad data detection. Third, the erroneous data are eliminated and the measure- ments are re-constructed according to the consistency patterns. Finally, the data quality is evaluated by comparing the raw measurements with the re-constructed measurements. The method can be applied to all array type logging tools, such as array induction tool and array resistivity tool. This paper describes the method and illustrates its application with the High Definition Lateral Log (HDLL, Baker Atlas) instru- ment. To demonstrate the efficiency of the method, several field examples are shown and discussed.     

Prediction of Young’s modulus of weathered igneous rocks using GRNN,RVM,and MPMR models with a new index

Young’s modulus(YM)of intact rock is an important parameter in the assessment of engineering behaviours of rock masses,and it cannot always be obtained in an economical and practical manner in laboratory experiments.The main purpose of this study is to examine the capability of the minimax probability machine regression(MPMR),relevance vector machine(RVM),and generalised regression neural network(GRNN)models for the prediction of YM.The other aim is to determine the usefulness of a new index,the n-durability index(ndrb),which is based on porosity and the slake durability index.According to the regression analysis performed in this study,the n-durability index as an explanatory parameter performs better than the P-wave velocity(Vp),porosity,and slake durability index in the models,considering the results herein as well as the existing literature.According to regression error characteristic curves,Taylor diagrams,and performance indices,the best prediction model is MPMR,while the worst is the GRNN model.Although GRNN is the worst of the soft computing models,its performance is slightly better than that of the multiple linear regression(MLR)model.According to the results of the study,the MPMR and RVM models with ndrb and Vp are successful tools that can predict the YM of igneous rock materials to different degrees.     

Rapid geometry analysis for earthquake-induced and rainfall-induced landslide dams in Taiwan

Stability analysis of the dam is important for disaster prevention and reduction. The dam＇s geometry plays an important role in understanding its stability. This study develops a rapid landslide dam geometry assessment method for both earthquake-induced and rainfall-induced landslide dams based on nine real cases collected in Chinese Taipei and 214 cases collected worldwide. For simplification purposes, a landslide dam is classified into triangular or trapezoidal. The rapid landslide dam geometry assessment method in this paper uses only satellite maps and the topographic maps to get landslide area, and then analyze the dam geometry. These maps are used to evaluate the area of the landslide and the slope of the river bed. Based on the evaluation information, the proposed method can calculate dam height, the length of the dam, and the angles of the dam in both upstream and downstream directions. These geometry parameters of a landslide dam provide important information for further dam stability analysis. The proposed methodology is applied to a real landslide dam case at Hsiaolin Village. The result shows that the proposed method can be used to assess the landslide dam geometry.     

An algorithm for multi-exponential inversion of T2 spectrum in nuclear magnetic resonance

NMR logging can provide the permeability parameter and abundant stratigraphical information such as total porosity,oil,gas and water saturation,oil viscosity,etc. And these physical parameters can be obtained by T2 spectrum inversion. NMR inversion is an important part in logging interpretation. The authors describe a multi-exponential inversion algorithm,solid iteration redress technique（ SIRT）,and apply the algorithm in real data and compare the results with those based on singular value decomposition（ SVD）. It shows that SIRT algorithm is easier to be understood and implemented,and the time spent in SIRT is much shorter than that of SVD algorithm. And the non-negative property of T2 spectrum is much easier to be implemented. It can match the results based on SVD very well. SIRT algorithm can be used in T2 spectrum inversion for NMR analysis.     

STRATEGIC ENVIRONMENTAL ASSESSMENT FORSUSTAINABLE DEVELOPMENT IN URBANIZATION PROCESS IN CHINA

This paper reviewed the development of Strategic Environmental Assessment (SEA) in China, expounded its functions and regional characteristics, and discussed the targets of SEA in the process of urbanization,and the main assessment indicators, procedures, techniques and methodologies of SEA. It concluded that SEA could be improved by the following recommendations: incorporating SEA in decision-making process, selecting experimental units for SEA trial run, and developing strategic evaluation tools and techniques. SEA can provide appropriate and up-to-date information on the impact of human activity on environment in the process of urbanization, and develop a plan of action targeting implementation of intervention for the rehabilitation and preservation of the ecological stability of a city. Therefore, SEA can be a supporting tool for decision-making toward achieving sustainable development.     

ON EVOLUTION OF MAN—LAND SYSTEM IN OASIS—Taking Minqin Basin as a Case

Human beings have had a tremendous impact on natural ecosystems and are now the principal power to change the biosphere.It is logical that we should pay close attention to the interaction between human systems and environ-mental systems.Taking Minqin bsin ,Gansu Province,as a case,this paper focuses on the evolution of regional physic-cal environments and the cultural systems by which people maintain their relationships with those environments.This pa-per presents the conceptural framework for the man-land system.Expecting to accelerate the regional sustainable develop-ment,it also analyses the evolutionary mechanism of regional man-land system.On the basis of reviewing and analyzing the evolution of man-land system in Minqin basin,the paper also brings forward an adujusting mode for the studied area, which consists of three aspects:to build up a concept that economic growth must harmonize with environmental qualityˊs and land productivityˊs improvement;to make a whole planning and management in the drainage area;and to push for-ward the technique of water-saving irrigation and establish water-saving agricultural system. Itˊs meaningful for resource exploitation and sustainable development of Chinese northwestern arid area,which is represented by Minqin basin,by under-standing what great changes the basin has experienced is experiencing at the global background,and studying its time standing what great changes the basin has experienced and is experiencing at the global background,and studying its time order and territorial structure ,the systemˊs character and law of evolution,trend and the regulating ways to improve man-land relationship.     

Eco-spatial Structure of Urban Agglomeration

In terms of ecological theory, this paper makes a comprehensive analysis of the mutualism and co- evolutionary mechanism between the eco-spatial structure and socio-economic development of the urban agglomeration, and maps out optimized modes of the eco-spatial structure of the urban agglomeration. The analysis is a case study of the urban agglomeration on different levels of global, national, provincial and local scales, on the basis of those conclusions are drawn: 1) Within the scope of the urban agglomeration, the cities should be reasonably sized and appropriately densified; the spatial combination of the urban agglomeration ought to be orderly, and its eco-spatial structure ought to be optimized and efficient; the relationship between the economic society and eco-spatial environment ought to be that of mutual benefit and co-evolution. 2) “The mode of corridor group network” is a certain trend evoked from the spatial structure of urban agglomeration. 3) The eco-spatial structure of urban agglomeration under “the mode of corridor group network” can further increase the environmental capacity of urban agglomeration, and is in favor of the harmonious relationship between man and nature.     

Reverse time migration based on normalized wavefield decomposition imaging condition

With the increasing complexity of prospecting objectives,reverse time migration (RTM)has attracted more and more attention due to its outstanding imaging quality.RTMis based on two-way wave equation,so it can avoid the limits of angle in traditional one-way wave equation migration,image reverse branch,prism waves and multi-reflected wave precisely and obtain accurate dynamic information.However,the huge demands for storage and computation as well as low frequency noises restrict its wide application.The normalized cross-correlation ima-ging conditions based on wave field decomposition are derived from traditional cross-correlation imaging condition, and it can eliminate the low-frequency noises effectively and improve the imaging resolution.The practical proce-dure includes separating source and receiver wave field into one-way components respectively,and conducting cross-correlation imaging condition to the post-separated wave field.In this way,the resolution and precision of the imaging result will be promoted greatly.     

TIDAL FRONTS AND THEIR INFLUENCE ON PRAWN POSTLARVAE IMMIGRATION IN ALBATROSS BAY, THE GULF OF CARPENTARIA, AUSTRALIA

Studies of dynamics in Albatross Bay, the Gulf of Carpentaria, Australia, show strong tides of which M_2is the dominant constituent in the region. Tidal fronts are present and they can be modeled by a 2-Dtidal model. Density currents have also been observed with seasonal variations due to the influence ofwet or dry seasons. This paper reviews the effects of these dynamics on the migration of prawn postlarvae in AlbatrossBay and its estuaries. It is found that through its vertical movement triggered by change of salinity theprawn postlarvae can be transported from the coast to the estuary by tides within 2.8 weeks. Howeverthis horizontal displacement mechanism may be destroyed by the tidal front in Albatross Bay. Densitycurrents may alter prawn postlarvae positions in Albatross Bay on a seasonal scale.     

STUDY ON THE SUBSIDING LAND EXTRACTION FROM LANDSAT TM IMAGE SUPPORTED BY GIS AND DOMAIN KNOWLEDGE

The subsiding land can be extracted from Remote Sensing image based on its spectral and spatial fea-tures,The features of subsiding land caused by mining,especially its RS information features and relative knowledge are proposed Three methods can be used to extract subsiding land from RS image ,The first is to categorize the region into different blocks (or layers) according to their features and apply corresponding strategies for each block,the second is to identify the changeable region based on GIS firstly and then to classify those regions.and the third is to post-process the classified image by traditional methods or ANN (Artificial Neural Network) methods based on domain knowledge and GIS.Two direct extraction methods are intrduced also.One is the extraction based on the water accumulating property of subsiding land ,and the other is based on the dynamic change of land cover in subsiding land.     

Delineation of an Urban Community Life Circle Based on a Machine-Learning Estimation of Spatiotemporal Behavioral Demand

Delineating life circles is an essential prerequisite for urban community life circle planning. Recent studies combined the environmental contexts with residents’ global positioning system(GPS) data to delineate the life circles. This method, however, is constrained by GPS data, and it can only be applied in the GPS surveyed communities. To address this limitation, this study developed a generalizable delineation method without the constraint of behavioral data. According to previous research, the community life circle consists of the walking-accessible range and internal structure. The core task to develop the generalizable method was to estimate the spatiotemporal behavioral demand for each plot of land to acquire the internal structure of the life circle, as the range can be delineated primarily based on environmental data. Therefore, behavioral demand estimation models were established through logistic regression and machine learning techniques, including decision trees and ensemble learning. The model with the lowest error rate was chosen as the final estimation model for each type of land. Finally, we used a community without GPS data as an example to demonstrate the effectiveness of the estimation models and delineation method. This article extends the existing literature by introducing spatiotemporal behavioral demand estimation models, which learn the relationships between environmental contexts, population composition and the existing delineated results based on GPS data to delineate the internal structure of the community life circle without employing behavioral data. Furthermore, the proposed method and delineation results also contributes to facilities adjustments and location selections in life circle planning, people-oriented transformation in urban planning, and activity space estimation of the population in evaluating and improving the urban policies.     

Using DNA damage to monitor water environment

DNA damage of aquatic organisms living in polluted environments can be used as a biomarker of the genotoxicity of toxic agents to organisms. This technique has been playing an important role in ecotoxicologlcal study and environmental risk assessment. In this article, main types of DNA damage caused by pollutants in water environments were reviewed; methods of detecting DNA damage were also documented for water environmental monitoring.