Zone Division Research Articles

Single Earthquake Bond Pricing Framework with Double Trigger Parameters Based on Multi Regional Seismic Information

The investor interest in multi-regional earthquake bonds may drop because high-risk locations are less appealing to investors than low-risk ones. Furthermore, a single parameter (earthquake magnitude) cannot accurately express the severity due to an earthquake. Therefore, the aim of this research is to propose valuing a framework for single earthquake bonds (SEB) using a double parameter trigger type, namely magnitude and depth of earthquakes, based on zone division according to seismic information. The zone division stage is divided into two stages. The first stage is to divide the covered area based on regional administrative boundaries and clustering based on the earthquake disaster risk index (EDRI), and the second stage involves clustering based on magnitude and depth of earthquakes and distance between earthquake events using the K-Means and K-Medoids algorithms. The distribution of double parameter triggers is modeled using the Archimedean copula. The result obtained is that the price of SEB based on the clustering result of EDRI categories and K-Means is higher than the price obtained by clustering EDRI categories and K-Medoids with maturities of less than 5 years. The result of this research is expected to assist the Special Purpose Vehicle in determining the price of SEB.

Grading Assessment of Temperature Thresholds and Suitable Temperature Zone Division for Typical Species in Marine Ranching

The construction of Marine Ranching is a safe, efficient, and sustainable way to achieve marine environmental protection and biological resource utilization. In recent years, the construction of Marine Ranching in coastal provinces in China has developed rapidly, but due to the late start, the ability to guarantee ecological security is weak. In this paper, Pseudosciaena crocea was selected as typical aquaculture species in Marine Ranching of the East China Sea, by integrating the experimental data from former studies effect of temperature on the Specific Growth Rate of this aquaculture species was evaluated via performing quadratic regression fitting, the results reveal that the optimum temperature ranges for Pseudosciaena crocea are 25.3– 30.7°C, the preferred range is 18.9–25.3°C, the unsuitable temperature ranges were lower than 18.9°C and higher than 30.7°C. Further, based on the aquaculture specifications, commodity specifications , and aquaculture cycle, the Specific Growth Rate to be achieved by the aquaculture species is determined, and the corresponding temperature threshold and graded evaluation were obtained, finally, the suitable water temperature distribution area of the Pseudosciaena crocea was evaluated based on the hydrological data observed in the East China Sea during 2020–2021.

ANALISIS PEMETAAN ZONA KONSERVASI AIR TANAH DI PROVINSI SULAWESI SELATAN MENGGUNAKAN APLIKASI QGIS

This study is titled Mapping Analysis of Groundwater Conservation Zones in South Sulawesi Province Using QGIS Application. This study aims to digitize the map of groundwater conservation zones in 13 regencies/cities in South Sulawesi Province and to find out the division of groundwater conservation zones in 13 regencies / cities in South Sulawesi while calculating the area of each zone. The method used in this study is to use data processing methods, where secondary maps in jpeg form are digitized and then analyzed to obtain results in the form of zone division and area of each zone. From the results of research that has been obtained, there are 5 types of groundwater conservation zones, namely critical zones, rare zones, vulnerable zones, safe zones, and addition zones. In 13 regencies/cities in South Sulawesi Province the most extensive zone is a safe zone with an area of 818912.42 hectares. However, the second largest zone (59626,923 hectares) is a vulnerable zone where in this zone can no longer absorb properly. For this reason, there needs to be a preventive way to overcome the problem.

Refined Urban Functional Zone Mapping by Integrating Open-Source Data

The determination of a reasonable spatial analysis unit is an essential step in urban functional zone (UFZ) division, which significantly affects the results. However, most studies on the division of functional zones are based on excessively large spatial units, such as blocks or traffic analysis zones (TAZs), which easily overlook the detailed characteristics of urban regions and introduce bias to the research conclusion. To address this issue, a refined zone segmentation method, namely, the Voronoi diagram for the polygon method, was proposed to generate refined spatial analysis units. Afterward, the functional topics of the spatial analysis unit were classified by a multiclass support vector machine (SVM) to produce the final UFZ map, where the functional topics of each spatial unit were obtained by coupling latent Dirichlet allocation (LDA). To verify the effectiveness of the proposed method, experiments were conducted in Beijing, China. The results indicated that the proposed segmentation method can generate fine-scale spatial units and provide fine-grained and higher accuracy UFZs (overall accuracy = 84%; kappa = 0.82).

Privacy-Preserving Cross-Zone Ride-Matching for Online Ride-Hailing Service

Although online ride-hailing supplies the nearest taxi matching for riders, the potential leakage of riders’ hobbies and physical condition raises privacy concerns. Since most of the privacy-preserving schemes do limited matching of riders and drivers in the same zone, in this paper, we propose a novel privacy-preserving cross-zone ride-matching scheme, namely, Cride, which extends one zone into multiple neighboring zones. Based on the zone division of a city, CRide allows distance computation between rider and driver across adjacent zones in the encrypted domain. Furthermore, towards efficiency improvement, a ciphertext packing technique is introduced. Theoretical analysis and experimental results suggest that CRide achieves a high ride-matching accuracy and acceptable efficiency without leaking privacy.

Late Quaternary Activity: Kouma Fault

The Kouma Fault, located at the northern foot of the Mangshan Mountain in Luoyang City, Henan Province, China, is an active fault newly discovered in the field seismic geological survey. The Kouma Fault is a normal fault that is approximately 30 km in length, near the east-west strike, and the north dip. The study on the nature of the Quaternary activity of the Kouma Fault is of great significance to the potential seismic source zone division, urban and rural land planning, site selection for major projects, the assessment and prevention of earthquake disaster risk. Through a series of seismic geological surveys, 12 fault outcrops were discovered, which were all distributed on the slopes of the loess gullies. The Kouma Fault cuts loess, clay, sand, and gravel. The fault planes and scratch can be seen clearly. The filling in the fault zone is dominated by clay, loess, and pebbles, among others. When the width of the fault zone is smaller (10–30 cm), most clay in the zone is laminated in occurrence. The laminated surface is almost parallel to the fault plane and the long axis direction of pebbles in the fault zone is almost parallel to the fault plane. When the width of the fault zone is greater (for instance, 1.6 m), the clay and loess in the fault zone are massive. The composite drilling geological section and trench reveal two paleoseismic events of the Kouma Fault, the paleoseismic event I occurred at the fault in the Middle Pleistocene; the paleoseismic event II occurred at the fault in the later period of the Late Pleistocene, and the vertical offset of the upper Pleistocene bottom boundary is 13.57 m. Based on the OSL age data in the fault G1 outcrop of the fault and TC1 and TC2 trenches, the latest activity age of the Kouma Fault may be defined within the range of (38.84 ± 4.69) to (40.90 ± 4.45) kaB.P. The latest active age of the Kouma fault is the late of Late Pleistocene. The average vertical slip rate of the fault during the Late Pleistocene has been 0.11 mm/a.

Data-driven traffic zone division in smart city: Framework and technology

The development of smart city and smart mobility has put forward requirements for the traffic zone division. The traditional traffic zone division is limited by data with low update frequency and low accuracy, which can hardly meet the needs. With the progress of ICTs, data-driven traffic zone division is developing gradually. Many scholars have tried to divide traffic zone through GPS and CDR. However, there are still shortcomings in the data-driven traffic zone division. On the one hand, different scholars use different data sources and division methods, and there is no unified framework that can describe different ways. On the other hand, many technical problems such as unsatisfactory zone boundary, single data source and clustering algorithm only considering similarity have not been solved well. Therefore, this paper first proposes a technical framework for data-driven traffic zone division, which describes the different methods uniformly by dividing the data-driven traffic zone division into four parts. Based on this framework, this paper proposes a traffic zone division method based on CSD, which solves the above technical problems successfully, and finally is applied in the traffic zone division of Foshan, China.

A novel zone-based machine learning approach for the prediction of the performance of industrial flares

Industrial flares are used to burn off unwanted gas during operation. If not combusted completely, intermediate products or incomplete combustion products are formed, and they will cause significant environmental and health issues. The EPA Refinery Sector Rule emphasizes smokeless flaring with combustion efficiency (CE) ≥ 96.5% and destruction and removal efficiency (DRE) ≥ 98% for all types of flares in the refineries. In this research, a novel zone-based modeling approach was developed for predicting CE and Opacity of steam assist flares. Flare CE data were partitioned into two zones based on the partition of the carbon and hydrogen atomic ratio (CHR), then random forest (RF) and Catboost algorithms were used to develop CE predictive models, respectively. This CHR-based zone partition has a clear implication in engineering. It was also found out that no zone division for flare Opacity prediction is needed, and both RF and Catboost algorithms generated good prediction results. All the models match extremely well with all the original experimental data. These predictive models under the same zone-partition use either RF or Catboost algorithm can both give superior prediction accuracy. This demonstrates the simplicity, general applicability, and high reliability of the zone-based ML approach.

The Effect of Serpent 2 Calculation Parameters on Evaluated Spent Nuclear Fuel Source Term

Abstract The estimation of spent nuclear fuel source term (decay heat, reactivity, nuclide inventory, etc.) has several sources of uncertainty such as uncertainties in nuclear data, uncertainties in the operation history, and choice of calculation parameters. In this work the effect of calculation parameters is studied by estimating the source term with the built-in burnup capability of serpent. The effect of the following parameters is considered: depletion zone division, burnup steps, unresolved resonance probability table sampling, Doppler-Broadening Rejection Correction (DBRC) and energy-dependent branching ratios. As a test case, a 2D Boiling Water Reactor (BWR) fuel assembly was modeled by first running a burnup calculation followed by a decay calculation. The following source term components were considered when investigating the effect of the studied parameters: total decay heat, photon emission rate and spontaneous fission rate. In general, the differences resulting from the use of different parameter variations were small for all three studied source term components. For the decay heat largest absolute relative difference was approximately 0.6% and for the photon emission rate approximately 1.1%. For the spontaneous fission rate maximum absolute relative difference of nearly 8% was observed. For all three components the variation of the depletion zone division resulted in the largest relative differences. Clear differences were also observed for burnup step length and DBRC variations. The use of unresolved resonance probability table sampling and energy-dependent branching ratios had an insignificant effect on the studied source term components.

ADMINISTRATIVE-TERRITORIAL DIVISION IN THE RSFSR (1920s–1930s)

The article discusses the process of changes in the administrative-territorial division of the RSFSR in the 20s–30s of the XX century. The article distinguishes several stages. Relying on historiography and sources, the author tries to analyze and summarize the historical experience in reorganization of the RSFSR administrative-territorial system from the “spontaneous” emergence of new administrative-territorial units against the background of the existing old-regime administrative-territorial units to the first consolidation reform, when governorates, volosts, uyezds were finally liquidated, as well as the second reform of the Soviet government, the essence of which was fragmentation. The study shows that all the transformations of the young Soviet republic in the studied area took place in connection with changes in the form of power organization, as well as with a change in the principles of regional policy and the direction of economic development of the country’s territories. At the present stage, opinions on the return to the governorate administration began to appear more and more often in the research literature, which became the subject of a detailed analysis by the author of this work. The article suggests and substantiates possible directions for improving the administrative-territorial division of the RSFSR after the reform in the 1920s–1930s in order to implement the policy of the Bolsheviks and further territorial development of the country. The main approaches to the formation of the RSFSR administrative-territorial division, identification of local socio-economic systems, determination of the optimal size of administrative-territorial entities are highlighted. The interrelation between the administrative-territorial division and economic zone division in Russia is substantiated.

Anti-collision zone division based hazard avoidance guidance for asteroid landing with constant thrust

Future asteroid landing and sample return missions will seek for landing sites with high scientific value, such as hazardous terrains, which means that the spacecraft needs the ability of autonomous hazard avoidance. Nowadays, the guidance algorithm based on artificial potential function plays an increasingly important role in hazard avoidance, but the local minimum problem makes that the spacecraft cannot reach the desired target landing point in some complex terrains. In this paper, a novel hazard avoidance guidance method which improves the traditional artificial potential function is developed. This method focuses on the impact zone analysis of hazards and defines anti-collision zone. In addition, a new repulsive potential function in logarithmic form is designed with continuity and fast numerical change rate in the anti-collision zone on this basis, and the problem of spacecraft falling into local minimum zone in complex terrains can be effectively avoided. Considering the practical application of constant thrust engine, the hazard avoidance control law with constant thrust sliding mode is designed to reduce switching frequency and fuel consumption. The performance of the proposed guidance law is verified through a set of simulations, as well as the global stability of the control system under uncertainty and perturbation conditions.

Improved object-based convolutional neural network (IOCNN) to classify very high-resolution remote sensing images

ABSTRACT The land cover classification of very high-resolution (VHR) remote sensing images is a challenging task. VHR images depict many complex objects with various shapes in complicated contexts. The deep learning-based method is a solution for such dif- ficult task and feature extraction. Nevertheless, this method cannot efficiently deal with images with complex scene structures. An improved object-based convolutional neural network (IOCNN) is designed to classify VHR images with zone division and convolutional position sampling techniques in this study. The method can achieve the best performance of each zone at its own optimized scales. Based on multi-scale convolutional deep features extracted from VHR images, the objects with irregular shapes can be classified using the approach. In this study, the zone-level scale adaption and multi-scale recognition of complex objects are achieved. The performance of IOCNN is compared with the state-of-the-art methods for feature extraction, including five object-based CNN approaches and two fully convolutional networks (FCNs). The results show that the classification performance of IOCNN is considerably stronger than that of state-of-the-art methods. The overall accuracies of the land cover classification in IOCNN are 91.65% and 93.49% on two tested images. The results demonstrate the practicability of IOCNN.

Biostratigraphy and reservoir characteristics of the Ordovician Wufeng Formation—Silurian Longmaxi Formation shale in the Sichuan Basin and its surrounding areas, China

Through graptolite identification in profiles, graptolite zone division, contour map compilation, and analysis of mineral composition, TOC content, lamina distribution features of shale samples, the biostratigraphic and reservoir characteristics of Ordovician Wufeng Formation—Silurian Longmaxi Formation in the Sichuan Basin and its peripheral are sorted out. There are 4 graptolite zones (WF1 to WF4) in Wufeng Formation and 9 (LM1 to LM9) in Longmaxi Formation, and the different graptolite zones can be calibrated by lithology and electrical property. The shale layers of these graptolite zones have two depocenters in the southwest and northeast, and differ in mineral composition, TOC, and lamina types. Among them, the graptolite zones of lower WF2 and WF4 are organic matter-poor massive hybrid shale, the upper part of WF1–WF2 and WF3 have horizontal bedding hybrid shale with organic matter, the LM1–LM4 mainly consist of organic-rich siliceous shale with horizontal bedding, and the LM5–LM9 graptolite zones consist of organic-lean hybrid shale with horizontal bedding. The mineral composition, TOC and lamina types of shale depend on the paleo-climate, paleo-water oxidation-reduction conditions, and paleo-sedimentation rate during its deposition. Deposited in oxygen-rich warm water, the lower parts of WF1 and WF2 graptolite zones have massive bedding, low TOC and silicon content. Deposited in cooler and oxygen-rich water, the WF4 has massive bedding, high calcium content and low TOC. Deposited in anoxic water with low rate, the upper part of WF2, WF3, and LM1–LM4 are composed of organic rich siliceous shale with horizontal bedding and high proportion of silt laminae. Deposited in oxygen rich water at a high rate, the graptolite zones LM5–LM9 have low contents of organic matter and siliceous content and high proportions of silt lamina.

Hough Transform-Based Angular Features for Learning-Free Handwritten Keyword Spotting.

Handwritten keyword spotting (KWS) is of great interest to the document image research community. In this work, we propose a learning-free keyword spotting method following query by example (QBE) setting for handwritten documents. It consists of four key processes: pre-processing, vertical zone division, feature extraction, and feature matching. The pre-processing step deals with the noise found in the word images, and the skewness of the handwritings caused by the varied writing styles of the individuals. Next, the vertical zone division splits the word image into several zones. The number of vertical zones is guided by the number of letters in the query word image. To obtain this information (i.e., number of letters in a query word image) during experimentation, we use the text encoding of the query word image. The user provides the information to the system. The feature extraction process involves the use of the Hough transform. The last step is feature matching, which first compares the features extracted from the word images and then generates a similarity score. The performance of this algorithm has been tested on three publicly available datasets: IAM, QUWI, and ICDAR KWS 2015. It is noticed that the proposed method outperforms state-of-the-art learning-free KWS methods considered here for comparison while evaluated on the present datasets. We also evaluate the performance of the present KWS model using state-of-the-art deep features and it is found that the features used in the present work perform better than the deep features extracted using InceptionV3, VGG19, and DenseNet121 models.

Typical Zone Division of Karst Kevelopment in China

Determining the typical area of karst development in China would provide positive reference for theoretical research of engineering geology in karst areas in China. By quantitative or semi-quantitative analyzing on the distribution characteristics, climatic conditions and formation lithology of carbonate rocks in China, regional difference characteristics of karst development in carbonate rock regions in China is evaluated, and finnally the representative karst zone in China is reasonably and theoretically delineated and demonstrated. Results show that: the pure Carbonate rocks in southwest of China in tropical and subtropical climate zone which are very favorable for karst development, distributes continuously and massively, and therefore can be considered as the most typical karst area,. this is also a very explanation that karst morphology develops completely. Especially, karst in the zone including Guangxi Zhuang Autonomous Region, southeast and north-east of Guizhou province, and southeastern of Chongqing Municipality, is the most representative karst region in southwest of china because of its the purest carbonate rocks.

Sequence-to-sequence deep learning model for building energy consumption prediction with dynamic simulation modeling

This study used a more detailed dataset approach to address the limitations of existing building energy prediction methods and to predict building energy demand more accurately. The EnergyPlus dynamic simulation program closely modeled building envelope performance, zone division, and heating, ventilation, and air conditioning systems. Building energy simulation used actual weather data and generated occupancy data. The occupancy, lighting, and equipment schedules for each zone were generated in 5-min intervals using the Lawrence Berkeley National Laboratory occupancy simulator. Summer electric power consumption based on Internet of Things information from the testbed building validated the model. When applying generated occupancy, lighting, and equipment schedules, the mean bias error of simulation similarly improved to 4.73%, and the coefficient of variation of the root-mean-squared error (Cv(RMSE]) improved to 12.26%. Subsequently, a demand prediction model was constructed as a sequence-to-sequence (seq2seq) model using long short-term memory (LSTM) cells in recurrent neural network (RNN) algorithms, then its accuracy was evaluated. In the seq2seq model, the learning performance based on the EnergyPlus data exhibited an RMSE of 4.48% and a weighted average percentage error of 3.07%. As a result of applying prediction methods while changing climate scenarios, prediction performance also satisfied statistically significant levels. The occupancy information and solar radiation were determined to exert the greatest influence on the building energy demand prediction. • The occupant schedule for each zone was generated in 5-min intervals using the LBNL occupancy simulator. • When applying the generated internal schedules, the MBE improved to 4.73%, and the Cv(RMSE) improved to 12.26%. • In the seq2seq model, the learning performance based on the EnergyPlus data exhibited an RMSE of 4.48% and a WAPE of 3.07%. • The occupant information and solar radiation were determined to exert the greatest influence on energy demand prediction.

Multi-zone prediction analysis of city-scale travel order demand.

Taxi order demand prediction is of tremendous importance for continuous upgrading of an intelligent transportation system to realise city-scale and personalised services. An accurate short-term taxi demand prediction model in both spatial and temporal relations can assist a city pre-allocate its resources and facilitate city-scale taxi operation management in a megacity. To address problems similar to the above, in this study, we proposed a multi-zone order demand prediction model to predict short-term taxi order demand in different zones at city-scale. A two-step methodology was developed, including order zone division and multi-zone order prediction. For the zone division step, the K-means++ spatial clustering algorithm was used, and its parameter k was estimated by the between–within proportion index. For the prediction step, six methods (backpropagation neural network, support vector regression, random forest, average fusion-based method, weighted fusion-based method, and k-nearest neighbour fusion-based method) were used for comparison. To demonstrate the performance, three multi-zone weighted accuracy indictors were proposed to evaluate the order prediction ability at city-scale. These models were implemented and validated on real-world taxi order demand data from a three-month consecutive collection in Shenzhen, China. Experiment on the city-scale taxi demand data demonstrated the superior prediction performance of the multi-zone order demand prediction model with the k-nearest neighbour fusion-based method based on the proposed accuracy indicator.

A scalable electrothermal model using a three‐dimensional thermal analysis model forGaN‐on‐diamondhigh‐electron‐mobility transistors

AbstractA scalable eletrothermal model is necessary for a high‐power amplifier design for accurate accounting of thermal effects. Toward this end, this study presents a scalable large‐signal model of gallium‐nitride (GaN) high‐electron‐mobility transistors (HEMTs) on diamond substrates. First, a three‐dimensional (3D) thermal analysis model was established. Then, a modified eletrothemal expression was proposed to predict thermal effects including scalability. For validation, the proposed model was implemented into quasi‐physics zone division (QPZD) large‐signal model. Results show that the proposed model accurately predicts theI–Vcurves of devices with different gate widths, gate fingers, and ambient temperatures. Moreover, the results of the model are consistent with on‐wafer measurements of 2 × 125, 4 × 125, and 10 × 50 μm of GaN‐on‐diamond HEMTs, indicating that the proposed model accurately predicts the DC–IVcurves, scattering parameters, and large‐signal performance. The findings of this study can be useful for designing microwave diamond–GaN HEMT circuits.

Numerical Prediction and Experimental Validation of the Low-temperature Oxidation Characteristics of Coal in a Large-scale Furnace in an Adiabatic Environment

ABSTRACT It is of great significance to study the characteristics of low-temperature oxidation of coal in an adiabatic environment for the prediction and control of coal spontaneous combustion. Coal spontaneous combustion experimentation can better simulate the field conditions and reflect and test the actual process underlying the spontaneous combustion of coal. Numerical simulation methods can provide a systematic solution for research on the dynamic evolution process of coal spontaneous combustion. In this study, a large-scale coal spontaneous combustion test bench was employed to test the oxidation heating characteristics and gas generation laws of Huangling coal samples from 303–443 K, revealing the spatiotemporal evolution characteristics of the gas concentration and temperature fields. Based on the theory of computational fluid dynamics (CFD), a mathematical model of the spontaneous combustion of coal in the adiabatic state is therefore constructed. The evolution law and distribution characteristics of the temperature and gas concentration fields of coal in the experimental furnace are simulated by Fluent. The results indicate that the position of the highest coal temperature in the experimental furnace mainly changed along the central axis and gradually moved down toward the air inlet as the coal temperature continued to rise. Above the critical temperature (353 K), the high-temperature point mobility was high, and only one-tenth of the ignition period could be utilized to travel along with more than one-third of the original distance from the intake side. In the furnace, O2, CO, and CO2 were distributed in the upper and lower layers at different concentration values. The concentration of O2 gradually decreased from the inlet side along the gas flow direction, but the concentrations of CO and CO2 demonstrated the opposite behavior. When the temperature exceeded 120°C, the consumption of O2 and the production of CO and CO2 gases were mainly distributed in the furnace body below 0.65 m. The simulation results are in line satisfactorily with the experimental results. This study provides theoretical support for field index gas and three zone division. The development of the model can improve the existing theory of coal spontaneous combustion and find a more systematic and reasonable criterion for judging coal spontaneous combustion.

A Big-Data-Driven Framework for Parking Demand Estimation in Urban Central Districts

Parking planning is a key issue in the process of urban transportation planning. To formulate a high-quality planning scheme, an accurate estimate of the parking demand is critical. Most previous published studies were based primarily on parking survey data, which is both costly and inaccurate. Owing to limited data sources and simplified models, most of the previous research estimates the parking demand without consideration for the relationship between parking demand, land use, and traffic attributes, thereby causing a lack of accuracy. Thus, this study proposes a big-data-driven framework for parking demand estimation. The framework contains two steps. The first step is the parking zone division method, which is based on the statistical information grid and multidensity clustering algorithms. The second step is parking demand estimation, which is extracted by support vector machines posed in the form of a machine learning regression problem. The framework is evaluated using a case in the city center in Cangzhou, China.