Softs of Fildes Peninsula, King George Island, the maritime Antarctic: Part I. Formation processes and pedogenetic particularities

Based on the data obtained from tens of the investigated soil profiles scattered over the Fildes Peninsula, King George Island, the maritime Antarctic, the soil-forming processes were discussed. It was concluded that on the weathering crusts resulted from various physical courses, the strong freeze-thaw action within regolith, significant organic matter accumulation, evident leaching and illuviation, as well as initial argillification dominated the formation and development of the soils on the Fildes Peninsula. Furthermore, this study indicated that the juvenility of genesis, poor profile-expression, variation in column thickness, etc. characterized the pedogenetic features of the soils of the Fildes Peninsula.     

Models of travel demand with endogenous preference change and heterogeneous agents

In the literatures of regional science, urban economics, and urban development planning, a working assumption is that individuals respond to incentives and regulations, given their preferences. Models for planning and policy analyses are used to consider what might occur if the incentives or regulations were different. In these models, however, preferences are usually assumed to be given and stable, and agents are usually assumed to be homogeneous. This paper focuses on the implications of making preferences in models of policy implementation endogenously determined and time varying heterogeneous agents. We consider first the recent literature on intertemporal choice and preference change, which cuts across many disciplines, and more briefly the literature on norm-regarding behavior. We then elaborate a simple model of transportation demand—from a static to a dynamic orientation, from fixed and exogenously given preferences of strictly self-regarding agents to endogenously determined and policy-induced preferences of heterogeneous agents—and illustrate its characteristics with simple numerical examples.     

基于交互多模型的水下目标跟踪方法

According to the requirements of real-time performance and reliability in underwater maneuvering target tracking as well as clarifying motion features of the underwater target, an interacting multiple model algorithm based on fuzzy logic inference （FIMM） is proposed. Maneuvering patterns of the target are represented by model sets, including the constant velocity model （CA）, the Singer mode~, and the nearly constant speed horizontal-turn model （HT） in FIMM technology. The simulation results show that compared to conventional IMM, the reliability and real-time performance of underwater target tracking can be improved by FIMM algorithm.     

Analytical methods for error propagation in planar space–time prisms

The space–time prism demarcates all locations in space–time that a mobile object or person can occupy during an episode of potential or unobserved movement. The prism is central to time geography as a measure of potential mobility and to mobile object databases as a measure of location possibilities given sampling error. This paper develops an analytical approach to assessing error propagation in space–time prisms and prism–prism intersections. We analyze the geometry of the prisms to derive a core set of geometric problems involving the intersection of circles and ellipses. Analytical error propagation techniques such as the Taylor linearization method based on the first-order partial derivatives are not available since explicit functions describing the intersections and their derivatives are unwieldy. However, since we have implicit functions describing prism geometry, we modify this approach using an implicit function theorem that provides the required first-order partials without the explicit expressions. We describe the general method as well as details for the two spatial dimensions case and provide example calculations.     

Distribution of Indus born mica along Gulf of Kachchh coast: Implications in understanding current dynamics

Indus is one of the major sources of sediments to the Gulf of Kachchh. Yet only its <63 micron fraction is studied in detail with regards to the offshore current dynamics. Hence here we present our study on characteristic signature of the Indus sediment load (i.e. mica minerals) in >63 micron size fraction along the coast of Gulf of Kachchh. The spatial distribution of mica minerals along the Gulf of Kachchh coast was studied which showed in general decreasing trend as we move along the northern and southern coast of the Gulf of Kachchh but, an increase in amount near the southern mouth at Okha. The study shows that the earlier proposed tidal barrier is ineffective in restricting movement of mica across the mouth of the gulf due to its characteristic transport mechanism. Also the presence of mudflats along the gulf of Kachchh coast plays a vital role as sediment receptors in the active sediment transport processes and mica minerals prove to be a promising simple tracer in studying the Indus born sediments in the region.     

A comparative study of sequential minimal optimization-based support vector machines,vote feature intervals,and logistic regression in landslide susceptibility assessment using GIS

Landslide susceptibility assessment using GIS has been done for part of Uttarakhand region of Himalaya (India) with the objective of comparing the predictive capability of three different machine learning methods, namely sequential minimal optimization-based support vector machines (SMOSVM), vote feature intervals (VFI), and logistic regression (LR) for spatial prediction of landslide occurrence. Out of these three methods, the SMOSVM and VFI are state-of-the-art methods for binary classification problems but have not been applied for landslide prediction, whereas the LR is known as a popular method for landslide susceptibility assessment. In the study, a total of 430 historical landslide polygons and 11 landslide affecting factors such as slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to rivers, distance to lineaments, and rainfall were selected for landslide analysis. For validation and comparison, statistical index-based methods and the receiver operating characteristic curve have been used. Analysis results show that all these models have good performance for landslide spatial prediction but the SMOSVM model has the highest predictive capability, followed by the VFI model, and the LR model, respectively. Thus, SMOSVM is a better model for landslide prediction and can be used for landslide susceptibility mapping of landslide-prone areas.     

Non-Linear Theory and Power-Law Models for Information Integration and Mineral Resources Quantitative Assessments

Singular physical or chemical processes may result in anomalous amounts of energy release or mass accumulation that, generally, are confined to narrow intervals in space or time. Singularity is a property of different types of non-linear natural processes including cloud formation, rainfall, hurricanes, flooding, landslides, earthquakes, wildfires, and mineralization. The end products of these non-linear processes can be modeled as fractals or multifractals. Hydrothermal processes in the Earth’s crust can result in ore deposits characterized by high concentrations of metals with fractal or multifractal properties. Here we show that the non-linear properties of the end products of singular mineralization processes can be applied for prediction of undiscovered mineral deposits and for quantitative mineral resource assessment, whether for mineral exploration or for regional, national and global planning for mineral resource utilization. In addition to the general theory and framework for the non-linear mineral resources assessment, this paper focuses on several power-law models proposed for characterizing non-linear properties of mineralization and for geoinformation extraction and integration. The theories, methods, and computer system discussed in this paper were validated using a case study dealing with hydrothermal Au mineral potential in southern Nova Scotia, Canada.