Spatial Analysis of Landscape Dynamics to Meteorological Changes in the Gulf of Mexico Coastal Region

The forest ecosystem is a dominant landscape in the Gulf of Mexico (GOM) coastal region. Currently, many studies have been carried out to identify factors that drive forest dynamics. Changes in meteorological conditions have been considered as the main factors affecting the forest dynamics. For this study, the statistical regression analysis was used for modeling forest dynamics. Meteorological impact analysis was driven by observed data from PRISM (parameter-elevation regressions on independent slopes model) climate dataset. The forest dynamics was characterized by an indicator, the normalized difference vegetation index (NDVI). The objectives of this study are to 1) to specify and estimate statistical regression models that account for forest dynamics in the Golf of Mexico coastal region, 2) to assess which model used to capture the relationship between forest dynamics and its explanatory variables with the best explanatory power, and 3) to use the best fitted regression model to explain forest dynamics. By using fixed-effects regression methods: ordinary least squares (OLS) and geographically weighted regression (GWR), the sample-point-based regression analysis showed that meteorological factors could generally explain more than half of variation in forest dynamics. In respect of the unexplained variation of forest dynamics, the necessity of using soil to explain forest dynamics was then discussed. The result suggested that the forest dynamics could be explained by both meteorological parameters and soil texture. One of the basic considerations in this study is to include the spatiotemporal heterogeneity caused by seasonality and forest types. The model explanatory power was found differ among forest types (spatially) and seasons (temporally). By constructing regression models with randomly varying intercepts and varying slopes, the linear mixed-effects model (LMM) was fitted on composite county-based data (e.g., precipitation, temperature and NDVI). The use of LMMs was proved to be appropriate for describing forest dynamics to mixed-effects induced by meteorological changes. Based on this finding, I concluded that meteorological changes could play a significant role in forest dynamics through both fixed-effects and random-effects

Modeling of hydraulic fracture propagation and height growth in layered formations

Microseismic observations and other field data suggest that hydraulic fractures are often not contained within a single layer. Acoustic log data show rock mechanical properties typically vary significantly between layers, leading to confining stress contrasts across bedding planes. Simulating the propagation of multiple hydraulic fractures in such a multi-layer environment represents a unique challenge when trying to achieve both numerical efficiency and accuracy. Among the concerning factors, fracture height growth and containment is increasingly drawing researchers’ attention. In this master’s thesis, an improved simplified 3D (S3D) hydraulic fracture propagation model is developed. The improved model is capable of simulating single and multiple non-planar fracture propagation and height growth in layered reservoir formations with different in-situ stresses, by employing a series of novel methods developed in this study. The S3D displacement discontinuity method (DDM) is extended to model fractures of non-uniform height by applying a new 3D correction factor. A stress correction factor is proposed to calculate the influence of stress contrast between layers on fracture opening. In the fracture propagation model, fracture width profile along vertical direction in a layered reservoir is calculated by a semi-analytical method introduced in this study. A novel fracture height growth methodology is then developed to predict fracture height in layered formations. The geometric transformation from tip propagation velocity to fracture height growth rate enables the model to avoid common pitfalls of over-predicting the fracture height. Test cases demonstrate that the improved S3D method can accurately model multiple static fractures with non-uniform fracture height, vertical offset and in-situ stress variation, while maintaining the considerably lower computation time. The proposed improved fracture propagation model is used to simulate the fracture propagation footprint recorded by a fracture experiment. Simulation results from the new fracture propagation model compare favorably with both the experimental data and simulation results from other researchers.Petroleum and Geosystems Engineerin

Characteristics Description of Potential User Segments on the E-Commerce Website oriented to Precision Marketing

In the increasingly competitive environment between e-commerce companies, for more accurate implementation of marketing strategies, e-commerce websites often choose to subdivide the consumer market of the enterprise to identify site users’ characteristics to find their needs. In this paper, we subdivide consumer market from the four dimensions of behavior, geography, demography and psychology and propose a model to describe the characteristics of potential user market segments. Based on the web log data and user transaction data, a classification algorithm is used to analyze user behavior data in Web log to find the potential user segments and the user\u27s descriptive characteristics in user transaction data are clustered to obtain the distribution of consumer characteristics under various product categories, then we use the product categories in e-commerce website as an intermediary to give every single potential user in potential user market segments the descriptive characteristics, which can provide data support for the realization of precision marketing. The proposed model is applied to the actual data of a certain insurance e-commerce platform, and based on the results, we gain some implications for marketing of the e-commerce website

Learning Humor Through AI: A Study on New Yorker\u27s Cartoon Caption Contests Using Deep Learning

My research focuses on predicting a cartoon caption\u27s wittiness using multi-modal deep learning models. Nowadays, deep learning is commonly used in image captioning tasks, during which the machine has to understand both natural languages and visual pictures. However, instead of aiming to describe a real-world scene accurately, my research seeks to train computers to learn humor inside both natural languages and visual images. Cartoons are the artistic medium that supposes to deliver visual humor, and their captions are also supposed to be interesting to add to the fun. Thus, I decided to use research on cartoons\u27 captions to see if deep learning models can, in some ways, learn human humor. I ended up using New Yorker\u27s Cartoon Captioning Contests as the dataset to train a multi-modal model that can predict a cartoon\u27s funniness. The model didn\u27t beat the benchmark in terms of accuracy of the classification task, but it eliminated some unsuccessful attempts and set us up for the future study on this topic