The beauty of collision

As a Chinese artist living in the United States, I’m researching the integration of Eastern and Western aesthetics, as well as the loss of identity that can occur when visual cultures begin to assimilate. My work endeavors to locate the connection between Eastern and Western arts through my own memories and experiences. From the age of five, I have studied calligraphy and traditional Chinese painting with my grandfather, who was a calligraphy professor. Today, I use the shapes of traditional Chinese hand fans as symbols of youth, drawing from memories of my mother and grandmother brandishing the fans to help me fall asleep and dream. I paint American coastal landscapes on the fans in a Chinese ink style, combining two aesthetics and places. Manipulating these memories of childhood while negotiating my adult life in the United States is how I question a sense of home and place in a multi-centered society. I also used various subjects from differing class, gender, disability, occupation, age, and religious expressions of Asian immigrant groups for my painting. This allows audiences to access the often divergent and complex ways Asian Immigrants live in the states during this period of discrimination. In this thesis, I will discuss whether Chinese painting can break thousands of years of tradition in terms of materials, expressions, forms, painting language, painting themes, and maintain relevance in today\u27s art world. I will also study the rapid development of science and technology and the background of this era in the information age and make individual judgments and innovation requirements for traditional Chinese painting\u27s future development

IMPUTING SOCIAL DEMOGRAPHIC INFORMATION BASED ON PASSIVELY COLLECTED LOCATION DATA AND MACHINE LEARNING METHODS

Multiple types of passively collected location data (PCLD) have emerged during the past 20 years. Its capability in travel demand analysis has also been studied and revealed. Unlike the traditional surveys whose sample is designed efficiently and carefully, PCLD features a non-probabilistic sample of dramatically larger size. However, PCLD barely contains any ground truth for both the human subjects involved and the movements they produce. The imputation for such missing information has been evaluated for years, including origin and destination, travel mode, trip purpose, etc. This research intends to advance the utilization of PCLD by imputing social demographic information, which can help to create a panorama for the large volume of travel behaviors observed and to further develop a rational weighting procedure for PCLD. The Conditional Inference Tree model has been employed to address the problems because of its abilities to avoid biased variable selection and overfitting

Human Mobility Trends during the COVID-19 Pandemic in the United States

In March of this year, COVID-19 was declared a pandemic and it continues to threaten public health. This global health crisis imposes limitations on daily movements, which have deteriorated every sector in our society. Understanding public reactions to the virus and the non-pharmaceutical interventions should be of great help to fight COVID-19 in a strategic way. We aim to provide tangible evidence of the human mobility trends by comparing the day-by-day variations across the U.S. Large-scale public mobility at an aggregated level is observed by leveraging mobile device location data and the measures related to social distancing. Our study captures spatial and temporal heterogeneity as well as the sociodemographic variations regarding the pandemic propagation and the non-pharmaceutical interventions. All mobility metrics adapted capture decreased public movements after the national emergency declaration. The population staying home has increased in all states and becomes more stable after the stay-at-home order with a smaller range of fluctuation. There exists overall mobility heterogeneity between the income or population density groups. The public had been taking active responses, voluntarily staying home more, to the in-state confirmed cases while the stay-at-home orders stabilize the variations. The study suggests that the public mobility trends conform with the government message urging to stay home. We anticipate our data-driven analysis offers integrated perspectives and serves as evidence to raise public awareness and, consequently, reinforce the importance of social distancing while assisting policymakers.Comment: 11 pages, 9 figure

Rationality of Analysts' Earnings Forecasts: UK Evidence

Analysts play crucial role in capital market. However, numerous prior researches provide evidence that analysts' earning forecasts are not efficient with respect to new information. Basu and Markov (2004) re-examine the efficiency of analysts' earning forecasts using both quadratic loss function and linear loss function. They argue that the forecasts are economically efficient under linear loss function. This dissertation replicates Basu and Markov (2004) method to examine the analysts'earnings forecast efficiency in UK market. The similar findings as Basu and Markov (2004) are obtained in my UK samples. The estimators of linear loss function is much closer to their predicted values than the estimators of quadratic loss function, and none of them are economically significant, indicating the analysts' earnings forecasts in UK market are economically efficient under linear loss function

Cyclic voltammetry: A simple method for determining contents of total and free iron ions in sodium ferric gluconate complex

Sodium ferric gluconate complex (SFGC) is the third generation of iron supplement of polysaccharide iron (III) complex (PIC). For evaluation of technological level and application value of the prepared SFGC, it would be of great significance to determine the iron content in SFGC in a simple but effective way. This paper introduces the cyclic voltammetry (CV) method for determination of iron content in SFGC. Under established optimal experimental conditions, the content of free iron ions can be directly scanned and calculated, while the total iron content can also be determined by completely acidifying SFGC into Fe3+ ions. After optimizing and screening, the optimal scanning conditions are determined as pH 3 and 0.05 V/s of the scanning rate. Prior CV measurements, 0.4 V of the enrichment potential, and 3 min of the enrichment time are found optimal. It has also been verified that CO32- ions present in the solution show little interference in the system within the experimental range of investigation. The contents of free Fe3+, Fe2+ ions and the total iron determined after acid-hydrolysis of SFGC can be accurately calculated according to the corresponding linear relationships between peak currents and iron concentrations. In this paper, the repeatability and accuracy of the method are verified, and its feasibility as a convenient and effective method to determine the iron supplements is confirmed

Estimating Warehouse Rental Price using Machine Learning Techniques

Boosted by the growing logistics industry and digital transformation, the sharing warehouse market is undergoing a rapid development. Both supply and demand sides in the warehouse rental business are faced with market perturbations brought by unprecedented peer competitions and information transparency. A key question faced by the participants is how to price warehouses in the open market. To understand the pricing mechanism, we built a real world warehouse dataset using data collected from the classified advertisements websites. Based on the dataset, we applied machine learning techniques to relate warehouse price with its relevant features, such as warehouse size, location and nearby real estate price. Four candidate models are used here: Linear Regression, Regression Tree, Random Forest Regression and Gradient Boosting Regression Trees. The case study in the Beijing area shows that warehouse rent is closely related to its location and land price. Models considering multiple factors have better skill in estimating warehouse rent, compared to singlefactor estimation. Additionally, tree models have better performance than the linear model, with the best model (Random Forest) achieving correlation coefficient of 0.57 in the test set. Deeper investigation of feature importance illustrates that distance from the city center plays the most important role in determining warehouse price in Beijing, followed by nearby real estate price and warehouse size