Modeling Financial Durations in Ultra-High-Frequency Data: A Markov-Switching Multifractal Approach

This thesis focus on the modeling and forecasting financial durations by the Markov-Switching Multifractal Duration (MSMD) model with using ultra-high-frequency (UHF) data. We first review the literatures of both of autoregressive conditional duration (ACD) models and MSMD models, and point out the limitations of applying these models in the analysis of UHF data. We then study the facts of inter-trade, price and volume durations from the NASDAQ TotalView-ITCH database for US stocks, which consists of high precision transaction time stamp. We reveal a discovery of spike and diurnal periodic pattern in intra-day durations and introduce a new procedure for the adjustment of periodicity. We discuss the properties of MSMD model and show that the model is capable of generating high persistence and long memory series. We assume that innovations follow exponential, gamma, Weibull, Burr or generalized gamma distributions and propose an improvement for the Gam-MSMD process. The new process captures dynamics of both of the shape and scale parameters in gamma distribution. We perform Maximum-Likelihood Estimation for the MSMD models. The empirical evidence we present here is in conformance with the theoretical properties of the model, and both gamma and Weibull are shown to better fit the data than the exponential in certain aspects. The estimation and simulation results show that higher number of the Markov components does not necessarily bring better performance. Finally, we compare the out-of-sample forecasting performance of the ACD and MSMD models based on inter-trade, price and volume durations of four major equities (MSFT, INTC, FB and QCOM) traded on NASDAQ, and conclude that the MSMD models outperform ACD models. The Gamma and Weibull innovations are superior to the exponential in all types of durations. The complex distributions, Burr and generalized gamma, could not provide any improvements on fitting or forecasting. We also show that the modified Gam-MSMD process performs better in terms of capturing the variance of sequence. | 121 page

Review of Tellurium Resources in the World and in China

Tellurium is an indispensable vitamin in modern high-tech fields, and it plays an irreplaceable and important role in many aspects such as national defense and medical treatment. Studies have shown that tellurium will be the best replacement for the next generation of green batteries. This article introduces the important uses of tellurium, summarizes the characteristics of different types of tellurium ores (independent tellurium and associated tellurium deposits), introduces the reserves and distribution of associated tellurium deposits in the world, and roughly predicts the demand for tellurium in major countries in the world this year

Spatial Transfer Learning for Estimating PM2.5 in Data-poor Regions

Air pollution, especially particulate matter 2.5 (PM2.5), is a pressing concern for public health and is difficult to estimate in developing countries (data-poor regions) due to a lack of ground sensors. Transfer learning models can be leveraged to solve this problem, as they use alternate data sources to gain knowledge (i.e., data from data-rich regions). However, current transfer learning methodologies do not account for dependencies between the source and the target domains. We recognize this transfer problem as spatial transfer learning and propose a new feature named Latent Dependency Factor (LDF) that captures spatial and semantic dependencies of both domains and is subsequently added to the feature spaces of the domains. We generate LDF using a novel two-stage autoencoder model that learns from clusters of similar source and target domain data. Our experiments show that transfer learning models using LDF have a 19.34% improvement over the baselines. We additionally support our experiments with qualitative findings.Comment: Accepted for publication at ECML-PKDD 202

Wildland Fires Worsened Population Exposure to Pm

As wildland fires become more frequent and intense, fire smoke has significantly worsened the ambient air quality, posing greater health risks. to better understand the impact of wildfire smoke on air quality, we developed a modeling system to estimate daily P

Discrimination of homocysteine, cysteine and glutathione using an aggregation-induced-emission-active hemicyanine dye

Elevated levels of homocysteine (Hcy) in blood are indicative of many high risk cardiovascular and neurodegenerative diseases. Thus, development of highly efficient and selective dyes for monitoring Hcy levels has attracted much attention. This paper reports the utilization of TPE-Cy, an aggregation-induced-emission active hemicyanine dye, as a probe for the detection of Hcy. More interestingly, this dye shows high selectivity to Hcy over cysteine, glutathione and other amino acids in weakly basic buffer solution

Crafting NPB with tetraphenylethene: a win–win strategy to create stable and efficient solid-state emitters with aggregation-induced emission feature, high hole-transporting property and efficient electroluminescence

N,N′-Di-(1-naphthyl)-N,N′-diphenyl-(1,1′-biphenyl)-4,4′-diamine (NPB) possesses high thermal and morphological stability and is one of the well-known hole-transporting materials for the fabrication of organic light-emitting diodes (OLEDs). Modification of NPB by the covalent integration of tetraphenylethene (TPE) into its structure dramatically changes its emission behavior: the resulting adduct (TPE–NPB) is highly emissive in the aggregated state, showing a novel phenomenon of aggregation-induced emission (AIE). The adduct is thermally and morphologically stable. Non-doped multilayer electroluminescence (EL) devices using TPE–NPB as an emitting layer were fabricated, which emitted green light with a maximum luminance and current efficiency of 11[thin space (1/6-em)]981 cd m−2 and 11.9 cd A−1, respectively. Even better device performances are observed in the bilayer device without NPB. Our strategy takes the full advantage of the AIE property in the solid state and retains the inherent properties of conventional luminophores. It opens a new avenue in the development of stable and efficient solid-state fluorescent materials for OLED application

Estimating PM\u3csub\u3e2.5\u3c/sub\u3ein Southern California using satellite data: Factors that affect model performance

Background: Studies of PM2.5 health effects are influenced by the spatiotemporal coverage and accuracy of exposure estimates. The use of satellite remote sensing data such as aerosol optical depth (AOD) in PM2.5 exposure modeling has increased recently in the US and elsewhere in the world. However, few studies have addressed this issue in southern California due to challenges with reflective surfaces and complex terrain. Methods: We examined the factors affecting the associations with satellite AOD using a two-stage spatial statistical model. The first stage estimated the temporal PM2.5/AOD relationships using a linear mixed effects model at 1 km resolution. The second stage accounted for spatial variation using geographically weighted regression. Goodness of fit for the final model was evaluated by comparing the daily PM2.5 concentrations generated by cross-validation (CV) with observations. These methods were applied to a region of southern California spanning from Los Angeles to San Diego. Results: Mean predicted PM2.5 concentration for the study domain was 8.84 µg m-3. Linear regression between CV predicted PM2.5 concentrations and observations had an R 2 of 0.80 and RMSE 2.25 µg m-3. The ratio of PM2.5 to PM10 proved an important variable in modifying the AOD/PM2.5 relationship (β = 14.79, p ≤ 0.001). Including this ratio improved model performance significantly (a 0.10 increase in CV R 2 and a 0.56 µg m-3 decrease in CV RMSE). Discussion: Utilizing the high-resolution MAIAC AOD, fine-resolution PM2.5 concentrations can be estimated where measurements are sparse. This study adds to the current literature using remote sensing data to achieve better exposure data in the understudied region of Southern California. Overall, we demonstrate the usefulness of MAIAC AOD and the importance of considering coarser particles in dust prone areas

Study on the Moderating Effect of Body Mass Index in Correlation of Anxiety and Depression Disorders

BackgroundAs two different kinds of mental disorders, anxiety disorder and depressive disorder could probably coexist in one with the proceeding of the illness. However, there are few studies on how to prevent and treat the coexistence of anxiety and depressive disorders.ObjectiveTo discuss the relationship between anxiety and depressive disorders, and the moderating effect of body mass index (BMI) in their relationship.MethodsBy use of simple random sampling, 86 outpatients and inpatients with anxiety disorders were selected from the First Affiliated Hospital of Nanchang University during June 1st to August, 31st, 2021. A self-made demographic questionnaire was used to obtain the demographic information. The Hamilton Anxiety Rating Scale was used to assess the anxiety level. The Hamilton Rating Scale for Depression was used to assess the depression level. Pearson correlation analyses were performed to assess the relationship between anxiety and depression disorders, and that between BMI and anxiety or depression disorder. Hierarchical regression analysis was adopted to explore the moderating effect of BMI on the relationship between anxiety and depressive disorders.ResultsEighty eligible cases were also diagnosed with depression. The anxiety prevalence was significantly increased with depression prevalence (r=0.70, P<0.01) . BMI had no significant linear correlation with anxiety prevalence (r=0.03, P>0.05) . BMI also had no significant linear correlation with depression prevalence (r=0.14, P>0.05) . BMI moderated the relationship between anxiety and depression disorders significantly (β=-0.16, P<0.01) .ConclusionBMI can weaken the effect of anxiety disorder on depression, helping prevent them from developing into comorbid mental disorders, providing new ideas for developing new dietary standard or exercise patterns for mental health prevention and treatment in the future and expands the research field of nutritional psychiatry to a certain extent

Comparison of multiple PM2.5 exposure products for estimating health benefits of emission controls over New York State, USA

Ambient exposure to fine particulate matter (PM2.5) is one of the top global health concerns. We estimate the PM2.5-related health benefits of emission reduction over New York State (NYS) from 2002 to 2012 using seven publicly available PM2.5 products that include information from ground-based observations, remote sensing and chemical transport models. While these PM2.5 products differ in spatial patterns, they show consistent decreases in PM2.5 by 28%–37% from 2002 to 2012. We evaluate these products using two sets of independent ground-based observations from the New York City Community Air Quality Survey (NYCCAS) Program for an urban area, and the Saint Regis Mohawk Tribe Air Quality Program for a remote area. Inclusion of satellite remote sensing improves the representativeness of surface PM2.5 in the remote area. Of the satellite-based products, only the statistical land use regression approach captures some of the spatial variability across New York City measured by NYCCAS. We estimate the PM2.5-related mortality burden by applying an integrated exposure-response function to the different PM2.5 products. The multi-product mean PM2.5-related mortality burden over NYS decreased by 5660 deaths (67%) from 8410 (95% confidence interval (CI): 4570–12 400) deaths in 2002 to 2750 (CI: 700–5790) deaths in 2012. We estimate a 28% uncertainty in the state-level PM2.5 mortality burden due to the choice of PM2.5 products, but such uncertainty is much smaller than the uncertainty (130%) associated with the exposure-response function