A Class of Second Order Difference Approximation for Solving Space Fractional Diffusion Equations

A class of second order approximations, called the weighted and shifted Gr\"{u}nwald difference operators, are proposed for Riemann-Liouville fractional derivatives, with their effective applications to numerically solving space fractional diffusion equations in one and two dimensions. The stability and convergence of our difference schemes for space fractional diffusion equations with constant coefficients in one and two dimensions are theoretically established. Several numerical examples are implemented to testify the efficiency of the numerical schemes and confirm the convergence order, and the numerical results for variable coefficients problem are also presented.Comment: 24 Page

Fintech vs. traditional financial services: how are investors reacting?

Financial technology (fintech) has experienced dramatic growth in the 21st century while the traditional finance sector is facing challenges of innovative and convenient services brought by financial technology in the U.S after the 2008 crisis. This dissertation intends to study whether investors view U.S fintech differently from traditional finance under the influence of macroeconomic variables (total non-farm payroll, S&P500 index, the spread of 10-year and 2-year Government Bonds, and 3-month LIBOR). We establish multiple linear regression models for U.S fintech (the KFTX index as a representative) and traditional finance (represented by the S&P 500 Financials Services Select Sector Index) respectively to investigate the relationship between the above four macroeconomic variables from 2016 to 2020 and then obtain their comparative model by the difference between their log returns in empirical analysis. We observe that total non-farm payroll, and S&P 500 index are both statistically relevant in explaining the variations of the S&P 500 Financials Services Select Sector Index and the KFTX index while the S&P 500 Financials Services Select Sector Index is also influenced by the positive and statistically significant effects of 3-month LIBOR and the spread of 10-year and 2-year Government Bonds. In addition, we figure out that when 3-month LIBOR or 10-year and 2-year Government Bonds spread rises, investors are inclined to buy more traditional financial stock represented by the S&P 500 Financials Services Select Sector Index than the fintech assets represented by the KFTX index.A tecnologia financeira (fintech) experimentou um crescimento dramático no século 21, enquanto o setor financeiro tradicional está enfrentando desafios de serviços inovadores e convenientes trazidos pela tecnologia financeira nos EUA após a crise de 2008. Esta dissertação pretende estudar se os investidores veem a fintech dos EUA de forma diferente das finanças tradicionais sob a influência de variáveis macroeconômicas (folha de pagamento não agrícola total, índice S & P500, spread de títulos do governo de 10 e 2 anos e LIBOR de 3 meses). Estabelecemos vários modelos de regressão linear para fintech dos EUA (o índice KFTX como representante) e finanças tradicionais (representado pelo índice S&P 500 Financials Services Select Sector), respectivamente, para investigar a relação entre as quatro variáveis macroeconômicas acima de 2016 a 2020 e, em seguida, obter seu modelo comparativo pela diferença entre seus retornos de log na análise empírica. Observamos que o total da folha de pagamento não agrícola e o índice S&P 500 são estatisticamente relevantes para explicar as variações do S&P 500 Financials Services Select Sector Index e do índice KFTX, enquanto o S&P 500 Financials Services Select Sector Index também é influenciado pelo índice positivo e efeitos estatisticamente significativos da LIBOR de 3 meses e do spread dos títulos do governo de 10 e 2 anos. Além disso, descobrimos que quando o spread da LIBOR de 3 meses ou dos títulos do governo de 10 e 2 anos aumenta, os investidores tendem a comprar mais ações financeiras tradicionais representadas pelo S&P 500 Financials Services Select Sector Index do que os ativos fintech representados por o índice KFTX

Genetically engineered mouse models for functional studies of SKP1-CUL1-F-box-protein (SCF) E3 ubiquitin ligases

The SCF (SKP1 (S-phase-kinase-associated protein 1), Cullin-1, F-box protein) E3 ubiquitin ligases, the founding member of Cullin-RING ligases (CRLs), are the largest family of E3 ubiquitin ligases in mammals. Each individual SCF E3 ligase consists of one adaptor protein SKP1, one scaffold protein cullin-1 (the first family member of the eight cullins), one F-box protein out of 69 family members, and one out of two RING (Really Interesting New Gene) family proteins RBX1/ROC1 or RBX2/ROC2/SAG/RNF7. Various combinations of these four components construct a large number of SCF E3s that promote the degradation of many key regulatory proteins in cell-context, temporally, and spatially dependent manners, thus controlling precisely numerous important cellular processes, including cell cycle progression, apoptosis, gene transcription, signal transduction, DNA replication, maintenance of genome integrity, and tumorigenesis. To understand how the SCF E3 ligases regulate these cellular processes and embryonic development under in vivo physiological conditions, a number of mouse models with transgenic (Tg) expression or targeted deletion of components of SCF have been established and characterized. In this review, we will provide a brief introduction to the ubiquitin-proteasome system (UPS) and the SCF E3 ubiquitin ligases, followed by a comprehensive overview on the existing Tg and knockout (KO) mouse models of the SCF E3s, and discuss the role of each component in mouse embryogenesis, cell proliferation, apoptosis, carcinogenesis, as well as other pathogenic processes associated with human diseases. We will end with a brief discussion on the future directions of this research area and the potential applications of the knowledge gained to more effective therapeutic interventions of human diseases

An Improved Approach for Automatic Parallel Parking in Narrow Parking Spaces

In 2014, there are more than 500,000 parking lot collisions, which is a 4% increase compare to 2010. Many cars are being produced every day, so that the parking spots are designed to be smaller, in the meantime, the size of cars has outgrown the size of parking space since the automakers have been making larger vehicles to favor customers\u27 demands for larger interior spaces. As a consequence of smaller parking spaces, the possibility of human operational errors is significantly increased, which subsequently leads to accidents and traffic problems. This paper proposes an automatic parking method for parallel parking, which can be used to park vehicles in a narrower space to reduce the chances of parking lot collisions. It is based on the kinematic model and could be easily combined with other automatic parking approaches such as fuzzy logic and artificial neural network, ultimately making the parallel parking process more effective

Correlation between Chlamydia Pneumoniae IgG Positive in Lung Cancer Patients and Cytokines Related to Radiation-induced Pulmonary Lesion

Background and objective There exsits intimate relationship between infection with chlamydia pneumoniae (Cpn) and lung cancer incidence. But few studies have been reported about radiation-induced pulmonary lesion in lung cancer patients infected with Cpn. The aim of this study is to explore the correlation between cytokines related to radiation-induced pulmonary lesion and Cpn IgG positive in lung cancer patients. Methods A total of 69 patients with lung cancer received chest radiotherapy. Blood samples were collected and frozen before radiotherapy (pre-RT), middle radiotherapy (mid-RT) and after radiotherapy (post-RT). Cpn IgG and levels of IL-1β, SP-A, TGF-β, and TNF-α were measured by enzymelinked immunosorbent assay (ELISA). Results In the total of 69 patients, 21 patients were Cpn IgG positive, 48 patients negative. The positive rate was 30.43%. In mid-RT concentration of IL-1β in Cpn IgG positive and negative group were (35.82±10.09) ng/L and (30.01±6.46) ng/L, with statistically significant difference (P < 0.05). Pre-RT and post-RT concentrations of IL-1β in Cpn IgG positive and negative group had no statistically significant difference. Mid-RT concentrations of SP-A in Cpn IgG positive group and negative group were (641.78±106.81) ng/L and (100.86±61.4) ng/L respectively, with statistically significant difference (P < 0.05). Post-RT concentration of SP-A in Cpn IgG positive and negative group were (657.47±115.19) ng/L and (93.23±47.15) ng/L respectively, with statistically significant difference (P < 0.05). Concentrations of TNF-α in Cpn IgG positive and negative group had no statistically significant difference. Concentrations of TGF-β in Cpn IgG positive group were (710.67±358.16) pg/mL in pre-RT, (1,002.06±542.16) pg/mL in mid-RT, (2,125.16±1,522.29) pg/mL in post-RT; those in negative group were (867.77±412.48) pg/mL, (914.05±425.70) pg/mL, (1,073.36±896.01) pg/mL. Concentration of TGF-β in post-RT between Cpn IgG positive and negative group had statistically significant difference (P < 0.05). Conclusion Cpn IgG positive in lung cancer patients influenced levels of IL-1β, SP-A, TGF-β during chest radiotherapy. This might aggravate radiation-induced pulmonary lesion

Uncertain Dynamic Characteristic Analysis for Structures with Spatially Dependent Random System Parameters

This work presents a robust non-deterministic free vibration analysis for engineering structures with random field parameters in the frame of stochastic finite element method. For this, considering the randomness and spatial correlation of structural physical parameters, a parameter setting model based on random field theory is proposed to represent the random uncertainty of parameters, and the stochastic dynamic characteristics of different structural systems are then analyzed by incorporating the presented parameter setting model with finite element method. First, Gauss random field theory is used to describe the uncertainty of structural material parameters, the random parameters are then characterized as the standard deviation and correlation length of the random field, and the random field parameters are then discretized with the Karhunen–Loeve expansion method. Moreover, based on the discretized random parameters and finite element method, structural dynamic characteristics analysis is addressed, and the probability distribution density function of the random natural frequency is estimated based on multi-dimensional kernel density estimation method. Finally, the random field parameters of the structures are quantified by using the maximum likelihood estimation method to verify the effectiveness of the proposed method and the applicability of the constructed model. The results indicate that (1) for the perspective of maximum likelihood estimation, the parameter setting at the maximum value point is highly similar to the input parameters; (2) the random field considering more parameters reflects a more realistic structure