New Insights on Time and Quantum Gravity

According to Einstein, a universal time does not exist. But what if time is different than what we think of it? Cosmic Microvawe Background Radiation was accepted as a reference for a universal clock and a new time concept has been constructed. According to this new concept, time was tackled as two-dimensional having both a wavelength and a frequency. What our clocks measure is actually a derivation of the frequency of time. A relativistic time dilation actually corresponds to an increase in the wavelength of time. At the point where time wavelength and time frequency is equal, where light is positioned, quantum-world and macro- world are seperated. Gravity was redefined with respect to time and the new two dimensional time fabric of the universe was speculated to be the source of dark energy causing the universe to expand. According to this new point of view quantum realm and macro-world can be better understood. This new time concept provide a basis for our understanding of quantum gravity and provide the long-sought answers to well known problems of it. A prediction of the presented theory is that the universe will expand at various rates at different regions due to the fact that particular surroundings will create different gravities and cause a different gravity- time wavelength effect yielding various time delays for calculating this rate of expansion

The Impact of COVID-19 and Its Policy Responses on Local Economy and Health Conditions

US states have implemented lockdown measures to contain the COVID-19 pandemic. We assess the impact of state policy responses on local economic and health conditions, with the goal to shed light on marginal health benefits and economic costs associated with social distancing. We find that lockdown measures are effective in alleviating disease severity, but yield significant contraction of the economy. Deteriorating health conditions are disruptive to the labor supply, financial health, and economic output. The adverse economic impact of lockdowns exceeds the economic damage brought by the disease itself, but health conditions better forecast economic contraction outcomes

Recurrent Chest Pain, as a Presenting Sign of Ovarian Endometrioma

Chest pain is a rare sign of thoracal endometriosis associated with endometrioma of the tubo-ovarian endometrioma. We report the case periodic episodes of chest pain concurrent with menstruation in a 35-year-old female, in which ovarian endometrioma was diagnosed and left-sided oophorectomy was performed. After surgery, patient underwent medical treatment which included a Gn-RH agonist and a combined oral contraceptive. In the follow-up period, there was no evidence of chest pain

Graph Neural Networks for Computational Chemistry

Graph Neural Networks are behind many pharmacological breakthroughs due to their innate ability to learn structural properties of molecules and accelerate high-throughput screening for favorable characteristics that could serve as a treatment or cure to a disease. Much of the world’s natural data, such as social networks and molecules, can be represented in the form of graphs. However, advancements in graph-based problems like chemistry have been lacking because graphs are a form of non-Euclidean data, and encoding them into a format that is compatible with deep learning is considerably more challenging. This thesis seeks to understand and benchmark the techniques used to preserve the structure and properties of a graph in the encoded form. Specifically, characteristics of a graph that are distinct in the graph form should be distinct in the encoded form. Preserving both the expressiveness and the distinctness of the encoded graph is a challenging task that has received a lot of attention in geometric deep learning. This work evaluates and compares various graph neural network methods on a large public dataset to quantify the expressive power of more detailed graph neural networks that consider dihedrals and bond information, for example. It becomes evident that simply constructing homogeneous graphs of nodes and edges is insufficient

How do large banking organizations manage their capital ratio?

Large banking organizations in the U.S. hold significantly more equity capital than the minimum required by bank regulators. This capital cushion has built up during a period of unusual profitability for the banking system, leading some observers to argue that the capital merely reflects recent profits. Others contend that the banks deliberately choose target capital levels based on their risk exposures and their counterparties’ sensitivities to default risk. In either case, the existence of “excess” capital makes it difficult to observe how banks manage their capital levels, particularly in response to regulatory changes (such as Basel II). We propose several hypotheses to explain this “excess” capital, and test these hypotheses using annual panel data for large, publicly traded U.S. bank holding companies (BHCs) from 1992 through 2006, and an innovative partial adjustment approach that allows both the target capital ratios and the speed of adjustment toward those targets to vary with firm-specific characteristics. We find evidence to suggest that large BHCs actively managed their capital ratios during our sample period. Our tests suggest that large BHCs choose target capital levels substantially above well-capitalized regulatory minima; that these targets increase with BHC risk but decrease with BHC size; that BHCs adjust toward these targets relatively quickly; and that adjustment speeds are faster for poorly capitalized BHCs, but slower (ceteris paribus) for BHCs under severe regulatory pressure.Banks and banking ; Capital

The Lattice Boltzmann Methods and Their Applications to Fluid Flows

The history of the Lattice Boltzmann Method and its application to fluid mechanics are investigated here. Detailed formulations are provided to form a basis for the Lattice Boltzmann Method and its many variations. These variations are designed to overcome shortcomings in the standard single relaxation time Lattice Boltzmann model. Presented here are: a model that utilizes the non-equilibrium parts of the stress tensor, the Regularized Lattice Boltzmann model; a model that converts over to momentum space, the Multi-Relaxation Time Lattice Boltzmann model; and a model that corrects itself using the entropy equation, the entropic Lattice Boltzmann model. Extensions for the Lattice Boltzmann method are derived that include: external forces, multiphase flows, and thermal flows. Various types of boundary conditions are modeled using different approaches. A detailed explanation on extracting common macroscopic flow properties in physical units is provided. These extracted properties can be used to check temporal and spatial convergence. A two dimensional, nine velocity model and a three dimensional, fifteen velocity model are used to provide examples of a number of the approaches mentioned. A two dimensional and three dimensional lid-driven cavity flow is used to illustrate these methods