Quasiparticle Levels at Large Interface Systems from Many-body Perturbation Theory: the XAF-GW method

We present a fully ab initio approach based on many-body perturbation theory in the GW approximation, to compute the quasiparticle levels of large interface systems without significant covalent interactions between the different components of the interface. The only assumption in our approach is that the polarizability matrix (chi) of the interface can be given by the sum of the polarizability matrices of individual components of the interface. We show analytically, using a two-state hybridized model, that this assumption is valid even in the presence of interface hybridization to form bonding and anti-bonding states, up to first order in the overlap matrix elements involved in the hybridization. We validate our approach by showing that the band structure obtained in our method is almost identical to that obtained using a regular GW calculation for bilayer black phosphorus, where interlayer hybridization is significant. Significant savings in computational time and memory are obtained by computing chi only for the smallest sub-unit cell of each component, and expanding (unfolding) the chi matrix to that in the unit cell of the interface. To treat interface hybridization, the full wavefunctions of the interface are used in computing the self-energy. We thus call the method XAF-GW (X: eXpand-chi, A: Add-chi, F: Full wavefunctions). Compared to GW-embedding type approaches in the literature, the XAF-GW approach is not limited to specific screening environments or to non-hybridized interface systems. XAF-GW can also be applied to systems with different dimensionalities, as well as to Moire superlattices such as in twisted bilayers. We illustrate the generality and usefulness of our approach by applying it to self-assembled PTCDA monolayers on Au(111) and Ag(111), and PTCDA monolayers on graphite-supported monolayer WSe2, where good agreement with experiment is obtained.Comment: More detailed proof of Add-Chi for hybridized states added in this versio

Traffic Crash Prediction Using Machine Learning Models

Traffic crashes account for most of casualties and injuries worldwide, and there has been growing concerns and studies regarding the contributing factors of traffic crashes. There are many factors causing or related to an occurrence of traffic crash, e.g., land use, traffic flow conditions, driver behavior and weather condition. This paper studied the spatial and temporal distribution of crashes on highway and developed real-time prediction models for crash occurrence. Traffic flow data, weather data, and crash data from multiple data sources were collected and processed to develop the model. Multiple machine learning models, such as SVM model and Decision Tree model, were used as the candidate models. It was found that weather, crash time, and traffic flow shortly prior to the crash occurrence are critical impacting factors for real-time crash prediction. The candidate models have low to moderate sensitivity to predict the crash occurrences due to limited sample size. To use the models in a traffic operations environment, a prediction tool with interactive map could be developed to proactively monitor crash hot spots and prepare staffing and resources for the potential crash occurrences

A Dual-Phase Health Capital Model and Its Application to Health Co-benefit Modelling of Decarbonisation

This thesis is developed in the context of investigating the health co-benefit of decarbonisation. Health co-benefit refers to the collateral benefit which arises from decarbonisation policies external to the main intended benefit of climate change mitigation via the reduction of Greenhouse Gases (GHG). Health co-benefit of this kind often arises via the corresponding reduction in air pollutants when GHG is reduced. This is because GHG and air pollutants such as particulate matter are often derived from the same source – the combustion of fossil fuels which drive economic activities. Existing literature in the health co-benefit of decarbonisation fail to give consider the effect of socio-economic variables such as income and education on the expected health co-benefits, and this is where the thesis begins. The backdrop of health co-benefit modelling and the need to incorporate socioeconomic considerations provide the impetus to develop a health economics model. However, in many ways this health economic model deviates from the health co-benefit studies methodologically and instead follows the tradition of the Health Capital Model developed by Grossman (1972). This is due to the micro-economic nature of this health economic model which employs standard economic theory and technique of optimisation, which differs from the fundamentally empirically driven approach of health co-benefit studies. The health economic model developed here is an opportunity to address some of the short-comings of the Health Capital Model. The health co-benefit background however provides some concrete context and inspiration for the application of the theoretical insights which can be drawn from this model. The main contribution of the model develop in this thesis from the theoretical point of view lies in the division of the lifecycle analysis of health into two distinct but related phases of childhood and adulthood. The two phases are specified with different assumptions reflecting the differing characteristics of childhood and adulthood. The most important distinction between the two phases is the manner in which investment in health capital (using time and goods resources) enters the modelling framework. In the childhood phase, health investment augments or increases the existing stock of health capital, while during the adulthood phase health investment prevents the decline of health but does not increase its stock. I believe this better reflects the biological behaviour of health over one’s life than the HCM which implicitly assumes that new stock of health and existing stock are perfectly substitutable. In my model, this substitutability is possible only during the childhood corresponding with the body and mental development. On the other hand, during adulthood when them body no longer grows, health investment may only preserve health. After developing the model, I went about to test it empirically. I used the Understanding Society youth questionnaire to test the child model and the British Household Panel Survey (BHPS) to test the adulthood model. Due to the way that optimisation problem was specified, the terminal end time conditional in the optimal control model became another endogenous variable. This variable is treated empirically as the life expectancy at the national level. I find that in general the empirical data strongly supports the theoretical propositions of my model. It should be noted here that since the main contribution of this thesis is in theoretical development, the empirical efforts were designed primarily with the intention of validating the propositions of the model, and not really for direct policy application. This is also reinforced by the use of ordered logit models where the coefficients of the independent variables on the dependent variable generally have no meaning, where we only concentrate on the signs of the relationship. Having successfully developed the model, it is applied in two policy settings. Firstly, through reformulation of the model gives the inclusion of socio-economic variables in the measure of Relative Risk (RR) a theoretical grounding. We utilised the Global Burden of Disease (GBD) data to compute RR across 180 countries in the world and regressed with World Bank data on ambient particulate matter pollution as well as GDP per capita. The former variable represents the exogenous rate of depreciation while the latter socio-economic variables, particularly income. I find that the RR is negatively associated with the GDP per capita at the national level. Using the estimated coefficients with the help of Professor Crawford-Brown we attempted to forecast how GDP per capita will interact with the health co-benefits of decarbonisation under a range of future scenarios. The second application of the model is in its use to predict the inequality implications of decarbonisation policy. This is performed by taking the second order partial derivative of an endogenous variable such as health, as will be described in detail later. This approach is sufficiently flexible to accommodate the prediction of inequality over range of policies and variables. The inequality implications and predictions according to this model are not tested empirically here. However, they are perhaps the most fruitful area for future research.Three Guiness Trust in which I was employed as a research assistan

A universal approach to coverage probability and throughput analysis for cellular networks

This paper proposes a novel tractable approach for accurately analyzing both the coverage probability and the achievable throughput of cellular networks. Specifically, we derive a new procedure referred to as the equivalent uniformdensity plane-entity (EUDPE)method for evaluating the other-cell interference. Furthermore, we demonstrate that our EUDPE method provides a universal and effective means to carry out the lower bound analysis of both the coverage probability and the average throughput for various base-station distribution models that can be found in practice, including the stochastic Poisson point process (PPP) model, a uniformly and randomly distributed model, and a deterministic grid-based model. The lower bounds of coverage probability and average throughput calculated by our proposed method agree with the simulated coverage probability and average throughput results and those obtained by the existing PPP-based analysis, if not better. Moreover, based on our new definition of cell edge boundary, we show that the cellular topology with randomly distributed base stations (BSs) only tends toward the Voronoi tessellation when the path-loss exponent is sufficiently high, which reveals the limitation of this popular network topology

Collectivist values for productive teamwork between Korean and Chinese employees

The global marketplace increasingly demands that cultural diverse people work together but studies have documented important barriers to inter-cultural collaboration. Researchers have argued the need to study intercultural interaction directly in order to develop knowledge that diverse people can use to overcome obstacles and work productively. This study proposes that collectivist values are a basis upon which Korean and Chinese colleagues working in joint ventures in China develop quality collegial relationships and thereby work productively together. Chinese employees completed measures of collectivist and individualist values in their relationships with a Korean colleague. The Korean partners completed measures of collegial relationships, productivity, and confidence of future collaboration. In addition to supporting that collectivist values can promote quality collegial relationships, findings support the theorizing that quality relationships facilitate productive collaborative work. Results suggest that collectivist values can be an important basis for Korean and Chinese employees to develop a common platform where they work together productively across cultural boundaries

Signal Timing Optimization for Corridors with Multiple Highway-Rail Grade Crossings Using Genetic Algorithm

Safety and efficiency are two critical issues at highway-rail grade crossings (HRGCs) and their nearby intersections. Standard traffic signal optimization programs are not designed to work on roadway networks that contain multiple HRGCs, because their underlying assumption is that the roadway traffic is in a steady-state.During a train event, steady-state conditions do not occur.This is particularly true for corridors that experience high train traffic (e.g., over 2 trains per hour). In this situation, the non-steadystate conditions predominate. This paper develops a simulation-based methodology for optimizing traffic signal timing plan on corridors of this kind.The primary goal is to maximize safety, and the secondary goal is to minimize delay. A Genetic Algorithm (GA) was used as the optimization approach in the proposed methodology. A new transition preemption strategy for dual tracks (TPS DT) and a train arrival prediction model were integrated in the proposed methodology. An urban road network withmultiple HRGCs in Lincoln, NE, was used as the study network.The microsimulation model VISSIMwas used for evaluation purposes and was calibrated to local traffic conditions. A sensitivity analysis with different train traffic scenarios was conducted. It was concluded that the methodology can significantly improve both the safety and efficiency of traffic corridors with HRGCs