1,295 research outputs found

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

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    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

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    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 universal approach to coverage probability and throughput analysis for cellular networks

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    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

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    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

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    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