Quantitatively Analyzing Phonon Spectral Contribution of Thermal Conductivity Based on Non-Equilibrium Molecular Dynamics Simulation I: From Space Fourier Transform

Probing detailed spectral dependence of phonon transport properties in bulk materials is critical to improve the function and performance of structures and devices in a diverse spectrum of technologies. Currently, such information can only be provided by the phonon spectral energy density (SED) or equivalently time domain normal mode analysis (TDNMA) methods in the framework of equilibrium molecular dynamics simulation (EMD), but has not been realized in non-equilibrium molecular dynamics simulations (NEMD) so far. In this paper we generate a new scheme directly based on NEMD and lattice dynamics theory, called time domain direct decomposition method (TDDDM), to predict the phonon mode specific thermal conductivity. Two benchmark cases of Lennard-Jones (LJ) Argon and Stillinger-Weber (SW) Si are studied by TDDDM to characterize contributions of individual phonon modes to overall thermal conductivity and the results are compared with that predicted using SED and TDNMA. Excellent agreements are found for both cases, which confirm the validity of our TDDDM approach. The biggest advantage of TDDDM is that it can be used to investigate the size effect of individual phonon modes in NEMD simulations, which cannot be tackled by SED and TDNMA in EMD simulations currently. We found that the phonon modes with mean free path larger than the system size are truncated in NEMD and contribute little to the overall thermal conductivity. The TDDDM provides direct physical origin for the well-known strong size effects in thermal conductivity prediction by NEMD

Architecture and Solutions For IOT Device Security

The Internet of Things (IOT) is a prominent technology that enables networked connection and data transfer between physical devices. It has found applications in home automation, healthcare, environmental monitoring, and industry. However, the exponential growth of IOT devices has raised concerns about network security, data leaks, and potential threats. This paper aims to provide an overview of security risks in various IOT areas, improve IOT application architectures, and explore recent security mechanisms and technologies. The conclusion highlights the importance of security attributes and discusses four technologies, namely blockchain, fog computing, edge computing, and machine learning, to enhance IOT security

Adaptive importance sampling for Deep Ritz

We introduce an adaptive sampling method for the Deep Ritz method aimed at solving partial differential equations (PDEs). Two deep neural networks are used. One network is employed to approximate the solution of PDEs, while the other one is a deep generative model used to generate new collocation points to refine the training set. The adaptive sampling procedure consists of two main steps. The first step is solving the PDEs using the Deep Ritz method by minimizing an associated variational loss discretized by the collocation points in the training set. The second step involves generating a new training set, which is then used in subsequent computations to further improve the accuracy of the current approximate solution. We treat the integrand in the variational loss as an unnormalized probability density function (PDF) and approximate it using a deep generative model called bounded KRnet. The new samples and their associated PDF values are obtained from the bounded KRnet. With these new samples and their associated PDF values, the variational loss can be approximated more accurately by importance sampling. Compared to the original Deep Ritz method, the proposed adaptive method improves accuracy, especially for problems characterized by low regularity and high dimensionality. We demonstrate the effectiveness of our new method through a series of numerical experiments

Receiver Operating Characteristic (ROC) Area Under the Curve (AUC): A Diagnostic Measure for Evaluating the Accuracy of Predictors of Education Outcomes

Early Warning Systems (EWS) and Early Warning Indictors (EWI) have recently emerged as an attractive domain for states and school districts interested in predicting student outcomes using data that schools already collect with the intention to better time and tailor interventions. However, current diagnostic measures used across the domain do not consider the dual issues of sensitivity and specificity of predictors, key components for considering accuracy. We apply signal detection theory using Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) analysis adapted from the engineering and medical domains, and using the pROC package in R. Using nationally generalizable data from the Education Longitudinal Study of 2002 (ELS:2002) we provide examples of applying ROC accuracy analysis to a variety of predictors of student outcomes, such as dropping out of high school, college enrollment, and postsecondary STEM degrees and careers. Keywords: ROC, AUC, Early Warning System, Early Warning Indicator, signal detection theory, dropout, college enrollment, Postsecondary STEM Degree, hard STEM career, soft STEM caree

Robust Fusion Filtering for Multisensor Time-Varying Uncertain Systems: The Finite Horizon Case

The robust H∞ fusion filtering problem is considered for linear time-varying uncertain systems observed by multiple sensors. A performance index function for this problem is defined as an indefinite quadratic inequality which is solved by the projection method in Krein space. On this basis, a robust centralized finite horizon H∞ fusion filtering algorithm is proposed. However, this centralized fusion method is with poor real time property, as the number of sensors increases. To resolve this difficulty, within the sequential fusion framework, the performance index function is described as a set of quadratic inequalities including an indefinite quadratic inequality. And a sequential robust finite horizon H∞ fusion filtering algorithm is given by solving this quadratic inequality group. Finally, two simulation examples for time-varying/time-invariant multisensor systems are exploited to demonstrate the effectiveness of the proposed methods in the respect of the real time property and filtering accuracy

Homo-junction bottom-gate amorphous In-Ga-Zn-O TFTs with metal induced source /drain regions

A fabrication process for homo-junction bottom-gate (HJBG) amorphous In–Ga–Zn–O (a-IGZO) thin-film transistors (TFTs) is proposed, in which the a-IGZO section as source/drain (S/D) region is induced to a low resistance state by coating a thin metal Al film and then performing a thermal annealing in oxygen, and that as channel region is protected from back etching by depositing and patterning a protective layer. Experimental results show that with a 5 nm Al film and a 200 ºC annealing, the sheet resistance of the S/D a-IGZO is 803 Ω/□ and keeps stable during subsequent thermal treatment. In addition, the annealing generated thin Al2O3 film contributes to improve the thermal stability and ambient atmosphere immunity of the fabricated HJBG TFTs. Please click Additional Files below to see the full abstract

Aggressiveness-regulated Multi-agent Stress Testing of Autonomous Vehicles

The emerging era of autonomous vehicles (AVs) presents unprecedented potential for transforming global transportation. As these vehicles begin to permeate our streets, the challenge of ensuring their safety, especially in unprecedented scenarios, looms large, due to the infrequent occurrence of high-risk scenarios within an essentially infinite number of test cases. This Master's thesis explores the intricate challenge of stress testing autonomous vehicles in simulated environments. The study delves into the application of multi-agent reinforcement learning (MARL) as a tool for stress testing AVs. Although MARL demands higher computational resources, it demonstrates strong ability in uncovering complex accident scenarios. This marks a shift from the state-of-the-art which deploys single-agent reinforcement algorithms that encounter limitations both in the quality of the generated accident scenarios and in their ability to generate complex accident scenarios as the number of traffic participants increases. Central to our approach is the integration of constraints that regulate the level of aggressiveness of traffic participants to induce more realistic and insightful accident scenarios. The thesis also presents the highway-attack-env, an environment for black-box AV testing that allows the assessment of both single and multi-agent reinforcement learning algorithms. The contributions of this research include the introduction of the aforementioned environment and a comprehensive benchmark, as well as a comparative analysis of single-agent and MARL algorithms, underscoring the superiority of the proposed multi-agent, aggressiveness-regulated methodology for AV validation

Wealth inequality and social mobility: A simulation-based modelling approach

We design a series of simulation-based thought experiments to deductively evaluate the causal effects of various factors on wealth inequality (the distribution) and social mobility (dynamics of the distribution). We find that uncertainty per se can lead to a “natural” degree of inequality and returns-related factors contribute more than earnings-related factors. Based on these identified factors, we construct an empirical, hybrid agent-based model to match the observed wealth inequality measures of the G7 countries and China. The estimated model can generate a power-law wealth distribution for the rich and a positively sloped intra-generational Great Gatsby curve. We also demonstrate how this hybrid model can be extended to a wide range of questions such as redistributive effects of tax and finance