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Semi-analytic shooting methods for Burgers equation
No full textWe implement new semi-analytic shooting methods for the stationary viscous Burgers' equation by modifying the classical time differencing methods. When the viscosity is small, a very stiff boundary layer appears and this boundary layer causes significant difficulties to approximate the solution for Burgers' equation. To overcome this issue and improve the numerical quality of the shooting methods with the classical Integrating Factor (IF) methods and Exponential Time Differencing (ETD) methods, we first employ the singular perturbation analysis for Burgers' equation, and derived the so-called correctors that approximate the stiff part of the solution. Then, we build our new semianalytic shooting methods for the stationary viscous Burgers' equation by embedding these correctors into the IF and ETD methods. By performing numerical simulations, we verify that our new schemes, enriched with the correctors, give much better approximations, compared with the classical schemes.(c) 2022 Elsevier B.V. All rights reserved
Managerial perspectives on climate change and stock price crash risk
Get PDFIn this study, we examine the effects of manager's perspectives on climate change on stock price crash risk. The analysis confirms that manager's climate change perspective is negatively associated with future stock price crash risk likelihood. Various channel tests show that investor attention and analyst coverage are potential channels through which a firm's climate change perspective improves financial stability and ultimately reduces crash risk. Our results are also robust to alternative climate change perspective measures
Controllable substitutional vanadiumdoping in wafer-scalemolybdenum disulfide films
No full textSubstitutional atomic doping of transition metal dichalcogenides (TMDs) in the chemical vapor deposition (CVD) process is a promising and effective strategy for modifying their physicochemical properties. However, the conventional CVD method only allows narrow-range modulation of the dopant concentration owing to the low reactivity of the precursors. Moreover, the growth of wafer-scale monolayer TMD films with high dopant concentrations is much more challenging. Herein, we report a facile doping approach based on liquid precursor-mediated CVD process for achieving high vanadium (V) doping in the MoS2 lattice with excellent doping uniformity and stability. The lateral growth of the host MoS2 lattice and the reactivity of the V precursor were simultaneously improved by introducing an alkali metal halide as a reaction promoter. The metal halide promoter enabled the wafer-scale synthesis of V-incorporated MoS2 monolayer film with excessively high doping concentrations. The excellent wafer-scale uniformity of the highly V-doped MoS2 film was confirmed through a series of microscopic, spectroscopic, and electrical analyses
Value Function Gradient Learning for Large-Scale Multistage Stochastic Programming Problems
No full textA stagewise decomposition algorithm called ???value function gradient learning??? (VFGL) is proposed for large-scale multistage stochastic convex programs. VFGL finds the parameter values that best fit the gradient of the value function within a given parametric family. Widely used decomposition algorithms for multistage stochastic programming, such as stochastic dual dynamic programming (SDDP), approximate the value function by adding linear subgradient cuts at each iteration. Although this approach has been successful for linear problems, nonlinear problems may suffer from the increasing size of each subproblem as the iteration proceeds. On the other hand, VFGL has a fixed number of parameters; thus, the size of the subproblems remains constant throughout the iteration. Furthermore, VFGL can learn the parameters by means of stochastic gradient descent, which means that it can be easil0y parallelized and does not require a scenario tree approximation of the underlying uncertainties. VFGL was compared with a deterministic equivalent formulation of the multistage stochastic programming problem and SDDP approaches for three illustrative examples: production planning, hydrothermal generation, and the lifetime financial planning problem. Numerical examples show that VFGL generates high-quality solutions and is computationally efficient
Offline Training-based Mitigation of IR Drop for ReRAM-based Deep Neural Network Accelerators
No full textRecently, ReRAM-based hardware accelerators showed unprecedented performance compared the digital accelerators. Technology scaling causes an inevitable increase in interconnect wire resistance, which leads to IR drops that could limit the performance of ReRAM-based accelerators. These IR drops deteriorate the signal integrity and quality especially in the Crossbar structures which are used to build high density ReRAMs. Hence, finding a software solution that can predict the effect of IR drop without involving expensive hardware or SPICE simulations, is very desirable. In this paper, we propose two neural networks models to predict the impact of the IR drop problem. These models are uded to evaluate the performance of the different deep neural networks (DNNs) models including binary and quantized neural networks showing similar performance (i.e., recognition accuracy) to the golden validation (i.e., SPICE-based DNN validation). In addition, these predication models are incorporated into DNNs training framework to efficiently retrain the DNN models and bridge the accuracy drop. To further enhance the validation accuracy, we propose incremental training methods. The DNN validation results, done through SPICE simulations, show very high improvement in performance close to the baseline performance, which demonstrates the efficacy of the proposed method even with challenging datasets such as CIFAR10 and SVHN
Receding Horizon-Based Infotaxis With Random Sampling for Source Search and Estimation in Complex Environments
No full textThis paper proposes a receding horizon-based information-theoretic source search and estimation strategy for a mobile sensor in an urban environment in which an invisible harmful substance is released into the atmosphere. The mobile sensor estimates the source term including its location and release rate by using sensor observations based on Bayesian inference. The sampling-based sequential Monte Carlo method, particle filter, is employed to estimate the source term state in a highly nonlinear and stochastic system. Infotaxis, the information-theoretic gradient-free search strategy is modified to find the optimal search path that maximizes the reduction of the entropy of the source term distribution. In particular, receding horizon Infotaxis is introduced to avoid falling into the local optima and to find more successful information gathering paths in obstacle-rich urban environments. Besides, a random sampling method is introduced to reduce the computational load of the receding horizon Infotaxis for real-time computation. The random sampling method samples the predicted future measurements based on current estimation of the source term and computes the optimal search path using sampled measurements rather than considering all possible future measurements. To demonstrate the benefit of the proposed approach, comprehensive numerical simulations are performed for various conditions. The proposed algorithm increases the success rate by about 30% and reduces the mean search time by about 40% compared with the existing information-theoretic search strategy
Virtual surface morphology generation of Ti-6Al-4V directed energy deposition via conditional generative adversarial network
Get PDFThe core challenge in directed energy deposition is to obtain high surface quality through process optimisation, which directly affects the mechanical properties of fabricated parts. However, for expensive materials like Ti-6Al-4V, the cost and time required to optimise process parameters can be excessive in inducing good surface quality. To mitigate these challenges, we propose a novel method with artificial intelligence to generate virtual surface morphology of Ti-6Al-4V parts by given process parameters. A high-resolution surface morphology image generation system has been developed by optimising conditional generative adversarial networks. The developed virtual surface matches experimental cases well with an Frechet inception distance score of 174, in the range of accurate matching. Microstructural analysis with parts fabricated with artificial intelligence guidance exhibited less textured microstructural behaviour on the surface which reduces the anisotropy in the columnar structure. This artificial intelligence guidance of virtual surface morphology can help to obtain high-quality parts cost-effectively
Dead layer estimation of an HPGe detector using MCNP6 and Geant4
No full textIn this study, Monte Carlo simulations were used to calculate the full-energy peak efficiency of a p-type coaxial high-purity germanium (HPGe) detector. The HPGe detector was modeled using MCNP6 and Geant4, and the thickness of the dead layer of germanium crystals was estimated for an accurate simulation. The dead layer was divided into front and side components, where a point source and a Marinelli beaker source were used to estimate each dead layer thickness. The model was validated by comparing the simulated as well as experimental results for the standard sources of cylindrical and Marinelli beakers. The Geant4 results and experimental results matched up to 4% in the 59.54???1836.05???keV energy range, while MCNP6 matched up to 6% when adjusted for coincidence summing effects. HPGe detector modeled in Monte Carlo simulations can be utilized for experimental validation and experimental setup prior to using actual HPGe detectors
DSCR1 deficiency ameliorates the A?? pathology of Alzheimer's disease by enhancing microglial activity
Get PDFMicroglial phagocytosis and clearance are important for the removal of amyloid-?? (A??) plaques in Alzheimer???s disease (AD). Chronic exposure of microglia to A?? plaques leads to microglial metabolic dysfunction, and dysregulation of microglia can accelerate the deposition of A?? plaques and cause learning and memory impairment. Thus, regulating microglial A?? clearance is crucial for the development of therapeutics for AD-related dementia. Here, Down syndrome critical region 1 (DSCR1) deficiency ameliorated A?? plaque deposition in the 5xFAD mouse model of AD by altering microglial activity; however, the A?? synthesis pathway was not affected. DSCR1 deficiency improved spatial learning and memory impairment in 5xFAD mice. Furthermore, DSCR1-deficient microglia exhibited accelerated lysosomal degradation of A?? after phagocytosis, and BV2 cells with stable knockdown of DSCR1 demonstrated enhanced lysosomal activity. RNA-sequencing analysis showed that the transcriptional signatures associated with responses to IFN-?? were significantly up-regulated in DSCR1-knockdown BV2 cells treated with A??. Our data strongly suggest that DSCR1 is a critical mediator of microglial degradation of amyloid plaques and a new potential microglial therapeutic target in AD
Filamentation near Hill's vortex
No full textFor the axi-symmetric incompressible Euler equations, we prove linear in time filamentation near Hill's vortex: there exists an arbitrary small outward perturbation growing linearly for all times. This is based on combining the recent nonlinear orbital stability obtained by the first author with a dynamical bootstrapping scheme for particle trajectories. These results rigorously confirm numerical simulations by Pozrikidis in 1986