Strong dopant dependence of electric transport in ion-gated MoS2

We report modifications of the temperature-dependent transport properties of $\mathrm{MoS_2}$ thin flakes via field-driven ion intercalation in an electric double layer transistor. We find that intercalation with $\mathrm{Li^+}$ ions induces the onset of an inhomogeneous superconducting state. Intercalation with $\mathrm{K^+}$ leads instead to a disorder-induced incipient metal-to-insulator transition. These findings suggest that similar ionic species can provide access to different electronic phases in the same material.Comment: 5 pages, 3 figure

An Expectation Maximization Algorithm to Model Failure Times by Continuous-Time Markov Chains

In many applications, the failure rate function may present a bathtub shape curve. In this paper, an expectation maximization algorithm is proposed to construct a suitable continuous-time Markov chain which models the failure time data by the first time reaching the absorbing state. Assume that a system is described by methods of supplementary variables, the device of stage, and so on. Given a data set, the maximum likelihood estimators of the initial distribution and the infinitesimal transition rates of the Markov chain can be obtained by our novel algorithm. Suppose that there are m transient states in the system and that there are n failure time data. The devised algorithm only needs to compute the exponential of m×m upper triangular matrices for O(nm2) times in each iteration. Finally, the algorithm is applied to two real data sets, which indicates the practicality and efficiency of our algorithm

The Design of Cube Calculus Machine Using Sram-Based Fpga Reconfigurable Hardware Dec’s Perle-1 Board

Presented in this thesis are new approaches to column compatibility checking and column-based input/output encoding for Curtis decompositions of switching functions. These approaches can be used in Curtis-type functional decomposition programs for applications in several scientific disciplines. Examples of applications are: minimization of combinational and sequential logic) mapping of logic functions to programmable logic devices such as CPLDs, MPGAs, and FPGAs, data encryption, data compression, pattern recognition) and image refinement. Presently, Curtis-type functional decomposition programs are used primarily for experimental purposes due to performance, quality, and compatibility issues. However) in the past few years a renewal of interest in the area of functional decomposition has resulted in significant improvements in performance and quality of multi-level decomposition programs. The goal of this thesis is to introduce algorithms that can significantly improve the performance and quality of Curtis-type decomposition programs. In doing so, it is hoped that a Curtis-type decomposition program, complete with efficient, high quality algorithms for decomposition, will be a feasible tool for use in one or more practical applications. Various testing and analyses were performed in order to evaluate the potential of algorithms presented in this thesis for use in a high quality Curtis-type decomposition program. Testing was done using a binary input, binary output Curtis-type decomposition program MULTIS/GUD. This program was implemented here at Portland State University by the Portland Oregon Logic Optimization Group

A simple high-performance current control strategy for V2G three-phase four-leg inverter with LCL filter

Electric vehicles (EVs) can behave as distributed energy storage devices for providing on-demand smart grid support service, that is an emerging Vehicle-to-Grid (V2G) technology. A high-performance and easy-implementation current control strategy for V2G Three-phase four-leg inverter with LCL filter is proposed. It consists of a deadbeat (DB) controller and a paralleled repetitive controller (RC). The DB controller is based on weighted average inductor current (WAIC) scheme, which simplifies the third-order LCL filter to be an equivalent 1st order L filter. The stability of the DB controlled inverter with the unmodelled system time delay is analyzed. DB controller is of very fast response and easy implementation, but is not immune to system time delay and various uncertainties. To overcome the disadvantages, a plug-in RC is added to reinforce the DB controller to remove harmonic distortion from the feed-in current in the presence of parameter uncertainties. A lab prototype of 10kW grid-connected three-phase four-leg inverter has been built up to validate the proposed current control strategy. The simulations and experiments are provided to demonstrate the validity of the proposed control strategy

Pore-scale simulation of gas displacement after water flooding using three-phase lattice Boltzmann method

Water flooding is a commonly used technique to improve oil recovery, although the amount of oil left in reservoirs after the procedure is still significant. Gas displacement after water flooding is an effective way to recover residual oil, but the occurrence state and flow principles of multiphase fluid after gas injection are still ambiguous. Therefore, the gas displacement process after water flooding should be studied on the pore scale to provide a basis for formulating a reasonable gas injection program. Most of the current pore-scale studies focus on two-phase flow, while simulations that account for the influence of oil-gas miscibility and injected water are seldom reported. In this work, the multi-component multi-phase Shan-Chen lattice Boltzmann model is used to simulate the gas displacement after water flooding in a porous medium, and the effects of injected water, viscosity ratio, pore structure, and miscibility are analyzed. It is established that the injected water will cause gas flow path variations and lead to premature gas channeling. Under the impact of capillary pressure, the water retained in the porous medium during the water flooding stage further imbibes into the tiny pores during gas injection and displaces the remaining oil. When miscibility is considered, the oil-gas interface disappears, eliminating the influence of the capillary effect on the fluid flow and enabling the recovery of remaining oil at the corner. This study sheds light on the gas displacement mechanisms after water flooding from the pore-scale perspective and provides a potential avenue for improving oil recovery.Document Type: Original articleCited as: Wang, S., Chen, L., Feng, Q., Chen, L., Fang, C., Cui, R. Pore-scale simulation of gas displacement after water flooding using three-phase lattice Boltzmann method. Capillarity, 2023, 6(2): 19-30. https://doi.org/10.46690/capi.2023.02.0

Efficient ticket routing by resolution sequence mining

IT problem management calls for quick identification of resolvers to reported problems. The efficiency of this process highly depends on ticket routing—transferring problem ticket among various expert groups in search of the right resolver to the ticket. To achieve efficient ticket routing, wise decision needs to be made at each step of ticket transfer to determine which expert group is likely to be, or to lead to the resolver. In this paper, we address the possibility of improving ticket routing efficiency by mining ticket resolution sequences alone, without accessing ticket content. To demonstrate this possibility, a Markov model is developed to statistically capture the right decisions that have been made toward problem resolution, where the order of the Markov model is carefully chosen according to the conditional entropy obtained from ticket data. We also design a search algorithm, called Variable-order Multiple active State search (VMS), that generates ticket transfer recommendations based on our model. The proposed framework is evaluated on a large set of realworld problem tickets. The results demonstrate that VMS significantly improves human decisions: Problem resolvers can often be identified with fewer ticket transfers

NETWORK DEVICE SYSTEM LOGGING SUMMARIZATION BASED ON LOW-RANK ADAPTATION AND CONTRASTIVE LEARNING

Techniques are presented herein that support the automatic generation of refined and summarized text from a system logging (syslog) message sequence. Aspects of the presented techniques employ an abstractive syslog summarization large language model (LLM) that is trained with contrastive learning and then fine-tuned using a Low-Rank Adaptation (LoRA) methodology. Under further aspects of the presented techniques, auxiliary text (such as network incident reports and application incident reports) is added to the prompt of the input of the LLM model to help the model generate a richer syslog summarization