148 research outputs found

    Optimizing Equitable Resource Allocation in Parallel Any-Scale Queues with Service Abandonment and its Application to Liver Transplant

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    We study the problem of equitably and efficiently allocating an arriving resource to multiple queues with customer abandonment. The problem is motivated by the cadaveric liver allocation system of the United States, which includes a large number of small-scale (in terms of yearly arrival intensities) patient waitlists with the possibility of patients abandoning (due to death) until the required service is completed (matched donor liver arrives). We model each waitlist as a GI/MI/1+GI queue, in which a virtual server receives a donor liver for the patient at the top of the waitlist, and patients may abandon while waiting or during service. To evaluate the performance of each queue, we develop a finite approximation technique as an alternative to fluid or diffusion approximations, which are inaccurate unless the queue's arrival intensity is large. This finite approximation for hundreds of queues is used within an optimization model to optimally allocate donor livers to each waitlist. A piecewise linear approximation of the optimization model is shown to provide the desired accuracy. Computational results show that solutions obtained in this way provide greater flexibility, and improve system performance when compared to solutions from the fluid models. Importantly, we find that appropriately increasing the proportion of livers allocated to waitlists with small scales or high mortality risks improves the allocation equity. This suggests a proportionately greater allocation of organs to smaller transplant centers and/or those with more vulnerable populations in an allocation policy. While our motivation is from liver allocation, the solution approach developed in this paper is applicable in other operational contexts with similar modeling frameworks.Comment: 48 Page

    On the number of frequency hypercubes Fn(4;2,2)F^n(4;2,2)

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    A frequency nn-cube Fn(4;2,2)F^n(4;2,2) is an nn-dimensional 4××44\times\cdots\times 4 array filled by 00s and 11s such that each line contains exactly two 11s. We classify the frequency 44-cubes F4(4;2,2)F^4(4;2,2), find a testing set of size 2525 for F3(4;2,2)F^3(4;2,2), and derive an upper bound on the number of Fn(4;2,2)F^n(4;2,2). Additionally, for any nn greater than 22, we construct an Fn(4;2,2)F^n(4;2,2) that cannot be refined to a latin hypercube, while each of its sub-Fn1(4;2,2)F^{n-1}(4;2,2) can. Keywords: frequency hypercube, frequency square, latin hypercube, testing set, MDS cod

    Boosting Feedback Efficiency of Interactive Reinforcement Learning by Adaptive Learning from Scores

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    Interactive reinforcement learning has shown promise in learning complex robotic tasks. However, the process can be human-intensive due to the requirement of large amount of interactive feedback. This paper presents a new method that uses scores provided by humans, instead of pairwise preferences, to improve the feedback efficiency of interactive reinforcement learning. Our key insight is that scores can yield significantly more data than pairwise preferences. Specifically, we require a teacher to interactively score the full trajectories of an agent to train a behavioral policy in a sparse reward environment. To avoid unstable scores given by human negatively impact the training process, we propose an adaptive learning scheme. This enables the learning paradigm to be insensitive to imperfect or unreliable scores. We extensively evaluate our method on robotic locomotion and manipulation tasks. The results show that the proposed method can efficiently learn near-optimal policies by adaptive learning from scores, while requiring less feedback compared to pairwise preference learning methods. The source codes are publicly available at https://github.com/SSKKai/Interactive-Scoring-IRL.Comment: Accepted by IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023

    The Cost-Effectiveness of Lowering Permissible Noise Levels Around U.S. Airports

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    Aircraft noise increases the risk of cardiovascular diseases and mental illness. The allowable limit for sound in the vicinity of an airport is 65 decibels (dB) averaged over a 24-h ‘day and night’ period (DNL) in the United States. We evaluate the trade-off between the cost and the health benefits of changing the regulatory DNL level from 65 dB to 55 dB using a Markov model. The study used LaGuardia Airport (LGA) as a case study. In compliance with 55 dB allowable limit of aircraft noise, sound insulation would be required for residential homes within the 55 dB to 65 dB DNL. A Markov model was built to assess the cost-effectiveness of installing sound insulation. One-way sensitivity analyses and Monte Carlo simulation were conducted to test uncertainty of the model. The incremental cost-effectiveness ratio of installing sound insulation for residents exposed to airplane noise from LGA was 11,163/QALYgained(9511,163/QALY gained (95% credible interval: cost-saving and life-saving to 93,054/QALY gained). Changing the regulatory standard for noise exposure around airports from 65 dB to 55 dB comes at a very good value

    A Novel Memristive Multilayer Feedforward Small-World Neural Network with Its Applications in PID Control

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    In this paper, we present an implementation scheme of memristor-based multilayer feedforward small-world neural network (MFSNN) inspirited by the lack of the hardware realization of the MFSNN on account of the need of a large number of electronic neurons and synapses. More specially, a mathematical closed-form charge-governed memristor model is presented with derivation procedures and the corresponding Simulink model is presented, which is an essential block for realizing the memristive synapse and the activation function in electronic neurons. Furthermore, we investigate a more intelligent memristive PID controller by incorporating the proposed MFSNN into intelligent PID control based on the advantages of the memristive MFSNN on computation speed and accuracy. Finally, numerical simulations have demonstrated the effectiveness of the proposed scheme

    Deintercalation of Al from MoAlB by molten salt etching to achieve a Mo2AlB2 compound and 2D MoB nanosheets

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    Two-dimensional (2D) MoB metal borides (MoB MBene) have attracted much attention due to their fascinating properties and functional applications. So far, work on the synthesis of 2D MoB nanosheets by acid or alkaline etching of MoAlB has not been very successful. It has been proposed that the 2D MoB MBene may be fabricated by chemical etching of a Mo2AlB2 precursor, but further investigations were not performed possibly due to the difficult preparation of the metastable Mo2AlB2 compound at high temperatures by solid-state reactions. Here, we report on the successful synthesis of the Mo2AlB2 compound and 2D MoB nanosheets by the deintercalation of Al from MoAlB through a ZnCl2 molten salt etching approach at relatively low temperatures. The influence of etching temperature, etching time, and starting mixtures on the formation of desirable phases have been investigated. A pure Mo2AlB2 compound was synthesized at temperatures below 600 ℃, while the 2D MoB MBene nanosheets were obtained at 700 ℃ through the molten salt etching of MoAlB. In addition, the present work further confirms that the MoB MBene can be prepared by etching the as-synthesized Mo2AlB2 precursor in LiF–HCl solution. Our work demonstrates that the molten salt etching is an effective method to prepare 2D MoB MBene

    Kinetic Analysis of Bio-Oil Aging by Using Pattern Search Method

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    Bio-oil derived from fast pyrolysis of lignocellulosic biomass is unstable, and aging would occur during its storage, handling, and transportation. The kinetic analysis of bio-oil aging is fundamental for the investigation of bio-oil aging mechanisms and the utilization of bio-oil as biofuels, biomaterials or biochemicals. The aging kinetic experiments of bio-oil from poplar wood pyrolysis were conducted at different aging temperatures of 303, 333, 353, and 363 K for different specified periods of time in capped glass vessels. The traditional method with two separate fittings was employed to fit experimental data, and the results indicated that the obtained kinetic parameters could not fit the experimental data well. An advanced approach for kinetic modeling of bio-oil aging has been developed by simultaneously processing experimental data at different aging temperatures and using the pattern search method. The aging kinetic model with the optimized parameters predicted the aging kinetic experimental data of the bio-oil sample very well for different aging temperatures

    A new opportunity for the emerging tellurium semiconductor: making resistive switching devices

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    Abstract: The development of the resistive switching cross-point array as the next-generation platform for high-density storage, in-memory computing and neuromorphic computing heavily relies on the improvement of the two component devices, volatile selector and nonvolatile memory, which have distinct operating current requirements. The perennial current-volatility dilemma that has been widely faced in various device implementations remains a major bottleneck. Here, we show that the device based on electrochemically active, low-thermal conductivity and low-melting temperature semiconducting tellurium filament can solve this dilemma, being able to function as either selector or memory in respective desired current ranges. Furthermore, we demonstrate one-selector-one-resistor behavior in a tandem of two identical Te-based devices, indicating the potential of Te-based device as a universal array building block. These nonconventional phenomena can be understood from a combination of unique electrical-thermal properties in Te. Preliminary device optimization efforts also indicate large and unique design space for Te-based resistive switching devices

    Anomalous stopping of laser-accelerated intense proton beam in dense ionized matter

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    Ultrahigh-intensity lasers (1018^{18}-1022^{22}W/cm2^{2}) have opened up new perspectives in many fields of research and application [1-5]. By irradiating a thin foil, an ultrahigh accelerating field (1012^{12} V/m) can be formed and multi-MeV ions with unprecedentedly high intensity (1010^{10}A/cm2^2) in short time scale (\simps) are produced [6-14]. Such beams provide new options in radiography [15], high-yield neutron sources [16], high-energy-density-matter generation [17], and ion fast ignition [18,19]. An accurate understanding of the nonlinear behavior of beam transport in matter is crucial for all these applications. We report here the first experimental evidence of anomalous stopping of a laser-generated high-current proton beam in well-characterized dense ionized matter. The observed stopping power is one order of magnitude higher than single-particle slowing-down theory predictions. We attribute this phenomenon to collective effects where the intense beam drives an decelerating electric field approaching 1GV/m in the dense ionized matter. This finding will have considerable impact on the future path to inertial fusion energy.Comment: 8 pages, 4 figure
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