A NOTE ON INCORPORATING TOOL LIFE VARIABILITY IN THE GEOMETRIC PROGRAMMING FORMULATION OF MACHINING ECONOMICS PROBLEMS

This note incorporates tool life variability in the geometric programming formulation of machining economics problems by transforming the probability of tool failure while machining a part to a function of the cutting variables for both Markovian and non-Markovian tool failure processes. Numerical examples show that the stochastic nature of tool life cannot be ignored, since it results in different optimal cutting variable values compared to the deterministic tool life case.     

Rollout sampling approximate policy iteration

Several researchers have recently investigated the connection between reinforcement learning and classification. We are motivated by proposals of approximate policy iteration schemes without value functions, which focus on policy representation using classifiers and address policy learning as a supervised learning problem. This paper proposes variants of an improved policy iteration scheme which addresses the core sampling problem in evaluating a policy through simulation as a multi-armed bandit machine. The resulting algorithm offers comparable performance to the previous algorithm achieved, however, with significantly less computational effort. An order of magnitude improvement is demonstrated experimentally in two standard reinforcement learning domains: inverted pendulum and mountain-car.     

Algorithm,software, and hardware optimizations for Delaunay mesh generation on simultaneous multithreaded architectures

This article focuses on the optimization of PCDM, a parallel, two-dimensional (2D) Delaunay mesh generation application, and its interaction with parallel architectures based on simultaneous multithreading (SMT) processors. We first present the step-by-step effect of a series of optimizations on performance. These optimizations improve the performance of PCDM by up to a factor of six. They target issues that very often limit the performance of scientific computing codes. We then evaluate the interaction of PCDM with a real SMT-based SMP system, using both high-level metrics, such as execution time, and low-level information from hardware performance counters.     

Optimal Control of Two-Stage Discrete Event Systems with Real-Time Constraints

We consider discrete event systems (DES) involving tasks with real-time constraints and seek to control processing times so as to minimize a cost function subject to each task meeting its own constraint. When tasks are processed over a single stage, it has been shown that there are structural properties of the optimal sample path that lead to very efficient solutions of such problems. When tasks are processed over multiple stages and are subject to end-to-end real-time constraints, these properties no longer hold and no obvious extensions are known. We consider a two-stage problem with homogeneous cost functions over all tasks at each stage and derive several new optimality properties. These properties lead to the idea of introducing “virtual” deadlines at the first stage, thus partially decoupling the stages so that the known efficient solutions for single-stage problems can be used. We prove that the solution obtained by an iterative virtual deadline algorithm (VDA) converges to the global optimal solution of the two-stage problem and illustrate the efficiency of the VDA through numerical examples.

Perturbation Analysis of Multiclass Stochastic Fluid Models

We use a stochastic fluid model (SFM) to capture the operation of finite-capacity queueing systems with multiple customer classes. We derive gradient estimators for class-dependent loss and workload related performance metrics with respect to any one of several threshold parameters used for buffer control. These estimators are shown to be unbiased and directly observable from a sample path without any knowledge of underlying stochastic characteristics of the traffic processes. This renders them computable in on-line environments and easily implementable in settings such as communication networks.     

Unraveling the Immune Microenvironment of Thymic Epithelial Tumors: Implications for Autoimmunity and Treatment

Thymic Epithelial Tumors (TETs) represent a rare tumor family, originating from the epithelial component of the thymus gland. Clinicopathologically, they are segregated into six major subtypes, associated with distinct histological features and clinical outcomes. Their emergence and evolution are accompanied by the generation of a complex tumor microenvironment (TME), dominated by phenotypically and functionally divergent immune cellular subsets, in different maturation states and in analogies that vary significantly among different subtypes. These heterogenous leukocyte populations exert either immune-permissive and tumor-suppressive functions or vice versa, and the dynamic equilibrium established among them either dictates the tumor immune milieu towards an immune-tolerance state or enables the development of a productive spontaneous tumoricidal response. The immunologically “hot” microenvironment, defining a significant proportion of TETs, makes them a promising candidate for the implementation of immune checkpoint inhibitors (ICIs). A number of phase I and II clinical trials have already demonstrated significant, type-specific clinical efficacy of PD-L1 inhibitors, even though substantial limitations in their utilization derive from their immune-mediated adverse effects. Moreover, the completed clinical studies involved relatively restricted patient samples and an expansion in the enrolled cohorts is required, so that more trustworthy conclusions regarding the benefit from ICIs in TETs can be extracted.     

Androgen Receptor Gene Pathway Upregulation and Radiation Resistance in Oligometastatic Prostate Cancer

Stereotactic ablative body radiotherapy (SABR) is currently used as a salvage intervention for men with oligometastatic prostate cancer (PC), and increasingly so since the results of the Stereotactic Ablative Body Radiotherapy for the Comprehensive Treatment of Oligometastatic Cancers (SABR-COMET) trial reported a significant improvement in overall survival with SABR. The addition of androgen deprivation therapy (ADT) to localised prostate radiotherapy improves survival as it sensitises PC to radiotherapy-induced cell death. The importance of the androgen receptor (AR) gene pathway in the development of resistance to radiotherapy is well established. In this review paper, we will examine the data to determine how we can overcome the upregulation of the AR pathway and suggest a strategy for improving outcomes in men with oligometastatic hormone-sensitive PC.     

Optimal control of batteries with fully and partially available rechargeability

Motivated by the increasing dependence of many systems on battery energy, we study the problem of optimally controlling how to discharge and recharge a non-ideal battery so as to maximize the work it can perform over a given time period and still maintain a desired final energy level. Modeling a battery as a dynamic system, we adopt a Kinetic Battery Model (KBM) and formulate a finite-horizon optimal control problem when recharging is always feasible under the constraint that discharging and recharging cannot occur at the same time. The solution is shown to be of bang–bang type with the property that the battery is always in recharging mode during the last part of the interval. When the length of the time horizon exceeds a critical value, we also show that the optimal policy includes chattering. Numerical results are included to illustrate our analysis. We then extend the problem to settings where recharging is only occasionally feasible and show that it can be reduced to a nonlinear optimization problem which can be solved at least numerically.     

SecMR – a secure multipath routing protocol for ad hoc networks

Multipath routing in ad hoc networks increases the resiliency against security attacks of collaborating malicious nodes, by maximizing the number of nodes that an adversary must compromise in order to take control of the communication. In this paper, we identify several attacks that render multipath routing protocols vulnerable to collaborating malicious nodes. We propose an on-demand multipath routing protocol, the secure multipath routing protocol (SecMR), and we analyze its security properties. Finally, through simulations, we evaluate the performance of the SecMR protocol in comparison with existing secure multipath routing protocols.