Providing for individual differences in civic instruction

Thesis (M.A.)--Boston University, 1946. This item was digitized by the Internet Archive

Solving the Top-percentile traffic routing problem by Approximate Dynamic Programming

Internet Service Providers (ISPs) have the ability to route their traffic over different network providers. This study investigates the optimal routing strategy under multihoming in the case where network providers charge ISPs according to top-percentile pricing (i.e. based on the ?th highest volume of traffic shipped). We call this problem the Top-percentile Traffic Routing Problem (TpTRP). The TpTRP is a multistage stochastic optimization problem. Routing decision for every time period should be made before knowing the amount of traffic that is to be sent. The stochastic nature of the problem forms the critical difficulty of this study. Solution approaches based on Stochastic Integer Programming or Stochastic Dynamic Programming (SDP) suffer from the curse of dimensionality, which restricts their applicability. To overcome this, we suggest to use Approximate Dynamic Programming, which exploits the structure of the problem to construct continuous approximations of the value functions in SDP. Thus, the curse of dimensionality is largely avoided

On the Effectiveness of Sequential Linear Programming for the Pooling Problem

The aim of this paper is to compare the performance of a local solution technique -- namely Sequential Linear Programming (SLP) employing random starting points -- with state-of-the-art global solvers such as Baron and more sophisticated local solvers such as Sequential Quadratic Programming and Interior Point for the pooling problem. These problems can have many local optima, and we present a small example that illustrates how this can occur. We demonstrate that SLP -- usually deemed obsolete since the arrival of fast reliable QP solvers, Interior Point Methods and sophisticated global solvers -- is still the method of choice for an important class of pooling problem when the criterion is the quality of the solution found within a given acceptable time budget. In addition we introduce a new formulation, the qq-formulation, for the case of fixed demands, that exclusively uses proportional variables. We compare the performance of SLP and the global solver Baron on the qq-formulation and other common formulations. While Baron with the qq-formulation generates weaker bounds than with the other formulations tested, for both SLP and Baron the qq-formulation finds the best solutions within a given time budget. The qq-formulation can be strengthened by pq-like cuts in which case the same bounds as for the pq-formulation are obtained. However the associated time penalty due to the additional constraints results in poorer solution quality within the time budget

Targeting colorectal cancer with human anti-EGFR monoclonocal antibodies: focus on panitumumab

The human anti-epidermal growth factor receptor (EGFR) monoclonal antibody, panitumumab, represents a significant advance in the treatment of colorectal cancer. The strategy to target this receptor is based on sound cancer biology demonstrating its essential role in colorectal carcinogenesis. Panitumumab, unlike its predecessor, cetuximab, is fully human and thus reduces the incidence of hypersensitivity reactions. But, in several clinical trials, unexpected toxicities have become more apparent, raising concerns of how readily panitumumab can succeed cetuximab. This paper reviews the development of this agent and the pivotal clinical trials that help our understanding of its optimal use in colorectal cancer treatment

Management of BRAF-mutant metastatic colorectal cancer: a review of treatment options and evidence-based guidelines

BRAF mutation; Metastatic colorectal cancer; Prognostic markersMutación BRAF; Cáncer colorrectal metastásico; Marcadores pronósticosMutació BRAF; Càncer colorectal metastàtic; Marcadors pronòsticsBackground Colorectal cancer (CRC) is still a leading cause of cancer-related deaths in the United States and worldwide, despite recent improvements in cancer management. CRC, like many malignancies, is a heterogeneous disease, with subtypes characterized by genetic alterations. One common mutation in CRC is in the BRAF gene (most commonly V600E substitution). This occurs in ∼10% of patients with metastatic CRC (mCRC) and is a marker of poor prognosis. Design Herein, we review the clinical and translational literature on the role of the BRAF V600E mutation in the pathogenesis of mCRC, its mechanisms as a prognostic marker, and its potential utility as a predictive marker of treatment response. We then summarize the current evidence-based recommendations for management of BRAF V600E-mutated mCRC, with a focus on recent clinical research advances in this setting. Results The current standard therapies for first-line treatment of BRAF-mutated mCRC are chemotherapy with bevacizumab as well as 5-fluorouracil, leucovorin, oxaliplatin, and irinotecan (FOLFOXIRI) plus bevacizumab in patients with a good performance status. Combination strategies involving mitogen-activated protein kinase (MAPK) pathway blockade have shown promising results for the treatment of patients with BRAF V600E-mutated mCRC. The Binimetinib, Encorafenib, And Cetuximab cOmbiNed to treat BRAF-mutant ColoRectal Cancer (BEACON CRC) study represents the largest study in this population to date and has given strong clinical evidence to support BRAF and epidermal growth factor receptor inhibition with the combination of encorafenib plus cetuximab. Conclusions The treatment of BRAF-mutated mCRC has evolved rapidly over the last several years. Recently, combination strategies involving MAPK pathway blockade have shown promising results in BRAF V600E-mutated mCRC, and other potential targets continue to be explored. In addition, a greater understanding of the role of BRAF V600E mutation in the pathogenesis of CRC should also continue to fuel advances in the management of patients with mCRC harboring this genetic aberration.This work was supported by Array BioPharma, which was acquired by Pfizer in July 2019 (no grant number)

Local solutions of the optimal power flow problem

The existence of locally optimal solutions to the AC optimal power flow problem (OPF) has been a question of interest for decades. This paper presents examples of local optima on a variety of test networks including modified versions of common networks. We show that local optima can occur because the feasible region is disconnected and/or because of nonlinearities in the constraints. Standard local optimization techniques are shown to converge to these local optima. The voltage bounds of all the examples in this paper are between ±5% and ±10% off-nominal. The examples with local optima are available in an online archive (http://www.maths.ed.ac.uk/optenergy/LocalOpt/) and can be used to test local or global optimization techniques for OPF. Finally we use our test examples to illustrate the behavior of a recent semi-definite programming approach that aims to find the global solution of OPF

MILP formulation for controlled islanding of power networks

This paper presents a flexible optimization approach to the problem of intentionally forming islands in a power network. A mixed integer linear programming (MILP) formulation is given for the problem of deciding simultaneously on the boundaries of the islands and adjustments to generators, so as to minimize the expected load shed while ensuring no system constraints are violated. The solution of this problem is, within each island, balanced in load and generation and satisfies steady-state DC power flow equations and operating limits. Numerical tests on test networks up to 300 buses show the method is computationally efficient. A subsequent AC optimal load shedding optimization on the islanded network model provides a solution that satisfies AC power flow. Time-domain simulations using second-order models of system dynamics show that if penalties were included in the MILP to discourage disconnecting lines and generators with large flows or outputs, the actions of network splitting and load shedding did not lead to a loss of stability

Optimization-Based Islanding of Power Networks Using Piecewise Linear AC Power Flow

In this paper, a flexible optimization-based framework for intentional islanding is presented. The decision is made of which transmission lines to switch in order to split the network while minimizing disruption, the amount of load shed, or grouping coherent generators. The approach uses a piecewise linear model of AC power flow, which allows the voltage and reactive power to be considered directly when designing the islands. Demonstrations on standard test networks show that solution of the problem provides islands that are balanced in real and reactive power, satisfy AC power flow laws, and have a healthy voltage profile