This paper proposes a simulation-based optimization (SO) method that enables the efficient use of complex stochastic urban traffic simulators to address various transportation problems. It presents a metamodel that integrates information from a simulator with an analytical queueing network model. The proposed metamodel combines a general-purpose component (a quadratic polynomial), which provides a detailed local approximation, with a physical component (the analytical queueing network model), which provides tractable analytical and global information. This combination leads to an SO framework that is computationally efficient and suitable for complex problems with very tight computational budgets.We integrate this metamodel within a derivative-free trust region algorithm. We evaluate the performance of this method considering a traffic signal control problem for the Swiss city of Lausanne, different demand scenarios and tight computational budgets. The method leads to well-performing signal plans. It leads to reduced, as well as more reliable, average travel times.Key words : Simulation-based optimization, Traffic control, Metamodel, Queueing IntroductionMicroscopic urban traffic simulators embed the most detailed traffic models. They represent individual vehicles and can account for vehicle-specific technologies/attributes. They represent individual travelers and embed detailed disaggregate behavioral models that describe how these travelers make travel decisions (e.g. departure time choice, mode choice, route choice, how travelers respond to real-time traffic information, how they decide to change lanes). They also provide a detailed representation of the underlying 1 Osorio and Bierlaire: A simulation-based optimization framework for urban transportation problems 2 Article submitted to Operations Research; manuscript no. (Please, provide the mansucript number!) supply network (e.g. variable message signs, public transport priorities). Thus, these traffic simulators can describe in detail the interactions between vehicle performance (e.g. instantaneous energy consumption, emissions), traveler behavior and the underlying transportation infrastructure, and yield a detailed description of traffic dynamics in urban networks.These simulators can provide accurate network performance estimates in the context of what-if analysis or sensitivity analysis. They are therefore often used to evaluate a set of predetermined transportation strategies (e.g., traffic management or network design strategies). Nevertheless, using them to derive appropriate strategies, i.e., to perform simulation-based optimization (SO), is an intricate task.We focus on transportation problems of the following form:The objective function f is usually the expected value of a stochastic network performance measure, F . The probability distribution function of F depends on the deterministic decision or control vector x and on deterministic exogenous parameters p. The feasible space Ω consists of a set of general (e.g., nonconvex) constraints that lin...

Liquid chromatography tandem mass spectrometry (LC-MS/MS) and multiple reaction monitoring mass spectrometry (MRM-MS) proteomics analyses were performed on eccrine sweat of healthy controls, and the results were compared with those from individuals diagnosed with schizophrenia (SZ). This is the first large scale study of the sweat proteome. First, we performed LC-MS/MS on pooled SZ samples and pooled control samples for global proteomics analysis. Results revealed a high abundance of diverse proteins and peptides in eccrine sweat. Most of the proteins identified from sweat samples were found to be different than the most abundant proteins from serum, which indicates that eccrine sweat is not simply a plasma transudate, and may thereby be a source of unique disease-associated biomolecules. A second independent set of patient and control sweat samples were analyzed by LC-MS/MS and spectral counting to determine qualitative protein differential abundances between the control and disease groups. Differential abundances of selected proteins, initially determined by spectral counting, were verified by MRM-MS analyses. Seventeen proteins showed a differential abundance of approximately two-fold or greater between the SZ pooled sample and the control pooled sample. This study demonstrates the utility of LC-MS/MS and MRM-MS as a viable strategy for the discovery and verification of potential sweat protein disease biomarkers.

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T his paper proposes a computationally efficient simulation-based optimization (SO) algorithm suitable to address large-scale generally constrained urban transportation problems. The algorithm is based on a novel metamodel formulation. We embed the metamodel within a derivative-free trust region algorithm and evaluate the performance of this SO approach considering tight computational budgets. We address a network-wide traffic signal control problem using a calibrated microscopic simulation model of evening peak period traffic of the full city of Lausanne, Switzerland, which consists of more than 600 links and 200 intersections. We control 99 signal phases of 17 intersections distributed throughout the entire network. This SO problem is a high-dimensional nonlinear constrained problem. It is considered large-scale and complex in the fields of derivative-free optimization, traffic signal optimization, and simulation-based optimization. We compare the performance of the proposed metamodel method to that of a traditional metamodel method and that of a widely used commercial signal control software. The proposed method systematically and efficiently identifies signal plans with improved average city-wide travel times.

Severe acute respiratory syndrome coronavirus 2 has spread rapidly around the globe. However, despite its high pathogenicity and transmissibility, the severity of the associated disease, COVID-19, varies widely. While the prognosis is favorable in most patients, critical illness, manifested by respiratory distress, thromboembolism, shock, and multi-organ failure, has been reported in about 5% of cases. Several studies have associated poor COVID-19 outcomes with the exhaustion of natural killer cells and cytotoxic T cells, lymphopenia, and elevated serum levels of D-dimer. In this article, we propose a common pathophysiological denominator for these negative prognostic markers, endogenous, angiotensin II toxicity. We hypothesize that, like in avian influenza, the outlook of COVID-19 is negatively correlated with the intracellular accumulation of angiotensin II promoted by the viral blockade of its degrading enzyme receptors. In this model, upregulated angiotensin II causes premature vascular senescence, leading to dysfunctional coagulation, and immunity. We further hypothesize that angiotensin II blockers and immune checkpoint inhibitors may be salutary for COVID-19 patients with critical illness by reversing both the clotting and immune defects (Graphical Abstract).