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The protein fluorescence and structural toolkit: Database and programs for the analysis of protein fluorescence and structural data

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PROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICS 2008年第4期71卷 1744-1754页

作者： Shen, Chi Menon, Rajiv Das, Dipanwita Bansal, Nidhi Nahar, Neha Guduru, Neelima Jaegle, Stephen Peckham, Joan Reshetnyak, Yana K. Univ Rhode Isl Dept Phys Kingston RI 02881 USA Univ Rhode Isl Dept Comp Sci & Stat Kingston RI 02881 USA

Protein fluorescence is a powerful tool for studying protein structure and dynamics if we have a means to interpret the spectral data in terms Of protein structural properties. Our previous research successfully provided this support through the development of individual software modules implementing the algorithms for fluorescence and structural analyses. Now we have integrated the developed software modules, introduced a new program for the assignment of tryptophan residues to spectral-structural classes, and created a web-based toolkit PFAST: protein fluorescence and structural toolkit. http://***/. PFAST contains three modules: (1) FCAT is a fluorescence-correlation analysis tool, which decomposes protein fluorescence spectra to reveal the spectral components of individual tryptophan residues or groups of tryptophan residues located close to each other, and assigns spectral components to one of five previously established spectral-structural classes. (2) SCAT is a structural-correlation analysis tool for the calculation of the structural parameters of the environment of tryptophan residues from the atomic structures of the proteins from the Protein Data Bank (PDB), and for the assignment of tryptophan residues to one of five spectral-structural classes. (3) The last module is a PFAST database that contains protein fluorescence and structural data obtained from results of the FCAT and SCAT analyses.

关键词： decomposition algorithms tryptophan fluorescence spectral-structural classes discriminant analysis classification score scientific databases scripted online database environment

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Disaggregated Bundle Methods for Distributed Market Clearing in Power Networks

Disaggregated Bundle Methods for Distributed Market Clearing...

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1st IEEE Global Conference on Signal and Information Processing (GlobalSIP)

作者： Zhang, Yu Gatsist, Nikolaos Giannakis, Georgios B. Univ Minnesota Dept ECE & DTC Minneapolis MN 55455 USA Univ Texas San Antonio Dept ECE San Antonio TX USA

ISBN: (纸本)9781479902484

A fast distributed approach is developed for the market clearing with large-scale demand response in electric power networks. In addition to conventional supply bids, demand offers from aggregators serving large numbers of residential smart appliances with different energy constraints are incorporated. Leveraging the Lagrangian relaxation based dual decomposition, the resulting optimization problem is decomposed into separate subproblems, and then solved in a distributed fashion by the market operator and each aggregator aided by the end-user smart meters. A disaggregated bundle method is adapted for solving the dual problem with a separable structure. Compared with the conventional dual update algorithms, the proposed approach exhibits faster convergence speed, which results in reduced communication overhead. Numerical results corroborate the effectiveness of the novel approach.

关键词： Aggregators decomposition algorithms demand response disaggregated bundle method market clearing

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Optimal Demand Response based on time-correlated utility in forward power markets

Optimal Demand Response based on time-correlated utility in ...

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IEEE PES Innovative Smart Grid Technologies Latin America (ISGT LATAM) Conference

作者： Montes de Oca, Sebastian Belzarena, Pablo Monzon, Pablo Univ Republica Fac Ingn Montevideo Uruguay

ISBN: (纸本)9781467366052

This paper is concerned with integration of demand response from small loads such as residential small appliance into forward electricity market. We use a simple model and formulate the problem as a dynamic program that maximizes social welfare. We focus particularly in those electric appliances with intertemporal consumption constraint. The satisfaction of end-users from operating them are captured through a utility function, which is also time-correlated. We pursued Lagrange decomposition in order to solve the high complexity problem optimization of the complete system. The main purpose is to optimally schedule a day in advance Local Service Entity's (LSE) procurement and final-users consumption in the forward electricity market.

关键词： Demand Response Smart Grid decomposition algorithms LSE

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Robust Optimization Models With Mixed-Integer Uncertainty Sets

Robust Optimization Models With Mixed-Integer Uncertainty Se...

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作者： Ansari Hadipour, Mehdi Oklahoma State University

学位级别：Ph.D., Doctor of Philosophy

In many real-world decision-making problems, some parameters are uncertain and decision-makers have to solve optimization models under uncertainty. In this study, we address two specific classes of problems with uncertain parameters and we are interested in solutions that are robust under any realization of uncertainty. The first one is a class of minimum-cost flow problems where the arcs are subject to multiple ripple-effect disruptions that increase their usage cost. The locations of the disruptions' epicenters are uncertain, and the decision-maker seeks a flow that minimizes cost assuming the worst-case realization of the disruptions. We evaluate the damage to each arc using different methods in which the arcs’ costs post-disruptions are represented with a mixed-integer feasible region. In the second class, we propose a novel problem that considers a decision-maker (a government agency or a public-private consortium) who seeks to efficiently allocate resources in retrofitting and recovery strategies to minimize social vulnerability under an uncertain tornado. As tornado paths cannot be forecast reliably, we model the problem using a two-stage robust optimization problem with a mixed-integer nonlinear uncertainty set that represents the tornado damage. In this study, our primary objective is to develop a mathematical and algorithmic framework for modeling and solving these specific problems. To accomplish this goal, we utilize robust optimization to formulate these problems; develop decomposition algorithms inspired by methods of mixed-integer optimization; and exploit the geometrical characteristics of the uncertainty sets to enhance the formulations and algorithms. We test our proposed approaches over real datasets and synthetic instances. The numerical experiments show that our methods provide sound decision-making strategies under uncertainty and achieve orders of magnitude improvements in computational times over standard approaches from the literature.

关键词： Bilevel programming decomposition algorithms Minimum cost flow Robust optimization Social vulnerability Tornado Decision-making

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A robust optimization model for distribution network design under a mixed integer set of scenarios

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COMPUTERS & OPERATIONS RESEARCH 2021年 136卷 105493-105493页

作者： Conde, Eduardo Leal, Marina Univ Seville Fac Math Dept Stat & Operat Res Campus Reina Mercedes Seville 41011 Spain

The design of a distribution network under uncertainty is addressed by using the minmax regret paradigm. Economic or technical uncertain factors may have a strong influence on the behavior of a distribution system and should be taken into account in the design process. These factors can affect to the construction and operating costs, origin or destination of the supplied commodities, required demands or arc capacities. In this paper, some of these uncertain parameters are supposed to be continuous while others are clearly discrete in nature, giving rise to a mixed integer set of scenarios under which the system may be assessed. We explore how to design a robust distribution network when the uncertainty in the continuous parameters is modeled through polyhedral sets combined with other sources of uncertainty for the discrete parameters. The corresponding optimization problem is solved using a Benders decomposition framework. Finally, a computational experiment with specialized solvers is carried out in order to test the efficiency of our algorithms.

关键词： Robust network design Minmax regret models and Benders decomposition algorithms

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Risk-averse multistage stochastic programs with expected conditional risk measures

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COMPUTERS & OPERATIONS RESEARCH 2024年 172卷

作者： Khatami, Maryam Silva, Thuener Pagnoncelli, Bernardo K. Ntaimo, Lewis Univ North Texas G Brint Ryan Coll Business Dept Informat Technol & Decis Sci Denton TX 76205 USA Pontifical Catholic Univ Rio de Janeiro Dept Ind Engn Rio De Janeiro Brazil Univ Cote dAzur SKEMA Business Sch Lille France Texas A&M Univ Dept Ind & Syst Engn College Stn TX USA

We study risk-averse multistage stochastic programs with expected conditional risk measures (ECRMs). ECRMs are attractive because they are time-consistent, which means that a plan made today will not be changed in the future if the problem is re-solved given a realization of the random variables. We show that the computational burden of solving the risk-averse problems based on ECRMs is the same as the risk-neutral ones. We consider ECRMs for both quantile and deviation mean-risk measures, deriving the Bellman equations in each case. Finally, we illustrate our results with extensive numerical computations for problems from two applications: hydrothermal scheduling and portfolio selection. The results show that the ECRM approach provides higher expected costs in the early stages to hedge against cost spikes in later stages for the hydrothermal scheduling problem. For the portfolio selection problem, the new approach gives well-diversified portfolios over time. Overall, the ECRM approach provides superior performance over the risk-neutral model under extreme scenario conditions.

关键词： Stochastic programming decomposition algorithms Expected conditional risk measures

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Impact of corpus callosum fiber tract crossing on polarimetric images of human brain histological sections: ex vivo studies in transmission configuration

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JOURNAL OF BIOMEDICAL OPTICS 2023年第10期28卷 102908-102908页

作者： Ivanov, Deyan Si, Lu Felger, Leonard Maragkou, Theoni Schucht, Philippe Schanne-Klein, Marie-Claire Ma, Hui Ossikovski, Razvigor Novikova, Tatiana Ecole Polytech Inst Polytech Paris CNRS LPICM Palaiseau France Tsinghua Univ Tsinghua Berkeley Shenzhen Inst Shenzhen Peoples R China Univ Bern Bern Univ Hosp Dept Neurosurg Inselspital Bern Switzerland Univ Bern Inst Tissue Med & Pathol Bern Switzerland Ecole Polytech Inst Polytech Paris Inserm CNRSLOB Palaiseau France Tsinghua Univ Dept Phys Beijing Peoples R China Florida Int Univ Dept Biomed Engn Miami FL 33199 USA

Significance: Imaging Mueller polarimetry is capable to trace in-plane orientation of brain fiber tracts by detecting the optical anisotropy of white matter of healthy brain. Brain tumor cells grow chaotically and destroy this anisotropy. Hence, the drop in scalar retardance values and randomization of the azimuth of the optical axis could serve as the optical marker for brain tumor zone ***: The presence of underlying crossing fibers can also affect the values of scalar retardance and the azimuth of the optical axis. We studied and analyzed the impact of fiber crossing on the polarimetric images of thin histological sections of brain corpus ***: We used the transmission Mueller microscope for imaging of two-layered stacks of thin sections of corpus callosum tissue to mimic the overlapping brain fiber tracts with different fiber orientations. The decomposition of the measured Mueller matrices was performed with differential and Lu-Chipman algorithms and completed by the statistical analysis of the maps of scalar retardance, azimuth of the optical axis, and ***: Our results indicate the sensitivity of Mueller polarimetry to different spatial arrangement of brain fiber tracts as seen in the maps of scalar retardance and azimuth of optical axis of two-layered stacks of corpus callosum sections The depolarization varies slightly (<15 % ) with the orientation of the optical axes in both corpus callosum stripes, but its value increases by 2.5 to 3 times with the stack ***: The crossing brain fiber tracts measured in transmission induce the drop in values of scalar retardance and randomization of the azimuth of the optical axis at optical path length of 15 mu m. It suggests that the presence of nerve fibers crossing within the depth of few microns will be also detected in polarimetric maps of brain white matter measured in reflection configuration.

关键词： corpus callosum brain fiber crossing Mueller polarimetry decomposition algorithms image processing

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An Efficient Solution Algorithm for Large-Scale Stochastic Mixed-Integer Linear Programs 1

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IFAC Proceedings Volumes 2012年第15期45卷 892-897页

作者： Arul Sundaramoorthy Xiang Li James M.B. Evans Paul I. Barton Process Systems Engineering Laboratory Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA (Tel: 617-253-6468) Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA (Tel: 617-253-1387) Process Systems Engineering Laboratory Department of Chemical Engineering Massachusetts Institute of Technology Cambridge MA 02139 USA (Tel: 617-253-6526)

In this work, we develop a decomposition strategy that exploits the underlying structure of two-stage stochastic programming (TSSP) formulations with mixed-integer linear programming (MILP) recourse problems. We demonstrate the effectiveness of the proposed algorithm using several examples of a capacity planning problem that arises in the pharmaceutical industry. The examples contain up to 10 potential products and nearly 60,000 scenarios. With the proposed decomposition algorithm, the solution time scales linearly with the number of scenarios, whereby a 10-product example with over 36 million binary variables, nearly 176 million continuous variables, and over 150 million constraints was solved in less than 3 hr of solver time.

关键词： decomposition algorithms Stochastic programming

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Recursive strategy for decomposing Betti tables of complete intersections

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INTERNATIONAL JOURNAL OF ALGEBRA AND COMPUTATION 2019年第7期29卷 1165-1191页

作者： Gibbons, Courtney R. Huben, Robert Stone, Branden Hamilton Coll Math Dept 198 Coll Hill Rd Clinton NY 13323 USA Univ Nebraska Dept Math POB 880130 Lincoln NE 68588 USA

In the spirit of Boij-Soderberg theory, we introduce a recursive decomposition algorithm for the Betti diagram of a complete intersection using the diagram of a complete intersection defined by a subset of the original generators. This alternative algorithm is the main tool that we use to investigate stability and compatibility of the Boij-Soderberg decompositions of related diagrams;indeed, when the biggest generating degree is sufficiently large, the alternative algorithm produces the Boij-Soderberg decomposition. We also provide a detailed analysis of the Boij-Soderberg decomposition for Betti diagrams of codimension four complete intersections where the largest generating degree satisfies the size condition.

关键词： Boij-Soderberg theory Betti diagrams complete intersections decomposition algorithms

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Two-Stage Stochastic Optimization for Optimal Power Flow Under Renewable Generation Uncertainty

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ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION 2014年第1期24卷 2-2页

作者： Dzung Phan Ghosh, Soumyadip IBM TJ Watson Res Ctr Dept Business Analyt & Math Sci New York NY 10598 USA

We propose a two-stage stochastic version of the classical economic dispatch problem with alternating-current power flow constraints, a nonconvex optimization formulation that is central to power transmission and distribution over an electricity grid. Certain generation decisions made in the first stage cannot further be changed in the second stage, where the uncertainty due to various factors such as renewable generation is realized. Any supply-demand mismatch in the second stage must be alleviated using high marginal cost power sources that can be tapped in short order. We solve a Sample-Average Approximation (SAA) of this formulation by capturing the uncertainty using a finite number of scenario samples. We propose two outer approximation algorithms to solve this nonconvex program to global optimality. We use recently discovered structural properties for the classical deterministic problem to show that when these properties hold the sequence of approximate solutions obtained under both alternatives has a limit point that is a globally optimal solution to the two-stage nonconvex SAA program. We also present an alternate local optimization approach to solving the SAA problem based on the Alternating Direction Method of Multipliers (ADMM). Numerical experiments for a variety of parameter settings were carried out to demonstrate the efficiency and usability of our method over ADMM for large practical instances.

关键词： Stochastic optimization optimal power flow decomposition algorithms sample-average approximation global solution

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