nonlinear programming has found useful applications in protein biophysics to help understand the microscopic exchange kinetics of data obtained using hydrogen-deuterium exchange mass spectrometry (HDX-MS). Finding a m...
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nonlinear programming has found useful applications in protein biophysics to help understand the microscopic exchange kinetics of data obtained using hydrogen-deuterium exchange mass spectrometry (HDX-MS). Finding a microscopic kinetic solution for HDX-MS data provides a window into local protein stability and energetics allowing them to be quantified and understood. Optimization of HDX-MS data is a significant challenge, however, due to the requirement to solve a large number of variables simultaneously with exceptionally large variable bounds. Modeled rates are frequently uncertain with an explicate dependency on the initial guess values. In order to enhance the search for a minimum solution in HDX-MS optimization, the ability of selected constrained variables to propagate throughout the data is considered. We reveal that locally bound constrained optimization induces a global effect on all variables. The global response to local constraints is large and surprisingly long-range, but the outcome is unpredictable, unexpectedly decreasing the overall accuracy of certain data sets depending on the stringency of the constraints. Utilizing previously described in-house validation criteria based on covariance matrices, a method is described that is able to accurately determine whether constraints benefit or impair the optimization of HDX-MS data. From this, we establish a new two-stage method for our online optimizer HDXmodeller that can effectively leverage locally bound variables to enhance HDX-MS data modeling.
Over the next decade, a massive number of plug-in electric vehicles (PEVs) will need to be integrated into current power grids. This is likely to give rise to unmanageable fluctuations in power demand and unacceptable...
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Over the next decade, a massive number of plug-in electric vehicles (PEVs) will need to be integrated into current power grids. This is likely to give rise to unmanageable fluctuations in power demand and unacceptable deviations in voltage. These negative impacts are difficult to mitigate because PEVs connect and disconnect from the grid randomly and each type of PEVs has different charging profiles. This paper presents a solution to these problems that involves coordination of power grid control and PEV charging. The proposed strategy minimises the overall costs of charging and power generation in meeting future increases in PEV charging demand and the operational constraints of the power grid. The solution is based on an on-off PEV charging strategy that is easy and convenient to implement online. The joint coordination problem is formulated by a mixed integer non-linear programming (MINP) with binary charging and continuous voltage variables and is solved by a highly novel computational algorithm. Its online implementation is based on a new model predictive control method that is free from prior assumptions about PEVs' arrival and charging information. Comprehensive simulations are provided to demonstrate the efficiency and practicality of the proposed methods.
In this paper, we propose two trust-region algorithms for unconstrained optimization. The trust-region algorithms minimize a model of the objective function within the trust-region, next update the size of the region ...
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In this paper, we propose two trust-region algorithms for unconstrained optimization. The trust-region algorithms minimize a model of the objective function within the trust-region, next update the size of the region and then repeat the procedure to find a first-order stationary point for the objective function. The size of the trust-region at each step is very critical to the effectiveness of the algorithm, particularly for large-scale problems, because minimizing the model at each step needs the gradient and the Hessian information of the objective function. Our modified trust-region algorithms are opportunistic in the sense that they explore beyond the trust-region if the boundary of the region prevents the algorithm from accepting a more beneficial point. It occurs when there is a very good agreement between the model and the objective function on the trust-region boundary, and we can find a step outside the trust-region with smaller value of the model while at which the agreement between the model and the objective function remains good. We show that the algorithms are convergent. Initial numerical experiments show that the proposed algorithms are more efficient than the traditional trust-region algorithm for a large majority of problems in the CUTEst suite.
In this work, movable antenna (MA) is invoked for enhancing the task completion performance of a mobile edge computing (MEC) system. Specifically, in the considered system model, a base station equipped with MAs assis...
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The range of multirotor Unmanned Aerial Vehicle (UAV) applications has grown significantly over the last decade. This is to be attributed to their simple mechanical design, along with hovering and maneuvering capabili...
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In recent years, the Internet of Medical Things (IoMT) has significantly boosted the healthcare industry. Federated learning (FL) can enhance the utilization of patient data while protecting privacy. Despite the great...
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In this paper, we examine coexistence of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) services within an intelligent reconfigurable surface (IRS)-assisted terahertz multi-cell ...
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In viral marketing campaigns, incentivized consumers can act as sales agents by sharing information. In this study, we investigate the problem of incentive rate determination over a network of consumers to maximize th...
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In viral marketing campaigns, incentivized consumers can act as sales agents by sharing information. In this study, we investigate the problem of incentive rate determination over a network of consumers to maximize the profit of a single good by a monopolist. For this purpose, we develop an epidemic spreading model to explore the dynamics of a viral marketing campaign under network externalities and incentivized individuals. We will examine two cases of homogeneous and heterogeneous incentive rates. In each case, we derive an N-intertwined dynamics model and obtain the existence and stability conditions of a trade-free or an endemic equilibrium. By treating the incentive as a control parameter, we investigate the problem of maximizing the monopolist's profit by formulating two nonlinear programming models. In the case of homogeneous incentive rates, results show that the optimal incentive is determined by devising a balance between the consumers' states in the Markov process. In the heterogeneous case, it is observed that despite the existence of a strong correlation with different centrality measures, the optimal incentive allocation cannot be solely determined by centrality measures. (C) 2020 Elsevier B.V. All rights reserved.
To cope with different types of distributed energy sources (DERs) and AC/DC loads, combined AC and DC distribution network has emerged as a potential solution for the forthcoming distribution network. However, upward ...
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This paper seeks to develop a reliable network of cross-docks by taking in to account disruption and reliability issues to hedge against heterogeneous risk of cross-docking failure. In real environments, applying a re...
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This paper seeks to develop a reliable network of cross-docks by taking in to account disruption and reliability issues to hedge against heterogeneous risk of cross-docking failure. In real environments, applying a recovery policy can be a feasible strategy to handle disruptions. Hence, in this study, a recovery policy has been addressed in the form of reallocating suppliers to alternative cross-docks or altering the transportation strategy to move shipments. In addition to cross-dock location design, the optimum capacity of opened cross-docks will be determined considering the loads that will be served by each cross-docking center under regular and disruption conditions. A mixed integer nonlinear programming formulation is presented for the problem and is then linearized to present an efficient model. In order to solve it, two Lagrangian relaxation algorithms are designed and tested on 40 problem instances with different values of parameters. The results achieved by GAMS/CPLEX are compared with those of two algorithms and some analyses are performed on the solutions. Moreover, as the case study, the focus has been placed on logistic part of a car-manufacturing company with a vast supply chain network, containing more than 600 suppliers. The logistic strategies have been applied in order to reduce the transportation cost through the supply chain network and diminish the disruption subsequences in such a network. Based on the results, some managerial recommendations are presented.
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