Background: Mutagenesis is commonly used to engineer proteins with desirable properties not present in the wild type (WT) protein, such as increased or decreased stability, reactivity, or solubility. Experimentalists ...
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Background: Mutagenesis is commonly used to engineer proteins with desirable properties not present in the wild type (WT) protein, such as increased or decreased stability, reactivity, or solubility. Experimentalists often have to choose a small subset of mutations from a large number of candidates to obtain the desired change, and computational techniques are invaluable to make the choices. While several such methods have been proposed to predict stability and reactivity mutagenesis, solubility has not received much attention. Results: We use concepts from computational geometry to define a three body scoring function that predicts the change in protein solubility due to mutations. The scoring function captures both sequence and structure information. By exploring the literature, we have assembled a substantial database of 137 single-and multiple-point solubility mutations. Our database is the largest such collection with structural information known so far. We optimize the scoring function using linearprogramming (LP) methods to derive its weights based on training. Starting with default values of 1, we find weights in the range [ 0,2] so that predictions of increase or decrease in solubility are optimized. We compare the LP method to the standard machine learning techniques of support vector machines (SVM) and the Lasso. Using statistics for leave-one-out (LOO), 10-fold, and 3-fold cross validations (CV) for training and prediction, we demonstrate that the LP method performs the best overall. For the LOOCV, the LP method has an overall accuracy of 81%.
In this paper, we present linear programming method in order to estimate the parameters of the DGM(1,1) model under the criterion of the minimization of mean absolute percentage error (MAPE) (some authors called avera...
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ISBN:
(纸本)9780769538884
In this paper, we present linear programming method in order to estimate the parameters of the DGM(1,1) model under the criterion of the minimization of mean absolute percentage error (MAPE) (some authors called average relative error). A published article is chosen for practical tests of this method, the results show that this method can obviously improve the simulation accuracy.
This paper considers the problem of evaluating the causal effect of ail exposure on ail Outcome in observational studies with both measured and unmeasured confounders between the exposure and the Outcome. Under such a...
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This paper considers the problem of evaluating the causal effect of ail exposure on ail Outcome in observational studies with both measured and unmeasured confounders between the exposure and the Outcome. Under such a situation, MacLehose et al. (Epidemiology 2005;16:548-555) applied linearprogramming optimization software to find the minimum and maximum possible Values Of file causal effect for specific numerical data. In this paper, we apply the symbolic Balke-Pearl linear programming method (Probabilistic counterfactuals: semantics, computation, and applications. Ph.D. Thesis, UCLA Cognitive Systems Laboratory, 1995;J. Amer Statist. Assoc. 1997;92:1172-1176) to derive the simple closed-form expressions for the lower and upper bounds on causal effects under various assumptions of monotonicity. These universal bounds enable epidemiologists and medical researchers to assess causal effects from observed data with minimum computational effort, and they further shed light on the accuracy of the assessment. Copyright (C) 2008 John Wiley & Sons, Ltd.
In this paper, we develop the linear programming method to obtain bounds for the cardinality of Grassmannian codes endowed with the chordal distance. We obtain a bound and its asymptotic version that generalize the we...
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In this paper, we develop the linear programming method to obtain bounds for the cardinality of Grassmannian codes endowed with the chordal distance. We obtain a bound and its asymptotic version that generalize the well-known bound for codes in the real projective space obtained by Kabatyanskiy and Levenshtein, and improve the Hamming bound for sufficiently large minimal distances.
In the optimum design of an FIR filter by the complex Chebyshev method, it is difficult to limit the increase of computational effort and to guarantee convergence to an optimum solution due to the nonlinearity of the ...
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In the optimum design of an FIR filter by the complex Chebyshev method, it is difficult to limit the increase of computational effort and to guarantee convergence to an optimum solution due to the nonlinearity of the problem and the instability of the frequency points for the optima. In this paper, the complex Chebyshev approximation problem is formulated as a linear semi-infinite programming problem by means of the real rotation theorem. An optimum design method is proposed using the three-phase method, a linear semi-infinite programmingmethod. In the present method, the design problem is first reduced to a linearprogramming problem with constraints and a provisional solution is derived by the simplex method. After the constraints corresponding to the degenerated optimum frequencies contained in the provisional solution are combined, the solutions are adjusted to satisfy the optimum condition so as to obtain an optimum solution. Since low accuracy is sufficient for the provisional solution, memory requirements and computational effort can be reduced. By means of a design example, it is shown that the proposed method is superior in terms of solution accuracy and computational effort to the technique based on the simplex. method. (C) 2003 Wiley Periodicals, Inc.
An approach for constructing vertical sections of the electrical conductivity of the earth on the basis of surface measurements is presented. Electromagnetic measurements are effected every few meters across strike us...
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ISBN:
(纸本)0780387422
An approach for constructing vertical sections of the electrical conductivity of the earth on the basis of surface measurements is presented. Electromagnetic measurements are effected every few meters across strike using pairs of horizontal and vertical coils, with the separation between Source and receiver varying in order to sample different depths. The data provides information about the vertical and horizontal distribution of electrical conductivity of a section of the earth. The recovering of the true conductivity distribution frorn the measurements is a nonlinear inverse problem that allows some useful linear approximations, which we exploit here using linearprogramming techniques. A regularization approach is considered using, epsilon-insensitive functions for both, the fitness to the data and the penalizing function. This representation enforces a model structure of blocks immersed in a homogeneous basement, a useful model in many practical instances.
In this paper, a linear programming method is adopted to numerically search the entire admissible PID settings in the frequency domain. The results are demonstrated in the coefficient plane and the coefficient space. ...
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In this paper, a linear programming method is adopted to numerically search the entire admissible PID settings in the frequency domain. The results are demonstrated in the coefficient plane and the coefficient space. Henceforth, the relationship between the PID coefficients and the specifications in terms of gain margin and phase margin to be satisfied is shown graphically. The developed method characterizes all the solution sets in the parameter plane according to the pre-specified specifications, and it also facilitates the tuning process effectively. This alternative PID tuning strategy provides a systematic design procedure. It can be evolved as a subject in the teaching course for control education.
Pumped storage hydropower stations are extensively used as peaking and reserve power plants. The current trend in this area is to increase their power output relative to the capacity of reservoirs, which results in a ...
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Pumped storage hydropower stations are extensively used as peaking and reserve power plants. The current trend in this area is to increase their power output relative to the capacity of reservoirs, which results in a shorter maximum duration of full-power operation while availability of the energy required for the pumping becomes less evident. This increases the possibility of reducing the effective power of pumped storage units in the case of a tight supply-demand balance in the power system. Although many studies have examined a role of pumped storage in generation systems, few studies have explicitly dealt with the power reduction. This paper proposes a new linearprogramming model that incorporates the reduction of effective power of pumped storage. The proposed model makes it possible to easily incorporate the power reduction in computations by employing hypothetical daily toad curves and dispatching loads to the curves. The model also includes LNG combined cycle power generation plants that are now being intensively implemented. The developed model is applied to a power system model. The sample study reveals that the reduction of effective power considerably varies from season to season and the reduction highly affects the optimal generation mix, that is, the optimal share of pumped storage. (C) 2001 Scripts Technica.
In this paper, we analyze the efficiency of a given robot movement schedule for the case of a flow shop robotic production cell with m different machines, one input conveyor, and one output conveyor. We begin with the...
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In this paper, we analyze the efficiency of a given robot movement schedule for the case of a flow shop robotic production cell with m different machines, one input conveyor, and one output conveyor. We begin with the case of one-robot cells and extend our results to multirobot cells. The paper studies the efficiency of a movement schedule for identical parts by defining a movement network associated with this schedule. This network models any cell layout and applies to multirobot cells. Using the movement network, we propose two cycle time evaluation methods, the first using linearprogramming and the second based on finding a longest path. The latter method generates a procedure to obtain an analytical formula for the cycle time. We extend the proposed methods to study the efficiency of a given input sequence (schedule) for different parts, that is, to determine the sequence processing time. The results obtained here allow us to quickly evaluate the efficiency of any given feasible movement schedule, for identical or different parts.
The ceaent industry process has a characteristic of broad frequency domain (from a minute to a few days). First we identified multi-dimensional AR model in high frequency domain and realized stability controls by stat...
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The ceaent industry process has a characteristic of broad frequency domain (from a minute to a few days). First we identified multi-dimensional AR model in high frequency domain and realized stability controls by state-space aethod. As the result, we have gotten satisfactory results of control. The other hand, we can't say certainly that normal setpoints provided by staffs and operators of the field are optimal. Then we identified a process of low frequency domain and simulated a optimal control by AR model and linear programming method. According to realizing this control, we can get dynamic optimum setpoints.
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