Non-optimized Model Research Articles

Game theoretical method for sum-rate maximization in full-duplex Massive MIMO Heterogeneous Networks

In this paper, we consider Massive MIMO in two-layer Heterogeneous Networks. The system has a large self-interference and co-channel interference due to operation in full-duplex mode. By using Game Theory, an optimized sum-rate is achieved. We investigate the potential sum-rate before and after the optimization under the power constraints. It is shown that the game theoretical method performed a very good access scheduling. Compared to non-optimized model, game theoretical method can achieve higher sum-rate.

Statistical evaluation of methods for identification of differentially abundant genes in comparative metagenomics.

BackgroundMetagenomics is the study of microbial communities by sequencing of genetic material directly from environmental or clinical samples. The genes present in the metagenomes are quantified by annotating and counting the generated DNA fragments. Identification of differentially abundant genes between metagenomes can provide important information about differences in community structure, diversity and biological function. Metagenomic data is however high-dimensional, contain high levels of biological and technical noise and have typically few biological replicates. The statistical analysis is therefore challenging and many approaches have been suggested to date.ResultsIn this article we perform a comprehensive evaluation of 14 methods for identification of differentially abundant genes between metagenomes. The methods are compared based on the power to detect differentially abundant genes and their ability to correctly estimate the type I error rate and the false discovery rate. We show that sample size, effect size, and gene abundance greatly affect the performance of all methods. Several of the methods also show non-optimal model assumptions and biased false discovery rate estimates, which can result in too large numbers of false positives. We also demonstrate that the performance of several of the methods differs substantially between metagenomic data sequenced by different technologies.ConclusionsTwo methods, primarily designed for the analysis of RNA sequencing data (edgeR and DESeq2) together with a generalized linear model based on an overdispersed Poisson distribution were found to have best overall performance. The results presented in this study may serve as a guide for selecting suitable statistical methods for identification of differentially abundant genes in metagenomes.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-016-2386-y) contains supplementary material, which is available to authorized users.

Graph-Based Salient Region Detection through Linear Neighborhoods

Pairwise neighboring relationships estimated by Gaussian weight function have been extensively adopted in the graph-based salient region detection methods recently. However, the learning of the parameters remains a problem as nonoptimal models will affect the detection results significantly. To tackle this challenge, we first apply the adjacent information provided by all neighbors of each node to construct the undirected weight graph, based on the assumption that every node can be optimally reconstructed by a linear combination of its neighbors. Then, the saliency detection is modeled as the process of graph labelling by learning from partially selected seeds (labeled data) in the graph. The promising experimental results presented on some datasets demonstrate the effectiveness and reliability of our proposed graph-based saliency detection method through linear neighborhoods.

Euler–Lagrange as Pseudo-metric of the RRT algorithm for optimal-time trajectory of flight simulation model in high-density obstacle environment

SUMMARYThis paper introduces a solution to the problem of steering an aerodynamical system, with non-holonomic constraints superimposed on dynamic equations of motion. The proposed approach is a dimensionality reduction of the Optimal Control Problem (OCP) with heavy path constraints to be solved by Rapidly-Exploring Random Tree (RRT) algorithm. In this research, we formulated and solved the OCP with Euler–Lagrange formula in order to find the optimal-time trajectory. The RRT constructs a non-collision path in static, high-dense obstacle environment (i.e. heavy path constraint). Based on a real-world aircraft model, our simulation results found the collision-free path and gave improvements of time and fuel consumption of the optimized Hamiltonian-based model over the original non-optimized model.

Prediction of non-hydrocarbon gas components in separator by using Hybrid Computational Intelligence models

Accurate prediction of non-hydrocarbon (Non-HC) gas components in the gas-oil separators reduces the cost of gas and oil production in petroleum engineering. However, this task is difficult because there is no known relation among the properties of crude oil and the separators. There are studies that attempt to predict hydrocarbons (HCs) components using either Computational Intelligence (CI) techniques or conventional techniques like Equitation-of-State (EOS) and Empirical Correlation (EC). In this paper, we explore the applicability of CI techniques such as Artificial Neural Network, Support Vector Regressions, and Adaptive Neuro-Fuzzy Inference System to predict the Non-HC gas components in gas-oil separator tank. Further, we incorporate Genetic Algorithms (GA) into the Hybrid Computational Intelligence (HCI) models to enhance the accuracy of prediction. GA is used to determine the most favorable values of the tuning parameters in the CI models. The performances of the CI and HCI models are compared with the performance of the conventional techniques like EOS and EC. The experimental results show that accuracy of prediction by CI and HCI models outperform the conventional methods for N2 and H2S gas components. Furthermore, the HCI models perform better than the non-optimized CI models while predicting the Non-HC gas components.

Improving thermal efficiency and increasing production rate in the double moving beds thermally coupled reactors by using differential evolution (DE) technique

According to the global requirements for energy saving and the control of global warming, multifunctional auto-thermal reactors as a novel concept in the process integration (PI) have risen up in the recent years. In the novel modification presented in this study, the required heat of endothermic naphtha reforming process has been supplied by nitrobenzene hydrogenation reaction. In addition, the enhancement of reactor performance, such as the increase of production rate, has become a key issue in the diverse industries. Thus, Differential Evolution (DE) technique is applied to optimize the operating conditions (temperature and pressure) and designing parameters of a thermally coupled reactor with double moving beds. Ultimately, the obtained results of the proposed model are compared with non-optimized and conventional model. This model results in noticeable reduction in the operational costs as well as enhancement of the net profit of the plant. The increase in the hydrogen and aromatic production shows the superiority of the proposed model.

NMR Parameters Determination through ACE Committee Machine with Genetic Implanted Fuzzy Logic and Genetic Implanted Neural Network

Free fluid porosity and rock permeability, undoubtedly the most critical parameters of hydrocarbon reservoir, could be obtained by processing of nuclear magnetic resonance (NMR) log. Despite conventional well logs (CWLs), NMR logging is very expensive and time-consuming. Therefore, idea of synthesizing NMR log from CWLs would be of a great appeal among reservoir engineers. For this purpose, three optimization strategies are followed. Firstly, artificial neural network (ANN) is optimized by virtue of hybrid genetic algorithm-pattern search (GA-PS) technique, then fuzzy logic (FL) is optimized by means of GA-PS, and eventually an alternative condition expectation (ACE) model is constructed using the concept of committee machine to combine outputs of optimized and non-optimized FL and ANN models. Results indicated that optimization of traditional ANN and FL model using GA-PS technique significantly enhances their performances. Furthermore, the ACE committee of aforementioned models produces more accurate and reliable results compared with a singular model performing alone.

GA-BP神经网络结合PCA的多基色颜色预测模型

Color partition method is the main method of multi-color reproduction. In this paper,the whole color area of 7 primary colors was divided into 6 partitions in group of 3 primary colors,and principal component analysis( PCA) was used to dimensionality reduction of the color spectrum reflectance. A three layers BP neural network was established to describe the transformation model of dot area coverage and spectral reflectance. Genetic algorithm( GA) was adopted for optimizing the weight threshold of BP NN to improve the prediction accuracy of model. Experimental results show that GA-BP model has higher prediction precision and stability compared with BP ANN and cell neugebauer model. When the number of training sample is 64 and test sample is 216 in the partition,the optimized model can predict color with the accuracy of 1. 669 mean ΔE*aband 0. 7% spectral RMSE. By comparing with the non-optimized model with the training sample numbers of 125,216,343( mean ΔE*abare 3. 267,2. 776,2. 175 and spectral RMSE are 0. 97%,0. 79%,0. 76%),the prediction accuracy of GA-BP model with the training sample numbers of 64 is equal to the accuracy of the non-optimized model with the training sample number of 343. The results show that GA-BP model can reach high precision color reproduction with small amount of samples,andhave good portability in practice.

Selective invocation of shape priors for deformable segmentation and morphologic classification of prostate cancer tissue microarrays

Shape based active contours have emerged as a natural solution to overlap resolution. However, most of these shape-based methods are computationally expensive. There are instances in an image where no overlapping objects are present and applying these schemes results in significant computational overhead without any accompanying, additional benefit. In this paper we present a novel adaptive active contour scheme (AdACM) that combines boundary and region based energy terms with a shape prior in a multi level set formulation. To reduce the computational overhead, the shape prior term in the variational formulation is only invoked for those instances in the image where overlaps between objects are identified; these overlaps being identified via a contour concavity detection scheme. By not having to invoke all three terms (shape, boundary, region) for segmenting every object in the scene, the computational expense of the integrated active contour model is dramatically reduced, a particularly relevant consideration when multiple objects have to be segmented on very large histopathological images. The AdACM was employed for the task of segmenting nuclei on 80 prostate cancer tissue microarray images from 40 patient studies. Nuclear shape based, architectural and textural features extracted from these segmentations were extracted and found to able to discriminate different Gleason grade patterns with a classification accuracy of 86% via a quadratic discriminant analysis (QDA) classifier. On average the AdACM model provided 60% savings in computational times compared to a non-optimized hybrid active contour model involving a shape prior.

Generating optimized configurable business process models in scenarios subject to uncertainty

ContextThe quality of business process models (i.e., software artifacts that capture the relations between the organizational units of a business) is essential for enhancing the management of business processes. However, such modeling is typically carried out manually. This is already challenging and time consuming when (1) input uncertainty exists, (2) activities are related, and (3) resource allocation has to be considered. When including optimization requirements regarding flexibility and robustness it becomes even more complicated potentially resulting into non-optimized models, errors, and lack of flexibility. ObjectiveTo facilitate the human work and to improve the resulting models in scenarios subject to uncertainty, we propose a software-supported approach for automatically creating configurable business process models from declarative specifications considering all the aforementioned requirements. MethodFirst, the scenario is modeled through a declarative language which allows the analysts to specify its variability and uncertainty. Thereafter, a set of optimized enactment plans (each one representing a potential execution alternative) are generated from such a model considering the input uncertainty. Finally, to deal with this uncertainty during run-time, a flexible configurable business process model is created from these plans. ResultsTo validate the proposed approach, we conduct a case study based on a real business which is subject to uncertainty. Results indicate that our approach improves the actual performance of the business and that the generated models support most of the uncertainty inherent to the business. ConclusionsThe proposed approach automatically selects the best part of the variability of a declarative specification. Unlike existing approaches, our approach considers input uncertainty, the optimization of multiple objective functions, as well as the resource and the control-flow perspectives. However, our approach also presents a few limitations: (1) it is focused on the control-flow and the data perspective is only partially addressed and (2) model attributes need to be estimated.

AUTOMATIC GENERATION OF OPTIMIZED BUSINESS PROCESS MODELS FROM CONSTRAINT-BASED SPECIFICATIONS

Business process (BP) models are usually defined manually by business analysts through imperative languages considering activity properties, constraints imposed on the relations between the activities as well as different performance objectives. Furthermore, allocating resources is an additional challenge since scheduling may significantly impact BP performance. Therefore, the manual specification of BP models can be very complex and time-consuming, potentially leading to non-optimized models or even errors. To overcome these problems, this work proposes the automatic generation of imperative optimized BP models from declarative specifications. The static part of these declarative specifications (i.e. control-flow and resource constraints) is expected to be useful on a long-term basis. This static part is complemented with information that is less stable and which is potentially unknown until starting the BP execution, i.e. estimates related to (1) number of process instances which are being executed within a particular timeframe, (2) activity durations, and (3) resource availabilities. Unlike conventional proposals, an imperative BP model optimizing a set of instances is created and deployed on a short-term basis. To provide for run-time flexibility the proposed approach additionally allows decisions to be deferred to run-time by using complex late-planning activities, and the imperative BP model to be dynamically adapted during run-time using replanning. To validate the proposed approach, different performance measures for a set of test models of varying complexity are analyzed. The results indicate that, despite the NP-hard complexity of the problems, a satisfactory number of suitable solutions can be produced.

Effect of the Simultaneous Interaction among Ascorbic Acid, Iron and pH on the Oxidative Stability of Oil-in-Water Emulsions

The objective of this study was to demonstrate how different factors can simultaneously influence the oxidative stability of an oil-in-water emulsion, and how these factors can be used to enlarge the variation range of oxidation markers, expressed as peroxide value (PV) and TBARS. Initially, a Plackett-Burman design was used to screen seven factors (temperature, pH, and iron, copper, ascorbyl palmitate, ascorbic acid, and sodium chloride concentrations). A temperature elevation of 30 to 60 °C reduced PV and TBARS, a pH change from 3.0 to 7.0 increased PV and reduced TBARS, and the presence of ascorbic acid (1 mmol/L) had no significant effect on PV but increased TBARS (p < 0.05). Thus, the temperature was fixed at 30 °C, and an emulsion was formulated with different combinations of ascorbic acid, iron, and pH according to a central composite rotatable design. Regression models were fitted to PV and TBARs responses and optimized to get the higher values of both markers of oxidation. The optimized emulsion contained 1.70 mmol/L AH (ascorbic acid) and 0.885 mmol/L FeSO(4) · 7H(2)O (1.0 mmol/L Fe(2+)) at pH 5.51 and 30 °C. The range of variation observed for oxidation markers in the optimized emulsion model (PV, 0-4.27 mequiv/L; TBARS, 0-13.55 mmol/L) was larger than the variation observed in the nonoptimized model (PV, 0-1.05 mequiv/L; TBARS, 0-1.00 mmol/L). The antioxidant activity of six compounds (Trolox, α-tocopherol, caffeic acid, gallic acid, catechin, and TBHQ) was evaluated using the optimized emulsion conditions. After application of the Tukey HSD post hoc statistical test, the samples that were not different (p < 0.05) in the nonoptimized emulsions showed a significant difference in the optimized emulsions. Considering the importance of the interactions on oxidation studies, our model represents a significant improvement in a direct methodology that can be applied to evaluate natural compounds under different combination of factors.

Adapted random sampling patterns for accelerated MRI

Variable density random sampling patterns have recently become increasingly popular for accelerated imaging strategies, as they lead to incoherent aliasing artifacts. However, the design of these sampling patterns is still an open problem. Current strategies use model assumptions like polynomials of different order to generate a probability density function that is then used to generate the sampling pattern. This approach relies on the optimization of design parameters which is very time consuming and therefore impractical for daily clinical use. This work presents a new approach that generates sampling patterns by making use of power spectra of existing reference data sets and hence requires neither parameter tuning nor an a priori mathematical model of the density of sampling points. The approach is validated with downsampling experiments, as well as with accelerated in vivo measurements. The proposed approach is compared with established sampling patterns, and the generalization potential is tested by using a range of reference images. Quantitative evaluation is performed for the downsampling experiments using RMS differences to the original, fully sampled data set. Our results demonstrate that the image quality of the method presented in this paper is comparable to that of an established model-based strategy when optimization of the model parameter is carried out and yields superior results to non-optimized model parameters. However, no random sampling pattern showed superior performance when compared to conventional Cartesian subsampling for the considered reconstruction strategy.

Numerical Simulation of Nonoptimal Dynamic Equilibrium Models

In this paper we present a recursive method for the numerical simulation of nonoptimal dynamic equilibrium models. This method builds upon a convergent operator over an expanded set of state variables. The fixed point of this operator defines the set of all Markovian equilibria. We study approximation properties of the operator as well as the convergence of the moments of simulated sample paths. We apply our numerical algorithm to various models with heterogeneous agents, incomplete financial markets, endogenous and exogenous borrowing constraints, taxes, and money.

Combination of the 2A/furin technology with an animal component free cell line development platform process

The recently described 2A/furin technology combines both chains of the antibody in a single open reading frame. Upon translation and secretion, the peptide is processed by the cell to generate native fully functional IgG antibodies. Here, we describe the results of an evaluation study of this technology for an industrial CHO cell line development process. The 2A/furin expression cassette setup was combined with a Novartis vector system. A transfection, selection, and cloning procedure in chemically defined media was established at Novartis and applied for a monoclonal test antibody. The productivity of 2A/furin-vector-derived clones in non-optimized generic shake flask fed-batch models was in a comparable range with clones derived from the reference control vector. Higher clonal production stability was seen for the majority of clones generated with the 2A/furin technology compared to the clones generated with the reference control vector. Product quality was analyzed by SDS-PAGE and no significant difference was detected between the two systems. Thus, it was shown that the 2A/furin technology can be successfully combined with a Novartis CHO expression system and platform. Due to the single ORF setup, the 2A/furin technology may therefore offer a suitable approach to reduce vector size and complexity.

The impact of monovalent ion force field model in nucleic acids simulations

Different classical models for monovalent ions (typically used to neutralize proteins or nucleic acids) are available in the literature and are widely used in molecular dynamics simulations without a great knowledge of their quality, consistency with the macromolecular force field and impact on the global simulation results. In this paper the ability of several of the most popular ion models to reproduce both quantum mechanics and experimental results is examined. Artefacts due to the use of incorrect ion models in molecular dynamics simulations of concentrated solutions of NaCl and KCl in water and of a short DNA duplex in 500 mM aqueous solutions of NaCl and KCl have been analyzed. Our results allow us to discuss the robustness and reliability of different ion models and to highlight the source of potential errors arising from non-optimal models. However, it is also found that the structural and dynamic characteristics of DNA (as an example of a heavily charged macromolecule) in near-physiological conditions are quite independent of the ion model used, providing support to most already-published simulations of macromolecules.

CASP8 results in context of previous experiments

The quality of structure models submitted to CASP8 is analyzed in the context of previous CASPs. To compare models from the latest experiment with their predecessors, we use the approaches consistent with the previous articles in this series. Using the basic evaluation measures accepted in CASP, there were no noticeable advances in the quality of the methods in any of the target difficulty categories. At the same time, there were three positive developments: (1) for set of the best models on each target, CASP8 registered the highest number of cases from all CASPs where alignment accuracy exceeded the maximum possible from the best template; (2) modeling accuracy of regions not present in the best template has improved; and (3) the loss in modeling quality from selection of nonoptimal models as the best ones submitted on the target has decreased.

Wealth Accounting, Exhaustible Resources and Social Welfare

The empirical literature on natural resource accounting uses methods which implicitly or explicitly entail measuring changes in total resource asset value when an exhaustible resource is depleted. In contrast, the growth theoretic literature on saving, social welfare and sustainable development is built upon a central finding, that the change in real wealth (as measured by net or ‘genuine’ saving) is proportional to the change in social welfare. We show that the change in total wealth exceeds the change in real wealth in optimal and non-optimal models of resource-extracting economies. This suggests that the change in social welfare is over-estimated when the change in total resource asset value is used as the measure of depletion. A simple empirical exercise, using World Bank data on ‘adjusted net saving’, reinforces the results from theory.

Modelling H.264/AVC VBR video traffic: comparison of a Markov and a self-similar source model

We compare the adequacy of two models for realistic video sources, namely the fractional Brownian motion (fBm) and Markov modulated fluid flow (Mmff) models. We use the effective bandwidth approach to get the probability that the buffer content exceeds a certain threshold. We use a formula in which the variance function, i.e., the variance of the traffic arriving in an interval of length ?, plays a central role and we model it as variance associated either with the Mmff or fBm model. We measure the variance function for an artificial source we construct, using Variable Bit Rate (VBR) H.264/AVC (Advanced Video Codec) video traces of real movies. There is a good correspondence between the buffer threshold exceeding probability obtained via trace-based simulations and the one predicted theoretically, based on the measured variance function. These we take as benchmark results against which we check both models--fBm and Mmff. First, we try to tune the model parameters such that their variance function matches the measured one over a large range of ? values, but this proves to be difficult. When matching the variance function over a short range of the ? of interest, we conclude that the Mmff model is better suited to model VBR video sources. We conduct an error sensitivity analysis with non-optimal model parameters and conclude that they do not influence considerably the buffer exceeding probability. Most reliable results are achieved with the measured variance function; if by some reason it needs to be modelled, the Mmff is preferred over the fBm model.

Matched-field replica model optimization and bottom property inversion in shallow water

Matched-field replica models based on an inaccurate knowledge of geoacoustic parameters such as bottom attenuation, shear, and interfacial sound-speed discontinuities, can predict an incorrect number of propagating modes for a shallow-water channel. The resulting degradation in the matched-field ambiguity surface can be substantially reduced by obtaining optimal replica models via modal-sum-limit optimization or bottom-property inversion. The use of these techniques for multi-tone (70, 95, 145, and 195 Hz) source-tow data recorded near San Diego during the first Shallow-Water Evaluation Cell Experiment (SWellEX-1) significantly increased matched-field correlation levels and improved source localization relative to results obtained with a previous nonoptimized model. The predicted number of propagating modes was also reduced substantially. The inversion for bottom properties (attenuation, interfacial sound-speed discontinuities, no shear) provided sediment attenuation estimates which agree well with Hamilton's models and were an order-of-magnitude greater than that used in the nonoptimized model, which accounts for the reduction in the number of modes. A simulated modal decomposition using the inverted optimal replica model verifies the number of modes predicted by the modal-sum-limit optimization.