A New Modified Firefly Algorithm
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Medha Gupta
2016-07-01
Full Text Available Nature inspired meta-heuristic algorithms studies the emergent collective intelligence of groups of simple agents. Firefly Algorithm is one of the new such swarm-based metaheuristic algorithm inspired by the flashing behavior of fireflies. The algorithm was first proposed in 2008 and since then has been successfully used for solving various optimization problems. In this work, we intend to propose a new modified version of Firefly algorithm (MoFA and later its performance is compared with the standard firefly algorithm along with various other meta-heuristic algorithms. Numerical studies and results demonstrate that the proposed algorithm is superior to existing algorithms.
Memetic firefly algorithm for combinatorial optimization
Fister, Iztok; Fister, Iztok; Brest, Janez
2012-01-01
Firefly algorithms belong to modern meta-heuristic algorithms inspired by nature that can be successfully applied to continuous optimization problems. In this paper, we have been applied the firefly algorithm, hybridized with local search heuristic, to combinatorial optimization problems, where we use graph 3-coloring problems as test benchmarks. The results of the proposed memetic firefly algorithm (MFFA) were compared with the results of the Hybrid Evolutionary Algorithm (HEA), Tabucol, and the evolutionary algorithm with SAW method (EA-SAW) by coloring the suite of medium-scaled random graphs (graphs with 500 vertices) generated using the Culberson random graph generator. The results of firefly algorithm were very promising and showed a potential that this algorithm could successfully be applied in near future to the other combinatorial optimization problems as well.
A multi-group firefly algorithm for numerical optimization
Tong, Nan; Fu, Qiang; Zhong, Caiming; Wang, Pengjun
2017-08-01
To solve the problem of premature convergence of firefly algorithm (FA), this paper analyzes the evolution mechanism of the algorithm, and proposes an improved Firefly algorithm based on modified evolution model and multi-group learning mechanism (IMGFA). A Firefly colony is divided into several subgroups with different model parameters. Within each subgroup, the optimal firefly is responsible for leading the others fireflies to implement the early global evolution, and establish the information mutual system among the fireflies. And then, each firefly achieves local search by following the brighter firefly in its neighbors. At the same time, learning mechanism among the best fireflies in various subgroups to exchange information can help the population to obtain global optimization goals more effectively. Experimental results verify the effectiveness of the proposed algorithm.
Firefly Algorithm for Economic Power Dispatching With Pollutants Emission
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Latifa DEKHICI
2012-01-01
Full Text Available Bio-inspired algorithms become among the most powerful algorithms for optimization. In this paper, we intend to provide one of the recent bio-inspired metaheuristic which is the Firefly Algorithm (FF to optimize power dispatching. For evaluation, we adapt the particle swarm optimization to the problem in the same way as the firefly algorithm. The application is done in an IEEE-14 and on two thermal plant networks. In one of the examples, we neglect power loss and pollutant emissions. The comparison with the particle swarm optimization (PSO, demonstrate the efficiency of firefly algorithm to reach the best cost in less than one second.
Cuckoo search and firefly algorithm theory and applications
2014-01-01
Nature-inspired algorithms such as cuckoo search and firefly algorithm have become popular and widely used in recent years in many applications. These algorithms are flexible, efficient and easy to implement. New progress has been made in the last few years, and it is timely to summarize the latest developments of cuckoo search and firefly algorithm and their diverse applications. This book will review both theoretical studies and applications with detailed algorithm analysis, implementation and case studies so that readers can benefit most from this book. Application topics are contributed by many leading experts in the field. Topics include cuckoo search, firefly algorithm, algorithm analysis, feature selection, image processing, travelling salesman problem, neural network, GPU optimization, scheduling, queuing, multi-objective manufacturing optimization, semantic web service, shape optimization, and others. This book can serve as an ideal reference for both graduates and researchers in computer scienc...
Bouc–Wen hysteresis model identification using Modified Firefly Algorithm
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Zaman, Mohammad Asif, E-mail: zaman@stanford.edu [Department of Electrical Engineering, Stanford University (United States); Sikder, Urmita [Department of Electrical Engineering and Computer Sciences, University of California, Berkeley (United States)
2015-12-01
The parameters of Bouc–Wen hysteresis model are identified using a Modified Firefly Algorithm. The proposed algorithm uses dynamic process control parameters to improve its performance. The algorithm is used to find the model parameter values that results in the least amount of error between a set of given data points and points obtained from the Bouc–Wen model. The performance of the algorithm is compared with the performance of conventional Firefly Algorithm, Genetic Algorithm and Differential Evolution algorithm in terms of convergence rate and accuracy. Compared to the other three optimization algorithms, the proposed algorithm is found to have good convergence rate with high degree of accuracy in identifying Bouc–Wen model parameters. Finally, the proposed method is used to find the Bouc–Wen model parameters from experimental data. The obtained model is found to be in good agreement with measured data. - Highlights: • We describe a new method to find the Bouc–Wen hysteresis model parameters. • We propose a Modified Firefly Algorithm. • We compare our method with existing methods to find that the proposed method performs better. • We use our model to fit experimental results. Good agreement is found.
Application of firefly algorithm to the dynamic model updating problem
Shabbir, Faisal; Omenzetter, Piotr
2015-04-01
Model updating can be considered as a branch of optimization problems in which calibration of the finite element (FE) model is undertaken by comparing the modal properties of the actual structure with these of the FE predictions. The attainment of a global solution in a multi dimensional search space is a challenging problem. The nature-inspired algorithms have gained increasing attention in the previous decade for solving such complex optimization problems. This study applies the novel Firefly Algorithm (FA), a global optimization search technique, to a dynamic model updating problem. This is to the authors' best knowledge the first time FA is applied to model updating. The working of FA is inspired by the flashing characteristics of fireflies. Each firefly represents a randomly generated solution which is assigned brightness according to the value of the objective function. The physical structure under consideration is a full scale cable stayed pedestrian bridge with composite bridge deck. Data from dynamic testing of the bridge was used to correlate and update the initial model by using FA. The algorithm aimed at minimizing the difference between the natural frequencies and mode shapes of the structure. The performance of the algorithm is analyzed in finding the optimal solution in a multi dimensional search space. The paper concludes with an investigation of the efficacy of the algorithm in obtaining a reference finite element model which correctly represents the as-built original structure.
Reliable Mobile Ad-Hoc Network Routing Using Firefly Algorithm
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D Jinil Persis
2016-05-01
Full Text Available Routing in Mobile Ad-hoc NETwork (MANET is a contemporary graph problem that is solved using various shortest path search techniques. The routing algorithms employed in modern routers use deterministic algorithms that extract an exact non-dominated set of solutions from the search space. The search efficiency of these algorithms is found to have an exponential time complexity in the worst case. Moreover this problem is a multi-objective optimization problem in nature for MANET and it is required to consider changing topology layout. This study attempts to employ a formulation incorporating objectives viz., delay, hop-distance, load, cost and reliability that has significant impact on network performance. Simulation with different random topologies has been carried out to illustrate the implementation of an exhaustive search algorithm and it is observed that the algorithm could handle small-scale networks limited to 15 nodes. A random search meta-heuristic that adopts the nature of firefly swarm has been proposed for larger networks to yield an approximated non-dominated path set. Firefly Algorithm is found to perform better than the exact algorithm in terms of scalability and computational time.
A LOAD BALANCING MODEL USING FIREFLY ALGORITHM IN CLOUD COMPUTING
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A. Paulin Florence
2014-01-01
Full Text Available Cloud computing is a model that points at streamlining the on-demand provisioning of software, hardware and data as services and providing end-users with flexible and scalable services accessible through the Internet. The main objective of the proposed approach is to maximize the resource utilization and provide a good balanced load among all the resources in cloud servers. Initially, a load model of every resource will be derived based on several factors such as, memory usage, processing time and access rate. Based on the newly derived load index, the current load will be computed for all the resources shared in virtual machine of cloud servers. Once the load index is computed for all the resources, load balancing operation will be initiated to effectively use the resources dynamically with the process of assigning resources to the corresponding node to reduce the load value. So, assigning of resources to proper nodes is an optimal distribution problem so that many optimization algorithms such as genetic algorithm and modified genetic algorithm are utilized for load balancing. These algorithms are not much effective in providing the neighbour solutions since it does not overcome exploration and exploration problem. So, utilizing the effective optimization procedure instead of genetic algorithm can lead to better load balancing since it is a traditional and old algorithm. Accordingly, I have planned to utilize a recent optimization algorithm, called firefly algorithm to do the load balancing operation in our proposed work. At first, the index table will be maintained by considering the availability of virtual servers and sequence of request. Then, load index will be computed based on the newly derived formulae. Based on load index, load balancing operation will be carried out using firefly algorithm. The performance analysis produced expected results and thus proved the proposed approach is efficient in optimizing schedules by balancing the
Directory of Open Access Journals (Sweden)
Nebojsa Bacanin
2014-01-01
portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results.
Umbarkar, A. J.; Balande, U. T.; Seth, P. D.
2017-06-01
The field of nature inspired computing and optimization techniques have evolved to solve difficult optimization problems in diverse fields of engineering, science and technology. The firefly attraction process is mimicked in the algorithm for solving optimization problems. In Firefly Algorithm (FA) sorting of fireflies is done by using sorting algorithm. The original FA is proposed with bubble sort for ranking the fireflies. In this paper, the quick sort replaces bubble sort to decrease the time complexity of FA. The dataset used is unconstrained benchmark functions from CEC 2005 [22]. The comparison of FA using bubble sort and FA using quick sort is performed with respect to best, worst, mean, standard deviation, number of comparisons and execution time. The experimental result shows that FA using quick sort requires less number of comparisons but requires more execution time. The increased number of fireflies helps to converge into optimal solution whereas by varying dimension for algorithm performed better at a lower dimension than higher dimension.
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Issa Ahmed Abed
2016-12-01
Full Text Available This paper present a method to enhance the firefly algorithm by coupling with a local search. The constructed technique is applied to identify the solar parameters model where the method has been proved its ability to obtain the photovoltaic parameters model. Standard firefly algorithm (FA, electromagnetism-like (EM algorithm, and electromagnetism-like without local (EMW search algorithm all are compared with the suggested method to test its capability to solve this model.
Continuous firefly algorithm applied to PWR core pattern enhancement
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Poursalehi, N., E-mail: npsalehi@yahoo.com [Engineering Department, Shahid Beheshti University, G.C., P.O. Box 1983963113, Tehran (Iran, Islamic Republic of); Zolfaghari, A.; Minuchehr, A.; Moghaddam, H.K. [Engineering Department, Shahid Beheshti University, G.C., P.O. Box 1983963113, Tehran (Iran, Islamic Republic of)
2013-05-15
Highlights: ► Numerical results indicate the reliability of CFA for the nuclear reactor LPO. ► The major advantages of CFA are its light computational cost and fast convergence. ► Our experiments demonstrate the ability of CFA to obtain the near optimal loading pattern. -- Abstract: In this research, the new meta-heuristic optimization strategy, firefly algorithm, is developed for the nuclear reactor loading pattern optimization problem. Two main goals in reactor core fuel management optimization are maximizing the core multiplication factor (K{sub eff}) in order to extract the maximum cycle energy and minimizing the power peaking factor due to safety constraints. In this work, we define a multi-objective fitness function according to above goals for the core fuel arrangement enhancement. In order to evaluate and demonstrate the ability of continuous firefly algorithm (CFA) to find the near optimal loading pattern, we developed CFA nodal expansion code (CFANEC) for the fuel management operation. This code consists of two main modules including CFA optimization program and a developed core analysis code implementing nodal expansion method to calculate with coarse meshes by dimensions of fuel assemblies. At first, CFA is applied for the Foxholes test case with continuous variables in order to validate CFA and then for KWU PWR using a decoding strategy for discrete variables. Results indicate the efficiency and relatively fast convergence of CFA in obtaining near optimal loading pattern with respect to considered fitness function. At last, our experience with the CFA confirms that the CFA is easy to implement and reliable.
Selection of parameters for advanced machining processes using firefly algorithm
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Rajkamal Shukla
2017-02-01
Full Text Available Advanced machining processes (AMPs are widely utilized in industries for machining complex geometries and intricate profiles. In this paper, two significant processes such as electric discharge machining (EDM and abrasive water jet machining (AWJM are considered to get the optimum values of responses for the given range of process parameters. The firefly algorithm (FA is attempted to the considered processes to obtain optimized parameters and the results obtained are compared with the results given by previous researchers. The variation of process parameters with respect to the responses are plotted to confirm the optimum results obtained using FA. In EDM process, the performance parameter “MRR” is increased from 159.70 gm/min to 181.6723 gm/min, while “Ra” and “REWR” are decreased from 6.21 μm to 3.6767 μm and 6.21% to 6.324 × 10−5% respectively. In AWJM process, the value of the “kerf” and “Ra” are decreased from 0.858 mm to 0.3704 mm and 5.41 mm to 4.443 mm respectively. In both the processes, the obtained results show a significant improvement in the responses.
A HYBRID FIREFLY ALGORITHM WITH FUZZY-C MEAN ALGORITHM FOR MRI BRAIN SEGMENTATION
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Mutasem K. Alsmadi
2014-01-01
Full Text Available Image processing is one of the essential tasks to extract suspicious region and robust features from the Magnetic Resonance Imaging (MRI. A numbers of the segmentation algorithms were developed in order to satisfy and increasing the accuracy of brain tumor detection. In the medical image processing brain image segmentation is considered as a complex and challenging part. Fuzzy c-means is unsupervised method that has been implemented for clustering of the MRI and different purposes such as recognition of the pattern of interest and image segmentation. However; fuzzy c-means algorithm still suffers many drawbacks, such as low convergence rate, getting stuck in the local minima and vulnerable to initialization sensitivity. Firefly algorithm is a new population-based optimization method that has been used successfully for solving many complex problems. This paper proposed a new dynamic and intelligent clustering method for brain tumor segmentation using the hybridization of Firefly Algorithm (FA with Fuzzy C-Means algorithm (FCM. In order to automatically segment MRI brain images and improve the capability of the FCM to automatically elicit the proper number and location of cluster centres and the number of pixels in each cluster in the abnormal (multiple sclerosis lesions MRI images. The experimental results proved the effectiveness of the proposed FAFCM in enhancing the performance of the traditional FCM clustering. Moreover; the superiority of the FAFCM with other state-of-the-art segmentation methods is shown qualitatively and quantitatively. Conclusion: A novel efficient and reliable clustering algorithm presented in this work, which is called FAFCM based on the hybridization of the firefly algorithm with fuzzy c-mean clustering algorithm. Automatically; the hybridized algorithm has the capability to cluster and segment MRI brain images.
Arterial cannula shape optimization by means of the rotational firefly algorithm
Tesch, K.; Kaczorowska, K.
2016-03-01
This article presents global optimization results of arterial cannula shapes by means of the newly modified firefly algorithm. The search for the optimal arterial cannula shape is necessary in order to minimize losses and prepare the flow that leaves the circulatory support system of a ventricle (i.e. blood pump) before it reaches the heart. A modification of the standard firefly algorithm, the so-called rotational firefly algorithm, is introduced. It is shown that the rotational firefly algorithm allows for better exploration of search spaces which results in faster convergence and better solutions in comparison with its standard version. This is particularly pronounced for smaller population sizes. Furthermore, it maintains greater diversity of populations for a longer time. A small population size and a low number of iterations are necessary to keep to a minimum the computational cost of the objective function of the problem, which comes from numerical solution of the nonlinear partial differential equations. Moreover, both versions of the firefly algorithm are compared to the state of the art, namely the differential evolution and covariance matrix adaptation evolution strategies.
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Chang Liu
2015-01-01
Full Text Available Path planning is a classic optimization problem which can be solved by many optimization algorithms. The complexity of three-dimensional (3D path planning for autonomous underwater vehicles (AUVs requires the optimization algorithm to have a quick convergence speed. This work provides a new 3D path planning method for AUV using a modified firefly algorithm. In order to solve the problem of slow convergence of the basic firefly algorithm, an improved method was proposed. In the modified firefly algorithm, the parameters of the algorithm and the random movement steps can be adjusted according to the operating process. At the same time, an autonomous flight strategy is introduced to avoid instances of invalid flight. An excluding operator was used to improve the effect of obstacle avoidance, and a contracting operator was used to enhance the convergence speed and the smoothness of the path. The performance of the modified firefly algorithm and the effectiveness of the 3D path planning method were proved through a varied set of experiments.
Testing a Firefly-Inspired Synchronization Algorithm in a Complex Wireless Sensor Network
Hao, Chuangbo; Song, Ping; Yang, Cheng; Liu, Xiongjun
2017-01-01
Data acquisition is the foundation of soft sensor and data fusion. Distributed data acquisition and its synchronization are the important technologies to ensure the accuracy of soft sensors. As a research topic in bionic science, the firefly-inspired algorithm has attracted widespread attention as a new synchronization method. Aiming at reducing the design difficulty of firefly-inspired synchronization algorithms for Wireless Sensor Networks (WSNs) with complex topologies, this paper presents a firefly-inspired synchronization algorithm based on a multiscale discrete phase model that can optimize the performance tradeoff between the network scalability and synchronization capability in a complex wireless sensor network. The synchronization process can be regarded as a Markov state transition, which ensures the stability of this algorithm. Compared with the Miroll and Steven model and Reachback Firefly Algorithm, the proposed algorithm obtains better stability and performance. Finally, its practicality has been experimentally confirmed using 30 nodes in a real multi-hop topology with low quality links. PMID:28282899
A clustering method of Chinese medicine prescriptions based on modified firefly algorithm.
Yuan, Feng; Liu, Hong; Chen, Shou-Qiang; Xu, Liang
2016-12-01
This paper is aimed to study the clustering method for Chinese medicine (CM) medical cases. The traditional K-means clustering algorithm had shortcomings such as dependence of results on the selection of initial value, trapping in local optimum when processing prescriptions form CM medical cases. Therefore, a new clustering method based on the collaboration of firefly algorithm and simulated annealing algorithm was proposed. This algorithm dynamically determined the iteration of firefly algorithm and simulates sampling of annealing algorithm by fitness changes, and increased the diversity of swarm through expansion of the scope of the sudden jump, thereby effectively avoiding premature problem. The results from confirmatory experiments for CM medical cases suggested that, comparing with traditional K-means clustering algorithms, this method was greatly improved in the individual diversity and the obtained clustering results, the computing results from this method had a certain reference value for cluster analysis on CM prescriptions.
A new approach to optic disc detection in human retinal images using the firefly algorithm.
Rahebi, Javad; Hardalaç, Fırat
2016-03-01
There are various methods and algorithms to detect the optic discs in retinal images. In recent years, much attention has been given to the utilization of the intelligent algorithms. In this paper, we present a new automated method of optic disc detection in human retinal images using the firefly algorithm. The firefly intelligent algorithm is an emerging intelligent algorithm that was inspired by the social behavior of fireflies. The population in this algorithm includes the fireflies, each of which has a specific rate of lighting or fitness. In this method, the insects are compared two by two, and the less attractive insects can be observed to move toward the more attractive insects. Finally, one of the insects is selected as the most attractive, and this insect presents the optimum response to the problem in question. Here, we used the light intensity of the pixels of the retinal image pixels instead of firefly lightings. The movement of these insects due to local fluctuations produces different light intensity values in the images. Because the optic disc is the brightest area in the retinal images, all of the insects move toward brightest area and thus specify the location of the optic disc in the image. The results of implementation show that proposed algorithm could acquire an accuracy rate of 100 % in DRIVE dataset, 95 % in STARE dataset, and 94.38 % in DiaRetDB1 dataset. The results of implementation reveal high capability and accuracy of proposed algorithm in the detection of the optic disc from retinal images. Also, recorded required time for the detection of the optic disc in these images is 2.13 s for DRIVE dataset, 2.81 s for STARE dataset, and 3.52 s for DiaRetDB1 dataset accordingly. These time values are average value.
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Gobind Preet Singh
2014-02-01
Full Text Available Today, in computer science, a computational challenge exists in finding a globally optimized solution from an enormously large search space. Various meta-heuristic methods can be used for finding the solution in a large search space. These methods can be explained as iterative search processes that efficiently perform the exploration and exploitation in the solution space. In this context, three such nature inspired meta-heuristic algorithms namely Krill Herd Algorithm (KH, Firefly Algorithm (FA and Cuckoo search Algorithm (CS can be used to find optimal solutions of various mathematical optimization problems. In this paper, the proposed algorithms were used to find the optimal solution of fifteen unimodal and multimodal benchmark test functions commonly used in the field of optimization and then compare their performances on the basis of efficiency, convergence, time and conclude that for both unimodal and multimodal optimization Cuckoo Search Algorithm via Lévy flight has outperformed others and for multimodal optimization Krill Herd algorithm is superior than Firefly algorithm but for unimodal optimization Firefly is superior than Krill Herd algorithm.
2014-01-01
Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrained mean-variance (CCMV) portfolio problem with entropy constraint was found in the literature. This paper introduces modified firefly algorithm (FA) for the CCMV portfolio model with entropy constraint. Firefly algorithm is one of the latest, very successful swarm intelligence algorithm; however, it exhibits some deficiencies when applied to constrained problems. To overcome lack of exploration power during early iterations, we modified the algorithm and tested it on standard portfolio benchmark data sets used in the literature. Our proposed modified firefly algorithm proved to be better than other state-of-the-art algorithms, while introduction of entropy diversity constraint further improved results. PMID:24991645
Multiuser Detection in MIMO-OFDM Wireless Communication System Using Hybrid Firefly Algorithm
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B. Sathish Kumar
2014-05-01
Full Text Available In recent years, future generation wireless communication technologies are most the prominent fields in which many innovative techniques are used for effective communication. Orthogonal frequency-division multiplexing is one of the important technologies used for communication in future generation technologies. Although it gives efficient results, it has some problems during the implementation in real-time. MIMO and OFDM are integrated to have both their benefits. But, noise and interference are the major issues in the MIMO OFDM systems. To overcome these issues multiuser detection method is used in MIMO OFDM. Several algorithms and mathematical formulations have been presented for solving multiuser detection problem in MIMO OFDM systems. The algorithms such as genetic simulated annealing algorithm, hybrid ant colony optimization algorithm are used for multiuser detection problem in previous studies. But, due to the limitations of those optimization algorithms, the results obtained are not significant. In this research, to overcome the noise and interference problems, hybrid firefly optimization algorithm based on the evolutionary algorithm is proposed. The proposed algorithm is compared with the existing multiuser detection algorithm such as particle swarm optimization, CEFM-GADA [complementary error function mutation (CEFM and a differential algorithm (DA genetic algorithm (GA] and Hybrid firefly optimization algorithm based on evolutionary algorithm. The simulation results shows that performance of the proposed algorithm is better than the existing algorithm and it provides a satisfactory trade-off between computational complexity and detection performance
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Guang-Dong Zhou
2015-01-01
Full Text Available Optimal sensor placement (OSP is an important task during the implementation of sophisticated structural health monitoring (SHM systems for large-scale structures. In this paper, a comparative study between the genetic algorithm (GA and the firefly algorithm (FA in solving the OSP problem is conducted. To overcome the drawback related to the inapplicability of the FA in optimization problems with discrete variables, some improvements are proposed, including the one-dimensional binary coding system, the Hamming distance between any two fireflies, and the semioriented movement scheme; also, a simple discrete firefly algorithm (SDFA is developed. The capabilities of the SDFA and the GA in finding the optimal sensor locations are evaluated using two disparate objective functions in a numerical example with a long-span benchmark cable-stayed bridge. The results show that the developed SDFA can find the optimal sensor configuration with high reliability. The comparative study indicates that the SDFA outperforms the GA in terms of algorithm complexity, computational efficiency, and result quality. The optimization mechanism of the FA has the potential to be extended to a wide range of optimization problems.
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M. Kandasamy
2014-12-01
Full Text Available A comparison study of Firefly Algorithm (FA and Bacterial Foraging Algorithm (BFO optimization is carried out by applying them to a Non Linear pH neutralization process. In process control engineering, the Proportional, Derivative, Integral controller tuning parameters are deciding the performance of the controller to ensure the good performance of the plant. The FA and BFO algorithms are applied to obtain the optimum values of controller parameters. The performance indicators such as servo response and regulatory response tests are carried out to evaluate the efficiency of the heuristic algorithm based controllers. The error minimization criterion such as Integral Absolute Error (IAE, Integral Square Error (ISE, Integral Time Square Error (ITSE, Integral Time Absolute Error (ITAE and Time domain specifications – rise time, Peak Overshoot and settling time are considered for the study of the performance of the controllers. The study indicates that, FA tuned PID controller provides marginally better set point tracking, load disturbance rejection, time domain specifications and error minimization for the Non Linear pH neutralization process compared to BFO tuned PID controller.
Extension of the firefly algorithm and preference rules for solving MINLP problems
Costa, M. Fernanda P.; Francisco, Rogério B.; Rocha, Ana Maria A. C.; Fernandes, Edite M. G. P.
2017-07-01
An extension of the firefly algorithm (FA) for solving mixed-integer nonlinear programming (MINLP) problems is presented. Although penalty functions are nowadays frequently used to handle integrality conditions and inequality and equality constraints, this paper proposes the implementation within the FA of a simple rounded-based heuristic and four preference rules to find and converge to MINLP feasible solutions. Preliminary numerical experiments are carried out to validate the proposed methodology.
A firefly algorithm for solving competitive location-design problem: a case study
Sadjadi, Seyed Jafar; Ashtiani, Milad Gorji; Ramezanian, Reza; Makui, Ahmad
2016-07-01
This paper aims at determining the optimal number of new facilities besides specifying both the optimal location and design level of them under the budget constraint in a competitive environment by a novel hybrid continuous and discrete firefly algorithm. A real-world application of locating new chain stores in the city of Tehran, Iran, is used and the results are analyzed. In addition, several examples have been solved to evaluate the efficiency of the proposed model and algorithm. The results demonstrate that the performed method provides good-quality results for the test problems.
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P. Balachennaiah
2016-06-01
Full Text Available This paper proposes a Firefly algorithm based technique to optimize the control variables for simultaneous optimization of real power loss and voltage stability limit of the transmission system. Mathematically, this issue can be formulated as nonlinear equality and inequality constrained optimization problem with an objective function integrating both real power loss and voltage stability limit. Transformers taps, unified power flow controller and its parameters have been included as control variables in the problem formulation. The effectiveness of the proposed algorithm has been tested on New England 39-bus system. Simulation results obtained with the proposed algorithm are compared with the real coded genetic algorithm for single objective of real power loss minimization and multi-objective of real power loss minimization and voltage stability limit maximization. Also, a classical optimization method known as interior point successive linear programming technique is considered here to compare the results of firefly algorithm for single objective of real power loss minimization. Simulation results confirm the potentiality of the proposed algorithm in solving optimization problems.
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K. Kumaravel
2015-05-01
Full Text Available Wireless Mesh Network (WMN uses the latest technology which helps in providing end users a high quality service referred to as the Internet’s “last mile”. Also considering WMN one of the most important technologies that are employed is multicast communication. Among the several issues routing which is significantly an important issue is addressed by every WMN technologies and this is done during the process of data transmission. The IEEE 802.11s Standard entails and sets procedures which need to be followed to facilitate interconnection and thus be able to devise an appropriate WMN. There has been introduction of several protocols by many authors which are mainly devised on the basis of machine learning and artificial intelligence. Multi-path routing may be considered as one such routing method which facilitates transmission of data over several paths, proving its capabilities as a useful strategy for achieving reliability in WMN. Though, multi-path routing in any manner cannot really guarantee deterministic transmission. As here there are multiple paths available for enabling data transmission from source to destination node. The algorithm that had been employed before in the studies conducted did not take in to consideration routing metrics which include energy aware metrics that are used for path selection during transferring of data. The following study proposes use of the hybrid multipath routing algorithm while taking in to consideration routing metrics which include energy, minimal loss for efficient path selection and transferring of data. Proposed algorithm here has two phases. In the first phase prim’s algorithm has been proposed so that in networks route discovery may be possible. For the second one the Hybrid firefly algorithm which is based on harmony search has been employed for selection of the most suitable and best through proper analysis of metrics which include energy awareness and minimal loss for every path that has
Continuous Firefly Algorithm for Optimal Tuning of Pid Controller in Avr System
Bendjeghaba, Omar
2014-01-01
This paper presents a tuning approach based on Continuous firefly algorithm (CFA) to obtain the proportional-integral- derivative (PID) controller parameters in Automatic Voltage Regulator system (AVR). In the tuning processes the CFA is iterated to reach the optimal or the near optimal of PID controller parameters when the main goal is to improve the AVR step response characteristics. Conducted simulations show the effectiveness and the efficiency of the proposed approach. Furthermore the proposed approach can improve the dynamic of the AVR system. Compared with particle swarm optimization (PSO), the new CFA tuning method has better control system performance in terms of time domain specifications and set-point tracking.
Endmember extraction from hyperspectral image based on discrete firefly algorithm (EE-DFA)
Zhang, Chengye; Qin, Qiming; Zhang, Tianyuan; Sun, Yuanheng; Chen, Chao
2017-04-01
This study proposed a novel method to extract endmembers from hyperspectral image based on discrete firefly algorithm (EE-DFA). Endmembers are the input of many spectral unmixing algorithms. Hence, in this paper, endmember extraction from hyperspectral image is regarded as a combinational optimization problem to get best spectral unmixing results, which can be solved by the discrete firefly algorithm. Two series of experiments were conducted on the synthetic hyperspectral datasets with different SNR and the AVIRIS Cuprite dataset, respectively. The experimental results were compared with the endmembers extracted by four popular methods: the sequential maximum angle convex cone (SMACC), N-FINDR, Vertex Component Analysis (VCA), and Minimum Volume Constrained Nonnegative Matrix Factorization (MVC-NMF). What's more, the effect of the parameters in the proposed method was tested on both synthetic hyperspectral datasets and AVIRIS Cuprite dataset, and the recommended parameters setting was proposed. The results in this study demonstrated that the proposed EE-DFA method showed better performance than the existing popular methods. Moreover, EE-DFA is robust under different SNR conditions.
Application of Firefly Algorithm for Parameter Estimation of Damped Compound Pendulum
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Saad Mohd Sazli
2016-01-01
Full Text Available This paper presents an investigation into the parameter estimation of the damped compound pendulum using Firefly algorithm method. In estimating the damped compound pendulum, the system necessarily needs a good model. Therefore, the aim of the work described in this paper is to obtain a dynamic model of the damped compound pendulum. By considering a discrete time form for the system, an autoregressive with exogenous input (ARX model structures was selected. In order to collect input-output data from the experiment, the PRBS signal is used to be input signal to regulate the motor speed. Where, the output signal is taken from position sensor. Firefly algorithm (FA algorithm is used to estimate the model parameters based on model 2nd orders. The model validation was done by comparing the measured output against the predicted output in terms of the closeness of both outputs via mean square error (MSE value. The performance of FA is measured in terms of mean square error (MSE.
Attia, Khalid A. M.; Nassar, Mohammed W. I.; El-Zeiny, Mohamed B.; Serag, Ahmed
2017-01-01
For the first time, a new variable selection method based on swarm intelligence namely firefly algorithm is coupled with three different multivariate calibration models namely, concentration residual augmented classical least squares, artificial neural network and support vector regression in UV spectral data. A comparative study between the firefly algorithm and the well-known genetic algorithm was developed. The discussion revealed the superiority of using this new powerful algorithm over the well-known genetic algorithm. Moreover, different statistical tests were performed and no significant differences were found between all the models regarding their predictabilities. This ensures that simpler and faster models were obtained without any deterioration of the quality of the calibration.
Directory of Open Access Journals (Sweden)
Eleonora Bottani
2017-06-01
Full Text Available This paper proposes a modified discrete firefly algorithm (DFA applied to the machine loading problem of the flexible manufacturing systems (FMSs starting from the mathematical formulation adopted by Swarnkar & Tiwari (2004. The aim of the problem is to identify the optimal jobs sequence that simultaneously maximizes the throughput and minimizes the system unbalance according to given technological constraints (e.g. available tool slots and machining time. The results of the algorithm proposed have been compared with the existing and most recent swarm-based approaches available in the open literature using as benchmark the set of ten problems proposed by Mukhopadhyay et al. (1992. The algorithm shows results that are comparable and sometimes even better than most of the other approaches considering both the quality of the results provided and the computational times obtained.
Wong, Ling Ai; Shareef, Hussain; Mohamed, Azah; Ibrahim, Ahmad Asrul
2014-01-01
This paper presents the application of enhanced opposition-based firefly algorithm in obtaining the optimal battery energy storage systems (BESS) sizing in photovoltaic generation integrated radial distribution network in order to mitigate the voltage rise problem. Initially, the performance of the original firefly algorithm is enhanced by utilizing the opposition-based learning and introducing inertia weight. After evaluating the performance of the enhanced opposition-based firefly algorithm (EOFA) with fifteen benchmark functions, it is then adopted to determine the optimal size for BESS. Two optimization processes are conducted where the first optimization aims to obtain the optimal battery output power on hourly basis and the second optimization aims to obtain the optimal BESS capacity by considering the state of charge constraint of BESS. The effectiveness of the proposed method is validated by applying the algorithm to the 69-bus distribution system and by comparing the performance of EOFA with conventional firefly algorithm and gravitational search algorithm. Results show that EOFA has the best performance comparatively in terms of mitigating the voltage rise problem.
Directory of Open Access Journals (Sweden)
Ling Ai Wong
2014-01-01
Full Text Available This paper presents the application of enhanced opposition-based firefly algorithm in obtaining the optimal battery energy storage systems (BESS sizing in photovoltaic generation integrated radial distribution network in order to mitigate the voltage rise problem. Initially, the performance of the original firefly algorithm is enhanced by utilizing the opposition-based learning and introducing inertia weight. After evaluating the performance of the enhanced opposition-based firefly algorithm (EOFA with fifteen benchmark functions, it is then adopted to determine the optimal size for BESS. Two optimization processes are conducted where the first optimization aims to obtain the optimal battery output power on hourly basis and the second optimization aims to obtain the optimal BESS capacity by considering the state of charge constraint of BESS. The effectiveness of the proposed method is validated by applying the algorithm to the 69-bus distribution system and by comparing the performance of EOFA with conventional firefly algorithm and gravitational search algorithm. Results show that EOFA has the best performance comparatively in terms of mitigating the voltage rise problem.
Exact Algorithms for Solving Stochastic Games
DEFF Research Database (Denmark)
Hansen, Kristoffer Arnsfelt; Koucky, Michal; Lauritzen, Niels;
2012-01-01
Shapley's discounted stochastic games, Everett's recursive games and Gillette's undiscounted stochastic games are classical models of game theory describing two-player zero-sum games of potentially infinite duration. We describe algorithms for exactly solving these games.......Shapley's discounted stochastic games, Everett's recursive games and Gillette's undiscounted stochastic games are classical models of game theory describing two-player zero-sum games of potentially infinite duration. We describe algorithms for exactly solving these games....
A Stochastic Collocation Algorithm for Uncertainty Analysis
Mathelin, Lionel; Hussaini, M. Yousuff; Zang, Thomas A. (Technical Monitor)
2003-01-01
This report describes a stochastic collocation method to adequately handle a physically intrinsic uncertainty in the variables of a numerical simulation. For instance, while the standard Galerkin approach to Polynomial Chaos requires multi-dimensional summations over the stochastic basis functions, the stochastic collocation method enables to collapse those summations to a one-dimensional summation only. This report furnishes the essential algorithmic details of the new stochastic collocation method and provides as a numerical example the solution of the Riemann problem with the stochastic collocation method used for the discretization of the stochastic parameters.
Trajectory averaging for stochastic approximation MCMC algorithms
Liang, Faming
2010-01-01
The subject of stochastic approximation was founded by Robbins and Monro [Ann. Math. Statist. 22 (1951) 400--407]. After five decades of continual development, it has developed into an important area in systems control and optimization, and it has also served as a prototype for the development of adaptive algorithms for on-line estimation and control of stochastic systems. Recently, it has been used in statistics with Markov chain Monte Carlo for solving maximum likelihood estimation problems and for general simulation and optimizations. In this paper, we first show that the trajectory averaging estimator is asymptotically efficient for the stochastic approximation MCMC (SAMCMC) algorithm under mild conditions, and then apply this result to the stochastic approximation Monte Carlo algorithm [Liang, Liu and Carroll J. Amer. Statist. Assoc. 102 (2007) 305--320]. The application of the trajectory averaging estimator to other stochastic approximation MCMC algorithms, for example, a stochastic approximation MLE al...
Directory of Open Access Journals (Sweden)
N. M. Okasha
2016-04-01
Full Text Available In this paper, an approach for conducting a Reliability-Based Design Optimization (RBDO of truss structures with linked-discrete design variables is proposed. The sections of the truss members are selected from the AISC standard tables and thus the design variables that represent the properties of each section are linked. Latin hypercube sampling is used in the evaluation of the structural reliability. The improved firefly algorithm is used for the optimization solution process. It was found that in order to use the improved firefly algorithm for efficiently solving problems of reliability-based design optimization with linked-discrete design variables; it needs to be modified as proposed in this paper to accelerate its convergence.
Debbarma, Sanjoy; Saikia, Lalit Chandra; Sinha, Nidul
2014-03-01
Present work focused on automatic generation control (AGC) of a three unequal area thermal systems considering reheat turbines and appropriate generation rate constraints (GRC). A fractional order (FO) controller named as I(λ)D(µ) controller based on crone approximation is proposed for the first time as an appropriate technique to solve the multi-area AGC problem in power systems. A recently developed metaheuristic algorithm known as firefly algorithm (FA) is used for the simultaneous optimization of the gains and other parameters such as order of integrator (λ) and differentiator (μ) of I(λ)D(µ) controller and governor speed regulation parameters (R). The dynamic responses corresponding to optimized I(λ)D(µ) controller gains, λ, μ, and R are compared with that of classical integer order (IO) controllers such as I, PI and PID controllers. Simulation results show that the proposed I(λ)D(µ) controller provides more improved dynamic responses and outperforms the IO based classical controllers. Further, sensitivity analysis confirms the robustness of the so optimized I(λ)D(µ) controller to wide changes in system loading conditions and size and position of SLP. Proposed controller is also found to have performed well as compared to IO based controllers when SLP takes place simultaneously in any two areas or all the areas. Robustness of the proposed I(λ)D(µ) controller is also tested against system parameter variations.
Directory of Open Access Journals (Sweden)
E.Kayalvizhi
2015-08-01
Full Text Available Mitigation of global warming gases from burning gasoline for transportation in vehicles is one of the biggest and most complex issues the world has ever faced. In an intention to eradicate the environmental crisis caused due to global warming, electric vehicles were been introduced that are powered by electric motor which works on the energy stored in a battery pack. Inspired by the research on power management in electric vehicles, this paper focuses on the development of an energy management system for electric vehicles (EMSEV to optimally balance the energy from battery pack. The proposed methodology uses firefly optimization algorithm to optimize the power consumption of the devices like electric motor, power steering, air conditioner, power window, automatic door locks, radio, speaker, horn, wiper, GPS, internal and external lights etc., from the battery in electric vehicles. Depending upon the distance to cover and the battery availability, the devices are made to switch down automatically through dynamic EDF scheduling. CAN protocol is used for effective communication between the devices and the controller. Simulation results are obtained using MATLAB.
Weighing Efficiency-Robustness in Supply Chain Disruption by Multi-Objective Firefly Algorithm
Directory of Open Access Journals (Sweden)
Tong Shu
2016-03-01
Full Text Available This paper investigates various supply chain disruptions in terms of scenario planning, including node disruption and chain disruption; namely, disruptions in distribution centers and disruptions between manufacturing centers and distribution centers. Meanwhile, it also focuses on the simultaneous disruption on one node or a number of nodes, simultaneous disruption in one chain or a number of chains and the corresponding mathematical models and exemplification in relation to numerous manufacturing centers and diverse products. Robustness of the design of the supply chain network is examined by weighing efficiency against robustness during supply chain disruptions. Efficiency is represented by operating cost; robustness is indicated by the expected disruption cost and the weighing issue is calculated by the multi-objective firefly algorithm for consistency in the results. It has been shown that the total cost achieved by the optimal target function is lower than that at the most effective time of supply chains. In other words, the decrease of expected disruption cost by improving robustness in supply chains is greater than the increase of operating cost by reducing efficiency, thus leading to cost advantage. Consequently, by approximating the Pareto Front Chart of weighing between efficiency and robustness, enterprises can choose appropriate efficiency and robustness for their longer-term development.
Stochastic Descent Analysis of Representation Learning Algorithms
Golden, Richard M.
2014-01-01
Although stochastic approximation learning methods have been widely used in the machine learning literature for over 50 years, formal theoretical analyses of specific machine learning algorithms are less common because stochastic approximation theorems typically possess assumptions which are difficult to communicate and verify. This paper presents a new stochastic approximation theorem for state-dependent noise with easily verifiable assumptions applicable to the analysis and design of import...
A modified firefly algorithm applied to the nuclear reload problem of a pressurized water reactor
Energy Technology Data Exchange (ETDEWEB)
Oliveira, Iona Maghali Santos de; Schirru, Roberto, E-mail: ioliveira@con.ufrj.b, E-mail: schirru@lmp.ufrj.b [Universidade Federal do Rio de Janeiro (PEN/COPPE/UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-Graduacao de Engenharia. Programa de Engenharia Nuclear
2011-07-01
The Nuclear Reactor Reload Problem (NRRP) is an issue of great importance and concern in nuclear engineering. It is the problem related with the periodic operation of replacing part of the fuel of a nuclear reactor. Traditionally, this procedure occurs after a period of operation called a cycle, or whenever the nuclear power plant is unable to continue operating at its nominal power. Studied for more than 40 years, the NRRP still remains a challenge for many optimization techniques due to its multiple objectives concerning economics, safety and reactor physics calculations. Characteristics such as non-linearity, multimodality and high dimensionality also make the NRRP a very complex optimization problem. In broad terms, it aims at getting the best arrangement of fuel in the nuclear reactor core that leads to a maximization of the operating time. The primary goal is to design fuel loading patterns (LPs) so that the core produces the required energy output in an economical way, without violating safety limits. Since multiple feasible solutions can be obtained to this problem, judicious optimization is required in order to identify the most economical among them. In this sense, this paper presents a new contribution in this area and introduces a modified firefly algorithm (FA) to perform LPs optimization for a pressurized water reactor. Based on the original FA introduced by Xin-She Yang in 2008, the proposed methodology seems to be very promising as an optimizer to the NRRP. The experiments performed and the comparisons with some well known best performing algorithms from the literature, confirm this statement. (author)
Goudarzi, Shidrokh; Haslina Hassan, Wan; Abdalla Hashim, Aisha-Hassan; Soleymani, Seyed Ahmad; Anisi, Mohammad Hossein; Zakaria, Omar M.
2016-01-01
This study aims to design a vertical handover prediction method to minimize unnecessary handovers for a mobile node (MN) during the vertical handover process. This relies on a novel method for the prediction of a received signal strength indicator (RSSI) referred to as IRBF-FFA, which is designed by utilizing the imperialist competition algorithm (ICA) to train the radial basis function (RBF), and by hybridizing with the firefly algorithm (FFA) to predict the optimal solution. The prediction accuracy of the proposed IRBF–FFA model was validated by comparing it to support vector machines (SVMs) and multilayer perceptron (MLP) models. In order to assess the model’s performance, we measured the coefficient of determination (R2), correlation coefficient (r), root mean square error (RMSE) and mean absolute percentage error (MAPE). The achieved results indicate that the IRBF–FFA model provides more precise predictions compared to different ANNs, namely, support vector machines (SVMs) and multilayer perceptron (MLP). The performance of the proposed model is analyzed through simulated and real-time RSSI measurements. The results also suggest that the IRBF–FFA model can be applied as an efficient technique for the accurate prediction of vertical handover. PMID:27438600
Directory of Open Access Journals (Sweden)
Padmavathi Kora
2017-06-01
Full Text Available Electrocardiogram (ECG, a non-invasive diagnostic technique, used for detecting cardiac arrhythmia. From last decade industry dealing with biomedical instrumentation and research, demanding an advancement in its ability to distinguish different cardiac arrhythmia. Atrial Fibrillation (AF is an irregular rhythm of the human heart. During AF, the atrial moments are quicker than the normal rate. As blood is not completely ejected out of atria, chances for the formation of blood clots in atrium. These abnormalities in the heart can be identified by the changes in the morphology of the ECG. The first step in the detection of AF is preprocessing of ECG, which removes noise using filters. Feature extraction is the next key process in this research. Recent feature extraction methods, such as Auto Regressive (AR modeling, Magnitude Squared Coherence (MSC and Wavelet Coherence (WTC using standard database (MIT-BIH, yielded a lot of features. Many of these features might be insignificant containing some redundant and non-discriminatory features that introduce computational burden and loss of performance. This paper presents fast Conjugate Symmetric Sequency Ordered Complex Hadamard Transform (CS-SCHT for extracting relevant features from the ECG signal. The sparse matrix factorization method is used for developing fast and efficient CS-SCHT algorithm and its computational performance is examined and compared to that of the HT and NCHT. The applications of the CS-SCHT in the ECG-based AF detection is also discussed. These fast CS-SCHT features are optimized using Hybrid Firefly and Particle Swarm Optimization (FFPSO to increase the performance of the classifier.
Trajectory averaging for stochastic approximation MCMC algorithms
Liang, Faming
2010-10-01
The subject of stochastic approximation was founded by Robbins and Monro [Ann. Math. Statist. 22 (1951) 400-407]. After five decades of continual development, it has developed into an important area in systems control and optimization, and it has also served as a prototype for the development of adaptive algorithms for on-line estimation and control of stochastic systems. Recently, it has been used in statistics with Markov chain Monte Carlo for solving maximum likelihood estimation problems and for general simulation and optimizations. In this paper, we first show that the trajectory averaging estimator is asymptotically efficient for the stochastic approximation MCMC (SAMCMC) algorithm under mild conditions, and then apply this result to the stochastic approximation Monte Carlo algorithm [Liang, Liu and Carroll J. Amer. Statist. Assoc. 102 (2007) 305-320]. The application of the trajectory averaging estimator to other stochastic approximationMCMC algorithms, for example, a stochastic approximation MLE algorithm for missing data problems, is also considered in the paper. © Institute of Mathematical Statistics, 2010.
Performance of a Distributed Stochastic Approximation Algorithm
Bianchi, Pascal; Hachem, Walid
2012-01-01
In this paper, a distributed stochastic approximation algorithm is studied. Applications of such algorithms include decentralized estimation, optimization, control or computing. The algorithm consists in two steps: a local step, where each node in a network updates a local estimate using a stochastic approximation algorithm with decreasing step size, and a gossip step, where a node computes a local weighted average between its estimates and those of its neighbors. Convergence of the estimates toward a consensus is established under weak assumptions. The approach relies on two main ingredients: the existence of a Lyapunov function for the mean field in the agreement subspace, and a contraction property of the random matrices of weights in the subspace orthogonal to the agreement subspace. A second order analysis of the algorithm is also performed under the form of a Central Limit Theorem. The Polyak-averaged version of the algorithm is also considered.
Algorithm refinement for stochastic partial differential equations.
Energy Technology Data Exchange (ETDEWEB)
Alexander, F. J. (Francis J.); Garcia, Alejandro L.,; Tartakovsky, D. M. (Daniel M.)
2001-01-01
A hybrid particle/continuum algorithm is formulated for Fickian diffusion in the fluctuating hydrodynamic limit. The particles are taken as independent random walkers; the fluctuating diffusion equation is solved by finite differences with deterministic and white-noise fluxes. At the interface between the particle and continuum computations the coupling is by flux matching, giving exact mass conservation. This methodology is an extension of Adaptive Mesh and Algorithm Refinement to stochastic partial differential equations. A variety of numerical experiments were performed for both steady and time-dependent scenarios. In all cases the mean and variance of density are captured correctly by the stochastic hybrid algorithm. For a non-stochastic version (i.e., using only deterministic continuum fluxes) the mean density is correct, but the variance is reduced except within the particle region, far from the interface. Extensions of the methodology to fluid mechanics applications are discussed.
Models and Algorithm for Stochastic Network Designs
Institute of Scientific and Technical Information of China (English)
Anthony Chen; Juyoung Kim; Seungjae Lee; Jaisung Choi
2009-01-01
The network design problem (NDP) is one of the most difficult and challenging problems in trans-portation. Traditional NDP models are often posed as a deterministic bilevel program assuming that all rele-vant inputs are known with certainty. This paper presents three stochastic models for designing transporta-tion networks with demand uncertainty. These three stochastic NDP models were formulated as the ex-pected value model, chance-constrained model, and dependent-chance model in a bilevel programming framework using different criteria to hedge against demand uncertainty. Solution procedures based on the traffic assignment algorithm, genetic algorithm, and Monte-Cado simulations were developed to solve these stochastic NDP models. The nonlinear and nonconvex nature of the bilevel program was handled by the genetic algorithm and traffic assignment algorithm, whereas the stochastic nature was addressed through simulations. Numerical experiments were conducted to evaluate the applicability of the stochastic NDP models and the solution procedure. Results from the three experiments show that the solution procedures are quite robust to different parameter settings.
Perspective: Stochastic algorithms for chemical kinetics.
Gillespie, Daniel T; Hellander, Andreas; Petzold, Linda R
2013-05-01
We outline our perspective on stochastic chemical kinetics, paying particular attention to numerical simulation algorithms. We first focus on dilute, well-mixed systems, whose description using ordinary differential equations has served as the basis for traditional chemical kinetics for the past 150 years. For such systems, we review the physical and mathematical rationale for a discrete-stochastic approach, and for the approximations that need to be made in order to regain the traditional continuous-deterministic description. We next take note of some of the more promising strategies for dealing stochastically with stiff systems, rare events, and sensitivity analysis. Finally, we review some recent efforts to adapt and extend the discrete-stochastic approach to systems that are not well-mixed. In that currently developing area, we focus mainly on the strategy of subdividing the system into well-mixed subvolumes, and then simulating diffusional transfers of reactant molecules between adjacent subvolumes together with chemical reactions inside the subvolumes.
Perspective: Stochastic algorithms for chemical kinetics
Gillespie, Daniel T.; Hellander, Andreas; Petzold, Linda R.
2013-05-01
We outline our perspective on stochastic chemical kinetics, paying particular attention to numerical simulation algorithms. We first focus on dilute, well-mixed systems, whose description using ordinary differential equations has served as the basis for traditional chemical kinetics for the past 150 years. For such systems, we review the physical and mathematical rationale for a discrete-stochastic approach, and for the approximations that need to be made in order to regain the traditional continuous-deterministic description. We next take note of some of the more promising strategies for dealing stochastically with stiff systems, rare events, and sensitivity analysis. Finally, we review some recent efforts to adapt and extend the discrete-stochastic approach to systems that are not well-mixed. In that currently developing area, we focus mainly on the strategy of subdividing the system into well-mixed subvolumes, and then simulating diffusional transfers of reactant molecules between adjacent subvolumes together with chemical reactions inside the subvolumes.
Rare event sampling with stochastic growth algorithms
Directory of Open Access Journals (Sweden)
Prellberg Thomas
2013-03-01
Full Text Available We discuss uniform sampling algorithms that are based on stochastic growth methods, using sampling of extreme configurations of polymers in simple lattice models as a motivation. We shall show how a series of clever enhancements to a fifty-odd year old algorithm, the Rosenbluth method, led to a cutting-edge algorithm capable of uniform sampling of equilibrium statistical mechanical systems of polymers in situations where competing algorithms failed to perform well. Examples range from collapsed homo-polymers near sticky surfaces to models of protein folding.
An exact accelerated stochastic simulation algorithm
Mjolsness, Eric; Orendorff, David; Chatelain, Philippe; Koumoutsakos, Petros
2009-04-01
An exact method for stochastic simulation of chemical reaction networks, which accelerates the stochastic simulation algorithm (SSA), is proposed. The present "ER-leap" algorithm is derived from analytic upper and lower bounds on the multireaction probabilities sampled by SSA, together with rejection sampling and an adaptive multiplicity for reactions. The algorithm is tested on a number of well-quantified reaction networks and is found experimentally to be very accurate on test problems including a chaotic reaction network. At the same time ER-leap offers a substantial speedup over SSA with a simulation time proportional to the 2/3 power of the number of reaction events in a Galton-Watson process.
Asymptotic normality of randomly truncated stochastic algorithms
Lelong, Jérôme
2010-01-01
We study the convergence rate of randomly truncated stochastic algorithms, which consist in the truncation of the standard Robbins-Monro procedure on an increasing sequence of compact sets. Such a truncation is often required in practice to ensure convergence when standard algorithms fail because the expected-value function grows too fast. In this work, we give a self contained proof of a central limit theorem for this algorithm under local assumptions on the expected-value function, which are fairly easy to check in practice.
Asymptotic normality of randomly truncated stochastic algorithms
Lelong, Jérôme
2010-01-01
We study the convergence rate of randomly truncated stochastic algorithms, which consist in the truncation of the standard Robbins-Monro procedure on an increasing sequence of compact sets. Such a truncation is often required in practice to ensure convergence when standard algorithms fail because the expected-value function grows too fast. In this work, we give a self contained proof of a central limit theorem for this algorithm under local assumptions on the expected-value function, which are fairly easy to check in practice.
Stochastic Evolutionary Algorithms for Planning Robot Paths
Fink, Wolfgang; Aghazarian, Hrand; Huntsberger, Terrance; Terrile, Richard
2006-01-01
A computer program implements stochastic evolutionary algorithms for planning and optimizing collision-free paths for robots and their jointed limbs. Stochastic evolutionary algorithms can be made to produce acceptably close approximations to exact, optimal solutions for path-planning problems while often demanding much less computation than do exhaustive-search and deterministic inverse-kinematics algorithms that have been used previously for this purpose. Hence, the present software is better suited for application aboard robots having limited computing capabilities (see figure). The stochastic aspect lies in the use of simulated annealing to (1) prevent trapping of an optimization algorithm in local minima of an energy-like error measure by which the fitness of a trial solution is evaluated while (2) ensuring that the entire multidimensional configuration and parameter space of the path-planning problem is sampled efficiently with respect to both robot joint angles and computation time. Simulated annealing is an established technique for avoiding local minima in multidimensional optimization problems, but has not, until now, been applied to planning collision-free robot paths by use of low-power computers.
Stochastic simulation algorithms and analysis
Asmussen, Soren
2007-01-01
Sampling-based computational methods have become a fundamental part of the numerical toolset of practitioners and researchers across an enormous number of different applied domains and academic disciplines. This book provides a broad treatment of such sampling-based methods, as well as accompanying mathematical analysis of the convergence properties of the methods discussed. The reach of the ideas is illustrated by discussing a wide range of applications and the models that have found wide usage. The first half of the book focuses on general methods, whereas the second half discusses model-specific algorithms. Given the wide range of examples, exercises and applications students, practitioners and researchers in probability, statistics, operations research, economics, finance, engineering as well as biology and chemistry and physics will find the book of value.
A novel nature inspired firefly algorithm with higher order neural network: Performance analysis
Directory of Open Access Journals (Sweden)
Janmenjoy Nayak
2016-03-01
Full Text Available The applications of both Feed Forward Neural network and Multilayer perceptron are very diverse and saturated. But the linear threshold unit of feed forward networks causes fast learning with limited capabilities, while due to multilayering, the back propagation of errors exhibits slow training speed in MLP. So, a higher order network can be constructed by correlating between the input variables to perform nonlinear mapping using the single layer of input units for overcoming the above drawbacks. In this paper, a Firefly based higher order neural network has been proposed for data classification for maintaining fast learning and avoids the exponential increase of processing units. A vast literature survey has been conducted to review the state of the art of the previous developed models. The performance of the proposed method has been tested with various benchmark datasets from UCI machine learning repository and compared with the performance of other established models. Experimental results imply that the proposed method is fast, steady, reliable and provides better classification accuracy than others.
ChePa, Noraziah; Hashim, Nor Laily; Yusof, Yuhanis; Hussain, Azham
2016-08-01
Flood evacuation centre is defined as a temporary location or area of people from disaster particularly flood as a rescue or precautionary measure. Gazetted evacuation centres are normally located at secure places which have small chances from being drowned by flood. However, due to extreme flood several evacuation centres in Kelantan were unexpectedly drowned. Currently, there is no study done on proposing a decision support aid to reallocate victims and resources of the evacuation centre when the situation getting worsens. Therefore, this study proposes a decision aid model to be utilized in realizing an adaptive emergency evacuation centre management system. This study undergoes two main phases; development of algorithm and models, and development of a web-based and mobile app. The proposed model operates using Firefly multi-objective optimization algorithm that creates an optimal schedule for the relocation of victims and resources for an evacuation centre. The proposed decision aid model and the adaptive system can be applied in supporting the National Security Council's respond mechanisms for handling disaster management level II (State level) especially in providing better management of the flood evacuating centres.
Directory of Open Access Journals (Sweden)
Akemi Gálvez
2013-01-01
Full Text Available Fitting spline curves to data points is a very important issue in many applied fields. It is also challenging, because these curves typically depend on many continuous variables in a highly interrelated nonlinear way. In general, it is not possible to compute these parameters analytically, so the problem is formulated as a continuous nonlinear optimization problem, for which traditional optimization techniques usually fail. This paper presents a new bioinspired method to tackle this issue. In this method, optimization is performed through a combination of two techniques. Firstly, we apply the indirect approach to the knots, in which they are not initially the subject of optimization but precomputed with a coarse approximation scheme. Secondly, a powerful bioinspired metaheuristic technique, the firefly algorithm, is applied to optimization of data parameterization; then, the knot vector is refined by using De Boor’s method, thus yielding a better approximation to the optimal knot vector. This scheme converts the original nonlinear continuous optimization problem into a convex optimization problem, solved by singular value decomposition. Our method is applied to some illustrative real-world examples from the CAD/CAM field. Our experimental results show that the proposed scheme can solve the original continuous nonlinear optimization problem very efficiently.
Application of Firefly Algorithm in Optimization of Chemical Production Process%萤火虫算法在化工过程优化中的应用
Institute of Scientific and Technical Information of China (English)
常晓萍; 杜文芳; 王凌; 何学甫
2014-01-01
近些年以来，以萤火虫算法为代表的群智能优化算法由于具有强大的寻优功能而得到了广泛的应用。在化工生产的过程中，采用萤火虫算法和Matlab语言对实际问题进行优化的同时还采用了苯-甲苯闪蒸过程对其进行了仿真计算。仿真结果表明，气相产物中苯的产量在有效的调节温度、压力以及分流系数的条件下能够达到最大，不仅实现了苯-甲苯闪蒸过程的最优化，同时还充分证明了在解决化工生产过程的优化问题上采用萤火虫算法是有效的。%In recent years, the swarm intelligence optimization algorithm represented by the firefly algorithm with powerful optimization seeking function has been widely used. In the process of chemical production, the actual problems are optimized by firefly algorithm and Matlab language, at the same time, and the simulation calculation is carried out by benzene-toluene flash process. The simulation results show that the output of benzene in the gaseous phase products can achieve maximum under the condition of effective regulation of temperature, pressure, and shunt coefficient, which not only realized the optimization of benzene-toluene flash process, but also fully demonstrated that it's effective to solve the optimization problem in the process of chemical production with the firefly algorithm.
Solving variational inequalities with Stochastic Mirror-Prox algorithm
Juditsky, Anatoli; Tauvel, Claire
2008-01-01
In this paper we consider iterative methods for stochastic variational inequalities (s.v.i.) with monotone operators. Our basic assumption is that the operator possesses both smooth and nonsmooth components. Further, only noisy observations of the problem data are available. We develop a novel Stochastic Mirror-Prox (SMP) algorithm for solving s.v.i. and show that with the convenient stepsize strategy it attains the optimal rates of convergence with respect to the problem parameters. We apply the SMP algorithm to Stochastic composite minimization and describe particular applications to Stochastic Semidefinite Feasability problem and Eigenvalue minimization.
Solving variational inequalities with stochastic mirror-prox algorithm
Directory of Open Access Journals (Sweden)
Anatoli B. Juditsky
2011-01-01
Full Text Available We consider iterative methods for stochastic variational inequalities (s.v.i. with monotone operators. Our basic assumption is that the operator possesses both smooth and nonsmooth components. Further, only noisy observations of the problem data are available. We develop a novel Stochastic Mirror-Prox (SMP algorithm for solving s.v.i. and show that with the convenient stepsize strategy it attains the optimal rates of convergence with respect to the problem parameters. We apply the SMP algorithm to Stochastic composite minimization and describe particular applications to Stochastic Semidefinite Feasability problem and Eigenvalue minimization.
Models and algorithms for stochastic online scheduling
Megow, N.; Uetz, Marc Jochen; Vredeveld, T.
We consider a model for scheduling under uncertainty. In this model, we combine the main characteristics of online and stochastic scheduling in a simple and natural way. Job processing times are assumed to be stochastic, but in contrast to traditional stochastic scheduling models, we assume that
Advanced Dynamically Adaptive Algorithms for Stochastic Simulations on Extreme Scales
Energy Technology Data Exchange (ETDEWEB)
Xiu, Dongbin [Univ. of Utah, Salt Lake City, UT (United States)
2017-03-03
The focus of the project is the development of mathematical methods and high-performance computational tools for stochastic simulations, with a particular emphasis on computations on extreme scales. The core of the project revolves around the design of highly efficient and scalable numerical algorithms that can adaptively and accurately, in high dimensional spaces, resolve stochastic problems with limited smoothness, even containing discontinuities.
一种混沌多样性控制的萤火虫优化算法%A firefly algorithm with chaotic diversity control
Institute of Scientific and Technical Information of China (English)
徐华丽; 苏守宝; 严仍荣; 马艳
2014-01-01
To overcome the disadvantage of premature convergence in the firefly algorithm , a firefly algorithm based on chaos diversity control (CDFA ) was proposed .Applying chaotic mapping ,CDFA achieved an initial firefly population that is high quality and uniformly distributed ;it then disturbed some individuals with low fitness values by chaotic mapping in the process of the search so as to keep the group's activity and reduce the possibility of falling into local optimum ;meanw hile , in order to increase the diversity of the population , the proposed algorithm used the physical reflection theory to control the position of the firefly outside the borders .Experimental results of bench mark functions show that CDFA can effectively improve the ability of the global search and local exploitation and has a better optimization precision and convergence rate than the basic FA .%针对基本萤火虫算法存在早熟停滞现象，提出了一种混沌多样性控制的萤火虫优化算法。运用混沌映射产生均匀分布的萤火虫初始位置，获得质量较好的初始解；在搜索过程中对适应值低的部分萤火虫进行混沌扰动，以保持群体活性，减小陷入局部最优的可能性；同时利用真实物理反弹理论对超越边界萤火虫位置进行控制，提高种群的多样性。用标准测试函数测试，实验结果表明，该算法能有效地提高了算法的全局搜索和局部开发能力，寻优精度和收敛速度明显提高。
A LEAP-FROG ALGORITHM FOR STOCHASTIC DYNAMICS
Van Gunsteren, W. F.; Berendsen, H. J. C.
1988-01-01
A third-order algorithm for stochastic dynamics (SD) simulations is proposed, identical to the powerful molecular dynamics leapfrog algorithm in the limit of infinitely small friction coefficient gamma. It belongs to the class of SD algorithms, in which the integration time step Delta t is not
A LEAP-FROG ALGORITHM FOR STOCHASTIC DYNAMICS
Van Gunsteren, W. F.; Berendsen, H. J. C.
1988-01-01
A third-order algorithm for stochastic dynamics (SD) simulations is proposed, identical to the powerful molecular dynamics leapfrog algorithm in the limit of infinitely small friction coefficient gamma. It belongs to the class of SD algorithms, in which the integration time step Delta t is not limit
Stochastic reaction–diffusion algorithms for macromolecular crowding
Sturrock, Marc
2016-06-01
Compartment-based (lattice-based) reaction–diffusion algorithms are often used for studying complex stochastic spatio-temporal processes inside cells. In this paper the influence of macromolecular crowding on stochastic reaction–diffusion simulations is investigated. Reaction–diffusion processes are considered on two different kinds of compartmental lattice, a cubic lattice and a hexagonal close packed lattice, and solved using two different algorithms, the stochastic simulation algorithm and the spatiocyte algorithm (Arjunan and Tomita 2010 Syst. Synth. Biol. 4, 35–53). Obstacles (modelling macromolecular crowding) are shown to have substantial effects on the mean squared displacement and average number of molecules in the domain but the nature of these effects is dependent on the choice of lattice, with the cubic lattice being more susceptible to the effects of the obstacles. Finally, improvements for both algorithms are presented.
Advanced Dynamically Adaptive Algorithms for Stochastic Simulations on Extreme Scales
Energy Technology Data Exchange (ETDEWEB)
Xiu, Dongbin [Purdue Univ., West Lafayette, IN (United States)
2016-06-21
The focus of the project is the development of mathematical methods and high-performance com- putational tools for stochastic simulations, with a particular emphasis on computations on extreme scales. The core of the project revolves around the design of highly e cient and scalable numer- ical algorithms that can adaptively and accurately, in high dimensional spaces, resolve stochastic problems with limited smoothness, even containing discontinuities.
Directory of Open Access Journals (Sweden)
Vincentius Raki Mahindhara
2017-01-01
Full Text Available Penggunaan relay arus lebih (over current relay pada industri memerlukan pengaturan beberapa parameter seperti arus pickup (Ip, time dial setting (TDS, serta waktu operasi (top. Dalam standard acuan dicantumkan batasan-batasan dan formulasi dalam menentukan parameter tersebut. Salah satu permasalahan adalah penentuan TDS pada relay inverse (Kode ANSI 51. Umumnya penentuan nilai TDS dilakukan dengan metode trial and error, hal ini dirasa kurang efektif sehingga diusulkan suatu metode baru dalam menentukan TDS pada sistem kelistrikan eksisting PT. Pupuk Kalimantan Timur. Digunakan algoritma adaptive firefly yang dimodifikasi dalam menyelesaikan permasalahan dengan mempertimbangkan kurva starting motor dan perbedaan tipe kurva antar relay
Performance analysis of stochastic gradient algorithms under weak conditions
Institute of Scientific and Technical Information of China (English)
DING Feng; YANG HuiZhong; LIU Fei
2008-01-01
By using the stochastic martingale theory, convergence properties of stochastic gradient (SG) identification algorithms are studied under weak conditions. The analysis indicates that the parameter estimates by the SG algorithms consistently converge to the true parameters, as long as the information vector is persistently exciting (i.e., the data product moment matrix has a bounded condition number) and that the process noises are zero mean and uncorrelated. These results remove the strict assumptions, made in existing references, that the noise variances and high-order moments exist, and the processes are stationary and ergodic and the strong persistent excitation condition holds. This contribution greatly relaxes the convergence conditions of stochastic gradient algorithms. The simulation results with bounded and unbounded noise variances confirm the convergence conclusions proposed.
Identification and estimation algorithm for stochastic neural system.
Nakao, M; Hara, K; Kimura, M; Sato, R
1984-01-01
An algorithm for the estimation of stochastic processes in a neural system is presented. This process is defined here as the continuous stochastic process reflecting the dynamics of the neural system which has some inputs and generates output spike trains. The algorithm proposed here is to identify the system parameters and then estimate the stochastic process called neural system process here. These procedures carried out on the basis of the output spike trains which are supposed to be the data observed in the randomly missing way by the threshold time function in the neural system. The algorithm is constructed with the well-known Kalman filters and realizes the estimation of the neural system process by cooperating with the algorithm for the parameter estimation of the threshold time function presented previously (Nakao et al., 1983). The performance of the algorithm is examined by applying it to the various spike trains simulated by some artificial models and also to the neural spike trains recorded in cat's optic tract fibers. The results in these applications are thought to prove the effectiveness of the algorithm proposed here to some extent. Such attempts, we think, will serve to improve the characterizing and modelling techniques of the stochastic neural systems.
Linear-scaling and parallelizable algorithms for stochastic quantum chemistry
Booth, George H; Alavi, Ali
2013-01-01
For many decades, quantum chemical method development has been dominated by algorithms which involve increasingly complex series of tensor contractions over one-electron orbital spaces. Procedures for their derivation and implementation have evolved to require the minimum amount of logic and rely heavily on computationally efficient library-based matrix algebra and optimized paging schemes. In this regard, the recent development of exact stochastic quantum chemical algorithms to reduce computational scaling and memory overhead requires a contrasting algorithmic philosophy, but one which when implemented efficiently can often achieve higher accuracy/cost ratios with small random errors. Additionally, they can exploit the continuing trend for massive parallelization which hinders the progress of deterministic high-level quantum chemical algorithms. In the Quantum Monte Carlo community, stochastic algorithms are ubiquitous but the discrete Fock space of quantum chemical methods is often unfamiliar, and the metho...
Stochastic Recursive Algorithms for Optimization Simultaneous Perturbation Methods
Bhatnagar, S; Prashanth, L A
2013-01-01
Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient perturbation approaches form a thread unifying all the algorithms considered. Simultaneous perturbation stochastic approximation and smooth fractional estimators for gradient- and Hessian-based methods are presented. These algorithms: • are easily implemented; • do not require an explicit system model; and • work with real or simulated data. Chapters on their application in service systems, vehicular traffic control and communications networks illustrate this point. The book is self-contained with necessary mathematical results placed in an appendix. The text provides easy-to-use, off-the-shelf algorithms that are given detailed mathematical treatment so the material presented will be of significant interest to practitioners, academic researchers and graduate students alike. The breadth of applications makes the book appropriate for reader from sim...
An identification algorithm for linear stochastic systems with time delays
Leondes, C. T.; Wong, E. C.
1982-01-01
Linear discrete stochastic control systems containing unknown multiple time delays, plant parameters and noise variances are considered. An algorithm is established which uses the maximum-likelihood technique to identify the unknown parameters. An estimated likelihood function is evaluated based on the previous parameter estimates, which in turn generates a new descent direction vector to update the unknown parameters. The delays and plant parameters are identified in their respective parameter spaces. An example of a second-order stochastic system has been implemented by digital simulation to demonstrate the applicability of the algorithm.
Institute of Scientific and Technical Information of China (English)
付强; 蒋睿奇; 王子龙; 李天霄
2015-01-01
Inacurate estimation of parameters of soil water characteristic curves can cause simulation results of soil moisture models. This paper introduced firefly algorithm to solve the nonlinear fitting problem of soil water characteristic curve model (Van Genuchten) parameters. In the algorithm principle, the force of attraction and light are based on the distance between different individuals, based on which the whole population is divided into a number of subgroups at the beginning, and every sub-population gathered around local optimal value. All sub-population can optimize at the same time, improving the efficiency, and the global optimal solution is found between all these local optimal values. Compared with the particle swarm algorithm, the firefly algorithm does not use perception such as individual best position, the global optimal position to control calculation, which avoids the potential defects such as premature convergence. In addition, the algorithm dose not set speed for fireflies, which avoids the problem that the speed exceeds a threshold. But, when firefly individuals approach the optimal value, it is likely to move longer than the distance between the individual and the optimal value, therefore resulting in the jump-over of the optimal value and reaching the other side. If this situation appears repeatedly, it will affect the convergence speed and accuracy of algorithm. So in this paper we turned the fixed step length into a variant step length that changed according to the between-firefly distance, and thus the firefly algorithm had a better global optimization ability at the beginning and rapidly found the position adjacent to the optimal value region, in the end it had a good local search ability and was able to find the overall optimal value. The test samples came from test field of College of Water Conservancy and Architecture in Northeast Agricultural University(126°45'32″E、45°44'41″N), China. Black soil in Harbin area can represent the
A Stochastic Approximation Frame Algorithm with Adaptive Directions
Institute of Scientific and Technical Information of China (English)
Zi Xu; YuHong Dai
2008-01-01
Stochastic approximation problem is to find some root or extremum of a non-linear function for which only noisy measurements of the function are available. The classical algorithm for stochastic approximation problem is the Robbins-Monro (RM) algorithm, which uses the noisy evaluation of the negative gradient direction as the iterative direction. In order to accelerate the RM algorithm, this paper gives a frame algorithm using adaptive iterative directions. At each iteration, the new algorithm goes towards either the noisy evaluation of the negative gradient direction or some other directions under some switch criterions. Two feasible choices of the criterions are pro-posed and two corresponding frame algorithms are formed. Different choices of the directions under the same given switch criterion in the frame can also form different algorithms. We also proposed the simultanous perturbation difference forms for the two frame algorithms. The almost surely convergence of the new algorithms are all established. The numerical experiments show that the new algorithms are promising.
Stochastic analysis and convergence velocity estimation of genetic algorithms
Institute of Scientific and Technical Information of China (English)
郭观七; 喻寿益
2003-01-01
Formulizations of mutation and crossover operators independent of representation of solutions are proposed. A kind of precisely quantitative Markov chain of populations of standard genetic algorithms is modeled. It is proved that inadequate parameters of mutation and crossover probabilities degenerate standard genetic algorithm to a class of random search algorithms without selection bias toward any solution based on fitness. After introducing elitist reservation, the stochastic matrix of Markov chain of the best-so-far individual with the highest fitness is derived.The average convergence velocity of genetic algorithms is defined as the mathematical expectation of the mean absorbing time steps that the best-so-far individual transfers from any initial solution to the global optimum. Using the stochastic matrix of the best-so-far individual, a theoretic method and the computing process of estimating the average convergence velocity are proposed.
On stochastic approximation algorithms for classes of PAC learning problems
Energy Technology Data Exchange (ETDEWEB)
Rao, N.S.V.; Uppuluri, V.R.R.; Oblow, E.M.
1994-03-01
The classical stochastic approximation methods are shown to yield algorithms to solve several formulations of the PAC learning problem defined on the domain [o,1]{sup d}. Under some assumptions on different ability of the probability measure functions, simple algorithms to solve some PAC learning problems are proposed based on networks of non-polynomial units (e.g. artificial neural networks). Conditions on the sizes of these samples required to ensure the error bounds are derived using martingale inequalities.
Fast Quantum Algorithms for Numerical Integrals and Stochastic Processes
Abrams, D.; Williams, C.
1999-01-01
We discuss quantum algorithms that calculate numerical integrals and descriptive statistics of stochastic processes. With either of two distinct approaches, one obtains an exponential speed increase in comparison to the fastest known classical deterministic algotithms and a quadratic speed increase incomparison to classical Monte Carlo methods.
On the Investigation of Stochastic Global Optimization Algorithms
Baritompa, B.; Hendrix, E.M.T.
2005-01-01
This discussion paper for the SGO 2001 Workshop considers the process of investigating stochastic global optimization algorithms. It outlines a general plan for the systematic study of their behavior. It raises questions about performance criteria, characteristics of test cases and classification of
Fast Quantum Algorithm for Predicting Descriptive Statistics of Stochastic Processes
Williams Colin P.
1999-01-01
Stochastic processes are used as a modeling tool in several sub-fields of physics, biology, and finance. Analytic understanding of the long term behavior of such processes is only tractable for very simple types of stochastic processes such as Markovian processes. However, in real world applications more complex stochastic processes often arise. In physics, the complicating factor might be nonlinearities; in biology it might be memory effects; and in finance is might be the non-random intentional behavior of participants in a market. In the absence of analytic insight, one is forced to understand these more complex stochastic processes via numerical simulation techniques. In this paper we present a quantum algorithm for performing such simulations. In particular, we show how a quantum algorithm can predict arbitrary descriptive statistics (moments) of N-step stochastic processes in just O(square root of N) time. That is, the quantum complexity is the square root of the classical complexity for performing such simulations. This is a significant speedup in comparison to the current state of the art.
Algorithm Refinement for Stochastic Partial Differential Equations. I. Linear Diffusion
Alexander, Francis J.; Garcia, Alejandro L.; Tartakovsky, Daniel M.
2002-10-01
A hybrid particle/continuum algorithm is formulated for Fickian diffusion in the fluctuating hydrodynamic limit. The particles are taken as independent random walkers; the fluctuating diffusion equation is solved by finite differences with deterministic and white-noise fluxes. At the interface between the particle and continuum computations the coupling is by flux matching, giving exact mass conservation. This methodology is an extension of Adaptive Mesh and Algorithm Refinement to stochastic partial differential equations. Results from a variety of numerical experiments are presented for both steady and time-dependent scenarios. In all cases the mean and variance of density are captured correctly by the stochastic hybrid algorithm. For a nonstochastic version (i.e., using only deterministic continuum fluxes) the mean density is correct, but the variance is reduced except in particle regions away from the interface. Extensions of the methodology to fluid mechanics applications are discussed.
Algorithm refinement for stochastic partial differential equations I. linear diffusion
Alexander, F J; Tartakovsky, D M
2002-01-01
A hybrid particle/continuum algorithm is formulated for Fickian diffusion in the fluctuating hydrodynamic limit. The particles are taken as independent random walkers; the fluctuating diffusion equation is solved by finite differences with deterministic and white-noise fluxes. At the interface between the particle and continuum computations the coupling is by flux matching, giving exact mass conservation. This methodology is an extension of Adaptive Mesh and Algorithm Refinement to stochastic partial differential equations. Results from a variety of numerical experiments are presented for both steady and time-dependent scenarios. In all cases the mean and variance of density are captured correctly by the stochastic hybrid algorithm. For a nonstochastic version (i.e., using only deterministic continuum fluxes) the mean density is correct, but the variance is reduced except in particle regions away from the interface. Extensions of the methodology to fluid mechanics applications are discussed.
Stochastic geometry, spatial statistics and random fields models and algorithms
2015-01-01
Providing a graduate level introduction to various aspects of stochastic geometry, spatial statistics and random fields, this volume places a special emphasis on fundamental classes of models and algorithms as well as on their applications, for example in materials science, biology and genetics. This book has a strong focus on simulations and includes extensive codes in Matlab and R, which are widely used in the mathematical community. It can be regarded as a continuation of the recent volume 2068 of Lecture Notes in Mathematics, where other issues of stochastic geometry, spatial statistics and random fields were considered, with a focus on asymptotic methods.
Stochastic Formal Correctness of Numerical Algorithms
Daumas, Marc; Lester, David; Martin-Dorel, Erik; Truffert, Annick
2009-01-01
We provide a framework to bound the probability that accumulated errors were never above a given threshold on numerical algorithms. Such algorithms are used for example in aircraft and nuclear power plants. This report contains simple formulas based on Levy's and Markov's inequalities and it presents a formal theory of random variables with a special focus on producing concrete results. We selected four very common applications that fit in our framework and cover the common practices of systems that evolve for a long time. We compute the number of bits that remain continuously significant in the first two applications with a probability of failure around one out of a billion, where worst case analysis considers that no significant bit remains. We are using PVS as such formal tools force explicit statement of all hypotheses and prevent incorrect uses of theorems.
Meta Heuristic Algorithms for Vehicle Routing Problem with Stochastic Demands
Directory of Open Access Journals (Sweden)
Geetha Shanmugam
2011-01-01
Full Text Available Problem statement: The shipment of goods from manufacturer to the consumer is a focal point of distribution logistics. In reality, the demand of consumers is not known a priori. This kind of distribution is dealt by Stochastic Vehicle Routing Problem (SVRP which is a NP-hard problem. In this proposed work, VRP with stochastic demand is considered. A probability distribution is considered as a random variable for stochastic demand of a customer. Approach: In this study, VRPSD is resolved using Meta heuristic algorithms such as Genetic Algorithm (GA, Particle Swarm Optimization (PSO and Hybrid PSO (HPSO. Dynamic Programming (DP is used to find the expected cost of each route generated by GA, PSO and HPSO. Results: The objective is to minimize the total expected cost of a priori route. The fitness value of a priori route is calculated using DP. In proposed HPSO, the initial particles are generated based Nearest Neighbor Heuristic (NNH. Elitism is used in HPSO for updating the particles. The algorithm is implemented using MATLAB7.0 and tested with problems having different number of customers. The results obtained are competitive in terms of execution time and memory usage. Conclusion: The computational time is reduced as polynomial time as O(nKQ time and the memory required is O(nQ. The ANOVA test is performed to compare the proposed HPSO with other heuristic algorithms.
STP: A Stochastic Tunneling Algorithm for Global Optimization
Energy Technology Data Exchange (ETDEWEB)
Oblow, E.M.
1999-05-20
A stochastic approach to solving continuous function global optimization problems is presented. It builds on the tunneling approach to deterministic optimization presented by Barhen et al, by combining a series of local descents with stochastic searches. The method uses a rejection-based stochastic procedure to locate new local minima descent regions and a fixed Lipschitz-like constant to reject unpromising regions in the search space, thereby increasing the efficiency of the tunneling process. The algorithm is easily implemented in low-dimensional problems and scales easily to large problems. It is less effective without further heuristics in these latter cases, however. Several improvements to the basic algorithm which make use of approximate estimates of the algorithms parameters for implementation in high-dimensional problems are also discussed. Benchmark results are presented, which show that the algorithm is competitive with the best previously reported global optimization techniques. A successful application of the approach to a large-scale seismology problem of substantial computational complexity using a low-dimensional approximation scheme is also reported.
基于萤火虫算法的神经网络CPI预测模型%Neural Network Prediction Model of CPI Using Firefly Algorithm Training
Institute of Scientific and Technical Information of China (English)
彭伟; 汪镭
2012-01-01
This paper uses firefly algorithm to train neural network model using for predicting consumer price index It specifies the model constructing process, train method and simulation By collecting data from Statistics Bureau, the papers sets up an algorithm model in matlab and calculate the simulating result It proves to be good in accuracy and satisfies the predicting standard The work will be positive in the macroeconomic policy decision%通过萤火虫算法来训练神经网络模型,将模型运用于消费者物价指数(CPI)的预测.阐述模型的建立,训练优化和仿真方法.收集国家统计局的统计数据,通过matlab建立算法模型,得到仿真结果.实验证明模型具有较高的预测精度,满足预测要求,对于未来我国的经济调控具有一定意义.
An SQP Algorithm for Recourse-based Stochastic Nonlinear Programming
Directory of Open Access Journals (Sweden)
Xinshun Ma
2016-05-01
Full Text Available The stochastic nonlinear programming problem with completed recourse and nonlinear constraints is studied in this paper. We present a sequential quadratic programming method for solving the problem based on the certainty extended nonlinear model. This algorithm is obtained by combing the active set method and filter method. The convergence of the method is established under some standard assumptions. Moreover, a practical design is presented and numerical results are provided.
A New Theoretical Framework for Analyzing Stochastic Global Optimization Algorithms
Institute of Scientific and Technical Information of China (English)
无
1999-01-01
In this paper, we develop a new theoretical framework by means of the absorbing Markov process theory for analyzing some stochastic global optimization algorithms. Applying the framework to the pure random search, we prove that the pure random search converges to the global minimum in probability and its time has geometry distribution. We also analyze the pure adaptive search by this framework and turn out that the pure adaptive search converges to the global minimum in probability and its time has Poisson distribution.
Computing gap free Pareto front approximations with stochastic search algorithms.
Schütze, Oliver; Laumanns, Marco; Tantar, Emilia; Coello, Carlos A Coello; Talbi, El-Ghazali
2010-01-01
Recently, a convergence proof of stochastic search algorithms toward finite size Pareto set approximations of continuous multi-objective optimization problems has been given. The focus was on obtaining a finite approximation that captures the entire solution set in some suitable sense, which was defined by the concept of epsilon-dominance. Though bounds on the quality of the limit approximation-which are entirely determined by the archiving strategy and the value of epsilon-have been obtained, the strategies do not guarantee to obtain a gap free approximation of the Pareto front. That is, such approximations A can reveal gaps in the sense that points f in the Pareto front can exist such that the distance of f to any image point F(a), a epsilon A, is "large." Since such gap free approximations are desirable in certain applications, and the related archiving strategies can be advantageous when memetic strategies are included in the search process, we are aiming in this work for such methods. We present two novel strategies that accomplish this task in the probabilistic sense and under mild assumptions on the stochastic search algorithm. In addition to the convergence proofs, we give some numerical results to visualize the behavior of the different archiving strategies. Finally, we demonstrate the potential for a possible hybridization of a given stochastic search algorithm with a particular local search strategy-multi-objective continuation methods-by showing that the concept of epsilon-dominance can be integrated into this approach in a suitable way.
Directory of Open Access Journals (Sweden)
Pratap Chandra Pradhan
2016-03-01
Full Text Available In this paper, a Firefly Algorithm (FA optimized fuzzy PID controller is proposed for Automatic Generation Control (AGC of multi-area multi-source power system. Initially, a two area six units power system is used and the gains of the fuzzy PID controller are optimized employing FA optimization technique using an ITAE criterion. The superiority of the proposed FA optimized fuzzy PID controller has been demonstrated by comparing the results with some recently published approaches such as optimal control and Differential Evolution (DE optimized PID controller for the identical interconnected power system. Then, physical constraints such as Time Delay (TD, reheat turbine and Generation Rate Constraint (GRC are included in the system model and the superiority of FA is demonstrated by comparing the results over DE, Gravitational Search Algorithm (GSA and Genetic Algorithm (GA optimization techniques for the same interconnected power system. Additionally, a Unified Power Flow Controller (UPFC is placed in the tie-line and Superconducting Magnetic Energy Storage (SMES units are considered in both areas. Simulation results show that the system performances are improved significantly with the proposed UPFC and SMES units. Sensitivity analysis of the system is performed by varying the system parameters and operating load conditions from their nominal values. It is observed that the optimum gains of the proposed controller need not be reset even if the system is subjected to wide variation in loading condition and system parameters. Finally, the effectiveness of the proposed controller design is verified by considering different types of load patterns.
Mannlein, Sally
2009-01-01
Most first-graders are interested in insects. In this article, the author describes a lesson, "Dragonflies and Fireflies," which is a first-grade lesson showing drawing, symmetry, neighboring colors (analogous) and watercolor techniques.
Algorithms to Solve Stochastic H2/H∞ Control with State-Dependent Noise
Directory of Open Access Journals (Sweden)
Ming Gao
2014-01-01
Full Text Available This paper is concerned with the algorithms which solve H2/H∞ control problems of stochastic systems with state-dependent noise. Firstly, the algorithms for the finite and infinite horizon H2/H∞ control of discrete-time stochastic systems are reviewed and studied. Secondly, two algorithms are proposed for the finite and infinite horizon H2/H∞ control of continuous-time stochastic systems, respectively. Finally, several numerical examples are presented to show the effectiveness of the algorithms.
Identification and estimation algorithm for stochastic neural system. II.
Nakao, M; Hara, K; Kimura, M; Sato, R
1985-01-01
The algorithm for identifying the stochastic neural system and estimating the system process which reflects the dynamics of the neural network are presented in this paper. The analogous algorithm has been proposed in our preceding paper (Nakao et al., 1984), which was based on the randomly missed observations of a system process only. Since the previous algorithm mentioned above was subject to an unfavorable effect of consecutively missed observations, to reduce such an effect the algorithm proposed here is designed additionally to observe an intensity process in a neural spike train as the information for the estimation. The algorithm is constructed with the extended Kalman filters because it is naturally expected that a nonlinear and time variant structure is necessary for the filters to realize the observation of an intensity process by means of mapping from a system process to an intensity process. The performance of the algorithm is examined by applying it to some artificial neural systems and also to cat's visual nervous systems. The results in these applications are thought to prove the effectiveness of the algorithm proposed here and its superiority to the algorithm proposed previously.
Bonus algorithm for large scale stochastic nonlinear programming problems
Diwekar, Urmila
2015-01-01
This book presents the details of the BONUS algorithm and its real world applications in areas like sensor placement in large scale drinking water networks, sensor placement in advanced power systems, water management in power systems, and capacity expansion of energy systems. A generalized method for stochastic nonlinear programming based on a sampling based approach for uncertainty analysis and statistical reweighting to obtain probability information is demonstrated in this book. Stochastic optimization problems are difficult to solve since they involve dealing with optimization and uncertainty loops. There are two fundamental approaches used to solve such problems. The first being the decomposition techniques and the second method identifies problem specific structures and transforms the problem into a deterministic nonlinear programming problem. These techniques have significant limitations on either the objective function type or the underlying distributions for the uncertain variables. Moreover, these ...
Efficient AM Algorithms for Stochastic ML Estimation of DOA
Directory of Open Access Journals (Sweden)
Haihua Chen
2016-01-01
Full Text Available The estimation of direction-of-arrival (DOA of signals is a basic and important problem in sensor array signal processing. To solve this problem, many algorithms have been proposed, among which the Stochastic Maximum Likelihood (SML is one of the most concerned algorithms because of its high accuracy of DOA. However, the estimation of SML generally involves the multidimensional nonlinear optimization problem. As a result, its computational complexity is rather high. This paper addresses the issue of reducing computational complexity of SML estimation of DOA based on the Alternating Minimization (AM algorithm. We have the following two contributions. First using transformation of matrix and properties of spatial projection, we propose an efficient AM (EAM algorithm by dividing the SML criterion into two components. One depends on a single variable parameter while the other does not. Second when the array is a uniform linear array, we get the irreducible form of the EAM criterion (IAM using polynomial forms. Simulation results show that both EAM and IAM can reduce the computational complexity of SML estimation greatly, while IAM is the best. Another advantage of IAM is that this algorithm can avoid the numerical instability problem which may happen in AM and EAM algorithms when more than one parameter converges to an identical value.
Stochastic algorithms for computing means of probability measures
Arnaudon, Marc; Phan, Anthony; Yang, Le
2011-01-01
Consider a probability measure supported by a regular geodesic ball in a manifold. For any p larger than or equal to 1 we define a stochastic algorithm which converges almost surely to the p-mean of the measure. Assuming furthermore that the functional to minimize is regular around the p-mean, we prove that a natural renormalization of the inhomogeneous Markov chain converges in law into an inhomogeneous diffusion process. We give an explicit expression of this process, as well as its local characteristic.
Roh, Min K; Daigle, Bernie J; Gillespie, Dan T; Petzold, Linda R
2011-12-21
In recent years there has been substantial growth in the development of algorithms for characterizing rare events in stochastic biochemical systems. Two such algorithms, the state-dependent weighted stochastic simulation algorithm (swSSA) and the doubly weighted SSA (dwSSA) are extensions of the weighted SSA (wSSA) by H. Kuwahara and I. Mura [J. Chem. Phys. 129, 165101 (2008)]. The swSSA substantially reduces estimator variance by implementing system state-dependent importance sampling (IS) parameters, but lacks an automatic parameter identification strategy. In contrast, the dwSSA provides for the automatic determination of state-independent IS parameters, thus it is inefficient for systems whose states vary widely in time. We present a novel modification of the dwSSA--the state-dependent doubly weighted SSA (sdwSSA)--that combines the strengths of the swSSA and the dwSSA without inheriting their weaknesses. The sdwSSA automatically computes state-dependent IS parameters via the multilevel cross-entropy method. We apply the method to three examples: a reversible isomerization process, a yeast polarization model, and a lac operon model. Our results demonstrate that the sdwSSA offers substantial improvements over previous methods in terms of both accuracy and efficiency.
The Forward-Reverse Algorithm for Stochastic Reaction Networks
Bayer, Christian
2015-01-07
In this work, we present an extension of the forward-reverse algorithm by Bayer and Schoenmakers [2] to the context of stochastic reaction networks (SRNs). We then apply this bridge-generation technique to the statistical inference problem of approximating the reaction coefficients based on discretely observed data. To this end, we introduce a two-phase iterative inference method in which we solve a set of deterministic optimization problems where the SRNs are replaced by the classical ODE rates; then, during the second phase, the Monte Carlo version of the EM algorithm is applied starting from the output of the previous phase. Starting from a set of over-dispersed seeds, the output of our two-phase method is a cluster of maximum likelihood estimates obtained by using convergence assessment techniques from the theory of Markov chain Monte Carlo.
SASS: a symmetry adapted stochastic search algorithm exploiting site symmetry.
Wheeler, Steven E; Schleyer, Paul V R; Schaefer, Henry F
2007-03-14
A simple symmetry adapted search algorithm (SASS) exploiting point group symmetry increases the efficiency of systematic explorations of complex quantum mechanical potential energy surfaces. In contrast to previously described stochastic approaches, which do not employ symmetry, candidate structures are generated within simple point groups, such as C2, Cs, and C2v. This facilitates efficient sampling of the 3N-6 Pople's dimensional configuration space and increases the speed and effectiveness of quantum chemical geometry optimizations. Pople's concept of framework groups [J. Am. Chem. Soc. 102, 4615 (1980)] is used to partition the configuration space into structures spanning all possible distributions of sets of symmetry equivalent atoms. This provides an efficient means of computing all structures of a given symmetry with minimum redundancy. This approach also is advantageous for generating initial structures for global optimizations via genetic algorithm and other stochastic global search techniques. Application of the SASS method is illustrated by locating 14 low-lying stationary points on the cc-pwCVDZ ROCCSD(T) potential energy surface of Li5H2. The global minimum structure is identified, along with many unique, nonintuitive, energetically favorable isomers.
Online Learning Algorithms for Stochastic Water-Filling
Gai, Yi
2011-01-01
Water-filling is the term for the classic solution to the problem of allocating constrained power to a set of parallel channels to maximize the total data-rate. It is used widely in practice, for example, for power allocation to sub-carriers in multi-user OFDM systems such as WiMax. The classic water-filling algorithm is deterministic and requires perfect knowledge of the channel gain to noise ratios. In this paper we consider how to do power allocation over stochastically time-varying (i.i.d.) channels with unknown gain to noise ratio distributions. We adopt an online learning framework based on stochastic multi-armed bandits. We consider two variations of the problem, one in which the goal is to find a power allocation to maximize $\\sum\\limits_i \\mathbb{E}[\\log(1 + SNR_i)]$, and another in which the goal is to find a power allocation to maximize $\\sum\\limits_i \\log(1 + \\mathbb{E}[SNR_i])$. For the first problem, we propose a \\emph{cognitive water-filling} algorithm that we call CWF1. We show that CWF1 obtai...
Stochastic Resonance algorithms to enhance damage detection in bearing faults
Directory of Open Access Journals (Sweden)
Castiglione Roberto
2015-01-01
Full Text Available Stochastic Resonance is a phenomenon, studied and mainly exploited in telecommunication, which permits the amplification and detection of weak signals by the assistance of noise. The first papers on this technique are dated early 80 s and were developed to explain the periodically recurrent ice ages. Other applications mainly concern neuroscience, biology, medicine and obviously signal analysis and processing. Recently, some researchers have applied the technique for detecting faults in mechanical systems and bearings. In this paper, we try to better understand the conditions of applicability and which is the best algorithm to be adopted for these purposes. In fact, to get the methodology profitable and efficient to enhance the signal spikes due to fault in rings and balls/rollers of bearings, some parameters have to be properly selected. This is a problem since in system identification this procedure should be as blind as possible. Two algorithms are analysed: the first exploits classical SR with three parameters mutually dependent, while the other uses Woods-Saxon potential, with three parameters yet but holding a different meaning. The comparison of the performances of the two algorithms and the optimal choice of their parameters are the scopes of this paper. Algorithms are tested on simulated and experimental data showing an evident capacity of increasing the signal to noise ratio.
Stochastic error whitening algorithm for linear filter estimation with noisy data.
Rao, Yadunandana N; Erdogmus, Deniz; Rao, Geetha Y; Principe, Jose C
2003-01-01
Mean squared error (MSE) has been the most widely used tool to solve the linear filter estimation or system identification problem. However, MSE gives biased results when the input signals are noisy. This paper presents a novel stochastic gradient algorithm based on the recently proposed error whitening criterion (EWC) to tackle the problem of linear filter estimation in the presence of additive white disturbances. We will briefly motivate the theory behind the new criterion and derive an online stochastic gradient algorithm. Convergence proof of the stochastic gradient algorithm is derived making mild assumptions. Further, we will propose some extensions to the stochastic gradient algorithm to ensure faster, step-size independent convergence. We will perform extensive simulations and compare the results with MSE as well as total-least squares in a parameter estimation problem. The stochastic EWC algorithm has many potential applications. We will use this in designing robust inverse controllers with noisy data.
Hierarchical Stochastic Simulation Algorithm for SBML Models of Genetic Circuits
Directory of Open Access Journals (Sweden)
Leandro eWatanabe
2014-11-01
Full Text Available This paper describes a hierarchical stochastic simulation algorithm which has been implemented within iBioSim, a tool used to model, analyze, and visualize genetic circuits. Many biological analysis tools flatten out hierarchy before simulation, but there are many disadvantages associated with this approach. First, the memory required to represent the model can quickly expand in the process. Second, the flattening process is computationally expensive. Finally, when modeling a dynamic cellular population within iBioSim, inlining the hierarchy of the model is inefficient since models must grow dynamically over time. This paper discusses a new approach to handle hierarchy on the fly to make the tool faster and more memory-efficient. This approach yields significant performance improvements as compared to the former flat analysis method.
An improved stochastic algorithm for temperature-dependent homogeneous gas phase reactions
Kraft, M
2003-01-01
We propose an improved stochastic algorithm for temperature-dependent homogeneous gas phase reactions. By combining forward and reverse reaction rates, a significant gain in computational efficiency is achieved. Two modifications of modelling the temperature dependence (with and without conservation of enthalpy) are introduced and studied quantitatively. The algorithm is tested for the combustion of n-heptane, which is a reference fuel component for internal combustion engines. The convergence of the algorithm is studied by a series of numerical experiments and the computational cost of the stochastic algorithm is compared with the DAE code DASSL. If less accuracy is needed the stochastic algorithm is faster on short simulation time intervals. The new stochastic algorithm is significantly faster than the original direct simulation algorithm in all cases considered.
Institute of Scientific and Technical Information of China (English)
朱靖; 章瑶易; 贺青
2016-01-01
为解决自动化仪表仓储系统货位优化问题，提出一种基于萤火虫算法的仓储货位优化方法，以仓储货位入库的时间为目标函数，利用最小时间法实现货位的最优分配，通过实际的案例，验证了萤火虫算法在立体表库货位优化中的有效性。%In order to solve the problem of the allotting allocation optimization in automated instrument warehouse sys-tem, improve the efficiency of storage system, this paper proposes the warehouse optimization method based on the firefly algorithm, which takes the storage location time as objective function, and adopts least time to achieve optimal allocation.The relevant case analysis verified the effectiveness of firefly algorithm in three-dimensional slotting opti-mization.
IPAC Firefly Development Roadmap
Wu, Xiuqin
2015-01-01
IPAC Firefly package has been developed in IRSA (NASA/IPAC Infrared Science Archive) in last six years. It is a software package utilizing state-of-the art AJAX technology to provide an interactive web user interface for astronomers. It has been used to build Spitzer Heritage Archive, WISE Image Service, Planck Visualization, PTF Image Service, and the new IRSA finder chart. It provides three major components: table display, FITS images visualization, and 2D plot. All three highly interactive...
Optimal Filtering Algorithm for Stochastic 2-D FMM Ⅱ with Multiplicative Noise
Institute of Scientific and Technical Information of China (English)
CHU Dongsheng; LIANG Meng; SHI Xin; ZHANG Ling
2004-01-01
A stochastic two-dimensional Fornasini-Marchesini's Model Ⅱ (2-D FMM Ⅱ) with multiplicative noise is given,and a filtering algorithm for this model, which is optimal in the sense of linear minimum-variance, is developed. The stochastic 2-D FMM Ⅱ with multiplicative noise can be reduced to a 1-D model, and the proposed optimal filtering algorithm for the stochastic 2-D FMM Ⅱ with multiplicative noise is obtained by using the state estimation theory of 1-D systems. An example is given to illustrate the validity of this algorithm.
Non-divergence of stochastic discrete time algorithms for PCA neural networks.
Lv, Jian Cheng; Yi, Zhang; Li, Yunxia
2015-02-01
Learning algorithms play an important role in the practical application of neural networks based on principal component analysis, often determining the success, or otherwise, of these applications. These algorithms cannot be divergent, but it is very difficult to directly study their convergence properties, because they are described by stochastic discrete time (SDT) algorithms. This brief analyzes the original SDT algorithms directly, and derives some invariant sets that guarantee the nondivergence of these algorithms in a stochastic environment by selecting proper learning parameters. Our theoretical results are verified by a series of simulation examples.
A Branch-and-Price Algorithm for the Capacitated Arc Routing Problem with Stochastic Demands
DEFF Research Database (Denmark)
Christiansen, Christian Holk; Lysgaard, Jens; Wøhlk, Sanne
2009-01-01
We address the Capacitated Arc Routing Problem with Stochastic Demands (CARPSD), which we formulate as a Set Partitioning Problem. The CARPSD is solved by a Branch-and-Price algorithm, which we apply without graph transformation. The demand's stochastic nature is incorporated into the pricing...... problem. Computational results are reported....
Kiesewetter, Simon; Drummond, Peter D.
2017-03-01
A variance reduction method for stochastic integration of Fokker-Planck equations is derived. This unifies the cumulant hierarchy and stochastic equation approaches to obtaining moments, giving a performance superior to either. We show that the brute force method of reducing sampling error by just using more trajectories in a sampled stochastic equation is not the best approach. The alternative of using a hierarchy of moment equations is also not optimal, as it may converge to erroneous answers. Instead, through Bayesian conditioning of the stochastic noise on the requirement that moment equations are satisfied, we obtain improved results with reduced sampling errors for a given number of stochastic trajectories. The method used here converges faster in time-step than Ito-Euler algorithms. This parallel optimized sampling (POS) algorithm is illustrated by several examples, including a bistable nonlinear oscillator case where moment hierarchies fail to converge.
Firefly: embracing future web technologies
Roby, W.; Wu, X.; Goldina, T.; Joliet, E.; Ly, L.; Mi, W.; Wang, C.; Zhang, Lijun; Ciardi, D.; Dubois-Felsmann, G.
2016-07-01
At IPAC/Caltech, we have developed the Firefly web archive and visualization system. Used in production for the last eight years in many missions, Firefly gives the scientist significant capabilities to study data. Firefly provided the first completely web based FITS viewer as well as a growing set of tabular and plotting visualizers. Further, it will be used for the science user interface of the LSST telescope which goes online in 2021. Firefly must meet the needs of archive access and visualization for the 2021 LSST telescope and must serve astronomers beyond the year 2030. Recently, our team has faced the fact that the technology behind Firefly software was becoming obsolete. We were searching for ways to utilize the current breakthroughs in maintaining stability, testability, speed, and reliability of large web applications, which Firefly exemplifies. In the last year, we have ported the Firefly to cutting edge web technologies. Embarking on this massive overhaul is no small feat to say the least. Choosing the technologies that will maintain a forward trajectory in a future development project is always hard and often overwhelming. When a team must port 150,000 lines of code for a production-level product there is little room to make poor choices. This paper will give an overview of the most modern web technologies and lessons learned in our conversion from GWT based system to React/Redux based system.
Convergence Rates of Finite Difference Stochastic Approximation Algorithms
2016-06-01
examine the rates of convergence for the Kiefer-Wolfowitz algorithm and the mirror descent algorithm , under various updating schemes using finite...dfferences as gradient approximations. It is shown that the convergence of these algorithms can be accelerated by controlling the implementation of the...Kiefer-Wolfowitz algorithm , mirror descent algorithm , finite-difference approximation, Monte Carlo methods REPORT DOCUMENTATION PAGE 11. SPONSOR
An improved firefly algorithm for the blocking flow shop scheduling problem%改进的萤火虫算法求解阻塞流水线调度问题
Institute of Scientific and Technical Information of China (English)
郭丽萍; 李向涛; 谷文祥; 殷明浩
2013-01-01
为了提高阻塞流水线调度问题的求解性能,提出了一种改进的萤火虫算法来求解阻塞流水线调度问题.首先,提出一种离散机制把个体的实数编码形式转换成离散的作业序列,从而使算法能够应用于离散问题求解;其次,设计一种双重初始化方法,并将NEH启发式方法应用到初始化中来,使算法有一个较优的初始化环境,提高初始种群的解的质量;此外,重新设计了算法中个体的移动方式来增大搜索域;最后,以一定概率对种群中的个体进行局部搜索,加强算法的局部搜索性能.通过对Taillard数据集中部分实例进行求解,实验结果验证了新算法的有效性.%In order to improve the solving performance of the blocking flow shop scheduling problem, the researcher proposes to examine an improved firefly algorithm. Firstly, a discrete mechanism was proposed to convert the real coded form of individuals to discrete job sequences, so that the algorithm could be used to deal with discrete problems. Secondly, a double-initialization method was designed and the NEH ( Nawaz-Enscore-Ham) heuristic method was applied to the initialization, which provided a superior initial environment and improved the quality of the solution of the initial population. In addition, the research paper focused on redesigning the movement pattern of individuals to enhance the search space. Finally, a local search algorithm was applied to individuals with a certain probability, for the purpose of enhancing the ability to search locally. The improved firefly algorithm was then tested utilizing the Taillard datasets, and the results verified the effectiveness of the new algorithm.
A variance propagation algorithm for stochastic heat and mass transfer problems in food processes
Scheerlinck, N.; Verboven, P.; Stigter, J.D.; Baerdemaeker, de J.; Impe, van J.F.; Nicolai, B.M.
2001-01-01
A variance propagation algorithm for stochastic coupled heat and mass transfer problems subjected to first order autoregressive random process boundary conditions was developed. The algorithm is based on the finite element formulation of Luikov's coupled heat and mass transfer equations and involves
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Dammann, Bernd; Madsen, Henrik
2014-01-01
This paper presents a decomposition algorithm for solving the optimal control problem (OCP) that arises in Mean-Variance Economic Model Predictive Control of stochastic linear systems. The algorithm applies the alternating direction method of multipliers to a reformulation of the OCP...
Kozel, Tomas; Stary, Milos
2016-10-01
Described models are used random forecasting period of flow line with different length. The length is shorter than 1 year. Forecasting period of flow line is transformed to line of managing discharges with same length as forecast. Adaptive managing is used only first value of line of discharges. Stochastic management is worked with dispersion of controlling discharge value. Main advantage stochastic management is fun of possibilities. In article is described construction and evaluation of adaptive stochastic model base on genetic algorithm (classic optimization method). Model was used for stochastic management of open large water reservoir with storage function. Genetic algorithm is used as optimization algorithm. Forecasted inflow is given to model and controlling discharge value is computed by model for chosen probability of controlling discharge value. Model was tested and validated on made up large open water reservoir. Results of stochastic model were evaluated for given probability and were compared to results of same model for 100% forecast (forecasted values are real values). The management of the large open water reservoir with storage function was done logically and with increased sum number of forecast from 300 to 500 the results given by model were better, but another increased from 500 to 750 and 1000 did not get expected improvement. Influence on course of management was tested for different length forecasted inflow and their sum number. Classical optimization model is needed too much time for calculation, therefore stochastic model base on genetic algorithm was used parallel calculation on cluster.
A Second-Order Stochastic Leap-Frog Algorithm for Langevin Simulation
Qiang, J; Qiang, Ji; Habib, Salman
2000-01-01
Langevin simulation provides an effective way to study collisional effects in beams by reducing the six-dimensional Fokker-Planck equation to a group of stochastic ordinary differential equations. These resulting equations usually have multiplicative noise since the diffusion coefficients in these equations are functions of position and time. Conventional algorithms, e.g. Euler and Heun, give only first order convergence of moments in a finite time interval. In this paper, a stochastic leap-frog algorithm for the numerical integration of Langevin stochastic differential equations with multiplicative noise is proposed and tested. The algorithm has a second-order convergence of moments in a finite time interval and requires the sampling of only one uniformly distributed random variable per time step. As an example, we apply the new algorithm to the study of a mechanical oscillator with multiplicative noise.
Fitting Social Network Models Using Varying Truncation Stochastic Approximation MCMC Algorithm
Jin, Ick Hoon
2013-10-01
The exponential random graph model (ERGM) plays a major role in social network analysis. However, parameter estimation for the ERGM is a hard problem due to the intractability of its normalizing constant and the model degeneracy. The existing algorithms, such as Monte Carlo maximum likelihood estimation (MCMLE) and stochastic approximation, often fail for this problem in the presence of model degeneracy. In this article, we introduce the varying truncation stochastic approximation Markov chain Monte Carlo (SAMCMC) algorithm to tackle this problem. The varying truncation mechanism enables the algorithm to choose an appropriate starting point and an appropriate gain factor sequence, and thus to produce a reasonable parameter estimate for the ERGM even in the presence of model degeneracy. The numerical results indicate that the varying truncation SAMCMC algorithm can significantly outperform the MCMLE and stochastic approximation algorithms: for degenerate ERGMs, MCMLE and stochastic approximation often fail to produce any reasonable parameter estimates, while SAMCMC can do; for nondegenerate ERGMs, SAMCMC can work as well as or better than MCMLE and stochastic approximation. The data and source codes used for this article are available online as supplementary materials. © 2013 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.
D-leaping: Accelerating stochastic simulation algorithms for reactions with delays
Bayati, Basil; Chatelain, Philippe; Koumoutsakos, Petros
2009-09-01
We propose a novel, accelerated algorithm for the approximate stochastic simulation of biochemical systems with delays. The present work extends existing accelerated algorithms by distributing, in a time adaptive fashion, the delayed reactions so as to minimize the computational effort while preserving their accuracy. The accuracy of the present algorithm is assessed by comparing its results to those of the corresponding delay differential equations for a representative biochemical system. In addition, the fluctuations produced from the present algorithm are comparable to those from an exact stochastic simulation with delays. The algorithm is used to simulate biochemical systems that model oscillatory gene expression. The results indicate that the present algorithm is competitive with existing works for several benchmark problems while it is orders of magnitude faster for certain systems of biochemical reactions.
A Cultural Algorithm for POMDPs from Stochastic Inventory Control
Prestwich, S.; Tarim, S.A.; Rossi, R.; Hnich, B.
2008-01-01
Reinforcement Learning algorithms such as SARSA with an eligibility trace, and Evolutionary Computation methods such as genetic algorithms, are competing approaches to solving Partially Observable Markov Decision Processes (POMDPs) which occur in many fields of Artificial Intelligence. A powerful fo
A Second-Order Stochastic Leap-Frog Algorithm for Multiplicative Noise Brownian Motion
Qiang, J; Qiang, Ji; Habib, Salman
1999-01-01
A stochastic leap-frog algorithm for the numerical integration of Brownianmotion stochastic differential equations with multiplicative noise is proposedand tested. The algorithm has a second-order convergence of moments in a finitetime interval and requires the sampling of only one uniformly distributedrandom variable per time step. The noise may be white or colored. We apply thealgorithm to a study of the approach towards equilibrium of an oscillatorcoupled nonlinearly to a heat bath and investigate the effect of themultiplicative noise (arising from the nonlinear coupling) on the relaxationtime. This allows us to test the regime of validity of the energy-envelopeapproximation method.
Modeling Signal Transduction Networks: A comparison of two Stochastic Kinetic Simulation Algorithms
Energy Technology Data Exchange (ETDEWEB)
Pettigrew, Michel F.; Resat, Haluk
2005-09-15
Simulations of a scalable four compartment reaction model based on the well known epidermal growth factor receptor (EGFR) signal transduction system are used to compare two stochastic algorithms ? StochSim and the Gibson-Gillespie. It is concluded that the Gibson-Gillespie is the algorithm of choice for most realistic cases with the possible exception of signal transduction networks characterized by a moderate number (< 100) of complex types, each with a very small population, but with a high degree of connectivity amongst the complex types. Keywords: Signal transduction networks, Stochastic simulation, StochSim, Gillespie
Frequency modulated weak signal detection based on stochastic resonance and genetic algorithm
Institute of Scientific and Technical Information of China (English)
XING; Hongyan; LU; Chunxia; ZHANG; Qiang
2016-01-01
Stochastic resonance system is subject to the restriction of small frequency parameter in weak signal detection,in order to solve this problem,a frequency modulated weak signal detection method based on stochastic resonance and genetic algorithm is presented in this paper. The frequency limit of stochastic resonance is eliminated by introducing carrier signal,which is multiplied with the measured signal to be injected in the stochastic resonance system,meanwhile,using genetic algorithm to optimize the carrier signal frequency,which determine the generated difference-frequency signal in the lowfrequency range,so as to achieve the stochastic resonance weak signal detection. Results showthat the proposed method is feasible and effective,which can significantly improve the output SNR of stochastic resonance,in addition,the system has the better self-adaptability,according to the operation result and output phenomenon,the unknown frequency of the signal to be measured can be obtained,so as to realize the weak signal detection of arbitrary frequency.
Institute of Scientific and Technical Information of China (English)
王雅琳; 陆向艳; 钟诚
2015-01-01
针对已有的基于链接分析的热点发现方法存在准确度较低、易受作弊链接影响、易产生主题漂移现象等问题，利用复杂网络簇结构具有高度主题相关的特点，提出一种融合应用链接分析和萤火虫算法聚类博文的热点话题发现算法。以博文页面为节点，与博文内容相同或相关的链接作为边，根据博文及博主的相关属性，综合评定页面权重，建立博客话题模型；运用萤火虫算法对博文进行聚类获得聚类中心，按页面权重将聚类中心从大到小排序，形成热点话题热度排行。实验结果表明，该方法能够发现精度更高、数量更多的博客热点话题。%To address the problem that the existing hotspot topics finding methods use link analysis to obtain the results ,which results in lower precision ,vulnerability to cheating links and emerging topic drift phenomenon ,a hotspot topic discovery algo‐rithm using link analysis and firefly algorithm to cluster Blog articles was proposed based on the feature of highly relevant subject of cluster structures in complex networks .Blog article pages and the links of the same or relative Blog articles were represented by nodes and edges respectively ,and the weights of Blog article pages were evaluated using the relative attributes of Blog articles and Bloggers ,and then the Blog topic model was established .Firefly algorithm was used to cluster the Blog articles to form the cluster centers ,and these cluster centers were sorted using the weights of the Blog article pages to obtain the ranking of hotspot topics .The experimental results show that the proposed method can find more Blog hotspot topics with higher precision .
Stochastic process variation in deep-submicron CMOS circuits and algorithms
Zjajo, Amir
2014-01-01
One of the most notable features of nanometer scale CMOS technology is the increasing magnitude of variability of the key device parameters affecting performance of integrated circuits. The growth of variability can be attributed to multiple factors, including the difficulty of manufacturing control, the emergence of new systematic variation-generating mechanisms, and most importantly, the increase in atomic-scale randomness, where device operation must be described as a stochastic process. In addition to wide-sense stationary stochastic device variability and temperature variation, existence of non-stationary stochastic electrical noise associated with fundamental processes in integrated-circuit devices represents an elementary limit on the performance of electronic circuits. In an attempt to address these issues, Stochastic Process Variation in Deep-Submicron CMOS: Circuits and Algorithms offers unique combination of mathematical treatment of random process variation, electrical noise and temperature and ne...
Directory of Open Access Journals (Sweden)
Arild Helseth
2015-12-01
Full Text Available Stochastic dual dynamic programming (SDDP has become a popular algorithm used in practical long-term scheduling of hydropower systems. The SDDP algorithm is computationally demanding, but can be designed to take advantage of parallel processing. This paper presents a novel parallel scheme for the SDDP algorithm, where the stage-wise synchronization point traditionally used in the backward iteration of the SDDP algorithm is partially relaxed. The proposed scheme was tested on a realistic model of a Norwegian water course, proving that the synchronization point relaxation significantly improves parallel efficiency.
Registration algorithm for sensor alignment based on stochastic fuzzy neural network
Institute of Scientific and Technical Information of China (English)
Li Jiao; Jing Zhongliang; He Jiaona; Wang An
2005-01-01
Multiple sensor registration is an important link in multi-sensors data fusion. The existed algorithm is all based on the assumption that system errors come from a fixed deviation set. But there are many other factors, which can result system errors. So traditional registration algorithms have limitation. This paper presents a registration algorithm for sensor alignment based on stochastic fuzzy neural network (SNFF), and utilized fuzzy clustering algorithm obtaining the number of fuzzy rules. Finally, the simulative result illuminate that this way could gain a satisfing result.
AN HYBRID STOCHASTIC-DETERMINISTIC OPTIMIZATION ALGORITHM FOR STRUCTURAL DAMAGE IDENTIFICATION
Nhamage, Idilson António; Lopez, Rafael Holdorf; Miguel, Leandro Fleck Fadel; Miguel, Letícia Fleck Fadel; Torii, André Jacomel
2017-01-01
Abstract. This paper presents a hybrid stochastic/deterministic optimization algorithm to solve the target optimization problem of vibration-based damage detection. The use of a numerical solution of the representation formula to locate the region of the global solution, i.e., to provide a starting point for the local optimizer, which is chosen to be the Nelder-Mead algorithm (NMA), is proposed. A series of numerical examples with different damage scenarios and noise levels was performed unde...
A branch-and-price algorithm for the capacitated vehicle routing problem with stochastic demands
DEFF Research Database (Denmark)
Christiansen, Christian Holk; Lysgaard, Jens
2007-01-01
This article introduces a new exact algorithm for the Capacitated Vehicle Routing Problem with Stochastic Demands (CVRPSD). The CVRPSD can be formulated as a Set Partitioning Problem and it is shown that the associated column generation subproblem can be solved using a dynamic programming scheme...
An approximation algorithm for a facility location problem with stochastic demands
Bumb, A.F.; van Ommeren, Jan C.W.
2004-01-01
In this article we propose, for any $\\epsilon>0$, a $2(1+\\epsilon)$-approximation algorithm for a facility location problem with stochastic demands. This problem can be described as follows. There are a number of locations, where facilities may be opened and a number of demand points, where requests
An approximation algorithm for a facility location problem with inventories and stochastic demands
Bumb, A.F.; van Ommeren, Jan C.W.; Megiddo, N.; Xu, Y.; Zhu, B.
2005-01-01
In this article we propose, for any $\\varepsilon$ > 0, a 2(1 + $\\varepsilon$)-approximation algorithm for a facility location problem with stochastic demands. At open facilities, inventory is kept such that arriving requests find a zero inventory with (at most) some pre-specified probability. The
An approximation algorithm for a facility location problem with stochastic demands
Bumb, A.F.; Ommeren, van J.C.W.
2004-01-01
In this article we propose, for any $\\epsilon>0$, a $2(1+\\epsilon)$-approximation algorithm for a facility location problem with stochastic demands. This problem can be described as follows. There are a number of locations, where facilities may be opened and a number of demand points, where requests
Institute of Scientific and Technical Information of China (English)
吴小康; 叶春明
2014-01-01
Personnel Flexibility Flow Shop Scheduling Problem is more realistic and practical than classical FSP .First , the personnel‐machine diagram is proposed .Second ,it extends the PM matrix and FI index to measure the personnel flexibil‐ity .After improving the classical FSP model ,it is used to solve the Car system problems and practical example through two steps by firefly algorithm at last .All of these prove that it is effective and feasible .%具有人员柔性的FS P问题较之于经典的FS P问题更具有现实性和应用前景。首先提出人员‐机器关系图并引出度量人员柔性的PM矩阵和FI指数，然后对传统FSP模型进行补充和改进，以萤火虫算法为工具，通过人员模式的选择和对应模式下的问题寻优两大步骤进行求解并利用经典Car类问题进行仿真，最后将其应用于实例求解。仿真和实例求解结果都充分说明了算法的有效性和可行性。
Roussel, Marc R; Zhu, Rui
2006-12-08
The quantitative modeling of gene transcription and translation requires a treatment of two key features: stochastic fluctuations due to the limited copy numbers of key molecules (genes, RNA polymerases, ribosomes), and delayed output due to the time required for biopolymer synthesis. Recently proposed algorithms allow for efficient simulations of such systems. However, it is critical to know whether the results of delay stochastic simulations agree with those from more detailed models of the transcription and translation processes. We present a generalization of previous delay stochastic simulation algorithms which allows both for multiple delays and for distributions of delay times. We show that delay stochastic simulations closely approximate simulations of a detailed transcription model except when two-body effects (e.g. collisions between polymerases on a template strand) are important. Finally, we study a delay stochastic model of prokaryotic transcription and translation which reproduces observations from a recent experimental study in which a single gene was expressed under the control of a repressed lac promoter in E. coli cells. This demonstrates our ability to quantitatively model gene expression using these new methods.
Roussel, Marc R.; Zhu, Rui
2006-12-01
The quantitative modeling of gene transcription and translation requires a treatment of two key features: stochastic fluctuations due to the limited copy numbers of key molecules (genes, RNA polymerases, ribosomes), and delayed output due to the time required for biopolymer synthesis. Recently proposed algorithms allow for efficient simulations of such systems. However, it is critical to know whether the results of delay stochastic simulations agree with those from more detailed models of the transcription and translation processes. We present a generalization of previous delay stochastic simulation algorithms which allows both for multiple delays and for distributions of delay times. We show that delay stochastic simulations closely approximate simulations of a detailed transcription model except when two-body effects (e.g. collisions between polymerases on a template strand) are important. Finally, we study a delay stochastic model of prokaryotic transcription and translation which reproduces observations from a recent experimental study in which a single gene was expressed under the control of a repressed lac promoter in E. coli cells. This demonstrates our ability to quantitatively model gene expression using these new methods.
The Fuzzy Modeling Algorithm for Complex Systems Based on Stochastic Neural Network
Institute of Scientific and Technical Information of China (English)
李波; 张世英; 李银惠
2002-01-01
A fuzzy modeling method for complex systems is studied. The notation of general stochastic neural network (GSNN) is presented and a new modeling method is given based on the combination of the modified Takagi and Sugeno's(MTS) fuzzy model and one-order GSNN. Using expectation-maximization (EM) algorithm, parameter estimation and model selection procedures are given. It avoids the shortcomings brought by other methods such as BP algorithm, when the number of parameters is large, BP algorithm is still difficult to apply directly without fine tuning and subjective tinkering. Finally, the simulated example demonstrates the effectiveness.
Directory of Open Access Journals (Sweden)
Suxiang He
2014-01-01
Full Text Available An implementable nonlinear Lagrange algorithm for stochastic minimax problems is presented based on sample average approximation method in this paper, in which the second step minimizes a nonlinear Lagrange function with sample average approximation functions of original functions and the sample average approximation of the Lagrange multiplier is adopted. Under a set of mild assumptions, it is proven that the sequences of solution and multiplier obtained by the proposed algorithm converge to the Kuhn-Tucker pair of the original problem with probability one as the sample size increases. At last, the numerical experiments for five test examples are performed and the numerical results indicate that the algorithm is promising.
An Efficient Forward-Reverse EM Algorithm for Statistical Inference in Stochastic Reaction Networks
Bayer, Christian
2016-01-06
In this work [1], we present an extension of the forward-reverse algorithm by Bayer and Schoenmakers [2] to the context of stochastic reaction networks (SRNs). We then apply this bridge-generation technique to the statistical inference problem of approximating the reaction coefficients based on discretely observed data. To this end, we introduce an efficient two-phase algorithm in which the first phase is deterministic and it is intended to provide a starting point for the second phase which is the Monte Carlo EM Algorithm.
Stochastic Analysis of an Iterative Semi Blind Adaptive Beamforming Algorithm
2009-06-24
beam- forming, demodulation, equalization, and decoding . In each stage, the initial beamformer weights computed by the TDMA training data are refined by...performance of the receiver for the Global System for Mobile commu- nications ( GSM ). In recent years, there has been work on techniques to mitigate the...Viterbi algorithm (SOVA) [5], and decoding . In this paper, we extend the iterative beamformer to in- corporate training-based as well as blind
An Analysis of Learning Algorithms in Complex Stochastic Environments
2007-06-01
speakers saying vowel phrases and resulted in a significant improvement in predictions during the refinement phase when contexts were added to the...with parameters for the agent, to both take actions and write the percepts it receives to a separate file. These two programs ran in tandem for...sensations, due to the recency threshold limiting the total number of percepts. A comparison of these two learning algorithms shows contrasting styles of
Directory of Open Access Journals (Sweden)
Eman Ali Hussain
2015-01-01
Full Text Available Absract In this project A new method for solving Stochastic Differential Equations SDEs deriving by Wiener process numerically will be construct and implement using Accelerated Genetic Algorithm AGA. An SDE is a differential equation in which one or more of the terms and hence the solutions itself is a stochastic process. Solving stochastic differential equations requires going away from the recognizable deterministic setting of ordinary and partial differential equations into a world where the evolution of a quantity has an inherent random component and where the expected behavior of this quantity can be described in terms of probability distributions. We applied our method on the Ito formula which is equivalent to the SDE to find approximation solution of the SDEs. Numerical experiments illustrate the behavior of the proposed method.
Banks, H Thomas; Hu, Shuhua; Joyner, Michele; Broido, Anna; Canter, Brandi; Gayvert, Kaitlyn; Link, Kathryn
2012-07-01
In this paper, we investigate three particular algorithms: a stochastic simulation algorithm (SSA), and explicit and implicit tau-leaping algorithms. To compare these methods, we used them to analyze two infection models: a Vancomycin-resistant enterococcus (VRE) infection model at the population level, and a Human Immunodeficiency Virus (HIV) within host infection model. While the first has a low species count and few transitions, the second is more complex with a comparable number of species involved. The relative efficiency of each algorithm is determined based on computational time and degree of precision required. The numerical results suggest that all three algorithms have the similar computational efficiency for the simpler VRE model, and the SSA is the best choice due to its simplicity and accuracy. In addition, we have found that with the larger and more complex HIV model, implementation and modification of tau-Leaping methods are preferred.
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
This paper addresses the problems of parameter estimation of multivariable stationary stochastic systems on the basis of observed output data. The main contribution is to employ the expectation-maximisation (EM) method as a means for computation of the maximum-likelihood (ML) parameter estimation of the system. Closed form of the expectation of the studied system subjected to Gaussian distribution noise is derived and paraneter choice that maximizes the expectation is also proposed. This results in an iterative algorithm for parameter estimation and the robust algorithm implementation based on technique of QR-factorization and Cholesky factorization is also discussed. Moreover, algorithmic properties such as non-decreasing likelihood value, necessary and sufficient conditions for the algorithm to arrive at a local stationary parameter, the convergence rate and the factors affecting the convergence rate are analyzed. Simulation study shows that the proposed algorithm has attractive properties such as numerical stability, and avoidance of difficult initial conditions.
An Algorithm for the Stochastic Simulation of Gene Expression and Heterogeneous Population Dynamics
Charlebois, Daniel A; Fraser, Dawn; Kaern, Mads
2011-01-01
We present an algorithm for the stochastic simulation of gene expression and heterogeneous population dynamics. The algorithm combines an exact method to simulate molecular-level fluctuations in single cells and a constant-number Monte Carlo method to simulate time-dependent statistical characteristics of growing cell populations. To benchmark performance, we compare simulation results with steadystate and time-dependent analytical solutions for several scenarios, including steadystate and time-dependent gene expression, and the effects on population heterogeneity of cell growth, division, and DNA replication. This comparison demonstrates that the algorithm provides an efficient and accurate approach to simulate how complex biological features influence gene expression. We also use the algorithm to model gene expression dynamics within "bet-hedging" cell populations during their adaption to environmental stress. These simulations indicate that the algorithm provides a framework suitable for simulating and ana...
GillespieSSA: Implementing the Gillespie Stochastic Simulation Algorithm in R
Directory of Open Access Journals (Sweden)
Mario Pineda-Krch
2008-02-01
Full Text Available The deterministic dynamics of populations in continuous time are traditionally described using coupled, first-order ordinary differential equations. While this approach is accurate for large systems, it is often inadequate for small systems where key species may be present in small numbers or where key reactions occur at a low rate. The Gillespie stochastic simulation algorithm (SSA is a procedure for generating time-evolution trajectories of finite populations in continuous time and has become the standard algorithm for these types of stochastic models. This article presents a simple-to-use and flexible framework for implementing the SSA using the high-level statistical computing language R and the package GillespieSSA. Using three ecological models as examples (logistic growth, Rosenzweig-MacArthur predator-prey model, and Kermack-McKendrick SIRS metapopulation model, this paper shows how a deterministic model can be formulated as a finite-population stochastic model within the framework of SSA theory and how it can be implemented in R. Simulations of the stochastic models are performed using four different SSA Monte Carlo methods: one exact method (Gillespie's direct method; and three approximate methods (explicit, binomial, and optimized tau-leap methods. Comparison of simulation results confirms that while the time-evolution trajectories obtained from the different SSA methods are indistinguishable, the approximate methods are up to four orders of magnitude faster than the exact methods.
Scalable Hierarchical Algorithms for stochastic PDEs and UQ
Litvinenko, Alexander
2015-01-07
H-matrices and Fast Multipole (FMM) are powerful methods to approximate linear operators coming from partial differential and integral equations as well as speed up computational cost from quadratic or cubic to log-linear (O(n log n)), where n number of degrees of freedom in the discretization. The storage is reduced to the log-linear as well. This hierarchical structure is a good starting point for parallel algorithms. Parallelization on shared and distributed memory systems was pioneered by Kriemann [1,2]. Since 2005, the area of parallel architectures and software is developing very fast. Progress in GPUs and Many-Core Systems (e.g. XeonPhi with 64 cores) motivated us to extend work started in [1,2,7,8].
Scalable Hierarchical Algorithms for stochastic PDEs and Uncertainty Quantification
Litvinenko, Alexander
2015-01-05
H-matrices and Fast Multipole (FMM) are powerful methods to approximate linear operators coming from partial differential and integral equations as well as speed up computational cost from quadratic or cubic to log-linear (O(n log n)), where n number of degrees of freedom in the discretization. The storage is reduced to the log-linear as well. This hierarchical structure is a good starting point for parallel algorithms. Parallelization on shared and distributed memory systems was pioneered by R. Kriemann, 2005. Since 2005, the area of parallel architectures and software is developing very fast. Progress in GPUs and Many-Core Systems (e.g. XeonPhi with 64 cores) motivated us to extend work started in [1,2,7,8].
Some Algorithms to Identify Rational Structures in Stochastic Processes with Expectations
Directory of Open Access Journals (Sweden)
Concepción González-Concepción
2007-01-01
Full Text Available This research was concerned with the identification of rational models in stochastic processes and we use the Padé-Laurent Approximation to identify a Transfer-Function Model with Expectations. We mention the T-table method and focus in studying the generalised epsilon-algorithm, emphasizing the main role of the statistical significance of their numerical entries. Empirical work is made for non-causal TF models using both simulated and economic real data.
An adaptive multi-level simulation algorithm for stochastic biological systems.
Lester, C; Yates, C A; Giles, M B; Baker, R E
2015-01-14
Discrete-state, continuous-time Markov models are widely used in the modeling of biochemical reaction networks. Their complexity often precludes analytic solution, and we rely on stochastic simulation algorithms (SSA) to estimate system statistics. The Gillespie algorithm is exact, but computationally costly as it simulates every single reaction. As such, approximate stochastic simulation algorithms such as the tau-leap algorithm are often used. Potentially computationally more efficient, the system statistics generated suffer from significant bias unless tau is relatively small, in which case the computational time can be comparable to that of the Gillespie algorithm. The multi-level method [Anderson and Higham, "Multi-level Monte Carlo for continuous time Markov chains, with applications in biochemical kinetics," SIAM Multiscale Model. Simul. 10(1), 146-179 (2012)] tackles this problem. A base estimator is computed using many (cheap) sample paths at low accuracy. The bias inherent in this estimator is then reduced using a number of corrections. Each correction term is estimated using a collection of paired sample paths where one path of each pair is generated at a higher accuracy compared to the other (and so more expensive). By sharing random variables between these paired paths, the variance of each correction estimator can be reduced. This renders the multi-level method very efficient as only a relatively small number of paired paths are required to calculate each correction term. In the original multi-level method, each sample path is simulated using the tau-leap algorithm with a fixed value of τ. This approach can result in poor performance when the reaction activity of a system changes substantially over the timescale of interest. By introducing a novel adaptive time-stepping approach where τ is chosen according to the stochastic behaviour of each sample path, we extend the applicability of the multi-level method to such cases. We demonstrate the
An adaptive multi-level simulation algorithm for stochastic biological systems
Energy Technology Data Exchange (ETDEWEB)
Lester, C., E-mail: lesterc@maths.ox.ac.uk; Giles, M. B.; Baker, R. E. [Mathematical Institute, Woodstock Road, Oxford OX2 6GG (United Kingdom); Yates, C. A. [Department of Mathematical Sciences, University of Bath, Bath BA2 7AY (United Kingdom)
2015-01-14
Discrete-state, continuous-time Markov models are widely used in the modeling of biochemical reaction networks. Their complexity often precludes analytic solution, and we rely on stochastic simulation algorithms (SSA) to estimate system statistics. The Gillespie algorithm is exact, but computationally costly as it simulates every single reaction. As such, approximate stochastic simulation algorithms such as the tau-leap algorithm are often used. Potentially computationally more efficient, the system statistics generated suffer from significant bias unless tau is relatively small, in which case the computational time can be comparable to that of the Gillespie algorithm. The multi-level method [Anderson and Higham, “Multi-level Monte Carlo for continuous time Markov chains, with applications in biochemical kinetics,” SIAM Multiscale Model. Simul. 10(1), 146–179 (2012)] tackles this problem. A base estimator is computed using many (cheap) sample paths at low accuracy. The bias inherent in this estimator is then reduced using a number of corrections. Each correction term is estimated using a collection of paired sample paths where one path of each pair is generated at a higher accuracy compared to the other (and so more expensive). By sharing random variables between these paired paths, the variance of each correction estimator can be reduced. This renders the multi-level method very efficient as only a relatively small number of paired paths are required to calculate each correction term. In the original multi-level method, each sample path is simulated using the tau-leap algorithm with a fixed value of τ. This approach can result in poor performance when the reaction activity of a system changes substantially over the timescale of interest. By introducing a novel adaptive time-stepping approach where τ is chosen according to the stochastic behaviour of each sample path, we extend the applicability of the multi-level method to such cases. We demonstrate the
Improved multiscale stochastic simulation algorithm%改进的多尺度随机算法
Institute of Scientific and Technical Information of China (English)
闫正楼; 彭新俊; 周文; 刘祥; 王翼飞
2009-01-01
多尺度随机模拟算法(multiscale stochastic simulation algorithm,MSSA)是模拟刚性化学反应系统的有效算法.但该算法对于慢-子系统上分子数目较大的刚性系统是低效的.为了有效地模拟该类系统,本文提出了τ-Leap算法改进的多尺度随机模拟算法(improved muhiscale stochastic simulation algorithm with τ-Leap,IMSS τ).算法将τ-Leap算法应用于IMSS算法来对其中的慢-子系统进行模拟,而舍弃了原方法中Gillespie的精确随机模拟算法(stochastic simulation algorithm,SSA),在精度损失较小的条件下,使模拟速度较IMSS算法提高了95%.数值模拟结果证实了IMSS τ的优点.
Genetic Algorithm and Tabu Search for Vehicle Routing Problems with Stochastic Demand
Ismail, Zuhaimy; Irhamah
2010-11-01
This paper presents a problem of designing solid waste collection routes, involving scheduling of vehicles where each vehicle begins at the depot, visits customers and ends at the depot. It is modeled as a Vehicle Routing Problem with Stochastic Demands (VRPSD). A data set from a real world problem (a case) is used in this research. We developed Genetic Algorithm (GA) and Tabu Search (TS) procedure and these has produced the best possible result. The problem data are inspired by real case of VRPSD in waste collection. Results from the experiment show the advantages of the proposed algorithm that are its robustness and better solution qualities.
Stochastic optimal phase retrieval algorithm for high-contrast imaging
Give'on, Amir; Kasdin, N. Jeremy; Vanderbei, Robert J.; Spergel, David N.; Littman, Michael G.; Gurfil, Pini
2003-12-01
The Princeton University Terrestrial Planet Finder (TPF) has been working on a novel method for direct imaging of extra solar planets using a shaped-pupil coronagraph. The entrance pupil of the coronagraph is optimized to have a point spread function (PSF) that provides the suppression level needed at the angular separation required for detection of extra solar planets. When integration time is to be minimized, the photon count at the planet location in the image plane is a Poisson distributed random process. The ultimate limitation of these high-dynamic-range imaging systems comes from scattering due to imperfections in the optical surfaces of the collecting system. The first step in correcting the wavefront errors is the estimation of the phase aberrations. The phase aberration caused by these imperfections is assumed to be a sum of two-dimensional sinusoidal functions. Its parameters are estimated using a global search with a genetic algorithm and a local optimization with the BFGS quasi-Newton method with a mixed quadratic and cubic line search procedure.
Cruz, Pedro
2011-01-01
It is shown the almost sure convergence and asymptotical normality of a generalization of Kesten's stochastic approximation algorithm for multidimensional case. In this generalization, the step increases or decreases if the scalar product of two subsequente increments of the estimates is positive or negative. This rule is intended to accelerate the entrance in the `stochastic behaviour' when initial conditions cause the algorithm to behave in a `deterministic fashion' for the starting iterations.
A multi-phase algorithm for a joint lot-sizing and pricing problem with stochastic demands
DEFF Research Database (Denmark)
Jenny Li, Hongyan; Thorstenson, Anders
2014-01-01
item over a ﬁnite multi-period planning horizon. Thece-dependent demands. The stochastic demand is captured by the scenario analysis approach, and this leads to a multiple-stage stochastic programming problem. Given the complexity of the stochastic programming problem, it is hard to determine optimal...... prices and lot sizes simultaneously. Therefore, we decompose the joint lot-sizing and pricing problem with stochastic demands and capacity constraints into a multi-phase decision process. In each phase, we solve the associated sub-problem to optimality. The decomposed decision process corresponds...... that the multi-phase heuristic algorithm solves the example problems effectively....
Rahman, Tuan A. Z.; Jalil, N. A. Abdul; As'arry, A.; Raja Ahmad, R. K.
2017-06-01
Support vector machine (SVM) has been known as one-state-of-the-art pattern recognition method. However, the SVM performance is particularly influenced byits parameter selection. This paper presents the parameter optimization of an SVM classifier using chaos-enhanced stochastic fractal search (SFS) algorithm to classify conditions of a ball bearing. The vibration data for normal and damaged conditions of the ball bearing system obtained from the Case Western Reserve University Bearing Data Centre. Features based on time and frequency domains were generated to characterize the ball bearing conditions. The performance of chaos-enhanced SFS algorithms in comparison to their predecessor algorithm is evaluated. In conclusion, the injection of chaotic maps into SFS algorithm improved its convergence speed and searching accuracy based on the statistical results of CEC 2015 benchmark test suites and their application to ball bearing fault diagnosis.
Institute of Scientific and Technical Information of China (English)
朱炜; 胡昊; 徐瑞华; 洪玲
2013-01-01
Based on the framework of method of successive averages(MSA), a modified stochastic user-equilibrium assignment algorithm was proposed, which can be used to calculate the passenger flow distribution of urban rail transit(URT) under network operation. In order to describe the congestion’s impact to passengers’ route choices, a generalized cost function with in-vehicle congestion was set up. Building on the k-th shortest path algorithm, a method for generating choice set with time constraint was embedded, considering the characteristics of network operation. A simple but efficient route choice model, which was derived from travel surveys for URT passengers in China, was introduced to perform the stochastic network loading at each iteration in the algorithm. Initial tests on the URT network in Shanghai City show that the methodology, with rational calculation time, promises to compute more precisely the passenger flow distribution of URT under network operation, compared with those practical algorithms used in today’s China.
Stochastic time-dependent vehicle routing problem: Mathematical models and ant colony algorithm
Directory of Open Access Journals (Sweden)
Zhengyu Duan
2015-11-01
Full Text Available This article addresses the stochastic time-dependent vehicle routing problem. Two mathematical models named robust optimal schedule time model and minimum expected schedule time model are proposed for stochastic time-dependent vehicle routing problem, which can guarantee delivery within the time windows of customers. The robust optimal schedule time model only requires the variation range of link travel time, which can be conveniently derived from historical traffic data. In addition, the robust optimal schedule time model based on robust optimization method can be converted into a time-dependent vehicle routing problem. Moreover, an ant colony optimization algorithm is designed to solve stochastic time-dependent vehicle routing problem. As the improvements in initial solution and transition probability, ant colony optimization algorithm has a good performance in convergence. Through computational instances and Monte Carlo simulation tests, robust optimal schedule time model is proved to be better than minimum expected schedule time model in computational efficiency and coping with the travel time fluctuations. Therefore, robust optimal schedule time model is applicable in real road network.
Firefly Clock Synchronization in an 802.15.4 Wireless Network
Directory of Open Access Journals (Sweden)
Elmenreich Wilfried
2009-01-01
Full Text Available This paper describes the design and implementation of a distributed self-stabilizing clock synchronization algorithm based on the biological example of Asian Fireflies. Huge swarms of these fireflies use the principle of pulse coupled oscillators in order to synchronously emit light flashes to attract mating partners. When applying this algorithm to real sensor networks, typically, nodes cannot receive messages while transmitting, which prevents the networked nodes from reaching synchronization. In order to counteract this deafness problem, we adopt a variant of the Reachback Firefly Algorithm to distribute the timing of light flashes in a given time window without affecting the quality of the synchronization. A case study implemented on 802.15.4 Zigbee nodes presents the application of this approach for a time-triggered communication scheduling and coordinated duty cycling in order to enhance the battery lifetime of the nodes.
Vrettas, Michail D; Opper, Manfred; Cornford, Dan
2015-01-01
This work introduces a Gaussian variational mean-field approximation for inference in dynamical systems which can be modeled by ordinary stochastic differential equations. This new approach allows one to express the variational free energy as a functional of the marginal moments of the approximating Gaussian process. A restriction of the moment equations to piecewise polynomial functions, over time, dramatically reduces the complexity of approximate inference for stochastic differential equation models and makes it comparable to that of discrete time hidden Markov models. The algorithm is demonstrated on state and parameter estimation for nonlinear problems with up to 1000 dimensional state vectors and compares the results empirically with various well-known inference methodologies.
Efficient generation and optimization of stochastic template banks by a neighboring cell algorithm
Fehrmann, Henning
2014-01-01
Placing signal templates (grid points) as efficiently as possible to cover a multi-dimensional parameter space is crucial in computing-intensive matched-filtering searches for gravitational waves, but also in similar searches in other fields of astronomy. To generate efficient coverings of arbitrary parameter spaces, stochastic template banks have been advocated, where templates are placed at random while rejecting those too close to others. However, in this simple scheme, for each new random point its distance to every template in the existing bank is computed. This rapidly increasing number of distance computations can render the acceptance of new templates computationally prohibitive, particularly for wide parameter spaces or in large dimensions. This work presents a neighboring cell algorithm that can dramatically improve the efficiency of constructing a stochastic template bank. By dividing the parameter space into sub-volumes (cells), for an arbitrary point an efficient hashing technique is exploited to...
Directory of Open Access Journals (Sweden)
Hosseinali Salemi
2016-04-01
Full Text Available Facility location models are observed in many diverse areas such as communication networks, transportation, and distribution systems planning. They play significant role in supply chain and operations management and are one of the main well-known topics in strategic agenda of contemporary manufacturing and service companies accompanied by long-lasting effects. We define a new approach for solving stochastic single source capacitated facility location problem (SSSCFLP. Customers with stochastic demand are assigned to set of capacitated facilities that are selected to serve them. It is demonstrated that problem can be transformed to deterministic Single Source Capacitated Facility Location Problem (SSCFLP for Poisson demand distribution. A hybrid algorithm which combines Lagrangian heuristic with adjusted mixture of Ant colony and Genetic optimization is proposed to find lower and upper bounds for this problem. Computational results of various instances with distinct properties indicate that proposed solving approach is efficient.
R-leaping: Accelerating the stochastic simulation algorithm by reaction leaps
Auger, Anne; Chatelain, Philippe; Koumoutsakos, Petros
2006-08-01
A novel algorithm is proposed for the acceleration of the exact stochastic simulation algorithm by a predefined number of reaction firings (R-leaping) that may occur across several reaction channels. In the present approach, the numbers of reaction firings are correlated binomial distributions and the sampling procedure is independent of any permutation of the reaction channels. This enables the algorithm to efficiently handle large systems with disparate rates, providing substantial computational savings in certain cases. Several mechanisms for controlling the accuracy and the appearance of negative species are described. The advantages and drawbacks of R-leaping are assessed by simulations on a number of benchmark problems and the results are discussed in comparison with established methods.
A Gillespie algorithm for non-Markovian stochastic processes: Laplace transform approach
Masuda, Naoki
2016-01-01
The Gillespie algorithm provides statistically exact methods to simulate stochastic dynamics modelled as interacting sequences of discrete events including systems of biochemical reactions or earthquakes, networks of queuing processes or spiking neurons, and epidemic and opinion formation processes on social networks. Empirically, inter-event times of various human activities, in particular human communication, and some natural phenomena are often distributed according to long-tailed distributions. The Gillespie algorithm and its extant variants either assume the Poisson process, which produces exponentially distributed inter-event times, not long-tailed distributions, assume particular functional forms for time courses of the event rate, or works for non-Poissonian renewal processes including the case of long-tailed distributions of inter-event times but at a high computational cost. In the present study, we propose an innovative Gillespie algorithm for renewal processes on the basis of the Laplace transform...
Sochi, Taha
2014-01-01
Several deterministic and stochastic multi-variable global optimization algorithms (Conjugate Gradient, Nelder-Mead, Quasi-Newton, and Global) are investigated in conjunction with energy minimization principle to resolve the pressure and volumetric flow rate fields in single ducts and networks of interconnected ducts. The algorithms are tested with seven types of fluid: Newtonian, power law, Bingham, Herschel-Bulkley, Ellis, Ree-Eyring and Casson. The results obtained from all those algorithms for all these types of fluid agree very well with the analytically derived solutions as obtained from the traditional methods which are based on the conservation principles and fluid constitutive relations. The results confirm and generalize the findings of our previous investigations that the energy minimization principle is at the heart of the flow dynamics systems. The investigation also enriches the methods of Computational Fluid Dynamics for solving the flow fields in tubes and networks for various types of Newtoni...
Alfonso, Lester; Zamora, Jose; Cruz, Pedro
2015-04-01
The stochastic approach to coagulation considers the coalescence process going in a system of a finite number of particles enclosed in a finite volume. Within this approach, the full description of the system can be obtained from the solution of the multivariate master equation, which models the evolution of the probability distribution of the state vector for the number of particles of a given mass. Unfortunately, due to its complexity, only limited results were obtained for certain type of kernels and monodisperse initial conditions. In this work, a novel numerical algorithm for the solution of the multivariate master equation for stochastic coalescence that works for any type of kernels and initial conditions is introduced. The performance of the method was checked by comparing the numerically calculated particle mass spectrum with analytical solutions obtained for the constant and sum kernels, with an excellent correspondence between the analytical and numerical solutions. In order to increase the speedup of the algorithm, software parallelization techniques with OpenMP standard were used, along with an implementation in order to take advantage of new accelerator technologies. Simulations results show an important speedup of the parallelized algorithms. This study was funded by a grant from Consejo Nacional de Ciencia y Tecnologia de Mexico SEP-CONACYT CB-131879. The authors also thanks LUFAC® Computacion SA de CV for CPU time and all the support provided.
Application of stochastic weighted algorithms to a multidimensional silica particle model
Energy Technology Data Exchange (ETDEWEB)
Menz, William J. [Department of Chemical Engineering and Biotechnology, University of Cambridge, New Museums Site, Pembroke Street, Cambridge CB2 3RA (United Kingdom); Patterson, Robert I.A.; Wagner, Wolfgang [Weierstrass Institute for Applied Analysis and Stochastics, Mohrenstrasse 39, Berlin 10117 (Germany); Kraft, Markus, E-mail: mk306@cam.ac.uk [Department of Chemical Engineering and Biotechnology, University of Cambridge, New Museums Site, Pembroke Street, Cambridge CB2 3RA (United Kingdom)
2013-09-01
Highlights: •Stochastic weighted algorithms (SWAs) are developed for a detailed silica model. •An implementation of SWAs with the transition kernel is presented. •The SWAs’ solutions converge to the direct simulation algorithm’s (DSA) solution. •The efficiency of SWAs is evaluated for this multidimensional particle model. •It is shown that SWAs can be used for coagulation problems in industrial systems. -- Abstract: This paper presents a detailed study of the numerical behaviour of stochastic weighted algorithms (SWAs) using the transition regime coagulation kernel and a multidimensional silica particle model. The implementation in the SWAs of the transition regime coagulation kernel and associated majorant rates is described. The silica particle model of Shekar et al. [S. Shekar, A.J. Smith, W.J. Menz, M. Sander, M. Kraft, A multidimensional population balance model to describe the aerosol synthesis of silica nanoparticles, Journal of Aerosol Science 44 (2012) 83–98] was used in conjunction with this coagulation kernel to study the convergence properties of SWAs with a multidimensional particle model. High precision solutions were calculated with two SWAs and also with the established direct simulation algorithm. These solutions, which were generated using large number of computational particles, showed close agreement. It was thus demonstrated that SWAs can be successfully used with complex coagulation kernels and high dimensional particle models to simulate real-world systems.
Solving the Vehicle Routing Problem with Stochastic Demands via Hybrid Genetic Algorithm-Tabu Search
Directory of Open Access Journals (Sweden)
Z. Ismail
2008-01-01
Full Text Available This study considers a version of the stochastic vehicle routing problem where customer demands are random variables with known probability distribution. A new scheme based on a hybrid GA and Tabu Search heuristic is proposed for this problem under a priori approach with preventive restocking. The relative performance of the proposed HGATS is compared to each GA and TS alone, on a set of randomly generated problems following some discrete probability distributions. The problem data are inspired by real case of VRPSD in waste collection. Results from the experiment show the advantages of the proposed algorithm that are its robustness and better solution qualities resulted.
Using genetic algorithm to solve a new multi-period stochastic optimization model
Zhang, Xin-Li; Zhang, Ke-Cun
2009-09-01
This paper presents a new asset allocation model based on the CVaR risk measure and transaction costs. Institutional investors manage their strategic asset mix over time to achieve favorable returns subject to various uncertainties, policy and legal constraints, and other requirements. One may use a multi-period portfolio optimization model in order to determine an optimal asset mix. Recently, an alternative stochastic programming model with simulated paths was proposed by Hibiki [N. Hibiki, A hybrid simulation/tree multi-period stochastic programming model for optimal asset allocation, in: H. Takahashi, (Ed.) The Japanese Association of Financial Econometrics and Engineering, JAFFE Journal (2001) 89-119 (in Japanese); N. Hibiki A hybrid simulation/tree stochastic optimization model for dynamic asset allocation, in: B. Scherer (Ed.), Asset and Liability Management Tools: A Handbook for Best Practice, Risk Books, 2003, pp. 269-294], which was called a hybrid model. However, the transaction costs weren't considered in that paper. In this paper, we improve Hibiki's model in the following aspects: (1) The risk measure CVaR is introduced to control the wealth loss risk while maximizing the expected utility; (2) Typical market imperfections such as short sale constraints, proportional transaction costs are considered simultaneously. (3) Applying a genetic algorithm to solve the resulting model is discussed in detail. Numerical results show the suitability and feasibility of our methodology.
Vilanova, Pedro
2016-01-07
In this work, we present an extension of the forward-reverse representation introduced in Simulation of forward-reverse stochastic representations for conditional diffusions , a 2014 paper by Bayer and Schoenmakers to the context of stochastic reaction networks (SRNs). We apply this stochastic representation to the computation of efficient approximations of expected values of functionals of SRN bridges, i.e., SRNs conditional on their values in the extremes of given time-intervals. We then employ this SRN bridge-generation technique to the statistical inference problem of approximating reaction propensities based on discretely observed data. To this end, we introduce a two-phase iterative inference method in which, during phase I, we solve a set of deterministic optimization problems where the SRNs are replaced by their reaction-rate ordinary differential equations approximation; then, during phase II, we apply the Monte Carlo version of the Expectation-Maximization algorithm to the phase I output. By selecting a set of over-dispersed seeds as initial points in phase I, the output of parallel runs from our two-phase method is a cluster of approximate maximum likelihood estimates. Our results are supported by numerical examples.
Energy Technology Data Exchange (ETDEWEB)
Xie Shaofei [Center for Instrumental Analysis, China Pharmaceutical University, Key Laboratory of Drug Quality Control and Pharmacovigilance, Ministry of Education, Nanjing 210009 (China); Xiang Bingren [Center for Instrumental Analysis, China Pharmaceutical University, Key Laboratory of Drug Quality Control and Pharmacovigilance, Ministry of Education, Nanjing 210009 (China)]. E-mail: cpuxsf@hotmail.com; Deng Haishan [Center for Instrumental Analysis, China Pharmaceutical University, Key Laboratory of Drug Quality Control and Pharmacovigilance, Ministry of Education, Nanjing 210009 (China); Xiang Suyun [Center for Instrumental Analysis, China Pharmaceutical University, Key Laboratory of Drug Quality Control and Pharmacovigilance, Ministry of Education, Nanjing 210009 (China); Lu Jun [Center for Instrumental Analysis, China Pharmaceutical University, Key Laboratory of Drug Quality Control and Pharmacovigilance, Ministry of Education, Nanjing 210009 (China)
2007-02-28
Based on the theory of stochastic resonance, an improved stochastic resonance algorithm with a new criterion for optimizing system parameters to enhance signal-to-noise ratio (SNR) of HPLC/UV chromatographic signal for trace analysis was presented in this study. Compared with the conventional criterion in stochastic resonance, the proposed one can ensure satisfactory SNR as well as good peak shape of chromatographic peak in output signal. Application of the criterion to experimental weak signals of HPLC/UV was investigated and the results showed an excellent quantitative relationship between different concentrations and responses.
Directory of Open Access Journals (Sweden)
Bruno H. Dias
2010-01-01
Full Text Available This paper presents a new approach for the expected cost-to-go functions modeling used in the stochastic dynamic programming (SDP algorithm. The SDP technique is applied to the long-term operation planning of electrical power systems. Using state space discretization, the Convex Hull algorithm is used for constructing a series of hyperplanes that composes a convex set. These planes represent a piecewise linear approximation for the expected cost-to-go functions. The mean operational costs for using the proposed methodology were compared with those from the deterministic dual dynamic problem in a case study, considering a single inflow scenario. This sensitivity analysis shows the convergence of both methods and is used to determine the minimum discretization level. Additionally, the applicability of the proposed methodology for two hydroplants in a cascade is demonstrated. With proper adaptations, this work can be extended to a complete hydrothermal system.
Chakraborty, Mithun; Konar, Amit
2008-01-01
The Proportional-Integral-Derivative Controller is widely used in industries for process control applications. Fractional-order PID controllers are known to outperform their integer-order counterparts. In this paper, we propose a new technique of fractional-order PID controller synthesis based on peak overshoot and rise-time specifications. Our approach is to construct an objective function, the optimization of which yields a possible solution to the design problem. This objective function is optimized using two popular bio-inspired stochastic search algorithms, namely Particle Swarm Optimization and Differential Evolution. With the help of a suitable example, the superiority of the designed fractional-order PID controller to an integer-order PID controller is affirmed and a comparative study of the efficacy of the two above algorithms in solving the optimization problem is also presented.
IPAC Firefly package goes open source
Wu, Xiuqin; Roby, William; Goldina, Tatiana; Ly, Loi; IRSA IPAC
2015-01-01
IPAC Firefly package has been developed in IRSA (NASA/IPAC Infrared Science Archive) in last six years. It is a software package utilizing state-of-the art AJAX technology to provide an interactive web user interface for astronomers. It has been used to build Spitzer Heritage Archive, WISE Image Service, Planck Visualization, PTF Image Service, and the new IRSA finder chart. It provides three major components: table display, FITS images visualization, and 2D plot. All three highly interactive components can work together using the same data model or separately to provide any combinations of interactivities among them. Firefly toolkits provide an easy way to put interactivities in an otherwise static web page. With a few lines of simple JavaScript embebded in a web page, Firefly toolkits can add manipulative functions to a static table, display a FITS image, or draw an XY 2D plot interactively.FIrefly will be in the GItHub soon.
Salah, Ahmad M.; Nelson, E. James; Williams, Gustavious P.
2010-04-01
We present algorithms and tools we developed to automatically link an overland flow model to a hydrodynamic water quality model with different spatial and temporal discretizations. These tools run the linked models which provide a stochastic simulation frame. We also briefly present the tools and algorithms we developed to facilitate and analyze stochastic simulations of the linked models. We demonstrate the algorithms by linking the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model for overland flow with the CE-QUAL-W2 model for water quality and reservoir hydrodynamics. GSSHA uses a two-dimensional horizontal grid while CE-QUAL-W2 uses a two-dimensional vertical grid. We implemented the algorithms and tools in the Watershed Modeling System (WMS) which allows modelers to easily create and use models. The algorithms are general and could be used for other models. Our tools create and analyze stochastic simulations to help understand uncertainty in the model application. While a number of examples of linked models exist, the ability to perform automatic, unassisted linking is a step forward and provides the framework to easily implement stochastic modeling studies.
Directory of Open Access Journals (Sweden)
Ahmad M Salah
2010-12-01
Full Text Available We present algorithms and tools we developed to automatically link an overland flow model to a hydrodynamic water quality model with different spatial and temporal discretizations. These tools run the linked models which provide a stochastic simulation frame. We also briefly present the tools and algorithms we developed to facilitate and analyze stochastic simulations of the linked models. We demonstrate the algorithms by linking the Gridded Surface Subsurface Hydrologic Analysis (GSSHA model for overland flow with the CE-QUAL-W2 model for water quality and reservoir hydrodynamics. GSSHA uses a two-dimensional horizontal grid while CE-QUAL-W2 uses a two-dimensional vertical grid. We implemented the algorithms and tools in the Watershed Modeling System (WMS which allows modelers to easily create and use models. The algorithms are general and could be used for other models. Our tools create and analyze stochastic simulations to help understand uncertainty in the model application. While a number of examples of linked models exist, the ability to perform automatic, unassisted linking is a step forward and provides the framework to easily implement stochastic modeling studies.
Bayer, Christian
2016-02-20
© 2016 Taylor & Francis Group, LLC. ABSTRACT: In this work, we present an extension of the forward–reverse representation introduced by Bayer and Schoenmakers (Annals of Applied Probability, 24(5):1994–2032, 2014) to the context of stochastic reaction networks (SRNs). We apply this stochastic representation to the computation of efficient approximations of expected values of functionals of SRN bridges, that is, SRNs conditional on their values in the extremes of given time intervals. We then employ this SRN bridge-generation technique to the statistical inference problem of approximating reaction propensities based on discretely observed data. To this end, we introduce a two-phase iterative inference method in which, during phase I, we solve a set of deterministic optimization problems where the SRNs are replaced by their reaction-rate ordinary differential equations approximation; then, during phase II, we apply the Monte Carlo version of the expectation-maximization algorithm to the phase I output. By selecting a set of overdispersed seeds as initial points in phase I, the output of parallel runs from our two-phase method is a cluster of approximate maximum likelihood estimates. Our results are supported by numerical examples.
Efficient generation and optimization of stochastic template banks by a neighboring cell algorithm
Fehrmann, Henning; Pletsch, Holger J.
2014-12-01
Placing signal templates (grid points) as efficiently as possible to cover a multidimensional parameter space is crucial in computing-intensive matched-filtering searches for gravitational waves, but also in similar searches in other fields of astronomy. To generate efficient coverings of arbitrary parameter spaces, stochastic template banks have been advocated, where templates are placed at random while rejecting those too close to others. However, in this simple scheme, for each new random point its distance to every template in the existing bank is computed. This rapidly increasing number of distance computations can render the acceptance of new templates computationally prohibitive, particularly for wide parameter spaces or in large dimensions. This paper presents a neighboring cell algorithm that can dramatically improve the efficiency of constructing a stochastic template bank. By dividing the parameter space into subvolumes (cells), for an arbitrary point an efficient hashing technique is exploited to obtain the index of its enclosing cell along with the parameters of its neighboring templates. Hence only distances to these neighboring templates in the bank are computed, massively lowering the overall computing cost, as demonstrated in simple examples. Furthermore, we propose a novel method based on this technique to increase the fraction of covered parameter space solely by directed template shifts, without adding any templates. As is demonstrated in examples, this method can be highly effective.
Paracousti-UQ: A Stochastic 3-D Acoustic Wave Propagation Algorithm.
Energy Technology Data Exchange (ETDEWEB)
Preston, Leiph
2017-09-01
Acoustic full waveform algorithms, such as Paracousti, provide deterministic solutions in complex, 3-D variable environments. In reality, environmental and source characteristics are often only known in a statistical sense. Thus, to fully characterize the expected sound levels within an environment, this uncertainty in environmental and source factors should be incorporated into the acoustic simulations. Performing Monte Carlo (MC) simulations is one method of assessing this uncertainty, but it can quickly become computationally intractable for realistic problems. An alternative method, using the technique of stochastic partial differential equations (SPDE), allows computation of the statistical properties of output signals at a fraction of the computational cost of MC. Paracousti-UQ solves the SPDE system of 3-D acoustic wave propagation equations and provides estimates of the uncertainty of the output simulated wave field (e.g., amplitudes, waveforms) based on estimated probability distributions of the input medium and source parameters. This report describes the derivation of the stochastic partial differential equations, their implementation, and comparison of Paracousti-UQ results with MC simulations using simple models.
The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond
Garivier, Aurélien
2011-01-01
This paper presents a finite-time analysis of the KL-UCB algorithm, an online, horizon-free index policy for stochastic bandit problems. We prove two distinct results: first, for arbitrary bounded rewards, the KL-UCB algorithm satisfies a uniformly better regret bound than UCB or UCB2; second, in the special case of Bernoulli rewards, it reaches the lower bound of Lai and Robbins. Furthermore, we show that simple adaptations of the KL-UCB algorithm are also optimal for specific classes of (possibly unbounded) rewards, including those generated from exponential families of distributions. A large-scale numerical study comparing KL-UCB with its main competitors (UCB, UCB2, UCB-Tuned, UCB-V, DMED) shows that KL-UCB is remarkably efficient and stable, including for short time horizons. KL-UCB is also the only method that always performs better than the basic UCB policy. Our regret bounds rely on deviations results of independent interest which are stated and proved in the Appendix. As a by-product, we also obtain ...
A new stochastic algorithm for proton exchange membrane fuel cell stack design optimization
Chakraborty, Uttara
2012-10-01
This paper develops a new stochastic heuristic for proton exchange membrane fuel cell stack design optimization. The problem involves finding the optimal size and configuration of stand-alone, fuel-cell-based power supply systems: the stack is to be configured so that it delivers the maximum power output at the load's operating voltage. The problem apparently looks straightforward but is analytically intractable and computationally hard. No exact solution can be found, nor is it easy to find the exact number of local optima; we, therefore, are forced to settle with approximate or near-optimal solutions. This real-world problem, first reported in Journal of Power Sources 131, poses both engineering challenges and computational challenges and is representative of many of today's open problems in fuel cell design involving a mix of discrete and continuous parameters. The new algorithm is compared against genetic algorithm, simulated annealing, and (1+1)-EA. Statistical tests of significance show that the results produced by our method are better than the best-known solutions for this problem published in the literature. A finite Markov chain analysis of the new algorithm establishes an upper bound on the expected time to find the optimum solution.
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Shaofei Xie
2012-02-01
Full Text Available Based on the theory of stochastic resonance, an adaptive single-well stochastic resonance (ASSR coupled with genetic algorithm was developed to enhance the signal-to-noise ratio of weak chromatographic signals. In conventional stochastic resonance algorithm, there are two or more parameters needed to be optimized and the proper parameters values were obtained by a universal searching within a given range. In the developed ASSR, the optimization of system parameter was simplified and automatic implemented. The ASSR was applied to the trace analysis of clenbuterol in human urine and it helped to significantly improve the limit of detection and limit of quantification of clenbuterol. Good linearity, precision and accuracy of the proposed method ensure that it could be an effective tool for trace analysis and the improvement of detective sensibility of current detectors.
A genetic algorithm based stochastic programming model for air quality management
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
This paper presents a model that can aid planners in defining the total allowable pollutant discharge in the planning region,accounting for the dynamic and stochastic character of meteorological conditions.This is accomplished by integrating Monte Carlo simulation and using genetic algorithm to solve the model.The model is demonstrated by using a realistic air urban-scale SO2 control problem in the Yuxi City of China.To evaluate effectiveness of the model,results of the approach are shown to compare with those of the linear deterministic procedures.This paper also provides a valuable insight into how air quality targets should be made when the air pollutant will not threat the residents'health.Finally,a discussion of the areas for further research are briefly delineated.
Badenhorst, Werner; Hanekom, Tania; Hanekom, Johan J
2016-12-01
This study presents the development of an alternative noise current term and novel voltage-dependent current noise algorithm for conductance-based stochastic auditory nerve fibre (ANF) models. ANFs are known to have significant variance in threshold stimulus which affects temporal characteristics such as latency. This variance is primarily caused by the stochastic behaviour or microscopic fluctuations of the node of Ranvier's voltage-dependent sodium channels of which the intensity is a function of membrane voltage. Though easy to implement and low in computational cost, existing current noise models have two deficiencies: it is independent of membrane voltage, and it is unable to inherently determine the noise intensity required to produce in vivo measured discharge probability functions. The proposed algorithm overcomes these deficiencies while maintaining its low computational cost and ease of implementation compared to other conductance and Markovian-based stochastic models. The algorithm is applied to a Hodgkin-Huxley-based compartmental cat ANF model and validated via comparison of the threshold probability and latency distributions to measured cat ANF data. Simulation results show the algorithm's adherence to in vivo stochastic fibre characteristics such as an exponential relationship between the membrane noise and transmembrane voltage, a negative linear relationship between the log of the relative spread of the discharge probability and the log of the fibre diameter and a decrease in latency with an increase in stimulus intensity.
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Haishan Deng
2014-01-01
Full Text Available Simultaneous determination of multiple weak chromatographic peaks via stochastic resonance algorithm attracts much attention in recent years. However, the optimization of the parameters is complicated and time consuming, although the single-well potential stochastic resonance algorithm (SSRA has already reduced the number of parameters to only one and simplified the process significantly. Even worse, it is often difficult to keep amplified peaks with beautiful peak shape. Therefore, multiobjective genetic algorithm was employed to optimize the parameter of SSRA for multiple optimization objectives (i.e., S/N and peak shape and multiple chromatographic peaks. The applicability of the proposed method was evaluated with an experimental data set of Sudan dyes, and the results showed an excellent quantitative relationship between different concentrations and responses.
Evaluation of a Geothermal Prospect Using a Stochastic Joint Inversion Algorithm
Tompson, A. F.; Mellors, R. J.; Ramirez, A.; Dyer, K.; Yang, X.; Trainor-Guitton, W.; Wagoner, J. L.
2013-12-01
A stochastic joint inverse algorithm to analyze diverse geophysical and hydrologic data for a geothermal prospect is developed. The purpose is to improve prospect evaluation by finding an ensemble of hydrothermal flow models that are most consistent with multiple types of data sets. The staged approach combines Bayesian inference within a Markov Chain Monte Carlo (MCMC) global search algorithm. The method is highly flexible and capable of accommodating multiple and diverse datasets as a means to maximize the utility of all available data to understand system behavior. An initial application is made at a geothermal prospect located near Superstition Mountain in the western Salton Trough in California. Readily available data include three thermal gradient exploration boreholes, borehole resistivity logs, magnetotelluric and gravity geophysical surveys, surface heat flux measurements, and other nearby hydrologic and geologic information. Initial estimates of uncertainty in structural or parametric characteristics of the prospect are used to drive large numbers of simulations of hydrothermal fluid flow and related geophysical processes using random realizations of the conceptual geothermal system. Uncertainty in the results is represented within a ranked subset of model realizations that best match all available data within a specified norm or tolerance. Statistical (posterior) characteristics of these solutions reflect reductions in the perceived (prior) uncertainties. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-ABS-641792.
A matrix product algorithm for stochastic dynamics on locally tree-like graphs
Barthel, Thomas; de Bacco, Caterina; Franz, Silvio
In this talk, I describe a novel algorithm for the efficient simulation of generic stochastic dynamics of classical degrees of freedom defined on the vertices of locally tree-like graphs. Such models correspond for example to spin-glass systems, Boolean networks, neural networks, or other technological, biological, and social networks. Building upon the cavity method and ideas from quantum many-body theory, the algorithm is based on a matrix product approximation of the so-called edge messages - conditional probabilities of vertex variable trajectories. The matrix product edge messages (MPEM) are constructed recursively. Computation costs and accuracy can be tuned by controlling the matrix dimensions of the MPEM in truncations. In contrast to Monte Carlo simulations, the approach has a better error scaling and works for both, single instances as well as the thermodynamic limit. Due to the absence of cancellation effects, observables with small expectation values can be evaluated accurately, allowing for the study of decay processes and temporal correlations with unprecedented accuracy. The method is demonstrated for the prototypical non-equilibrium Glauber dynamics of an Ising spin system. Reference: arXiv:1508.03295.
An efficient algorithm for the stochastic simulation of the hybridization of DNA to microarrays
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Laurenzi Ian J
2009-12-01
Full Text Available Abstract Background Although oligonucleotide microarray technology is ubiquitous in genomic research, reproducibility and standardization of expression measurements still concern many researchers. Cross-hybridization between microarray probes and non-target ssDNA has been implicated as a primary factor in sensitivity and selectivity loss. Since hybridization is a chemical process, it may be modeled at a population-level using a combination of material balance equations and thermodynamics. However, the hybridization reaction network may be exceptionally large for commercial arrays, which often possess at least one reporter per transcript. Quantification of the kinetics and equilibrium of exceptionally large chemical systems of this type is numerically infeasible with customary approaches. Results In this paper, we present a robust and computationally efficient algorithm for the simulation of hybridization processes underlying microarray assays. Our method may be utilized to identify the extent to which nucleic acid targets (e.g. cDNA will cross-hybridize with probes, and by extension, characterize probe robustnessusing the information specified by MAGE-TAB. Using this algorithm, we characterize cross-hybridization in a modified commercial microarray assay. Conclusions By integrating stochastic simulation with thermodynamic prediction tools for DNA hybridization, one may robustly and rapidly characterize of the selectivity of a proposed microarray design at the probe and "system" levels. Our code is available at http://www.laurenzi.net.
Genetic Algorithm Based Framework for Automation of Stochastic Modeling of Multi-Season Streamflows
Srivastav, R. K.; Srinivasan, K.; Sudheer, K.
2009-05-01
bootstrap (MABB) ) based on the explicit objective functions of minimizing the relative bias and relative root mean square error in estimating the storage capacity of the reservoir. The optimal parameter set of the hybrid model is obtained based on the search over a multi- dimensional parameter space (involving simultaneous exploration of the parametric (PAR(1)) as well as the non-parametric (MABB) components). This is achieved using the efficient evolutionary search based optimization tool namely, non-dominated sorting genetic algorithm - II (NSGA-II). This approach helps in reducing the drudgery involved in the process of manual selection of the hybrid model, in addition to predicting the basic summary statistics dependence structure, marginal distribution and water-use characteristics accurately. The proposed optimization framework is used to model the multi-season streamflows of River Beaver and River Weber of USA. In case of both the rivers, the proposed GA-based hybrid model yields a much better prediction of the storage capacity (where simultaneous exploration of both parametric and non-parametric components is done) when compared with the MLE-based hybrid models (where the hybrid model selection is done in two stages, thus probably resulting in a sub-optimal model). This framework can be further extended to include different linear/non-linear hybrid stochastic models at other temporal and spatial scales as well.
Pinto da Silva, Luís; Esteves da Silva, Joaquim C. G.
2014-01-01
Firefly bioluminescence presents a light emitting profile with a form of a flash, due to the firefly luciferase-catalyzed formation of inhibitory products. These impair the binding of the substrate luciferin to the active site of the enzyme. However, this luciferase catalyzed pathways may not be the only ones responsible for the flash profile. The oxidation and isomerisation of the substrate luciferin lead to the formation of compounds that are also known inhibitors of firefly bioluminescence. So, the objective of this Letter was to analyze if these reactions could be capable of interfering with the bioluminescence reaction.
Modulations in the light of the firefly
Indian Academy of Sciences (India)
Anurup Gohain Barua
2013-03-01
Continuous light could be produced from the firefly by making it inhale vapours of ethyl acetate. Here we perform such a control experiment on the Indian species of the firefly Luciola praeusta Kiesenwetter 1874 (Coleoptera : Lampyridae : Luciolinae), and analyse the light in the microsecond time scale. The amplitude of the continuous train of triangular pulses is apparently altered in accordance with the instantaneous values of a hypothetical signal, which exhibits pulse amplitude modulation (PAM). In addition to sampling in amplitude, this scheme apparently provides sampling in time, representing pulse width modulation (PWM). A Fourier transform spectrum of this waveform shows the `carrier’ frequency and the accompanying `side bands’.
Dubois, Anne; Lavielle, Marc; Gsteiger, Sandro; Pigeolet, Etienne; Mentré, France
2011-09-20
In this work, we develop a bioequivalence analysis using nonlinear mixed effects models (NLMEM) that mimics the standard noncompartmental analysis (NCA). We estimate NLMEM parameters, including between-subject and within-subject variability and treatment, period and sequence effects. We explain how to perform a Wald test on a secondary parameter, and we propose an extension of the likelihood ratio test for bioequivalence. We compare these NLMEM-based bioequivalence tests with standard NCA-based tests. We evaluate by simulation the NCA and NLMEM estimates and the type I error of the bioequivalence tests. For NLMEM, we use the stochastic approximation expectation maximisation (SAEM) algorithm implemented in monolix. We simulate crossover trials under H(0) using different numbers of subjects and of samples per subject. We simulate with different settings for between-subject and within-subject variability and for the residual error variance. The simulation study illustrates the accuracy of NLMEM-based geometric means estimated with the SAEM algorithm, whereas the NCA estimates are biased for sparse design. NCA-based bioequivalence tests show good type I error except for high variability. For a rich design, type I errors of NLMEM-based bioequivalence tests (Wald test and likelihood ratio test) do not differ from the nominal level of 5%. Type I errors are inflated for sparse design. We apply the bioequivalence Wald test based on NCA and NLMEM estimates to a three-way crossover trial, showing that Omnitrope®; (Sandoz GmbH, Kundl, Austria) powder and solution are bioequivalent to Genotropin®; (Pfizer Pharma GmbH, Karlsruhe, Germany). NLMEM-based bioequivalence tests are an alternative to standard NCA-based tests. However, caution is needed for small sample size and highly variable drug.
Elenchezhiyan, M; Prakash, J
2015-09-01
In this work, state estimation schemes for non-linear hybrid dynamic systems subjected to stochastic state disturbances and random errors in measurements using interacting multiple-model (IMM) algorithms are formulated. In order to compute both discrete modes and continuous state estimates of a hybrid dynamic system either an IMM extended Kalman filter (IMM-EKF) or an IMM based derivative-free Kalman filters is proposed in this study. The efficacy of the proposed IMM based state estimation schemes is demonstrated by conducting Monte-Carlo simulation studies on the two-tank hybrid system and switched non-isothermal continuous stirred tank reactor system. Extensive simulation studies reveal that the proposed IMM based state estimation schemes are able to generate fairly accurate continuous state estimates and discrete modes. In the presence and absence of sensor bias, the simulation studies reveal that the proposed IMM unscented Kalman filter (IMM-UKF) based simultaneous state and parameter estimation scheme outperforms multiple-model UKF (MM-UKF) based simultaneous state and parameter estimation scheme.
Stochastic modeling for trajectories drift in the ocean: Application of Density Clustering Algorithm
Shchekinova, E Y
2015-01-01
The aim of this study is to address the effects of wind-induced drift on a floating sea objects using high--resolution ocean forecast data and atmospheric data. Two applications of stochastic Leeway model for prediction of trajectories drift in the Mediterranean sea are presented: long-term simulation of sea drifters in the western Adriatic sea (21.06.2009-23.06.2009) and numerical reconstruction of the Elba accident (21.06.2009-23.06.2009). Long-term simulations in the western Adriatic sea are performed using wind data from the European Center for Medium-Range Weather Forecast (ECMWF) and currents from the Adriatic Forecasting System (AFS). An algorithm of spatial clustering is proposed to identify the most probable search areas with a high density of drifters. The results are compared for different simulation scenarios using different categories of drifters and forcing fields. The reconstruction of sea object drift near to the Elba Island is performed using surface currents from the Mediterranean Forecastin...
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Jibing Wu
2017-01-01
Full Text Available Clustering analysis is a basic and essential method for mining heterogeneous information networks, which consist of multiple types of objects and rich semantic relations among different object types. Heterogeneous information networks are ubiquitous in the real-world applications, such as bibliographic networks and social media networks. Unfortunately, most existing approaches, such as spectral clustering, are designed to analyze homogeneous information networks, which are composed of only one type of objects and links. Some recent studies focused on heterogeneous information networks and yielded some research fruits, such as RankClus and NetClus. However, they often assumed that the heterogeneous information networks usually follow some simple schemas, such as bityped network schema or star network schema. To overcome the above limitations, we model the heterogeneous information network as a tensor without the restriction of network schema. Then, a tensor CP decomposition method is adapted to formulate the clustering problem in heterogeneous information networks. Further, we develop two stochastic gradient descent algorithms, namely, SGDClus and SOSClus, which lead to effective clustering multityped objects simultaneously. The experimental results on both synthetic datasets and real-world dataset have demonstrated that our proposed clustering framework can model heterogeneous information networks efficiently and outperform state-of-the-art clustering methods.
Selçuk K. İşleyen; Ö. Faruk Baykoç
2008-01-01
We define a special case for the vehicle routing problem with stochastic demands (SC-VRPSD) where customer demands are normally distributed. We propose a new linear model for computing the expected length of a tour in SC-VRPSD. The proposed model is based on the integration of the “Traveling Salesman Problem” (TSP) and the Assignment Problem. For large-scale problems, we also use an Iterated Local Search (ILS) algorithm in order to reach an effective solution.
Energy Technology Data Exchange (ETDEWEB)
Volkov, M V; Garanin, S G; Dolgopolov, Yu V; Kopalkin, A V; Kulikov, S M; Sinyavin, D N; Starikov, F A; Sukharev, S A; Tyutin, S V; Khokhlov, S V; Chaparin, D A [Russian Federal Nuclear Center ' All-Russian Research Institute of Experimental Physics' , Sarov, Nizhnii Novgorod region (Russian Federation)
2014-11-30
A seven-channel fibre laser system operated by the master oscillator – multichannel power amplifier scheme is the phase locked using a stochastic parallel gradient algorithm. The phase modulators on lithium niobate crystals are controlled by a multichannel electronic unit with the microcontroller processing signals in real time. The dynamic phase locking of the laser system with the bandwidth of 14 kHz is demonstrated, the time of phasing is 3 – 4 ms. (fibre and integrated-optical structures)
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Patricio Orio
Full Text Available BACKGROUND: The phenomena that emerge from the interaction of the stochastic opening and closing of ion channels (channel noise with the non-linear neural dynamics are essential to our understanding of the operation of the nervous system. The effects that channel noise can have on neural dynamics are generally studied using numerical simulations of stochastic models. Algorithms based on discrete Markov Chains (MC seem to be the most reliable and trustworthy, but even optimized algorithms come with a non-negligible computational cost. Diffusion Approximation (DA methods use Stochastic Differential Equations (SDE to approximate the behavior of a number of MCs, considerably speeding up simulation times. However, model comparisons have suggested that DA methods did not lead to the same results as in MC modeling in terms of channel noise statistics and effects on excitability. Recently, it was shown that the difference arose because MCs were modeled with coupled gating particles, while the DA was modeled using uncoupled gating particles. Implementations of DA with coupled particles, in the context of a specific kinetic scheme, yielded similar results to MC. However, it remained unclear how to generalize these implementations to different kinetic schemes, or whether they were faster than MC algorithms. Additionally, a steady state approximation was used for the stochastic terms, which, as we show here, can introduce significant inaccuracies. MAIN CONTRIBUTIONS: We derived the SDE explicitly for any given ion channel kinetic scheme. The resulting generic equations were surprisingly simple and interpretable--allowing an easy, transparent and efficient DA implementation, avoiding unnecessary approximations. The algorithm was tested in a voltage clamp simulation and in two different current clamp simulations, yielding the same results as MC modeling. Also, the simulation efficiency of this DA method demonstrated considerable superiority over MC methods
Directory of Open Access Journals (Sweden)
Huiyi Hu
2013-01-01
speed of the stochastic gradient algorithm. The key term separation principle can simplify the identification model of the input nonlinear system, and the decomposition technique can enhance computational efficiencies of identification algorithms. The simulation results show that the proposed algorithm is effective for estimating the parameters of IN-CARAR systems.
Udink ten Cate, A.J.
1985-01-01
Discrete-time least-squares algorithms for recursive parameter estimation have continuous-time counterparts, which minimize a quadratic functional. The continuous-time algorithms can also include (in)equality constraints. Asymptotic convergence is demonstrated by means of Lyapunov methods. The constrained algorithms are applied in a stabilized output error configuration for parameter estimation in stochastic linear systems.
First lasing, capabilities, and flexibilities of FIREFLY
Energy Technology Data Exchange (ETDEWEB)
Berryman, K.W.; Smith, T.I. [Stanford Univ., CA (United States)
1995-12-31
FIREFLY is a free electron law that was designed to produce picosecond pulses of light in the range between 15 and 100 microns. It uses an inexpensive electromagnetic wiggler and variable outcoupling to provide maximum flexibility for user experiments. FIREFLY first lased on November 23, 1994, and has now operated from 15 to 65 microns. It has lased in both a traditional undulator configuration and as an optical klystron, and has also lased on the third harmonic between 9 and 11microns. We present measurements, of optical spectral width and pulse width at a range of wavelengths in both configurations. We also compare direct measurements of electron beam extraction, efficiency with observed optical power for fundamental, third harmonic, arid optical klystron operation. We discuss wavelength switching between adjacent peaks in the gain spectrum of an optical klystron, observed for the first time in FIREFLY. Finally, we focus on issues relevant to experimentation with FIREFLY, including continuously variable outcoupling, optical mode quality, and beam handling in the far-infrared.
Pan, Indranil; Das, Saptarshi; Gupta, Amitava
2011-01-01
An optimal PID and an optimal fuzzy PID have been tuned by minimizing the Integral of Time multiplied Absolute Error (ITAE) and squared controller output for a networked control system (NCS). The tuning is attempted for a higher order and a time delay system using two stochastic algorithms viz. the Genetic Algorithm (GA) and two variants of Particle Swarm Optimization (PSO) and the closed loop performances are compared. The paper shows that random variation in network delay can be handled efficiently with fuzzy logic based PID controllers over conventional PID controllers.
STOCHASTIC APPROXIMATION ALGORITHMS WITH EXPANDING TRUNCATIONS%扩展截尾的随机逼近算法
Institute of Scientific and Technical Information of China (English)
陈翰馥
2012-01-01
注意到系统控制及相关领域中相当一类问题可归结为参数估计,而后者又可转化为未知函数的求根问题,首先介绍用带噪声量测递推地求根方法,即经典的随机逼近算法,并针对它的不足,引入扩展截尾的随机逼近算法(SAAWET),给出它的一般收敛定理.接着介绍应用SAAWET解决线性随机系统系数辨识及定阶, Hammerstein,Wiener,NARX等非线性系统的辨识,非线性随机系统的迭代学习控制及适应调节,以及其它一些问题.所给出的估计都是递推的,并且以概率1收敛到真值.%It is noticed that a considerable class of problems arising from systems and control and related fields may be reduced to parameter estimation, which, in turn, can be transformed to a root-seeking problem for unknown functions. The paper first, introduces the root-seeking method based on the noisy observations, i.e., the classical stochastic approximation algorithm. Against the restrictions of applying the classical algorithm, the stochastic approximation algorithm with expanding truncations (SAAWET) is introduced, and its general convergence theorem is demonstrated as well. Then, SAAWET is applied to solve problems like coefficient identification and order determination of linear stochastic systems, identification of Hammerstein, Wiener, and NARX systems, iterative learning control and adaptive regulation for nonlinear stochastic systems, and some others. All estimates given by the method are recursive and converge to the corresponding true values with probability one.
Directory of Open Access Journals (Sweden)
Bin Liu
2012-01-01
Full Text Available The wagon flow scheduling plays a very important role in transportation activities in railway bureau. However, it is difficult to implement in the actual decision-making process of wagon flow scheduling that compiled under certain environment, because of the interferences of uncertain information, such as train arrival time, train classify time, train assemble time, and flexible train-size limitation. Based on existing research results, considering the stochasticity of all kinds of train operation time and fuzziness of train-size limitation of the departure train, aimed at maximizing the satisfaction of departure train-size limitation and minimizing the wagon residence time at railway station, a stochastic chance-constrained fuzzy multiobjective model for flexible wagon flow scheduling problem is established in this paper. Moreover, a hybrid intelligent algorithm based on ant colony optimization (ACO and genetic algorithm (GA is also provided to solve this model. Finally, the rationality and effectiveness of the model and algorithm are verified through a numerical example, and the results prove that the accuracy of the train work plan could be improved by the model and algorithm; consequently, it has a good robustness and operability.
McDonnell, Mark D; Mohan, Ashutosh; Stricker, Christian
2013-01-01
The release of neurotransmitter vesicles after arrival of a pre-synaptic action potential (AP) at cortical synapses is known to be a stochastic process, as is the availability of vesicles for release. These processes are known to also depend on the recent history of AP arrivals, and this can be described in terms of time-varying probabilities of vesicle release. Mathematical models of such synaptic dynamics frequently are based only on the mean number of vesicles released by each pre-synaptic AP, since if it is assumed there are sufficiently many vesicle sites, then variance is small. However, it has been shown recently that variance across sites can be significant for neuron and network dynamics, and this suggests the potential importance of studying short-term plasticity using simulations that do generate trial-to-trial variability. Therefore, in this paper we study several well-known conceptual models for stochastic availability and release. We state explicitly the random variables that these models describe and propose efficient algorithms for accurately implementing stochastic simulations of these random variables in software or hardware. Our results are complemented by mathematical analysis and statement of pseudo-code algorithms.
Directory of Open Access Journals (Sweden)
Tushar Tyagi
2016-11-01
Full Text Available This paper presents solution of multi-objective optimal dispatch (MOOD problem of solar-wind-thermal system by improved stochastic fractal search (ISFSA algorithm. Stochastic fractal search (SFSA is inspired by the phenomenon of natural growth called fractal. It utilizes the concept of creating fractals for conducting a search through the problem domain with the help of two main operations diffusion and updating. To improve the exploration and exploitation capability of SFSA, scale factor is used in place of random operator. The SFSA and proposed ISFSA is implemented and tested on six different multi objective complex test systems of power system. TOPSIS is used here as a decision making tool to find the best compromise solution between the two conflicting objectives. The outcomes of simulation results are also compared with recent reported methods to confirm the superiority and validation of proposed approach.
Identification of paralogous genes of firefly luciferase in the Japanese firefly, Luciola cruciata.
Oba, Yuichi; Sato, Mitsunori; Ohta, Yuichiro; Inouye, Satoshi
2006-03-01
Two homologous genes of firefly luciferase, LcLL1 and LcLL2, were cloned from the Japanese firefly Luciola cruciata, and were expressed and characterized. The gene product of LcLL1 had long-chain fatty acyl-CoA synthetic activity, but not luciferase activity. The other gene product of LcLL2 did not show enzymatic activities of acyl-CoA synthetase and luciferase. RT-PCR analysis showed that the transcript of LcLL1 was abundant in larva but very low in adult, while LcLL2 was expressed in both larva and adult. Phylogenetic analysis indicated that LcLL1 and LcLL2 are paralogous genes of firefly luciferase. Recently, we found that CG6178 in Drosophila melanogaster is an orthologue of firefly luciferase and shows fatty acyl-CoA synthetic activity, but not luciferase activity. These results suggest that firefly luciferase might be evolved from a fatty acyl-CoA synthetase by gene duplication in insects.
Mohamed, Majeed; Narayan Kar, Indra
2015-11-01
This paper focuses on a stochastic version of contraction theory to construct observer-controller structure for a flight dynamic system with noisy velocity measurement. A nonlinear stochastic observer is designed to estimate the pitch rate, the pitch angle, and the velocity of an aircraft example model using stochastic contraction theory. Estimated states are used to compute feedback control for solving a tracking problem. The structure and gain selection of the observer is carried out using Itô's stochastic differential equations and the contraction theory. The contraction property of integrated observer-controller structure is derived to ensure the exponential convergence of the trajectories of closed-loop nonlinear system. The upper bound of the state estimation error is explicitly derived and the efficacy of the proposed observer-controller structure has been shown through the numerical simulations.
Wei, Lin-Yang; Qi, Hong; Ren, Ya-Tao; Ruan, Li-Ming
2016-11-01
Inverse estimation of the refractive index distribution in one-dimensional participating media with graded refractive index (GRI) is investigated. The forward radiative transfer problem is solved by the Chebyshev collocation spectral method. The stochastic particle swarm optimization (SPSO) algorithm is employed to retrieve three kinds of GRI distribution, i.e. the linear, sinusoidal and quadratic GRI distribution. The retrieval accuracy of GRI distribution with different wall emissivity, optical thickness, absorption coefficients and scattering coefficients are discussed thoroughly. To improve the retrieval accuracy of quadratic GRI distribution, a double-layer model is proposed to supply more measurement information. The influence of measurement errors upon the precision of estimated results is also investigated. Considering the GRI distribution is unknown beforehand in practice, a quadratic function is employed to retrieve the linear GRI by SPSO algorithm. All the results show that the SPSO algorithm is applicable to retrieve different GRI distributions in participating media accurately even with noisy data.
Wong, Yin Mei; Wilkie, Joshua
2006-01-01
Since the introduction of the Black-Scholes model stochastic processes have played an increasingly important role in mathematical finance. In many cases prices, volatility and other quantities can be modeled using stochastic ordinary differential equations. Available methods for solving such equations have until recently been markedly inferior to analogous methods for deterministic ordinary differential equations. Recently, a number of methods which employ variable stepsizes to control local ...
Tavakkoli-Moghaddam, Reza; Alinaghian, Mehdi; Salamat-Bakhsh, Alireza; Norouzi, Narges
2012-05-01
A vehicle routing problem is a significant problem that has attracted great attention from researchers in recent years. The main objectives of the vehicle routing problem are to minimize the traveled distance, total traveling time, number of vehicles and cost function of transportation. Reducing these variables leads to decreasing the total cost and increasing the driver's satisfaction level. On the other hand, this satisfaction, which will decrease by increasing the service time, is considered as an important logistic problem for a company. The stochastic time dominated by a probability variable leads to variation of the service time, while it is ignored in classical routing problems. This paper investigates the problem of the increasing service time by using the stochastic time for each tour such that the total traveling time of the vehicles is limited to a specific limit based on a defined probability. Since exact solutions of the vehicle routing problem that belong to the category of NP-hard problems are not practical in a large scale, a hybrid algorithm based on simulated annealing with genetic operators was proposed to obtain an efficient solution with reasonable computational cost and time. Finally, for some small cases, the related results of the proposed algorithm were compared with results obtained by the Lingo 8 software. The obtained results indicate the efficiency of the proposed hybrid simulated annealing algorithm.
Multicolor Bioluminescence Obtained Using Firefly Luciferin.
Kiyama, Masahiro; Saito, Ryohei; Iwano, Satoshi; Obata, Rika; Niwa, Haruki; Maki, Shojiro A
2016-01-01
Firefly bioluminescence is widely used in life science research as a useful analysis tool. For example, the adenosine-5`-triphosphate (ATP)-dependent enzymatic firefly bioluminescence reaction has long been utilized as a microbial monitoring tool. Rapid and sensitive firefly luciferin-luciferase combinations are used not only to measure cell viability but also for reporter-gene assays. Recently, bioluminescence was utilized as a noninvasive, real-time imaging tool for living subjects to monitor cells and biological events. However, the number of commercialized luciferase genes is limited and tissue-permeable near-infrared (NIR) region emitting light is required for in vivo imaging. In this review, recent studies describing synthetic luciferin analogues predicted to have red-shifted bioluminescence are summarized. Luciferase substrates emitting red, green, and blue light that were designed and developed in our laboratory are presented. The longest emission wavelength of the synthesized luciferin analogues was recorded at 675 nm, which is within the NIR region. This compound is now commercially available as "Aka Lumine®".
Institute of Scientific and Technical Information of China (English)
Jing CHEN; Rui-feng DING
2014-01-01
Based on the work in Ding and Ding (2008), we develop a modifi ed stochastic gradient (SG) parameter estimation algorithm for a dual-rate Box-Jenkins model by using an auxiliary model. We simplify the complex dual-rate Box-Jenkins model to two fi nite impulse response (FIR) models, present an auxiliary model to estimate the missing outputs and the unknown noise variables, and compute all the unknown parameters of the system with colored noises. Simulation results indicate that the proposed method is effective.
Jacobson, R. A.
1975-01-01
Difficulties arise in guiding a solar electric propulsion spacecraft due to nongravitational accelerations caused by random fluctuations in the magnitude and direction of the thrust vector. These difficulties may be handled by using a low thrust guidance law based on the linear-quadratic-Gaussian problem of stochastic control theory with a minimum terminal miss performance criterion. Explicit constraints are imposed on the variances of the control parameters, and an algorithm based on the Hilbert space extension of a parameter optimization method is presented for calculation of gains in the guidance law. The terminal navigation of a 1980 flyby mission to the comet Encke is used as an example.
Directory of Open Access Journals (Sweden)
2009-03-01
Full Text Available We define a special case for the vehicle routing problem with stochastic demands (SC-VRPSD where customer demands are normally distributed. We propose a new linear model for computing the expected length of a tour in SC-VRPSD. The proposed model is based on the integration of the “Traveling Salesman Problem” (TSP and the Assignment Problem. For large-scale problems, we also use an Iterated Local Search (ILS algorithm in order to reach an effective solution.
Institute of Scientific and Technical Information of China (English)
HUANG Haijun; WANG Shouyang; Michael G. H. Bell
2001-01-01
In this paper, a bi-level formulation of the continuous networkdesign problem (NDP) is proposed on the basis of logit stochastic user equilibrium (SUE) assignment with elastic demand. The model determines the link capacity improvements by maximizing net economic benefit while considering changes in demand and traffic distribution in network.The derivatives of equilibrium link flows and objective function with respect to capacity expansion variables, which are analytically derived, can be computed without having to first find path choice information. These derivatives are employed to develop a quasiNewton algorithm with the BFGS(Broyden-Fletcher-Goldfarb-Shanno) formula for solving the nonlinear, nonconvex but differentiable SUE-constrained network design problem. The SUE assignment with elastic demand is solved by using the method of successive averages in conjunction with Bell's matrix inversion logit assignment method. Simple and complex example networks are presented to illustrate the model and the algorithm.
Argoti, A.; Fan, L. T.; Cruz, J.; Chou, S. T.
2008-01-01
The stochastic simulation of chemical reactions, specifically, a simple reversible chemical reaction obeying the first-order, i.e., linear, rate law, has been presented by Martinez-Urreaga and his collaborators in this journal. The current contribution is intended to complement and augment their work in two aspects. First, the simple reversible…
Methods for High-Order Multi-Scale and Stochastic Problems Analysis, Algorithms, and Applications
2016-10-17
weakly interacting particles . 15. SUBJECT TERMS Discontinuous Galerkin method, high order finite difference approximation, hyperbolic conservation...laws, finite element method, Bernstein-Bezier finite elements, weakly interacting particle systems, accelerated Monte Carlo, stochastic networks 16...tessellate. Again, a possible solution readily suggests itself whereby pyramidal elements are used as a means of interfacing between the tetrahedra with
Bisimulation Algorithms for StochasticProcess Algebras and their BDD-based Implementation
Katoen, Joost P.; Hermanns, H.; Siegle, M.
1999-01-01
Stochastic process algebras have been introduced in order to enable compositional performance analysis. The size of the state space is a limiting factor, especially if the system consists of many cooperating components. To fight state space explosion, various proposals for compositional aggregation
The "Best" Algorithm for Solving Stochastic Mixed Integer Programs
2006-01-01
focuses on solving problems for which all second-stage scenarios can be enu- merated (Klein Haneveld and van der Vlerk 1998, Ahmed 2004). Two exceptions... Haneveld , W. K. and M. H. van der Vlerk. 1998. Stochastic integer programming: state of the art. Available via <http://citeseer.ist.psu. edu
Directory of Open Access Journals (Sweden)
Yun Li
2016-12-01
Full Text Available Based on stochastic modeling of Coriolis vibration gyros by the Allan variance technique, this paper discusses Angle Random Walk (ARW, Rate Random Walk (RRW and Markov process gyroscope noises which have significant impacts on the North-finding accuracy. A new continuous rotation alignment algorithm for a Coriolis vibration gyroscope Inertial Measurement Unit (IMU is proposed in this paper, in which the extended observation equations are used for the Kalman filter to enhance the estimation of gyro drift errors, thus improving the north-finding accuracy. Theoretical and numerical comparisons between the proposed algorithm and the traditional ones are presented. The experimental results show that the new continuous rotation alignment algorithm using the extended observation equations in the Kalman filter is more efficient than the traditional two-position alignment method. Using Coriolis vibration gyros with bias instability of 0.1°/h, a north-finding accuracy of 0.1° (1σ is achieved by the new continuous rotation alignment algorithm, compared with 0.6° (1σ north-finding accuracy for the two-position alignment and 1° (1σ for the fixed-position alignment.
Li, Yun; Wu, Wenqi; Jiang, Qingan; Wang, Jinling
2016-01-01
Based on stochastic modeling of Coriolis vibration gyros by the Allan variance technique, this paper discusses Angle Random Walk (ARW), Rate Random Walk (RRW) and Markov process gyroscope noises which have significant impacts on the North-finding accuracy. A new continuous rotation alignment algorithm for a Coriolis vibration gyroscope Inertial Measurement Unit (IMU) is proposed in this paper, in which the extended observation equations are used for the Kalman filter to enhance the estimation of gyro drift errors, thus improving the north-finding accuracy. Theoretical and numerical comparisons between the proposed algorithm and the traditional ones are presented. The experimental results show that the new continuous rotation alignment algorithm using the extended observation equations in the Kalman filter is more efficient than the traditional two-position alignment method. Using Coriolis vibration gyros with bias instability of 0.1°/h, a north-finding accuracy of 0.1° (1σ) is achieved by the new continuous rotation alignment algorithm, compared with 0.6° (1σ) north-finding accuracy for the two-position alignment and 1° (1σ) for the fixed-position alignment. PMID:27983585
Stochastic Averaging and Stochastic Extremum Seeking
Liu, Shu-Jun
2012-01-01
Stochastic Averaging and Stochastic Extremum Seeking develops methods of mathematical analysis inspired by the interest in reverse engineering and analysis of bacterial convergence by chemotaxis and to apply similar stochastic optimization techniques in other environments. The first half of the text presents significant advances in stochastic averaging theory, necessitated by the fact that existing theorems are restricted to systems with linear growth, globally exponentially stable average models, vanishing stochastic perturbations, and prevent analysis over infinite time horizon. The second half of the text introduces stochastic extremum seeking algorithms for model-free optimization of systems in real time using stochastic perturbations for estimation of their gradients. Both gradient- and Newton-based algorithms are presented, offering the user the choice between the simplicity of implementation (gradient) and the ability to achieve a known, arbitrary convergence rate (Newton). The design of algorithms...
Adaptive Firefly Optimization on Reducing High Dimensional Weighted Word Affinity Graph
Directory of Open Access Journals (Sweden)
Dr. Poonam Yadhav
2014-12-01
Full Text Available Document analysis and retrieval system can best define an efficient information retrieval system. Among various processing stages in a document analysis and retrieval system, feature descriptors at processing volume limit require more importance while developing the system. This is mainly because of the increase in probability of getting high dimensional semantic description. This increases the vitality of opting a robust dimensionality reduction method for our retrieval system. Principle Component Analysis (PCA, Independent Component Analysis (ICA, etc are the most popular dimensionality reduction methods. However, they are highly complex while handling nonlinear data with multiple characteristics. Optimization algorithms can be a good alternative for the traditional methods. In fact, classical optimization algorithms such as Genetic Algorithm (GA, Particle Swarm Optimization (PSO, etc have been widely applied. However, the data handling remains inefficient under current data exploding scenario. In our previous work, we have exploited Firefly Algorithm (FA to solve the optimization problem. Due to parameter selection dilemma in traditional FA, this paper concentrates on using Adaptive Firefly Algorithm (AFA. AFA adaptively varies step search of solutions and hence improves the convergence rate of the algorithm. As a result, near – optimal solution can be obtained qualitatively. We further recommend the dimensionality reduction method to handle weighted word affinity graph to improve the retrieval efficiency.
Nature-inspired optimization algorithms
Yang, Xin-She
2014-01-01
Nature-Inspired Optimization Algorithms provides a systematic introduction to all major nature-inspired algorithms for optimization. The book's unified approach, balancing algorithm introduction, theoretical background and practical implementation, complements extensive literature with well-chosen case studies to illustrate how these algorithms work. Topics include particle swarm optimization, ant and bee algorithms, simulated annealing, cuckoo search, firefly algorithm, bat algorithm, flower algorithm, harmony search, algorithm analysis, constraint handling, hybrid methods, parameter tuning
M. Dyer; R. Kannan; L. Stougie (Leen)
2014-01-01
htmlabstractWe consider maximising a concave function over a convex set by a simplerandomised algorithm. The strength of the algorithm is that it requires only approximatefunction evaluations for the concave function and a weak membership oraclefor the convex set. Under smoothness conditions on the
Directory of Open Access Journals (Sweden)
Ching-Hung Lee
2011-01-01
Full Text Available This paper proposes a new type fuzzy neural systems, denoted IT2RFNS-A (interval type-2 recurrent fuzzy neural system with asymmetric membership function, for nonlinear systems identification and control. To enhance the performance and approximation ability, the triangular asymmetric fuzzy membership function (AFMF and TSK-type consequent part are adopted for IT2RFNS-A. The gradient information of the IT2RFNS-A is not easy to obtain due to the asymmetric membership functions and interval valued sets. The corresponding stable learning is derived by simultaneous perturbation stochastic approximation (SPSA algorithm which guarantees the convergence and stability of the closed-loop systems. Simulation and comparison results for the chaotic system identification and the control of Chua's chaotic circuit are shown to illustrate the feasibility and effectiveness of the proposed method.
Parasuraman, Ramviyas; Molinari, Luca; Kershaw, Keith; Di Castro, Mario; Masi, Alessandro; Ferre, Manuel
2014-01-01
The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS). When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities), there is a possibility that some electronic components may fail randomly (due to radiation effects), which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the “server-relay-client” framework that uses (multiple) relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO) algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide red...
Leimkuhler, Ben; Tuckerman, Mark E
2013-01-01
Molecular dynamics is one of the most commonly used approaches for studying the dynamics and statistical distributions of many physical, chemical, and biological systems using atomistic or coarse-grained models. It is often the case, however, that the interparticle forces drive motion on many time scales, and the efficiency of a calculation is limited by the choice of time step, which must be sufficiently small that the fastest force components are accurately integrated. Multiple time-stepping algorithms partially alleviate this inefficiency by assigning to each time scale an appropriately chosen step-size. However, such approaches are limited by resonance phenomena, wherein motion on the fastest time scales limits the step sizes associated with slower time scales. In atomistic models of biomolecular systems, for example, resonances limit the largest time step to around 5-6 fs. In this paper, we introduce a set of stochastic isokinetic equations of motion that are shown to be rigorously ergodic and that can b...
Parasuraman, Ramviyas; Fabry, Thomas; Molinari, Luca; Kershaw, Keith; Di Castro, Mario; Masi, Alessandro; Ferre, Manuel
2014-12-12
The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS). When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities), there is a possibility that some electronic components may fail randomly (due to radiation effects), which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the "server-relay-client" framework that uses (multiple) relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO) algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide redundant networking abilities. We use pre-processing techniques, such as exponential moving averaging and spatial averaging filters on the RSS data for smoothing. We apply a receiver spatial diversity concept and employ a position controller on the relay node using a stochastic gradient ascent method for self-positioning the relay node to achieve the RSS balancing task. The effectiveness of the proposed solution is validated by extensive simulations and field experiments in CERN facilities. For the field trials, we used a youBot mobile robot platform as the relay node, and two stand-alone Raspberry Pi computers as the client and server nodes. The algorithm has been proven to be robust to noise in the radio signals and to work effectively even under non-line-of-sight conditions.
Directory of Open Access Journals (Sweden)
Ramviyas Parasuraman
2014-12-01
Full Text Available The reliability of wireless communication in a network of mobile wireless robot nodes depends on the received radio signal strength (RSS. When the robot nodes are deployed in hostile environments with ionizing radiations (such as in some scientific facilities, there is a possibility that some electronic components may fail randomly (due to radiation effects, which causes problems in wireless connectivity. The objective of this paper is to maximize robot mission capabilities by maximizing the wireless network capacity and to reduce the risk of communication failure. Thus, in this paper, we consider a multi-node wireless tethering structure called the “server-relay-client” framework that uses (multiple relay nodes in between a server and a client node. We propose a robust stochastic optimization (RSO algorithm using a multi-sensor-based RSS sampling method at the relay nodes to efficiently improve and balance the RSS between the source and client nodes to improve the network capacity and to provide redundant networking abilities. We use pre-processing techniques, such as exponential moving averaging and spatial averaging filters on the RSS data for smoothing. We apply a receiver spatial diversity concept and employ a position controller on the relay node using a stochastic gradient ascent method for self-positioning the relay node to achieve the RSS balancing task. The effectiveness of the proposed solution is validated by extensive simulations and field experiments in CERN facilities. For the field trials, we used a youBot mobile robot platform as the relay node, and two stand-alone Raspberry Pi computers as the client and server nodes. The algorithm has been proven to be robust to noise in the radio signals and to work effectively even under non-line-of-sight conditions.
Energy Technology Data Exchange (ETDEWEB)
Dulikravich, George S.; Sikka, Vinod K.; Muralidharan, G.
2006-06-01
The goal of this project was to adapt and use an advanced semi-stochastic algorithm for constrained multiobjective optimization and combine it with experimental testing and verification to determine optimum concentrations of alloying elements in heat-resistant and corrosion-resistant H-series stainless steel alloys that will simultaneously maximize a number of alloy's mechanical and corrosion properties.
Creation of High Efficient Firefly Luciferase
Nakatsu, Toru
Firefly emits visible yellow-green light. The bioluminescence reaction is carried out by the enzyme luciferase. The bioluminescence of luciferase is widely used as an excellent tool for monitoring gene expression, the measurement of the amount of ATP and in vivo imaging. Recently a study of the cancer metastasis is carried out by in vivo luminescence imaging system, because luminescence imaging is less toxic and more useful for long-term assay than fluorescence imaging by GFP. However the luminescence is much dimmer than fluorescence. Then bioluminescence imaging in living organisms demands the high efficient luciferase which emits near infrared lights or enhances the emission intensity. Here I introduce an idea for creating the high efficient luciferase based on the crystal structure.
Fiber optic biosensor of immobilized firefly luciferase
Institute of Scientific and Technical Information of China (English)
蔡谨; 吉鑫松; 等
2002-01-01
Luciferase from firefly lantern extract was immobilized on CNBr-activated Sepharose 4B,The kinetic properties of immobilized luciferase were extensively studied.The Km' for D-luciferin is 11.9umol/L,the optimum pH and temperature for Sepharose-bound enzyme were 7.8 and 25℃ respectively.A luminescence fiber optic biosensor,making use of immobilized crude luciferase was developed for assay of ATP.The peak light intensity was linear with respect to ATP concentration in range of 10-9-10-5mol/L.A biological application was also demonstrated with the determination of serum ATP from rats bred in low versus normal oxygen environments.
Fiber optic biosensor of immobilized firefly luciferase
Institute of Scientific and Technical Information of China (English)
蔡谨; 孟文芳; 吉鑫松
2002-01-01
Luciferase from firefly lantern extract was immobilized on CNBr-activated Sepharose 4B. The kinetic properties of immobilized luciferase were extensively studied. The Km′ for D-luciferin is 11.9 μmol/L, the optimum pH and temperature for Sepharose-bound enzyme were 7.8 and 25℃ respectively. A luminescence fiber optic biosensor, making use of immobilized crude luciferase, was developed for assay of ATP. The peak light intensity was linear with respect to ATP concentration in range of 10-9-10-5 mol/L. A biological application was also demonstrated with the determination of serum ATP from rats bred in low versus normal oxygen environments.
Proposing an Algorithm for R&Q Inventory Control Model with Stochastic Demand Influenced by Shortage
Directory of Open Access Journals (Sweden)
Parviz fattahi
2013-08-01
Full Text Available In this article, the continuous - review inventory control system has been studied. A new constraint of demand dependent on the average percent of product shortage has been added to the problem. It means that the average demand has a direct relationship with shortage in a period. This constraint, which is related to the costs of credit loss of the organization due to product shortage, has been considered in the inventory model. In this paper, the mathematical model of this problem has been presented and then, two heuristic approaches based on the genetic and simulated annealing algorithms are developed. Computational results indicate that the simulated annealing algorithm can provide better results compare to the genetic algorithm.
Energy Technology Data Exchange (ETDEWEB)
Atanassov, E.; Dimitrov, D., E-mail: d.slavov@bas.bg, E-mail: emanouil@parallel.bas.bg, E-mail: gurov@bas.bg; Gurov, T. [Institute of Information and Communication Technologies, BAS, Acad. G. Bonchev str., bl. 25A, 1113 Sofia (Bulgaria)
2015-10-28
The recent developments in the area of high-performance computing are driven not only by the desire for ever higher performance but also by the rising costs of electricity. The use of various types of accelerators like GPUs, Intel Xeon Phi has become mainstream and many algorithms and applications have been ported to make use of them where available. In Financial Mathematics the question of optimal use of computational resources should also take into account the limitations on space, because in many use cases the servers are deployed close to the exchanges. In this work we evaluate various algorithms for option pricing that we have implemented for different target architectures in terms of their energy and space efficiency. Since it has been established that low-discrepancy sequences may be better than pseudorandom numbers for these types of algorithms, we also test the Sobol and Halton sequences. We present the raw results, the computed metrics and conclusions from our tests.
Cardot, Hervé; Zitt, Pierre-André
2011-01-01
With the progress of measurement apparatus and the development of automatic sensors it is not unusual anymore to get thousands of samples of observations taking values in high dimension spaces such as functional spaces. In such large samples of high dimensional data, outlying curves may not be uncommon and even a few individuals may corrupt simple statistical indicators such as the mean trajectory. We focus here on the estimation of the geometric median which is a direct generalization of the real median and has nice robustness properties. The geometric median being defined as the minimizer of a simple convex functional that is differentiable everywhere when the distribution has no atoms, it is possible to estimate it with online gradient algorithms. Such algorithms are very fast and can deal with large samples. Furthermore they also can be simply updated when the data arrive sequentially. We state the almost sure consistency and the L2 rates of convergence of the stochastic gradient estimator as well as the ...
Ivanova, Violeta M.; Sousa, Rita; Murrihy, Brian; Einstein, Herbert H.
2014-06-01
This paper presents results from research conducted at MIT during 2010-2012 on modeling of natural rock fracture systems with the GEOFRAC three-dimensional stochastic model. Following a background summary of discrete fracture network models and a brief introduction of GEOFRAC, the paper provides a thorough description of the newly developed mathematical and computer algorithms for fracture intensity, aperture, and intersection representation, which have been implemented in MATLAB. The new methods optimize, in particular, the representation of fracture intensity in terms of cumulative fracture area per unit volume, P32, via the Poisson-Voronoi Tessellation of planes into polygonal fracture shapes. In addition, fracture apertures now can be represented probabilistically or deterministically whereas the newly implemented intersection algorithms allow for computing discrete pathways of interconnected fractures. In conclusion, results from a statistical parametric study, which was conducted with the enhanced GEOFRAC model and the new MATLAB-based Monte Carlo simulation program FRACSIM, demonstrate how fracture intensity, size, and orientations influence fracture connectivity.
User extensibility of the Firefly astronomical visualization software
Dubois-Felsmann, Gregory P.; Goldina, Tatiana; Ly, Loi; Roby, William; Wu, Xiuqin; Zhang, Lijun
2016-01-01
We have developed mechanisms for extending the functionality of the open-source Firefly astronomical visualization software with user-supplied code. Firefly is a toolkit for the construction of Web-based applications for visualizing astronomical images and tabular data, with the software distribution also including a basic general-purpose pre-built application. The Firefly tools are the base for NASA's IRSA archive as well as other web applications developed at IPAC.Recent releases include new public APIs allowing the extension of Firefly functionality in various ways. New Javascript APIs allow customization of the interface presented in the browser, including the ability to define buttons for custom actions that can be performed on points, lines, and regions in images. New Python APIs allow the invocation of operations in a Firefly-based application, allowing it to serve as a display engine for FITS images and other astronomical data. In addition, the Firefly web server side has been enhanced with the ability to invoke user-supplied processes that can produce either image or tabular results based on operations on data from the application or external sources. For instance, the user can define an operation to perform source detection on a graphically selected region in an image and return the results for display as a table and/or x-y plot. User processes can be defined in any language supported on the server host; our current efforts have focused on Python. This mechanism has been used to support the integration of Firefly with the LSST project's software stack, with reusable "tasks" from the LSST stack configurable as extensions to Firefly.
Why is firefly oxyluciferin a notoriously labile substance?
Maltsev, Oleg V; Nath, Naba K; Naumov, Panče; Hintermann, Lukas
2014-01-13
The chemistry of firefly bioluminescence is important for numerous applications in biochemistry and analytical chemistry. The emitter of this bioluminescent system, firefly oxyluciferin, is difficult to handle. The cause of its lability was clarified while its synthesis was reinvestigated. A side product was identified and characterized by NMR spectroscopy and X-ray crystallography. The reason for the lability of oxyluciferin is now ascribed to autodimerization of the coexisting enol and keto forms in a Mannich-type reaction.
Directory of Open Access Journals (Sweden)
Maxim Ioan
2009-05-01
Full Text Available In our paper we build a reccurence from generalized Garman equation and discretization of 3-dimensional domain. From reccurence we build an algorithm for computing values of an option based on time, momentan volatility of support and value of support on a
Firefly as a novel swarm intelligence variable selection method in spectroscopy.
Goodarzi, Mohammad; dos Santos Coelho, Leandro
2014-12-10
A critical step in multivariate calibration is wavelength selection, which is used to build models with better prediction performance when applied to spectral data. Up to now, many feature selection techniques have been developed. Among all different types of feature selection techniques, those based on swarm intelligence optimization methodologies are more interesting since they are usually simulated based on animal and insect life behavior to, e.g., find the shortest path between a food source and their nests. This decision is made by a crowd, leading to a more robust model with less falling in local minima during the optimization cycle. This paper represents a novel feature selection approach to the selection of spectroscopic data, leading to more robust calibration models. The performance of the firefly algorithm, a swarm intelligence paradigm, was evaluated and compared with genetic algorithm and particle swarm optimization. All three techniques were coupled with partial least squares (PLS) and applied to three spectroscopic data sets. They demonstrate improved prediction results in comparison to when only a PLS model was built using all wavelengths. Results show that firefly algorithm as a novel swarm paradigm leads to a lower number of selected wavelengths while the prediction performance of built PLS stays the same.
Community Detection Algorithm Combining Stochastic Block Model and Attribute Data Clustering
Kataoka, Shun; Kobayashi, Takuto; Yasuda, Muneki; Tanaka, Kazuyuki
2016-11-01
We propose a new algorithm to detect the community structure in a network that utilizes both the network structure and vertex attribute data. Suppose we have the network structure together with the vertex attribute data, that is, the information assigned to each vertex associated with the community to which it belongs. The problem addressed this paper is the detection of the community structure from the information of both the network structure and the vertex attribute data. Our approach is based on the Bayesian approach that models the posterior probability distribution of the community labels. The detection of the community structure in our method is achieved by using belief propagation and an EM algorithm. We numerically verified the performance of our method using computer-generated networks and real-world networks.
Community Detection Algorithm Combining Stochastic Block Model and Attribute Data Clustering
Kataoka, Shun; Yasuda, Muneki; Tanaka, Kazuyuki
2016-01-01
We propose a new algorithm to detect the community structure in a network that utilizes both the network structure and vertex attribute data. Suppose we have the network structure together with the vertex attribute data, that is, the information assigned to each vertex associated with the community to which it belongs. The problem addressed this paper is the detection of the community structure from the information of both the network structure and the vertex attribute data. Our approach is based on the Bayesian approach that models the posterior probability distribution of the community labels. The detection of the community structure in our method is achieved by using belief propagation and an EM algorithm. We numerically verified the performance of our method using computer-generated networks and real-world networks.
On Convergence of the Nelder-Mead Simplex Algorithm for Unconstrained Stochastic Optimization
1995-05-01
the best point. Reklaitis , Ravindran, and Ragsdell (1983) further classify direct-search methods into two classes: heuristic techniques and...restricted conditions" ( Reklaitis et al., 1983, p. 75). The Nelder-Mead algorithm is a heuristic direct-search method. An example of a theoretically...Pressure Liquid Cliromatography. Analytica Clumica Ada, 93, 211-219. Reklaitis , G. V., Ravindran, A., & Ragsdell, K. M. (1983). Engineering
2012-03-01
Rao discusses the most common gradient search algorithms in [71] along with some heuristic ones that use some added randomness to direct the search... aircrews and mission planners who have interest in this type of problem. 98 Target T=Threat T x1 x2 B ou nd ar y B ou nd ar y T θ V (tf , x1(tf ), x2(tf...processing. The most valuable product to mission planners and aircrews is not the set of statistics describing expected values, variances, and covariances and
A multispin algorithm for the Kob-Andersen stochastic dynamics on regular lattices
Boccagna, Roberto
2017-07-01
The aim of the paper is to propose an algorithm based on the Multispin Coding technique for the Kob-Andersen glassy dynamics. We first give motivations to speed up the numerical simulation in the context of spin glass models [M. Mezard, G. Parisi, M. Virasoro, Spin Glass Theory and Beyond (World Scientific, Singapore, 1987)]; after defining the Markovian dynamics as in [W. Kob, H.C. Andersen, Phys. Rev. E 48, 4364 (1993)] as well as the related interesting observables, we extend it to the more general framework of random regular graphs, listing at the same time some known analytical results [C. Toninelli, G. Biroli, D.S. Fisher, J. Stat. Phys. 120, 167 (2005)]. The purpose of this work is a dual one; firstly, we describe how bitwise operators can be used to build up the algorithm by carefully exploiting the way data are stored on a computer. Since it was first introduced [M. Creutz, L. Jacobs, C. Rebbi, Phys. Rev. D 20, 1915 (1979); C. Rebbi, R.H. Swendsen, Phys. Rev. D 21, 4094 (1980)], this technique has been widely used to perform Monte Carlo simulations for Ising and Potts spin systems; however, it can be successfully adapted to more complex systems in which microscopic parameters may assume boolean values. Secondly, we introduce a random graph in which a characteristic parameter allows to tune the possible transition point. A consistent part is devoted to listing the numerical results obtained by running numerical simulations.
An exact algorithm for the vehicle routing problem with stochastic demands
Energy Technology Data Exchange (ETDEWEB)
Louveaux, F.
1994-12-31
The classical deterministic Vehicle Routing Problem (VRP) can be defined as follows. Let G = (V, E) be an undirected graph where V = {l_brace}v{sub 1}, {center_dot}{center_dot}, v{sub n}{r_brace} is a set of vertices representing cities or customers, and E = {l_brace}(v{sub i}, V{sub j}) : i < j; v{sub i}, v{sub j} {element_of} V{r_brace} is an edge set. With each vertex v{sub i}(i {>=} 2) (v{sub i}, v{sub j}) is associated a non-negative cost (distance, travel time) c{sub ij}. Vertex v{sub 1} represents a depot at which are based m identical vehicle of capacity Q > 0. Depending on the version of the problem considered, the value of m is either fixed, or bounded above by a constant {<=} m. The VRP consists of determining vehicle routes in such a way that (i) all routes start and end at the depot; (ii) each vertex other than the depot is visited exactly once; (iii) the total demand of any given route does not exceed Q; (iv) the total distance traveled by all vehicles is minimized. In the Stochastic Vehicle Routing Problem (SVRP), the demand associated with vertex v{sub i} is a random variable {xi}{sub i}. As a result, it is no longer possible to assume that vehicle routes may be followed as planned. The SRVP is modeled in two stages. In the first stage, a priori vehicle routes satisyfing conditions (i) and (ii) are constructed, without full information on the demands. In the second stage, when this information becomes available, routes are followed as planned, until the accumulated demand attains or exceeds the vehicle capacity. In this case, failure is said to occur and a recourse action is taken: the vehicle returns to the depot to unload, and resumes its visits at the point of failure. The SVRP consists of determining an a priori set of routes so as to minimize the expected cost of the second stage solution. The corresponding model can be solved using the relaxation approach in Laporte and Louveaux.
Crystal structure of firefly luciferase throws light on a superfamily of adenylate-forming enzymes
National Research Council Canada - National Science Library
Conti, Elena; Franks, Nick P; Brick, Peter
1996-01-01
Firefly luciferase is a 62 kDa protein that catalyzes the production of light. In the presence of MgATP and molecular oxygen, the enzyme oxidizes its substrate, firefly luciferin, emitting yellow-green light...
Directory of Open Access Journals (Sweden)
Tarek H. M. Abou-El-Enien
2015-04-01
Full Text Available This paper extended TOPSIS (Technique for Order Preference by Similarity Ideal Solution method for solving Two-Level Large Scale Linear Multiobjective Optimization Problems with Stochastic Parameters in the righthand side of the constraints (TL-LSLMOP-SPrhs of block angular structure. In order to obtain a compromise ( satisfactory solution to the (TL-LSLMOP-SPrhs of block angular structure using the proposed TOPSIS method, a modified formulas for the distance function from the positive ideal solution (PIS and the distance function from the negative ideal solution (NIS are proposed and modeled to include all the objective functions of the two levels. In every level, as the measure of ―Closeness‖ dp-metric is used, a k-dimensional objective space is reduced to two –dimentional objective space by a first-order compromise procedure. The membership functions of fuzzy set theory is used to represent the satisfaction level for both criteria. A single-objective programming problem is obtained by using the max-min operator for the second –order compromise operaion. A decomposition algorithm for generating a compromise ( satisfactory solution through TOPSIS approach is provided where the first level decision maker (FLDM is asked to specify the relative importance of the objectives. Finally, an illustrative numerical example is given to clarify the main results developed in the paper.
Varouchakis, Emmanouil
2017-04-01
Reliable temporal modelling of groundwater level is significant for efficient water resources management in hydrological basins and for the prevention of possible desertification effects. In this work we propose a stochastic data driven approach of temporal monitoring and prediction that can incorporate auxiliary information. More specifically, we model the temporal (mean annual and biannual) variation of groundwater level by means of a discrete time autoregressive exogenous variable model (ARX model). The ARX model parameters and its predictions are estimated by means of the Kalman filter adaptation algorithm (KFAA). KFAA is suitable for sparsely monitored basins that do not allow for an independent estimation of the ARX model parameters. Three new modified versions of the original form of the ARX model are proposed and investigated: the first considers a larger time scale, the second a larger time delay in terms of the groundwater level input and the third considers the groundwater level difference between the last two hydrological years, which is incorporated in the model as a third input variable. We apply KFAA to time series of groundwater level values from Mires basin in the island of Crete. In addition to precipitation measurements, we use pumping data as exogenous variables. We calibrate the ARX model based on the groundwater level for the years 1981 to 2006 and use it to successfully predict the mean annual and biannual groundwater level for recent years (2007-2010).
Halko, Nathan; Tropp, Joel A
2009-01-01
Low-rank matrix approximations, such as the truncated singular value decomposition and the rank-revealing QR decomposition, play a central role in data analysis and scientific computing. This work surveys recent research which demonstrates that \\emph{randomization} offers a powerful tool for performing low-rank matrix approximation. These techniques exploit modern computational architectures more fully than classical methods and open the possibility of dealing with truly massive data sets. In particular, these techniques offer a route toward principal component analysis (PCA) for petascale data. This paper presents a modular framework for constructing randomized algorithms that compute partial matrix decompositions. These methods use random sampling to identify a subspace that captures most of the action of a matrix. The input matrix is then compressed - either explicitly or implicitly - to this subspace, and the reduced matrix is manipulated deterministically to obtain the desired low-rank factorization. In ...
Sparse adaptive Taylor approximation algorithms for parametric and stochastic elliptic PDEs
Chkifa, Abdellah
2012-11-29
The numerical approximation of parametric partial differential equations is a computational challenge, in particular when the number of involved parameter is large. This paper considers a model class of second order, linear, parametric, elliptic PDEs on a bounded domain D with diffusion coefficients depending on the parameters in an affine manner. For such models, it was shown in [9, 10] that under very weak assumptions on the diffusion coefficients, the entire family of solutions to such equations can be simultaneously approximated in the Hilbert space V = H0 1(D) by multivariate sparse polynomials in the parameter vector y with a controlled number N of terms. The convergence rate in terms of N does not depend on the number of parameters in V, which may be arbitrarily large or countably infinite, thereby breaking the curse of dimensionality. However, these approximation results do not describe the concrete construction of these polynomial expansions, and should therefore rather be viewed as benchmark for the convergence analysis of numerical methods. The present paper presents an adaptive numerical algorithm for constructing a sequence of sparse polynomials that is proved to converge toward the solution with the optimal benchmark rate. Numerical experiments are presented in large parameter dimension, which confirm the effectiveness of the adaptive approach. © 2012 EDP Sciences, SMAI.
Directory of Open Access Journals (Sweden)
Yuichi Oba
Full Text Available BACKGROUND: Bioluminescence in fireflies and click beetles is produced by a luciferase-luciferin reaction. The luminescence property and protein structure of firefly luciferase have been investigated, and its cDNA has been used for various assay systems. The chemical structure of firefly luciferin was identified as the D-form in 1963 and studies on the biosynthesis of firefly luciferin began early in the 1970's. Incorporation experiments using (14C-labeled compounds were performed, and cysteine and benzoquinone/hydroquinone were proposed to be biosynthetic component for firefly luciferin. However, there have been no clear conclusions regarding the biosynthetic components of firefly luciferin over 30 years. METHODOLOGY/PRINCIPAL FINDINGS: Incorporation studies were performed by injecting stable isotope-labeled compounds, including L-[U-(13C3]-cysteine, L-[1-(13C]-cysteine, L-[3-(13C]-cysteine, 1,4-[D6]-hydroquinone, and p-[2,3,5,6-D]-benzoquinone, into the adult lantern of the living Japanese firefly Luciola lateralis. After extracting firefly luciferin from the lantern, the incorporation of stable isotope-labeled compounds into firefly luciferin was identified by LC/ESI-TOF-MS. The positions of the stable isotope atoms in firefly luciferin were determined by the mass fragmentation of firefly luciferin. CONCLUSIONS: We demonstrated for the first time that D- and L-firefly luciferins are biosynthesized in the lantern of the adult firefly from two L-cysteine molecules with p-benzoquinone/1,4-hydroquinone, accompanied by the decarboxylation of L-cysteine.
Octopamine: presence in firefly lantern suggests a transmitter role.
Robertson, H A; Carlson, A D
1976-01-01
The lantern of the adult firefly is innervated by a nerve supply with endings similar to other known monoaminergic nerves. However, catecholamines could not be detected in the nerve endings and noradrenaline and adrenaline were relatively weak agonists in eliciting a flash of light when applied to the lantern in vitro. Firefly lanterns were assayed for octopamine using a sensitive, specific enzymatic assay in which octopamine is enzymatically converted to 3H-synephrine and quantitated by liquid scintillation counting. The presence of substantial amounts (125 ng/g tissue) of octopamine was demonstrated and confirmed by thin layer chromatography. On the basis of this and earlier work, it is suggested that the nerves to the firefly lantern are octopaminergic.
Evaluation of the performance of different firefly algorithms to the ...
African Journals Online (AJOL)
3Department of Electrical Power and Machines, Faculty of Engineering, Ain ... DOI: http://dx.doi.org/10.4314/ijest.v9i2.1. 1. ... In this paper, these three variants are applied to solve the nonlinear ELD problem. ..... Learning and Computing, Vol.
Directory of Open Access Journals (Sweden)
Milinkovitch Michel C
2010-07-01
Full Text Available Abstract Background The development, in the last decade, of stochastic heuristics implemented in robust application softwares has made large phylogeny inference a key step in most comparative studies involving molecular sequences. Still, the choice of a phylogeny inference software is often dictated by a combination of parameters not related to the raw performance of the implemented algorithm(s but rather by practical issues such as ergonomics and/or the availability of specific functionalities. Results Here, we present MetaPIGA v2.0, a robust implementation of several stochastic heuristics for large phylogeny inference (under maximum likelihood, including a Simulated Annealing algorithm, a classical Genetic Algorithm, and the Metapopulation Genetic Algorithm (metaGA together with complex substitution models, discrete Gamma rate heterogeneity, and the possibility to partition data. MetaPIGA v2.0 also implements the Likelihood Ratio Test, the Akaike Information Criterion, and the Bayesian Information Criterion for automated selection of substitution models that best fit the data. Heuristics and substitution models are highly customizable through manual batch files and command line processing. However, MetaPIGA v2.0 also offers an extensive graphical user interface for parameters setting, generating and running batch files, following run progress, and manipulating result trees. MetaPIGA v2.0 uses standard formats for data sets and trees, is platform independent, runs in 32 and 64-bits systems, and takes advantage of multiprocessor and multicore computers. Conclusions The metaGA resolves the major problem inherent to classical Genetic Algorithms by maintaining high inter-population variation even under strong intra-population selection. Implementation of the metaGA together with additional stochastic heuristics into a single software will allow rigorous optimization of each heuristic as well as a meaningful comparison of performances among these
Energy Technology Data Exchange (ETDEWEB)
Cheng, Mulin, E-mail: mulinch@caltech.edu; Hou, Thomas Y., E-mail: hou@cms.caltech.edu; Zhang, Zhiwen, E-mail: zhangzw@caltech.edu
2013-06-01
We propose a dynamically bi-orthogonal method (DyBO) to solve time dependent stochastic partial differential equations (SPDEs). The objective of our method is to exploit some intrinsic sparse structure in the stochastic solution by constructing the sparsest representation of the stochastic solution via a bi-orthogonal basis. It is well-known that the Karhunen–Loeve expansion (KLE) minimizes the total mean squared error and gives the sparsest representation of stochastic solutions. However, the computation of the KL expansion could be quite expensive since we need to form a covariance matrix and solve a large-scale eigenvalue problem. The main contribution of this paper is that we derive an equivalent system that governs the evolution of the spatial and stochastic basis in the KL expansion. Unlike other reduced model methods, our method constructs the reduced basis on-the-fly without the need to form the covariance matrix or to compute its eigendecomposition. In the first part of our paper, we introduce the derivation of the dynamically bi-orthogonal formulation for SPDEs, discuss several theoretical issues, such as the dynamic bi-orthogonality preservation and some preliminary error analysis of the DyBO method. We also give some numerical implementation details of the DyBO methods, including the representation of stochastic basis and techniques to deal with eigenvalue crossing. In the second part of our paper [11], we will present an adaptive strategy to dynamically remove or add modes, perform a detailed complexity analysis, and discuss various generalizations of this approach. An extensive range of numerical experiments will be provided in both parts to demonstrate the effectiveness of the DyBO method.
He, Xiaojun; Ma, Haotong; Luo, Chuanxin
2016-10-01
The optical multi-aperture imaging system is an effective way to magnify the aperture and increase the resolution of telescope optical system, the difficulty of which lies in detecting and correcting of co-phase error. This paper presents a method based on stochastic parallel gradient decent algorithm (SPGD) to correct the co-phase error. Compared with the current method, SPGD method can avoid detecting the co-phase error. This paper analyzed the influence of piston error and tilt error on image quality based on double-aperture imaging system, introduced the basic principle of SPGD algorithm, and discuss the influence of SPGD algorithm's key parameters (the gain coefficient and the disturbance amplitude) on error control performance. The results show that SPGD can efficiently correct the co-phase error. The convergence speed of the SPGD algorithm is improved with the increase of gain coefficient and disturbance amplitude, but the stability of the algorithm reduced. The adaptive gain coefficient can solve this problem appropriately. This paper's results can provide the theoretical reference for the co-phase error correction of the multi-aperture imaging system.
Rolland, J; Simonnet, E
2015-01-01
In this paper we compute and analyse the transition rates and duration of reactive trajectories of the stochastic 1-D Allen-Cahn equations for both the Freidlin-Wentzell regime (weak noise or temperature limit) and finite-amplitude white noise, as well as for small and large domain. We demonstrate that extremely rare reactive trajectories corresponding to direct transitions between two metastable states are efficiently computed using an algorithm called adaptive multilevel splitting. This algorithm is dedicated to the computation of rare events and is able to provide ensembles of reactive trajectories in a very efficient way. In the small noise limit, our numerical results are in agreement with large-deviation predictions such as instanton-like solutions, mean first passages and escape probabilities. We show that the duration of reactive trajectories follows a Gumbel distribution like for one degree of freedom systems. Moreover, the mean duration growths logarithmically with the inverse temperature. The prefa...
between-home and between-city variability in residential pollutant infiltration. This is likely a result of differences in home ventilation, or air exchange rates (AER). The Stochastic Human Exposure and Dose Simulation (SHEDS) model is a population exposure model that uses a pro...
Transient transfection and expression of firefly luciferase in Giardia lamblia.
1995-01-01
We have developed a gene transfer system for the protozoan parasite Giardia lamblia. This organism is responsible for many cases of diarrhea worldwide and is considered to be one of the most primitive eukaryotes. Expression of a heterologous gene was detected in this parasite after electroporation with appropriate DNA constructs. We constructed a series of transfection plasmids using flanking sequences of the Giardia glutamate dehydrogenase (GDH) gene to drive expression of the firefly lucife...
Excited-State Dynamics of Oxyluciferin in Firefly Luciferase
Snellenburg, Joris J.
2016-11-23
The color variations of light emitted by some natural and mutant luciferases are normally attributed to collective factors referred to as microenvironment effects; however, the exact nature of these interactions between the emitting molecule (oxyluciferin) and the active site remains elusive. Although model studies of noncomplexed oxyluciferin and its variants have greatly advanced the understanding of its photochemistry, extrapolation of the conclusions to the real system requires assumptions about the polarity and proticity of the active site. To decipher the intricate excited-state dynamics, global and target analysis is performed here for the first time on the steady-state and time-resolved spectra of firefly oxyluciferin complexed with luciferase from the Japanese firefly (Luciola cruciata). The experimental steady-state and time resolved luminescence spectra of the oxyluciferin/luciferase complex in solution are compared with the broadband time-resolved firefly bioluminescence recorded in vivo. The results demonstrate that de-excitation of the luminophore results in a complex cascade of photoinduced proton transfer processes and can be interpreted by the pH dependence of the emitted light. It is confirmed that proton transfer is the central event in the spectrochemistry of this system for which any assignment of the pH dependent emission to a single chemical species would be an oversimplification.
自适应随机共振算法用于微弱信号检测%Application of Adaptive Stochastic Resonance Algorithm in Weak Signal Detection
Institute of Scientific and Technical Information of China (English)
王利亚; 印春生; 蔡文生; 潘忠孝
2001-01-01
An adaptive algorithm for extracting weak signal based on the theory of stochastic resonance(SR) was proposed. It was implemented by investigating the self-correlation of the outputs of the bistable system(with different μ). The results show that the self-correlation will reach maximum when a sudden change of output occurs, and hence prove that SR is sensitive sharply to the change of the parameters μ, and also provide one novel method to express the outputs which hasnt been solved till now. The adaptive algorithm was also applied to analyze the weak Laser-Raman spectrum of a CCl4 sample(liquid in capillary) which was measured with SPEX-1403 Laser-Raman spectrometer from 250 cm-1 to 418 cm-1 on 5 mW(output power of laser) . The spectrum was analyzed with the adaptive algorithm by varying the data to (-4.13, 3.50) and declining μ from 2.0 with step 0.01. The results obtained show that the adaptive algorithm was able to detect the weak signal automatically and correctly.
Bin Liu
2012-01-01
The wagon flow scheduling plays a very important role in transportation activities in railway bureau. However, it is difficult to implement in the actual decision-making process of wagon flow scheduling that compiled under certain environment, because of the interferences of uncertain information, such as train arrival time, train classify time, train assemble time, and flexible train-size limitation. Based on existing research results, considering the stochasticity of all kinds of train oper...
Institute of Scientific and Technical Information of China (English)
李玲; 凌晨
2014-01-01
研究含有限样本的随机非线性互补问题的数值求解方法。在将问题等价转化为非线性方程组的基础上，给出一个光滑化Levenberg-Marquardt算法。该算法具有全局收敛性，并在局部误差界条件下，还拥有局部的二次收敛性质。所做的数值例子结果表明，所给算法具有较好的实际计算效果。%A class of stochastic nonlinear complementary problems ( SNCP) with finite elements is studied in this paper .Based upon converting the original problem to nonlinear equations equally , a smoothing Levenberg-Marquardt algorithm for solving SNCP is presented , which is shown to be globally convergent .Moreover, this algorithm is shown to converge locally quadratically under an error bound condition .Some numerical results show that presented algorithm has good actual properties .
Chow, Sy-Miin; Lu, Zhaohua; Sherwood, Andrew; Zhu, Hongtu
2016-03-01
The past decade has evidenced the increased prevalence of irregularly spaced longitudinal data in social sciences. Clearly lacking, however, are modeling tools that allow researchers to fit dynamic models to irregularly spaced data, particularly data that show nonlinearity and heterogeneity in dynamical structures. We consider the issue of fitting multivariate nonlinear differential equation models with random effects and unknown initial conditions to irregularly spaced data. A stochastic approximation expectation-maximization algorithm is proposed and its performance is evaluated using a benchmark nonlinear dynamical systems model, namely, the Van der Pol oscillator equations. The empirical utility of the proposed technique is illustrated using a set of 24-h ambulatory cardiovascular data from 168 men and women. Pertinent methodological challenges and unresolved issues are discussed.
Directory of Open Access Journals (Sweden)
Bailing Liu
2015-01-01
Full Text Available Facility location, inventory control, and vehicle routes scheduling are three key issues to be settled in the design of logistics system for e-commerce. Due to the online shopping features of e-commerce, customer returns are becoming much more than traditional commerce. This paper studies a three-phase supply chain distribution system consisting of one supplier, a set of retailers, and a single type of product with continuous review (Q, r inventory policy. We formulate a stochastic location-inventory-routing problem (LIRP model with no quality defects returns. To solve the NP-hand problem, a pseudo-parallel genetic algorithm integrating simulated annealing (PPGASA is proposed. The computational results show that PPGASA outperforms GA on optimal solution, computing time, and computing stability.
Roslan, Norzeana; Sulaiman, Norela
2015-09-01
This study was conducted to study the firefly species found in Tasik Chini, the soil factors that suitable for larval development fireflies flashes, and the sociological aspects of the community's availability to engage in firefly ecotourism. This was achieved through firefly sampling, soil analysis, abiotic data collection and by questionnaire surveys from local community perceptions and knowledge on fireflies and ecotourism. Fireflies sampling were conducted from December 2011 to January 2013 at Kampung Melai and Kampung Cenahan. Three non-synchronize fireflies genus were found, namely Colophotia sp., Pygoluciola sp., and Pyrocoelia sp. A total of 25 questionnaires were given to four groups of respondents consisting orang asli (5 respondents), boat operator (2 respondents), resort workers (5 respondents) and FELDA residents (13 respondents). The questionnaires were analysed using Rasch Winstep Software based on Rasch Measurement Model. Results of the survey indicated that the local community was not ready for ecotourism in their area. Meanwhile, the soil pH was very acidic and the heavy metals concentration was high, which is not good for the development of firefly larvae. In conclusion, Tasik Chini was not having the potential for ecotourism. Despite the fact, improvement of soils with soil remediation methods can be apply for enhancing larvae development and having more awareness campaign of ecotourism to local community.
Building Twilight "Light Sensors" to Study the Effects of Light Pollution on Fireflies
Thancharoen, Anchana; Branham, Marc A.; Lloyd, James E.
2008-01-01
Light pollution negatively affects many nocturnal organisms. We outline two experiments that can be conducted by students to examine the effects of light pollution on firefly behavior. Inexpensive electronic light sensors, which are easy to construct and calibrate, are used to sample light levels along transects in spaces where fireflies are…
Search for an optimal light-extracting surface derived from the morphology of a firefly lantern
Bay, Annick; Sarrazin, Michaël; Vigneron, Jean Pol
2013-02-01
Fireflies light up our warm summer evenings. There is more physics behind these little animals than any one of us could imagine. We analyze from a physical point of view one structure found on the firefly lantern, the one which best improves light extraction. Moreover, simulations will be done to show why this specific structure may be more effective than one designed by humans.
A New Metaheuristic Bat-Inspired Algorithm
Yang, Xin-She
2010-01-01
Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed.
Directory of Open Access Journals (Sweden)
James E Skinner
2009-08-01
Full Text Available James E Skinner1, Michael Meyer2, Brian A Nester3, Una Geary4, Pamela Taggart4, Antoinette Mangione4, George Ramalanjaona5, Carol Terregino6, William C Dalsey41Vicor Technologies, Inc., Boca Raton, FL, USA; 2Max Planck Institute for Experimental Physiology, Goettingen, Germany; 3Lehigh Valley Hospital, Allentown, PA, USA; 4Albert Einstein Medical Center, Philadelphia, PA, USA; 5North Shore University Hospital, Plainview, NY, USA; 6Cooper Medical Center, Camden, NJ, USAObjective: Comparative algorithmic evaluation of heartbeat series in low-to-high risk cardiac patients for the prospective prediction of risk of arrhythmic death (AD.Background: Heartbeat variation reflects cardiac autonomic function and risk of AD. Indices based on linear stochastic models are independent risk factors for AD in post-myocardial infarction (post-MI cohorts. Indices based on nonlinear deterministic models have superior predictability in retrospective data.Methods: Patients were enrolled (N = 397 in three emergency departments upon presenting with chest pain and were determined to be at low-to-high risk of acute MI (>7%. Brief ECGs were recorded (15 min and R-R intervals assessed by three nonlinear algorithms (PD2i, DFA, and ApEn and four conventional linear-stochastic measures (SDNN, MNN, 1/f-Slope, LF/HF. Out-of-hospital AD was determined by modified Hinkle–Thaler criteria.Results: All-cause mortality at one-year follow-up was 10.3%, with 7.7% adjudicated to be AD. The sensitivity and relative risk for predicting AD was highest at all time-points for the nonlinear PD2i algorithm (p ≤ 0.001. The sensitivity at 30 days was 100%, specificity 58%, and relative risk >100 (p ≤ 0.001; sensitivity at 360 days was 95%, specificity 58%, and relative risk >11.4 (p ≤ 0.001.Conclusions: Heartbeat analysis by the time-dependent nonlinear PD2i algorithm is comparatively the superior test.Keywords: autonomic nervous system, regulatory systems, electrophysiology, heart rate
Simulation of Stochastic Partial Differential Equations and Stochastic Active Contours
Lang, Annika
2007-01-01
This thesis discusses several aspects of the simulation of stochastic partial differential equations. First, two fast algorithms for the approximation of infinite dimensional Gaussian random fields with given covariance are introduced. Later Hilbert space-valued Wiener processes are constructed out of these random fields. A short introduction to infinite-dimensional stochastic analysis and stochastic differential equations is given. Furthermore different definitions of numerical stability for...
Didari, Azadeh; Pinar Mengüç, M.
2017-08-01
Advances in nanotechnology and nanophotonics are inextricably linked with the need for reliable computational algorithms to be adapted as design tools for the development of new concepts in energy harvesting, radiative cooling, nanolithography and nano-scale manufacturing, among others. In this paper, we provide an outline for such a computational tool, named NF-RT-FDTD, to determine the near-field radiative transfer between structured surfaces using Finite Difference Time Domain method. NF-RT-FDTD is a direct and non-stochastic algorithm, which accounts for the statistical nature of the thermal radiation and is easily applicable to any arbitrary geometry at thermal equilibrium. We present a review of the fundamental relations for far- and near-field radiative transfer between different geometries with nano-scale surface and volumetric features and gaps, and then we discuss the details of the NF-RT-FDTD formulation, its application to sample geometries and outline its future expansion to more complex geometries. In addition, we briefly discuss some of the recent numerical works for direct and indirect calculations of near-field thermal radiation transfer, including Scattering Matrix method, Finite Difference Time Domain method (FDTD), Wiener Chaos Expansion, Fluctuating Surface Current (FSC), Fluctuating Volume Current (FVC) and Thermal Discrete Dipole Approximations (TDDA).
Mignon, David; Simonson, Thomas
2016-07-15
Computational protein design depends on an energy function and an algorithm to search the sequence/conformation space. We compare three stochastic search algorithms: a heuristic, Monte Carlo (MC), and a Replica Exchange Monte Carlo method (REMC). The heuristic performs a steepest-descent minimization starting from thousands of random starting points. The methods are applied to nine test proteins from three structural families, with a fixed backbone structure, a molecular mechanics energy function, and with 1, 5, 10, 20, 30, or all amino acids allowed to mutate. Results are compared to an exact, "Cost Function Network" method that identifies the global minimum energy conformation (GMEC) in favorable cases. The designed sequences accurately reproduce experimental sequences in the hydrophobic core. The heuristic and REMC agree closely and reproduce the GMEC when it is known, with a few exceptions. Plain MC performs well for most cases, occasionally departing from the GMEC by 3-4 kcal/mol. With REMC, the diversity of the sequences sampled agrees with exact enumeration where the latter is possible: up to 2 kcal/mol above the GMEC. Beyond, room temperature replicas sample sequences up to 10 kcal/mol above the GMEC, providing thermal averages and a solution to the inverse protein folding problem. © 2016 Wiley Periodicals, Inc.
Stochastic volatility and stochastic leverage
DEFF Research Database (Denmark)
Veraart, Almut; Veraart, Luitgard A. M.
This paper proposes the new concept of stochastic leverage in stochastic volatility models. Stochastic leverage refers to a stochastic process which replaces the classical constant correlation parameter between the asset return and the stochastic volatility process. We provide a systematic...... treatment of stochastic leverage and propose to model the stochastic leverage effect explicitly, e.g. by means of a linear transformation of a Jacobi process. Such models are both analytically tractable and allow for a direct economic interpretation. In particular, we propose two new stochastic volatility...... models which allow for a stochastic leverage effect: the generalised Heston model and the generalised Barndorff-Nielsen & Shephard model. We investigate the impact of a stochastic leverage effect in the risk neutral world by focusing on implied volatilities generated by option prices derived from our new...
DEFF Research Database (Denmark)
Bottolo, Leonardo; Chadeau-Hyam, Marc; Hastie, David I
2013-01-01
and maximize genetic variant detection. We integrated our algorithm with a new Bayesian strategy for multi-phenotype analysis to identify the specific contribution of each SNP to different trait combinations and study genetic regulation of lipid metabolism in the Gutenberg Health Study (GHS). Despite...
A transgenic rat with ubiquitous expression of firefly luciferase gene
Hakamata, Yoji; Murakami, Takashi; Kobayashi, Eiji
2006-02-01
In vivo imaging strategies provide cellular and molecular events in real time that helps us to understand biological processes in living animals. The development of molecular tags such as green fluorescent proteins and luciferase from the firefly Photinus pyralis has lead to a revolution in the visualization of complex biochemical processes. We developed a novel inbred transgenic rat strain containing firefly luciferase based on the transgenic (Tg) technique in rats. This Tg rat expressed the luciferase gene ubiquitously under control of the ROSA26 promoter. Cellular immune responsiveness against the luciferase protein was evaluated using conventional skin grafting and resulted in the long-term acceptance of Tg rat skin on wild-type rats. Strikingly, organ transplant with heart and small bowel demonstrated organ viability and graft survival, suggesting that cells from luciferase-Tg are transplantable to track their fate. Taking advantage of the less immunogenic luciferase, we also tested the role of hepatocyte-infusion in a liver injury model, and bone marrow-derived cells in a skin defect model. Employed in conjunction with modern advances in optical imaging, this luciferase-Tg rat system provides an innovative animal tool and a new means of facilitating biomedical research such as in the case of regeneration medicine.
Accretion stream mapping with genetically modified "fire-flies"
Bridge, C M; Cropper, M; Ramsay, G
2004-01-01
We apply an eclipse mapping technique using `genetically modified fire-flies' to the eclipse light curves of HU Aqr and EP Dra. The technique makes as few assumptions as possible about the location of accretion stream material, allowing the emission to be located anywhere within the Roche lobe of the white dwarf. We model two consecutive eclipses in the UBVR_c band for HU Aqr, and four consecutive `white'-light eclipses for EP Dra, to determine the changing brightness distribution of stream material. We find fire-fly distributions which are consistent with accretion through a curtain of material in both HU Aqr and EP Dra, and show that the previously assumed two part ballistic and magnetic trajectory is a good approximation for polars. Model fits to the colour band data of HU Aqr indicate that the material confined to the magnetic field lines is brightest, and most of the emission originates from close to the white dwarf. There is evidence for emission from close to a calculated ballistic stream in both HU Aq...
Biologically inspired LED lens from cuticular nanostructures of firefly lantern.
Kim, Jae-Jun; Lee, Youngseop; Kim, Ha Gon; Choi, Ki-Ju; Kweon, Hee-Seok; Park, Seongchong; Jeong, Ki-Hun
2012-11-13
Cuticular nanostructures found in insects effectively manage light for light polarization, structural color, or optical index matching within an ultrathin natural scale. These nanostructures are mainly dedicated to manage incoming light and recently inspired many imaging and display applications. A bioluminescent organ, such as a firefly lantern, helps to out-couple light from the body in a highly efficient fashion for delivering strong optical signals in sexual communication. However, the cuticular nanostructures, except the light-producing reactions, have not been well investigated for physical principles and engineering biomimetics. Here we report a unique observation of high-transmission nanostructures on a firefly lantern and its biological inspiration for highly efficient LED illumination. Both numerical and experimental results clearly reveal high transmission through the nanostructures inspired from the lantern cuticle. The nanostructures on an LED lens surface were fabricated by using a large-area nanotemplating and reconfigurable nanomolding with heat-induced shear thinning. The biologically inspired LED lens, distinct from a smooth surface lens, substantially increases light transmission over visible ranges, comparable to conventional antireflection coating. This biological inspiration can offer new opportunities for increasing the light extraction efficiency of high-power LED packages.
Stochastic Electrochemical Kinetics
Beruski, O
2016-01-01
A model enabling the extension of the Stochastic Simulation Algorithm to electrochemical systems is proposed. The physical justifications and constraints for the derivation of a chemical master equation are provided and discussed. The electrochemical driving forces are included in the mathematical framework, and equations are provided for the associated electric responses. The implementation for potentiostatic and galvanostatic systems is presented, with results pointing out the stochastic nature of the algorithm. The electric responses presented are in line with the expected results from the theory, providing a new tool for the modeling of electrochemical kinetics.
Directory of Open Access Journals (Sweden)
Rui Zhang
2013-01-01
Full Text Available We consider a parallel machine scheduling problem with random processing/setup times and adjustable production rates. The objective functions to be minimized consist of two parts; the first part is related with the due date performance (i.e., the tardiness of the jobs, while the second part is related with the setting of machine speeds. Therefore, the decision variables include both the production schedule (sequences of jobs and the production rate of each machine. The optimization process, however, is significantly complicated by the stochastic factors in the manufacturing system. To address the difficulty, a simulation-based three-stage optimization framework is presented in this paper for high-quality robust solutions to the integrated scheduling problem. The first stage (crude optimization is featured by the ordinal optimization theory, the second stage (finer optimization is implemented with a metaheuristic called differential evolution, and the third stage (fine-tuning is characterized by a perturbation-based local search. Finally, computational experiments are conducted to verify the effectiveness of the proposed approach. Sensitivity analysis and practical implications are also discussed.
Theoretical tuning of the firefly bioluminescence spectra by the modification of oxyluciferin
Cheng, Yuan-Yuan; Zhu, Jia; Liu, Ya-Jun
2014-01-01
Extending the firefly bioluminescence is of practical significance for the improved visualization of living cells and the development of a multicolor reporter. Tuning the color of bioluminescence in fireflies mainly involves the modification of luciferase and luciferin. In this Letter, we theoretically studied the emission spectra of 9 firefly oxyluciferin analogs in the gas phase and in solutions. Three density functionals, including B3LYP, CAM-B3LYP and M06-2X, were employed to theoretically predict the efficiently luminescent analogs. The reliable functionals for calculating the targeted systems were suggested. The luminescence efficiency, solvent effects, and substituent effects are discussed based on the calculated results.
The light of the firefly under the influence of ethyl acetate
Indian Academy of Sciences (India)
Anurup Gohain Barua; Subhash Rajbongshi
2010-06-01
When a firefly is made to inhale ethyl acetate vapour, a constant glow appears after a few minutes from its abdominal lantern. This control experiment has been performed by a few workers to record the emission spectrum of the firefly. However, a time-resolved experiment performed by us on this continuous light emitted by the species Luciola praeusta Kiesenwetter 1874 (Coleoptera : Lampyridae : Luciolinae) reveals that it is composed of a continuous train of tiny pulses! The nature of the pulses suggests that an oscillatory chemical reaction continues in the microsecond time scale in the lantern of the anaesthetized firefly.
High sensitive GPS signal acquisition algorithm based on stochastic resonance%基于随机共振的高灵敏度GPS信号捕获算法
Institute of Scientific and Technical Information of China (English)
莫建文; 欧阳缮; 肖海林; 孙希延
2011-01-01
针对微弱环境下全球定位系统(global position system,GPS)信号捕获问题,提出了基于随机共振(stochastic resonance,SR)的GPS信号捕获算法.该算法首先用部分匹配滤波器对GPS信号进行分段相关预处理,然后利用SR提高预处理后信号的信噪比(signal-to-noise ratio,SNR),SR中引入了二次采样和自适应技术,解决了传统SR不适合捕获大频率信号(频率远大于1 Hz的信号)及SR参数难以与被捕获信号匹配的难题,最后采用快速傅里叶变换(fast Fourier transform,FFT)谱分析技术,实现在较短的相关累积时间内获得较高的捕获灵敏度.实验表明,算法能明显提高系统捕获性能,在相关累积时间为10 ms,信号SNR为-38 dB的情况下,算法捕获GPS信号的正确检测率达到100%.%Aiming at the acquisition problem of weak global position system (GPS) signals, a GPS signal acquisition algorithm based on stochastic resonance (SR) is proposed. After a partial-matched filter is adopted in GPS signal pre-processing, the signal-to-noise ratio (SNR) is improved by employing SR. By introducing twice sampling and adaptive techniques in SR, signals with the large frequence can be acquired, and the parameters of SR can match up with the acquired siganls. Fast Fourier transform (FFT) spectrum analysis is used to realize higher sensitive acquisition of GPS signals in shorter coherent integration time. Experiment results show that the acquisition system improves acquiring performance obviously using the proposed algorithm, and it can be seen that correct detection probabilities achieve lOO％ for a coherent integration time of 1O ms and SNR of -38 dB.
Stochastic Programming with Simple Integer Recourse
Louveaux, François V.; van der Vlerk, Maarten H.
1993-01-01
Stochastic integer programs are notoriously difficult. Very few properties are known and solution algorithms are very scarce. In this paper, we introduce the class of stochastic programs with simple integer recourse, a natural extension of the simple recourse case extensively studied in stochastic c
Rached, Nadhir B.
2014-01-06
A new hybrid adaptive MC forward Euler algorithm for SDEs with singular coefficients and non-smooth observables is developed. This adaptive method is based on the derivation of a new error expansion with computable leading order terms. When a non-smooth binary payoff is considered, the new adaptive method achieves the same complexity as the uniform discretization with smooth problems. Moreover, the new developed algorithm is extended to the multilevel Monte Carlo (MLMC) forward Euler setting which reduces the complexity from O(TOL-3) to O(TOL-2(log(TOL))2). For the binary option case, it recovers the standard multilevel computational cost O(TOL-2(log(TOL))2). When considering a higher order Milstein scheme, a similar complexity result was obtained by Giles using the uniform time stepping for one dimensional SDEs, see [2]. The difficulty to extend Giles’ Milstein MLMC method to the multidimensional case is an argument for the flexibility of our new constructed adaptive MLMC forward Euler method which can be easily adapted to this setting. Similarly, the expected complexity O(TOL-2(log(TOL))2) is reached for the multidimensional case and verified numerically.
Energy Technology Data Exchange (ETDEWEB)
Mehrotra, Sanjay [Northwestern Univ., Evanston, IL (United States)
2016-09-07
The support from this grant resulted in seven published papers and a technical report. Two papers are published in SIAM J. on Optimization [87, 88]; two papers are published in IEEE Transactions on Power Systems [77, 78]; one paper is published in Smart Grid [79]; one paper is published in Computational Optimization and Applications [44] and one in INFORMS J. on Computing [67]). The works in [44, 67, 87, 88] were funded primarily by this DOE grant. The applied papers in [77, 78, 79] were also supported through a subcontract from the Argonne National Lab. We start by presenting our main research results on the scenario generation problem in Sections 1–2. We present our algorithmic results on interior point methods for convex optimization problems in Section 3. We describe a new ‘central’ cutting surface algorithm developed for solving large scale convex programming problems (as is the case with our proposed research) with semi-infinite number of constraints in Section 4. In Sections 5–6 we present our work on two application problems of interest to DOE.
Godinho, Sérgio; Guiomar, Nuno; Gil, Artur
2016-07-01
This study aims to develop and propose a methodological approach for montado ecosystem mapping using Landsat 8 multi-spectral data, vegetation indices, and the Stochastic Gradient Boosting (SGB) algorithm. Two Landsat 8 scenes (images from spring and summer 2014) of the same area in southern Portugal were acquired. Six vegetation indices were calculated for each scene: the Enhanced Vegetation Index (EVI), the Short-Wave Infrared Ratio (SWIR32), the Carotenoid Reflectance Index 1 (CRI1), the Green Chlorophyll Index (CIgreen), the Normalised Multi-band Drought Index (NMDI), and the Soil-Adjusted Total Vegetation Index (SATVI). Based on this information, two datasets were prepared: (i) Dataset I only included multi-temporal Landsat 8 spectral bands (LS8), and (ii) Dataset II included the same information as Dataset I plus vegetation indices (LS8 + VIs). The integration of the vegetation indices into the classification scheme resulted in a significant improvement in the accuracy of Dataset II's classifications when compared to Dataset I (McNemar test: Z-value = 4.50), leading to a difference of 4.90% in overall accuracy and 0.06 in the Kappa value. For the montado ecosystem, adding vegetation indices in the classification process showed a relevant increment in producer and user accuracies of 3.64% and 6.26%, respectively. By using the variable importance function from the SGB algorithm, it was found that the six most prominent variables (from a total of 24 tested variables) were the following: EVI_summer; CRI1_spring; SWIR32_spring; B6_summer; B5_summer; and CIgreen_summer.
Rolland, Joran; Bouchet, Freddy; Simonnet, Eric
2016-01-01
In this article we compute and analyse the transition rates and duration of reactive trajectories of the stochastic 1-D Allen-Cahn equations for both the Freidlin-Wentzell regime (weak noise or temperature limit) and finite-amplitude white noise, as well as for small and large domain. We demonstrate that extremely rare reactive trajectories corresponding to direct transitions between two metastable states are efficiently computed using an algorithm called adaptive multilevel splitting. This algorithm is dedicated to the computation of rare events and is able to provide ensembles of reactive trajectories in a very efficient way. In the small noise limit, our numerical results are in agreement with large-deviation predictions such as instanton-like solutions, mean first passages and escape probabilities. We show that the duration of reactive trajectories follows a Gumbel distribution like for one degree of freedom systems. Moreover, the mean duration growths logarithmically with the inverse temperature. The prefactor given by the potential curvature grows exponentially with size. The main novelty of our work is that we also perform an analysis of reactive trajectories for large noises and large domains. In this case, we show that the position of the reactive front is essentially a random walk. This time, the mean duration grows linearly with the inverse temperature and quadratically with the size. Using a phenomenological description of the system, we are able to calculate the transition rate, although the dynamics is described by neither Freidlin-Wentzell or Eyring-Kramers type of results. Numerical results confirm our analysis.
Rached, Nadhir B.
2013-12-01
The Monte Carlo forward Euler method with uniform time stepping is the standard technique to compute an approximation of the expected payoff of a solution of an Itô SDE. For a given accuracy requirement TOL, the complexity of this technique for well behaved problems, that is the amount of computational work to solve the problem, is O(TOL-3). A new hybrid adaptive Monte Carlo forward Euler algorithm for SDEs with non-smooth coefficients and low regular observables is developed in this thesis. This adaptive method is based on the derivation of a new error expansion with computable leading-order terms. The basic idea of the new expansion is the use of a mixture of prior information to determine the weight functions and posterior information to compute the local error. In a number of numerical examples the superior efficiency of the hybrid adaptive algorithm over the standard uniform time stepping technique is verified. When a non-smooth binary payoff with either GBM or drift singularity type of SDEs is considered, the new adaptive method achieves the same complexity as the uniform discretization with smooth problems. Moreover, the new developed algorithm is extended to the MLMC forward Euler setting which reduces the complexity from O(TOL-3) to O(TOL-2(log(TOL))2). For the binary option case with the same type of Itô SDEs, the hybrid adaptive MLMC forward Euler recovers the standard multilevel computational cost O(TOL-2(log(TOL))2). When considering a higher order Milstein scheme, a similar complexity result was obtained by Giles using the uniform time stepping for one dimensional SDEs. The difficulty to extend Giles\\' Milstein MLMC method to the multidimensional case is an argument for the flexibility of our new constructed adaptive MLMC forward Euler method which can be easily adapted to this setting. Similarly, the expected complexity O(TOL-2(log(TOL))2) is reached for the multidimensional case and verified numerically.
Chen, Linfeng; Shi, Xiaodi; Li, Mingzhu; Hu, Junping; Sun, Shufeng; Su, Bin; Wen, Yongqiang; Han, Dong; Jiang, Lei; Song, Yanlin
2015-08-01
Fireflies have drawn considerable attention for thousands of years due to their highly efficient bioluminescence, which is important for fundamental research and photonic applications. However, there are few reports on the reflector layer (RL) of firefly lantern, which contributes to the bright luminescence. Here we presented the detailed microstructure of the RL consisting of random hollow granules, which had high reflectance in the range from 450 nm to 800 nm. Inspired by the firefly lantern, artificial films with high reflectance in the visible region were fabricated using hollow silica microparticles mimicking the structure of the RL. Additionally, the bioinspired structures provided an efficient RL for the chemiluminescence system and could substantially enhance the initial chemiluminescence intensity. The work not only provides new insight into the bright bioluminescence of fireflies, but also is importance for the design of photonic materials for theranostics, detection, and imaging.
Effect of very high magnetic field on the optical properties of firefly light emitter oxyluciferin
Energy Technology Data Exchange (ETDEWEB)
Zhou, Weihang; Nakamura, Daisuke [Institute for Solid State Physics, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8581 (Japan); Wang, Yu [Institute for Solid State Physics, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8581 (Japan); State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences (China); Mochizuki, Toshimitsu [Institute for Solid State Physics, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8581 (Japan); Fukushima Renewable Energy Institute, National Institute of Advanced Industrial Science and Technology, 2-2-9 Machiike-dai, Koriyama, Fukushima 963-0215 (Japan); Akiyama, Hidefumi [Institute for Solid State Physics, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8581 (Japan); Takeyama, Shojiro, E-mail: takeyama@issp.u-tokyo.ac.jp [Institute for Solid State Physics, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba 277-8581 (Japan)
2015-09-15
Magnetic field effect on enzymatic reactions is under intensive study in the past decades. Recently, it was reported that firefly bioluminescence was suppressed and red-shifted significantly when exposed to external magnetic field. However in this work, by means of selective excitation, we confirmed that emission properties of firefly light emitter “oxyluciferin” are completely immune to external magnetic field of up to 53 T. These findings pose strong contrast to existing relevant results. Potential reasons for the discrepancies found and the underlying physics towards the understanding of firefly bioluminescence were discussed. - Highlights: • Effect of ultra-high magnetic field on the optical properties of firefly light emitter oxyluciferin was reported. • Emission properties of oxyluciferin were confirmed to be immune to external high magnetic fields up to 53 T. • .Potential reasons for the discrepancies between our results and previous reports and the underlying physics were discussed.
Excited-state proton transfer of firefly dehydroluciferin.
Presiado, Itay; Erez, Yuval; Simkovitch, Ron; Shomer, Shay; Gepshtein, Rinat; Pinto da Silva, Luís; Esteves da Silva, Joaquim C G; Huppert, Dan
2012-11-08
Steady-state and time-resolved emission techniques were used to study the protolytic processes in the excited state of dehydroluciferin, a nonbioluminescent product of the firefly enzyme luciferase. We found that the ESPT rate coefficient is only 1.1 × 10(10) s(-1), whereas those of d-luciferin and oxyluciferin are 3.7 × 10(10) and 2.1 × 10(10) s(-1), respectively. We measured the ESPT rate in water-methanol mixtures, and we found that the rate decreases nonlinearly as the methanol content in the mixture increases. The deprotonated form of dehydroluciferin has a bimodal decay with short- and long-time decay components, as was previously found for both D-luciferin and oxyluciferin. In weakly acidic aqueous solutions, the deprotonated form's emission is efficiently quenched. We attribute this observation to the ground-state protonation of the thiazole nitrogen, whose pK(a) value is ~3.
Preparation and epitope characterization of monoclonal antibodies against firefly luciferase
Institute of Scientific and Technical Information of China (English)
徐沁; 丁建芳; 胡红雨; 许根俊
1999-01-01
The 6-His tagged firefly luciferase was highly expressed in E. coli and purified to homogeneity by affinity chromatography and gel filtration. After immunizing Balb/c mice with the antigen, 6 hybridomas clones were found to secrete monoelonal antibodies (mAbs) and the mAbs were also purified separately. The competitive binding experiments show that 2 mAbs can bind heat-denatured antigen or its proteolytic fragments but not the native lueiferase, suggesting that their epitopes might be accommodated in the internal segments of the protein. On the other hand, the other 4 mAbs are capable of binding both native and denatured antigens. It infers that their epitopes locate in the segments on the protein surface. The results also suggest that the six mAbs are all sequence-specific.
Stability Analysis for Stochastic Optimization Problems
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Stochastic optimization offers a means of considering the objectives and constrains with stochastic parameters. However, it is generally difficult to solve the stochastic optimization problem by employing conventional methods for nonlinear programming when the number of random variables involved is very large. Neural network models and algorithms were applied to solve the stochastic optimization problem on the basis of the stability theory. Stability for stochastic programs was discussed. If random vector sequence converges to the random vector in the original problem in distribution, the optimal value of the corresponding approximation problems converges to the optimal value of the original stochastic optimization problem.
Search for an optimal light-extracting surface derived from the morphology of a firefly lantern
Bay, Annick; Vigneron, Jean Pol
2012-01-01
Fireflies lighten up our warm summer evenings. There is more physic behind these little animals than anyone of us could imagine. In this paper we analyze from a physical point of view one structure found on the firefly lantern, the one which best improves light extraction. Moreover, simulations will be done to show why this specific structure may be more effective than a "human-thought" one.
Stochastic Shadowing and Stochastic Stability
Todorov, Dmitry
2014-01-01
The notion of stochastic shadowing property is introduced. Relations to stochastic stability and standard shadowing are studied. Using tent map as an example it is proved that, in contrast to what happens for standard shadowing, there are significantly non-uniformly hyperbolic systems that satisfy stochastic shadowing property.
Directory of Open Access Journals (Sweden)
Leonardo Bottolo
Full Text Available Genome-wide association studies (GWAS yielded significant advances in defining the genetic architecture of complex traits and disease. Still, a major hurdle of GWAS is narrowing down multiple genetic associations to a few causal variants for functional studies. This becomes critical in multi-phenotype GWAS where detection and interpretability of complex SNP(s-trait(s associations are complicated by complex Linkage Disequilibrium patterns between SNPs and correlation between traits. Here we propose a computationally efficient algorithm (GUESS to explore complex genetic-association models and maximize genetic variant detection. We integrated our algorithm with a new Bayesian strategy for multi-phenotype analysis to identify the specific contribution of each SNP to different trait combinations and study genetic regulation of lipid metabolism in the Gutenberg Health Study (GHS. Despite the relatively small size of GHS (n = 3,175, when compared with the largest published meta-GWAS (n > 100,000, GUESS recovered most of the major associations and was better at refining multi-trait associations than alternative methods. Amongst the new findings provided by GUESS, we revealed a strong association of SORT1 with TG-APOB and LIPC with TG-HDL phenotypic groups, which were overlooked in the larger meta-GWAS and not revealed by competing approaches, associations that we replicated in two independent cohorts. Moreover, we demonstrated the increased power of GUESS over alternative multi-phenotype approaches, both Bayesian and non-Bayesian, in a simulation study that mimics real-case scenarios. We showed that our parallel implementation based on Graphics Processing Units outperforms alternative multi-phenotype methods. Beyond multivariate modelling of multi-phenotypes, our Bayesian model employs a flexible hierarchical prior structure for genetic effects that adapts to any correlation structure of the predictors and increases the power to identify
Kanie, Shusei; Nishikawa, Toshio; Ojika, Makoto; Oba, Yuichi
2016-04-21
Firefly luciferin, the substrate for the bioluminescence reaction of luminous beetles, possesses a benzothiazole ring, which is rare in nature. Here, we demonstrate a novel one-pot reaction to give firefly luciferin in a neutral buffer from p-benzoquinone and cysteine without any synthetic reagents or enzymes. The formation of firefly luciferin was low in yield in various neutral buffers, whereas it was inhibited or completely prevented in acidic or basic buffers, in organic solvents, or under a nitrogen atmosphere. Labelling analysis of the firefly luciferin using stable isotopic cysteines showed that the benzothiazole ring was formed via the decarboxylation and carbon-sulfur bond rearrangement of cysteine. These findings imply that the biosynthesis of firefly luciferin can be developed/evolved from the non-enzymatic production of firefly luciferin using common primary biosynthetic units, p-benzoquinone and cysteine.
Directory of Open Access Journals (Sweden)
Sara Nakhjirkan
2017-09-01
in green supply chain. Vehicle routing between distribution centres and customers has been considered in the model. Establishment place of distribution centres among potential places is determined by the model. The distributors use continuous review policy (r, Q to control the inventory. The proposed model object is to find an optimal supply chain with minimum costs. To validate the proposed model and measure its compliance with real world problems, GAMS IDE/Cplex has been used. In order to measure the efficiency of the proposed model in large scale problems, a genetic algorithm has been used. The results confirm the efficiency of the proposed model as a practical tool for decision makers to solve location-inventory-routing problems in green supply chain. The proposed GA could reduce the solving time by 85% while reaching on the average 97% of optimal solution compared with exact method.
Stochastic superparameterization in quasigeostrophic turbulence
Energy Technology Data Exchange (ETDEWEB)
Grooms, Ian, E-mail: grooms@cims.nyu.edu [Center for Atmosphere Ocean Science, Courant Institute of Mathematical Sciences, New York University, 251 Mercer St., New York, NY 10012 (United States); Majda, Andrew J., E-mail: jonjon@cims.nyu.edu [Center for Atmosphere Ocean Science, Courant Institute of Mathematical Sciences, New York University, 251 Mercer St., New York, NY 10012 (United States); Center for Prototype Climate Modelling, NYU-Abu Dhabi (United Arab Emirates)
2014-08-15
In this article we expand and develop the authors' recent proposed methodology for efficient stochastic superparameterization algorithms for geophysical turbulence. Geophysical turbulence is characterized by significant intermittent cascades of energy from the unresolved to the resolved scales resulting in complex patterns of waves, jets, and vortices. Conventional superparameterization simulates large scale dynamics on a coarse grid in a physical domain, and couples these dynamics to high-resolution simulations on periodic domains embedded in the coarse grid. Stochastic superparameterization replaces the nonlinear, deterministic eddy equations on periodic embedded domains by quasilinear stochastic approximations on formally infinite embedded domains. The result is a seamless algorithm which never uses a small scale grid and is far cheaper than conventional SP, but with significant success in difficult test problems. Various design choices in the algorithm are investigated in detail here, including decoupling the timescale of evolution on the embedded domains from the length of the time step used on the coarse grid, and sensitivity to certain assumed properties of the eddies (e.g. the shape of the assumed eddy energy spectrum). We present four closures based on stochastic superparameterization which elucidate the properties of the underlying framework: a ‘null hypothesis’ stochastic closure that uncouples the eddies from the mean, a stochastic closure with nonlinearly coupled eddies and mean, a nonlinear deterministic closure, and a stochastic closure based on energy conservation. The different algorithms are compared and contrasted on a stringent test suite for quasigeostrophic turbulence involving two-layer dynamics on a β-plane forced by an imposed background shear. The success of the algorithms developed here suggests that they may be fruitfully applied to more realistic situations. They are expected to be particularly useful in providing accurate and
A practical guide to stochastic simulations of reaction-diffusion processes
Erban, Radek; Chapman, Jonathan; Maini, Philip
2007-01-01
A practical introduction to stochastic modelling of reaction-diffusion processes is presented. No prior knowledge of stochastic simulations is assumed. The methods are explained using illustrative examples. The article starts with the classical Gillespie algorithm for the stochastic modelling of chemical reactions. Then stochastic algorithms for modelling molecular diffusion are given. Finally, basic stochastic reaction-diffusion methods are presented. The connections between stochastic simul...
Previous exposure assessment panel studies have observed considerable seasonal, between-home and between-city variability in residential pollutant infiltration. This is likely a result of differences in home ventilation, or air exchange rates (AER). The Stochastic Human Exposure ...
Variance decomposition in stochastic simulators
Le Maître, O. P.
2015-06-28
This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.
Variance decomposition in stochastic simulators.
Le Maître, O P; Knio, O M; Moraes, A
2015-06-28
This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.
Variance decomposition in stochastic simulators
Le Maître, O. P.; Knio, O. M.; Moraes, A.
2015-06-01
This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.
Multistage quadratic stochastic programming
Lau, Karen K.; Womersley, Robert S.
2001-04-01
Quadratic stochastic programming (QSP) in which each subproblem is a convex piecewise quadratic program with stochastic data, is a natural extension of stochastic linear programming. This allows the use of quadratic or piecewise quadratic objective functions which are essential for controlling risk in financial and project planning. Two-stage QSP is a special case of extended linear-quadratic programming (ELQP). The recourse functions in QSP are piecewise quadratic convex and Lipschitz continuous. Moreover, they have Lipschitz gradients if each QP subproblem is strictly convex and differentiable. Using these properties, a generalized Newton algorithm exhibiting global and superlinear convergence has been proposed recently for the two stage case. We extend the generalized Newton algorithm to multistage QSP and show that it is globally and finitely convergent under suitable conditions. We present numerical results on randomly generated data and modified publicly available stochastic linear programming test sets. Efficiency schemes on different scenario tree structures are discussed. The large-scale deterministic equivalent of the multistage QSP is also generated and their accuracy compared.
Firefly courtship as the basis of the synchronization-response principle
Ramírez Ávila, G. M.; Deneubourg, J.-L.; Guisset, J.-L.; Wessel, N.; Kurths, J.
2011-06-01
Response to synchronization seems to be a widespread phenomenon specially in biological systems. We highlight this phenomenon studying the courtship of flashing fireflies in which a typical collective rhythm occurring only among the males arises and it is followed by a response of the females. Based on a model issued from electronic fireflies, we explain the synchronization of the males and the active responses of the females in the courtship of mingled (both sexes) populations of fireflies. The model also explains the courtship behavior of other species whose interactions follow the same logic even if their physical features are different. Moreover, the model can make predictions on the behavior of mingled and mixed (natural and artificial) groups of such animals. This finding could be considered as the basis of a new principle, namely the synchronization-response.
In vivo bioluminescence emissions of the firefly Luciola praeusta at low temperatures.
Sharma, Upamanyu; Goswami, Angana; Rabha, Mana Mohan; Barua, Anurup Gohain
2016-08-01
Dependences of light emission from fireflies on external factors like temperature and magnetic field have been studied in recent times. Interesting conclusions have been drawn and hypotheses put forward in those studies. Here we report steady-state and time-resolved emissions of the Indian species of the firefly Luciola praeusta Kiesenwetter 1874 (Coleoptera: Lampyridae: Luciolinae) at temperatures below 20°C. Intensity profiles of emission spectra remain the same as those recorded at normal or high temperatures. Two-flash combinations are frequently formed, giving the appearance of the resolution of a simple flash into two. Simple flashes also become abnormally broad with no uniformity in the increase of their durations. The flashes obtained from fireflies at low temperatures are compared and contrasted with the ones under a strong static magnetic field.
Institute of Scientific and Technical Information of China (English)
满飞宇; 陈云鹤
2011-01-01
目前国内外对随机需求多车辆路径问题的研究还很少,本文针对标准hopfield神经网络容易陷入局部极值点等问题,以总路程最短和总使用车辆数最少为目标,提出了一种基于退火策略的混沌神经网络的求解随机需求多车辆路径问题的算法,该算法既可以使混沌运动有足够长的进程以提高粗搜索性能,又可以随混沌动态的减弱使收敛速度加快。实验结果表明,该算法优化车辆路径更佳,是解决随机需求多车辆路径问题的有效方法。%In recent times,the research of multiple vehicles routing problem（VRP） with stochastic demand is not consummate in domestic and abroad.However,this paper indicates a kind of annealing chaotic neural network to multiple vehicles routing problem with stochastic demand,targeting to the shortest total distance and the least number of vehicles,while standard Hopfield neural network easy to fall into local optimum.In this algorithm,chaos lasts long enough to improve the search performance of crude,and also convergence rate is larger while chaotic dynamics weakens.According to the experimental results,vehicle routing is better using this algorithm,the algorithm is a effective method to solving multiple vehicles routing problem with stochastic demand.
McKean, Henry P
2005-01-01
This little book is a brilliant introduction to an important boundary field between the theory of probability and differential equations. -E. B. Dynkin, Mathematical Reviews This well-written book has been used for many years to learn about stochastic integrals. The book starts with the presentation of Brownian motion, then deals with stochastic integrals and differentials, including the famous Itô lemma. The rest of the book is devoted to various topics of stochastic integral equations, including those on smooth manifolds. Originally published in 1969, this classic book is ideal for supplemen
Parzen, Emanuel
2015-01-01
Well-written and accessible, this classic introduction to stochastic processes and related mathematics is appropriate for advanced undergraduate students of mathematics with a knowledge of calculus and continuous probability theory. The treatment offers examples of the wide variety of empirical phenomena for which stochastic processes provide mathematical models, and it develops the methods of probability model-building.Chapter 1 presents precise definitions of the notions of a random variable and a stochastic process and introduces the Wiener and Poisson processes. Subsequent chapters examine
FireFly: reconfigurable optical wireless networking data centers
Kavehrad, Mohsen; Deng, Peng; Gupta, H.; Longtin, J.; Das, S. R.; Sekar, V.
2017-01-01
We explore a novel, free-space optics based approach for building data center interconnects. Data centers (DCs) are a critical piece of today's networked applications in both private and public sectors. The key factors that have driven this trend are economies of scale, reduced management costs, better utilization of hardware via statistical multiplexing, and the ability to elastically scale applications in response to changing workload patterns. A robust DC network fabric is fundamental to the success of DCs and to ensure that the network does not become a bottleneck for high-performance applications. In this context, DC network design must satisfy several goals: high performance (e.g., high throughput and low latency), low equipment and management cost, robustness to dynamic traffic patterns, incremental expandability to add new servers or racks, and other practical concerns such as cabling complexity, and power and cooling costs. Current DC network architectures do not seem to provide a satisfactory solution, with respect to the above requirements. In particular, traditional static (wired) networks are either overprovisioned or oversubscribed. Recent works have tried to overcome the above limitations by augmenting a static (wired) "core" with some flexible links (RF-wireless or optical). These augmented architectures show promise, but offer only incremental improvement in performance. Specifically, RFwireless based augmented solutions also offer only limited performance improvement, due to inherent interference and range constraints of RF links. This paper explores an alternative design point—a fully flexible and all-wireless DC interrack network based on free-space optical (FSO) links. We call this FireFly as in; Free-space optical Inter-Rack nEtwork with high FLexibilitY. We will present our designs and tests using various configurations that can help the performance and reliability of the FSO links.
Indian Academy of Sciences (India)
A Gohain Barua; S Hazarika; N M Saikia; G D Baruah
2009-06-01
We recorded the in vivo emission and time-resolved spectra of the firefly Luciola praeusta Kiesenwetter 1874 (Coleoptera : Lampyridae : Luciolinae). The emission spectrum shows that the full width at half maximum (FWHM) value for this particular species is 55 nm, which is significantly narrower than the in vivo half-widths reported till now. The time-resolved spectrum reveals that a flash of about 100 ms duration is, in fact, composed of a number of microsecond pulses. This suggests that the speed of the enzyme-catalysed chemiluminescence reaction in the firefly for the emission of light is much faster than was previously believed.
Indian Academy of Sciences (India)
A Gohain Barua; S Hazarika; N M Saikia; G D Baruah
2009-09-01
We recorded the in vivo emission and time-resolved spectra of the firefly Luciola praeusta Kiesenwetter 1874 (Coleoptera : Lampyridae : Luciolinae). The emission spectrum shows that the full width at half maximum (FWHM) value for this particular species is 55 nm, which is significantly narrower than the in vivo half-widths reported till now. The time-resolved spectrum reveals that a flash of about 100 ms duration is, in fact, composed of a number of microsecond pulses. This suggests that the speed of the enzyme-catalysed chemiluminescence reaction in the firefly for the emission of light is much faster than was previously believed.
Stochastic superparameterization in quasigeostrophic turbulence
Grooms, Ian
2013-01-01
In this article we expand and develop the authors' recent proposed methodology for efficient stochastic superparameterization (SP) algorithms for geophysical turbulence. Geophysical turbulence is characterized by significant intermittent cascades of energy from the unresolved to the resolved scales resulting in complex patterns of waves, jets, and vortices. Conventional SP simulates large scale dynamics on a coarse grid in a physical domain, and couples these dynamics to high-resolution simulations on periodic domains embedded in the coarse grid. Stochastic SP replaces the nonlinear, deterministic eddy equations on periodic embedded domains by quasilinear stochastic approximations on formally infinite embedded domains. The result is a seamless algorithm which never uses a small scale grid and is far cheaper than conventional SP, but with significant success in difficult test problems. Various design choices in the algorithm are investigated in detail here, including decoupling the timescale of evolution on th...
Schneider, Johannes J
2007-01-01
This book addresses stochastic optimization procedures in a broad manner. The first part offers an overview of relevant optimization philosophies; the second deals with benchmark problems in depth, by applying a selection of optimization procedures. Written primarily with scientists and students from the physical and engineering sciences in mind, this book addresses a larger community of all who wish to learn about stochastic optimization techniques and how to use them.
Institute of Scientific and Technical Information of China (English)
王连稳; 蔡延光
2013-01-01
研究了随机需求车辆路径优化问题(VRPSD)，在只允许路由失败一次和允许部分服务的情况下，给出了应用人工蜂群算法(ABC)用于求解该问题的具体方案。并针对需求为二项分布的VRPSD问题做了Matlab仿真试验，结果验证了该算法解决VRPSD的可行性。%This paper studied the vehicle routing problem with stochastic demands(VRPSD),and give specific methods with the application of Artificial Bee Colony algorithm(ABC) to solve the problem occurring in the situation that only one time routing failure and partial service are al owed.Matlab simulation has been done for a type of vehicle routing problem with stochastic demand which fol ows binomial distribution,demonstrating the feasibility of the algorithm to solve VRPSD.
Stanger-Hall, Kathrin F; Lloyd, James E
2015-03-01
Animal communication is an intriguing topic in evolutionary biology. In this comprehensive study of visual signal evolution, we used a phylogenetic approach to study the evolution of the flash communication system of North American fireflies. The North American firefly genus Photinus contains 35 described species with simple ON-OFF visual signals, and information on habitat types, sympatric congeners, and predators. This makes them an ideal study system to test hypotheses on the evolution of male and female visual signal traits. Our analysis of 34 Photinus species suggests two temporal pattern generators: one for flash duration and one for flash intervals. Reproductive character displacement was a main factor for signal divergence in male flash duration among sympatric Photinus species. Male flash pattern intervals (i.e., the duration of the dark periods between signals) were positively correlated with the number of sympatric Photuris fireflies, which include predators of Photinus. Females of different Photinus species differ in their response preferences to male traits. As in other communication systems, firefly male sexual signals seem to be a compromise between optimizing mating success (sexual selection) and minimizing predation risk (natural selection). An integrative model for Photinus signal evolution is proposed.
Institute of Scientific and Technical Information of China (English)
周柏; 陶卿; 储德军
2015-01-01
几乎所有的稀疏随机算法都来源于在线形式，只能获得平均输出方式的收敛速率，对于强凸优化问题无法达到最优的瞬时收敛速率.文中避开在线形式转到随机模式，直接研究随机优化算法.首先在含有 L1正则化项的稀疏优化问题中加入 L2正则化项，使之具有强凸特性.然后将黑箱优化方法中的随机步长策略引入到当前通用的结构优化算法 COMID 中，得到基于随机步长的混合正则化镜面下降稀疏随机优化算法.最后通过分析 L1正则化问题中软阈值方法的求解特点，证明算法具有最优的瞬时收敛速率.实验表明，文中算法的稀疏性优于 COMID.%Almost all sparse stochastic algorithms are developed from the online setting, and only the convergence rate of average output can be obtained. The optimal rate for strongly convex optimization problems can not be reached as well. The stochastic optimization algorithms are directly studied instead of the online to batch conversation in this paper. Firstly, by incorporating the L2 regularizer into the L1-regularized sparse optimization problems, the strong convexity can be obtained. Then, by introducing the random step-size strategy from the black-box optimization method to the state-of-the-art algorithm-composite objective mirror descent (COMID), a sparse stochastic optimization algorithm based introducing on random step-size hybrid regularized mirror descent ( RS-HMD) is achieved. Finally, based on the analysis of characteristics of soft threshold methods in solving the L1-regularized problem, the optimal individual convergence rate is proved. Experimental results demonstrate that sparsity of RS-HMD is better than that of COMID.
Stochastic models: theory and simulation.
Energy Technology Data Exchange (ETDEWEB)
Field, Richard V., Jr.
2008-03-01
Many problems in applied science and engineering involve physical phenomena that behave randomly in time and/or space. Examples are diverse and include turbulent flow over an aircraft wing, Earth climatology, material microstructure, and the financial markets. Mathematical models for these random phenomena are referred to as stochastic processes and/or random fields, and Monte Carlo simulation is the only general-purpose tool for solving problems of this type. The use of Monte Carlo simulation requires methods and algorithms to generate samples of the appropriate stochastic model; these samples then become inputs and/or boundary conditions to established deterministic simulation codes. While numerous algorithms and tools currently exist to generate samples of simple random variables and vectors, no cohesive simulation tool yet exists for generating samples of stochastic processes and/or random fields. There are two objectives of this report. First, we provide some theoretical background on stochastic processes and random fields that can be used to model phenomena that are random in space and/or time. Second, we provide simple algorithms that can be used to generate independent samples of general stochastic models. The theory and simulation of random variables and vectors is also reviewed for completeness.
Chang, Mou-Hsiung
2015-01-01
The classical probability theory initiated by Kolmogorov and its quantum counterpart, pioneered by von Neumann, were created at about the same time in the 1930s, but development of the quantum theory has trailed far behind. Although highly appealing, the quantum theory has a steep learning curve, requiring tools from both probability and analysis and a facility for combining the two viewpoints. This book is a systematic, self-contained account of the core of quantum probability and quantum stochastic processes for graduate students and researchers. The only assumed background is knowledge of the basic theory of Hilbert spaces, bounded linear operators, and classical Markov processes. From there, the book introduces additional tools from analysis, and then builds the quantum probability framework needed to support applications to quantum control and quantum information and communication. These include quantum noise, quantum stochastic calculus, stochastic quantum differential equations, quantum Markov semigrou...
Stochastic partial differential equations
Chow, Pao-Liu
2014-01-01
Preliminaries Introduction Some Examples Brownian Motions and Martingales Stochastic Integrals Stochastic Differential Equations of Itô Type Lévy Processes and Stochastic IntegralsStochastic Differential Equations of Lévy Type Comments Scalar Equations of First Order Introduction Generalized Itô's Formula Linear Stochastic Equations Quasilinear Equations General Remarks Stochastic Parabolic Equations Introduction Preliminaries Solution of Stochastic Heat EquationLinear Equations with Additive Noise Some Regularity Properties Stochastic Reaction-Diffusion Equations Parabolic Equations with Grad
Solving Simple Stochastic Games with Few Random Vertices
Gimbert, H.; Horn, F.
2009-01-01
Simple stochastic games are two-player zero-sum stochastic games with turn-based moves, perfect information, and reachability winning conditions. We present two new algorithms computing the values of simple stochastic games. Both of them rely on the existence of optimal permutation strategies, a cla
Multistage stochastic optimization
Pflug, Georg Ch
2014-01-01
Multistage stochastic optimization problems appear in many ways in finance, insurance, energy production and trading, logistics and transportation, among other areas. They describe decision situations under uncertainty and with a longer planning horizon. This book contains a comprehensive treatment of today’s state of the art in multistage stochastic optimization. It covers the mathematical backgrounds of approximation theory as well as numerous practical algorithms and examples for the generation and handling of scenario trees. A special emphasis is put on estimation and bounding of the modeling error using novel distance concepts, on time consistency and the role of model ambiguity in the decision process. An extensive treatment of examples from electricity production, asset liability management and inventory control concludes the book
Stochastic Constraint Programming
Walsh, Toby
2009-01-01
To model combinatorial decision problems involving uncertainty and probability, we introduce stochastic constraint programming. Stochastic constraint programs contain both decision variables (which we can set) and stochastic variables (which follow a probability distribution). They combine together the best features of traditional constraint satisfaction, stochastic integer programming, and stochastic satisfiability. We give a semantics for stochastic constraint programs, and propose a number...
Kumar, Manjeet; Rawat, Tarun Kumar; Aggarwal, Apoorva
2017-03-01
In this paper, a new meta-heuristic optimization technique, called interior search algorithm (ISA) with Lèvy flight is proposed and applied to determine the optimal parameters of an unknown infinite impulse response (IIR) system for the system identification problem. ISA is based on aesthetics, which is commonly used in interior design and decoration processes. In ISA, composition phase and mirror phase are applied for addressing the nonlinear and multimodal system identification problems. System identification using modified-ISA (M-ISA) based method involves faster convergence, single parameter tuning and does not require derivative information because it uses a stochastic random search using the concepts of Lèvy flight. A proper tuning of control parameter has been performed in order to achieve a balance between intensification and diversification phases. In order to evaluate the performance of the proposed method, mean square error (MSE), computation time and percentage improvement are considered as the performance measure. To validate the performance of M-ISA based method, simulations has been carried out for three benchmarked IIR systems using same order and reduced order system. Genetic algorithm (GA), particle swarm optimization (PSO), cat swarm optimization (CSO), cuckoo search algorithm (CSA), differential evolution using wavelet mutation (DEWM), firefly algorithm (FFA), craziness based particle swarm optimization (CRPSO), harmony search (HS) algorithm, opposition based harmony search (OHS) algorithm, hybrid particle swarm optimization-gravitational search algorithm (HPSO-GSA) and ISA are also used to model the same examples and simulation results are compared. Obtained results confirm the efficiency of the proposed method.
Energy Technology Data Exchange (ETDEWEB)
Bisognano, J.; Leemann, C.
1982-03-01
Stochastic cooling is the damping of betatron oscillations and momentum spread of a particle beam by a feedback system. In its simplest form, a pickup electrode detects the transverse positions or momenta of particles in a storage ring, and the signal produced is amplified and applied downstream to a kicker. The time delay of the cable and electronics is designed to match the transit time of particles along the arc of the storage ring between the pickup and kicker so that an individual particle receives the amplified version of the signal it produced at the pick-up. If there were only a single particle in the ring, it is obvious that betatron oscillations and momentum offset could be damped. However, in addition to its own signal, a particle receives signals from other beam particles. In the limit of an infinite number of particles, no damping could be achieved; we have Liouville's theorem with constant density of the phase space fluid. For a finite, albeit large number of particles, there remains a residue of the single particle damping which is of practical use in accumulating low phase space density beams of particles such as antiprotons. It was the realization of this fact that led to the invention of stochastic cooling by S. van der Meer in 1968. Since its conception, stochastic cooling has been the subject of much theoretical and experimental work. The earliest experiments were performed at the ISR in 1974, with the subsequent ICE studies firmly establishing the stochastic cooling technique. This work directly led to the design and construction of the Antiproton Accumulator at CERN and the beginnings of p anti p colliding beam physics at the SPS. Experiments in stochastic cooling have been performed at Fermilab in collaboration with LBL, and a design is currently under development for a anti p accumulator for the Tevatron.
Modelling and application of stochastic processes
1986-01-01
The subject of modelling and application of stochastic processes is too vast to be exhausted in a single volume. In this book, attention is focused on a small subset of this vast subject. The primary emphasis is on realization and approximation of stochastic systems. Recently there has been considerable interest in the stochastic realization problem, and hence, an attempt has been made here to collect in one place some of the more recent approaches and algorithms for solving the stochastic realiza tion problem. Various different approaches for realizing linear minimum-phase systems, linear nonminimum-phase systems, and bilinear systems are presented. These approaches range from time-domain methods to spectral-domain methods. An overview of the chapter contents briefly describes these approaches. Also, in most of these chapters special attention is given to the problem of developing numerically ef ficient algorithms for obtaining reduced-order (approximate) stochastic realizations. On the application side,...
Segmentation of stochastic images with a stochastic random walker method.
Pätz, Torben; Preusser, Tobias
2012-05-01
We present an extension of the random walker segmentation to images with uncertain gray values. Such gray-value uncertainty may result from noise or other imaging artifacts or more general from measurement errors in the image acquisition process. The purpose is to quantify the influence of the gray-value uncertainty onto the result when using random walker segmentation. In random walker segmentation, a weighted graph is built from the image, where the edge weights depend on the image gradient between the pixels. For given seed regions, the probability is evaluated for a random walk on this graph starting at a pixel to end in one of the seed regions. Here, we extend this method to images with uncertain gray values. To this end, we consider the pixel values to be random variables (RVs), thus introducing the notion of stochastic images. We end up with stochastic weights for the graph in random walker segmentation and a stochastic partial differential equation (PDE) that has to be solved. We discretize the RVs and the stochastic PDE by the method of generalized polynomial chaos, combining the recent developments in numerical methods for the discretization of stochastic PDEs and an interactive segmentation algorithm. The resulting algorithm allows for the detection of regions where the segmentation result is highly influenced by the uncertain pixel values. Thus, it gives a reliability estimate for the resulting segmentation, and it furthermore allows determining the probability density function of the segmented object volume.
Institute of Scientific and Technical Information of China (English)
肖世昌; 孙树栋; 国欢; 金梅; 杨宏安
2015-01-01
提出一种混合分布估计算法用于求解具有随机工时的Job shop调度问题。建立随机Job shop调度问题(Stochastic Job shop scheduling problem, SJSSP)数学模型并给出随机期望值模型的评价方法。为提高种群多样性，将(μ+λ)-进化策略(Evolutionary strategy, ES)的重组、变异过程引入分布估计算法(Estimation of distribution algorithm, EDA)，构造一种混合分布估计算法，ES-EDA。根据所采用的基于工序的编码方式，对父代工序继承率的概念进行了定义，并为重组过程设计基于父代工序继承率的个体重组方法，该方法不仅能使子代有效继承父代的优良特征，同时可避免非法解的产生。在标准算例FT06、FT10、FT20的基础上构造加工时间随机的3组算例，并选择文献中的5种算法作为混合分布估计算法的对比算法，仿真试验结果表明混合分布估计算法在优化性能方面具有明显优势。%A hybrid estimation of distribution algorithm(EDA) is proposed to solve the stochastic job shop scheduling problem (SJSSP) with stochastic processing times. The mathematic model of the SJSSP and the evaluation method of stochastically expected model are constructed. To enhance the population diversity, the recombination and mutation process of (μ+λ)-Evolutionary strategy are incorporated in the EDA, thus a hybrid EDA, i.e. ES-EDA is constructed. Based on the encoding method of chromosome adopted in this research, the concept of Inherit rate of the operations in parent individual is defined. Then a new recombination method based on the Inherit rate of the operations in parent individual is designed. This recombination method can not only make the offspring inheriting the excellent characteristics of the parent effectively, but also avoiding infeasible solutions. Three problem instances with stochastic processing times for simulation experiment are constructed based on the benchmark instances FT06, FT10
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
A simple and rapid expression and purification method of recombinant firefly luciferase was developed for bacteria detection. A modified luciferase gene from North American firefly Photinus pyralis was cloned into pET28a expression vector and the recombinant protein was produced in Escherichia coli BL21. The recombinant luciferase,equipped with a polyhistidine affinity tag,was purified by immobilized metal ion affinity chromatography (IMAC). The approach generated an abundant expression and an efficient pur...
Eichhorn, Ralf; Aurell, Erik
2014-04-01
'Stochastic thermodynamics as a conceptual framework combines the stochastic energetics approach introduced a decade ago by Sekimoto [1] with the idea that entropy can consistently be assigned to a single fluctuating trajectory [2]'. This quote, taken from Udo Seifert's [3] 2008 review, nicely summarizes the basic ideas behind stochastic thermodynamics: for small systems, driven by external forces and in contact with a heat bath at a well-defined temperature, stochastic energetics [4] defines the exchanged work and heat along a single fluctuating trajectory and connects them to changes in the internal (system) energy by an energy balance analogous to the first law of thermodynamics. Additionally, providing a consistent definition of trajectory-wise entropy production gives rise to second-law-like relations and forms the basis for a 'stochastic thermodynamics' along individual fluctuating trajectories. In order to construct meaningful concepts of work, heat and entropy production for single trajectories, their definitions are based on the stochastic equations of motion modeling the physical system of interest. Because of this, they are valid even for systems that are prevented from equilibrating with the thermal environment by external driving forces (or other sources of non-equilibrium). In that way, the central notions of equilibrium thermodynamics, such as heat, work and entropy, are consistently extended to the non-equilibrium realm. In the (non-equilibrium) ensemble, the trajectory-wise quantities acquire distributions. General statements derived within stochastic thermodynamics typically refer to properties of these distributions, and are valid in the non-equilibrium regime even beyond the linear response. The extension of statistical mechanics and of exact thermodynamic statements to the non-equilibrium realm has been discussed from the early days of statistical mechanics more than 100 years ago. This debate culminated in the development of linear response
Time-ordered product expansions for computational stochastic system biology.
Mjolsness, Eric
2013-06-01
The time-ordered product framework of quantum field theory can also be used to understand salient phenomena in stochastic biochemical networks. It is used here to derive Gillespie's stochastic simulation algorithm (SSA) for chemical reaction networks; consequently, the SSA can be interpreted in terms of Feynman diagrams. It is also used here to derive other, more general simulation and parameter-learning algorithms including simulation algorithms for networks of stochastic reaction-like processes operating on parameterized objects, and also hybrid stochastic reaction/differential equation models in which systems of ordinary differential equations evolve the parameters of objects that can also undergo stochastic reactions. Thus, the time-ordered product expansion can be used systematically to derive simulation and parameter-fitting algorithms for stochastic systems.
带有随机需求的车辆路径选择问题算法研究%A Solution Algorithm for Vehicle Routing Problem with Stochastic Demands
Institute of Scientific and Technical Information of China (English)
吕萌; 朱金福; 王瑞凯
2012-01-01
This paper proposes a new solution algorithm to the Vehicle Routing Problem with Stochastic Demands ( VRPSD). The theoretical basis of this algorithm is to transform a given VRPSD problem into solving a limited set of Capacitated Vehicle Routing Problem (CVRP) instances, and then the VRPSD problem can be solved by solving the corresponding CVRP problem. This paper recommends using the concept of safety stock to enhance the reliability of the solutions to above problem. This study also uses Monte Carlo simulation to estimate the reliability each possible path under different level of safety stock. Finally, an example is used to demonstrate the reliability and generality of the described algorithm.%为带有随机需求的车辆路径问题( VRPSD)提供一个具有通用性的算法,将一个VRPSD问题转化成带有运力限制的车辆路径问题(CVRP)的有限集,通过解CVRP问题来解决VRPSD问题.为提高解的可靠性引入了安全库存的概念,并运用了蒙特卡罗仿真方法,得到了在不同安全库存水平下每条路径的可靠性.用实例检验所述方法的可靠性和通用性.
Crisan, Dan
2011-01-01
"Stochastic Analysis" aims to provide mathematical tools to describe and model high dimensional random systems. Such tools arise in the study of Stochastic Differential Equations and Stochastic Partial Differential Equations, Infinite Dimensional Stochastic Geometry, Random Media and Interacting Particle Systems, Super-processes, Stochastic Filtering, Mathematical Finance, etc. Stochastic Analysis has emerged as a core area of late 20th century Mathematics and is currently undergoing a rapid scientific development. The special volume "Stochastic Analysis 2010" provides a sa
Solving Simple Stochastic Games with Few Random Vertices
Gimbert, Hugo; Horn, Florian
2007-01-01
Simple stochastic games are two-player zero-sum stochastic games with turn-based moves, perfect information, and reachability winning conditions. We present two new algorithms computing the values of simple stochastic games. Both of them rely on the existence of optimal permutation strategies, a class of positional strategies derived from permutations of the random vertices. The "permutation-enumeration" algorithm performs an exhaustive search among these strategies, while the "permutation-im...
Stochastic multireference Epstein-Nesbet perturbation theory
Sharma, Sandeep; Jeanmairet, Guillaume; Alavi, Ali; Umrigar, C J
2016-01-01
We extend the recently proposed heat-bath configuration interaction (HCI) method [Holmes, Tubman, Umrigar, J. Chem. Theory Comput. 12, 3674 (2016)], by introducing a stochastic algorithm for performing multireference Epstein-Nesbet perturbation theory, in order to completely eliminate the severe memory bottleneck of the original method. The proposed stochastic algorithm has several attractive features. First, there is no sign problem that plagues several quantum Monte Carlo methods. Second, instead of using Metropolis-Hastings sampling, we use the Alias method to directly sample determinants from the reference wavefunction, thus avoiding correlations between consecutive samples. Third, in addition to removing the memory bottleneck, stochastic-HCI (s-HCI) is faster than the deterministic variant for most systems if a stochastic error of 0.1 mHa is acceptable. Fourth, within the s-HCI algorithm one can trade memory for a modest increase in computer time. Fifth, the perturbative calculation is embarrassingly par...
Institute of Scientific and Technical Information of China (English)
熊娟
2013-01-01
针对粒子群算法跟踪空中目标的缺点,提出随机占优精英裂变算法.首先对父体大权值粒子裂变,形成子代个体空间,覆盖小权值粒子；接着采用精英策略,计算个体在选择过程中被选中的概率,使大部分较优个体参与进化,用极小欧几里德距离计算拥挤距离；然后采用分布函数来确定采样数据的随机占优关系；最后给出了空中目标运动模型.实验仿真表明,滤波轨迹接近空中运动目标的轨迹,在整个观测区域内具有很好的跟踪效果,位置误差标准值小.%Considering the disadvantages of particle swarm optimization algorithm in aerial target tracking, stochastic dominance elite fission algorithm was proposed. After the fission of the parent body weight value particle, individual space was formed, which covered with small weight particle. Then, the elitist strategy was adopted for calculating the probability of an individual being selected, made the most of fine individuals participate in evolution. The crowding distance was calculated out with minimum Euclidean distance, and the distribution function was used to determine the sampling data of stochastic dominance relation. Finally, the aerial target motion model was given. The experimental simulation showed that: 1 ) the filtering trajectoriy is approximate to the aerial target moving trajectory; and 2) the tracking effect is satisfactory during the entire observation area.
Stochastic learning via optimizing the variational inequalities.
Tao, Qing; Gao, Qian-Kun; Chu, De-Jun; Wu, Gao-Wei
2014-10-01
A wide variety of learning problems can be posed in the framework of convex optimization. Many efficient algorithms have been developed based on solving the induced optimization problems. However, there exists a gap between the theoretically unbeatable convergence rate and the practically efficient learning speed. In this paper, we use the variational inequality (VI) convergence to describe the learning speed. To this end, we avoid the hard concept of regret in online learning and directly discuss the stochastic learning algorithms. We first cast the regularized learning problem as a VI. Then, we present a stochastic version of alternating direction method of multipliers (ADMMs) to solve the induced VI. We define a new VI-criterion to measure the convergence of stochastic algorithms. While the rate of convergence for any iterative algorithms to solve nonsmooth convex optimization problems cannot be better than O(1/√t), the proposed stochastic ADMM (SADMM) is proved to have an O(1/t) VI-convergence rate for the l1-regularized hinge loss problems without strong convexity and smoothness. The derived VI-convergence results also support the viewpoint that the standard online analysis is too loose to analyze the stochastic setting properly. The experiments demonstrate that SADMM has almost the same performance as the state-of-the-art stochastic learning algorithms but its O(1/t) VI-convergence rate is capable of tightly characterizing the real learning speed.
Directory of Open Access Journals (Sweden)
Jorge A. Ruiz-Vanoye
2012-07-01
Full Text Available In this paper, we show a survey of meta-heuristics algorithms based on grouping of animals by social behavior for the Traveling Salesman Problem, and propose a new classification of meta-heuristics algorithms (not based on swarm intelligence theory based on grouping of animals: swarm algorithms, schools algorithms, flocks algorithms and herds algorithms: a The swarm algorithms (inspired by the insect swarms and zooplankton swarms: Ant Colony Optimization algorithm – ACO (inspired by the research on the behavior of ant colonies, Firefly Algorithm (based on fireflies, Marriage in Honey Bees Optimization Algorithm - MBO algorithm (inspired by the Honey Bee, Wasp Swarm Algorithm (inspired on the Parasitic wasps, Termite Algorithm (inspired by the termites, Mosquito swarms Algorithm – MSA (inspired by mosquito swarms, zooplankton swarms Algorithm - ZSA (inspired by the Zooplankton and Bumblebees Swarms Algorithm – BSA (inspired by Bumblebees. b The school algorithms (inspired by the fish schools: The Particle Swarm Optimization algorithm – PSO (inspired by social behavior and movement dynamics of fish or schooling. c The flock algorithms (inspired by the bird flocks: the flocking algorithm, and the Particle Swarm Optimization algorithm (inspired on the dynamics of the birds, d The herd and pack Algorithms (inspired by the mammal herds and packs: bat algorithm (inspired by bat, wolf pack search algorithm - WPS (inspired by wolfs, Rats herds algorithm - RATHA (inspired by Rats, Dolphins Herds Algorithm - DHA (inspired by Dolphins and the feral-dogs herd algorithm - FDHA (inspired by feral-dogs herd.
Firefly: A HOT camera core for thermal imagers with enhanced functionality
Pillans, Luke; Harmer, Jack; Edwards, Tim
2015-06-01
Raising the operating temperature of mercury cadmium telluride infrared detectors from 80K to above 160K creates new applications for high performance infrared imagers by vastly reducing the size, weight and power consumption of the integrated cryogenic cooler. Realizing the benefits of Higher Operating Temperature (HOT) requires a new kind of infrared camera core with the flexibility to address emerging applications in handheld, weapon mounted and UAV markets. This paper discusses the Firefly core developed to address these needs by Selex ES in Southampton UK. Firefly represents a fundamental redesign of the infrared signal chain reducing power consumption and providing compatibility with low cost, low power Commercial Off-The-Shelf (COTS) computing technology. This paper describes key innovations in this signal chain: a ROIC purpose built to minimize power consumption in the proximity electronics, GPU based image processing of infrared video, and a software customisable infrared core which can communicate wirelessly with other Battlespace systems.
Bioheterojunction Effect on Fluorescence Origin and Efficiency Improvement of Firefly Chromophores
Cai, Duanjun; Milne, Bruce F; Nogueira, Fernando; 10.1021/jz1009532
2010-01-01
We propose the heterojunction effect in the analysis of the fluorescence mechanism of the firefly chromophore. Following this analysis, and with respect to the HOMO-LUMO gap alignment between the chromophore's functional fragments, three main heterojunction types (I, II, and I*) are identified. Time-dependent density-functional theory optical absorption calculations for the firefly chromophore show that the strongest excitation appears in the deprotonated anion state of the keto form. This can be explained by its high HOMO-LUMO overlap due to strong bio-heterojunction confinement. It is also found that the nitrogen atom in the thiazolyl rings, due to its larger electronegativity, plays a key role in the emission process, its importance growing when HOMO and LUMO overlap at its location. This principle is applied to enhance the chromophore's fluorescence efficiency and to guide the functionalization of molecular optoelectronic devices.
Alam, Rabeka; Karam, Liliana M.; Doane, Tennyson L.; Zylstra, Joshua; Fontaine, Danielle M.; Branchini, Bruce R.; Maye, Mathew M.
2014-12-01
The bioluminescence resonance energy transfer (BRET) between firefly luciferase enzymes and semiconductive quantum dots (QDs) with near infrared emission is described. The QD were phase transferred to aqueous buffers using a histidine mediated phase transfer route, and incubated with a hexahistidine tagged, green emitting variant of firefly luciferase from Photinus pyralis (PPyGRTS). The PPyGRTS were bound to the QD interface via the hexahistidine tag, which effectively displaces the histidine layer and binds directly to the QD interfaces, allowing for short donor-acceptor distances (˜5.5 nm). Due to this, high BRET efficiency ratios of ˜5 were obtained. These PPyGRTS-QD bio-nano conjugates were characterized by transmission electron microscopy, thermal gravimetric analysis, Fourier transform infrared spectroscopy and BRET emission studies. The final optimized conjugate was easily observable by night vision imaging, demonstrating the potential of these materials in imaging and signaling/sensing applications.
Bioinspired Molecular Lantern: Tuning the Firefly Oxyluciferin Emission with Host-Guest Chemistry.
Saleh, Na'il; Suwaid, Abdul Rahman Ba; Alhalabi, Ahmad; Abuibaid, Ahmed Z A; Maltsev, Oleg V; Hintermann, Lukas; Naumov, Panče
2016-08-11
Fireflies generate flashes of visible light via luciferase-catalyzed chemiexcitation of the substrate (luciferin) to the first excited state of the emitter (oxyluciferin). Microenvironment effects are often invoked to explain the effects of the luciferase active pocket on the emission; however, the exceedingly complex spectrochemistry and synthetic burdens have precluded elucidation of the nature of these interactions. To decipher the effects of microenvironment on the light emission, here the hydrophobic interior of cucurbit[7]uril (CB7) is used to mimic the nonpolar active pocket of luciferase. The hydrophobic interior of CB7 induces shifts of the ground-state pKas by 1.9-2.5 units to higher values. Upon sequestration, the emission maxima of neutral firefly oxyluciferin and its conjugate monodeprotonated base are blue-shifted by 40 and 39 nm, respectively, resulting in visual color changes of the emitted light.
Li, Jian-Feng; Zhang, Dandan
2014-07-01
This unit describes the split firefly luciferase complementation (SFLC) assay, a high-throughput quantitative method that can be used to investigate protein-protein interactions (PPIs) in plant mesophyll protoplasts. In SFLC, the two proteins to be tested for interaction are expressed as chimeric proteins, each fused to a different half of firefly luciferase. If the proteins interact, a functional luciferase can be transitorily reconstituted, and is detected using the cell-permeable substrate D-luciferin. An advantage of the SFLC assay is that dynamic changes in PPIs in a cell can be detected in a near real-time manner. Another advantage is the unusually high DNA co-transfection and protein expression efficiencies that can be achieved in plant protoplasts, thereby enhancing the throughput of the method.
Ab initio investigation on the structures and spectra of the firefly luciferin
Institute of Scientific and Technical Information of China (English)
LIU; YaJun; FANG; WeiHai
2007-01-01
The ground state (S0) geometry of the firefly luciferin (LH2) was optimized by both DFT B3LYP and CASSCF methods. The vertical excitation energies (Tv) of three low-lying states (S1, S2, and S3) were calculated by TD-DFT B3LYP//CASSCF method. The S1 geometry was optimized by CASSCF method. Its Tv and the transition energy (Te) were calculated by MS-CASPT2//CASSCF method. Both the TD-DFT and MS-CASPT2 calculated S1 state Tv values agree with the experimental one. The IPEA shift greatly affects the MS-CASPT2 calculated Tv values. Some important excited states of LH2 and oxyluciferin (oxyLH2) are charge-transfer states and have more than one dominant configuration, so for deeply researching the firefly bioluminescence, the multireference calculations are desired.
Institute of Scientific and Technical Information of China (English)
赵学良; 肖永松
2011-01-01
For mnltiple-input single-output (MISO) output-error systems, estimates from the conventional recursive least squares parameter identification method are biased. In order to euhanlce accuracy and speed of convergence for stochastic gradient identification algorithm, an auxiliary model based multi-innovation extended stochastic gradient (AM-MI-ESG) algorithm is presented by replacing the unknown unmeasurable variables in the information vector with the outputs of the auxiliary model, and introducing the innovation length and expanding the scalar innovation to an innovation vector. The AM-M1-ESG algorithm uses not only the current data and innovation but also the past data and innovation at each iteration, thus the parameter estimation accuracy and convergence rate can be improved. The simulation results show that the proposed algorithm is effective.%对于有色噪声干扰的输出误差多输入单输出(MISO)系统,常规的递推最小二乘辨识方法给出的参数估计是有偏的.为了提高随机梯度辨识方法的收敛精度和速度,用辅助模型的输出代替辨识模型信息向量中的未知不可测变量,推导出其辅助模型增广随机梯度辨识算法;再引入新息长度扩展标量新息为新息向量,提出了基于辅助模型的MISO系统多新息增广随机梯度辨识算法.所得算法在每一次的迭代中不仅使用了当前数据和新息,而且使用了过去数据和新息,提高了参数估计精度和收敛速度.仿真例子验证了算法的有效性.
Stochastic geometry for image analysis
Descombes, Xavier
2013-01-01
This book develops the stochastic geometry framework for image analysis purpose. Two main frameworks are described: marked point process and random closed sets models. We derive the main issues for defining an appropriate model. The algorithms for sampling and optimizing the models as well as for estimating parameters are reviewed. Numerous applications, covering remote sensing images, biological and medical imaging, are detailed. This book provides all the necessary tools for developing an image analysis application based on modern stochastic modeling.
Niwa, Kazuki
2016-01-01
Quantum yield (QY), which is defined as the probability of photon production by a single bio/chemiluminescence reaction, is an important factor to characterize luminescence light intensity emitted diffusively from the reaction solution mixture. Here, methods to measure number of photons to determine QY according to the techniques of national radiometry standards are described. As an example, experiments using firefly bioluminescence reactions are introduced.
Hirano, Takashi
2016-01-01
Firefly shows bioluminescence by "luciferin-luciferase" (L-L) reaction using luciferin, luciferase, ATP and O2. The chemical photon generation by an enzymatic reaction is widely utilized for analytical methods including biological imaging in the life science fields. To expand photondetecting analyses with firefly bioluminescence, it is important for users to understand the chemical basis of the L-L reaction. In particular, the emission color variation of the L-L reaction is one of the distinguishing characteristics for multicolor luciferase assay and in vivo imaging. From the viewpoint of fundamental chemistry, this review explains the recent progress in the studies on the molecular mechanism of emission color variation after showing the outline of the reaction mechanism of the whole L-L reaction. On the basis of the mechanism, the progresses in organic synthesis of luciferin analogs modulating their emission colors are also presented to support further developments of red/near infrared in vivo biological imaging utility of firefly bioluminescence.
Martin, Gavin J; Branham, Marc A; Whiting, Michael F; Bybee, Seth M
2017-02-01
Fireflies are some of the most captivating organisms on the planet. They have a rich history as subjects of scientific study, especially in relation to their bioluminescent behavior. Yet, the phylogenetic relationships of fireflies are still poorly understood. Here, we present the first total evidence approach to reconstruct lampyrid phylogeny using both a molecular matrix from six loci and an extensive morphological matrix. Using this phylogeny we test the hypothesis that adult bioluminescence evolved after the origin of the firefly clade. The ancestral state of adult bioluminescence is recovered as non-bioluminescent with one to six gains and five to ten subsequent losses. The monophyly of the family, as well as the subfamilies is also tested. Ototretinae, Cyphonocerinae, Luciolinae (incl. Pristolycus), Amydetinae, "cheguevarinae" sensu Jeng 2008, and Photurinae are highly supported as monophyletic. With the exception of four taxa, Lampyrinae is also recovered as monophyletic with high support. Based on phylogenetic and morphological data Lamprohiza, Phausis, and Lamprigera are transferred to Lampyridae incertae sedis.
Firefly: an optical lithographic system for the fabrication of holographic security labels
Calderón, Jorge; Rincón, Oscar; Amézquita, Ricardo; Pulido, Iván.; Amézquita, Sebastián.; Bernal, Andrés.; Romero, Luis; Agudelo, Viviana
2016-03-01
This paper introduces Firefly, an optical lithography origination system that has been developed to produce holographic masters of high quality. This mask-less lithography system has a resolution of 418 nm half-pitch, and generates holographic masters with the optical characteristics required for security applications of level 1 (visual verification), level 2 (pocket reader verification) and level 3 (forensic verification). The holographic master constitutes the main core of the manufacturing process of security holographic labels used for the authentication of products and documents worldwide. Additionally, the Firefly is equipped with a software tool that allows for the hologram design from graphic formats stored in bitmaps. The software is capable of generating and configuring basic optical effects such as animation and color, as well as effects of high complexity such as Fresnel lenses, engraves and encrypted images, among others. The Firefly technology gathers together optical lithography, digital image processing and the most advanced control systems, making possible a competitive equipment that challenges the best technologies in the industry of holographic generation around the world. In this paper, a general description of the origination system is provided as well as some examples of its capabilities.
Institute of Scientific and Technical Information of China (English)
葛显龙; 王旭; 代应
2011-01-01
在分析集货式动态配送网络中车辆调度问题的基础上,建立基于时间轴的动态车辆调度模型,根据随机需求信息把动态配送问题转换成一系列静态配送问题,并设计量子遗传算法时静态配送问题求解.建立动态车辆调度问题的数学优化模型,设计基于并行节约算法动态插入随机需求信息的混合量子遗传算法,对动态模型进行实时再优化.最后,结合算例时模型和算法进行计算仿真,取得了较好的试验结果.%On the basis of analyzing the vehicle routing problem of dynamic distribution network, the dynamic vehicle routing problem with time axis is proposed. According to the stochastic demand information, the dynamic distribution problem is transformed into a series of static distribution problems, and the improved genetic algorithm is designed to solve them, The mathematical model of dynamic vehiele routing problems is established, and hybrid quantum genetic algorithm based on parallel economy algorithm which can insert random dynamic demand information is designed for real-time optimization of the dynamic model. Finally, combining with a simulated example, the model and algorithm is tested, which achieve good results.
Stochastic power flow modeling
Energy Technology Data Exchange (ETDEWEB)
1980-06-01
The stochastic nature of customer demand and equipment failure on large interconnected electric power networks has produced a keen interest in the accurate modeling and analysis of the effects of probabilistic behavior on steady state power system operation. The principle avenue of approach has been to obtain a solution to the steady state network flow equations which adhere both to Kirchhoff's Laws and probabilistic laws, using either combinatorial or functional approximation techniques. Clearly the need of the present is to develop sound techniques for producing meaningful data to serve as input. This research has addressed this end and serves to bridge the gap between electric demand modeling, equipment failure analysis, etc., and the area of algorithm development. Therefore, the scope of this work lies squarely on developing an efficient means of producing sensible input information in the form of probability distributions for the many types of solution algorithms that have been developed. Two major areas of development are described in detail: a decomposition of stochastic processes which gives hope of stationarity, ergodicity, and perhaps even normality; and a powerful surrogate probability approach using proportions of time which allows the calculation of joint events from one dimensional probability spaces.
Institute of Scientific and Technical Information of China (English)
陆琳; 谭清美
2006-01-01
针对一类随机需求车辆路径问题(stochastic vehicle routing problem,SVRP),结合现实生活中长期客户服务记录所隐含的统计性知识构建新的统计学模型,并将种群搜索与轨迹搜索算法相结合提出了一种新的混合粒子群优化算法.该算法通过引入导引式局部搜索,来减小粒子群搜索陷入局优的可能性以获得更优化解.仿真计算证明混合粒子群优化算法的有效性.同时,该算法也拓展了VRP的算法空间.
Directory of Open Access Journals (Sweden)
Romanu Ekaterini
2006-01-01
Full Text Available This article shows the similarities between Claude Debussy’s and Iannis Xenakis’ philosophy of music and work, in particular the formers Jeux and the latter’s Metastasis and the stochastic works succeeding it, which seem to proceed parallel (with no personal contact to what is perceived as the evolution of 20th century Western music. Those two composers observed the dominant (German tradition as outsiders, and negated some of its elements considered as constant or natural by "traditional" innovators (i.e. serialists: the linearity of musical texture, its form and rhythm.
Principal axes for stochastic dynamics.
Vasconcelos, V V; Raischel, F; Haase, M; Peinke, J; Wächter, M; Lind, P G; Kleinhans, D
2011-09-01
We introduce a general procedure for directly ascertaining how many independent stochastic sources exist in a complex system modeled through a set of coupled Langevin equations of arbitrary dimension. The procedure is based on the computation of the eigenvalues and the corresponding eigenvectors of local diffusion matrices. We demonstrate our algorithm by applying it to two examples of systems showing Hopf bifurcation. We argue that computing the eigenvectors associated to the eigenvalues of the diffusion matrix at local mesh points in the phase space enables one to define vector fields of stochastic eigendirections. In particular, the eigenvector associated to the lowest eigenvalue defines the path of minimum stochastic forcing in phase space, and a transform to a new coordinate system aligned with the eigenvectors can increase the predictability of the system.
Principal axes for stochastic dynamics
Vasconcelos, V V; Haase, M; Peinke, J; Wächter, M; Lind, P G; Kleinhans, D
2011-01-01
We introduce a general procedure for directly ascertaining how many independent stochastic sources exist in a complex system modeled through a set of coupled Langevin equations of arbitrary dimension. The procedure is based on the computation of the eigenvalues and the corresponding eigenvectors of local diffusion matrices. We demonstrate our algorithm by applying it to two examples of systems showing Hopf-bifurcation. We argue that computing the eigenvectors associated to the eigenvalues of the diffusion matrix at local mesh points in the phase space enables one to define vector fields of stochastic eigendirections. In particular, the eigenvector associated to the lowest eigenvalue defines the path of minimum stochastic forcing in phase space, and a transform to a new coordinate system aligned with the eigenvectors can increase the predictability of the system.
Applied probability and stochastic processes
Sumita, Ushio
1999-01-01
Applied Probability and Stochastic Processes is an edited work written in honor of Julien Keilson. This volume has attracted a host of scholars in applied probability, who have made major contributions to the field, and have written survey and state-of-the-art papers on a variety of applied probability topics, including, but not limited to: perturbation method, time reversible Markov chains, Poisson processes, Brownian techniques, Bayesian probability, optimal quality control, Markov decision processes, random matrices, queueing theory and a variety of applications of stochastic processes. The book has a mixture of theoretical, algorithmic, and application chapters providing examples of the cutting-edge work that Professor Keilson has done or influenced over the course of his highly-productive and energetic career in applied probability and stochastic processes. The book will be of interest to academic researchers, students, and industrial practitioners who seek to use the mathematics of applied probability i...
Online Advertisement, Optimization and Stochastic Networks
Bo,; Srikant, R
2010-01-01
In this paper, we propose a stochastic model to describe how modern search service providers charge client companies based on users' queries for their related "adwords" by using certain advertisement assignment strategies. We formulate an optimization problem to maximize the long-term average revenue for the service provider under each client's long-term average budget constraint, and design an online algorithm which captures the stochastic properties of users' queries and click-through behaviors. We solve the optimization problem by making connections to scheduling problems in wireless networks, queueing theory and stochastic networks. With a small customizable parameter $\\epsilon$ which is the step size used in each iteration of the online algorithm, we have shown that our online algorithm achieves a long-term average revenue which is within $O(\\epsilon)$ of the optimal revenue and the overdraft level of this algorithm is upper-bounded by $O(1/\\epsilon)$.
Institute of Scientific and Technical Information of China (English)
刘艳君; 丁锋
2016-01-01
It is an issue that multivariable systems with high dimensions have many parameters, resulting in heavy computational costs in identification methods. Therefore, a coupled stochastic gradient algorithm is derived for multivariable systems based on the coupling identification concept. The identification model is decomposed into several single-output systems, and the parameter estimates are coupled during the subsystem identification by using the gradient search. The convergence properties are analyzed by using the martingale convergence theorem. Compared with the recursive least squares algorithm and the coupled least squares algorithm, the proposed algorithm has less computational load. The convergence rate can be improved by introducing a forgetting factor. Performance analysis verifies that the proposed algorithm converges. The simulation results show the effectiveness of the proposed algorithm.%针对多变量系统维数大、参数多、一般的辨识算法计算量大的问题，基于耦合辨识概念，推导多变量系统的耦合随机梯度算法，利用鞅收敛定理分析算法的收敛性能。算法的主要思想是将系统模型分解为多个单输出子系统，在子系统的递推辨识过程中，将每个子系统的参数估计值耦合起来。所提出算法与最小二乘算法和耦合最小二乘算法相比，具有较少的计算量，收敛速度可以通过引入遗忘因子得到改善。性能分析表明了所提出算法收敛，仿真实例验证了算法的有效性。
Stochastic Optimization of Wind Turbine Power Factor Using Stochastic Model of Wind Power
DEFF Research Database (Denmark)
Chen, Peiyuan; Siano, Pierluigi; Bak-Jensen, Birgitte
2010-01-01
. The optimization algorithm utilizes the stochastic models of wind power generation (WPG) and load demand to take into account their stochastic variation. The stochastic model of WPG is developed on the basis of a limited autoregressive integrated moving average (LARIMA) model by introducing a crosscorrelation......This paper proposes a stochastic optimization algorithm that aims to minimize the expectation of the system power losses by controlling wind turbine (WT) power factors. This objective of the optimization is subject to the probability constraints of bus voltage and line current requirements...... structure to the LARIMA model. The proposed stochastic optimization is carried out on a 69-bus distribution system. Simulation results confirm that, under various combinations of WPG and load demand, the system power losses are considerably reduced with the optimal setting of WT power factor as compared...
Learning from nature: Nature-inspired algorithms
DEFF Research Database (Denmark)
Albeanu, Grigore; Madsen, Henrik; Popentiu-Vladicescu, Florin
2016-01-01
During last decade, the nature has inspired researchers to develop new algorithms. The largest collection of nature-inspired algorithms is biology-inspired: swarm intelligence (particle swarm optimization, ant colony optimization, cuckoo search, bees' algorithm, bat algorithm, firefly algorithm etc...... on collective social behaviour of organisms, researchers have developed optimization strategies taking into account not only the individuals, but also groups and environment. However, learning from nature, new classes of approaches can be identified, tested and compared against already available algorithms....... This work reviews the most effective nature-inspired algorithms and describes learning strategies based on nature oriented thinking. Examples and the benefits obtained from applying nature-inspired strategies in test generation, learners group optimization, and artificial immune systems for learning...
Wolf Pack Algorithm for Unconstrained Global Optimization
Directory of Open Access Journals (Sweden)
Hu-Sheng Wu
2014-01-01
Full Text Available The wolf pack unites and cooperates closely to hunt for the prey in the Tibetan Plateau, which shows wonderful skills and amazing strategies. Inspired by their prey hunting behaviors and distribution mode, we abstracted three intelligent behaviors, scouting, calling, and besieging, and two intelligent rules, winner-take-all generation rule of lead wolf and stronger-survive renewing rule of wolf pack. Then we proposed a new heuristic swarm intelligent method, named wolf pack algorithm (WPA. Experiments are conducted on a suit of benchmark functions with different characteristics, unimodal/multimodal, separable/nonseparable, and the impact of several distance measurements and parameters on WPA is discussed. What is more, the compared simulation experiments with other five typical intelligent algorithms, genetic algorithm, particle swarm optimization algorithm, artificial fish swarm algorithm, artificial bee colony algorithm, and firefly algorithm, show that WPA has better convergence and robustness, especially for high-dimensional functions.
Institute of Scientific and Technical Information of China (English)
商丽媛; 谭清美
2014-01-01
The hub location is an important issue for the hub-and-spoke network optimization design, and hub covering is a type of the hub location problem. The uncertainty of the hub station construction costs and the uncertainty of the distance between two nodes are considered. The model of stochastic��-robust multiple allocation hub set covering problem is proposed with combination of stochastic optimization and robust optimization. Binary quantum-behaved particle swarm optimization algorithm is improved based on immunity thought. Immune quantum-behaved particle swarm optimization algorithm is proposed to solve the stochastic��-robust multiple allocation hub set covering model. The simulation example of this model is given, and the result shows the feasibility and effectiveness of the proposed model and algorithm.%枢纽站选址是轴辐式网络优化设计的重要问题，枢纽站覆盖则是该问题的一个类型。考虑枢纽站建站成本和节点间运输距离的不确定性，结合随机优化和鲁棒优化方法，建立了完备轴辐式网络中多分配枢纽站集覆盖问题的随机��-鲁棒优化模型；采用二进制编码，对量子粒子群算法进行改进，加入免疫思想，设计了免疫量子粒子群求解算法。最后通过算例对模型进行仿真计算，结果表明了该模型及算法的可行性和有效性。
Institute of Scientific and Technical Information of China (English)
韩玉艳; 巩敦卫; 张勇
2015-01-01
For the blocking lot-streaming flow shop scheduling problem with stochastic processing time,a method is proposed to transform it into a determinate one using Monte Carlo sampling method.An improved artificial bee colony algorithm is developed, in which a harmony search and local search based on insertion operators are adopted to balance the algorithm’s capability in explo-ration and exploitation.The proposed algorithm is applied to 24 instances of blocking lot-streaming flow shop scheduling prob-lem.The experimental results show that the improved algorithm can generate solutions with high quality and reduce the influence resulting from uncertainties.%针对含有随机加工时间的阻塞批量流水线调度问题，利用蒙特卡洛采样方法，将不确定加工时间的阻塞批量流水线调度问题转化为确定加工时间的阻塞批量调度问题。采用改进的人工蜂群算法，对上述转化后的调度问题进行求解。算法中加入了和声搜索和基于插入操作的局部搜索算子，以改进全局探索和局部开发能力，并将改进的算法应用到阻塞批量调度的24个算例中。仿真实验结果表明，改进的人工蜂群算法能够降低调度中的不确定因素带来的影响，产生高质量的解。
一类随机顾客车辆路径问题及其算法%Stochastic Customer Vehicle Routing Problem and Its Algorithm Analysis
Institute of Scientific and Technical Information of China (English)
陆琳; 蔡绍洪
2010-01-01
针对一类随机顾客车辆路径问题(Vehicle routing problem with stochastic customer,VRPSC),探讨了VRPSC的实时动态规划机制,并结合运送货物需求量的不同特性,分析了车辆供货中遇到服务路线失败时的两种不同服务策略并构建了相应的模型.设计了针对VRPSC的蚁群算法,并选用60个节点的基准问题对VRPSC的动态模型进行了仿真计算.结果表明,对顾客信息进行数据挖掘以获取较精确经验概率以及采用部分服务策略均有助于缩短车辆总行驶路径,为有效降低车辆的运行成本提供了科学依据.
Energy Technology Data Exchange (ETDEWEB)
Zhang, Qichun; Zhou, Jinglin; Wang, Hong; Chai, Tianyou
2016-08-31
In this paper, stochastic coupling attenuation is investigated for a class of multi-variable bilinear stochastic systems and a novel output feedback m-block backstepping controller with linear estimator is designed, where gradient descent optimization is used to tune the design parameters of the controller. It has been shown that the trajectories of the closed-loop stochastic systems are bounded in probability sense and the stochastic coupling of the system outputs can be effectively attenuated by the proposed control algorithm. Moreover, the stability of the stochastic systems is analyzed and the effectiveness of the proposed method has been demonstrated using a simulated example.
Stochastic Vehicle Routing with Recourse
Goertz, Inge Li; Saket, Rishi
2012-01-01
We study the classic Vehicle Routing Problem in the setting of stochastic optimization with recourse. StochVRP is a two-stage optimization problem, where demand is satisfied using two routes: fixed and recourse. The fixed route is computed using only a demand distribution. Then after observing the demand instantiations, a recourse route is computed -- but costs here become more expensive by a factor lambda. We present an O(log^2 n log(n lambda))-approximation algorithm for this stochastic routing problem, under arbitrary distributions. The main idea in this result is relating StochVRP to a special case of submodular orienteering, called knapsack rank-function orienteering. We also give a better approximation ratio for knapsack rank-function orienteering than what follows from prior work. Finally, we provide a Unique Games Conjecture based omega(1) hardness of approximation for StochVRP, even on star-like metrics on which our algorithm achieves a logarithmic approximation.
Berger, Frank; Paulmurugan, Ramasamy; Bhaumik, Srabani; Gambhir, Sanjiv Sam
2008-01-01
Purpose Firefly luciferase catalyzes the oxidative decarboxylation of D-luciferin to oxyluciferin in the presence of cofactors, producing bioluminescence. This reaction is used in optical bioluminescence-based molecular imaging approaches to detect the expression of the firefly luciferase reporter g
Stochastic Simulations of Cellular Biological Processes
2007-06-01
model kinetics of a system of chemical reactions is to use a stochastic 2. Stochastic Simulation Algorithm approach in terms of the Chemical Master...number of processors and running time) for interactive disk spae ad, herfor, my ceat meory simulations. Therefore, in addition to running in an...management problems for simulations involving a large inteative mode, foNScan as o run in ’n number of long runs or for large reaction networks. interactive
Lanchier, Nicolas
2017-01-01
Three coherent parts form the material covered in this text, portions of which have not been widely covered in traditional textbooks. In this coverage the reader is quickly introduced to several different topics enriched with 175 exercises which focus on real-world problems. Exercises range from the classics of probability theory to more exotic research-oriented problems based on numerical simulations. Intended for graduate students in mathematics and applied sciences, the text provides the tools and training needed to write and use programs for research purposes. The first part of the text begins with a brief review of measure theory and revisits the main concepts of probability theory, from random variables to the standard limit theorems. The second part covers traditional material on stochastic processes, including martingales, discrete-time Markov chains, Poisson processes, and continuous-time Markov chains. The theory developed is illustrated by a variety of examples surrounding applications such as the ...
Energy Technology Data Exchange (ETDEWEB)
Blaskiewicz, M.
2011-01-01
Stochastic Cooling was invented by Simon van der Meer and was demonstrated at the CERN ISR and ICE (Initial Cooling Experiment). Operational systems were developed at Fermilab and CERN. A complete theory of cooling of unbunched beams was developed, and was applied at CERN and Fermilab. Several new and existing rings employ coasting beam cooling. Bunched beam cooling was demonstrated in ICE and has been observed in several rings designed for coasting beam cooling. High energy bunched beams have proven more difficult. Signal suppression was achieved in the Tevatron, though operational cooling was not pursued at Fermilab. Longitudinal cooling was achieved in the RHIC collider. More recently a vertical cooling system in RHIC cooled both transverse dimensions via betatron coupling.
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
In this paper, the stochastic flow of mappings generated by a Feller convolution semigroup on a compact metric space is studied. This kind of flow is the generalization of superprocesses of stochastic flows and stochastic diffeomorphism induced by the strong solutions of stochastic differential equations.
Automated Flight Routing Using Stochastic Dynamic Programming
Ng, Hok K.; Morando, Alex; Grabbe, Shon
2010-01-01
Airspace capacity reduction due to convective weather impedes air traffic flows and causes traffic congestion. This study presents an algorithm that reroutes flights in the presence of winds, enroute convective weather, and congested airspace based on stochastic dynamic programming. A stochastic disturbance model incorporates into the reroute design process the capacity uncertainty. A trajectory-based airspace demand model is employed for calculating current and future airspace demand. The optimal routes minimize the total expected traveling time, weather incursion, and induced congestion costs. They are compared to weather-avoidance routes calculated using deterministic dynamic programming. The stochastic reroutes have smaller deviation probability than the deterministic counterpart when both reroutes have similar total flight distance. The stochastic rerouting algorithm takes into account all convective weather fields with all severity levels while the deterministic algorithm only accounts for convective weather systems exceeding a specified level of severity. When the stochastic reroutes are compared to the actual flight routes, they have similar total flight time, and both have about 1% of travel time crossing congested enroute sectors on average. The actual flight routes induce slightly less traffic congestion than the stochastic reroutes but intercept more severe convective weather.
Investigating Performance of Various Natural Computing Algorithms
Directory of Open Access Journals (Sweden)
Bharat V. Chawda
2017-01-01
Full Text Available Nature is there since millenniums. Natural elements have withstood harsh complexities since years and have proved their efficiency in tackling them. This aspect has inspired many researchers to design algorithms based on phenomena in the natural world since the last couple of decades. Such algorithms are known as natural computing algorithms or nature inspired algorithms. These algorithms have established their ability to solve a large number of real-world complex problems by providing optimal solutions within the reasonable time duration. This paper presents an investigation by assessing the performance of some of the well-known natural computing algorithms with their variations. These algorithms include Genetic Algorithms, Ant Colony Optimization, River Formation Dynamics, Firefly Algorithm and Cuckoo Search. The Traveling Sales man Problem is used here as a test bed problem for performance evaluation of these algorithms. It is a kind of combinatorial optimization problem and known as one the most famous NP-Hard problems. It is simple and easy to understand, but at the same time, very difficult to find the optimal solution in a reasonable time – particularly with the increase in a number of cities. The source code for the above natural computing algorithms is developed in MATLAB R2015b and applied on several TSP instances given in TSPLIB library. Results obtained are analyzed based on various criteria such as tour length, required iterations, convergence time and quality of solutions. Conclusions derived from this analysis help to establish the superiority of Firefly Algorithms over the other algorithms in comparative terms.
Stochastic Blind Motion Deblurring
Xiao, Lei
2015-05-13
Blind motion deblurring from a single image is a highly under-constrained problem with many degenerate solutions. A good approximation of the intrinsic image can therefore only be obtained with the help of prior information in the form of (often non-convex) regularization terms for both the intrinsic image and the kernel. While the best choice of image priors is still a topic of ongoing investigation, this research is made more complicated by the fact that historically each new prior requires the development of a custom optimization method. In this paper, we develop a stochastic optimization method for blind deconvolution. Since this stochastic solver does not require the explicit computation of the gradient of the objective function and uses only efficient local evaluation of the objective, new priors can be implemented and tested very quickly. We demonstrate that this framework, in combination with different image priors produces results with PSNR values that match or exceed the results obtained by much more complex state-of-the-art blind motion deblurring algorithms.
Directory of Open Access Journals (Sweden)
Jongdee To-im
2012-01-01
Full Text Available A firefly learning module for the sustainable development was developed for Thai secondary school students in the study province. A deeper connection between environment, social and economic dimensions, which lies at the core of sustainability, became the key issue for this learning module. Also an important dimension of the module was the empowerment of the students themselves. Through brainstorming and ensuring activities, students were expected to act at the local level and to develop a deeper sense of responsibility. This study aimed at to develop learning module based on both the principle of inquiry approaches and the collaboration of a community of learners. Mixed methods paradigm was employed for data collection and analysis. Four data collection techniques: classroom observations, interviews, written documents, and questionnaire were employed. The Statistical Package for the Social Sciences was applied for quantitative data analysis. The qualitative data were analyzed using open and axial coding techniques. The analyzed data were categorized to describe context of developed learning module, the students’ conceptual understanding, and awareness toward ecosystems and firefly conservation. The study involved one-9th grade class of twenty students from one school in Samutsongkhram Province, Thailand. The results indicated that the developed learning module improved students’ conceptual understanding, perceptions, and self-reported behavior toward ecosystems and firefly conservation. The results of the effectiveness of this learning module clearly showed that the students gained significantly higher score in conceptual understanding and perceptions after participating in this learning module. The results from interviews showed that the students changed from a poor to a very good level of understanding after involvement in this learning module. The results also indicated that none of the students remained at the poor level after
Sobczyk, K
1985-01-01
This is a concise, unified exposition of the existing methods of analysis of linear stochastic waves with particular reference to the most recent results. Both scalar and vector waves are considered. Principal attention is concentrated on wave propagation in stochastic media and wave scattering at stochastic surfaces. However, discussion extends also to various mathematical aspects of stochastic wave equations and problems of modelling stochastic media.
Digital simulation and modeling of nonlinear stochastic systems
Energy Technology Data Exchange (ETDEWEB)
Richardson, J M; Rowland, J R
1981-04-01
Digitally generated solutions of nonlinear stochastic systems are not unique but depend critically on the numerical integration algorithm used. Some theoretical and practical implications of this dependence are examined. The Ito-Stratonovich controversy concerning the solution of nonlinear stochastic systems is shown to be more than a theoretical debate on maintaining Markov properties as opposed to utilizing the computational rules of ordinary calculus. The theoretical arguments give rise to practical considerations in the formation and solution of discrete models from continuous stochastic systems. Well-known numerical integration algorithms are shown not only to provide different solutions for the same stochastic system but also to correspond to different stochastic integral definitions. These correspondences are proved by considering first and second moments of solutions that result from different integration algorithms and then comparing the moments to those arising from various stochastic integral definitions. This algorithm-dependence of solutions is in sharp contrast to the deterministic and linear stochastic cases in which unique solutions are determined by any convergent numerical algorithm. Consequences of the relationship between stochastic system solutions and simulation procedures are presented for a nonlinear filtering example. Monte Carlo simulations and statistical tests are applied to the example to illustrate the determining role which computational procedures play in generating solutions.
Digital simulation and modeling of nonlinear stochastic systems
Energy Technology Data Exchange (ETDEWEB)
Richardson, J M; Rowland, J R
1980-01-01
Digitally generated solutions of nonlinear stochastic systems are not unique, but depend critically on the numerical integration algorithm used. Some theoretical and practical implications of this dependence are examined. The Ito-Stratonovich controversy concerning the solution of nonlinear stochastic systems is shown to be more than a theoretical debate on maintaining Markov properties as opposed to utilizing the computational rules of ordinary calculus. The theoretical arguments give rise to practical considerations in the formation and solution of discrete models from continuous stochastic systems. Well-known numerical integration algorithms are shown not only to provide different solutions for the same stochastic system, but also to correspond to different stochastic integral definitions. These correspondences are proved by considering first and second moments of solutions resulting from different integration algorithms and comparing the moments to those arising from various stochastic integral definitions. Monte Carlo simulations and statistical tests are applied to illustrate the determining role that computational procedures play in generating solutions. This algorithm dependence of solutions is in sharp contrast to the deterministic and linear stochastic cases, in which unique solutions are determined by any convergent numerical algorithm. Consequences of this relationship between stochastic system solutions and simulation procedures are presented for a nonlinear filtering example. 2 figures.
Online Stochastic Ad Allocation: Efficiency and Fairness
Feldman, Jon; Korula, Nitish; Mirrokni, Vahab S; Stein, Cliff
2010-01-01
We study the efficiency and fairness of online stochastic display ad allocation algorithms from a theoretical and practical standpoint. In particular, we study the problem of maximizing efficiency in the presence of stochastic information. In this setting, each advertiser has a maximum demand for impressions of display ads that will arrive online. In our model, inspired by the concept of free disposal in economics, we assume that impressions that are given to an advertiser above her demand are given to her for free. Our main theoretical result is to present a training-based algorithm that achieves a (1-\\epsilon)-approximation guarantee in the random order stochastic model. In the corresponding online matching problem, we learn a dual variable for each advertiser, based on data obtained from a sample of impressions. We also discuss different fairness measures in online ad allocation, based on comparison to an ideal offline fair solution, and develop algorithms to compute "fair" allocations. We then discuss sev...
Stochastic homothetically revealed preference for tight stochastic demand functions
Jan Heufer
2009-01-01
This paper strengthens the framework of stochastic revealed preferences introduced by Bandyopadhyay et al. (1999, 2004) for stochastic homothetically revealed preferences for tight stochastic demand functions.
Quantum/molecular mechanics study of firefly bioluminescence on luciferase oxidative conformation
Pinto da Silva, Luís; Esteves da Silva, Joaquim C. G.
2014-07-01
This is the first report of a computational study of the color tuning mechanism of firefly bioluminescence, using the oxidative conformation of luciferase. The results of these calculations demonstrated that the electrostatic field generated by luciferase is fundamental both for the emission shift and efficiency. Further calculations indicated that a shift in emission is achieved by modulating the energy, at different degrees, of the emissive and ground states. These differences in energy modulation will then lead to changes in the energy gap between the states.
Virus-mediated expression of firefly luciferase in the parasitic protozoan Giardia lamblia.
1995-01-01
Giardia lamblia, a prevalent human pathogen and one of the lineages that branched earliest from prokaryotes, can be infected with a double-stranded RNA virus, giardiavirus (GLV). The 6,277-bp viral genome has been previously cloned (A.L. Wang, H.-M. Yang, K.A. Shen, and C.C. Wang, Proc. Natl. Acad. Sci. USA 90:8595-8599, 1993; C.-H. Wu, C.C. Wang, H.M. Yang, and A.L. Wang, Gene, in press) and was converted to a transfection vector for G. lamblia in the present study. By flanking the firefly l...
Gross anatomy of central nervous system in firefly, Pteroptyx tener (Coleoptera: Lampyridae)
Hudawiyah, Nur; Wahida, O. Nurul; Norela, S.
2015-09-01
This paper describes for the first time the organization and fine structure of the central nervous system (CNS) in the fireflies, Pteroptyx tener (Coleoptera: Lampyridae). The morphology of the CNS was examined by using Carl Zeiss AxioScope A1 photomicroscope with iSolution Lite software. Some specific structural features such as the localization of protocerebrum, deutocerebrum and tritocerebrum in the brain region were analyzed. Other than that, the nerve cord and its peripheral structure were also analyzed. This study suggests that, there is a very obvious difference between male and female central nervous system which illustrates that they may differ in function in controlling physiological and behavioral activities.
Stochastic reconstruction of sandstones
Manwart; Torquato; Hilfer
2000-07-01
A simulated annealing algorithm is employed to generate a stochastic model for a Berea sandstone and a Fontainebleau sandstone, with each a prescribed two-point probability function, lineal-path function, and "pore size" distribution function, respectively. We find that the temperature decrease of the annealing has to be rather quick to yield isotropic and percolating configurations. A comparison of simple morphological quantities indicates good agreement between the reconstructions and the original sandstones. Also, the mean survival time of a random walker in the pore space is reproduced with good accuracy. However, a more detailed investigation by means of local porosity theory shows that there may be significant differences of the geometrical connectivity between the reconstructed and the experimental samples.
Improved bounds for stochastic matching
Li, Jian
2010-01-01
In this paper we study stochastic matching problems that are motivated by applications in online dating and kidney exchange programs. We consider two probing models: edge probing and matching probing. Our main result is an algorithm that finds a matching-probing strategy attaining a small constant approximation ratio. An interesting aspect of our approach is that we compare the cost our solution to the best edge-probing strategy. Thus, we indirectly show that the best matching-probing strategy is only a constant factor away from the best edge-probing strategy. Even though our algorithm has a slightly worse approximation ratio than a greedy algorithm for edge-probing strategies, we show that the two algorithms can be combined to get improved approximations.
Institute of Scientific and Technical Information of China (English)
邵言剑; 陶卿; 姜纪远; 周柏
2014-01-01
Stochastic gradient descent (SGD) is one of the efficient methods for dealing with large-scale data. Recent research shows that the black-box SGD method can reach an O(1/T) convergence rate for strongly-convex problems. However, for solving the regularized problem with L1 plus L2 terms, the convergence rate of the structural optimization method such as COMID (composite objective mirror descent) can only attain O(lnT/T). In this paper, a weighted algorithm based on COMID is presented, to keep the sparsity imposed by the L1 regularization term. A prove is provided to show that it achieves an O(1/T) convergence rate. Furthermore, the proposed scheme takes the advantage of computation on-the-fly so that the computational costs are reduced. The experimental results demonstrate the correctness of theoretic analysis and effectiveness of the proposed algorithm.%随机梯度下降(SGD)算法是处理大规模数据的有效方法之一。黑箱方法SGD在强凸条件下能达到最优的O(1/T)收敛速率，但对于求解L1+L2正则化学习问题的结构优化算法，如COMID(composite objective mirror descent)仅具有O(lnT/T)的收敛速率。提出一种能够保证稀疏性基于COMID的加权算法，证明了其不仅具有O(1/T)的收敛速率，还具有on-the-fly计算的优点，从而减少了计算代价。实验结果表明了理论分析的正确性和所提算法的有效性。
AESS: Accelerated Exact Stochastic Simulation
Jenkins, David D.; Peterson, Gregory D.
2011-12-01
The Stochastic Simulation Algorithm (SSA) developed by Gillespie provides a powerful mechanism for exploring the behavior of chemical systems with small species populations or with important noise contributions. Gene circuit simulations for systems biology commonly employ the SSA method, as do ecological applications. This algorithm tends to be computationally expensive, so researchers seek an efficient implementation of SSA. In this program package, the Accelerated Exact Stochastic Simulation Algorithm (AESS) contains optimized implementations of Gillespie's SSA that improve the performance of individual simulation runs or ensembles of simulations used for sweeping parameters or to provide statistically significant results. Program summaryProgram title: AESS Catalogue identifier: AEJW_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEJW_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: University of Tennessee copyright agreement No. of lines in distributed program, including test data, etc.: 10 861 No. of bytes in distributed program, including test data, etc.: 394 631 Distribution format: tar.gz Programming language: C for processors, CUDA for NVIDIA GPUs Computer: Developed and tested on various x86 computers and NVIDIA C1060 Tesla and GTX 480 Fermi GPUs. The system targets x86 workstations, optionally with multicore processors or NVIDIA GPUs as accelerators. Operating system: Tested under Ubuntu Linux OS and CentOS 5.5 Linux OS Classification: 3, 16.12 Nature of problem: Simulation of chemical systems, particularly with low species populations, can be accurately performed using Gillespie's method of stochastic simulation. Numerous variations on the original stochastic simulation algorithm have been developed, including approaches that produce results with statistics that exactly match the chemical master equation (CME) as well as other approaches that approximate the CME. Solution
ada: An R Package for Stochastic Boosting
Directory of Open Access Journals (Sweden)
Mark Culp
2006-09-01
Full Text Available Boosting is an iterative algorithm that combines simple classification rules with ‘mediocre’ performance in terms of misclassification error rate to produce a highly accurate classification rule. Stochastic gradient boosting provides an enhancement which incorporates a random mechanism at each boosting step showing an improvement in performance and speed in generating the ensemble. ada is an R package that implements three popular variants of boosting, together with a version of stochastic gradient boosting. In addition, useful plots for data analytic purposes are provided along with an extension to the multi-class case. The algorithms are illustrated with synthetic and real data sets.
ada: An R Package for Stochastic Boosting
Directory of Open Access Journals (Sweden)
Mark Culp
2006-09-01
Full Text Available Boosting is an iterative algorithm that combines simple classification rules with "mediocre" performance in terms of misclassification error rate to produce a highly accurate classification rule. Stochastic gradient boosting provides an enhancement which incorporates a random mechanism at each boosting step showing an improvement in performance and speed in generating the ensemble. ada is an R package that implements three popular variants of boosting, together with a version of stochastic gradient boosting. In addition, useful plots for data analytic purposes are provided along with an extension to the multi-class case. The algorithms are illustrated with synthetic and real data sets.
Brew, C A J
1995-01-01
In this paper we provide a probabilistic interpretation for typed feature structures very similar to those used by Pollard and Sag. We begin with a version of the interpretation which lacks a treatment of re-entrant feature structures, then provide an extended interpretation which allows them. We sketch algorithms allowing the numerical parameters of our probabilistic interpretations of HPSG to be estimated from corpora.
Genome Size in North American Fireflies: Substantial Variation Likely Driven by Neutral Processes
Johnston, J. Spencer; Stanger-Hall, Kathrin F.; Hjelmen, Carl E.; Hanrahan, Shawn J.; Korunes, Katharine; Hall, David
2017-01-01
Abstract Eukaryotic genomes show tremendous size variation across taxa. Proximate explanations for genome size variation include differences in ploidy and amounts of noncoding DNA, especially repetitive DNA. Ultimate explanations include selection on physiological correlates of genome size such as cell size, which in turn influence body size, resulting in the often-observed correlation between body size and genome size. In this study, we examined body size and repetitive DNA elements in relationship to the evolution of genome size in North American representatives of a single beetle family, the Lampyridae (fireflies). The 23 species considered represent an excellent study system because of the greater than 5-fold range of genome sizes, documented here using flow cytometry, and the 3-fold range in body size, measured using pronotum width. We also identified common genomic repetitive elements using low-coverage sequencing. We found a positive relationship between genome size and repetitive DNA, particularly retrotransposons. Both genome size and these elements were evolving as expected given phylogenetic relatedness. We also tested whether genome size varied with body size and found no relationship. Together, our results suggest that genome size is evolving neutrally in fireflies. PMID:28541478
The evolution of adult light emission color in North American fireflies.
Hall, David W; Sander, Sarah E; Pallansch, Jennifer C; Stanger-Hall, Kathrin F
2016-09-01
Firefly species (Lampyridae) vary in the color of their adult bioluminescence. It has been hypothesized that color is selected to enhance detection by conspecifics. One mechanism to improve visibility of the signal is to increase contrast against ambient light. High contrast implies that fireflies active early in the evening will emit yellower luminescence to contrast against ambient light reflected from green vegetation, especially in habitats with high vegetation cover. Another mechanism to improve visibility is to use reflection off the background to enhance the light signal. Reflectance predicts that sedentary females will produce greener light to maximize reflection off the green vegetation on which they signal. To test these predictions, we recorded over 7500 light emission spectra and determined peak emission wavelength for 675 males, representing 24 species, at 57 field sites across the Eastern United States. We found support for both hypotheses: males active early in more vegetated habitats produced yellower flashes in comparison to later-active males with greener flashes. Further, in two of the eight species with female data, female light emissions were significantly greener as compared to males.
Scaling Laws in the Transient Dynamics of Firefly-like Oscillators
Energy Technology Data Exchange (ETDEWEB)
Rubido, N; Cabeza, C; Marti, A [Igua 4225, Instituto de Fisica, Facultad de Ciencias, Montevideo (Uruguay); Ramirez Avila, G M, E-mail: nrubido@fisica.edu.uy [Institut fuer Physik, Humboldt Universitaet zu Berlin (Germany)
2011-03-01
Fireflies constitute a paradigm of pulse-coupled oscillators. In order to tackle the problems related to synchronisation transients of pulse-coupled oscillators, a Light-Controlled Oscillator (LCO) model is presented. A single LCO constitutes a one-dimensional relaxation oscillator described by two distinct time-scales meant to mimic fireflies in the sense that: it is capable of emitting light in a pulse-like fashion and detect the emitted by others in order to adjust its oscillation. We present dynamical results for two interacting LCOs in the torus for all possible coupling configurations. Transient times to the synchronous limit cycle are obtained experimentally and numerically as a function of initial conditions and coupling strengths. Scaling laws are found based on dimensional analysis and critical exponents calculated, thus, global dynamic is restricted. Furthermore, an analytical orthogonal transformation that allows to calculate Floquet multipliers directly from the time series is presented. As a consequence, local dynamics is also fully characterized. This transformation can be easily extended to a system with an arbitrary number of interacting LCOs.
Kröger, Ronald H H; Gislén, Anna
2004-01-01
The camera eyes of fishes and cephalopods have come forth by convergent evolution. In a variety of vertebrates capable of color vision, longitudinal chromatic aberration (LCA) of the optical system is corrected for by the exactly tuned longitudinal spherical aberration (LSA) of the crystalline lens. The LSA leads to multiple focal lengths, such that several wavelengths can be focused on the retina. We investigated whether that is also the case in the firefly squid (Watasenia scintillans), a cephalopod species that is likely to have color vision. It was found that the lens of W. scintillans is virtually free of LSA and uncorrected for LCA. However, the eye does not suffer from LCA because of a banked retina. Photoreceptors sensitive to short and long wavelengths are located at appropriate distances from the lens, such that they receive well-focused images. Such a design is an excellent solution for the firefly squid because a large area of the retina is monochromatically organized and it allows for double use of the surface area in the dichromatically organized part of the retina. However, it is not a universal solution since compensation for LCA by a banked retina requires that eye size and/or spectral separation between photopigments is small.
Raev, M. D.; Sharkov, E. A.; Tikhonov, V. V.; Repina, I. A.; Komarova, N. Yu.
2015-12-01
The GLOBAL-RT database (DB) is composed of long-term radio heat multichannel observation data received from DMSP F08-F17 satellites; it is permanently supplemented with new data on the Earth's exploration from the space department of the Space Research Institute, Russian Academy of Sciences. Arctic ice-cover areas for regions higher than 60° N latitude were calculated using the DB polar version and NASA Team 2 algorithm, which is widely used in foreign scientific literature. According to the analysis of variability of Arctic ice cover during 1987-2014, 2 months were selected when the Arctic ice cover was maximal (February) and minimal (September), and the average ice cover area was calculated for these months. Confidence intervals of the average values are in the 95-98% limits. Several approximations are derived for the time dependences of the ice-cover maximum and minimum over the period under study. Regression dependences were calculated for polynomials from the first degree (linear) to sextic. It was ascertained that the minimal root-mean-square error of deviation from the approximated curve sharply decreased for the biquadratic polynomial and then varied insignificantly: from 0.5593 for the polynomial of third degree to 0.4560 for the biquadratic polynomial. Hence, the commonly used strictly linear regression with a negative time gradient for the September Arctic ice cover minimum over 30 years should be considered incorrect.
Elliptical Antenna Array Synthesis Using Backtracking Search Optimisation Algorithm
Directory of Open Access Journals (Sweden)
Kerim Guney
2016-04-01
Full Text Available The design of the elliptical antenna arrays is relatively new research area in the antenna array community. Backtracking search optimisation algorithm (BSA is employed for the synthesis of elliptical antenna arrays having different number of array elements. For this aim, BSA is used to calculate the optimum angular position and amplitude values of the array elements. BSA is a population-based iterative evolutionary algorithm. The remarkable properties of BSA are that it has a good optimisation performance, simple implementation structure, and few control parameters. The results of BSA are compared with those of self-adaptive differential evolution algorithm, firefly algorithm, biogeography based optimisation algorithm, and genetic algorithm. The results show that BSA can reach better solutions than the compared optimisation algorithms. Iterative performances of BSA are also compared with those of bacterial foraging algorithm and differential search algorithm.
Solving stochastic multiobjective vehicle routing problem using probabilistic metaheuristic
Directory of Open Access Journals (Sweden)
Gannouni Asmae
2017-01-01
closed form expression. This novel approach is based on combinatorial probability and can be incorporated in a multiobjective evolutionary algorithm. (iiProvide probabilistic approaches to elitism and diversification in multiobjective evolutionary algorithms. Finally, The behavior of the resulting Probabilistic Multi-objective Evolutionary Algorithms (PrMOEAs is empirically investigated on the multi-objective stochastic VRP problem.
Stochastic Approaches to Interactive Multi-Criteria Optimization Problems
1986-01-01
A stochastic approach to the development of interactive algorithms for multicriteria optimization is discussed in this paper. These algorithms are based on the idea of a random search and the use of a decision-maker who can compare any two decisions. The questions of both theoretical analysis (proof of convergence, investigation of stability) and practical implementation of these algorithms are discussed.
The stochastic integrable AKNS hierarchy
Arnaudon, Alexis
2015-01-01
We derive a stochastic AKNS hierarchy using geometrical methods. The integrability is shown via a stochastic zero curvature relation associated with a stochastic isospectral problem. We expose some of the stochastic integrable partial differential equations which extend the stochastic KdV equation discovered by M. Wadati in 1983 for all the AKNS flows. We also show how to find stochastic solitons from the stochastic evolution of the scattering data of the stochastic IST. We finally expose som...
Exact and Approximate Stochastic Simulation of Intracellular Calcium Dynamics
Directory of Open Access Journals (Sweden)
Nicolas Wieder
2011-01-01
pathways. The purpose of the present paper is to provide an overview of the aforementioned simulation approaches and their mutual relationships in the spectrum ranging from stochastic to deterministic algorithms.
Moawia Alghalith
2012-01-01
We present new stochastic differential equations, that are more general and simpler than the existing Ito-based stochastic differential equations. As an example, we apply our approach to the investment (portfolio) model.
Stochastic processes - quantum physics
Energy Technology Data Exchange (ETDEWEB)
Streit, L. (Bielefeld Univ. (Germany, F.R.))
1984-01-01
The author presents an elementary introduction to stochastic processes. He starts from simple quantum mechanics and considers problems in probability, finally presenting quantum dynamics in terms of stochastic processes.
Stochastic Generalized Method of Moments
Yin, Guosheng
2011-08-16
The generalized method of moments (GMM) is a very popular estimation and inference procedure based on moment conditions. When likelihood-based methods are difficult to implement, one can often derive various moment conditions and construct the GMM objective function. However, minimization of the objective function in the GMM may be challenging, especially over a large parameter space. Due to the special structure of the GMM, we propose a new sampling-based algorithm, the stochastic GMM sampler, which replaces the multivariate minimization problem by a series of conditional sampling procedures. We develop the theoretical properties of the proposed iterative Monte Carlo method, and demonstrate its superior performance over other GMM estimation procedures in simulation studies. As an illustration, we apply the stochastic GMM sampler to a Medfly life longevity study. Supplemental materials for the article are available online. © 2011 American Statistical Association.
Stochastic vehicle routing with recourse
DEFF Research Database (Denmark)
Gørtz, Inge Li; Nagarajan, Viswanath; Saket, Rishi
2012-01-01
We study the classic Vehicle Routing Problem in the setting of stochastic optimization with recourse. StochVRP is a two-stage problem, where demand is satisfied using two routes: fixed and recourse. The fixed route is computed using only a demand distribution. Then after observing the demand...... instantiations, a recourse route is computed - but costs here become more expensive by a factor λ. We present an O(log2n ·log(nλ))-approximation algorithm for this stochastic routing problem, under arbitrary distributions. The main idea in this result is relating StochVRP to a special case of submodular...... orienteering, called knapsack rank-function orienteering. We also give a better approximation ratio for knapsack rank-function orienteering than what follows from prior work. Finally, we provide a Unique Games Conjecture based ω(1) hardness of approximation for StochVRP, even on star-like metrics on which our...
Stochastic tools in turbulence
Lumey, John L
2012-01-01
Stochastic Tools in Turbulence discusses the available mathematical tools to describe stochastic vector fields to solve problems related to these fields. The book deals with the needs of turbulence in relation to stochastic vector fields, particularly, on three-dimensional aspects, linear problems, and stochastic model building. The text describes probability distributions and densities, including Lebesgue integration, conditional probabilities, conditional expectations, statistical independence, lack of correlation. The book also explains the significance of the moments, the properties of the
Multiscale stochastic simulations of chemical reactions with regulated scale separation
Energy Technology Data Exchange (ETDEWEB)
Koumoutsakos, Petros, E-mail: petros@ethz.ch [Chair of Computational Science, Clausiusstrasse 33, ETH Zurich, CH-8092 (Switzerland); Feigelman, Justin [Chair of Computational Science, Clausiusstrasse 33, ETH Zurich, CH-8092 (Switzerland)
2013-07-01
We present a coupling of multiscale frameworks with accelerated stochastic simulation algorithms for systems of chemical reactions with disparate propensities. The algorithms regulate the propensities of the fast and slow reactions of the system, using alternating micro and macro sub-steps simulated with accelerated algorithms such as τ and R-leaping. The proposed algorithms are shown to provide significant speedups in simulations of stiff systems of chemical reactions with a trade-off in accuracy as controlled by a regulating parameter. More importantly, the error of the methods exhibits a cutoff phenomenon that allows for optimal parameter choices. Numerical experiments demonstrate that hybrid algorithms involving accelerated stochastic simulations can be, in certain cases, more accurate while faster, than their corresponding stochastic simulation algorithm counterparts.
Stochastic gradient descent on Riemannian manifolds
Bonnabel, Silvere
2011-01-01
Stochastic gradient descent is a simple appproach to find the local minima of a function whose evaluations are corrupted by noise. In this paper, mostly motivated by machine learning applications, we develop a procedure extending stochastic gradient descent algorithms to the case where the function is defined on a Riemannian manifold. We prove that, as in the Euclidian case, the descent algorithm converges to a critical point of the cost function. The algorithm has numerous potential applications, and we show several well-known algorithms can be cast in our versatile geometric framework. We also address the gain tuning issue in connection with the tools of the recent theory of symmetry-preserving observers.
Energy Technology Data Exchange (ETDEWEB)
Brennan,J.M.; Blaskiewicz, M. M.; Severino, F.
2009-05-04
After the success of longitudinal stochastic cooling of bunched heavy ion beam in RHIC, transverse stochastic cooling in the vertical plane of Yellow ring was installed and is being commissioned with proton beam. This report presents the status of the effort and gives an estimate, based on simulation, of the RHIC luminosity with stochastic cooling in all planes.
Institute of Scientific and Technical Information of China (English)
周溪召
2001-01-01
Because the randomness of transportation information has not been considered in present practical transportation planning ,the accuracy or efficiency of transportation planning is depreciated.In order to overcome this drawback,the combinatorial model simulaneously involving OD distribution of trips in a transportation network and stochastic user equilibrium assignment(SUEA) of trips to routes in each OD pairs was developped on the basis of analysis on random ness in choices of route and destination for a given mode. It was proved that the solution of the combinatorial model was unique and it satisfied Wardropian principle of SUEA and requirement of OD distribution by introducing Langrangian function.Finally,the algorithm of model was given by using the direction hunting method.%目前交通规划实践缺乏考虑交通信息的随机性，从而降低了它所得结果的准确性.为此，通过分析出行路径选择和目标选择的随机性，建立了交通网络OD分布与随机平衡（或均衡）分配的组合模型，通过引入拉格朗日函数，证明了模型最优解满足随机用户平衡条件和OD分布的要求且最优解是唯一的;最后给出了模型的方向搜索算法.
Institute of Scientific and Technical Information of China (English)
肖世昌; 孙树栋; 杨宏安
2015-01-01
A hybrid estimition of distribution algorithm(HEDA) is proposed to solve the stochastic Job shop scheduling problem with earliness/tardiness penalty. To enhance the population diversity of the HEDA, the inherit rate of the operations in parent chromosome is defined and a new recombination method is proposed base on it. This recombination method not only can make the offspring inherit the excellent characteristics of the parent effectively, but also can avoid infeasible solution. To improve the precision of the individual evaluation, the optimal computing budget allocation is adopted in the phase of individual selection. Simulation results show the effectiveness and robustness of HEDA.%针对加工时间具有随机特性的Job shop调度问题，提出基于分布估计算法的混合算法。为增强分布估计算法的种群多样性，定义了父代工序继承率并设计一种可保留父代个体优良结构特征的重组方法，该方法在继承父代个体优良结构特征的同时避免了非法解的产生。在个体选择评价阶段，采用最优计算量分配策略为每个个体分配模拟量以提高个体评价的精确性。仿真算例表明了所提出算法的有效性和鲁棒性。
Financial planning via multi-stage stochastic programs
Energy Technology Data Exchange (ETDEWEB)
Mulvey, J.
1994-12-31
This paper presents a framework for modeling financial applications, including asset allocation, as multi-stage stochastic programs. Starting with the classic single-period Markowitz model, we show that additional realistic issues can be addressed via multi-period stochastic programming. The advantages of integrating liabilities, assets, and investment goals are discussed. An investor must design a dynamic model for the stochastic parameters and link this process to an integrated asset-liability system if he wants to measure portfolio risk over time. Partial analysis leads to errors in risk evaluation. Algorithms for solving the resulting large-scale nonlinear stochastic programs are described. Topics for future research are proposed.
Stanger-Hall, Kathrin F; Lloyd, James E; Hillis, David M
2007-10-01
Representatives of the beetle family Lampyridae ("fireflies", "lightningbugs") are well known for their use of light signals for species recognition during mate search. However, not all species in this family use light for mate attraction, but use chemical signals instead. The lampyrids have a worldwide distribution with more than 2000 described species, but very little is known about their phylogenetic relationships. Within North America, some lampyrids use pheromones as the major mating signal whereas others use visual signals such as extended glows or short light flashes. Here, we use a phylogenetic approach to illuminate the relationships of North American lampyrids and the evolution of their mating signals. Specifically, to establish the first phylogeny of all North American lampyrid genera, we sequenced nuclear (18S) and mitochondrial (16S and COI) genes to investigate the phylogenetic relationships of 26 species from 16 North American (NA) genera and one species from the genus Pterotus that was removed recently from the Lampyridae. To test the monophyly of the NA firefly fauna we sequenced the same genes from three European lampyrids and three Asian lampyrids, and included all available Genbank data (27 additional Asian lampyrids and a former lampyrid from Asia, Rhagophthalmus). Our results show that the North American lampyrids are not monophyletic. Different subgroups are closely related to species from Europe, Asia and tropical America, respectively. The present classification of fireflies into subfamilies and tribes is not, for the most part, supported by our phylogenetic analysis. Two former lampyrid genera, Pterotus and Rhagophthalmus, which have recently been removed from this family, are in fact nested within the Lampyridae. Further, we found that the use of light as a sexual signal may have originated one or four times among lampyrids, followed by nine or four losses, respectively. Short flashes originated at least twice and possibly three times
Institute of Scientific and Technical Information of China (English)
岳强; 宋瑞; 徐梁; 刘志谦
2011-01-01
In order to improve the informationization, planning and management of public transit< scientific public transit planning and management strategies are needed. Transit flow assignment is one of the key aspects of transit planning and management. This paper reviews the transit flow assignment research at both home and abroad. Based on the concept of urban general transit network, connection, connection segment and attraction connection set, this paper proposes a volume-restrained &. Timetable-based stochastic user equilibrium transit assignment model (VRT-SUE). The principle and solving steps of VRT-SUE algorithm are discussed and a numerical example is presented to showcase the proposed VRT-SUE. Assignment results show that the algorithm can well represent the influences of the departure time, arrival time, transfer number and cost onto traveler's behavior.%提高公共交通规划管理及信息化水平,需要制定科学的公交规划管理策略,公交客流分配是公交规划与管理的核心内容之一.为促进公交配流理论研究,提高公交客流分配结果科学性,分析了国内外公交配流研究现状,对于城市广义公交网络,结合公交时刻表,定义了公交连接、连接片段以及吸引连接集合,建立了容量限制下基于时刻表的公交网络随机用户均衡配流算法(VRT-SUE),对算法原理和求解步骤进行了探讨.构建实例公交网络进行分析测试,配流结果显示该模型能够很好的反映出出发时间、到达时间、换乘次数及费用等对乘客公交出行的影响,算法是合理和有效的.
A NOTE ON THE STOCHASTIC ROOTS OF STOCHASTIC MATRICES
Institute of Scientific and Technical Information of China (English)
Qi-Ming HE; Eldon GUNN
2003-01-01
In this paper, we study the stochastic root matrices of stochastic matrices. All stochastic roots of 2×2 stochastic matrices are found explicitly. A method based on characteristic polynomial of matrix is developed to find all real root matrices that are functions of the original 3×3 matrix, including all possible (function) stochastic root matrices. In addition, we comment on some numerical methods for computing stochastic root matrices of stochastic matrices.
Computational singular perturbation analysis of stochastic chemical systems with stiffness
Wang, Lijin; Han, Xiaoying; Cao, Yanzhao; Najm, Habib N.
2017-04-01
Computational singular perturbation (CSP) is a useful method for analysis, reduction, and time integration of stiff ordinary differential equation systems. It has found dominant utility, in particular, in chemical reaction systems with a large range of time scales at continuum and deterministic level. On the other hand, CSP is not directly applicable to chemical reaction systems at micro or meso-scale, where stochasticity plays an non-negligible role and thus has to be taken into account. In this work we develop a novel stochastic computational singular perturbation (SCSP) analysis and time integration framework, and associated algorithm, that can be used to not only construct accurately and efficiently the numerical solutions to stiff stochastic chemical reaction systems, but also analyze the dynamics of the reduced stochastic reaction systems. The algorithm is illustrated by an application to a benchmark stochastic differential equation model, and numerical experiments are carried out to demonstrate the effectiveness of the construction.
Stochastic skyline route planning under time-varying uncertainty
DEFF Research Database (Denmark)
Yang, Bin; Guo, Chenjuan; Jensen, Christian S.;
2014-01-01
-dependent and uncertain. To support such uses, we propose techniques that enable the construction of a multi-cost, time-dependent, uncertain graph (MTUG) model of a road network based on GPS data from vehicles that traversed the road network. Based on the MTUG, we define stochastic skyline routes that consider multiple...... costs and time-dependent uncertainty, and we propose efficient algorithms to retrieve stochastic skyline routes for a given source-destination pair and a start time. Empirical studies with three road networks in Denmark and a substantial GPS data set offer insight into the design properties of the MTUG...... and the efficiency of the stochastic skyline routing algorithms....
Ogawa, Shigeyoshi
2017-01-01
This book presents an elementary introduction to the theory of noncausal stochastic calculus that arises as a natural alternative to the standard theory of stochastic calculus founded in 1944 by Professor Kiyoshi Itô. As is generally known, Itô Calculus is essentially based on the "hypothesis of causality", asking random functions to be adapted to a natural filtration generated by Brownian motion or more generally by square integrable martingale. The intention in this book is to establish a stochastic calculus that is free from this "hypothesis of causality". To be more precise, a noncausal theory of stochastic calculus is developed in this book, based on the noncausal integral introduced by the author in 1979. After studying basic properties of the noncausal stochastic integral, various concrete problems of noncausal nature are considered, mostly concerning stochastic functional equations such as SDE, SIE, SPDE, and others, to show not only the necessity of such theory of noncausal stochastic calculus but ...
Stochastic Lie group integrators
Malham, Simon J A
2007-01-01
We present Lie group integrators for nonlinear stochastic differential equations with non-commutative vector fields whose solution evolves on a smooth finite dimensional manifold. Given a Lie group action that generates transport along the manifold, we pull back the stochastic flow on the manifold to the Lie group via the action, and subsequently pull back the flow to the corresponding Lie algebra via the exponential map. We construct an approximation to the stochastic flow in the Lie algebra via closed operations and then push back to the Lie group and then to the manifold, thus ensuring our approximation lies in the manifold. We call such schemes stochastic Munthe-Kaas methods after their deterministic counterparts. We also present stochastic Lie group integration schemes based on Castell--Gaines methods. These involve using an underlying ordinary differential integrator to approximate the flow generated by a truncated stochastic exponential Lie series. They become stochastic Lie group integrator schemes if...
Robust red-emission spectra and yields in firefly bioluminescence against temperature changes
Mochizuki, Toshimitsu; Wang, Yu; Hiyama, Miyabi; Akiyama, Hidefumi
2014-05-01
We measured the quantitative spectra of firefly (Photinus pyralis) bioluminescence at various temperatures to investigate the temperature dependence of the luciferin-luciferase reaction at 15-34 °C. The quantitative spectra were decomposed very well into red (1.9 eV), orange (2.0 eV), and green (2.2 eV) Gaussian components. The intensity of the green component was the only temperature sensitive quantity that linearly decreased as the temperature increased at pH 7 and 8. We found the quantitative bioluminescence spectra to be robust below 2.0 eV against temperature and other experimental conditions. The revealed robustness of the red emissions should be useful for quantitative applications such as adenosine-5'-triphosphate detection.
Improved light extraction in the bioluminescent lantern of a Photuris firefly (Lampyridae)
Bay, Annick; Suhonen, Heikki; Vigneron, Jean Pol
2012-01-01
A common problem of light sources emitting from an homogeneous high-refractive index medium into air is the loss of photons by total internal reflection. Bioluminescent organisms, as well as artificial devices, have to face this problem. It is expected that life, with its mechanisms for evolution, would have selected appropriate optical structures to get around this problem, at least partially. The morphology of the lantern of a specific firefly in the genus Photuris has been examined. The optical properties of the different parts of this lantern have been modelled, in order to determine their positive or adverse effect with regard to the global light extraction. We conclude that the most efficient pieces of the lantern structure are the misfit of the external scales (which produce abrupt roughness in air) and the lowering of the refractive index at the level of the cluster of photocytes, where the bioluminescent production takes place.
Singh, Nishant; Ojha, Sandeep; Bhattacharya, Alok; Bhattacharya, Sudha
2012-05-01
Entamoeba invadens is used as a model system to study trophozoite to cyst differentiation since Entamoeba histolytica, the causative agent of amoebiasis cannot encyst in culture. However, a system for introduction of cloned genes in E. invadens is not available. Here we report an electroporation-based method for transfection of E. invadens tophozoites and demonstrate the expression of firefly luciferase reporter gene driven from the E. invadens ribosomal protein L3 promoter. The efficiency of luciferase expression driven from the promoters of three different E. invadens genes (rpl3, rps10 and h2b) was tested and found to correlate with the in vivo expression levels of the respective gene. This system will permit the analysis of regulatory elements required for gene expression in E. invadens.
Improved light extraction in the bioluminescent lantern of a Photuris firefly (Lampyridae)
Bay, Annick; Cloetens, Peter; Suhonen, Heikki; Vigneron, Jean Pol
2013-01-01
A common problem of light sources emitting from an homogeneous high-refractive index medium into air is the loss of photons by total internal reflection. Bioluminescent organisms, as well as artificial devices, have to face this problem. It is expected that life, with its mechanisms for evolution, would have selected appropriate optical structures to get around this problem, at least partially. The morphology of the lantern of a specific firefly in the genus Photuris has been examined. The optical properties of the different parts of this lantern have been modeled, in order to determine their positive or adverse effect with regard to the global light extraction. We conclude that the most efficient pieces of the lantern structure are the misfit of the external scales (which produce abrupt roughness in air) and the lowering of the refractive index at the level of the cluster of photocytes, where the bioluminescent production takes place.
Numerical methods for stochastic partial differential equations with white noise
Zhang, Zhongqiang
2017-01-01
This book covers numerical methods for stochastic partial differential equations with white noise using the framework of Wong-Zakai approximation. The book begins with some motivational and background material in the introductory chapters and is divided into three parts. Part I covers numerical stochastic ordinary differential equations. Here the authors start with numerical methods for SDEs with delay using the Wong-Zakai approximation and finite difference in time. Part II covers temporal white noise. Here the authors consider SPDEs as PDEs driven by white noise, where discretization of white noise (Brownian motion) leads to PDEs with smooth noise, which can then be treated by numerical methods for PDEs. In this part, recursive algorithms based on Wiener chaos expansion and stochastic collocation methods are presented for linear stochastic advection-diffusion-reaction equations. In addition, stochastic Euler equations are exploited as an application of stochastic collocation methods, where a numerical compa...
Planning under uncertainty solving large-scale stochastic linear programs
Energy Technology Data Exchange (ETDEWEB)
Infanger, G. (Stanford Univ., CA (United States). Dept. of Operations Research Technische Univ., Vienna (Austria). Inst. fuer Energiewirtschaft)
1992-12-01
For many practical problems, solutions obtained from deterministic models are unsatisfactory because they fail to hedge against certain contingencies that may occur in the future. Stochastic models address this shortcoming, but up to recently seemed to be intractable due to their size. Recent advances both in solution algorithms and in computer technology now allow us to solve important and general classes of practical stochastic problems. We show how large-scale stochastic linear programs can be efficiently solved by combining classical decomposition and Monte Carlo (importance) sampling techniques. We discuss the methodology for solving two-stage stochastic linear programs with recourse, present numerical results of large problems with numerous stochastic parameters, show how to efficiently implement the methodology on a parallel multi-computer and derive the theory for solving a general class of multi-stage problems with dependency of the stochastic parameters within a stage and between different stages.
Modestova, Yulia; Koksharov, Mikhail I; Ugarova, Natalia N
2014-09-01
Firefly luciferase is a two-domain enzyme that catalyzes the bioluminescent reaction of firefly luciferin oxidation. Color of the emitted light depends on the structure of the enzyme, yet the exact color-tuning mechanism remains unknown by now, and the role of the C-domain in it is rarely discussed, because a very few color-shifting mutations in the C-domain were described. Recently we reported a strong red-shifting mutation E457K in the C-domain; the bioluminescence spectra of this enzyme were independent of temperature or pH. In the present study we investigated the role of the residue E457 in the enzyme using the Luciola mingrelica luciferase with a thermostabilized N-domain as a parent enzyme for site-directed mutagenesis. We obtained a set of mutants and studied their catalytic properties, thermal stability and bioluminescence spectra. Experimental spectra were represented as a sum of two components (bioluminescence spectra of putative "red" and "green" emitters); λmax of these components were constant for all the mutants, but the ratio of these emitters was defined by temperature and mutations in the C-domain. We suggest that each emitter is stabilized by a specific conformation of the active site; thus, enzymes with two forms of the active site coexist in the reactive media. The rigid structure of the C-domain is crucial for maintaining the conformation corresponding to the "green" emitter. We presume that the emitters are the keto- and enol forms of oxyluciferin.
Constrained Stochastic Extended Redundancy Analysis.
DeSarbo, Wayne S; Hwang, Heungsun; Stadler Blank, Ashley; Kappe, Eelco
2015-06-01
We devise a new statistical methodology called constrained stochastic extended redundancy analysis (CSERA) to examine the comparative impact of various conceptual factors, or drivers, as well as the specific predictor variables that contribute to each driver on designated dependent variable(s). The technical details of the proposed methodology, the maximum likelihood estimation algorithm, and model selection heuristics are discussed. A sports marketing consumer psychology application is provided in a Major League Baseball (MLB) context where the effects of six conceptual drivers of game attendance and their defining predictor variables are estimated. Results compare favorably to those obtained using traditional extended redundancy analysis (ERA).
Memristors Empower Spiking Neurons With Stochasticity
Al-Shedivat, Maruan
2015-06-01
Recent theoretical studies have shown that probabilistic spiking can be interpreted as learning and inference in cortical microcircuits. This interpretation creates new opportunities for building neuromorphic systems driven by probabilistic learning algorithms. However, such systems must have two crucial features: 1) the neurons should follow a specific behavioral model, and 2) stochastic spiking should be implemented efficiently for it to be scalable. This paper proposes a memristor-based stochastically spiking neuron that fulfills these requirements. First, the analytical model of the memristor is enhanced so it can capture the behavioral stochasticity consistent with experimentally observed phenomena. The switching behavior of the memristor model is demonstrated to be akin to the firing of the stochastic spike response neuron model, the primary building block for probabilistic algorithms in spiking neural networks. Furthermore, the paper proposes a neural soma circuit that utilizes the intrinsic nondeterminism of memristive switching for efficient spike generation. The simulations and analysis of the behavior of a single stochastic neuron and a winner-take-all network built of such neurons and trained on handwritten digits confirm that the circuit can be used for building probabilistic sampling and pattern adaptation machinery in spiking networks. The findings constitute an important step towards scalable and efficient probabilistic neuromorphic platforms. © 2011 IEEE.
Directory of Open Access Journals (Sweden)
Kenta Takada
2012-04-01
Full Text Available Seasonal changes in the popularity of fireflies [usually Genji-fireflies (Luciola cruciata Motschulsky in Japan] and Japanese rhinoceros beetles [Allomyrina dichotoma (Linne] were investigated to examine whether contemporary Japanese are interested in visible emergence of these insects as seasonal events. The popularity of fireflies and Japanese rhinoceros beetles was assessed by the Google search volume of their Japanese names, “Hotaru” and “Kabuto-mushi” in Japanese Katakana script using Google Trends. The search volume index for fireflies and Japanese rhinoceros beetles was distributed across seasons with a clear peak in only particular times of each year from 2004 to 2011. In addition, the seasonal peak of popularity for fireflies occurred at the beginning of June, whereas that for Japanese rhinoceros beetles occurred from the middle of July to the beginning of August. Thus seasonal peak of each species coincided with the peak period of the emergence of each adult stage. These findings indicated that the Japanese are interested in these insects primarily during the time when the two species are most visibly abundant. Although untested, this could suggest that fireflies and Japanese rhinoceros beetles are perceived by the general public as indicators or symbols of summer in Japan.
Stochastic Engine Convergence Diagnostics
Energy Technology Data Exchange (ETDEWEB)
Glaser, R
2001-12-11
The stochastic engine uses a Markov Chain Monte Carlo (MCMC) sampling device to allow an analyst to construct a reasonable estimate of the state of nature that is consistent with observed data and modeling assumptions. The key engine output is a sample from the posterior distribution, which is the conditional probability distribution of the state of nature, given the data. In applications the state of nature may refer to a complicated, multi-attributed feature like the lithology map of a volume of earth, or to a particular related parameter of interest, say the centroid of the largest contiguous sub-region of specified lithology type. The posterior distribution, which we will call f, can be thought of as the best stochastic description of the state of nature that incorporates all pertinent physical and theoretical models as well as observed data. Characterization of the posterior distribution is the primary goal in the Bayesian statistical paradigm. In applications of the stochastic engine, however, analytical calculation of the posterior distribution is precluded, and only a sample drawn from the distribution is feasible. The engine's MCMC technique, which employs the Metropolis-Hastings algorithm, provides a sample in the form of a sequence (chain) of possible states of nature, x{sup (1)}, x{sup (2)}, ..., x{sup (T)}, .... The sequencing is motivated by consideration of comparative likelihoods of the data. Asymptotic results ensure that the sample ultimately spans the entire posterior distribution and reveals the actual state frequencies that characterize the posterior. In mathematical jargon, the sample is an ergodic Markov chain with stationary distribution f. What this means is that once the chain has gone a sufficient number of steps, T{sub 0}, the (unconditional) distribution of the state, x{sup (T)}, at any step T {ge} T{sub 0} is the same (i.e., is ''stationary''), and is the posterior distribution, f. We call T{sub 0} the &apos
Fan sub-equation method for Wick-type stochastic partial differential equations
Energy Technology Data Exchange (ETDEWEB)
Zhang Sheng, E-mail: zhshaeng@yahoo.com.c [Department of Mathematics, Bohai University, Jinzhou 121013 (China); School of Mathematical Sciences, Dalian University of Technology, Dalian 116024 (China); Zhang Hongqing [School of Mathematical Sciences, Dalian University of Technology, Dalian 116024 (China)
2010-09-13
An improved algorithm is devised for using Fan sub-equation method to solve Wick-type stochastic partial differential equations. Applying the improved algorithm to the Wick-type generalized stochastic KdV equation, we obtain more general Jacobi and Weierstrass elliptic function solutions, hyperbolic and trigonometric function solutions, exponential function solutions and rational solutions.
Moderate Deviations for Recursive Stochastic Algorithms
2014-08-02
measures of Markov chains : Lower bounds. Ann. Probab., 25:259�284, 1997. [8] T. Dean and P. Dupuis. Splitting for rare event simulation : A large...we mention their usefulness in the design and analysis of Monte Carlo schemes. It is well known that accelerated Monte Carlo schemes (e.g...fast�continuous time ergodic Markov chain , and in [19] this is extended to a small noise di¤usion whose coe¢ cients depend on the �fast� Markov chain
Identification methods for nonlinear stochastic systems.
Fullana, Jose-Maria; Rossi, Maurice
2002-03-01
Model identifications based on orbit tracking methods are here extended to stochastic differential equations. In the present approach, deterministic and statistical features are introduced via the time evolution of ensemble averages and variances. The aforementioned quantities are shown to follow deterministic equations, which are explicitly written within a linear as well as a weakly nonlinear approximation. Based on such equations and the observed time series, a cost function is defined. Its minimization by simulated annealing or backpropagation algorithms then yields a set of best-fit parameters. This procedure is successfully applied for various sampling time intervals, on a stochastic Lorenz system.
Dynamic and stochastic multi-project planning
Melchiors, Philipp
2015-01-01
This book deals with dynamic and stochastic methods for multi-project planning. Based on the idea of using queueing networks for the analysis of dynamic-stochastic multi-project environments this book addresses two problems: detailed scheduling of project activities, and integrated order acceptance and capacity planning. In an extensive simulation study, the book thoroughly investigates existing scheduling policies. To obtain optimal and near optimal scheduling policies new models and algorithms are proposed based on the theory of Markov decision processes and Approximate Dynamic programming.
Relaxed Half-Stochastic Belief Propagation
Leduc-Primeau, François; Mannor, Shie; Gross, Warren J
2012-01-01
Low-density parity-check codes are attractive for high throughput applications because of their low decoding complexity per bit, but also because all the codeword bits can be decoded in parallel. However, achieving this in a circuit implementation is complicated by the number of wires required to exchange messages between processing nodes. Decoding algorithms that exchange binary messages are interesting for fully-parallel implementations because they can reduce the number and the length of the wires, and increase logic density. This paper introduces the Relaxed Half-Stochastic (RHS) decoding algorithm, a binary message belief propagation (BP) algorithm that achieves a coding gain comparable to the best known BP algorithms that use real-valued messages. We derive the RHS algorithm by starting from the well-known Sum-Product algorithm, and then derive a low-complexity version suitable for circuit implementation. We present extensive simulation results on two standardized codes having different rates and constr...
Institute of Scientific and Technical Information of China (English)
单德山; 徐敏
2011-01-01
以某自锚式悬索桥模型试验为研究背景,采用数据驱动随机子空间识别算法和改进稳定图方法对桥梁结构运营模态分析进行研究.为解决数据驱动随机子空间识别中的系统定阶和虚假模态问题,采用奇异熵增量进行系统定阶,并对稳定图进行改进,实现了虚假模态的识别与剔除,最终达到了精确识别桥梁结构模态参数的目的.采用模型试验在不同数据采集方案下的测试数据,识别该模型桥相应测试条件下的模态参数,将识别结果分别与ANSYS理论计算值、DASY-Lab模态参数识别结果进行比较,验证了所提方法及自编程序的正确性,该方法可应用于桥梁结构的运营模态分析中.%The model test of a self-anchored suspension bridge is taken as the research background, the data-driven stochastic subspace identification (Data-Driven SSI) algorithm and the improved stabilization diagram method are employed to study the operational modal analysis of bridge structure. For solving the problem of the system order determination and false modal parameters in the Data-Driven SSI, the singular entropy increment is introduced to determine the system order and to improve the stability diagram, the false modal parameters are therefore identified and eliminated and eventually the goal of accurately identifying the modal parameters of the bridge structure is attained. By using the testing data of the model test under different data acquisition cases, the modal parameters of the bridge structure Under the corresponding testing condi-tions are identified, the identified modal parameters are respectively compared to the values of the ANSYS theoretic calculation and to the results of DASYLab modal parameter identification. The correctness of the method proposed herewith and the self-complied program has been verified and the method can be applied to the operational model analysis of the bridge structure.
System Optimization Using a Parallel Stochastic Approach
Directory of Open Access Journals (Sweden)
ZAPLATILEK, K.
2013-05-01
Full Text Available This paper describes an original stochastic algorithm based on a parallel approach. The algorithm is suitable especially for a real technical system optimization. A few independent pseudorandom generators are used. They generate independent variable vectors along all of the optimized system axes. Local optimal values are used to define a final pseudorandom generator with a narrower interval around the global optimum. Theoretical foundations are introduced and a few practical experiments are presented. The described method is also suitable for the quality classification of the pseudorandom generators using the selected RGB color scheme. Main advantages of this approach are discussed. The algorithm was developed in the MATLAB environment.
Fundamentals of Stochastic Networks
Ibe, Oliver C
2011-01-01
An interdisciplinary approach to understanding queueing and graphical networks In today's era of interdisciplinary studies and research activities, network models are becoming increasingly important in various areas where they have not regularly been used. Combining techniques from stochastic processes and graph theory to analyze the behavior of networks, Fundamentals of Stochastic Networks provides an interdisciplinary approach by including practical applications of these stochastic networks in various fields of study, from engineering and operations management to communications and the physi
1996-01-01
Giardiavirus (GLV) is a 6,277-bp double-stranded RNA virus of Giardia lamblia, one of the earliest eukaryotic divergents from the prokaryotes. Our previous success in GLV-mediated transfection of G. lamblia has provided an effective way of monitoring the mechanisms underlining GLV gene replication and mRNA translation in this organism. Here we have investigated the cis-acting signals in the GLV genome that regulate replication, transcription, and translation of an inserted firefly luciferase ...
Fluctuations as stochastic deformation
Kazinski, P. O.
2008-04-01
A notion of stochastic deformation is introduced and the corresponding algebraic deformation procedure is developed. This procedure is analogous to the deformation of an algebra of observables like deformation quantization, but for an imaginary deformation parameter (the Planck constant). This method is demonstrated on diverse relativistic and nonrelativistic models with finite and infinite degrees of freedom. It is shown that under stochastic deformation the model of a nonrelativistic particle interacting with the electromagnetic field on a curved background passes into the stochastic model described by the Fokker-Planck equation with the diffusion tensor being the inverse metric tensor. The first stochastic correction to the Newton equations for this system is found. The Klein-Kramers equation is also derived as the stochastic deformation of a certain classical model. Relativistic generalizations of the Fokker-Planck and Klein-Kramers equations are obtained by applying the procedure of stochastic deformation to appropriate relativistic classical models. The analog of the Fokker-Planck equation associated with the stochastic Lorentz-Dirac equation is derived too. The stochastic deformation of the models of a free scalar field and an electromagnetic field is investigated. It turns out that in the latter case the obtained stochastic model describes a fluctuating electromagnetic field in a transparent medium.
Singular stochastic differential equations
Cherny, Alexander S
2005-01-01
The authors introduce, in this research monograph on stochastic differential equations, a class of points termed isolated singular points. Stochastic differential equations possessing such points (called singular stochastic differential equations here) arise often in theory and in applications. However, known conditions for the existence and uniqueness of a solution typically fail for such equations. The book concentrates on the study of the existence, the uniqueness, and, what is most important, on the qualitative behaviour of solutions of singular stochastic differential equations. This is done by providing a qualitative classification of isolated singular points, into 48 possible types.
Energy Technology Data Exchange (ETDEWEB)
Shi, Junwei; Zhang, Huan; Fang, Liurong; Xi, Yongqiang; Zhou, Yanrong; Luo, Rui; Wang, Dang, E-mail: wangdang511@126.com; Xiao, Shaobo; Chen, Huanchun
2014-10-03
Highlights: • We developed a novel firefly luciferase based biosensor to detect apoptosis. • The novel biosensor 233-DnaE-DEVDG was reliable, sensitive and convenient. • 233-DnaE-DEVDG faithfully indicated ESAT-6 family proteins of Mycobacterium tuberculosis induced apoptosis. • EsxA, esxT and esxL in ESAT-6 family proteins induced apoptosis. • Activation of nuclear factor-κB (NF-κB) participated in esxT-induced apoptosis. - Abstract: The activation of caspase-3 is a key surrogate marker for detecting apoptosis. To quantitate caspase-3 activity, we constructed a biosensor comprising a recombinant firefly luciferase containing a caspase-3 cleavage site. When apoptosis was induced, caspase-3 cleavage of the biosensor activated firefly luciferase by a factor greater than 25. The assay conveniently detected apoptosis in real time, indicating that it will facilitate drug discovery. We screened ESAT-6 family proteins of Mycobacterium tuberculosis and found that esxA, esxT and esxL induced apoptosis. Further, activation of nuclear factor-κB (NF-κB) and the NF-κB-regulated genes encoding tumor necrosis factor-α (TNF-α) and tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) participated in esxT-induced apoptosis. We conclude that this assay is useful for high-throughput screening to identify and characterize proteins and drugs that regulate apoptosis.
Optimization algorithms and applications
Arora, Rajesh Kumar
2015-01-01
Choose the Correct Solution Method for Your Optimization ProblemOptimization: Algorithms and Applications presents a variety of solution techniques for optimization problems, emphasizing concepts rather than rigorous mathematical details and proofs. The book covers both gradient and stochastic methods as solution techniques for unconstrained and constrained optimization problems. It discusses the conjugate gradient method, Broyden-Fletcher-Goldfarb-Shanno algorithm, Powell method, penalty function, augmented Lagrange multiplier method, sequential quadratic programming, method of feasible direc
Stochastic longshore current dynamics
Restrepo, Juan M.; Venkataramani, Shankar
2016-12-01
We develop a stochastic parametrization, based on a 'simple' deterministic model for the dynamics of steady longshore currents, that produces ensembles that are statistically consistent with field observations of these currents. Unlike deterministic models, stochastic parameterization incorporates randomness and hence can only match the observations in a statistical sense. Unlike statistical emulators, in which the model is tuned to the statistical structure of the observation, stochastic parametrization are not directly tuned to match the statistics of the observations. Rather, stochastic parameterization combines deterministic, i.e physics based models with stochastic models for the "missing physics" to create hybrid models, that are stochastic, but yet can be used for making predictions, especially in the context of data assimilation. We introduce a novel measure of the utility of stochastic models of complex processes, that we call consistency of sensitivity. A model with poor consistency of sensitivity requires a great deal of tuning of parameters and has a very narrow range of realistic parameters leading to outcomes consistent with a reasonable spectrum of physical outcomes. We apply this metric to our stochastic parametrization and show that, the loss of certainty inherent in model due to its stochastic nature is offset by the model's resulting consistency of sensitivity. In particular, the stochastic model still retains the forward sensitivity of the deterministic model and hence respects important structural/physical constraints, yet has a broader range of parameters capable of producing outcomes consistent with the field data used in evaluating the model. This leads to an expanded range of model applicability. We show, in the context of data assimilation, the stochastic parametrization of longshore currents achieves good results in capturing the statistics of observation that were not used in tuning the model.
A Stochastic Employment Problem
Wu, Teng
2013-01-01
The Stochastic Employment Problem(SEP) is a variation of the Stochastic Assignment Problem which analyzes the scenario that one assigns balls into boxes. Balls arrive sequentially with each one having a binary vector X = (X[subscript 1], X[subscript 2],...,X[subscript n]) attached, with the interpretation being that if X[subscript i] = 1 the ball…
Stochastic Convection Parameterizations
Teixeira, Joao; Reynolds, Carolyn; Suselj, Kay; Matheou, Georgios
2012-01-01
computational fluid dynamics, radiation, clouds, turbulence, convection, gravity waves, surface interaction, radiation interaction, cloud and aerosol microphysics, complexity (vegetation, biogeochemistry, radiation versus turbulence/convection stochastic approach, non-linearities, Monte Carlo, high resolutions, large-Eddy Simulations, cloud structure, plumes, saturation in tropics, forecasting, parameterizations, stochastic, radiation-clod interaction, hurricane forecasts
Instantaneous stochastic perturbation theory
Lüscher, Martin
2015-01-01
A form of stochastic perturbation theory is described, where the representative stochastic fields are generated instantaneously rather than through a Markov process. The correctness of the procedure is established to all orders of the expansion and for a wide class of field theories that includes all common formulations of lattice QCD.
Verhoosel, C.V.; Gutiérrez, M.A.; Hulshoff, S.J.
2006-01-01
The field of fluid-structure interaction is combined with the field of stochastics to perform a stochastic flutter analysis. Various methods to directly incorporate the effects of uncertainties in the flutter analysis are investigated. The panel problem with a supersonic fluid flowing over it is con
Dynamic analysis of stochastic transcription cycles.
Directory of Open Access Journals (Sweden)
Claire V Harper
2011-04-01
Full Text Available In individual mammalian cells the expression of some genes such as prolactin is highly variable over time and has been suggested to occur in stochastic pulses. To investigate the origins of this behavior and to understand its functional relevance, we quantitatively analyzed this variability using new mathematical tools that allowed us to reconstruct dynamic transcription rates of different reporter genes controlled by identical promoters in the same living cell. Quantitative microscopic analysis of two reporter genes, firefly luciferase and destabilized EGFP, was used to analyze the dynamics of prolactin promoter-directed gene expression in living individual clonal and primary pituitary cells over periods of up to 25 h. We quantified the time-dependence and cyclicity of the transcription pulses and estimated the length and variation of active and inactive transcription phases. We showed an average cycle period of approximately 11 h and demonstrated that while the measured time distribution of active phases agreed with commonly accepted models of transcription, the inactive phases were differently distributed and showed strong memory, with a refractory period of transcriptional inactivation close to 3 h. Cycles in transcription occurred at two distinct prolactin-promoter controlled reporter genes in the same individual clonal or primary cells. However, the timing of the cycles was independent and out-of-phase. For the first time, we have analyzed transcription dynamics from two equivalent loci in real-time in single cells. In unstimulated conditions, cells showed independent transcription dynamics at each locus. A key result from these analyses was the evidence for a minimum refractory period in the inactive-phase of transcription. The response to acute signals and the result of manipulation of histone acetylation was consistent with the hypothesis that this refractory period corresponded to a phase of chromatin remodeling which significantly
Greenwood, Priscilla E
2016-01-01
This book describes a large number of open problems in the theory of stochastic neural systems, with the aim of enticing probabilists to work on them. This includes problems arising from stochastic models of individual neurons as well as those arising from stochastic models of the activities of small and large networks of interconnected neurons. The necessary neuroscience background to these problems is outlined within the text, so readers can grasp the context in which they arise. This book will be useful for graduate students and instructors providing material and references for applying probability to stochastic neuron modeling. Methods and results are presented, but the emphasis is on questions where additional stochastic analysis may contribute neuroscience insight. An extensive bibliography is included. Dr. Priscilla E. Greenwood is a Professor Emerita in the Department of Mathematics at the University of British Columbia. Dr. Lawrence M. Ward is a Professor in the Department of Psychology and the Brain...
Stochastic volatility selected readings
Shephard, Neil
2005-01-01
Neil Shephard has brought together a set of classic and central papers that have contributed to our understanding of financial volatility. They cover stocks, bonds and currencies and range from 1973 up to 2001. Shephard, a leading researcher in the field, provides a substantial introduction in which he discusses all major issues involved. General Introduction N. Shephard. Part I: Model Building. 1. A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices, (P. K. Clark). 2. Financial Returns Modelled by the Product of Two Stochastic Processes: A Study of Daily Sugar Prices, 1961-7, S. J. Taylor. 3. The Behavior of Random Variables with Nonstationary Variance and the Distribution of Security Prices, B. Rosenberg. 4. The Pricing of Options on Assets with Stochastic Volatilities, J. Hull and A. White. 5. The Dynamics of Exchange Rate Volatility: A Multivariate Latent Factor ARCH Model, F. X. Diebold and M. Nerlove. 6. Multivariate Stochastic Variance Models. 7. Stochastic Autoregressive...
Institute of Scientific and Technical Information of China (English)
李继猛; 张金凤; 张云刚; 岳宁
2016-01-01
Aiming at the impact feature extraction problem of gear faults under strong background noises,a gear fault diagnosis method was proposed based on adaptive stochastic resonance and sparse code shrinkage algorithm.In order to achieve the effective extraction of periodic impact features,cor-related kurtosis was adopted as the measurement index of stochastic resonance,which was used to construct the fitness function of genetic algorithm.According to the maximum of fitness function,the optimal stochastic resonance system parameters could be selected,so as to achieve the adaptive extrac-tion of the periodic impact features from the vibration signals submerged by strong noises.Then, sparse code shrinkage algorithm was applied to further reduce the noises from the detection results of stochastic resonance,thereby the impact features were highlighted to improve the identification accu-racy of gear faults.The experimental and engineering application results indicate that the proposed method can realize the enhancement and extraction of impact features of gear faults,which will pro-vide a basis for gear fault diagnosis.%针对强背景噪声下齿轮故障冲击特征提取问题，提出了一种基于自适应随机共振和稀疏编码收缩算法的齿轮故障诊断方法。该方法选用相关峭度作为随机共振检测周期性冲击分量的测度函数，借助遗传算法实现信号中周期性冲击特征的自适应提取；在此基础上，利用稀疏编码收缩算法对随机共振检测结果做进一步降噪处理，从而凸显冲击特征，提高故障识别精度。试验和工程实例分析结果表明，该方法可实现齿轮故障冲击特征的增强提取，为齿轮故障诊断提供依据。
Identification and stochastic control of helicopter dynamic modes
Molusis, J. A.; Bar-Shalom, Y.
1983-01-01
A general treatment of parameter identification and stochastic control for use on helicopter dynamic systems is presented. Rotor dynamic models, including specific applications to rotor blade flapping and the helicopter ground resonance problem are emphasized. Dynamic systems which are governed by periodic coefficients as well as constant coefficient models are addressed. The dynamic systems are modeled by linear state variable equations which are used in the identification and stochastic control formulation. The pure identification problem as well as the stochastic control problem which includes combined identification and control for dynamic systems is addressed. The stochastic control problem includes the effect of parameter uncertainty on the solution and the concept of learning and how this is affected by the control's duel effect. The identification formulation requires algorithms suitable for on line use and thus recursive identification algorithms are considered. The applications presented use the recursive extended kalman filter for parameter identification which has excellent convergence for systems without process noise.
The reaction process of firefly bioluminescence triggered by photolysis of caged-ATP.
Kageyama, Takeshi; Tanaka, Masatoshi; Sekiya, Takao; Ohno, Shin-Ya; Wada, Naohisa
2011-01-01
The reaction process of firefly bioluminescence was studied by photolyzing caged-ATP to adenosine triphosphate (ATP) within 100 ms. The intensity of luminescence increases markedly to reach a maximum within 1 s, maintains almost the same intensity up to 5 s and then decays monotonically. The rise γ(1) and decay γ(2) rate constants were determined to be about 5 s(-1) and 1 × 10(-2) s(-1), respectively, so as to phenomenologically fit the time course. A second luminescence peak appears after around 350 s. The dependence of the rate constants on the concentrations of reactants and a viscous reagent revealed that two kinds of reaction contribute the observed time course: (1) an intrinsic reaction by ATP photolyzed from caged-ATP that is already trapped in luciferase; and (2) a diffusion-controlled reaction by free ATP in the buffer solution outside luciferase. Numerical analysis based on reaction kinetics related γ(1) and γ(2) to the rate constants of a three-step reaction model, and accurately described the effects of concentration of reactants and a viscous reagent on the time courses of bioluminescence. Thus, it has been clearly concluded that the binding mode of caged-ATP at the catalytic center of luciferase is very different from that of ATP.
Firefly Luciferase Mutants Allow Substrate-Selective Bioluminescence Imaging in the Mouse Brain.
Adams, Spencer T; Mofford, David M; Reddy, G S Kiran Kumar; Miller, Stephen C
2016-04-11
Bioluminescence imaging is a powerful approach for visualizing specific events occurring inside live mice. Animals can be made to glow in response to the expression of a gene, the activity of an enzyme, or the growth of a tumor. But bioluminescence requires the interaction of a luciferase enzyme with a small-molecule luciferin, and its scope has been limited by the mere handful of natural combinations. Herein, we show that mutants of firefly luciferase can discriminate between natural and synthetic substrates in the brains of live mice. When using adeno-associated viral (AAV) vectors to express luciferases in the brain, we found that mutant luciferases that are inactive or weakly active with d-luciferin can light up brightly when treated with the aminoluciferins CycLuc1 and CycLuc2 or their respective FAAH-sensitive luciferin amides. Further development of selective luciferases promises to expand the power of bioluminescence and allow multiple events to be imaged in the same live animal.
Gaussia Luciferase for Bioluminescence Tumor Monitoring in Comparison with Firefly Luciferase
Directory of Open Access Journals (Sweden)
Yusuke Inoue
2011-09-01
Full Text Available Gaussia luciferase (Gluc is a secreted reporter, and its expression in living animals can be assessed by in vivo bioluminescence imaging (BLI or blood assays. We characterized Gluc as an in vivo reporter in comparison with firefly luciferase (Fluc. Mice were inoculated subcutaneously with tumor cells expressing both Fluc and Gluc and underwent Flue BLI, Gluc BLI, blood assays of Glue activity, and caliper measurement. In Gluc BLI, the signal from the tumor peaked immediately and then decreased rapidly. In the longitudinal monitoring, all measures indicated an increase in tumor burden early after cell inoculation. However, the increase reached plateaus in Gluc BLI and Fluc BLI despite a continuous increase in the caliper measurement and Gluc blood assay. Significant correlations were found between the measures, and the correlation between the blood signal and caliper volume was especially high. Gluc allows tumor monitoring in mice and should be applicable to dual-reporter assessment in combination with Fluc. The Gluc blood assay appears to provide a reliable indicator of viable tumor burden, and the combination of a blood assay and in vivo BLI using Glue should be promising for quantifying and localizing the tumors.
Presiado, Itay; Erez, Yuval; Huppert, Dan
2010-12-30
Steady-state and time-resolved techniques were employed to study the excited-state proton transfer (ESPT) from d-luciferin, the natural substrate of the firefly luciferase, to the mild acetate base in aqueous solutions. We found that in 1 M aqueous solutions of acetate or higher, a proton transfer (PT) process to the acetate takes place within 30 ps in both H(2)O and D(2)O solutions. The time-resolved emission signal is composed of three components. We found that the short-time component decay time is 300 and 600 fs in H(2)O and D(2)O, respectively. This component is attributed either to a PT process via the shortest water bridged complex available, ROH··H(2)O··Ac(-), or to PT taking place within a contact ion pair. The second time component of 2000 and 3000 fs for H(2)O and D(2)O, respectively, is attributed to ROH* acetate complex, whose proton wire is longer by one water molecule. The decay rate of the third, long-time component is proportional to the acetate concentration. We attribute it to the diffusion-assisted reaction as well as to PT process to the solvent.
Maltsev, Oleg V; Yue, Ling; Rebarz, Mateusz; Hintermann, Lukas; Sliwa, Michel; Ruckebusch, Cyril; Pejov, Ljupčo; Liu, Ya-Jun; Naumov, Panče
2014-08-18
The chemical complexity of oxyluciferin (OxyLH2), the light-emitting molecule in the bioluminescence of fireflies, originates from the possibility of keto/enol tautomerism and single or double deprotonation. Herein, we present detailed infrared spectroscopic analysis of OxyLH2 and several of its chemical isomers and isotopomers. To facilitate the future characterization of its biogenic forms, we provide accurate assignments of the solid-state and solution FTIR spectra of OxyLH2 based on comparison to six isotopically labeled variants ([2-(13)C]-OxyLH2, [3-(15)N]-OxyLH2, [4-(13)C]-OxyLH2, [5-(13)C]-OxyLH2, [2'-(13)C]-OxyLH2, [3'-(15)N]-OxyLH2), five closely related structural analogues, and theoretically computed spectra. The computed DFT harmonic vibrational force fields (B3LYP and M06 functionals with basis sets of varying flexibility up to 6-311++G**) reproduce well the observed shifts in the IR spectra of both isotopically labeled and structurally related analogues.
Receptors for /sup 3/H-octopamine in the adult firefly light organ
Energy Technology Data Exchange (ETDEWEB)
Hashemzadeh, H.; Hollingworth, R.M.; Voliva, A.
1985-08-05
/sup 3/H-Octopamine binds reversibly and with high affinity to sites on adult firefly light organ membranes. The binding is characterized by multiple affinities. Scatchard analysis supported a two site binding model with a tentative Kd value of about 1 nM for the high affinity component. The more abundant lower affinity site had a Kd value of about 60 nM. Guanyl nucleotides (Gpp(NH)p and GTP) greatly reduced the apparent number of octopamine binding sites. Competition studies with known octopaminergic agonists including the formamidine pesticides chlordimeform (CDM) and N-demethylchlordimeform (DCDM) showed the following rank order of potencies in displacing octopamine: DCDM > octopamine = synephrine > naphazoline > clonidine > CDM. It was also observed that phentolamine was much more active than propranolol in antagonizing OA-binding. These relative activities are similar to the abilities of the same compounds to alter adenylate cyclase activity in light organ homogenates. Together with the effect of GTP on binding, these results suggest that the binding sites are functional octopamine receptors of the light organ. 27 references, 3 figures, 1 table.
StochPy: a comprehensive, user-friendly tool for simulating stochastic biological processes.
Directory of Open Access Journals (Sweden)
Timo R Maarleveld
Full Text Available Single-cell and single-molecule measurements indicate the importance of stochastic phenomena in cell biology. Stochasticity creates spontaneous differences in the copy numbers of key macromolecules and the timing of reaction events between genetically-identical cells. Mathematical models are indispensable for the study of phenotypic stochasticity in cellular decision-making and cell survival. There is a demand for versatile, stochastic modeling environments with extensive, preprogrammed statistics functions and plotting capabilities that hide the mathematics from the novice users and offers low-level programming access to the experienced user. Here we present StochPy (Stochastic modeling in Python, which is a flexible software tool for stochastic simulation in cell biology. It provides various stochastic simulation algorithms, SBML support, analyses of the probability distributions of molecule copy numbers and event waiting times, analyses of stochastic time series, and a range of additional statistical functions and plotting facilities for stochastic simulations. We illustrate the functionality of StochPy with stochastic models of gene expression, cell division, and single-molecule enzyme kinetics. StochPy has been successfully tested against the SBML stochastic test suite, passing all tests. StochPy is a comprehensive software package for stochastic simulation of the molecular control networks of living cells. It allows novice and experienced users to study stochastic phenomena in cell biology. The integration with other Python software makes StochPy both a user-friendly and easily extendible simulation tool.
Robustness of Populations in Stochastic Environments
DEFF Research Database (Denmark)
Gießen, Christian; Kötzing, Timo
2014-01-01
We consider stochastic versions of OneMax and Leading-Ones and analyze the performance of evolutionary algorithms with and without populations on these problems. It is known that the (1+1) EA on OneMax performs well in the presence of very small noise, but poorly for higher noise levels. We extend...... these results to LeadingOnes and to many different noise models, showing how the application of drift theory can significantly simplify and generalize previous analyses. Most surprisingly, even small populations (of size _(log n)) can make evolutionary algorithms perform well for high noise levels, well outside...... the abilities of the (1+1) EA! Larger population sizes are even more beneficial; we consider both parent and o_spring populations. In this sense, populations are robust in these stochastic settings....
Robustness of Populations in Stochastic Environments
DEFF Research Database (Denmark)
Gießen, Christian; Kötzing, Timo
2016-01-01
We consider stochastic versions of OneMax and LeadingOnes and analyze the performance of evolutionary algorithms with and without populations on these problems. It is known that the (1+1) EA on OneMax performs well in the presence of very small noise, but poorly for higher noise levels. We extend...... these results to LeadingOnes and to many different noise models, showing how the application of drift theory can significantly simplify and generalize previous analyses. Most surprisingly, even small populations (of size Θ(logn)) can make evolutionary algorithms perform well for high noise levels, well outside...... the abilities of the (1+1) EA. Larger population sizes are even more beneficial; we consider both parent and offspring populations. In this sense, populations are robust in these stochastic settings....
Stochastic Simulation of Process Calculi for Biology
Phillips, Andrew; Paulevé, Loïc; 10.4204/EPTCS.40.1
2010-01-01
Biological systems typically involve large numbers of components with complex, highly parallel interactions and intrinsic stochasticity. To model this complexity, numerous programming languages based on process calculi have been developed, many of which are expressive enough to generate unbounded numbers of molecular species and reactions. As a result of this expressiveness, such calculi cannot rely on standard reaction-based simulation methods, which require fixed numbers of species and reactions. Rather than implementing custom stochastic simulation algorithms for each process calculus, we propose to use a generic abstract machine that can be instantiated to a range of process calculi and a range of reaction-based simulation algorithms. The abstract machine functions as a just-in-time compiler, which dynamically updates the set of possible reactions and chooses the next reaction in an iterative cycle. In this short paper we give a brief summary of the generic abstract machine, and show how it can be instant...
Stochastic Combinatorial Optimization under Probabilistic Constraints
Agrawal, Shipra; Ye, Yinyu
2008-01-01
In this paper, we present approximation algorithms for combinatorial optimization problems under probabilistic constraints. Specifically, we focus on stochastic variants of two important combinatorial optimization problems: the k-center problem and the set cover problem, with uncertainty characterized by a probability distribution over set of points or elements to be covered. We consider these problems under adaptive and non-adaptive settings, and present efficient approximation algorithms for the case when underlying distribution is a product distribution. In contrast to the expected cost model prevalent in stochastic optimization literature, our problem definitions support restrictions on the probability distributions of the total costs, via incorporating constraints that bound the probability with which the incurred costs may exceed a given threshold.
Stochastic linear dynamical programming in order to apply it in energy modelling
Energy Technology Data Exchange (ETDEWEB)
El Hachem, S.
1995-11-01
This thesis contributes to the development of new algorithms for the computation of stochastic dynamic problem and its mini-maxi variant for the case of imperfect knowledge on random data. The proposed algorithms are scenarios aggregation type. It also contributes to integrate these algorithms in a decision support approach and to discuss the stochastic modeling of two energy problems: the refining and the portfolio gas contracts. (author). 112 refs., 5 tabs.
Models of stochastic gene expression and Weyl algebra
Vidal, Samuel,; Petitot, Michel; Boulier, François; Lemaire, François; Kuttler, Celine
2010-01-01
International audience; This paper presents a symbolic algorithm for computing the ODE systems which describe the evolution of the moments associated to a chemical reaction system, considered from a stochastic point of view. The algorithm, which is formulated in the Weyl algebra, seems more efficient than the corresponding method, based on partial derivatives. In particular, an efficient method for handling conservation laws is presented. The output of the algorithm can be used for a further ...
Bayesian Variable Selection via Particle Stochastic Search.
Shi, Minghui; Dunson, David B
2011-02-01
We focus on Bayesian variable selection in regression models. One challenge is to search the huge model space adequately, while identifying high posterior probability regions. In the past decades, the main focus has been on the use of Markov chain Monte Carlo (MCMC) algorithms for these purposes. In this article, we propose a new computational approach based on sequential Monte Carlo (SMC), which we refer to as particle stochastic search (PSS). We illustrate PSS through applications to linear regression and probit models.
2nd International Conference on Harmony Search Algorithm
Geem, Zong
2016-01-01
The Harmony Search Algorithm (HSA) is one of the most well-known techniques in the field of soft computing, an important paradigm in the science and engineering community. This volume, the proceedings of the 2nd International Conference on Harmony Search Algorithm 2015 (ICHSA 2015), brings together contributions describing the latest developments in the field of soft computing with a special focus on HSA techniques. It includes coverage of new methods that have potentially immense application in various fields. Contributed articles cover aspects of the following topics related to the Harmony Search Algorithm: analytical studies; improved, hybrid and multi-objective variants; parameter tuning; and large-scale applications. The book also contains papers discussing recent advances on the following topics: genetic algorithms; evolutionary strategies; the firefly algorithm and cuckoo search; particle swarm optimization and ant colony optimization; simulated annealing; and local search techniques. This book ...
Sequential stochastic optimization
Cairoli, Renzo
1996-01-01
Sequential Stochastic Optimization provides mathematicians and applied researchers with a well-developed framework in which stochastic optimization problems can be formulated and solved. Offering much material that is either new or has never before appeared in book form, it lucidly presents a unified theory of optimal stopping and optimal sequential control of stochastic processes. This book has been carefully organized so that little prior knowledge of the subject is assumed; its only prerequisites are a standard graduate course in probability theory and some familiarity with discrete-paramet
Kraenkel, R. A.; da Silva, D. J. Pamplona
2010-01-01
We consider the dynamics of a biological population described by the Fisher-Kolmogorov-Petrovskii-Piskunov (FKPP) equation in the case where the spatial domain consists of alternating favorable and adverse patches whose sizes are distributed randomly. For the one-dimensional case we define a stochastic analogue of the classical critical patch size. We address the issue of persistence of a population and we show that the minimum fraction of the length of favorable segments to the total length is always smaller in the stochastic case than in a periodic arrangement. In this sense, spatial stochasticity favors viability of a population.
Fundamentals of Stochastic Filtering
Crisan, Dan
2008-01-01
The objective of stochastic filtering is to determine the best estimate for the state of a stochastic dynamical system from partial observations. The solution of this problem in the linear case is the well known Kalman-Bucy filter which has found widespread practical application. The purpose of this book is to provide a rigorous mathematical treatment of the non-linear stochastic filtering problem using modern methods. Particular emphasis is placed on the theoretical analysis of numerical methods for the solution of the filtering problem via particle methods. The book should provide sufficient
Stochastic Simulation of Process Calculi for Biology
Directory of Open Access Journals (Sweden)
Andrew Phillips
2010-10-01
Full Text Available Biological systems typically involve large numbers of components with complex, highly parallel interactions and intrinsic stochasticity. To model this complexity, numerous programming languages based on process calculi have been developed, many of which are expressive enough to generate unbounded numbers of molecular species and reactions. As a result of this expressiveness, such calculi cannot rely on standard reaction-based simulation methods, which require fixed numbers of species and reactions. Rather than implementing custom stochastic simulation algorithms for each process calculus, we propose to use a generic abstract machine that can be instantiated to a range of process calculi and a range of reaction-based simulation algorithms. The abstract machine functions as a just-in-time compiler, which dynamically updates the set of possible reactions and chooses the next reaction in an iterative cycle. In this short paper we give a brief summary of the generic abstract machine, and show how it can be instantiated with the stochastic simulation algorithm known as Gillespie's Direct Method. We also discuss the wider implications of such an abstract machine, and outline how it can be used to simulate multiple calculi simultaneously within a common framework.
Stochastic Optimization Approaches for Solving Sudoku
Perez, Meir
2008-01-01
In this paper the Sudoku problem is solved using stochastic search techniques and these are: Cultural Genetic Algorithm (CGA), Repulsive Particle Swarm Optimization (RPSO), Quantum Simulated Annealing (QSA) and the Hybrid method that combines Genetic Algorithm with Simulated Annealing (HGASA). The results obtained show that the CGA, QSA and HGASA are able to solve the Sudoku puzzle with CGA finding a solution in 28 seconds, while QSA finding a solution in 65 seconds and HGASA in 1.447 seconds. This is mainly because HGASA combines the parallel searching of GA with the flexibility of SA. The RPSO was found to be unable to solve the puzzle.
Directory of Open Access Journals (Sweden)
Christley Scott
2010-07-01
Full Text Available Abstract Background Stochastic effects can be important for the behavior of processes involving small population numbers, so the study of stochastic models has become an important topic in the burgeoning field of computational systems biology. However analysis techniques for stochastic models have tended to lag behind their deterministic cousins due to the heavier computational demands of the statistical approaches for fitting the models to experimental data. There is a continuing need for more effective and efficient algorithms. In this article we focus on the parameter inference problem for stochastic kinetic models of biochemical reactions given discrete time-course observations of either some or all of the molecular species. Results We propose an algorithm for inference of kinetic rate parameters based upon maximum likelihood using stochastic gradient descent (SGD. We derive a general formula for the gradient of the likelihood function given discrete time-course observations. The formula applies to any explicit functional form of the kinetic rate laws such as mass-action, Michaelis-Menten, etc. Our algorithm estimates the gradient of the likelihood function by reversible jump Markov chain Monte Carlo sampling (RJMCMC, and then gradient descent method is employed to obtain the maximum likelihood estimation of parameter values. Furthermore, we utilize flux balance analysis and show how to automatically construct reversible jump samplers for arbitrary biochemical reaction models. We provide RJMCMC sampling algorithms for both fully observed and partially observed time-course observation data. Our methods are illustrated with two examples: a birth-death model and an auto-regulatory gene network. We find good agreement of the inferred parameters with the actual parameters in both models. Conclusions The SGD method proposed in the paper presents a general framework of inferring parameters for stochastic kinetic models. The method is
Stochastic differential equations and applications
Friedman, Avner
2006-01-01
This text develops the theory of systems of stochastic differential equations, and it presents applications in probability, partial differential equations, and stochastic control problems. Originally published in two volumes, it combines a book of basic theory and selected topics with a book of applications.The first part explores Markov processes and Brownian motion; the stochastic integral and stochastic differential equations; elliptic and parabolic partial differential equations and their relations to stochastic differential equations; the Cameron-Martin-Girsanov theorem; and asymptotic es
Frédéric, Pierret
2014-01-01
The equations of celestial mechanics that govern the variation of the orbital elements are completely derived for stochastic perturbation which generalized the classic perturbation equations which are used since Gauss, starting from Newton's equation and it's solution. The six most understandable orbital element, the semi-major axis, the eccentricity, the inclination, the longitude of the ascending node, the pericenter angle and the mean motion are express in term of the angular momentum vector $\\textbf{H}$ per unit of mass and the energy $E$ per unit of mass. We differentiate those expressions using It\\^o's theory of differential equations due to the stochastic nature of the perturbing force. The result is applied to the two-body problem perturbed by a stochastic dust cloud and also perturbed by a stochastic dynamical oblateness of the central body.
Doberkat, Ernst-Erich
2009-01-01
Combining coalgebraic reasoning, stochastic systems and logic, this volume presents the principles of coalgebraic logic from a categorical perspective. Modal logics are also discussed, including probabilistic interpretations and an analysis of Kripke models.
Stochastic modelling of turbulence
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Sørensen, Emil Hedevang Lohse
This thesis addresses stochastic modelling of turbulence with applications to wind energy in mind. The primary tool is ambit processes, a recently developed class of computationally tractable stochastic processes based on integration with respect to Lévy bases. The subject of ambit processes...... stochastic turbulence model based on ambit processes is proposed. It is shown how a prescribed isotropic covariance structure can be reproduced. Non-Gaussian turbulence models are obtained through non-Gaussian Lévy bases or through volatility modulation of Lévy bases. As opposed to spectral models operating...... is dissipated into heat due to the internal friction caused by viscosity. An existing stochastic model, also expressed in terms of ambit processes, is extended and shown to give a universal and parsimonious description of the turbulent energy dissipation. The volatility modulation, referred to above, has...
Stochastic calculus with infinitesimals
Herzberg, Frederik
2013-01-01
Stochastic analysis is not only a thriving area of pure mathematics with intriguing connections to partial differential equations and differential geometry. It also has numerous applications in the natural and social sciences (for instance in financial mathematics or theoretical quantum mechanics) and therefore appears in physics and economics curricula as well. However, existing approaches to stochastic analysis either presuppose various concepts from measure theory and functional analysis or lack full mathematical rigour. This short book proposes to solve the dilemma: By adopting E. Nelson's "radically elementary" theory of continuous-time stochastic processes, it is based on a demonstrably consistent use of infinitesimals and thus permits a radically simplified, yet perfectly rigorous approach to stochastic calculus and its fascinating applications, some of which (notably the Black-Scholes theory of option pricing and the Feynman path integral) are also discussed in the book.
Stochastic processes inference theory
Rao, Malempati M
2014-01-01
This is the revised and enlarged 2nd edition of the authors’ original text, which was intended to be a modest complement to Grenander's fundamental memoir on stochastic processes and related inference theory. The present volume gives a substantial account of regression analysis, both for stochastic processes and measures, and includes recent material on Ridge regression with some unexpected applications, for example in econometrics. The first three chapters can be used for a quarter or semester graduate course on inference on stochastic processes. The remaining chapters provide more advanced material on stochastic analysis suitable for graduate seminars and discussions, leading to dissertation or research work. In general, the book will be of interest to researchers in probability theory, mathematical statistics and electrical and information theory.
Notes on the Stochastic Exponential and Logarithm
Larsson, Martin; Ruf, Johannes
2017-01-01
Stochastic exponentials are defined for semimartingales on stochastic intervals, and stochastic logarithms are defined for nonnegative semimartingales, up to the first time the semimartingale hits zero continuously. In the case of (nonnegative) local supermartingales, these two stochastic transformations are inverse to each other. The reciprocal of a stochastic exponential is again a stochastic exponential on a stochastic interval.
Solving Simple Stochastic Games with Few Random Vertices
Gimbert, Hugo
2007-01-01
We present a new algorithm for solving Simple Stochastic Games (SSGs). This algorithm is based on an exhaustive search of a special kind of positional optimal strategies, the \\emph{f-strategies}. The running time is O(|Vr| (|V||E|+|p|)), where |V|, |Vr|, |E| and |p| are respectively the number of vertices, random vertices and edges, and the maximum bit-length of a transition probability. Our algorithm improves existing algorithms for solving SSGs in three aspects. First, our algorithm performs well on SSGs with few random vertices, second it does not rely on linear or quadratic programming, third it applies to all SSGs, not only stopping SSGs.
Geometric Stochastic Resonance
Ghosh, Pulak Kumar; Savel'ev, Sergey E; Nori, Franco
2015-01-01
A Brownian particle moving across a porous membrane subject to an oscillating force exhibits stochastic resonance with properties which strongly depend on the geometry of the confining cavities on the two sides of the membrane. Such a manifestation of stochastic resonance requires neither energetic nor entropic barriers, and can thus be regarded as a purely geometric effect. The magnitude of this effect is sensitive to the geometry of both the cavities and the pores, thus leading to distinctive optimal synchronization conditions.
Yu, Hyeong Won; Chung, Joon Woo; Yi, Jin Wook; Song, Ra-Yeong; Lee, Joon-Hyop; Kwon, Hyungju; Kim, Su-Jin; Chai, Young Jun; Choi, June Young; Lee, Kyu Eun
2017-07-01
It is unclear whether near-infrared (NIR) light-induced indocyanine green (ICG) fluorescence can effectively identify, and thus permit the preservation of, parathyroid glands in bilateral axillo-breast approach (BABA) robotic thyroidectomy. This case-control study with a prospectively recruited consecutive series and a retrospectively selected control group assessed the usefulness of ICG with Firefly(R) technology to identify the parathyroid glands intraoperatively during BABA robotic thyroidectomy. All consecutive patients (N = 22) who were scheduled to undergo BABA robotic thyroidectomy for papillary thyroid carcinoma in December 2013-August 2015 and met the study eligibility criteria were recruited prospectively. ICG fluorescence was used with the Firefly system (NIR illuminator: 805 nm; filter: 825 nm) integrated in the da Vinci Si robot system to identify the lower parathyroid glands. Parathyroid hormone levels were recorded on postoperative days 0, 1, 2, and 14. Propensity score matching was used to identify an age-, gender-, tumor size-, and operation type-matched group of control patients who underwent BABA robotic thyroidectomy without the Firefly system. The two groups were compared in terms of parathyroid-related outcomes. ICG fluorescence-mediated identification of the parathyroid and thyroid glands required on average (range) 203 ± 89 (125-331) and 207 ± 112 (130-356) s, respectively. The mean (range) fluorescence duration in these glands was 20.8 ± 6.0 (16.6-35.8) and 20.1 ± 7.3 (15.5-33.8) min, respectively. The ICG group had a significantly lower rate of incidental parathyroidectomy than the control group (0 vs. 15.9%, P = 0.048). ICG with NIR light may feasibly and safely identify the parathyroid glands in BABA robotic thyroidectomy.
Directory of Open Access Journals (Sweden)
John R Lue
2016-01-01
Full Text Available Endometriosis continues to be a major primary gynecologic etiology of chronic pelvic pain. The symptom profile, which includes cyclic pelvic pain, dysmenorrhea, and dyspareunia or dyschezia, is nonspecific and does not correlate with the extent or severity of disease. Trans-vaginal or trans-rectal ultrasound, as well as magnetic resonance imaging, can help visualize endometriomas and deeply infiltrating endometriosis. Additionally, there have been no serum marker tests available so far. However, even intraoperatively, the diagnosis may be missed, leading to under diagnosis and delayed or noninitiation of treatment. There are thought to be three distinct endometriotic lesions of the pelvis that are seen laparoscopically. The first is that which is visible on the pelvic peritoneal surface or the surface of the ovary, which is commonly termed peritoneal endometriosis. Second, endometriotic lesions that occur within the ovary and form cysts that are often lined with endometrioid mucosa are termed endometriomas. Lastly, rectovaginal endometriomas are endometriotic lesions that contain a mixture of adipose and fibrous tissue located between the rectum and vagina. All of these lesions can be singular or multiple and the pelvis may contain one or all three types of lesions. The shared histologic feature with all three lesions is the presence of endometrial epithelial cells or endometrial stroma. During a diagnostic procedure, the da-Vinci robot and its firefly mode allow for three dimensional visualization and seven degrees of instrument articulation for meticulous dissection of fibrotic areas of peritoneum that may contain deep infiltrating lesions of endometriosis. This case report describes a relatively new and innovative technique for effectively diagnosing and successfully treating endometriosis when other less invasive methods have failed.
Molecular basis for the high-affinity binding and stabilization of firefly luciferase by PTC124
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Auld, Douglas S.; Lovell, Scott; Thorne, Natasha; Lea, Wendy A.; Maloney, David J.; Shen, Min; Rai, Ganesha; Battaile, Kevin P.; Thomas, Craig J.; Simeonov, Anton; Hanzlik, Robert P.; Inglese, James (NIH); (Kansas); (HWMRI)
2010-04-07
Firefly luciferase (FLuc), an ATP-dependent bioluminescent reporter enzyme, is broadly used in chemical biology and drug discovery assays. PTC124 Ataluren; (3-[5-(2-fluorophenyl)-1,2,4-oxadiazol-3-yl]benzoic acid) discovered in an FLuc-based assay targeting nonsense codon suppression, is an unusually potent FLuc-inhibitor. Paradoxically, PTC124 and related analogs increase cellular FLuc activity levels by posttranslational stabilization. In this study, we show that FLuc inhibition and stabilization is the result of an inhibitory product formed during the FLuc-catalyzed reaction between its natural substrate, ATP, and PTC124. A 2.0 {angstrom} cocrystal structure revealed the inhibitor to be the acyl-AMP mixed-anhydride adduct PTC124-AMP, which was subsequently synthesized and shown to be a high-affinity multisubstrate adduct inhibitor (MAI; KD = 120 pM) of FLuc. Biochemical assays, liquid chromatography/mass spectrometry, and near-attack conformer modeling demonstrate that formation of this novel MAI is absolutely dependent upon the precise positioning and reactivity of a key meta-carboxylate of PTC124 within the FLuc active site. We also demonstrate that the inhibitory activity of PTC124-AMP is relieved by free coenzyme A, a component present at high concentrations in luciferase detection reagents used for cell-based assays. This explains why PTC124 can appear to increase, instead of inhibit, FLuc activity in cell-based reporter gene assays. To our knowledge, this is an unusual example in which the 'off-target' effect of a small molecule is mediated by an MAI mechanism.
Uric Acid Spherulites in the Reflector Layer of Firefly Light Organ
Goh, King-Siang; Sheu, Hwo-Shuenn; Hua, Tzu-En; Kang, Mei-Hua; Li, Chia-Wei
2013-01-01
Background In firefly light organs, reflector layer is a specialized tissue which is believed to play a key role for increasing the bioluminescence intensity through reflection. However, the nature of this unique tissue remains elusive. In this report, we investigated the role, fine structure and nature of the reflector layer in the light organ of adult Luciola cerata. Principal Findings Our results indicated that the reflector layer is capable of reflecting bioluminescence, and contains abundant uric acid. Electron microscopy (EM) demonstrated that the cytosol of the reflector layer's cells is filled with densely packed spherical granules, which should be the uric acid granules. These granules are highly regular in size (∼700 nm in diameter), and exhibit a radial internal structure. X-ray diffraction (XRD) analyses revealed that an intense single peak pattern with a d-spacing value of 0.320 nm is specifically detected in the light organ, and is highly similar to the diffraction peak pattern and d-spacing value of needle-formed crystals of monosodium urate monohydrate. However, the molar ratio evaluation of uric acid to various cations (K+, Na+, Ca2+ and Mg2+) in the light organ deduced that only a few uric acid molecules were in the form of urate salts. Thus, non-salt uric acid should be the source of the diffraction signal detected in the light organ. Conclusions In the light organ, the intense single peak diffraction signal might come from a unique needle-like uric acid form, which is different from other known structures of non-salt uric acid form. The finding of a radial structure in the granules of reflector layer implies that the spherical uric acid granules might be formed by the radial arrangement of needle-formed packing matter. PMID:23441187
Firefly bioluminescent assay of ATP in the presence of ATP extractant by using liposomes.
Kamidate, Tamio; Yanashita, Kenji; Tani, Hirofumi; Ishida, Akihiko; Notani, Mizuyo
2006-01-01
Liposomes containing phosphatidylcholine (PC) and cholesterol (Chol) were applied to the enhancer for firefly bioluminescence (BL) assay for ATP in the presence of cationic surfactants using as an extractant for the release of ATP from living cells. Benzalkonium chloride (BAC) was used as an ATP extractant. However, BAC seriously inhibited the activity of luciferase, thus resulting in the remarkable decrease in the sensitivity of the BL assay for ATP. On the other hand, we found that BAC was associated with liposomes to form cationic liposomes containing BAC. The association rate of BAC with liposomes was faster than that of BAC with luciferase. As a result, the inhibitory effect of BAC on luciferase was eliminated in the presence of liposomes. In addition, cationic liposomes thus formed enhanced BL emission. BL measurement conditions were optimized in terms of liposome charge type, liposome size, and total concentration of PC and Chol. ATP can be sensitively determined without dilution of analytical samples by using liposomes. The detection limit of ATP with and without liposomes was 100 amol and 25 fmol in aqueous ATP standard solutions containing 0.06% BAC, respectively. The method was applied to the determination of ATP in Escherichia coli extracts. The BL intensity was linear from 4 x 10(4) to 1 x 10(7) cells mL(-1) in the absence of liposomes. On the other hand, the BL intensity was linear from 4 x 10(3) to 4 x 10(6) cells mL(-1) in the presence of liposomes. The detection limit of ATP in E. coli extracts was improved by a factor of 10 via use of liposomes.