On a numerical solving of random generated hexamatrix games

NASA Astrophysics Data System (ADS)

Orlov, Andrei; Strekalovskiy, Alexander

2016-10-01

In this paper, we develop a global search method for finding a Nash equilibrium in a hexamatrix game (polymatrix game of three players). The method, on the one hand, is based on the equivalence theorem of the problem of finding a Nash equilibrium in the game and a special mathematical optimization problem, and, on the other hand, on the usage of Global Search Theory for solving the latter problem. The efficiency of this approach is demonstrated by the results of computational testing.

MIP models and hybrid algorithms for simultaneous job splitting and scheduling on unrelated parallel machines.

PubMed

Eroglu, Duygu Yilmaz; Ozmutlu, H Cenk

2014-01-01

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms.

TemperSAT: A new efficient fair-sampling random k-SAT solver

NASA Astrophysics Data System (ADS)

Fang, Chao; Zhu, Zheng; Katzgraber, Helmut G.

The set membership problem is of great importance to many applications and, in particular, database searches for target groups. Recently, an approach to speed up set membership searches based on the NP-hard constraint-satisfaction problem (random k-SAT) has been developed. However, the bottleneck of the approach lies in finding the solution to a large SAT formula efficiently and, in particular, a large number of independent solutions is needed to reduce the probability of false positives. Unfortunately, traditional random k-SAT solvers such as WalkSAT are biased when seeking solutions to the Boolean formulas. By porting parallel tempering Monte Carlo to the sampling of binary optimization problems, we introduce a new algorithm (TemperSAT) whose performance is comparable to current state-of-the-art SAT solvers for large k with the added benefit that theoretically it can find many independent solutions quickly. We illustrate our results by comparing to the currently fastest implementation of WalkSAT, WalkSATlm.

A comparative study of controlled random search algorithms with application to inverse aerofoil design

NASA Astrophysics Data System (ADS)

Manzanares-Filho, N.; Albuquerque, R. B. F.; Sousa, B. S.; Santos, L. G. C.

2018-06-01

This article presents a comparative study of some versions of the controlled random search algorithm (CRSA) in global optimization problems. The basic CRSA, originally proposed by Price in 1977 and improved by Ali et al. in 1997, is taken as a starting point. Then, some new modifications are proposed to improve the efficiency and reliability of this global optimization technique. The performance of the algorithms is assessed using traditional benchmark test problems commonly invoked in the literature. This comparative study points out the key features of the modified algorithm. Finally, a comparison is also made in a practical engineering application, namely the inverse aerofoil shape design.

A Novel Harmony Search Algorithm Based on Teaching-Learning Strategies for 0-1 Knapsack Problems

PubMed Central

Tuo, Shouheng; Yong, Longquan; Deng, Fang'an

2014-01-01

To enhance the performance of harmony search (HS) algorithm on solving the discrete optimization problems, this paper proposes a novel harmony search algorithm based on teaching-learning (HSTL) strategies to solve 0-1 knapsack problems. In the HSTL algorithm, firstly, a method is presented to adjust dimension dynamically for selected harmony vector in optimization procedure. In addition, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to improve the performance of HS algorithm. Another improvement in HSTL method is that the dynamic strategies are adopted to change the parameters, which maintains the proper balance effectively between global exploration power and local exploitation power. Finally, simulation experiments with 13 knapsack problems show that the HSTL algorithm can be an efficient alternative for solving 0-1 knapsack problems. PMID:24574905

A novel harmony search algorithm based on teaching-learning strategies for 0-1 knapsack problems.

PubMed

Tuo, Shouheng; Yong, Longquan; Deng, Fang'an

2014-01-01

To enhance the performance of harmony search (HS) algorithm on solving the discrete optimization problems, this paper proposes a novel harmony search algorithm based on teaching-learning (HSTL) strategies to solve 0-1 knapsack problems. In the HSTL algorithm, firstly, a method is presented to adjust dimension dynamically for selected harmony vector in optimization procedure. In addition, four strategies (harmony memory consideration, teaching-learning strategy, local pitch adjusting, and random mutation) are employed to improve the performance of HS algorithm. Another improvement in HSTL method is that the dynamic strategies are adopted to change the parameters, which maintains the proper balance effectively between global exploration power and local exploitation power. Finally, simulation experiments with 13 knapsack problems show that the HSTL algorithm can be an efficient alternative for solving 0-1 knapsack problems.

Identification of problems in search strategies in Cochrane Reviews.

PubMed

Franco, Juan Víctor Ariel; Garrote, Virginia Laura; Escobar Liquitay, Camila Micaela; Vietto, Valeria

2018-05-15

Search strategies are essential for the adequate retrieval of studies in a systematic review (SR). Our objective was to identify problems in the design and reporting of search strategies in a sample of new Cochrane SRs first published in The Cochrane Library in 2015. We took a random sample of 70 new Cochrane SRs of interventions published in 2015. We evaluated their design and reporting of search strategies using the recommendations from the Cochrane Handbook for Systematic Reviews of Interventions, the Methodological Expectations of Cochrane Intervention Reviews, and the Peer Review of Electronic Search Strategies evidence-based guideline. Most reviews complied with the reporting standards in the Cochrane Handbook and the Methodological Expectations of Cochrane Intervention Reviews; however, 8 SRs did not search trials registers, 3 SRs included language restrictions, and there was inconsistent reporting of contact with individuals and searches of the gray literature. We found problems in the design of the search strategies in 73% of reviews (95% CI, 60-84%) and 53% of these contained problems (95% CI, 38-69%) that could limit both the sensitivity and precision of the search strategies. We found limitations in the design and reporting of search strategies. We consider that a greater adherence to the guidelines could improve their quality. Copyright © 2018 John Wiley & Sons, Ltd.

MIP Models and Hybrid Algorithms for Simultaneous Job Splitting and Scheduling on Unrelated Parallel Machines

PubMed Central

Ozmutlu, H. Cenk

2014-01-01

We developed mixed integer programming (MIP) models and hybrid genetic-local search algorithms for the scheduling problem of unrelated parallel machines with job sequence and machine-dependent setup times and with job splitting property. The first contribution of this paper is to introduce novel algorithms which make splitting and scheduling simultaneously with variable number of subjobs. We proposed simple chromosome structure which is constituted by random key numbers in hybrid genetic-local search algorithm (GAspLA). Random key numbers are used frequently in genetic algorithms, but it creates additional difficulty when hybrid factors in local search are implemented. We developed algorithms that satisfy the adaptation of results of local search into the genetic algorithms with minimum relocation operation of genes' random key numbers. This is the second contribution of the paper. The third contribution of this paper is three developed new MIP models which are making splitting and scheduling simultaneously. The fourth contribution of this paper is implementation of the GAspLAMIP. This implementation let us verify the optimality of GAspLA for the studied combinations. The proposed methods are tested on a set of problems taken from the literature and the results validate the effectiveness of the proposed algorithms. PMID:24977204

Tuning Monotonic Basin Hopping: Improving the Efficiency of Stochastic Search as Applied to Low-Thrust Trajectory Optimization

NASA Technical Reports Server (NTRS)

Englander, Jacob A.; Englander, Arnold C.

2014-01-01

Trajectory optimization methods using monotonic basin hopping (MBH) have become well developed during the past decade [1, 2, 3, 4, 5, 6]. An essential component of MBH is a controlled random search through the multi-dimensional space of possible solutions. Historically, the randomness has been generated by drawing random variable (RV)s from a uniform probability distribution. Here, we investigate the generating the randomness by drawing the RVs from Cauchy and Pareto distributions, chosen because of their characteristic long tails. We demonstrate that using Cauchy distributions (as first suggested by J. Englander [3, 6]) significantly improves monotonic basin hopping (MBH) performance, and that Pareto distributions provide even greater improvements. Improved performance is defined in terms of efficiency and robustness. Efficiency is finding better solutions in less time. Robustness is efficiency that is undiminished by (a) the boundary conditions and internal constraints of the optimization problem being solved, and (b) by variations in the parameters of the probability distribution. Robustness is important for achieving performance improvements that are not problem specific. In this work we show that the performance improvements are the result of how these long-tailed distributions enable MBH to search the solution space faster and more thoroughly. In developing this explanation, we use the concepts of sub-diffusive, normally-diffusive, and super-diffusive random walks (RWs) originally developed in the field of statistical physics.

System Design under Uncertainty: Evolutionary Optimization of the Gravity Probe-B Spacecraft

NASA Technical Reports Server (NTRS)

Pullen, Samuel P.; Parkinson, Bradford W.

1994-01-01

This paper discusses the application of evolutionary random-search algorithms (Simulated Annealing and Genetic Algorithms) to the problem of spacecraft design under performance uncertainty. Traditionally, spacecraft performance uncertainty has been measured by reliability. Published algorithms for reliability optimization are seldom used in practice because they oversimplify reality. The algorithm developed here uses random-search optimization to allow us to model the problem more realistically. Monte Carlo simulations are used to evaluate the objective function for each trial design solution. These methods have been applied to the Gravity Probe-B (GP-B) spacecraft being developed at Stanford University for launch in 1999, Results of the algorithm developed here for GP-13 are shown, and their implications for design optimization by evolutionary algorithms are discussed.

Computer Software for Intelligent Systems.

ERIC Educational Resources Information Center

Lenat, Douglas B.

1984-01-01

Discusses the development and nature of computer software for intelligent systems, indicating that the key to intelligent problem-solving lies in reducing the random search for solutions. Formal reasoning methods, expert systems, and sources of power in problem-solving are among the areas considered. Specific examples of such software are…

Automated parameterization of intermolecular pair potentials using global optimization techniques

NASA Astrophysics Data System (ADS)

Krämer, Andreas; Hülsmann, Marco; Köddermann, Thorsten; Reith, Dirk

2014-12-01

In this work, different global optimization techniques are assessed for the automated development of molecular force fields, as used in molecular dynamics and Monte Carlo simulations. The quest of finding suitable force field parameters is treated as a mathematical minimization problem. Intricate problem characteristics such as extremely costly and even abortive simulations, noisy simulation results, and especially multiple local minima naturally lead to the use of sophisticated global optimization algorithms. Five diverse algorithms (pure random search, recursive random search, CMA-ES, differential evolution, and taboo search) are compared to our own tailor-made solution named CoSMoS. CoSMoS is an automated workflow. It models the parameters' influence on the simulation observables to detect a globally optimal set of parameters. It is shown how and why this approach is superior to other algorithms. Applied to suitable test functions and simulations for phosgene, CoSMoS effectively reduces the number of required simulations and real time for the optimization task.

A Multi-Start Evolutionary Local Search for the Two-Echelon Location Routing Problem

NASA Astrophysics Data System (ADS)

Nguyen, Viet-Phuong; Prins, Christian; Prodhon, Caroline

This paper presents a new hybrid metaheuristic between a greedy randomized adaptive search procedure (GRASP) and an evolutionary/iterated local search (ELS/ILS), using Tabu list to solve the two-echelon location routing problem (LRP-2E). The GRASP uses in turn three constructive heuristics followed by local search to generate the initial solutions. From a solution of GRASP, an intensification strategy is carried out by a dynamic alternation between ELS and ILS. In this phase, each child is obtained by mutation and evaluated through a splitting procedure of giant tour followed by a local search. The tabu list, defined by two characteristics of solution (total cost and number of trips), is used to avoid searching a space already explored. The results show that our metaheuristic clearly outperforms all previously published methods on LRP-2E benchmark instances. Furthermore, it is competitive with the best meta-heuristic published for the single-echelon LRP.

Dynamic Grover search: applications in recommendation systems and optimization problems

NASA Astrophysics Data System (ADS)

Chakrabarty, Indranil; Khan, Shahzor; Singh, Vanshdeep

2017-06-01

In the recent years, we have seen that Grover search algorithm (Proceedings, 28th annual ACM symposium on the theory of computing, pp. 212-219, 1996) by using quantum parallelism has revolutionized the field of solving huge class of NP problems in comparisons to classical systems. In this work, we explore the idea of extending Grover search algorithm to approximate algorithms. Here we try to analyze the applicability of Grover search to process an unstructured database with a dynamic selection function in contrast to the static selection function used in the original work (Grover in Proceedings, 28th annual ACM symposium on the theory of computing, pp. 212-219, 1996). We show that this alteration facilitates us to extend the application of Grover search to the field of randomized search algorithms. Further, we use the dynamic Grover search algorithm to define the goals for a recommendation system based on which we propose a recommendation algorithm which uses binomial similarity distribution space giving us a quadratic speedup over traditional classical unstructured recommendation systems. Finally, we see how dynamic Grover search can be used to tackle a wide range of optimization problems where we improve complexity over existing optimization algorithms.

Spatial Cytoskeleton Organization Supports Targeted Intracellular Transport

NASA Astrophysics Data System (ADS)

Hafner, Anne E.; Rieger, Heiko

2018-03-01

The efficiency of intracellular cargo transport from specific source to target locations is strongly dependent upon molecular motor-assisted motion along the cytoskeleton. Radial transport along microtubules and lateral transport along the filaments of the actin cortex underneath the cell membrane are characteristic for cells with a centrosome. The interplay between the specific cytoskeleton organization and the motor performance realizes a spatially inhomogeneous intermittent search strategy. In order to analyze the efficiency of such intracellular search strategies we formulate a random velocity model with intermittent arrest states. We evaluate efficiency in terms of mean first passage times for three different, frequently encountered intracellular transport tasks: i) the narrow escape problem, which emerges during cargo transport to a synapse or other specific region of the cell membrane, ii) the reaction problem, which considers the binding time of two particles within the cell, and iii) the reaction-escape problem, which arises when cargo must be released at a synapse only after pairing with another particle. Our results indicate that cells are able to realize efficient search strategies for various intracellular transport tasks economically through a spatial cytoskeleton organization that involves only a narrow actin cortex rather than a cell body filled with randomly oriented actin filaments.

Efficient search of multiple types of targets

NASA Astrophysics Data System (ADS)

Wosniack, M. E.; Raposo, E. P.; Viswanathan, G. M.; da Luz, M. G. E.

2015-12-01

Random searches often take place in fragmented landscapes. Also, in many instances like animal foraging, significant benefits to the searcher arise from visits to a large diversity of patches with a well-balanced distribution of targets found. Up to date, such aspects have been widely ignored in the usual single-objective analysis of search efficiency, in which one seeks to maximize just the number of targets found per distance traversed. Here we address the problem of determining the best strategies for the random search when these multiple-objective factors play a key role in the process. We consider a figure of merit (efficiency function), which properly "scores" the mentioned tasks. By considering random walk searchers with a power-law asymptotic Lévy distribution of step lengths, p (ℓ ) ˜ℓ-μ , with 1 <μ ≤3 , we show that the standard optimal strategy with μopt≈2 no longer holds universally. Instead, optimal searches with enhanced superdiffusivity emerge, including values as low as μopt≈1.3 (i.e., tending to the ballistic limit). For the general theory of random search optimization, our findings emphasize the necessity to correctly characterize the multitude of aims in any concrete metric to compare among possible candidates to efficient strategies. In the context of animal foraging, our results might explain some empirical data pointing to stronger superdiffusion (μ <2 ) in the search behavior of different animal species, conceivably associated to multiple goals to be achieved in fragmented landscapes.

Initialization and Restart in Stochastic Local Search: Computing a Most Probable Explanation in Bayesian Networks

NASA Technical Reports Server (NTRS)

Mengshoel, Ole J.; Wilkins, David C.; Roth, Dan

2010-01-01

For hard computational problems, stochastic local search has proven to be a competitive approach to finding optimal or approximately optimal problem solutions. Two key research questions for stochastic local search algorithms are: Which algorithms are effective for initialization? When should the search process be restarted? In the present work we investigate these research questions in the context of approximate computation of most probable explanations (MPEs) in Bayesian networks (BNs). We introduce a novel approach, based on the Viterbi algorithm, to explanation initialization in BNs. While the Viterbi algorithm works on sequences and trees, our approach works on BNs with arbitrary topologies. We also give a novel formalization of stochastic local search, with focus on initialization and restart, using probability theory and mixture models. Experimentally, we apply our methods to the problem of MPE computation, using a stochastic local search algorithm known as Stochastic Greedy Search. By carefully optimizing both initialization and restart, we reduce the MPE search time for application BNs by several orders of magnitude compared to using uniform at random initialization without restart. On several BNs from applications, the performance of Stochastic Greedy Search is competitive with clique tree clustering, a state-of-the-art exact algorithm used for MPE computation in BNs.

Solving Capacitated Closed Vehicle Routing Problem with Time Windows (CCVRPTW) using BRKGA with local search

NASA Astrophysics Data System (ADS)

Prasetyo, H.; Alfatsani, M. A.; Fauza, G.

2018-05-01

The main issue in vehicle routing problem (VRP) is finding the shortest route of product distribution from the depot to outlets to minimize total cost of distribution. Capacitated Closed Vehicle Routing Problem with Time Windows (CCVRPTW) is one of the variants of VRP that accommodates vehicle capacity and distribution period. Since the main problem of CCVRPTW is considered a non-polynomial hard (NP-hard) problem, it requires an efficient and effective algorithm to solve the problem. This study was aimed to develop Biased Random Key Genetic Algorithm (BRKGA) that is combined with local search to solve the problem of CCVRPTW. The algorithm design was then coded by MATLAB. Using numerical test, optimum algorithm parameters were set and compared with the heuristic method and Standard BRKGA to solve a case study on soft drink distribution. Results showed that BRKGA combined with local search resulted in lower total distribution cost compared with the heuristic method. Moreover, the developed algorithm was found to be successful in increasing the performance of Standard BRKGA.

A random-key encoded harmony search approach for energy-efficient production scheduling with shared resources

NASA Astrophysics Data System (ADS)

Garcia-Santiago, C. A.; Del Ser, J.; Upton, C.; Quilligan, F.; Gil-Lopez, S.; Salcedo-Sanz, S.

2015-11-01

When seeking near-optimal solutions for complex scheduling problems, meta-heuristics demonstrate good performance with affordable computational effort. This has resulted in a gravitation towards these approaches when researching industrial use-cases such as energy-efficient production planning. However, much of the previous research makes assumptions about softer constraints that affect planning strategies and about how human planners interact with the algorithm in a live production environment. This article describes a job-shop problem that focuses on minimizing energy consumption across a production facility of shared resources. The application scenario is based on real facilities made available by the Irish Center for Manufacturing Research. The formulated problem is tackled via harmony search heuristics with random keys encoding. Simulation results are compared to a genetic algorithm, a simulated annealing approach and a first-come-first-served scheduling. The superior performance obtained by the proposed scheduler paves the way towards its practical implementation over industrial production chains.

A k-Vector Approach to Sampling, Interpolation, and Approximation

NASA Astrophysics Data System (ADS)

Mortari, Daniele; Rogers, Jonathan

2013-12-01

The k-vector search technique is a method designed to perform extremely fast range searching of large databases at computational cost independent of the size of the database. k-vector search algorithms have historically found application in satellite star-tracker navigation systems which index very large star catalogues repeatedly in the process of attitude estimation. Recently, the k-vector search algorithm has been applied to numerous other problem areas including non-uniform random variate sampling, interpolation of 1-D or 2-D tables, nonlinear function inversion, and solution of systems of nonlinear equations. This paper presents algorithms in which the k-vector search technique is used to solve each of these problems in a computationally-efficient manner. In instances where these tasks must be performed repeatedly on a static (or nearly-static) data set, the proposed k-vector-based algorithms offer an extremely fast solution technique that outperforms standard methods.

Tuning Monotonic Basin Hopping: Improving the Efficiency of Stochastic Search as Applied to Low-Thrust Trajectory Optimization

NASA Technical Reports Server (NTRS)

Englander, Jacob; Englander, Arnold

2014-01-01

Trajectory optimization methods using MBH have become well developed during the past decade. An essential component of MBH is a controlled random search through the multi-dimensional space of possible solutions. Historically, the randomness has been generated by drawing RVs from a uniform probability distribution. Here, we investigate the generating the randomness by drawing the RVs from Cauchy and Pareto distributions, chosen because of their characteristic long tails. We demonstrate that using Cauchy distributions (as first suggested by Englander significantly improves MBH performance, and that Pareto distributions provide even greater improvements. Improved performance is defined in terms of efficiency and robustness, where efficiency is finding better solutions in less time, and robustness is efficiency that is undiminished by (a) the boundary conditions and internal constraints of the optimization problem being solved, and (b) by variations in the parameters of the probability distribution. Robustness is important for achieving performance improvements that are not problem specific. In this work we show that the performance improvements are the result of how these long-tailed distributions enable MBH to search the solution space faster and more thoroughly. In developing this explanation, we use the concepts of sub-diffusive, normally-diffusive, and super-diffusive RWs originally developed in the field of statistical physics.

An evolutionary strategy based on partial imitation for solving optimization problems

NASA Astrophysics Data System (ADS)

Javarone, Marco Alberto

2016-12-01

In this work we introduce an evolutionary strategy to solve combinatorial optimization tasks, i.e. problems characterized by a discrete search space. In particular, we focus on the Traveling Salesman Problem (TSP), i.e. a famous problem whose search space grows exponentially, increasing the number of cities, up to becoming NP-hard. The solutions of the TSP can be codified by arrays of cities, and can be evaluated by fitness, computed according to a cost function (e.g. the length of a path). Our method is based on the evolution of an agent population by means of an imitative mechanism, we define 'partial imitation'. In particular, agents receive a random solution and then, interacting among themselves, may imitate the solutions of agents with a higher fitness. Since the imitation mechanism is only partial, agents copy only one entry (randomly chosen) of another array (i.e. solution). In doing so, the population converges towards a shared solution, behaving like a spin system undergoing a cooling process, i.e. driven towards an ordered phase. We highlight that the adopted 'partial imitation' mechanism allows the population to generate solutions over time, before reaching the final equilibrium. Results of numerical simulations show that our method is able to find, in a finite time, both optimal and suboptimal solutions, depending on the size of the considered search space.

Advanced fitness landscape analysis and the performance of memetic algorithms.

PubMed

Merz, Peter

2004-01-01

Memetic algorithms (MAs) have demonstrated very effective in combinatorial optimization. This paper offers explanations as to why this is so by investigating the performance of MAs in terms of efficiency and effectiveness. A special class of MAs is used to discuss efficiency and effectiveness for local search and evolutionary meta-search. It is shown that the efficiency of MAs can be increased drastically with the use of domain knowledge. However, effectiveness highly depends on the structure of the problem. As is well-known, identifying this structure is made easier with the notion of fitness landscapes: the local properties of the fitness landscape strongly influence the effectiveness of the local search while the global properties strongly influence the effectiveness of the evolutionary meta-search. This paper also introduces new techniques for analyzing the fitness landscapes of combinatorial problems; these techniques focus on the investigation of random walks in the fitness landscape starting at locally optimal solutions as well as on the escape from the basins of attractions of current local optima. It is shown for NK-landscapes and landscapes of the unconstrained binary quadratic programming problem (BQP) that a random walk to another local optimum can be used to explain the efficiency of recombination in comparison to mutation. Moreover, the paper shows that other aspects like the size of the basins of attractions of local optima are important for the efficiency of MAs and a local search escape analysis is proposed. These simple analysis techniques have several advantages over previously proposed statistical measures and provide valuable insight into the behaviour of MAs on different kinds of landscapes.

Optimization and universality of Brownian search in a basic model of quenched heterogeneous media

NASA Astrophysics Data System (ADS)

Godec, Aljaž; Metzler, Ralf

2015-05-01

The kinetics of a variety of transport-controlled processes can be reduced to the problem of determining the mean time needed to arrive at a given location for the first time, the so-called mean first-passage time (MFPT) problem. The occurrence of occasional large jumps or intermittent patterns combining various types of motion are known to outperform the standard random walk with respect to the MFPT, by reducing oversampling of space. Here we show that a regular but spatially heterogeneous random walk can significantly and universally enhance the search in any spatial dimension. In a generic minimal model we consider a spherically symmetric system comprising two concentric regions with piecewise constant diffusivity. The MFPT is analyzed under the constraint of conserved average dynamics, that is, the spatially averaged diffusivity is kept constant. Our analytical calculations and extensive numerical simulations demonstrate the existence of an optimal heterogeneity minimizing the MFPT to the target. We prove that the MFPT for a random walk is completely dominated by what we term direct trajectories towards the target and reveal a remarkable universality of the spatially heterogeneous search with respect to target size and system dimensionality. In contrast to intermittent strategies, which are most profitable in low spatial dimensions, the spatially inhomogeneous search performs best in higher dimensions. Discussing our results alongside recent experiments on single-particle tracking in living cells, we argue that the observed spatial heterogeneity may be beneficial for cellular signaling processes.

Comparison of Decisions Quality of Heuristic Methods with Limited Depth-First Search Techniques in the Graph Shortest Path Problem

NASA Astrophysics Data System (ADS)

Vatutin, Eduard

2017-12-01

The article deals with the problem of analysis of effectiveness of the heuristic methods with limited depth-first search techniques of decision obtaining in the test problem of getting the shortest path in graph. The article briefly describes the group of methods based on the limit of branches number of the combinatorial search tree and limit of analyzed subtree depth used to solve the problem. The methodology of comparing experimental data for the estimation of the quality of solutions based on the performing of computational experiments with samples of graphs with pseudo-random structure and selected vertices and arcs number using the BOINC platform is considered. It also shows description of obtained experimental results which allow to identify the areas of the preferable usage of selected subset of heuristic methods depending on the size of the problem and power of constraints. It is shown that the considered pair of methods is ineffective in the selected problem and significantly inferior to the quality of solutions that are provided by ant colony optimization method and its modification with combinatorial returns.

Enhancing Social Inclusion of Children with Externalizing Problems through Classroom Seating Arrangements: A Randomized Controlled Trial

ERIC Educational Resources Information Center

van den Berg, Y. H. M.; Stoltz, S.

2018-01-01

Inclusive education has brought new challenges for teachers, including the search for a suitable place in the classroom for children with externalizing problems. In the current study, we examined whether a careful rearrangement of the classroom seats could promote social acceptance and more prosocial behaviors for children with externalizing…

Broadcasting satellite service synthesis using gradient and cyclic coordinate search procedures

NASA Technical Reports Server (NTRS)

Reilly, C. H.; Mount-Campbell, C. A.; Gonsalvez, D. J.; Martin, C. H.; Levis, C. A.; Wang, C. W.

1986-01-01

Two search techniques are considered for solving satellite synthesis problems. Neither is likely to find a globally optimal solution. In order to determine which method performs better and what factors affect their performance, we design an experiment and solve the same problem under a variety of starting solution configuration-algorithm combinations. Since there is no randomization in the experiment, we present results of practical, rather than statistical, significance. Our implementation of a cyclic coordinate search procedure clearly finds better synthesis solutions than our implementation of a gradient search procedure does with our objective of maximizing the minimum C/I ratio computed at test points on the perimeters of the intended service areas. The length of the available orbital arc and the configuration of the starting solution are shown to affect the quality of the solutions found.

Broadcasting satellite service synthesis using gradient and cyclic coordinate search procedures

NASA Technical Reports Server (NTRS)

Reilly, C. H.; Mount-Campbell, C. A.; Gonsalvez, D. J.; Martin, C. H.; Levis, C. A.

1986-01-01

Two search techniques are considered for solving satellite synthesis problems. Neither is likely to find a globally optimal solution. In order to determine which method performs better and what factors affect their performance, an experiment is designed and the same problem is solved under a variety of starting solution configuration-algorithm combinations. Since there is no randomization in the experiment, results of practical, rather than statistical, significance are presented. Implementation of a cyclic coordinate search procedure clearly finds better synthesis solutions than implementation of a gradient search procedure does with the objective of maximizing the minimum C/I ratio computed at test points on the perimeters of the intended service areas. The length of the available orbital arc and the configuration of the starting solution are shown to affect the quality of the solutions found.

On multiple crack identification by ultrasonic scanning

NASA Astrophysics Data System (ADS)

Brigante, M.; Sumbatyan, M. A.

2018-04-01

The present work develops an approach which reduces operator equations arising in the engineering problems to the problem of minimizing the discrepancy functional. For this minimization, an algorithm of random global search is proposed, which is allied to some genetic algorithms. The efficiency of the method is demonstrated by the solving problem of simultaneous identification of several linear cracks forming an array in an elastic medium by using the circular Ultrasonic scanning.

Searching for patterns in remote sensing image databases using neural networks

NASA Technical Reports Server (NTRS)

Paola, Justin D.; Schowengerdt, Robert A.

1995-01-01

We have investigated a method, based on a successful neural network multispectral image classification system, of searching for single patterns in remote sensing databases. While defining the pattern to search for and the feature to be used for that search (spectral, spatial, temporal, etc.) is challenging, a more difficult task is selecting competing patterns to train against the desired pattern. Schemes for competing pattern selection, including random selection and human interpreted selection, are discussed in the context of an example detection of dense urban areas in Landsat Thematic Mapper imagery. When applying the search to multiple images, a simple normalization method can alleviate the problem of inconsistent image calibration. Another potential problem, that of highly compressed data, was found to have a minimal effect on the ability to detect the desired pattern. The neural network algorithm has been implemented using the PVM (Parallel Virtual Machine) library and nearly-optimal speedups have been obtained that help alleviate the long process of searching through imagery.

Dynamical analysis of Grover's search algorithm in arbitrarily high-dimensional search spaces

NASA Astrophysics Data System (ADS)

Jin, Wenliang

2016-01-01

We discuss at length the dynamical behavior of Grover's search algorithm for which all the Walsh-Hadamard transformations contained in this algorithm are exposed to their respective random perturbations inducing the augmentation of the dimension of the search space. We give the concise and general mathematical formulations for approximately characterizing the maximum success probabilities of finding a unique desired state in a large unsorted database and their corresponding numbers of Grover iterations, which are applicable to the search spaces of arbitrary dimension and are used to answer a salient open problem posed by Grover (Phys Rev Lett 80:4329-4332, 1998).

Optimisation in radiotherapy. III: Stochastic optimisation algorithms and conclusions.

PubMed

Ebert, M

1997-12-01

This is the final article in a three part examination of optimisation in radiotherapy. Previous articles have established the bases and form of the radiotherapy optimisation problem, and examined certain types of optimisation algorithm, namely, those which perform some form of ordered search of the solution space (mathematical programming), and those which attempt to find the closest feasible solution to the inverse planning problem (deterministic inversion). The current paper examines algorithms which search the space of possible irradiation strategies by stochastic methods. The resulting iterative search methods move about the solution space by sampling random variates, which gradually become more constricted as the algorithm converges upon the optimal solution. This paper also discusses the implementation of optimisation in radiotherapy practice.

Search of exploration opportunity for near earth objects based on analytical gradients

NASA Astrophysics Data System (ADS)

Ren, Y.; Cui, P. Y.; Luan, E. J.

2008-01-01

The problem of searching for exploration opportunity of near Earth objects is investigated. For rendezvous missions, the analytical gradients of performance index with respect to free parameters are derived by combining the calculus of variation with the theory of state-transition matrix. Then, some initial guesses are generated random in the search space, and the performance index is optimized with the guidance of analytical gradients from these initial guesses. This method not only keeps the property of global search in traditional method, but also avoids the blindness in the traditional exploration opportunity search; hence, the computing speed could be increased greatly. Furthermore, by using this method, the search precision could be controlled effectively.

Creating targeted initial populations for genetic product searches in heterogeneous markets

NASA Astrophysics Data System (ADS)

Foster, Garrett; Turner, Callaway; Ferguson, Scott; Donndelinger, Joseph

2014-12-01

Genetic searches often use randomly generated initial populations to maximize diversity and enable a thorough sampling of the design space. While many of these initial configurations perform poorly, the trade-off between population diversity and solution quality is typically acceptable for small-scale problems. Navigating complex design spaces, however, often requires computationally intelligent approaches that improve solution quality. This article draws on research advances in market-based product design and heuristic optimization to strategically construct 'targeted' initial populations. Targeted initial designs are created using respondent-level part-worths estimated from discrete choice models. These designs are then integrated into a traditional genetic search. Two case study problems of differing complexity are presented to illustrate the benefits of this approach. In both problems, targeted populations lead to computational savings and product configurations with improved market share of preferences. Future research efforts to tailor this approach and extend it towards multiple objectives are also discussed.

Fast optimization algorithms and the cosmological constant

NASA Astrophysics Data System (ADS)

Bao, Ning; Bousso, Raphael; Jordan, Stephen; Lackey, Brad

2017-11-01

Denef and Douglas have observed that in certain landscape models the problem of finding small values of the cosmological constant is a large instance of a problem that is hard for the complexity class NP (Nondeterministic Polynomial-time). The number of elementary operations (quantum gates) needed to solve this problem by brute force search exceeds the estimated computational capacity of the observable Universe. Here we describe a way out of this puzzling circumstance: despite being NP-hard, the problem of finding a small cosmological constant can be attacked by more sophisticated algorithms whose performance vastly exceeds brute force search. In fact, in some parameter regimes the average-case complexity is polynomial. We demonstrate this by explicitly finding a cosmological constant of order 10-120 in a randomly generated 1 09-dimensional Arkani-Hamed-Dimopoulos-Kachru landscape.

A random forest learning assisted "divide and conquer" approach for peptide conformation search.

PubMed

Chen, Xin; Yang, Bing; Lin, Zijing

2018-06-11

Computational determination of peptide conformations is challenging as it is a problem of finding minima in a high-dimensional space. The "divide and conquer" approach is promising for reliably reducing the search space size. A random forest learning model is proposed here to expand the scope of applicability of the "divide and conquer" approach. A random forest classification algorithm is used to characterize the distributions of the backbone φ-ψ units ("words"). A random forest supervised learning model is developed to analyze the combinations of the φ-ψ units ("grammar"). It is found that amino acid residues may be grouped as equivalent "words", while the φ-ψ combinations in low-energy peptide conformations follow a distinct "grammar". The finding of equivalent words empowers the "divide and conquer" method with the flexibility of fragment substitution. The learnt grammar is used to improve the efficiency of the "divide and conquer" method by removing unfavorable φ-ψ combinations without the need of dedicated human effort. The machine learning assisted search method is illustrated by efficiently searching the conformations of GGG/AAA/GGGG/AAAA/GGGGG through assembling the structures of GFG/GFGG. Moreover, the computational cost of the new method is shown to increase rather slowly with the peptide length.

Approximation algorithms for the min-power symmetric connectivity problem

NASA Astrophysics Data System (ADS)

Plotnikov, Roman; Erzin, Adil; Mladenovic, Nenad

2016-10-01

We consider the NP-hard problem of synthesis of optimal spanning communication subgraph in a given arbitrary simple edge-weighted graph. This problem occurs in the wireless networks while minimizing the total transmission power consumptions. We propose several new heuristics based on the variable neighborhood search metaheuristic for the approximation solution of the problem. We have performed a numerical experiment where all proposed algorithms have been executed on the randomly generated test samples. For these instances, on average, our algorithms outperform the previously known heuristics.

Search Algorithms as a Framework for the Optimization of Drug Combinations

PubMed Central

Coquin, Laurence; Schofield, Jennifer; Feala, Jacob D.; Reed, John C.; McCulloch, Andrew D.; Paternostro, Giovanni

2008-01-01

Combination therapies are often needed for effective clinical outcomes in the management of complex diseases, but presently they are generally based on empirical clinical experience. Here we suggest a novel application of search algorithms—originally developed for digital communication—modified to optimize combinations of therapeutic interventions. In biological experiments measuring the restoration of the decline with age in heart function and exercise capacity in Drosophila melanogaster, we found that search algorithms correctly identified optimal combinations of four drugs using only one-third of the tests performed in a fully factorial search. In experiments identifying combinations of three doses of up to six drugs for selective killing of human cancer cells, search algorithms resulted in a highly significant enrichment of selective combinations compared with random searches. In simulations using a network model of cell death, we found that the search algorithms identified the optimal combinations of 6–9 interventions in 80–90% of tests, compared with 15–30% for an equivalent random search. These findings suggest that modified search algorithms from information theory have the potential to enhance the discovery of novel therapeutic drug combinations. This report also helps to frame a biomedical problem that will benefit from an interdisciplinary effort and suggests a general strategy for its solution. PMID:19112483

Exploration Opportunity Search of Near-earth Objects Based on Analytical Gradients

NASA Astrophysics Data System (ADS)

Ren, Yuan; Cui, Ping-Yuan; Luan, En-Jie

2008-07-01

The problem of search of opportunity for the exploration of near-earth minor objects is investigated. For rendezvous missions, the analytical gradients of the performance index with respect to the free parameters are derived using the variational calculus and the theory of state-transition matrix. After generating randomly some initial guesses in the search space, the performance index is optimized, guided by the analytical gradients, leading to the local minimum points representing the potential launch opportunities. This method not only keeps the global-search property of the traditional method, but also avoids the blindness in the latter, thereby increasing greatly the computing speed. Furthermore, with this method, the searching precision could be controlled effectively.

A Comparison of Techniques for Scheduling Fleets of Earth-Observing Satellites

NASA Technical Reports Server (NTRS)

Globus, Al; Crawford, James; Lohn, Jason; Pryor, Anna

2003-01-01

Earth observing satellite (EOS) scheduling is a complex real-world domain representative of a broad class of over-subscription scheduling problems. Over-subscription problems are those where requests for a facility exceed its capacity. These problems arise in a wide variety of NASA and terrestrial domains and are .XI important class of scheduling problems because such facilities often represent large capital investments. We have run experiments comparing multiple variants of the genetic algorithm, hill climbing, simulated annealing, squeaky wheel optimization and iterated sampling on two variants of a realistically-sized model of the EOS scheduling problem. These are implemented as permutation-based methods; methods that search in the space of priority orderings of observation requests and evaluate each permutation by using it to drive a greedy scheduler. Simulated annealing performs best and random mutation operators outperform our squeaky (more intelligent) operator. Furthermore, taking smaller steps towards the end of the search improves performance.

Solving optimization problems by the public goods game

NASA Astrophysics Data System (ADS)

Javarone, Marco Alberto

2017-09-01

We introduce a method based on the Public Goods Game for solving optimization tasks. In particular, we focus on the Traveling Salesman Problem, i.e. a NP-hard problem whose search space exponentially grows increasing the number of cities. The proposed method considers a population whose agents are provided with a random solution to the given problem. In doing so, agents interact by playing the Public Goods Game using the fitness of their solution as currency of the game. Notably, agents with better solutions provide higher contributions, while those with lower ones tend to imitate the solution of richer agents for increasing their fitness. Numerical simulations show that the proposed method allows to compute exact solutions, and suboptimal ones, in the considered search spaces. As result, beyond to propose a new heuristic for combinatorial optimization problems, our work aims to highlight the potentiality of evolutionary game theory beyond its current horizons.

Hybrid Self-Adaptive Evolution Strategies Guided by Neighborhood Structures for Combinatorial Optimization Problems.

PubMed

Coelho, V N; Coelho, I M; Souza, M J F; Oliveira, T A; Cota, L P; Haddad, M N; Mladenovic, N; Silva, R C P; Guimarães, F G

2016-01-01

This article presents an Evolution Strategy (ES)--based algorithm, designed to self-adapt its mutation operators, guiding the search into the solution space using a Self-Adaptive Reduced Variable Neighborhood Search procedure. In view of the specific local search operators for each individual, the proposed population-based approach also fits into the context of the Memetic Algorithms. The proposed variant uses the Greedy Randomized Adaptive Search Procedure with different greedy parameters for generating its initial population, providing an interesting exploration-exploitation balance. To validate the proposal, this framework is applied to solve three different [Formula: see text]-Hard combinatorial optimization problems: an Open-Pit-Mining Operational Planning Problem with dynamic allocation of trucks, an Unrelated Parallel Machine Scheduling Problem with Setup Times, and the calibration of a hybrid fuzzy model for Short-Term Load Forecasting. Computational results point out the convergence of the proposed model and highlight its ability in combining the application of move operations from distinct neighborhood structures along the optimization. The results gathered and reported in this article represent a collective evidence of the performance of the method in challenging combinatorial optimization problems from different application domains. The proposed evolution strategy demonstrates an ability of adapting the strength of the mutation disturbance during the generations of its evolution process. The effectiveness of the proposal motivates the application of this novel evolutionary framework for solving other combinatorial optimization problems.

[NSAID-gastropathy and Helicobacter pylori: more questions than answers].

PubMed

Shirinskaia, N V; Akhmedov, V A

2010-01-01

NSAID-gastropathy is a very serious problem as for gastroenterology, so for cardiology and rheumatology. On a present moment the influence of a Helicobacter pylori in ulcerogenesis in patients taking NSAID for a long period is a very unclear especially treatment position. For the analysis of this problem was made a systemic literature search from 1966 till 2009 years and all randomizes, meta-analysis was reviewed.

Recourse-based facility-location problems in hybrid uncertain environment.

PubMed

Wang, Shuming; Watada, Junzo; Pedrycz, Witold

2010-08-01

The objective of this paper is to study facility-location problems in the presence of a hybrid uncertain environment involving both randomness and fuzziness. A two-stage fuzzy-random facility-location model with recourse (FR-FLMR) is developed in which both the demands and costs are assumed to be fuzzy-random variables. The bounds of the optimal objective value of the two-stage FR-FLMR are derived. As, in general, the fuzzy-random parameters of the FR-FLMR can be regarded as continuous fuzzy-random variables with an infinite number of realizations, the computation of the recourse requires solving infinite second-stage programming problems. Owing to this requirement, the recourse function cannot be determined analytically, and, hence, the model cannot benefit from the use of techniques of classical mathematical programming. In order to solve the location problems of this nature, we first develop a technique of fuzzy-random simulation to compute the recourse function. The convergence of such simulation scenarios is discussed. In the sequel, we propose a hybrid mutation-based binary ant-colony optimization (MBACO) approach to the two-stage FR-FLMR, which comprises the fuzzy-random simulation and the simplex algorithm. A numerical experiment illustrates the application of the hybrid MBACO algorithm. The comparison shows that the hybrid MBACO finds better solutions than the one using other discrete metaheuristic algorithms, such as binary particle-swarm optimization, genetic algorithm, and tabu search.

Problem-based learning in dental education: a systematic review of the literature.

PubMed

Bassir, Seyed Hossein; Sadr-Eshkevari, Pooyan; Amirikhorheh, Shaden; Karimbux, Nadeem Y

2014-01-01

The purpose of this systematic review was to compare the effectiveness of problem-based learning (PBL) with that of traditional (non-PBL) approaches in dental education. The search strategy included electronic and manual searches of studies published up to October 2012. The PICO (Population, Intervention, Comparator, and Outcome) framework was utilized to guide the inclusion or exclusion of studies. The search strategy identified 436 articles, seventeen of which met the inclusion criteria. No randomized controlled trial was found comparing the effectiveness of PBL with that of lecture-based approach at the level of an entire curriculum. Three randomized controlled trials had evaluated the effectiveness of PBL at a single course level. The quality assessment rated four studies as being of moderate quality, while the other studies were assessed as being of weak quality. This review concludes that there are a very limited number of well-designed controlled studies evaluating the effectiveness of PBL in dental education. The data in those studies reveal that PBL does not negatively influence the acquisition of factual knowledge in dental students and PBL enhances the ability of students in applying their knowledge to clinical situations. In addition, PBL positively affects students' perceived preparedness.

Time-optimal trajectory planning for underactuated spacecraft using a hybrid particle swarm optimization algorithm

NASA Astrophysics Data System (ADS)

Zhuang, Yufei; Huang, Haibin

2014-02-01

A hybrid algorithm combining particle swarm optimization (PSO) algorithm with the Legendre pseudospectral method (LPM) is proposed for solving time-optimal trajectory planning problem of underactuated spacecrafts. At the beginning phase of the searching process, an initialization generator is constructed by the PSO algorithm due to its strong global searching ability and robustness to random initial values, however, PSO algorithm has a disadvantage that its convergence rate around the global optimum is slow. Then, when the change in fitness function is smaller than a predefined value, the searching algorithm is switched to the LPM to accelerate the searching process. Thus, with the obtained solutions by the PSO algorithm as a set of proper initial guesses, the hybrid algorithm can find a global optimum more quickly and accurately. 200 Monte Carlo simulations results demonstrate that the proposed hybrid PSO-LPM algorithm has greater advantages in terms of global searching capability and convergence rate than both single PSO algorithm and LPM algorithm. Moreover, the PSO-LPM algorithm is also robust to random initial values.

Reliable Facility Location Problem with Facility Protection

PubMed Central

Tang, Luohao; Zhu, Cheng; Lin, Zaili; Shi, Jianmai; Zhang, Weiming

2016-01-01

This paper studies a reliable facility location problem with facility protection that aims to hedge against random facility disruptions by both strategically protecting some facilities and using backup facilities for the demands. An Integer Programming model is proposed for this problem, in which the failure probabilities of facilities are site-specific. A solution approach combining Lagrangian Relaxation and local search is proposed and is demonstrated to be both effective and efficient based on computational experiments on random numerical examples with 49, 88, 150 and 263 nodes in the network. A real case study for a 100-city network in Hunan province, China, is presented, based on which the properties of the model are discussed and some managerial insights are analyzed. PMID:27583542

Improved hybrid optimization algorithm for 3D protein structure prediction.

PubMed

Zhou, Changjun; Hou, Caixia; Wei, Xiaopeng; Zhang, Qiang

2014-07-01

A new improved hybrid optimization algorithm - PGATS algorithm, which is based on toy off-lattice model, is presented for dealing with three-dimensional protein structure prediction problems. The algorithm combines the particle swarm optimization (PSO), genetic algorithm (GA), and tabu search (TS) algorithms. Otherwise, we also take some different improved strategies. The factor of stochastic disturbance is joined in the particle swarm optimization to improve the search ability; the operations of crossover and mutation that are in the genetic algorithm are changed to a kind of random liner method; at last tabu search algorithm is improved by appending a mutation operator. Through the combination of a variety of strategies and algorithms, the protein structure prediction (PSP) in a 3D off-lattice model is achieved. The PSP problem is an NP-hard problem, but the problem can be attributed to a global optimization problem of multi-extremum and multi-parameters. This is the theoretical principle of the hybrid optimization algorithm that is proposed in this paper. The algorithm combines local search and global search, which overcomes the shortcoming of a single algorithm, giving full play to the advantage of each algorithm. In the current universal standard sequences, Fibonacci sequences and real protein sequences are certified. Experiments show that the proposed new method outperforms single algorithms on the accuracy of calculating the protein sequence energy value, which is proved to be an effective way to predict the structure of proteins.

Multiobjective optimization approach: thermal food processing.

PubMed

Abakarov, A; Sushkov, Y; Almonacid, S; Simpson, R

2009-01-01

The objective of this study was to utilize a multiobjective optimization technique for the thermal sterilization of packaged foods. The multiobjective optimization approach used in this study is based on the optimization of well-known aggregating functions by an adaptive random search algorithm. The applicability of the proposed approach was illustrated by solving widely used multiobjective test problems taken from the literature. The numerical results obtained for the multiobjective test problems and for the thermal processing problem show that the proposed approach can be effectively used for solving multiobjective optimization problems arising in the food engineering field.

Randomized Approaches for Nearest Neighbor Search in Metric Space When Computing the Pairwise Distance Is Extremely Expensive

NASA Astrophysics Data System (ADS)

Wang, Lusheng; Yang, Yong; Lin, Guohui

Finding the closest object for a query in a database is a classical problem in computer science. For some modern biological applications, computing the similarity between two objects might be very time consuming. For example, it takes a long time to compute the edit distance between two whole chromosomes and the alignment cost of two 3D protein structures. In this paper, we study the nearest neighbor search problem in metric space, where the pair-wise distance between two objects in the database is known and we want to minimize the number of distances computed on-line between the query and objects in the database in order to find the closest object. We have designed two randomized approaches for indexing metric space databases, where objects are purely described by their distances with each other. Analysis and experiments show that our approaches only need to compute O(logn) objects in order to find the closest object, where n is the total number of objects in the database.

SPIN or LURCH : a Comparative Assessment of Model Checking and Stochastic Search for Temporal Properties in Procedural Code

NASA Technical Reports Server (NTRS)

Powell, John D.; Owens, David; Menzies, Tim

2004-01-01

The difficulty of how to test large systems, such as the one on board a NASA robotic remote explorer (RRE) vehicle, is fundamentally a search issue: the global state space representing all possible has yet to be solved, even after many decades of work. Randomized algorithms have been known to outperform their deterministic counterparts for search problems representing a wide range of applications. In the case study presented here, the LURCH randomized algorithm proved to be adequate to the task of testing a NASA RRE vehicle. LURCH found all the errors found by an earlier analysis of a more complete method (SPIN). Our empirical results are that LURCH can scale to much larger models than standard model checkers like SMV and SPIN. Further, the LURCH analysis was simpler than the SPIN analysis. The simplicity and scalability of LURCH are two compelling reasons for experimenting further with this tool.

Ringed Seal Search for Global Optimization via a Sensitive Search Model.

PubMed

Saadi, Younes; Yanto, Iwan Tri Riyadi; Herawan, Tutut; Balakrishnan, Vimala; Chiroma, Haruna; Risnumawan, Anhar

2016-01-01

The efficiency of a metaheuristic algorithm for global optimization is based on its ability to search and find the global optimum. However, a good search often requires to be balanced between exploration and exploitation of the search space. In this paper, a new metaheuristic algorithm called Ringed Seal Search (RSS) is introduced. It is inspired by the natural behavior of the seal pup. This algorithm mimics the seal pup movement behavior and its ability to search and choose the best lair to escape predators. The scenario starts once the seal mother gives birth to a new pup in a birthing lair that is constructed for this purpose. The seal pup strategy consists of searching and selecting the best lair by performing a random walk to find a new lair. Affected by the sensitive nature of seals against external noise emitted by predators, the random walk of the seal pup takes two different search states, normal state and urgent state. In the normal state, the pup performs an intensive search between closely adjacent lairs; this movement is modeled via a Brownian walk. In an urgent state, the pup leaves the proximity area and performs an extensive search to find a new lair from sparse targets; this movement is modeled via a Levy walk. The switch between these two states is realized by the random noise emitted by predators. The algorithm keeps switching between normal and urgent states until the global optimum is reached. Tests and validations were performed using fifteen benchmark test functions to compare the performance of RSS with other baseline algorithms. The results show that RSS is more efficient than Genetic Algorithm, Particles Swarm Optimization and Cuckoo Search in terms of convergence rate to the global optimum. The RSS shows an improvement in terms of balance between exploration (extensive) and exploitation (intensive) of the search space. The RSS can efficiently mimic seal pups behavior to find best lair and provide a new algorithm to be used in global optimization problems.

A Darwinian approach to control-structure design

NASA Technical Reports Server (NTRS)

Zimmerman, David C.

1993-01-01

Genetic algorithms (GA's), as introduced by Holland (1975), are one form of directed random search. The form of direction is based on Darwin's 'survival of the fittest' theories. GA's are radically different from the more traditional design optimization techniques. GA's work with a coding of the design variables, as opposed to working with the design variables directly. The search is conducted from a population of designs (i.e., from a large number of points in the design space), unlike the traditional algorithms which search from a single design point. The GA requires only objective function information, as opposed to gradient or other auxiliary information. Finally, the GA is based on probabilistic transition rules, as opposed to deterministic rules. These features allow the GA to attack problems with local-global minima, discontinuous design spaces and mixed variable problems, all in a single, consistent framework.

Inverse atmospheric radiative transfer problems - A nonlinear minimization search method of solution. [aerosol pollution monitoring

NASA Technical Reports Server (NTRS)

Fymat, A. L.

1976-01-01

The paper studies the inversion of the radiative transfer equation describing the interaction of electromagnetic radiation with atmospheric aerosols. The interaction can be considered as the propagation in the aerosol medium of two light beams: the direct beam in the line-of-sight attenuated by absorption and scattering, and the diffuse beam arising from scattering into the viewing direction, which propagates more or less in random fashion. The latter beam has single scattering and multiple scattering contributions. In the former case and for single scattering, the problem is reducible to first-kind Fredholm equations, while for multiple scattering it is necessary to invert partial integrodifferential equations. A nonlinear minimization search method, applicable to the solution of both types of problems has been developed, and is applied here to the problem of monitoring aerosol pollution, namely the complex refractive index and size distribution of aerosol particles.

A Sustainable City Planning Algorithm Based on TLBO and Local Search

NASA Astrophysics Data System (ADS)

Zhang, Ke; Lin, Li; Huang, Xuanxuan; Liu, Yiming; Zhang, Yonggang

2017-09-01

Nowadays, how to design a city with more sustainable features has become a center problem in the field of social development, meanwhile it has provided a broad stage for the application of artificial intelligence theories and methods. Because the design of sustainable city is essentially a constraint optimization problem, the swarm intelligence algorithm of extensive research has become a natural candidate for solving the problem. TLBO (Teaching-Learning-Based Optimization) algorithm is a new swarm intelligence algorithm. Its inspiration comes from the “teaching” and “learning” behavior of teaching class in the life. The evolution of the population is realized by simulating the “teaching” of the teacher and the student “learning” from each other, with features of less parameters, efficient, simple thinking, easy to achieve and so on. It has been successfully applied to scheduling, planning, configuration and other fields, which achieved a good effect and has been paid more and more attention by artificial intelligence researchers. Based on the classical TLBO algorithm, we propose a TLBO_LS algorithm combined with local search. We design and implement the random generation algorithm and evaluation model of urban planning problem. The experiments on the small and medium-sized random generation problem showed that our proposed algorithm has obvious advantages over DE algorithm and classical TLBO algorithm in terms of convergence speed and solution quality.

On the efficiency of a randomized mirror descent algorithm in online optimization problems

NASA Astrophysics Data System (ADS)

Gasnikov, A. V.; Nesterov, Yu. E.; Spokoiny, V. G.

2015-04-01

A randomized online version of the mirror descent method is proposed. It differs from the existing versions by the randomization method. Randomization is performed at the stage of the projection of a subgradient of the function being optimized onto the unit simplex rather than at the stage of the computation of a subgradient, which is common practice. As a result, a componentwise subgradient descent with a randomly chosen component is obtained, which admits an online interpretation. This observation, for example, has made it possible to uniformly interpret results on weighting expert decisions and propose the most efficient method for searching for an equilibrium in a zero-sum two-person matrix game with sparse matrix.

Bridging the Gap Between Theory and Practice: Structure and Randomization in Large Scale Combinatorial Search

DTIC Science & Technology

2012-01-17

PROJECT NUMBER 5e. TASK NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) 8. PERFORMING ORGANIZATION...world problems. Our work brings together techniques from constraint programming, mathematical programming, and satisfiability in a symbiotic way to...power-­‐law  search  tree  model  for  complete  or  exact  methods     (See [9] for a detailed description of this work and

Using sobol sequences for planning computer experiments

NASA Astrophysics Data System (ADS)

Statnikov, I. N.; Firsov, G. I.

2017-12-01

Discusses the use for research of problems of multicriteria synthesis of dynamic systems method of Planning LP-search (PLP-search), which not only allows on the basis of the simulation model experiments to revise the parameter space within specified ranges of their change, but also through special randomized nature of the planning of these experiments is to apply a quantitative statistical evaluation of influence of change of varied parameters and their pairwise combinations to analyze properties of the dynamic system.Start your abstract here...

Spatial Search by Quantum Walk is Optimal for Almost all Graphs.

PubMed

Chakraborty, Shantanav; Novo, Leonardo; Ambainis, Andris; Omar, Yasser

2016-03-11

The problem of finding a marked node in a graph can be solved by the spatial search algorithm based on continuous-time quantum walks (CTQW). However, this algorithm is known to run in optimal time only for a handful of graphs. In this work, we prove that for Erdös-Renyi random graphs, i.e., graphs of n vertices where each edge exists with probability p, search by CTQW is almost surely optimal as long as p≥log^{3/2}(n)/n. Consequently, we show that quantum spatial search is in fact optimal for almost all graphs, meaning that the fraction of graphs of n vertices for which this optimality holds tends to one in the asymptotic limit. We obtain this result by proving that search is optimal on graphs where the ratio between the second largest and the largest eigenvalue is bounded by a constant smaller than 1. Finally, we show that we can extend our results on search to establish high fidelity quantum communication between two arbitrary nodes of a random network of interacting qubits, namely, to perform quantum state transfer, as well as entanglement generation. Our work shows that quantum information tasks typically designed for structured systems retain performance in very disordered structures.

Search protocols for hidden forensic objects beneath floors and within walls.

PubMed

Ruffell, Alastair; Pringle, Jamie K; Forbes, Shari

2014-04-01

The burial of objects (human remains, explosives, weapons) below or behind concrete, brick, plaster or tiling may be associated with serious crime and are difficult locations to search. These are quite common forensic search scenarios but little has been published on them to-date. Most documented discoveries are accidental or from suspect/witness testimony. The problem in locating such hidden objects means a random or chance-based approach is not advisable. A preliminary strategy is presented here, based on previous studies, augmented by primary research where new technology or applications are required. This blend allows a rudimentary search workflow, from remote desktop study, to non-destructive investigation through to recommendations as to how the above may inform excavation, demonstrated here with a case study from a homicide investigation. Published case studies on the search for human remains demonstrate the problems encountered when trying to find and recover sealed-in and sealed-over locations. Established methods include desktop study, photography, geophysics and search dogs: these are integrated with new technology (LiDAR and laser scanning; photographic rectification; close-quarter aerial imagery; ground-penetrating radar on walls and gamma-ray/neutron activation radiography) to propose this possible search strategy. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Randomly iterated search and statistical competency as powerful inversion tools for deformation source modeling: Application to volcano interferometric synthetic aperture radar data

NASA Astrophysics Data System (ADS)

Shirzaei, M.; Walter, T. R.

2009-10-01

Modern geodetic techniques provide valuable and near real-time observations of volcanic activity. Characterizing the source of deformation based on these observations has become of major importance in related monitoring efforts. We investigate two random search approaches, simulated annealing (SA) and genetic algorithm (GA), and utilize them in an iterated manner. The iterated approach helps to prevent GA in general and SA in particular from getting trapped in local minima, and it also increases redundancy for exploring the search space. We apply a statistical competency test for estimating the confidence interval of the inversion source parameters, considering their internal interaction through the model, the effect of the model deficiency, and the observational error. Here, we present and test this new randomly iterated search and statistical competency (RISC) optimization method together with GA and SA for the modeling of data associated with volcanic deformations. Following synthetic and sensitivity tests, we apply the improved inversion techniques to two episodes of activity in the Campi Flegrei volcanic region in Italy, observed by the interferometric synthetic aperture radar technique. Inversion of these data allows derivation of deformation source parameters and their associated quality so that we can compare the two inversion methods. The RISC approach was found to be an efficient method in terms of computation time and search results and may be applied to other optimization problems in volcanic and tectonic environments.

Surgery for post-vitrectomy cataract

PubMed Central

Do, Diana V; Gichuhi, Stephen; Vedula, Satyanarayana S; Hawkins, Barbara S

2014-01-01

Background Cataract formation or acceleration can occur after intraocular surgery, especially following vitrectomy, a surgical technique for removing the vitreous which is used in the treatment of disorders that affect the posterior segment of the eye. The underlying problem that led to vitrectomy may limit the benefit from cataract surgery. Objectives The objective of this review was to evaluate the effectiveness and safety of surgery for post-vitrectomy cataract with respect to visual acuity, quality of life, and other outcomes. Search methods We searched CENTRAL (which contains the Cochrane Eyes and Vision Group Trials Register) (The Cochrane Library 2013, Issue 4), Ovid MEDLINE, Ovid MEDLINE in-Process and Other Non-Indexed Citations, Ovid MEDLINE Daily Update, Ovid OLDMED-LINE (January 1946 to May 2013), EMBASE (January 1980 to May 2013, Latin American and Caribbean Health Sciences Literature Database (LILACS) (January 1982 to May 2013), PubMed (January 1946 to May 2013), the metaRegister of Controlled Trials (mRCT) (www.controlled-trials.com), ClinicalTrials.gov (www.clinicaltrial.gov) and the WHO International Clinical Trials Registry Platform (ICTRP) (www.who.int/ictrp/search/en). We did not use any date or language restrictions in the electronic searches for trials. We last searched the electronic databases on 22 May 2013. Selection criteria We planned to include randomized and quasi-randomized controlled trials comparing cataract surgery with no surgery in adult patients who developed cataract following vitrectomy. Data collection and analysis Two authors screened the search results independently according to the standard methodological procedures expected by The Cochrane Collaboration. Main results We found no randomized or quasi-randomized controlled trials comparing cataract surgery with no cataract surgery for patients who developed cataracts following vitrectomy surgery. Authors' conclusions There is no evidence from randomized or quasi-randomized controlled trials on which to base clinical recommendations for surgery for post-vitrectomy cataract. There is a clear need for randomized controlled trials to address this evidence gap. Such trials should stratify participants by their age, the retinal disorder leading to vitrectomy, and the status of the underlying disease process in the contralateral eye. Outcomes assessed in such trials may include gain of vision on the Early Treatment Diabetic Retinopathy Study (ETDRS) scale, quality of life, and adverse events such as posterior capsular rupture. Both short-term (six-month) and long-term (one-year or two-year) outcomes should be examined. PMID:24357418

A cognitive-behavioral and mindfulness-based group sleep intervention improves behavior problems in at-risk adolescents by improving perceived sleep quality.

PubMed

Blake, Matthew J; Snoep, Lian; Raniti, Monika; Schwartz, Orli; Waloszek, Joanna M; Simmons, Julian G; Murray, Greg; Blake, Laura; Landau, Elizabeth R; Dahl, Ronald E; Bootzin, Richard; McMakin, Dana L; Dudgeon, Paul; Trinder, John; Allen, Nicholas B

2017-12-01

The aim of this study was to test whether a cognitive-behavioral and mindfulness-based group sleep intervention would improve behavior problems in at-risk adolescents, and whether these improvements were specifically related to improvements in sleep. Secondary analysis of a randomized controlled trial conducted with 123 adolescent participants (female = 60%; mean age = 14.48, range 12.04-16.31 years) who had high levels of sleep problems and anxiety symptoms. Participants were randomized into either a sleep improvement intervention (n = 63) or an active control "study skills" intervention (n = 60). Participants completed sleep and behavior problems questionnaires, wore an actiwatch and completed a sleep diary for five school nights, both before and after the intervention. Parallel multiple mediation models showed that postintervention improvements in social problems, attention problems, and aggressive behaviors were specifically mediated by moderate improvements in self-reported sleep quality on school nights, but were not mediated by moderate improvements in actigraphy-assessed sleep onset latency or sleep diary-measured sleep efficiency on school nights. This study provides evidence, using a methodologically rigorous design, that a cognitive-behavioral and mindfulness-based group sleep intervention improved behavior problems in at-risk adolescent by improving perceived sleep quality on school nights. These findings suggest that sleep interventions could be directed towards adolescents with behavior problems. This study was part of The SENSE Study (Sleep and Education: learning New Skills Early). URL: ACTRN12612001177842; http://www.anzctr.org.au/TrialSearch.aspx?searchTxt=ACTRN12612001177842&isBasic=True. Copyright © 2017 Elsevier Ltd. All rights reserved.

A methodology for airplane parameter estimation and confidence interval determination in nonlinear estimation problems. Ph.D. Thesis - George Washington Univ., Apr. 1985

NASA Technical Reports Server (NTRS)

Murphy, P. C.

1986-01-01

An algorithm for maximum likelihood (ML) estimation is developed with an efficient method for approximating the sensitivities. The ML algorithm relies on a new optimization method referred to as a modified Newton-Raphson with estimated sensitivities (MNRES). MNRES determines sensitivities by using slope information from local surface approximations of each output variable in parameter space. With the fitted surface, sensitivity information can be updated at each iteration with less computational effort than that required by either a finite-difference method or integration of the analytically determined sensitivity equations. MNRES eliminates the need to derive sensitivity equations for each new model, and thus provides flexibility to use model equations in any convenient format. A random search technique for determining the confidence limits of ML parameter estimates is applied to nonlinear estimation problems for airplanes. The confidence intervals obtained by the search are compared with Cramer-Rao (CR) bounds at the same confidence level. The degree of nonlinearity in the estimation problem is an important factor in the relationship between CR bounds and the error bounds determined by the search technique. Beale's measure of nonlinearity is developed in this study for airplane identification problems; it is used to empirically correct confidence levels and to predict the degree of agreement between CR bounds and search estimates.

Searching for Buried Treasure: Uncovering Discovery in Discovery-Based Learning

ERIC Educational Resources Information Center

Chase, Kiera; Abrahamson, Dor

2018-01-01

Forty 4th and 9th grade students participated individually in tutorial interviews centered on a problem-solving activity designed for learning basic algebra mechanics through diagrammatic modeling of an engaging narrative about a buccaneering giant burying and unearthing her treasure on a desert island. Participants were randomly assigned to…

Ringed Seal Search for Global Optimization via a Sensitive Search Model

PubMed Central

Saadi, Younes; Yanto, Iwan Tri Riyadi; Herawan, Tutut; Balakrishnan, Vimala; Chiroma, Haruna; Risnumawan, Anhar

2016-01-01

The efficiency of a metaheuristic algorithm for global optimization is based on its ability to search and find the global optimum. However, a good search often requires to be balanced between exploration and exploitation of the search space. In this paper, a new metaheuristic algorithm called Ringed Seal Search (RSS) is introduced. It is inspired by the natural behavior of the seal pup. This algorithm mimics the seal pup movement behavior and its ability to search and choose the best lair to escape predators. The scenario starts once the seal mother gives birth to a new pup in a birthing lair that is constructed for this purpose. The seal pup strategy consists of searching and selecting the best lair by performing a random walk to find a new lair. Affected by the sensitive nature of seals against external noise emitted by predators, the random walk of the seal pup takes two different search states, normal state and urgent state. In the normal state, the pup performs an intensive search between closely adjacent lairs; this movement is modeled via a Brownian walk. In an urgent state, the pup leaves the proximity area and performs an extensive search to find a new lair from sparse targets; this movement is modeled via a Levy walk. The switch between these two states is realized by the random noise emitted by predators. The algorithm keeps switching between normal and urgent states until the global optimum is reached. Tests and validations were performed using fifteen benchmark test functions to compare the performance of RSS with other baseline algorithms. The results show that RSS is more efficient than Genetic Algorithm, Particles Swarm Optimization and Cuckoo Search in terms of convergence rate to the global optimum. The RSS shows an improvement in terms of balance between exploration (extensive) and exploitation (intensive) of the search space. The RSS can efficiently mimic seal pups behavior to find best lair and provide a new algorithm to be used in global optimization problems. PMID:26790131

A novel global Harmony Search method based on Ant Colony Optimisation algorithm

NASA Astrophysics Data System (ADS)

Fouad, Allouani; Boukhetala, Djamel; Boudjema, Fares; Zenger, Kai; Gao, Xiao-Zhi

2016-03-01

The Global-best Harmony Search (GHS) is a stochastic optimisation algorithm recently developed, which hybridises the Harmony Search (HS) method with the concept of swarm intelligence in the particle swarm optimisation (PSO) to enhance its performance. In this article, a new optimisation algorithm called GHSACO is developed by incorporating the GHS with the Ant Colony Optimisation algorithm (ACO). Our method introduces a novel improvisation process, which is different from that of the GHS in the following aspects. (i) A modified harmony memory (HM) representation and conception. (ii) The use of a global random switching mechanism to monitor the choice between the ACO and GHS. (iii) An additional memory consideration selection rule using the ACO random proportional transition rule with a pheromone trail update mechanism. The proposed GHSACO algorithm has been applied to various benchmark functions and constrained optimisation problems. Simulation results demonstrate that it can find significantly better solutions when compared with the original HS and some of its variants.

A New Framework for Analysis of Coevolutionary Systems-Directed Graph Representation and Random Walks.

PubMed

Chong, Siang Yew; Tiňo, Peter; He, Jun; Yao, Xin

2017-11-20

Studying coevolutionary systems in the context of simplified models (i.e., games with pairwise interactions between coevolving solutions modeled as self plays) remains an open challenge since the rich underlying structures associated with pairwise-comparison-based fitness measures are often not taken fully into account. Although cyclic dynamics have been demonstrated in several contexts (such as intransitivity in coevolutionary problems), there is no complete characterization of cycle structures and their effects on coevolutionary search. We develop a new framework to address this issue. At the core of our approach is the directed graph (digraph) representation of coevolutionary problems that fully captures structures in the relations between candidate solutions. Coevolutionary processes are modeled as a specific type of Markov chains-random walks on digraphs. Using this framework, we show that coevolutionary problems admit a qualitative characterization: a coevolutionary problem is either solvable (there is a subset of solutions that dominates the remaining candidate solutions) or not. This has an implication on coevolutionary search. We further develop our framework that provides the means to construct quantitative tools for analysis of coevolutionary processes and demonstrate their applications through case studies. We show that coevolution of solvable problems corresponds to an absorbing Markov chain for which we can compute the expected hitting time of the absorbing class. Otherwise, coevolution will cycle indefinitely and the quantity of interest will be the limiting invariant distribution of the Markov chain. We also provide an index for characterizing complexity in coevolutionary problems and show how they can be generated in a controlled manner.

An Algorithm for Efficient Maximum Likelihood Estimation and Confidence Interval Determination in Nonlinear Estimation Problems

NASA Technical Reports Server (NTRS)

Murphy, Patrick Charles

1985-01-01

An algorithm for maximum likelihood (ML) estimation is developed with an efficient method for approximating the sensitivities. The algorithm was developed for airplane parameter estimation problems but is well suited for most nonlinear, multivariable, dynamic systems. The ML algorithm relies on a new optimization method referred to as a modified Newton-Raphson with estimated sensitivities (MNRES). MNRES determines sensitivities by using slope information from local surface approximations of each output variable in parameter space. The fitted surface allows sensitivity information to be updated at each iteration with a significant reduction in computational effort. MNRES determines the sensitivities with less computational effort than using either a finite-difference method or integrating the analytically determined sensitivity equations. MNRES eliminates the need to derive sensitivity equations for each new model, thus eliminating algorithm reformulation with each new model and providing flexibility to use model equations in any format that is convenient. A random search technique for determining the confidence limits of ML parameter estimates is applied to nonlinear estimation problems for airplanes. The confidence intervals obtained by the search are compared with Cramer-Rao (CR) bounds at the same confidence level. It is observed that the degree of nonlinearity in the estimation problem is an important factor in the relationship between CR bounds and the error bounds determined by the search technique. The CR bounds were found to be close to the bounds determined by the search when the degree of nonlinearity was small. Beale's measure of nonlinearity is developed in this study for airplane identification problems; it is used to empirically correct confidence levels for the parameter confidence limits. The primary utility of the measure, however, was found to be in predicting the degree of agreement between Cramer-Rao bounds and search estimates.

Using Genetic Programming with Prior Formula Knowledge to Solve Symbolic Regression Problem.

PubMed

Lu, Qiang; Ren, Jun; Wang, Zhiguang

2016-01-01

A researcher can infer mathematical expressions of functions quickly by using his professional knowledge (called Prior Knowledge). But the results he finds may be biased and restricted to his research field due to limitation of his knowledge. In contrast, Genetic Programming method can discover fitted mathematical expressions from the huge search space through running evolutionary algorithms. And its results can be generalized to accommodate different fields of knowledge. However, since GP has to search a huge space, its speed of finding the results is rather slow. Therefore, in this paper, a framework of connection between Prior Formula Knowledge and GP (PFK-GP) is proposed to reduce the space of GP searching. The PFK is built based on the Deep Belief Network (DBN) which can identify candidate formulas that are consistent with the features of experimental data. By using these candidate formulas as the seed of a randomly generated population, PFK-GP finds the right formulas quickly by exploring the search space of data features. We have compared PFK-GP with Pareto GP on regression of eight benchmark problems. The experimental results confirm that the PFK-GP can reduce the search space and obtain the significant improvement in the quality of SR.

An analysis of iterated local search for job-shop scheduling.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Whitley, L. Darrell; Howe, Adele E.; Watson, Jean-Paul

2003-08-01

Iterated local search, or ILS, is among the most straightforward meta-heuristics for local search. ILS employs both small-step and large-step move operators. Search proceeds via iterative modifications to a single solution, in distinct alternating phases. In the first phase, local neighborhood search (typically greedy descent) is used in conjunction with the small-step operator to transform solutions into local optima. In the second phase, the large-step operator is applied to generate perturbations to the local optima obtained in the first phase. Ideally, when local neighborhood search is applied to the resulting solution, search will terminate at a different local optimum, i.e.,more » the large-step perturbations should be sufficiently large to enable escape from the attractor basins of local optima. ILS has proven capable of delivering excellent performance on numerous N P-Hard optimization problems. [LMS03]. However, despite its implicity, very little is known about why ILS can be so effective, and under what conditions. The goal of this paper is to advance the state-of-the-art in the analysis of meta-heuristics, by providing answers to this research question. They focus on characterizing both the relationship between the structure of the underlying search space and ILS performance, and the dynamic behavior of ILS. The analysis proceeds in the context of the job-shop scheduling problem (JSP) [Tai94]. They begin by demonstrating that the attractor basins of local optima in the JSP are surprisingly weak, and can be escaped with high probaiblity by accepting a short random sequence of less-fit neighbors. this result is used to develop a new ILS algorithms for the JSP, I-JAR, whose performance is competitive with tabu search on difficult benchmark instances. They conclude by developing a very accurate behavioral model of I-JAR, which yields significant insights into the dynamics of search. The analysis is based on a set of 100 random 10 x 10 problem instances, in addition to some widely used benchmark instances. Both I-JAR and the tabu search algorithm they consider are based on the N1 move operator introduced by van Laarhoven et al. [vLAL92]. The N1 operator induces a connected search space, such that it is always possible to move from an arbitrary solution to an optimal solution; this property is integral to the development of a behavioral model of I-JAR. However, much of the analysis generalizes to other move operators, including that of Nowicki and Smutnick [NS96]. Finally the models are based on the distance between two solutions, which they take as the well-known disjunctive graph distance [MBK99].« less

A systematic review of randomized controlled trials of interventions designed to decrease child abuse in high-risk families

PubMed Central

Levey, Elizabeth J.; Gelaye, Bizu; Bain, Paul; Rondon, Marta B.; Borba, Christina P.C.; Henderson, David C.; Williams, Michelle A.

2017-01-01

Child abuse is a global problem, and parents with histories of childhood abuse are at increased risk of abusing their offspring. The objective of this systematic review is to provide a clear overview of the existing literature of randomized controlled trials evaluating the effectiveness of interventions to prevent child abuse. PubMed, PsychINFO, Web of Science, Sociological Abstracts, and CINAHL were systematically searched and expanded by hand search. This review includes all randomized controlled trials (RCTs) of interventions designed to prevent abuse among mothers identified as high-risk. Of the eight studies identified, only three found statistically significant reductions in abuse by any measure, and only two found reductions in incidents reported to child protective services. While much has been written about child abuse in high-risk families, few RCTs have been performed. Only home visitation has a significant evidence base for reducing child abuse, and the findings vary considerably. Also, data from low- and middle-income countries are limited. PMID:28110205

Statistical mechanics of budget-constrained auctions

NASA Astrophysics Data System (ADS)

Altarelli, F.; Braunstein, A.; Realpe-Gomez, J.; Zecchina, R.

2009-07-01

Finding the optimal assignment in budget-constrained auctions is a combinatorial optimization problem with many important applications, a notable example being in the sale of advertisement space by search engines (in this context the problem is often referred to as the off-line AdWords problem). On the basis of the cavity method of statistical mechanics, we introduce a message-passing algorithm that is capable of solving efficiently random instances of the problem extracted from a natural distribution, and we derive from its properties the phase diagram of the problem. As the control parameter (average value of the budgets) is varied, we find two phase transitions delimiting a region in which long-range correlations arise.

Multiobjective Resource-Constrained Project Scheduling with a Time-Varying Number of Tasks

PubMed Central

Abello, Manuel Blanco

2014-01-01

In resource-constrained project scheduling (RCPS) problems, ongoing tasks are restricted to utilizing a fixed number of resources. This paper investigates a dynamic version of the RCPS problem where the number of tasks varies in time. Our previous work investigated a technique called mapping of task IDs for centroid-based approach with random immigrants (McBAR) that was used to solve the dynamic problem. However, the solution-searching ability of McBAR was investigated over only a few instances of the dynamic problem. As a consequence, only a small number of characteristics of McBAR, under the dynamics of the RCPS problem, were found. Further, only a few techniques were compared to McBAR with respect to its solution-searching ability for solving the dynamic problem. In this paper, (a) the significance of the subalgorithms of McBAR is investigated by comparing McBAR to several other techniques; and (b) the scope of investigation in the previous work is extended. In particular, McBAR is compared to a technique called, Estimation Distribution Algorithm (EDA). As with McBAR, EDA is applied to solve the dynamic problem, an application that is unique in the literature. PMID:24883398

Greedy algorithms in disordered systems

NASA Astrophysics Data System (ADS)

Duxbury, P. M.; Dobrin, R.

1999-08-01

We discuss search, minimal path and minimal spanning tree algorithms and their applications to disordered systems. Greedy algorithms solve these problems exactly, and are related to extremal dynamics in physics. Minimal cost path (Dijkstra) and minimal cost spanning tree (Prim) algorithms provide extremal dynamics for a polymer in a random medium (the KPZ universality class) and invasion percolation (without trapping) respectively.

The grasshopper problem

NASA Astrophysics Data System (ADS)

Goulko, Olga; Kent, Adrian

2017-11-01

We introduce and physically motivate the following problem in geometric combinatorics, originally inspired by analysing Bell inequalities. A grasshopper lands at a random point on a planar lawn of area 1. It then jumps once, a fixed distance d, in a random direction. What shape should the lawn be to maximize the chance that the grasshopper remains on the lawn after jumping? We show that, perhaps surprisingly, a disc-shaped lawn is not optimal for any d>0. We investigate further by introducing a spin model whose ground state corresponds to the solution of a discrete version of the grasshopper problem. Simulated annealing and parallel tempering searches are consistent with the hypothesis that, for d<π-1/2, the optimal lawn resembles a cogwheel with n cogs, where the integer n is close to π (arcsin⁡(√{π }d / 2 )) -1. We find transitions to other shapes for d ≳π-1 / 2.

Application of firefly algorithm to the dynamic model updating problem

NASA Astrophysics Data System (ADS)

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.

Hybridisations of Variable Neighbourhood Search and Modified Simplex Elements to Harmony Search and Shuffled Frog Leaping Algorithms for Process Optimisations

NASA Astrophysics Data System (ADS)

Aungkulanon, P.; Luangpaiboon, P.

2010-10-01

Nowadays, the engineering problem systems are large and complicated. An effective finite sequence of instructions for solving these problems can be categorised into optimisation and meta-heuristic algorithms. Though the best decision variable levels from some sets of available alternatives cannot be done, meta-heuristics is an alternative for experience-based techniques that rapidly help in problem solving, learning and discovery in the hope of obtaining a more efficient or more robust procedure. All meta-heuristics provide auxiliary procedures in terms of their own tooled box functions. It has been shown that the effectiveness of all meta-heuristics depends almost exclusively on these auxiliary functions. In fact, the auxiliary procedure from one can be implemented into other meta-heuristics. Well-known meta-heuristics of harmony search (HSA) and shuffled frog-leaping algorithms (SFLA) are compared with their hybridisations. HSA is used to produce a near optimal solution under a consideration of the perfect state of harmony of the improvisation process of musicians. A meta-heuristic of the SFLA, based on a population, is a cooperative search metaphor inspired by natural memetics. It includes elements of local search and global information exchange. This study presents solution procedures via constrained and unconstrained problems with different natures of single and multi peak surfaces including a curved ridge surface. Both meta-heuristics are modified via variable neighbourhood search method (VNSM) philosophy including a modified simplex method (MSM). The basic idea is the change of neighbourhoods during searching for a better solution. The hybridisations proceed by a descent method to a local minimum exploring then, systematically or at random, increasingly distant neighbourhoods of this local solution. The results show that the variant of HSA with VNSM and MSM seems to be better in terms of the mean and variance of design points and yields.

Frequency assignments for HFDF receivers in a search and rescue network

NASA Astrophysics Data System (ADS)

Johnson, Krista E.

1990-03-01

This thesis applies a multiobjective linear programming approach to the problem of assigning frequencies to high frequency direction finding (HFDF) receivers in a search-and-rescue network in order to maximize the expected number of geolocations of vessels in distress. The problem is formulated as a multiobjective integer linear programming problem. The integrality of the solutions is guaranteed by the totally unimodularity of the A-matrix. Two approaches are taken to solve the multiobjective linear programming problem: (1) the multiobjective simplex method as implemented in ADBASE; and (2) an iterative approach. In this approach, the individual objective functions are weighted and combined in a single additive objective function. The resulting single objective problem is expressed as a network programming problem and solved using SAS NETFLOW. The process is then repeated with different weightings for the objective functions. The solutions obtained from the multiobjective linear programs are evaluated using a FORTRAN program to determine which solution provides the greatest expected number of geolocations. This solution is then compared to the sample mean and standard deviation for the expected number of geolocations resulting from 10,000 random frequency assignments for the network.

Bi-criteria travelling salesman subtour problem with time threshold

NASA Astrophysics Data System (ADS)

Kumar Thenepalle, Jayanth; Singamsetty, Purusotham

2018-03-01

This paper deals with the bi-criteria travelling salesman subtour problem with time threshold (BTSSP-T), which comes from the family of the travelling salesman problem (TSP) and is NP-hard in the strong sense. The problem arises in several application domains, mainly in routing and scheduling contexts. Here, the model focuses on two criteria: total travel distance and gains attained. The BTSSP-T aims to determine a subtour that starts and ends at the same city and visits a subset of cities at a minimum travel distance with maximum gains, such that the time spent on the tour does not exceed the predefined time threshold. A zero-one integer-programming problem is adopted to formulate this model with all practical constraints, and it includes a finite set of feasible solutions (one for each tour). Two algorithms, namely, the Lexi-Search Algorithm (LSA) and the Tabu Search (TS) algorithm have been developed to solve the BTSSP-T problem. The proposed LSA implicitly enumerates the feasible patterns and provides an efficient solution with backtracking, whereas the TS, which is metaheuristic, will give the better approximate solution. A numerical example is demonstrated in order to understand the search mechanism of the LSA. Numerical experiments are carried out in the MATLAB environment, on the different benchmark instances available in the TSPLIB domain as well as on randomly generated test instances. The experimental results show that the proposed LSA works better than the TS algorithm in terms of solution quality and, computationally, both LSA and TS are competitive.

A Modified Artificial Bee Colony Algorithm for p-Center Problems

PubMed Central

Yurtkuran, Alkın

2014-01-01

The objective of the p-center problem is to locate p-centers on a network such that the maximum of the distances from each node to its nearest center is minimized. The artificial bee colony algorithm is a swarm-based meta-heuristic algorithm that mimics the foraging behavior of honey bee colonies. This study proposes a modified ABC algorithm that benefits from a variety of search strategies to balance exploration and exploitation. Moreover, random key-based coding schemes are used to solve the p-center problem effectively. The proposed algorithm is compared to state-of-the-art techniques using different benchmark problems, and computational results reveal that the proposed approach is very efficient. PMID:24616648

Search for Directed Networks by Different Random Walk Strategies

NASA Astrophysics Data System (ADS)

Zhu, Zi-Qi; Jin, Xiao-Ling; Huang, Zhi-Long

2012-03-01

A comparative study is carried out on the efficiency of five different random walk strategies searching on directed networks constructed based on several typical complex networks. Due to the difference in search efficiency of the strategies rooted in network clustering, the clustering coefficient in a random walker's eye on directed networks is defined and computed to be half of the corresponding undirected networks. The search processes are performed on the directed networks based on Erdös—Rényi model, Watts—Strogatz model, Barabási—Albert model and clustered scale-free network model. It is found that self-avoiding random walk strategy is the best search strategy for such directed networks. Compared to unrestricted random walk strategy, path-iteration-avoiding random walks can also make the search process much more efficient. However, no-triangle-loop and no-quadrangle-loop random walks do not improve the search efficiency as expected, which is different from those on undirected networks since the clustering coefficient of directed networks are smaller than that of undirected networks.

Development of program package for investigation and modeling of carbon nanostructures in diamond like carbon films with the help of Raman scattering and infrared absorption spectra line resolving

NASA Astrophysics Data System (ADS)

Hayrapetyan, David B.; Hovhannisyan, Levon; Mantashyan, Paytsar A.

2013-04-01

The analysis of complex spectra is an actual problem for modern science. The work is devoted to the creation of a software package, which analyzes spectrum in the different formats, possesses by dynamic knowledge database and self-study mechanism, performs automated analysis of the spectra compound based on knowledge database by application of certain algorithms. In the software package as searching systems, hyper-spherical random search algorithms, gradient algorithms and genetic searching algorithms were used. The analysis of Raman and IR spectrum of diamond-like carbon (DLC) samples were performed by elaborated program. After processing the data, the program immediately displays all the calculated parameters of DLC.

Progress developing the JAXA next generation satellite data repository (G-Portal).

NASA Astrophysics Data System (ADS)

Ikehata, Y.

2016-12-01

JAXA has been operating the "G-Portal" as a repository for search and access data of Earth observation satellite related JAXA since February 2013. The G-Portal handles ten satellites data; GPM, TRMM, Aqua, ADEOS-II, ALOS (search only), ALOS-2 (search only), MOS-1, MOS-1b, ERS-1 and JERS-1. G-Portal plans to import future satellites GCOM-C and EarthCARE. Except for ALOS and ALOS-2, all of these data are open and free. The G-Portal supports web search, catalogue search (CSW and OpenSearch) and direct download by SFTP for data access. However, the G-Portal has some problems about performance and usability. For example, about performance, the G-Portal is based on 10Gbps network and uses scale out architecture. (Conceptual design was reported in AGU fall meeting 2015. (IN23D-1748)) In order to improve those problems, JAXA is developing the next generation repository since February 2016. This paper describes usability problems improvements and challenges towards the next generation system. The improvements and challenges include the following points. Current web interface uses "step by step" design and URL is generated randomly. For that reason, users must see the Web page and click many times to get desired satellite data. So, Web design will be changed completely from "step by step" to "1 page" and URL will be based on REST (REpresentational State Transfer). Regarding direct download, the current method(SFTP) is very hard to use because of anomaly port assign and key-authentication. So, we will support FTP protocol. Additionally, the next G-Portal improve catalogue service. Currently catalogue search is available only to limited users including NASA, ESA and CEOS due to performance and reliability issue, but we will remove this limitation. Furthermore, catalogue search client function will be implemented to take in other agencies satellites catalogue. Users will be able to search satellite data across agencies.

On the use of harmony search algorithm in the training of wavelet neural networks

NASA Astrophysics Data System (ADS)

Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline

2015-10-01

Wavelet neural networks (WNNs) are a class of feedforward neural networks that have been used in a wide range of industrial and engineering applications to model the complex relationships between the given inputs and outputs. The training of WNNs involves the configuration of the weight values between neurons. The backpropagation training algorithm, which is a gradient-descent method, can be used for this training purpose. Nonetheless, the solutions found by this algorithm often get trapped at local minima. In this paper, a harmony search-based algorithm is proposed for the training of WNNs. The training of WNNs, thus can be formulated as a continuous optimization problem, where the objective is to maximize the overall classification accuracy. Each candidate solution proposed by the harmony search algorithm represents a specific WNN architecture. In order to speed up the training process, the solution space is divided into disjoint partitions during the random initialization step of harmony search algorithm. The proposed training algorithm is tested onthree benchmark problems from the UCI machine learning repository, as well as one real life application, namely, the classification of electroencephalography signals in the task of epileptic seizure detection. The results obtained show that the proposed algorithm outperforms the traditional harmony search algorithm in terms of overall classification accuracy.

Recursive Branching Simulated Annealing Algorithm

NASA Technical Reports Server (NTRS)

Bolcar, Matthew; Smith, J. Scott; Aronstein, David

2012-01-01

This innovation is a variation of a simulated-annealing optimization algorithm that uses a recursive-branching structure to parallelize the search of a parameter space for the globally optimal solution to an objective. The algorithm has been demonstrated to be more effective at searching a parameter space than traditional simulated-annealing methods for a particular problem of interest, and it can readily be applied to a wide variety of optimization problems, including those with a parameter space having both discrete-value parameters (combinatorial) and continuous-variable parameters. It can take the place of a conventional simulated- annealing, Monte-Carlo, or random- walk algorithm. In a conventional simulated-annealing (SA) algorithm, a starting configuration is randomly selected within the parameter space. The algorithm randomly selects another configuration from the parameter space and evaluates the objective function for that configuration. If the objective function value is better than the previous value, the new configuration is adopted as the new point of interest in the parameter space. If the objective function value is worse than the previous value, the new configuration may be adopted, with a probability determined by a temperature parameter, used in analogy to annealing in metals. As the optimization continues, the region of the parameter space from which new configurations can be selected shrinks, and in conjunction with lowering the annealing temperature (and thus lowering the probability for adopting configurations in parameter space with worse objective functions), the algorithm can converge on the globally optimal configuration. The Recursive Branching Simulated Annealing (RBSA) algorithm shares some features with the SA algorithm, notably including the basic principles that a starting configuration is randomly selected from within the parameter space, the algorithm tests other configurations with the goal of finding the globally optimal solution, and the region from which new configurations can be selected shrinks as the search continues. The key difference between these algorithms is that in the SA algorithm, a single path, or trajectory, is taken in parameter space, from the starting point to the globally optimal solution, while in the RBSA algorithm, many trajectories are taken; by exploring multiple regions of the parameter space simultaneously, the algorithm has been shown to converge on the globally optimal solution about an order of magnitude faster than when using conventional algorithms. Novel features of the RBSA algorithm include: 1. More efficient searching of the parameter space due to the branching structure, in which multiple random configurations are generated and multiple promising regions of the parameter space are explored; 2. The implementation of a trust region for each parameter in the parameter space, which provides a natural way of enforcing upper- and lower-bound constraints on the parameters; and 3. The optional use of a constrained gradient- search optimization, performed on the continuous variables around each branch s configuration in parameter space to improve search efficiency by allowing for fast fine-tuning of the continuous variables within the trust region at that configuration point.

A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems

PubMed Central

Cao, Leilei; Xu, Lihong; Goodman, Erik D.

2016-01-01

A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared. PMID:27293421

A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems.

PubMed

Cao, Leilei; Xu, Lihong; Goodman, Erik D

2016-01-01

A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared.

Universal principles governing multiple random searchers on complex networks: The logarithmic growth pattern and the harmonic law

NASA Astrophysics Data System (ADS)

Weng, Tongfeng; Zhang, Jie; Small, Michael; Harandizadeh, Bahareh; Hui, Pan

2018-03-01

We propose a unified framework to evaluate and quantify the search time of multiple random searchers traversing independently and concurrently on complex networks. We find that the intriguing behaviors of multiple random searchers are governed by two basic principles—the logarithmic growth pattern and the harmonic law. Specifically, the logarithmic growth pattern characterizes how the search time increases with the number of targets, while the harmonic law explores how the search time of multiple random searchers varies relative to that needed by individual searchers. Numerical and theoretical results demonstrate these two universal principles established across a broad range of random search processes, including generic random walks, maximal entropy random walks, intermittent strategies, and persistent random walks. Our results reveal two fundamental principles governing the search time of multiple random searchers, which are expected to facilitate investigation of diverse dynamical processes like synchronization and spreading.

Some practical problems in implementing randomization.

PubMed

Downs, Matt; Tucker, Kathryn; Christ-Schmidt, Heidi; Wittes, Janet

2010-06-01

While often theoretically simple, implementing randomization to treatment in a masked, but confirmable, fashion can prove difficult in practice. At least three categories of problems occur in randomization: (1) bad judgment in the choice of method, (2) design and programming errors in implementing the method, and (3) human error during the conduct of the trial. This article focuses on these latter two types of errors, dealing operationally with what can go wrong after trial designers have selected the allocation method. We offer several case studies and corresponding recommendations for lessening the frequency of problems in allocating treatment or for mitigating the consequences of errors. Recommendations include: (1) reviewing the randomization schedule before starting a trial, (2) being especially cautious of systems that use on-demand random number generators, (3) drafting unambiguous randomization specifications, (4) performing thorough testing before entering a randomization system into production, (5) maintaining a dataset that captures the values investigators used to randomize participants, thereby allowing the process of treatment allocation to be reproduced and verified, (6) resisting the urge to correct errors that occur in individual treatment assignments, (7) preventing inadvertent unmasking to treatment assignments in kit allocations, and (8) checking a sample of study drug kits to allow detection of errors in drug packaging and labeling. Although we performed a literature search of documented randomization errors, the examples that we provide and the resultant recommendations are based largely on our own experience in industry-sponsored clinical trials. We do not know how representative our experience is or how common errors of the type we have seen occur. Our experience underscores the importance of verifying the integrity of the treatment allocation process before and during a trial. Clinical Trials 2010; 7: 235-245. http://ctj.sagepub.com.

Hybrid Metaheuristics for Solving a Fuzzy Single Batch-Processing Machine Scheduling Problem

PubMed Central

Molla-Alizadeh-Zavardehi, S.; Tavakkoli-Moghaddam, R.; Lotfi, F. Hosseinzadeh

2014-01-01

This paper deals with a problem of minimizing total weighted tardiness of jobs in a real-world single batch-processing machine (SBPM) scheduling in the presence of fuzzy due date. In this paper, first a fuzzy mixed integer linear programming model is developed. Then, due to the complexity of the problem, which is NP-hard, we design two hybrid metaheuristics called GA-VNS and VNS-SA applying the advantages of genetic algorithm (GA), variable neighborhood search (VNS), and simulated annealing (SA) frameworks. Besides, we propose three fuzzy earliest due date heuristics to solve the given problem. Through computational experiments with several random test problems, a robust calibration is applied on the parameters. Finally, computational results on different-scale test problems are presented to compare the proposed algorithms. PMID:24883359

An effective PSO-based memetic algorithm for flow shop scheduling.

PubMed

Liu, Bo; Wang, Ling; Jin, Yi-Hui

2007-02-01

This paper proposes an effective particle swarm optimization (PSO)-based memetic algorithm (MA) for the permutation flow shop scheduling problem (PFSSP) with the objective to minimize the maximum completion time, which is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. In the proposed PSO-based MA (PSOMA), both PSO-based searching operators and some special local searching operators are designed to balance the exploration and exploitation abilities. In particular, the PSOMA applies the evolutionary searching mechanism of PSO, which is characterized by individual improvement, population cooperation, and competition to effectively perform exploration. On the other hand, the PSOMA utilizes several adaptive local searches to perform exploitation. First, to make PSO suitable for solving PFSSP, a ranked-order value rule based on random key representation is presented to convert the continuous position values of particles to job permutations. Second, to generate an initial swarm with certain quality and diversity, the famous Nawaz-Enscore-Ham (NEH) heuristic is incorporated into the initialization of population. Third, to balance the exploration and exploitation abilities, after the standard PSO-based searching operation, a new local search technique named NEH_1 insertion is probabilistically applied to some good particles selected by using a roulette wheel mechanism with a specified probability. Fourth, to enrich the searching behaviors and to avoid premature convergence, a simulated annealing (SA)-based local search with multiple different neighborhoods is designed and incorporated into the PSOMA. Meanwhile, an effective adaptive meta-Lamarckian learning strategy is employed to decide which neighborhood to be used in SA-based local search. Finally, to further enhance the exploitation ability, a pairwise-based local search is applied after the SA-based search. Simulation results based on benchmarks demonstrate the effectiveness of the PSOMA. Additionally, the effects of some parameters on optimization performances are also discussed.

Lévy flight artificial bee colony algorithm

NASA Astrophysics Data System (ADS)

Sharma, Harish; Bansal, Jagdish Chand; Arya, K. V.; Yang, Xin-She

2016-08-01

Artificial bee colony (ABC) optimisation algorithm is a relatively simple and recent population-based probabilistic approach for global optimisation. The solution search equation of ABC is significantly influenced by a random quantity which helps in exploration at the cost of exploitation of the search space. In the ABC, there is a high chance to skip the true solution due to its large step sizes. In order to balance between diversity and convergence in the ABC, a Lévy flight inspired search strategy is proposed and integrated with ABC. The proposed strategy is named as Lévy Flight ABC (LFABC) has both the local and global search capability simultaneously and can be achieved by tuning the Lévy flight parameters and thus automatically tuning the step sizes. In the LFABC, new solutions are generated around the best solution and it helps to enhance the exploitation capability of ABC. Furthermore, to improve the exploration capability, the numbers of scout bees are increased. The experiments on 20 test problems of different complexities and five real-world engineering optimisation problems show that the proposed strategy outperforms the basic ABC and recent variants of ABC, namely, Gbest-guided ABC, best-so-far ABC and modified ABC in most of the experiments.

Optimal random Lévy-loop searching: New insights into the searching behaviours of central-place foragers

NASA Astrophysics Data System (ADS)

Reynolds, A. M.

2008-04-01

A random Lévy-looping model of searching is devised and optimal random Lévy-looping searching strategies are identified for the location of a single target whose position is uncertain. An inverse-square power law distribution of loop lengths is shown to be optimal when the distance between the centre of the search and the target is much shorter than the size of the longest possible loop in the searching pattern. Optimal random Lévy-looping searching patterns have recently been observed in the flight patterns of honeybees (Apis mellifera) when attempting to locate their hive and when searching after a known food source becomes depleted. It is suggested that the searching patterns of desert ants (Cataglyphis) are consistent with the adoption of an optimal Lévy-looping searching strategy.

A systematic review of randomized controlled trials of interventions designed to decrease child abuse in high-risk families.

PubMed

Levey, Elizabeth J; Gelaye, Bizu; Bain, Paul; Rondon, Marta B; Borba, Christina P C; Henderson, David C; Williams, Michelle A

2017-03-01

Child abuse is a global problem, and parents with histories of childhood abuse are at increased risk of abusing their offspring. The objective of this systematic review is to provide a clear overview of the existing literature of randomized controlled trials evaluating the effectiveness of interventions to prevent child abuse. PubMed, PsychINFO, Web of Science, Sociological Abstracts, and CINAHL were systematically searched and expanded by hand search. This review includes all randomized controlled trials (RCTs) of interventions designed to prevent abuse among mothers identified as high-risk. Of the eight studies identified, only three found statistically significant reductions in abuse by any measure, and only two found reductions in incidents reported to child protective services. While much has been written about child abuse in high-risk families, few RCTs have been performed. Only home visitation has a significant evidence base for reducing child abuse, and the findings vary considerably. Also, data from low- and middle-income countries are limited. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Amoeba-Inspired Heuristic Search Dynamics for Exploring Chemical Reaction Paths.

PubMed

Aono, Masashi; Wakabayashi, Masamitsu

2015-09-01

We propose a nature-inspired model for simulating chemical reactions in a computationally resource-saving manner. The model was developed by extending our previously proposed heuristic search algorithm, called "AmoebaSAT [Aono et al. 2013]," which was inspired by the spatiotemporal dynamics of a single-celled amoeboid organism that exhibits sophisticated computing capabilities in adapting to its environment efficiently [Zhu et al. 2013]. AmoebaSAT is used for solving an NP-complete combinatorial optimization problem [Garey and Johnson 1979], "the satisfiability problem," and finds a constraint-satisfying solution at a speed that is dramatically faster than one of the conventionally known fastest stochastic local search methods [Iwama and Tamaki 2004] for a class of randomly generated problem instances [ http://www.cs.ubc.ca/~hoos/5/benchm.html ]. In cases where the problem has more than one solution, AmoebaSAT exhibits dynamic transition behavior among a variety of the solutions. Inheriting these features of AmoebaSAT, we formulate "AmoebaChem," which explores a variety of metastable molecules in which several constraints determined by input atoms are satisfied and generates dynamic transition processes among the metastable molecules. AmoebaChem and its developed forms will be applied to the study of the origins of life, to discover reaction paths for which expected or unexpected organic compounds may be formed via unknown unstable intermediates and to estimate the likelihood of each of the discovered paths.

A Study of Periodical Literature Searching at an Urban Research Library: Problems and Patterns.

ERIC Educational Resources Information Center

Carey, Kevin

Patron use of periodical indexes at an urban academic research library was studied over a 7-week period in the summer of 1982. A total of 98 patrons were interviewed on a random basis as they used various periodical and newspaper indexes. The questionnaire was designed to gather information on citation elements, the methods patrons use to locate…

LETTER TO THE EDITOR: Phase transition in a random fragmentation problem with applications to computer science

NASA Astrophysics Data System (ADS)

Dean, David S.; Majumdar, Satya N.

2002-08-01

We study a fragmentation problem where an initial object of size x is broken into m random pieces provided x > x0 where x0 is an atomic cut-off. Subsequently, the fragmentation process continues for each of those daughter pieces whose sizes are bigger than x0. The process stops when all the fragments have sizes smaller than x0. We show that the fluctuation of the total number of splitting events, characterized by the variance, generically undergoes a nontrivial phase transition as one tunes the branching number m through a critical value m = mc. For m < mc, the fluctuations are Gaussian where as for m > mc they are anomalously large and non-Gaussian. We apply this general result to analyse two different search algorithms in computer science.

Meta-RaPS Algorithm for the Aerial Refueling Scheduling Problem

NASA Technical Reports Server (NTRS)

Kaplan, Sezgin; Arin, Arif; Rabadi, Ghaith

2011-01-01

The Aerial Refueling Scheduling Problem (ARSP) can be defined as determining the refueling completion times for each fighter aircraft (job) on multiple tankers (machines). ARSP assumes that jobs have different release times and due dates, The total weighted tardiness is used to evaluate schedule's quality. Therefore, ARSP can be modeled as a parallel machine scheduling with release limes and due dates to minimize the total weighted tardiness. Since ARSP is NP-hard, it will be more appropriate to develop a pproimate or heuristic algorithm to obtain solutions in reasonable computation limes. In this paper, Meta-Raps-ATC algorithm is implemented to create high quality solutions. Meta-RaPS (Meta-heuristic for Randomized Priority Search) is a recent and promising meta heuristic that is applied by introducing randomness to a construction heuristic. The Apparent Tardiness Rule (ATC), which is a good rule for scheduling problems with tardiness objective, is used to construct initial solutions which are improved by an exchanging operation. Results are presented for generated instances.

Effects of therapy for dysphagia in Parkinson's disease: systematic review.

PubMed

Baijens, Laura W J; Speyer, Renée

2009-03-01

This systematic review explores the effects of dysphagia treatment for Parkinson's disease. The review includes rehabilitative, surgical, pharmacologic, and other treatments. Only oropharyngeal dysphagia is selected for this literature search, excluding dysphagia due to esophageal or gastric disorders. The effects of deep brain stimulation on dysphagia are not included. In general, the literature concerning dysphagia treatment in Parkinson's disease is rather limited. Most effect studies show diverse methodologic problems. Multiple case studies and trials are identified by searching biomedical literature databases PubMed and Embase, and by hand-searching reference lists. The conclusions of most studies cannot be compared with one another because of heterogeneous therapy methods and outcome measures. Further research based on randomized controlled trials to determine the effectiveness of different therapies for dysphagia in Parkinson's disease is required.

Application of multivariable search techniques to structural design optimization

NASA Technical Reports Server (NTRS)

Jones, R. T.; Hague, D. S.

1972-01-01

Multivariable optimization techniques are applied to a particular class of minimum weight structural design problems: the design of an axially loaded, pressurized, stiffened cylinder. Minimum weight designs are obtained by a variety of search algorithms: first- and second-order, elemental perturbation, and randomized techniques. An exterior penalty function approach to constrained minimization is employed. Some comparisons are made with solutions obtained by an interior penalty function procedure. In general, it would appear that an interior penalty function approach may not be as well suited to the class of design problems considered as the exterior penalty function approach. It is also shown that a combination of search algorithms will tend to arrive at an extremal design in a more reliable manner than a single algorithm. The effect of incorporating realistic geometrical constraints on stiffener cross-sections is investigated. A limited comparison is made between minimum weight cylinders designed on the basis of a linear stability analysis and cylinders designed on the basis of empirical buckling data. Finally, a technique for locating more than one extremal is demonstrated.

An energy-based perturbation and a taboo strategy for improving the searching ability of stochastic structural optimization methods

NASA Astrophysics Data System (ADS)

Cheng, Longjiu; Cai, Wensheng; Shao, Xueguang

2005-03-01

An energy-based perturbation and a new idea of taboo strategy are proposed for structural optimization and applied in a benchmark problem, i.e., the optimization of Lennard-Jones (LJ) clusters. It is proved that the energy-based perturbation is much better than the traditional random perturbation both in convergence speed and searching ability when it is combined with a simple greedy method. By tabooing the most wide-spread funnel instead of the visited solutions, the hit rate of other funnels can be significantly improved. Global minima of (LJ) clusters up to 200 atoms are found with high efficiency.

How evolutionary crystal structure prediction works--and why.

PubMed

Oganov, Artem R; Lyakhov, Andriy O; Valle, Mario

2011-03-15

Once the crystal structure of a chemical substance is known, many properties can be predicted reliably and routinely. Therefore if researchers could predict the crystal structure of a material before it is synthesized, they could significantly accelerate the discovery of new materials. In addition, the ability to predict crystal structures at arbitrary conditions of pressure and temperature is invaluable for the study of matter at extreme conditions, where experiments are difficult. Crystal structure prediction (CSP), the problem of finding the most stable arrangement of atoms given only the chemical composition, has long remained a major unsolved scientific problem. Two problems are entangled here: search, the efficient exploration of the multidimensional energy landscape, and ranking, the correct calculation of relative energies. For organic crystals, which contain a few molecules in the unit cell, search can be quite simple as long as a researcher does not need to include many possible isomers or conformations of the molecules; therefore ranking becomes the main challenge. For inorganic crystals, quantum mechanical methods often provide correct relative energies, making search the most critical problem. Recent developments provide useful practical methods for solving the search problem to a considerable extent. One can use simulated annealing, metadynamics, random sampling, basin hopping, minima hopping, and data mining. Genetic algorithms have been applied to crystals since 1995, but with limited success, which necessitated the development of a very different evolutionary algorithm. This Account reviews CSP using one of the major techniques, the hybrid evolutionary algorithm USPEX (Universal Structure Predictor: Evolutionary Xtallography). Using recent developments in the theory of energy landscapes, we unravel the reasons evolutionary techniques work for CSP and point out their limitations. We demonstrate that the energy landscapes of chemical systems have an overall shape and explore their intrinsic dimensionalities. Because of the inverse relationships between order and energy and between the dimensionality and diversity of an ensemble of crystal structures, the chances that a random search will find the ground state decrease exponentially with increasing system size. A well-designed evolutionary algorithm allows for much greater computational efficiency. We illustrate the power of evolutionary CSP through applications that examine matter at high pressure, where new, unexpected phenomena take place. Evolutionary CSP has allowed researchers to make unexpected discoveries such as a transparent phase of sodium, a partially ionic form of boron, complex superconducting forms of calcium, a novel superhard allotrope of carbon, polymeric modifications of nitrogen, and a new class of compounds, perhydrides. These methods have also led to the discovery of novel hydride superconductors including the "impossible" LiH(n) (n=2, 6, 8) compounds, and CaLi(2). We discuss extensions of the method to molecular crystals, systems of variable composition, and the targeted optimization of specific physical properties. © 2011 American Chemical Society

Adaptive infinite impulse response system identification using modified-interior search algorithm with Lèvy flight.

PubMed

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. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

A Method for Estimating View Transformations from Image Correspondences Based on the Harmony Search Algorithm.

PubMed

Cuevas, Erik; Díaz, Margarita

2015-01-01

In this paper, a new method for robustly estimating multiple view relations from point correspondences is presented. The approach combines the popular random sampling consensus (RANSAC) algorithm and the evolutionary method harmony search (HS). With this combination, the proposed method adopts a different sampling strategy than RANSAC to generate putative solutions. Under the new mechanism, at each iteration, new candidate solutions are built taking into account the quality of the models generated by previous candidate solutions, rather than purely random as it is the case of RANSAC. The rules for the generation of candidate solutions (samples) are motivated by the improvisation process that occurs when a musician searches for a better state of harmony. As a result, the proposed approach can substantially reduce the number of iterations still preserving the robust capabilities of RANSAC. The method is generic and its use is illustrated by the estimation of homographies, considering synthetic and real images. Additionally, in order to demonstrate the performance of the proposed approach within a real engineering application, it is employed to solve the problem of position estimation in a humanoid robot. Experimental results validate the efficiency of the proposed method in terms of accuracy, speed, and robustness.

A modified generalized extremal optimization algorithm for the quay crane scheduling problem with interference constraints

NASA Astrophysics Data System (ADS)

Guo, Peng; Cheng, Wenming; Wang, Yi

2014-10-01

The quay crane scheduling problem (QCSP) determines the handling sequence of tasks at ship bays by a set of cranes assigned to a container vessel such that the vessel's service time is minimized. A number of heuristics or meta-heuristics have been proposed to obtain the near-optimal solutions to overcome the NP-hardness of the problem. In this article, the idea of generalized extremal optimization (GEO) is adapted to solve the QCSP with respect to various interference constraints. The resulting GEO is termed the modified GEO. A randomized searching method for neighbouring task-to-QC assignments to an incumbent task-to-QC assignment is developed in executing the modified GEO. In addition, a unidirectional search decoding scheme is employed to transform a task-to-QC assignment to an active quay crane schedule. The effectiveness of the developed GEO is tested on a suite of benchmark problems introduced by K.H. Kim and Y.M. Park in 2004 (European Journal of Operational Research, Vol. 156, No. 3). Compared with other well-known existing approaches, the experiment results show that the proposed modified GEO is capable of obtaining the optimal or near-optimal solution in a reasonable time, especially for large-sized problems.

Does oral language underpin the development of later behavior problems? A longitudinal meta-analysis.

PubMed

Chow, Jason C; Ekholm, Erik; Coleman, Heather

2018-05-24

The purpose of this article is to estimate the overall weighted mean effect of the relation between early language skills and later behavior problems in school-aged children. A systematic literature search yielded 19,790 unduplicated reports, and a structured search strategy and identification procedure yielded 25 unique data sets, with 114 effect sizes for analysis. Eligible reports were then coded, and effect sizes were extracted and synthesized via robust variance estimation and random-effects meta-analytic techniques. The overall correlation between early language and later behavior problems was negative and small (r = -.14, 95% confidence interval [CI] [-.16, -.11]), and controlling for demographic variables did not reduce the magnitude of the inverse relationship between language skill and problem behavior (r = -.16). Moderator analyses identified receptive language, parent-reported behavior measures, gender, and age as significant predictors of the association between language and behavior. This article corroborates the consistent findings of previous meta-analytic and longitudinal studies and further identifies areas, particularly around measurement, for future research. Furthermore, prospective longitudinal evaluations of the relations between language deficits and behavior problems with different types of measures (teacher-/parent-report, direct assessment, classroom observation) is warranted. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Enabling search over encrypted multimedia databases

NASA Astrophysics Data System (ADS)

Lu, Wenjun; Swaminathan, Ashwin; Varna, Avinash L.; Wu, Min

2009-02-01

Performing information retrieval tasks while preserving data confidentiality is a desirable capability when a database is stored on a server maintained by a third-party service provider. This paper addresses the problem of enabling content-based retrieval over encrypted multimedia databases. Search indexes, along with multimedia documents, are first encrypted by the content owner and then stored onto the server. Through jointly applying cryptographic techniques, such as order preserving encryption and randomized hash functions, with image processing and information retrieval techniques, secure indexing schemes are designed to provide both privacy protection and rank-ordered search capability. Retrieval results on an encrypted color image database and security analysis of the secure indexing schemes under different attack models show that data confidentiality can be preserved while retaining very good retrieval performance. This work has promising applications in secure multimedia management.

A Comparison of Techniques for Scheduling Earth-Observing Satellites

NASA Technical Reports Server (NTRS)

Globus, Al; Crawford, James; Lohn, Jason; Pryor, Anna

2004-01-01

Scheduling observations by coordinated fleets of Earth Observing Satellites (EOS) involves large search spaces, complex constraints and poorly understood bottlenecks, conditions where evolutionary and related algorithms are often effective. However, there are many such algorithms and the best one to use is not clear. Here we compare multiple variants of the genetic algorithm: stochastic hill climbing, simulated annealing, squeaky wheel optimization and iterated sampling on ten realistically-sized EOS scheduling problems. Schedules are represented by a permutation (non-temperal ordering) of the observation requests. A simple deterministic scheduler assigns times and resources to each observation request in the order indicated by the permutation, discarding those that violate the constraints created by previously scheduled observations. Simulated annealing performs best. Random mutation outperform a more 'intelligent' mutator. Furthermore, the best mutator, by a small margin, was a novel approach we call temperature dependent random sampling that makes large changes in the early stages of evolution and smaller changes towards the end of search.

A random utility model of delay discounting and its application to people with externalizing psychopathology.

PubMed

Dai, Junyi; Gunn, Rachel L; Gerst, Kyle R; Busemeyer, Jerome R; Finn, Peter R

2016-10-01

Previous studies have demonstrated that working memory capacity plays a central role in delay discounting in people with externalizing psychopathology. These studies used a hyperbolic discounting model, and its single parameter-a measure of delay discounting-was estimated using the standard method of searching for indifference points between intertemporal options. However, there are several problems with this approach. First, the deterministic perspective on delay discounting underlying the indifference point method might be inappropriate. Second, the estimation procedure using the R2 measure often leads to poor model fit. Third, when parameters are estimated using indifference points only, much of the information collected in a delay discounting decision task is wasted. To overcome these problems, this article proposes a random utility model of delay discounting. The proposed model has 2 parameters, 1 for delay discounting and 1 for choice variability. It was fit to choice data obtained from a recently published data set using both maximum-likelihood and Bayesian parameter estimation. As in previous studies, the delay discounting parameter was significantly associated with both externalizing problems and working memory capacity. Furthermore, choice variability was also found to be significantly associated with both variables. This finding suggests that randomness in decisions may be a mechanism by which externalizing problems and low working memory capacity are associated with poor decision making. The random utility model thus has the advantage of disclosing the role of choice variability, which had been masked by the traditional deterministic model. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

The problem of latent attentional capture: Easy visual search conceals capture by task-irrelevant abrupt onsets.

PubMed

Gaspelin, Nicholas; Ruthruff, Eric; Lien, Mei-Ching

2016-08-01

Researchers are sharply divided regarding whether irrelevant abrupt onsets capture spatial attention. Numerous studies report that they do and a roughly equal number report that they do not. This puzzle has inspired numerous attempts at reconciliation, none gaining general acceptance. The authors propose that abrupt onsets routinely capture attention, but the size of observed capture effects depends critically on how long attention dwells on distractor items which, in turn, depends critically on search difficulty. In a series of spatial cuing experiments, the authors show that irrelevant abrupt onsets produce robust capture effects when visual search is difficult, but not when search is easy. Critically, this effect occurs even when search difficulty varies randomly across trials, preventing any strategic adjustments of the attentional set that could modulate probability of capture by the onset cue. The authors argue that easy visual search provides an insensitive test for stimulus-driven capture by abrupt onsets: even though onsets truly capture attention, the effects of capture can be latent. This observation helps to explain previous failures to find capture by onsets, nearly all of which used an easy visual search. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

The Problem of Latent Attentional Capture: Easy Visual Search Conceals Capture by Task-Irrelevant Abrupt Onsets

PubMed Central

Gaspelin, Nicholas; Ruthruff, Eric; Lien, Mei-Ching

2016-01-01

Researchers are sharply divided regarding whether irrelevant abrupt onsets capture spatial attention. Numerous studies report that they do and a roughly equal number report that they do not. This puzzle has inspired numerous attempts at reconciliation, none gaining general acceptance. We propose that abrupt onsets routinely capture attention, but the size of observed capture effects depends critically on how long attention dwells on distractor items which, in turn, depends critically on search difficulty. In a series of spatial cuing experiments, we show that irrelevant abrupt onsets produce robust capture effects when visual search is difficult, but not when search is easy. Critically, this effect occurs even when search difficulty varies randomly across trials, preventing any strategic adjustments of the attentional set that could modulate probability of capture by the onset cue. We argue that easy visual search provides an insensitive test for stimulus-driven capture by abrupt onsets: even though onsets truly capture attention, the effects of capture can be latent. This observation helps to explain previous failures to find capture by onsets, nearly all of which employed an easy visual search. PMID:26854530

A critical appraisal of the reporting quality of published randomized controlled trials in the fall injuries.

PubMed

Asghari Jafarabadi, Mohammad; Sadeghi-Bazrgani, Homayoun; Dianat, Iman

2018-06-01

To evaluate the quality of reporting in published randomized controlled trials (RTCs) in the field of fall injuries. The 188 RTCs published between 2001 and 2011, indexed in EMBASE and Medline databases were extracted through searching by appropriate keywords and EMTree classification terms. The evaluation trustworthiness was assured through parallel evaluations of two experts in epidemiology and biostatistics. About 40%-75% of papers had problems in reporting random allocation method, allocation concealment, random allocation implementation, blinding and similarity among groups, intention to treat and balancing benefits and harms. Moreover, at least 10% of papers inappropriately/not reported the design, protocol violations, sample size justification, subgroup/adjusted analyses, presenting flow diagram, drop outs, recruitment time, baseline data, suitable effect size on outcome, ancillary analyses, limitations and generalizability. Considering the shortcomings found and due to the importance of the RCTs for fall injury prevention programmes, their reporting quality should be improved.

On salesmen and tourists: Two-step optimization in deterministic foragers

NASA Astrophysics Data System (ADS)

Maya, Miguel; Miramontes, Octavio; Boyer, Denis

2017-02-01

We explore a two-step optimization problem in random environments, the so-called restaurant-coffee shop problem, where a walker aims at visiting the nearest and better restaurant in an area and then move to the nearest and better coffee-shop. This is an extension of the Tourist Problem, a one-step optimization dynamics that can be viewed as a deterministic walk in a random medium. A certain amount of heterogeneity in the values of the resources to be visited causes the emergence of power-laws distributions for the steps performed by the walker, similarly to a Lévy flight. The fluctuations of the step lengths tend to decrease as a consequence of multiple-step planning, thus reducing the foraging uncertainty. We find that the first and second steps of each planned movement play very different roles in heterogeneous environments. The two-step process improves only slightly the foraging efficiency compared to the one-step optimization, at a much higher computational cost. We discuss the implications of these findings for animal and human mobility, in particular in relation to the computational effort that informed agents should deploy to solve search problems.

Mutually beneficial relationship in optimization between search-space smoothing and stochastic search

NASA Astrophysics Data System (ADS)

Hasegawa, Manabu; Hiramatsu, Kotaro

2013-10-01

The effectiveness of the Metropolis algorithm (MA) (constant-temperature simulated annealing) in optimization by the method of search-space smoothing (SSS) (potential smoothing) is studied on two types of random traveling salesman problems. The optimization mechanism of this hybrid approach (MASSS) is investigated by analyzing the exploration dynamics observed in the rugged landscape of the cost function (energy surface). The results show that the MA can be successfully utilized as a local search algorithm in the SSS approach. It is also clarified that the optimization characteristics of these two constituent methods are improved in a mutually beneficial manner in the MASSS run. Specifically, the relaxation dynamics generated by employing the MA work effectively even in a smoothed landscape and more advantage is taken of the guiding function proposed in the idea of SSS; this mechanism operates in an adaptive manner in the de-smoothing process and therefore the MASSS method maintains its optimization function over a wider temperature range than the MA.

A protein-dependent side-chain rotamer library.

PubMed

Bhuyan, Md Shariful Islam; Gao, Xin

2011-12-14

Protein side-chain packing problem has remained one of the key open problems in bioinformatics. The three main components of protein side-chain prediction methods are a rotamer library, an energy function and a search algorithm. Rotamer libraries summarize the existing knowledge of the experimentally determined structures quantitatively. Depending on how much contextual information is encoded, there are backbone-independent rotamer libraries and backbone-dependent rotamer libraries. Backbone-independent libraries only encode sequential information, whereas backbone-dependent libraries encode both sequential and locally structural information. However, side-chain conformations are determined by spatially local information, rather than sequentially local information. Since in the side-chain prediction problem, the backbone structure is given, spatially local information should ideally be encoded into the rotamer libraries. In this paper, we propose a new type of backbone-dependent rotamer library, which encodes structural information of all the spatially neighboring residues. We call it protein-dependent rotamer libraries. Given any rotamer library and a protein backbone structure, we first model the protein structure as a Markov random field. Then the marginal distributions are estimated by the inference algorithms, without doing global optimization or search. The rotamers from the given library are then re-ranked and associated with the updated probabilities. Experimental results demonstrate that the proposed protein-dependent libraries significantly outperform the widely used backbone-dependent libraries in terms of the side-chain prediction accuracy and the rotamer ranking ability. Furthermore, without global optimization/search, the side-chain prediction power of the protein-dependent library is still comparable to the global-search-based side-chain prediction methods.

Simulated Annealing in the Variable Landscape

NASA Astrophysics Data System (ADS)

Hasegawa, Manabu; Kim, Chang Ju

An experimental analysis is conducted to test whether the appropriate introduction of the smoothness-temperature schedule enhances the optimizing ability of the MASSS method, the combination of the Metropolis algorithm (MA) and the search-space smoothing (SSS) method. The test is performed on two types of random traveling salesman problems. The results show that the optimization performance of the MA is substantially improved by a single smoothing alone and slightly more by a single smoothing with cooling and by a de-smoothing process with heating. The performance is compared to that of the parallel tempering method and a clear advantage of the idea of smoothing is observed depending on the problem.

Optimising mobile phase composition, its flow-rate and column temperature in HPLC using taboo search.

PubMed

Guillaume, Y C; Peyrin, E

2000-03-06

A chemometric methodology is proposed to study the separation of seven p-hydroxybenzoic esters in reversed phase liquid chromatography (RPLC). Fifteen experiments were found to be necessary to find a mathematical model which linked a novel chromatographic response function (CRF) with the column temperature, the water fraction in the mobile phase and its flow rate. The CRF optimum was determined using a new algorithm based on Glover's taboo search (TS). A flow-rate of 0.9 ml min(-1) with a water fraction of 0.64 in the ACN-water mixture and a column temperature of 10 degrees C gave the most efficient separation conditions. The usefulness of TS was compared with the pure random search (PRS) and simplex search (SS). As demonstrated by calculations, the algorithm avoids entrapment in local minima and continues the search to give a near-optimal final solution. Unlike other methods of global optimisation, this procedure is generally applicable, easy to implement, derivative free, conceptually simple and could be used in the future for much more complex optimisation problems.

Practicing evidence based medicine at the bedside: a randomized controlled pilot study in undergraduate medical students assessing the practicality of tablets, smartphones, and computers in clinical life.

PubMed

Friederichs, Hendrik; Marschall, Bernhard; Weissenstein, Anne

2014-12-05

Practicing evidence-based medicine is an important aspect of providing good medical care. Accessing external information through literature searches on computer-based systems can effectively achieve integration in clinical care. We conducted a pilot study using smartphones, tablets, and stationary computers as search devices at the bedside. The objective was to determine possible differences between the various devices and assess students' internet use habits. In a randomized controlled pilot study, 120 students were divided in three groups. One control group solved clinical problems on a computer and two intervention groups used mobile devices at the bedside. In a questionnaire, students were asked to report their internet use habits as well as their satisfaction with their respective search tool using a 5-point Likert scale. Of 120 surveys, 94 (78.3%) complete data sets were analyzed. The mobility of the tablet (3.90) and the smartphone (4.39) was seen as a significant advantage over the computer (2.38, p < .001). However, for performing an effective literature search at the bedside, the computer (3.22) was rated superior to both tablet computers (2.13) and smartphones (1.68). No significant differences were detected between tablets and smartphones except satisfaction with screen size (tablet 4.10, smartphone 2.00, p < .001). Using a mobile device at the bedside to perform an extensive search is not suitable for students who prefer using computers. However, mobility is regarded as a substantial advantage, and therefore future applications might facilitate quick and simple searches at the bedside.

Determination of Slope Safety Factor with Analytical Solution and Searching Critical Slip Surface with Genetic-Traversal Random Method

PubMed Central

2014-01-01

In the current practice, to determine the safety factor of a slope with two-dimensional circular potential failure surface, one of the searching methods for the critical slip surface is Genetic Algorithm (GA), while the method to calculate the slope safety factor is Fellenius' slices method. However GA needs to be validated with more numeric tests, while Fellenius' slices method is just an approximate method like finite element method. This paper proposed a new method to determine the minimum slope safety factor which is the determination of slope safety factor with analytical solution and searching critical slip surface with Genetic-Traversal Random Method. The analytical solution is more accurate than Fellenius' slices method. The Genetic-Traversal Random Method uses random pick to utilize mutation. A computer automatic search program is developed for the Genetic-Traversal Random Method. After comparison with other methods like slope/w software, results indicate that the Genetic-Traversal Random Search Method can give very low safety factor which is about half of the other methods. However the obtained minimum safety factor with Genetic-Traversal Random Search Method is very close to the lower bound solutions of slope safety factor given by the Ansys software. PMID:24782679

An ILP based memetic algorithm for finding minimum positive influence dominating sets in social networks

NASA Astrophysics Data System (ADS)

Lin, Geng; Guan, Jian; Feng, Huibin

2018-06-01

The positive influence dominating set problem is a variant of the minimum dominating set problem, and has lots of applications in social networks. It is NP-hard, and receives more and more attention. Various methods have been proposed to solve the positive influence dominating set problem. However, most of the existing work focused on greedy algorithms, and the solution quality needs to be improved. In this paper, we formulate the minimum positive influence dominating set problem as an integer linear programming (ILP), and propose an ILP based memetic algorithm (ILPMA) for solving the problem. The ILPMA integrates a greedy randomized adaptive construction procedure, a crossover operator, a repair operator, and a tabu search procedure. The performance of ILPMA is validated on nine real-world social networks with nodes up to 36,692. The results show that ILPMA significantly improves the solution quality, and is robust.

Effect of domestic violence training

PubMed Central

Zaher, Eman; Keogh, Kelly; Ratnapalan, Savithiri

2014-01-01

Abstract Objective To describe and evaluate the effectiveness of domestic violence education in improving physicians’ knowledge, recognition, and management of abused women. Data sources The Cochrane Database of Systematic Reviews, MEDLINE, PubMed, PsycINFO, ERIC, and EMBASE were searched for articles published between January 1, 2000, and November 1, 2012. This search was supplemented by manual searches for relevant articles using a combined text-word and MeSH-heading search strategy. Study selection Randomized controlled trials were selected that used educational interventions among physicians and provided data on the effects of the interventions. Synthesis Nine randomized controlled trials were included that described different educational approaches with various outcome measures. Three studies examined the effects of educational interventions among postgraduate trainee physicians and found an increase in knowledge but no change in behaviour with regard to identifying victims of domestic violence. Six studies examined educational interventions for practising physicians. Three of these studies used multifaceted physician training that combined education with system support interventions to change physician behaviour, such as increasing general awareness of domestic violence with brochures and posters, providing aids to remind physicians how to identify victims, facilitating physician access to victim support services, and providing audits and feedback. Multifaceted educational interventions included interactive workshops, Web-based learning, and experiential training. Another study used focus-group discussions and training, and showed improved domestic violence reporting among physicians. The remaining 2 studies showed improved perceptions of practising physicians’ self-efficacy using problem-based online learning. Conclusion It was difficult to determine the most effective educational strategy, as the educational interventions and the outcome measures varied among the selected studies. Brief interventions for postgraduate trainee physicians improved knowledge but did not seem to affect behaviour. Online education using a problem-based learning format improved practising physicians’ perceptions, knowledge, and skills in managing domestic violence. Physician training combined with system support interventions seemed to benefit domestic violence victims and increase referrals to domestic violence support resources. PMID:25022633

Emergence of Lévy Walks from Second-Order Stochastic Optimization

NASA Astrophysics Data System (ADS)

Kuśmierz, Łukasz; Toyoizumi, Taro

2017-12-01

In natural foraging, many organisms seem to perform two different types of motile search: directed search (taxis) and random search. The former is observed when the environment provides cues to guide motion towards a target. The latter involves no apparent memory or information processing and can be mathematically modeled by random walks. We show that both types of search can be generated by a common mechanism in which Lévy flights or Lévy walks emerge from a second-order gradient-based search with noisy observations. No explicit switching mechanism is required—instead, continuous transitions between the directed and random motions emerge depending on the Hessian matrix of the cost function. For a wide range of scenarios, the Lévy tail index is α =1 , consistent with previous observations in foraging organisms. These results suggest that adopting a second-order optimization method can be a useful strategy to combine efficient features of directed and random search.

Estimation of distribution algorithm with path relinking for the blocking flow-shop scheduling problem

NASA Astrophysics Data System (ADS)

Shao, Zhongshi; Pi, Dechang; Shao, Weishi

2018-05-01

This article presents an effective estimation of distribution algorithm, named P-EDA, to solve the blocking flow-shop scheduling problem (BFSP) with the makespan criterion. In the P-EDA, a Nawaz-Enscore-Ham (NEH)-based heuristic and the random method are combined to generate the initial population. Based on several superior individuals provided by a modified linear rank selection, a probabilistic model is constructed to describe the probabilistic distribution of the promising solution space. The path relinking technique is incorporated into EDA to avoid blindness of the search and improve the convergence property. A modified referenced local search is designed to enhance the local exploitation. Moreover, a diversity-maintaining scheme is introduced into EDA to avoid deterioration of the population. Finally, the parameters of the proposed P-EDA are calibrated using a design of experiments approach. Simulation results and comparisons with some well-performing algorithms demonstrate the effectiveness of the P-EDA for solving BFSP.

Evolutionary optimization of biopolymers and sequence structure maps

DOE Office of Scientific and Technical Information (OSTI.GOV)

Reidys, C.M.; Kopp, S.; Schuster, P.

1996-06-01

Searching for biopolymers having a predefined function is a core problem of biotechnology, biochemistry and pharmacy. On the level of RNA sequences and their corresponding secondary structures we show that this problem can be analyzed mathematically. The strategy will be to study the properties of the RNA sequence to secondary structure mapping that is essential for the understanding of the search process. We show that to each secondary structure s there exists a neutral network consisting of all sequences folding into s. This network can be modeled as a random graph and has the following generic properties: it is densemore » and has a giant component within the graph of compatible sequences. The neutral network percolates sequence space and any two neutral nets come close in terms of Hamming distance. We investigate the distribution of the orders of neutral nets and show that above a certain threshold the topology of neutral nets allows to find practically all frequent secondary structures.« less

Heuristic approach to image registration

NASA Astrophysics Data System (ADS)

Gertner, Izidor; Maslov, Igor V.

2000-08-01

Image registration, i.e. correct mapping of images obtained from different sensor readings onto common reference frame, is a critical part of multi-sensor ATR/AOR systems based on readings from different types of sensors. In order to fuse two different sensor readings of the same object, the readings have to be put into a common coordinate system. This task can be formulated as optimization problem in a space of all possible affine transformations of an image. In this paper, a combination of heuristic methods is explored to register gray- scale images. The modification of Genetic Algorithm is used as the first step in global search for optimal transformation. It covers the entire search space with (randomly or heuristically) scattered probe points and helps significantly reduce the search space to a subspace of potentially most successful transformations. Due to its discrete character, however, Genetic Algorithm in general can not converge while coming close to the optimum. Its termination point can be specified either as some predefined number of generations or as achievement of a certain acceptable convergence level. To refine the search, potential optimal subspaces are searched using more delicate and efficient for local search Taboo and Simulated Annealing methods.

School Locker Searches and the Fourth Amendment.

ERIC Educational Resources Information Center

Bjorklun, Eugene C.

1995-01-01

Because school lockers are potential hiding places for weapons and drugs, some schools are eliminating them. Searching student lockers on a random basis raises legal questions. Examines the legality of random locker searches based upon the guidelines for student searches set forth by the Supreme Court in "New Jersey v. T.L.O." and lower…

Searching for globally optimal functional forms for interatomic potentials using genetic programming with parallel tempering.

PubMed

Slepoy, A; Peters, M D; Thompson, A P

2007-11-30

Molecular dynamics and other molecular simulation methods rely on a potential energy function, based only on the relative coordinates of the atomic nuclei. Such a function, called a force field, approximately represents the electronic structure interactions of a condensed matter system. Developing such approximate functions and fitting their parameters remains an arduous, time-consuming process, relying on expert physical intuition. To address this problem, a functional programming methodology was developed that may enable automated discovery of entirely new force-field functional forms, while simultaneously fitting parameter values. The method uses a combination of genetic programming, Metropolis Monte Carlo importance sampling and parallel tempering, to efficiently search a large space of candidate functional forms and parameters. The methodology was tested using a nontrivial problem with a well-defined globally optimal solution: a small set of atomic configurations was generated and the energy of each configuration was calculated using the Lennard-Jones pair potential. Starting with a population of random functions, our fully automated, massively parallel implementation of the method reproducibly discovered the original Lennard-Jones pair potential by searching for several hours on 100 processors, sampling only a minuscule portion of the total search space. This result indicates that, with further improvement, the method may be suitable for unsupervised development of more accurate force fields with completely new functional forms. Copyright (c) 2007 Wiley Periodicals, Inc.

A Method for Estimating View Transformations from Image Correspondences Based on the Harmony Search Algorithm

PubMed Central

Cuevas, Erik; Díaz, Margarita

2015-01-01

In this paper, a new method for robustly estimating multiple view relations from point correspondences is presented. The approach combines the popular random sampling consensus (RANSAC) algorithm and the evolutionary method harmony search (HS). With this combination, the proposed method adopts a different sampling strategy than RANSAC to generate putative solutions. Under the new mechanism, at each iteration, new candidate solutions are built taking into account the quality of the models generated by previous candidate solutions, rather than purely random as it is the case of RANSAC. The rules for the generation of candidate solutions (samples) are motivated by the improvisation process that occurs when a musician searches for a better state of harmony. As a result, the proposed approach can substantially reduce the number of iterations still preserving the robust capabilities of RANSAC. The method is generic and its use is illustrated by the estimation of homographies, considering synthetic and real images. Additionally, in order to demonstrate the performance of the proposed approach within a real engineering application, it is employed to solve the problem of position estimation in a humanoid robot. Experimental results validate the efficiency of the proposed method in terms of accuracy, speed, and robustness. PMID:26339228

Improving the outcomes of children affected by parental substance abuse: a review of randomized controlled trials

PubMed Central

Calhoun, Stacy; Conner, Emma; Miller, Melodi; Messina, Nena

2015-01-01

Substance abuse is a major public health concern that impacts not just the user but also the user’s family. The effect that parental substance abuse has on children has been given substantial attention over the years. Findings from the literature suggest that children of substance-abusing parents have a high risk of developing physical and mental health and behavioral problems. A number of intervention programs have been developed for parents who have a substance abuse problem. There have also been a number of interventions that have been developed for children who have at least one parent with a substance abuse problem. However, it remains unclear how we can best mitigate the negative effects that parental substance abuse has on children due to the scarcity of evaluations that utilize rigorous methodologies such as experimental designs. The purpose of this study is to review randomized controlled trials of intervention programs targeting parents with substance abuse problems and/or children with at least one parent with a substance abuse problem in order to identify programs that show some promise in improving the behavioral and mental health outcomes of children affected by parental substance abuse. Four randomized controlled trials that met our eligibility criteria were identified using major literature search engines. The findings from this review suggest that interventions that focus on improving parenting practices and family functioning may be effective in reducing problems in children affected by parental substance abuse. However, further research utilizing rigorous methodologies are needed in order to identify other successful interventions that can improve the outcomes of these children long after the intervention has ended. PMID:25670915

Bullied children and psychosomatic problems: a meta-analysis.

PubMed

Gini, Gianluca; Pozzoli, Tiziana

2013-10-01

A previous meta-analysis showed that being bullied during childhood is related to psychosomatic problems, but many other studies have been published since then, including some longitudinal studies. We performed a new meta-analysis to quantify the association between peer victimization and psychosomatic complaints in the school-aged population. We searched online databases up to April 2012, and bibliographies of retrieved studies and of narrative reviews, for studies that examined the association between being bullied and psychosomatic complaints in children and adolescents. The original search identified 119 nonduplicated studies, of which 30 satisfied the prestated inclusion criteria. Two separate random effects meta-analyses were performed on 6 longitudinal studies (odds ratio = 2.39, 95% confidence interval, 1.76 to 3.24) and 24 cross-sectional studies (odds ratio = 2.17, 95% confidence interval, 1.91 to 2.46), respectively. Results showed that bullied children and adolescents have a significantly higher risk for psychosomatic problems than non-bullied agemates. In the cross-sectional studies, the magnitude of effect size significantly decreased with the increase of the proportion of female participants in the study sample. No other moderators were statistically significant. The association between being bullied and psychosomatic problems was confirmed. Given that school bullying is a widespread phenomenon in many countries around the world, the present results indicate that bullying should be considered a significant international public health problem.

Optimizing random searches on three-dimensional lattices

NASA Astrophysics Data System (ADS)

Yang, Benhao; Yang, Shunkun; Zhang, Jiaquan; Li, Daqing

2018-07-01

Search is a universal behavior related to many types of intelligent individuals. While most studies have focused on search in two or infinite-dimensional space, it is still missing how search can be optimized in three-dimensional space. Here we study random searches on three-dimensional (3d) square lattices with periodic boundary conditions, and explore the optimal search strategy with a power-law step length distribution, p(l) ∼l-μ, known as Lévy flights. We find that compared to random searches on two-dimensional (2d) lattices, the optimal exponent μopt on 3d lattices is relatively smaller in non-destructive case and remains similar in destructive case. We also find μopt decreases as the lattice length in z direction increases under high target density. Our findings may help us to understand the role of spatial dimension in search behaviors.

A Multiuser Manufacturing Resource Service Composition Method Based on the Bees Algorithm

PubMed Central

Xie, Yongquan; Zhou, Zude; Pham, Duc Truong; Xu, Wenjun; Ji, Chunqian

2015-01-01

In order to realize an optimal resource service allocation in current open and service-oriented manufacturing model, multiuser resource service composition (RSC) is modeled as a combinational and constrained multiobjective problem. The model takes into account both subjective and objective quality of service (QoS) properties as representatives to evaluate a solution. The QoS properties aggregation and evaluation techniques are based on existing researches. The basic Bees Algorithm is tailored for finding a near optimal solution to the model, since the basic version is only proposed to find a desired solution in continuous domain and thus not suitable for solving the problem modeled in our study. Particular rules are designed for handling the constraints and finding Pareto optimality. In addition, the established model introduces a trusted service set to each user so that the algorithm could start by searching in the neighbor of more reliable service chains (known as seeds) than those randomly generated. The advantages of these techniques are validated by experiments in terms of success rate, searching speed, ability of avoiding ingenuity, and so forth. The results demonstrate the effectiveness of the proposed method in handling multiuser RSC problems. PMID:26339232

ISE: An Integrated Search Environment. The manual

NASA Technical Reports Server (NTRS)

Chu, Lon-Chan

1992-01-01

Integrated Search Environment (ISE), a software package that implements hierarchical searches with meta-control, is described in this manual. ISE is a collection of problem-independent routines to support solving searches. Mainly, these routines are core routines for solving a search problem and they handle the control of searches and maintain the statistics related to searches. By separating the problem-dependent and problem-independent components in ISE, new search methods based on a combination of existing methods can be developed by coding a single master control program. Further, new applications solved by searches can be developed by coding the problem-dependent parts and reusing the problem-independent parts already developed. Potential users of ISE are designers of new application solvers and new search algorithms, and users of experimental application solvers and search algorithms. The ISE is designed to be user-friendly and information rich. In this manual, the organization of ISE is described and several experiments carried out on ISE are also described.

Randomized trial for answers to clinical questions: evaluating a pre-appraised versus a MEDLINE search protocol.

PubMed

Patel, Manesh R; Schardt, Connie M; Sanders, Linda L; Keitz, Sheri A

2006-10-01

The paper compares the speed, validity, and applicability of two different protocols for searching the primary medical literature. A randomized trial involving medicine residents was performed. An inpatient general medicine rotation was used. Thirty-two internal medicine residents were block randomized into four groups of eight. Success rate of each search protocol was measured by perceived search time, number of questions answered, and proportion of articles that were applicable and valid. Residents randomized to the MEDLINE-first (protocol A) group searched 120 questions, and residents randomized to the MEDLINE-last (protocol B) searched 133 questions. In protocol A, 104 answers (86.7%) and, in protocol B, 117 answers (88%) were found to clinical questions. In protocol A, residents reported that 26 (25.2%) of the answers were obtained quickly or rated as "fast" (<5 minutes) as opposed to 55 (51.9%) in protocol B, (P = 0.0004). A subset of questions and articles (n = 79) were reviewed by faculty who found that both protocols identified similar numbers of answer articles that addressed the questions and were felt to be valid using critical appraisal criteria. For resident-generated clinical questions, both protocols produced a similarly high percentage of applicable and valid articles. The MEDLINE-last search protocol was perceived to be faster. However, in the MEDLINE-last protocol, a significant portion of questions (23%) still required searching MEDLINE to find an answer.

Coevolutionary Free Lunches

NASA Technical Reports Server (NTRS)

Wolpert, David H.; Macready, William G.

2005-01-01

Recent work on the mathematical foundations of optimization has begun to uncover its rich structure. In particular, the "No Free Lunch" (NFL) theorems state that any two algorithms are equivalent when their performance is averaged across all possible problems. This highlights the need for exploiting problem-specific knowledge to achieve better than random performance. In this paper we present a general framework covering more search scenarios. In addition to the optimization scenarios addressed in the NFL results, this framework covers multi-armed bandit problems and evolution of multiple co-evolving players. As a particular instance of the latter, it covers "self-play" problems. In these problems the set of players work together to produce a champion, who then engages one or more antagonists in a subsequent multi-player game. In contrast to the traditional optimization case where the NFL results hold, we show that in self-play there are free lunches: in coevolution some algorithms have better performance than other algorithms, averaged across all possible problems. We consider the implications of these results to biology where there is no champion.

An efficient genetic algorithm for maximum coverage deployment in wireless sensor networks.

PubMed

Yoon, Yourim; Kim, Yong-Hyuk

2013-10-01

Sensor networks have a lot of applications such as battlefield surveillance, environmental monitoring, and industrial diagnostics. Coverage is one of the most important performance metrics for sensor networks since it reflects how well a sensor field is monitored. In this paper, we introduce the maximum coverage deployment problem in wireless sensor networks and analyze the properties of the problem and its solution space. Random deployment is the simplest way to deploy sensor nodes but may cause unbalanced deployment and therefore, we need a more intelligent way for sensor deployment. We found that the phenotype space of the problem is a quotient space of the genotype space in a mathematical view. Based on this property, we propose an efficient genetic algorithm using a novel normalization method. A Monte Carlo method is adopted to design an efficient evaluation function, and its computation time is decreased without loss of solution quality using a method that starts from a small number of random samples and gradually increases the number for subsequent generations. The proposed genetic algorithms could be further improved by combining with a well-designed local search. The performance of the proposed genetic algorithm is shown by a comparative experimental study. When compared with random deployment and existing methods, our genetic algorithm was not only about twice faster, but also showed significant performance improvement in quality.

A Model with Darwinian Dynamics on a Rugged Landscape

NASA Astrophysics Data System (ADS)

Brotto, Tommaso; Bunin, Guy; Kurchan, Jorge

2017-02-01

We discuss the population dynamics with selection and random diffusion, keeping the total population constant, in a fitness landscape associated with Constraint Satisfaction, a paradigm for difficult optimization problems. We obtain a phase diagram in terms of the size of the population and the diffusion rate, with a glass phase inside which the dynamics keeps searching for better configurations, and outside which deleterious `mutations' spoil the performance. The phase diagram is analogous to that of dense active matter in terms of temperature and drive.

Full glowworm swarm optimization algorithm for whole-set orders scheduling in single machine.

PubMed

Yu, Zhang; Yang, Xiaomei

2013-01-01

By analyzing the characteristics of whole-set orders problem and combining the theory of glowworm swarm optimization, a new glowworm swarm optimization algorithm for scheduling is proposed. A new hybrid-encoding schema combining with two-dimensional encoding and random-key encoding is given. In order to enhance the capability of optimal searching and speed up the convergence rate, the dynamical changed step strategy is integrated into this algorithm. Furthermore, experimental results prove its feasibility and efficiency.

A New TS Algorithm for Solving Low-Carbon Logistics Vehicle Routing Problem with Split Deliveries by Backpack-From a Green Operation Perspective.

PubMed

Xia, Yangkun; Fu, Zhuo; Tsai, Sang-Bing; Wang, Jiangtao

2018-05-10

In order to promote the development of low-carbon logistics and economize logistics distribution costs, the vehicle routing problem with split deliveries by backpack is studied. With the help of the model of classical capacitated vehicle routing problem, in this study, a form of discrete split deliveries was designed in which the customer demand can be split only by backpack. A double-objective mathematical model and the corresponding adaptive tabu search (TS) algorithm were constructed for solving this problem. By embedding the adaptive penalty mechanism, and adopting the random neighborhood selection strategy and reinitialization principle, the global optimization ability of the new algorithm was enhanced. Comparisons with the results in the literature show the effectiveness of the proposed algorithm. The proposed method can save the costs of low-carbon logistics and reduce carbon emissions, which is conducive to the sustainable development of low-carbon logistics.

Enabling the extended compact genetic algorithm for real-parameter optimization by using adaptive discretization.

PubMed

Chen, Ying-ping; Chen, Chao-Hong

2010-01-01

An adaptive discretization method, called split-on-demand (SoD), enables estimation of distribution algorithms (EDAs) for discrete variables to solve continuous optimization problems. SoD randomly splits a continuous interval if the number of search points within the interval exceeds a threshold, which is decreased at every iteration. After the split operation, the nonempty intervals are assigned integer codes, and the search points are discretized accordingly. As an example of using SoD with EDAs, the integration of SoD and the extended compact genetic algorithm (ECGA) is presented and numerically examined. In this integration, we adopt a local search mechanism as an optional component of our back end optimization engine. As a result, the proposed framework can be considered as a memetic algorithm, and SoD can potentially be applied to other memetic algorithms. The numerical experiments consist of two parts: (1) a set of benchmark functions on which ECGA with SoD and ECGA with two well-known discretization methods: the fixed-height histogram (FHH) and the fixed-width histogram (FWH) are compared; (2) a real-world application, the economic dispatch problem, on which ECGA with SoD is compared to other methods. The experimental results indicate that SoD is a better discretization method to work with ECGA. Moreover, ECGA with SoD works quite well on the economic dispatch problem and delivers solutions better than the best known results obtained by other methods in existence.

Coverage-maximization in networks under resource constraints.

PubMed

Nandi, Subrata; Brusch, Lutz; Deutsch, Andreas; Ganguly, Niloy

2010-06-01

Efficient coverage algorithms are essential for information search or dispersal in all kinds of networks. We define an extended coverage problem which accounts for constrained resources of consumed bandwidth B and time T . Our solution to the network challenge is here studied for regular grids only. Using methods from statistical mechanics, we develop a coverage algorithm with proliferating message packets and temporally modulated proliferation rate. The algorithm performs as efficiently as a single random walker but O(B(d-2)/d) times faster, resulting in significant service speed-up on a regular grid of dimension d . The algorithm is numerically compared to a class of generalized proliferating random walk strategies and on regular grids shown to perform best in terms of the product metric of speed and efficiency.

Honey Bees Inspired Optimization Method: The Bees Algorithm.

PubMed

Yuce, Baris; Packianather, Michael S; Mastrocinque, Ernesto; Pham, Duc Truong; Lambiase, Alfredo

2013-11-06

Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective behavior is usually very complex. The collective behavior of a swarm of social organisms emerges from the behaviors of the individuals of that swarm. Researchers have developed computational optimization methods based on biology such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony. The aim of this paper is to describe an optimization algorithm called the Bees Algorithm, inspired from the natural foraging behavior of honey bees, to find the optimal solution. The algorithm performs both an exploitative neighborhood search combined with random explorative search. In this paper, after an explanation of the natural foraging behavior of honey bees, the basic Bees Algorithm and its improved versions are described and are implemented in order to optimize several benchmark functions, and the results are compared with those obtained with different optimization algorithms. The results show that the Bees Algorithm offering some advantage over other optimization methods according to the nature of the problem.

On Searching Available Channels with Asynchronous MAC-Layer Spectrum Sensing

NASA Astrophysics Data System (ADS)

Jiang, Chunxiao; Ma, Xin; Chen, Canfeng; Ma, Jian; Ren, Yong

Dynamic spectrum access has become a focal issue recently, in which identifying the available spectrum plays a rather important role. Lots of work has been done concerning secondary user (SU) synchronously accessing primary user's (PU's) network. However, on one hand, SU may have no idea about PU's communication protocols; on the other, it is possible that communications among PU are not based on synchronous scheme at all. In order to address such problems, this paper advances a strategy for SU to search available spectrums with asynchronous MAC-layer sensing. With this method, SUs need not know the communication mechanisms in PU's network when dynamically accessing. We will focus on four aspects: 1) strategy for searching available channels; 2) vacating strategy when PUs come back; 3) estimation of channel parameters; 4) impact of SUs' interference on PU's data rate. The simulations show that our search strategy not only can achieve nearly 50% less interference probability than equal allocation of total search time, but also well adapts to time-varying channels. Moreover, access by our strategies can attain 150% more access time than random access. The moment matching estimator shows good performance in estimating and tracing time-varying channels.

Efficient and Accurate Optimal Linear Phase FIR Filter Design Using Opposition-Based Harmony Search Algorithm

PubMed Central

Saha, S. K.; Dutta, R.; Choudhury, R.; Kar, R.; Mandal, D.; Ghoshal, S. P.

2013-01-01

In this paper, opposition-based harmony search has been applied for the optimal design of linear phase FIR filters. RGA, PSO, and DE have also been adopted for the sake of comparison. The original harmony search algorithm is chosen as the parent one, and opposition-based approach is applied. During the initialization, randomly generated population of solutions is chosen, opposite solutions are also considered, and the fitter one is selected as a priori guess. In harmony memory, each such solution passes through memory consideration rule, pitch adjustment rule, and then opposition-based reinitialization generation jumping, which gives the optimum result corresponding to the least error fitness in multidimensional search space of FIR filter design. Incorporation of different control parameters in the basic HS algorithm results in the balancing of exploration and exploitation of search space. Low pass, high pass, band pass, and band stop FIR filters are designed with the proposed OHS and other aforementioned algorithms individually for comparative optimization performance. A comparison of simulation results reveals the optimization efficacy of the OHS over the other optimization techniques for the solution of the multimodal, nondifferentiable, nonlinear, and constrained FIR filter design problems. PMID:23844390

Efficient and accurate optimal linear phase FIR filter design using opposition-based harmony search algorithm.

PubMed

Saha, S K; Dutta, R; Choudhury, R; Kar, R; Mandal, D; Ghoshal, S P

2013-01-01

In this paper, opposition-based harmony search has been applied for the optimal design of linear phase FIR filters. RGA, PSO, and DE have also been adopted for the sake of comparison. The original harmony search algorithm is chosen as the parent one, and opposition-based approach is applied. During the initialization, randomly generated population of solutions is chosen, opposite solutions are also considered, and the fitter one is selected as a priori guess. In harmony memory, each such solution passes through memory consideration rule, pitch adjustment rule, and then opposition-based reinitialization generation jumping, which gives the optimum result corresponding to the least error fitness in multidimensional search space of FIR filter design. Incorporation of different control parameters in the basic HS algorithm results in the balancing of exploration and exploitation of search space. Low pass, high pass, band pass, and band stop FIR filters are designed with the proposed OHS and other aforementioned algorithms individually for comparative optimization performance. A comparison of simulation results reveals the optimization efficacy of the OHS over the other optimization techniques for the solution of the multimodal, nondifferentiable, nonlinear, and constrained FIR filter design problems.

Incremental social learning in particle swarms.

PubMed

de Oca, Marco A Montes; Stutzle, Thomas; Van den Enden, Ken; Dorigo, Marco

2011-04-01

Incremental social learning (ISL) was proposed as a way to improve the scalability of systems composed of multiple learning agents. In this paper, we show that ISL can be very useful to improve the performance of population-based optimization algorithms. Our study focuses on two particle swarm optimization (PSO) algorithms: a) the incremental particle swarm optimizer (IPSO), which is a PSO algorithm with a growing population size in which the initial position of new particles is biased toward the best-so-far solution, and b) the incremental particle swarm optimizer with local search (IPSOLS), in which solutions are further improved through a local search procedure. We first derive analytically the probability density function induced by the proposed initialization rule applied to new particles. Then, we compare the performance of IPSO and IPSOLS on a set of benchmark functions with that of other PSO algorithms (with and without local search) and a random restart local search algorithm. Finally, we measure the benefits of using incremental social learning on PSO algorithms by running IPSO and IPSOLS on problems with different fitness distance correlations.

Insight and search in Katona's five-square problem.

PubMed

Ollinger, Michael; Jones, Gary; Knoblich, Günther

2014-01-01

Insights are often productive outcomes of human thinking. We provide a cognitive model that explains insight problem solving by the interplay of problem space search and representational change, whereby the problem space is constrained or relaxed based on the problem representation. By introducing different experimental conditions that either constrained the initial search space or helped solvers to initiate a representational change, we investigated the interplay of problem space search and representational change in Katona's five-square problem. Testing 168 participants, we demonstrated that independent hints relating to the initial search space and to representational change had little effect on solution rates. However, providing both hints caused a significant increase in solution rates. Our results show the interplay between problem space search and representational change in insight problem solving: The initial problem space can be so large that people fail to encounter impasse, but even when representational change is achieved the resulting problem space can still provide a major obstacle to finding the solution.

Validating the random search model for two targets of different difficulty.

PubMed

Chan, Alan H S; Yu, Ruifeng

2010-02-01

A random visual search model was fitted to 1,788 search times obtained from a nonidentical double-target search task. 30 Hong Kong Chinese (13 men, 17 women) ages 18 to 33 years (M = 23, SD = 6.8) took part in the experiment voluntarily. The overall adequacy and prediction accuracy of the model for various search time parameters (mean and median search times and response times) for both individual and pooled data show that search strategy may reasonably be inferred from search time distributions. The results also suggested the general applicability of the random search model for describing the search behavior of a large number of participants performing the type of search used here, as well as the practical feasibility of its application for determination of stopping policy for optimization of an inspection system design. Although the data generally conformed to the model the search for the more difficult target was faster than expected. The more difficult target was usually detected after the easier target and it is suggested that some degree of memory-guided searching may have been used for the second target. Some abnormally long search times were observed and it is possible that these might have been due to the characteristics of visual lobes, nonoptimum interfixation distances and inappropriate overlapping of lobes, as has been previously reported.

A novel ternary content addressable memory design based on resistive random access memory with high intensity and low search energy

NASA Astrophysics Data System (ADS)

Han, Runze; Shen, Wensheng; Huang, Peng; Zhou, Zheng; Liu, Lifeng; Liu, Xiaoyan; Kang, Jinfeng

2018-04-01

A novel ternary content addressable memory (TCAM) design based on resistive random access memory (RRAM) is presented. Each TCAM cell consists of two parallel RRAM to both store and search for ternary data. The cell size of the proposed design is 8F2, enable a ∼60× cell area reduction compared with the conventional static random access memory (SRAM) based implementation. Simulation results also show that the search delay and energy consumption of the proposed design at the 64-bit word search are 2 ps and 0.18 fJ/bit/search respectively at 22 nm technology node, where significant improvements are achieved compared to previous works. The desired characteristics of RRAM for implementation of the high performance TCAM search chip are also discussed.

Quantity and quality assessment of randomized controlled trials on orthodontic practice in PubMed.

PubMed

Shimada, Tatsuo; Takayama, Hisako; Nakamura, Yoshiki

2010-07-01

To find current high-quality evidence for orthodontic practice within a reasonable time, we tested the performance of a PubMed search. PubMed was searched using publication type randomized controlled trial and medical subject heading term "orthodontics" for articles published between 2003 and 2007. The PubMed search results were compared with those from a hand search of four orthodontic journals to determine the sensitivity of PubMed search. We evaluated the precision of the PubMed search result and assessed the quality of individual randomized controlled trials using the Jadad scale. Sensitivity and precision were 97.46% and 58.12%, respectively. In PubMed, of the 277 articles retrieved, 161 (58.12%) were randomized controlled trials on orthodontic practice, and 115 of the 161 articles (71.42%) were published in four orthodontic journals: American Journal of Orthodontics and Dentofacial Orthopedics, The Angle Orthodontist, the European Journal of Orthodontics, and the Journal of Orthodontics. Assessment by the Jadad scale revealed 60 high-quality randomized controlled trials on orthodontic practice, of which 45 (75%) were published in these four journals. PubMed is a highly desirable search engine for evidence-based orthodontic practice. To stay current and get high-quality evidence, it is reasonable to look through four orthodontic journals: American Journal of Orthodontics and Dentofacial Orthopedics, The Angle Orthodontist, the European Journal of Orthodontics, and the Journal of Orthodontics.

Spectrum sharing between a surveillance radar and secondary Wi-Fi networks

NASA Astrophysics Data System (ADS)

Hessar, Farzad; Roy, Sumit

2016-06-01

Co-existence between unlicensed networks that share spectrum spatio-temporally with terrestrial (e.g. Air Traffic Control) and shipborne radars in 3-GHz band is attracting significant interest. Similar to every primary-secondary coexistence scenario, interference from unlicensed devices to a primary receiver must be within acceptable bounds. In this work, we formulate the spectrum sharing problem between a pulsed, search radar (primary) and 802.11 WLAN as the secondary. We compute the protection region for such a search radar for a) a single secondary user (initially) as well as b) a random spatial distribution of multiple secondary users. Furthermore, we also analyze the interference to the WiFi devices from the radar's transmissions to estimate the impact on achievable WLAN throughput as a function of distance to the primary radar.

Asymmetry in search.

PubMed

Kaindl, H; Kainz, G; Radda, K

2001-01-01

Most of the work on search in artificial intelligence (AI) deals with one search direction only-mostly forward search-although it is known that a structural asymmetry of the search graph causes differences in the efficiency of searching in the forward or the backward direction, respectively. In the case of symmetrical graph structure, however, current theory would not predict such differences in efficiency. In several classes of job sequencing problems, we observed a phenomenon of asymmetry in search that relates to the distribution of the are costs in the search graph. This phenomenon can be utilized for improving the search efficiency by a new algorithm that automatically selects the search direction. We demonstrate fur a class of job sequencing problems that, through the utilization of this phenomenon, much more difficult problems can be solved-according to our best knowledge-than by the best published approach, and on the same problems, the running time is much reduced. As a consequence, we propose to check given problems for asymmetrical distribution of are costs that may cause asymmetry in search.

Searches for Randomized Controlled Trials of Drugs in MEDLINE and EMBASE Using Only Generic Drug Names Compared with Searches Applied in Current Practice in Systematic Reviews

ERIC Educational Resources Information Center

Waffenschmidt, Siw; Guddat, Charlotte

2015-01-01

Background: It is unclear which terms should be included in bibliographic searches for randomized controlled trials (RCTs) of drugs, and identifying relevant drug terms can be extremely laborious. The aim of our analysis was to determine whether a bibliographic search using only the generic drug name produces sufficient results for the generation…

Hybrid intelligent optimization methods for engineering problems

NASA Astrophysics Data System (ADS)

Pehlivanoglu, Yasin Volkan

The purpose of optimization is to obtain the best solution under certain conditions. There are numerous optimization methods because different problems need different solution methodologies; therefore, it is difficult to construct patterns. Also mathematical modeling of a natural phenomenon is almost based on differentials. Differential equations are constructed with relative increments among the factors related to yield. Therefore, the gradients of these increments are essential to search the yield space. However, the landscape of yield is not a simple one and mostly multi-modal. Another issue is differentiability. Engineering design problems are usually nonlinear and they sometimes exhibit discontinuous derivatives for the objective and constraint functions. Due to these difficulties, non-gradient-based algorithms have become more popular in recent decades. Genetic algorithms (GA) and particle swarm optimization (PSO) algorithms are popular, non-gradient based algorithms. Both are population-based search algorithms and have multiple points for initiation. A significant difference from a gradient-based method is the nature of the search methodologies. For example, randomness is essential for the search in GA or PSO. Hence, they are also called stochastic optimization methods. These algorithms are simple, robust, and have high fidelity. However, they suffer from similar defects, such as, premature convergence, less accuracy, or large computational time. The premature convergence is sometimes inevitable due to the lack of diversity. As the generations of particles or individuals in the population evolve, they may lose their diversity and become similar to each other. To overcome this issue, we studied the diversity concept in GA and PSO algorithms. Diversity is essential for a healthy search, and mutations are the basic operators to provide the necessary variety within a population. After having a close scrutiny of the diversity concept based on qualification and quantification studies, we improved new mutation strategies and operators to provide beneficial diversity within the population. We called this new approach as multi-frequency vibrational GA or PSO. They were applied to different aeronautical engineering problems in order to study the efficiency of these new approaches. These implementations were: applications to selected benchmark test functions, inverse design of two-dimensional (2D) airfoil in subsonic flow, optimization of 2D airfoil in transonic flow, path planning problems of autonomous unmanned aerial vehicle (UAV) over a 3D terrain environment, 3D radar cross section minimization problem for a 3D air vehicle, and active flow control over a 2D airfoil. As demonstrated by these test cases, we observed that new algorithms outperform the current popular algorithms. The principal role of this multi-frequency approach was to determine which individuals or particles should be mutated, when they should be mutated, and which ones should be merged into the population. The new mutation operators, when combined with a mutation strategy and an artificial intelligent method, such as, neural networks or fuzzy logic process, they provided local and global diversities during the reproduction phases of the generations. Additionally, the new approach also introduced random and controlled diversity. Due to still being population-based techniques, these methods were as robust as the plain GA or PSO algorithms. Based on the results obtained, it was concluded that the variants of the present multi-frequency vibrational GA and PSO were efficient algorithms, since they successfully avoided all local optima within relatively short optimization cycles.

Application of Multi-Objective Human Learning Optimization Method to Solve AC/DC Multi-Objective Optimal Power Flow Problem

NASA Astrophysics Data System (ADS)

Cao, Jia; Yan, Zheng; He, Guangyu

2016-06-01

This paper introduces an efficient algorithm, multi-objective human learning optimization method (MOHLO), to solve AC/DC multi-objective optimal power flow problem (MOPF). Firstly, the model of AC/DC MOPF including wind farms is constructed, where includes three objective functions, operating cost, power loss, and pollutant emission. Combining the non-dominated sorting technique and the crowding distance index, the MOHLO method can be derived, which involves individual learning operator, social learning operator, random exploration learning operator and adaptive strategies. Both the proposed MOHLO method and non-dominated sorting genetic algorithm II (NSGAII) are tested on an improved IEEE 30-bus AC/DC hybrid system. Simulation results show that MOHLO method has excellent search efficiency and the powerful ability of searching optimal. Above all, MOHLO method can obtain more complete pareto front than that by NSGAII method. However, how to choose the optimal solution from pareto front depends mainly on the decision makers who stand from the economic point of view or from the energy saving and emission reduction point of view.

Partial branch and bound algorithm for improved data association in multiframe processing

NASA Astrophysics Data System (ADS)

Poore, Aubrey B.; Yan, Xin

1999-07-01

A central problem in multitarget, multisensor, and multiplatform tracking remains that of data association. Lagrangian relaxation methods have shown themselves to yield near optimal answers in real-time. The necessary improvement in the quality of these solutions warrants a continuing interest in these methods. These problems are NP-hard; the only known methods for solving them optimally are enumerative in nature with branch-and-bound being most efficient. Thus, the development of methods less than a full branch-and-bound are needed for improving the quality. Such methods as K-best, local search, and randomized search have been proposed to improve the quality of the relaxation solution. Here, a partial branch-and-bound technique along with adequate branching and ordering rules are developed. Lagrangian relaxation is used as a branching method and as a method to calculate the lower bound for subproblems. The result shows that the branch-and-bound framework greatly improves the resolution quality of the Lagrangian relaxation algorithm and yields better multiple solutions in less time than relaxation alone.

A Hybrid Search Algorithm for Swarm Robots Searching in an Unknown Environment

PubMed Central

Li, Shoutao; Li, Lina; Lee, Gordon; Zhang, Hao

2014-01-01

This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency. PMID:25386855

A hybrid search algorithm for swarm robots searching in an unknown environment.

PubMed

Li, Shoutao; Li, Lina; Lee, Gordon; Zhang, Hao

2014-01-01

This paper proposes a novel method to improve the efficiency of a swarm of robots searching in an unknown environment. The approach focuses on the process of feeding and individual coordination characteristics inspired by the foraging behavior in nature. A predatory strategy was used for searching; hence, this hybrid approach integrated a random search technique with a dynamic particle swarm optimization (DPSO) search algorithm. If a search robot could not find any target information, it used a random search algorithm for a global search. If the robot found any target information in a region, the DPSO search algorithm was used for a local search. This particle swarm optimization search algorithm is dynamic as all the parameters in the algorithm are refreshed synchronously through a communication mechanism until the robots find the target position, after which, the robots fall back to a random searching mode. Thus, in this searching strategy, the robots alternated between two searching algorithms until the whole area was covered. During the searching process, the robots used a local communication mechanism to share map information and DPSO parameters to reduce the communication burden and overcome hardware limitations. If the search area is very large, search efficiency may be greatly reduced if only one robot searches an entire region given the limited resources available and time constraints. In this research we divided the entire search area into several subregions, selected a target utility function to determine which subregion should be initially searched and thereby reduced the residence time of the target to improve search efficiency.

Emergence of an optimal search strategy from a simple random walk

PubMed Central

Sakiyama, Tomoko; Gunji, Yukio-Pegio

2013-01-01

In reports addressing animal foraging strategies, it has been stated that Lévy-like algorithms represent an optimal search strategy in an unknown environment, because of their super-diffusion properties and power-law-distributed step lengths. Here, starting with a simple random walk algorithm, which offers the agent a randomly determined direction at each time step with a fixed move length, we investigated how flexible exploration is achieved if an agent alters its randomly determined next step forward and the rule that controls its random movement based on its own directional moving experiences. We showed that our algorithm led to an effective food-searching performance compared with a simple random walk algorithm and exhibited super-diffusion properties, despite the uniform step lengths. Moreover, our algorithm exhibited a power-law distribution independent of uniform step lengths. PMID:23804445

Emergence of an optimal search strategy from a simple random walk.

PubMed

Sakiyama, Tomoko; Gunji, Yukio-Pegio

2013-09-06

In reports addressing animal foraging strategies, it has been stated that Lévy-like algorithms represent an optimal search strategy in an unknown environment, because of their super-diffusion properties and power-law-distributed step lengths. Here, starting with a simple random walk algorithm, which offers the agent a randomly determined direction at each time step with a fixed move length, we investigated how flexible exploration is achieved if an agent alters its randomly determined next step forward and the rule that controls its random movement based on its own directional moving experiences. We showed that our algorithm led to an effective food-searching performance compared with a simple random walk algorithm and exhibited super-diffusion properties, despite the uniform step lengths. Moreover, our algorithm exhibited a power-law distribution independent of uniform step lengths.

A hybrid, auto-adaptive and rule-based multi-agent approach using evolutionary algorithms for improved searching

NASA Astrophysics Data System (ADS)

Izquierdo, Joaquín; Montalvo, Idel; Campbell, Enrique; Pérez-García, Rafael

2016-08-01

Selecting the most appropriate heuristic for solving a specific problem is not easy, for many reasons. This article focuses on one of these reasons: traditionally, the solution search process has operated in a given manner regardless of the specific problem being solved, and the process has been the same regardless of the size, complexity and domain of the problem. To cope with this situation, search processes should mould the search into areas of the search space that are meaningful for the problem. This article builds on previous work in the development of a multi-agent paradigm using techniques derived from knowledge discovery (data-mining techniques) on databases of so-far visited solutions. The aim is to improve the search mechanisms, increase computational efficiency and use rules to enrich the formulation of optimization problems, while reducing the search space and catering to realistic problems.

Multiple-variable neighbourhood search for the single-machine total weighted tardiness problem

NASA Astrophysics Data System (ADS)

Chung, Tsui-Ping; Fu, Qunjie; Liao, Ching-Jong; Liu, Yi-Ting

2017-07-01

The single-machine total weighted tardiness (SMTWT) problem is a typical discrete combinatorial optimization problem in the scheduling literature. This problem has been proved to be NP hard and thus provides a challenging area for metaheuristics, especially the variable neighbourhood search algorithm. In this article, a multiple variable neighbourhood search (m-VNS) algorithm with multiple neighbourhood structures is proposed to solve the problem. Special mechanisms named matching and strengthening operations are employed in the algorithm, which has an auto-revising local search procedure to explore the solution space beyond local optimality. Two aspects, searching direction and searching depth, are considered, and neighbourhood structures are systematically exchanged. Experimental results show that the proposed m-VNS algorithm outperforms all the compared algorithms in solving the SMTWT problem.

Highlighting the Mechanism of the Quantum Speedup by Time-Symmetric and Relational Quantum Mechanics

NASA Astrophysics Data System (ADS)

Castagnoli, Giuseppe

2016-03-01

Bob hides a ball in one of four drawers. Alice is to locate it. Classically she has to open up to three drawers, quantally just one. The fundamental reason for this quantum speedup is not known. The usual representation of the quantum algorithm is limited to the process of solving the problem. We extend it to the process of setting the problem. The number of the drawer with the ball becomes a unitary transformation of the random outcome of the preparation measurement. This extended, time-symmetric, representation brings in relational quantum mechanics. It is with respect to Bob and any external observer and cannot be with respect to Alice. It would tell her the number of the drawer with the ball before she opens any drawer. To Alice, the projection of the quantum state due to the preparation measurement should be retarded at the end of her search; in the input state of the search, the drawer number is determined to Bob and undetermined to Alice. We show that, mathematically, one can ascribe any part of the selection of the random outcome of the preparation measurement to the final Alice's measurement. Ascribing half of it explains the speedup of the present algorithm. This leaves the input state to Bob unaltered and projects that to Alice on a state of lower entropy where she knows half of the number of the drawer with the ball in advance. The quantum algorithm turns out to be a sum over histories in each of which Alice knows in advance that the ball is in a pair of drawers and locates it by opening one of the two. In the sample of quantum algorithms examined, the part of the random outcome of the initial measurement selected by the final measurement is one half or slightly above it. Conversely, given an oracle problem, the assumption it is one half always corresponds to an existing quantum algorithm and gives the order of magnitude of the number of oracle queries required by the optimal one.

The effectiveness of problem-based learning on development of nursing students' critical thinking: a systematic review and meta-analysis.

PubMed

Kong, Ling-Na; Qin, Bo; Zhou, Ying-qing; Mou, Shao-yu; Gao, Hui-Ming

2014-03-01

The objective of this systematic review and meta-analysis was to estimate the effectiveness of problem-based learning in developing nursing students' critical thinking. Searches of PubMed, EMBASE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Proquest, Cochrane Central Register of Controlled Trials (CENTRAL) and China National Knowledge Infrastructure (CNKI) were undertaken to identify randomized controlled trails from 1965 to December 2012, comparing problem-based learning with traditional lectures on the effectiveness of development of nursing students' critical thinking, with no language limitation. The mesh-terms or key words used in the search were problem-based learning, thinking, critical thinking, nursing, nursing education, nurse education, nurse students, nursing students and pupil nurse. Two reviewers independently assessed eligibility and extracted data. Quality assessment was conducted independently by two reviewers using the Cochrane Collaboration's Risk of Bias Tool. We analyzed critical thinking scores (continuous outcomes) using a standardized mean difference (SMD) or weighted mean difference (WMD) with a 95% confidence intervals (CIs). Heterogeneity was assessed using the Cochran's Q statistic and I(2) statistic. Publication bias was assessed by means of funnel plot and Egger's test of asymmetry. Nine articles representing eight randomized controlled trials were included in the meta-analysis. Most studies were of low risk of bias. The pooled effect size showed problem-based learning was able to improve nursing students' critical thinking (overall critical thinking scores SMD=0.33, 95%CI=0.13-0.52, P=0.0009), compared with traditional lectures. There was low heterogeneity (overall critical thinking scores I(2)=45%, P=0.07) in the meta-analysis. No significant publication bias was observed regarding overall critical thinking scores (P=0.536). Sensitivity analysis showed that the result of our meta-analysis was reliable. Most effect sizes for subscales of the California Critical Thinking Dispositions Inventory (CCTDI) and Bloom's Taxonomy favored problem-based learning, while effect sizes for all subscales of the California Critical Thinking Skills Test (CCTST) and most subscales of the Watson-Glaser Critical Thinking Appraisal (WCGTA) were inconclusive. The results of the current meta-analysis indicate that problem-based learning might help nursing students to improve their critical thinking. More research with larger sample size and high quality in different nursing educational contexts are required. Copyright © 2013 Elsevier Ltd. All rights reserved.

Exploration versus exploitation in space, mind, and society

PubMed Central

Hills, Thomas T.; Todd, Peter M.; Lazer, David; Redish, A. David; Couzin, Iain D.

2015-01-01

Search is a ubiquitous property of life. Although diverse domains have worked on search problems largely in isolation, recent trends across disciplines indicate that the formal properties of these problems share similar structures and, often, similar solutions. Moreover, internal search (e.g., memory search) shows similar characteristics to external search (e.g., spatial foraging), including shared neural mechanisms consistent with a common evolutionary origin across species. Search problems and their solutions also scale from individuals to societies, underlying and constraining problem solving, memory, information search, and scientific and cultural innovation. In summary, search represents a core feature of cognition, with a vast influence on its evolution and processes across contexts and requiring input from multiple domains to understand its implications and scope. PMID:25487706

A Multiuser Detector Based on Artificial Bee Colony Algorithm for DS-UWB Systems

PubMed Central

Liu, Xiaohui

2013-01-01

Artificial Bee Colony (ABC) algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. The ABC algorithm was developed to solve optimizing numerical problems and revealed premising results in processing time and solution quality. In ABC, a colony of artificial bees search for rich artificial food sources; the optimizing numerical problems are converted to the problem of finding the best parameter which minimizes an objective function. Then, the artificial bees randomly discover a population of initial solutions and then iteratively improve them by employing the behavior: moving towards better solutions by means of a neighbor search mechanism while abandoning poor solutions. In this paper, an efficient multiuser detector based on a suboptimal code mapping multiuser detector and artificial bee colony algorithm (SCM-ABC-MUD) is proposed and implemented in direct-sequence ultra-wideband (DS-UWB) systems under the additive white Gaussian noise (AWGN) channel. The simulation results demonstrate that the BER and the near-far effect resistance performances of this proposed algorithm are quite close to those of the optimum multiuser detector (OMD) while its computational complexity is much lower than that of OMD. Furthermore, the BER performance of SCM-ABC-MUD is not sensitive to the number of active users and can obtain a large system capacity. PMID:23983638

Search efficiency of biased migration towards stationary or moving targets in heterogeneously structured environments

NASA Astrophysics Data System (ADS)

Azimzade, Youness; Mashaghi, Alireza

2017-12-01

Efficient search acts as a strong selective force in biological systems ranging from cellular populations to predator-prey systems. The search processes commonly involve finding a stationary or mobile target within a heterogeneously structured environment where obstacles limit migration. An open generic question is whether random or directionally biased motions or a combination of both provide an optimal search efficiency and how that depends on the motility and density of targets and obstacles. To address this question, we develop a simple model that involves a random walker searching for its targets in a heterogeneous medium of bond percolation square lattice and used mean first passage time (〈T 〉 ) as an indication of average search time. Our analysis reveals a dual effect of directional bias on the minimum value of 〈T 〉 . For a homogeneous medium, directionality always decreases 〈T 〉 and a pure directional migration (a ballistic motion) serves as the optimized strategy, while for a heterogeneous environment, we find that the optimized strategy involves a combination of directed and random migrations. The relative contribution of these modes is determined by the density of obstacles and motility of targets. Existence of randomness and motility of targets add to the efficiency of search. Our study reveals generic and simple rules that govern search efficiency. Our findings might find application in a number of areas including immunology, cell biology, ecology, and robotics.

Improved multi-objective ant colony optimization algorithm and its application in complex reasoning

NASA Astrophysics Data System (ADS)

Wang, Xinqing; Zhao, Yang; Wang, Dong; Zhu, Huijie; Zhang, Qing

2013-09-01

The problem of fault reasoning has aroused great concern in scientific and engineering fields. However, fault investigation and reasoning of complex system is not a simple reasoning decision-making problem. It has become a typical multi-constraint and multi-objective reticulate optimization decision-making problem under many influencing factors and constraints. So far, little research has been carried out in this field. This paper transforms the fault reasoning problem of complex system into a paths-searching problem starting from known symptoms to fault causes. Three optimization objectives are considered simultaneously: maximum probability of average fault, maximum average importance, and minimum average complexity of test. Under the constraints of both known symptoms and the causal relationship among different components, a multi-objective optimization mathematical model is set up, taking minimizing cost of fault reasoning as the target function. Since the problem is non-deterministic polynomial-hard(NP-hard), a modified multi-objective ant colony algorithm is proposed, in which a reachability matrix is set up to constrain the feasible search nodes of the ants and a new pseudo-random-proportional rule and a pheromone adjustment mechinism are constructed to balance conflicts between the optimization objectives. At last, a Pareto optimal set is acquired. Evaluation functions based on validity and tendency of reasoning paths are defined to optimize noninferior set, through which the final fault causes can be identified according to decision-making demands, thus realize fault reasoning of the multi-constraint and multi-objective complex system. Reasoning results demonstrate that the improved multi-objective ant colony optimization(IMACO) can realize reasoning and locating fault positions precisely by solving the multi-objective fault diagnosis model, which provides a new method to solve the problem of multi-constraint and multi-objective fault diagnosis and reasoning of complex system.

Coevolutionary Free Lunches

NASA Technical Reports Server (NTRS)

Wolpert, David H.; Macready, William G.

2005-01-01

Recent work on the foundations of optimization has begun to uncover its underlying rich structure. In particular, the "No Free Lunch" (NFL) theorems [WM97] state that any two algorithms are equivalent when their performance is averaged across all possible problems. This highlights the need for exploiting problem-specific knowledge to achieve better than random performance. In this paper we present a general framework covering most search scenarios. In addition to the optimization scenarios addressed in the NFL results, this framework covers multi-armed bandit problems and evolution of multiple co-evolving agents. As a particular instance of the latter, it covers "self-play" problems. In these problems the agents work together to produce a champion, who then engages one or more antagonists in a subsequent multi-player game In contrast to the traditional optimization case where the NFL results hold, we show that in self-play there are free lunches: in coevolution some algorithms have better performance than other algorithms, averaged across all possible problems. However in the typical coevolutionary scenarios encountered in biology, where there is no champion, NFL still holds.

3D statistical shape models incorporating 3D random forest regression voting for robust CT liver segmentation

NASA Astrophysics Data System (ADS)

Norajitra, Tobias; Meinzer, Hans-Peter; Maier-Hein, Klaus H.

2015-03-01

During image segmentation, 3D Statistical Shape Models (SSM) usually conduct a limited search for target landmarks within one-dimensional search profiles perpendicular to the model surface. In addition, landmark appearance is modeled only locally based on linear profiles and weak learners, altogether leading to segmentation errors from landmark ambiguities and limited search coverage. We present a new method for 3D SSM segmentation based on 3D Random Forest Regression Voting. For each surface landmark, a Random Regression Forest is trained that learns a 3D spatial displacement function between the according reference landmark and a set of surrounding sample points, based on an infinite set of non-local randomized 3D Haar-like features. Landmark search is then conducted omni-directionally within 3D search spaces, where voxelwise forest predictions on landmark position contribute to a common voting map which reflects the overall position estimate. Segmentation experiments were conducted on a set of 45 CT volumes of the human liver, of which 40 images were randomly chosen for training and 5 for testing. Without parameter optimization, using a simple candidate selection and a single resolution approach, excellent results were achieved, while faster convergence and better concavity segmentation were observed, altogether underlining the potential of our approach in terms of increased robustness from distinct landmark detection and from better search coverage.

Sample size determination for bibliographic retrieval studies

PubMed Central

Yao, Xiaomei; Wilczynski, Nancy L; Walter, Stephen D; Haynes, R Brian

2008-01-01

Background Research for developing search strategies to retrieve high-quality clinical journal articles from MEDLINE is expensive and time-consuming. The objective of this study was to determine the minimal number of high-quality articles in a journal subset that would need to be hand-searched to update or create new MEDLINE search strategies for treatment, diagnosis, and prognosis studies. Methods The desired width of the 95% confidence intervals (W) for the lowest sensitivity among existing search strategies was used to calculate the number of high-quality articles needed to reliably update search strategies. New search strategies were derived in journal subsets formed by 2 approaches: random sampling of journals and top journals (having the most high-quality articles). The new strategies were tested in both the original large journal database and in a low-yielding journal (having few high-quality articles) subset. Results For treatment studies, if W was 10% or less for the lowest sensitivity among our existing search strategies, a subset of 15 randomly selected journals or 2 top journals were adequate for updating search strategies, based on each approach having at least 99 high-quality articles. The new strategies derived in 15 randomly selected journals or 2 top journals performed well in the original large journal database. Nevertheless, the new search strategies developed using the random sampling approach performed better than those developed using the top journal approach in a low-yielding journal subset. For studies of diagnosis and prognosis, no journal subset had enough high-quality articles to achieve the expected W (10%). Conclusion The approach of randomly sampling a small subset of journals that includes sufficient high-quality articles is an efficient way to update or create search strategies for high-quality articles on therapy in MEDLINE. The concentrations of diagnosis and prognosis articles are too low for this approach. PMID:18823538

Insight into the ten-penny problem: guiding search by constraints and maximization.

PubMed

Öllinger, Michael; Fedor, Anna; Brodt, Svenja; Szathmáry, Eörs

2017-09-01

For a long time, insight problem solving has been either understood as nothing special or as a particular class of problem solving. The first view implicates the necessity to find efficient heuristics that restrict the search space, the second, the necessity to overcome self-imposed constraints. Recently, promising hybrid cognitive models attempt to merge both approaches. In this vein, we were interested in the interplay of constraints and heuristic search, when problem solvers were asked to solve a difficult multi-step problem, the ten-penny problem. In three experimental groups and one control group (N = 4 × 30) we aimed at revealing, what constraints drive problem difficulty in this problem, and how relaxing constraints, and providing an efficient search criterion facilitates the solution. We also investigated how the search behavior of successful problem solvers and non-solvers differ. We found that relaxing constraints was necessary but not sufficient to solve the problem. Without efficient heuristics that facilitate the restriction of the search space, and testing the progress of the problem solving process, the relaxation of constraints was not effective. Relaxing constraints and applying the search criterion are both necessary to effectively increase solution rates. We also found that successful solvers showed promising moves earlier and had a higher maximization and variation rate across solution attempts. We propose that this finding sheds light on how different strategies contribute to solving difficult problems. Finally, we speculate about the implications of our findings for insight problem solving.

Effects of physiotherapy interventions on balance in multiple sclerosis: a systematic review and meta-analysis of randomized controlled trials.

PubMed

Paltamaa, Jaana; Sjögren, Tuulikki; Peurala, Sinikka H; Heinonen, Ari

2012-10-01

To determine the effects of physiotherapy interventions on balance in people with multiple sclerosis. A systematic literature search was conducted in Medline, Cinahl, Embase, PEDro, both electronically and by manual search up to March 2011. Randomized controlled trials of physiotherapy interventions in people with multiple sclerosis, with an outcome measure linked to the International Classification of Functioning, Disability and Health (ICF) category of "Changing and maintaining body position", were included. The quality of studies was determined by the van Tulder criteria. Meta-analyses were performed in subgroups according to the intervention. After screening 233 full-text papers, 11 studies were included in a qualitative analysis and 7 in a meta-analysis. The methodological quality of the studies ranged from poor to moderate. Low evidence was found for the efficacy of specific balance exercises, physical therapy based on an individualized problem-solving approach, and resistance and aerobic exercises on improving balance among ambulatory people with multiple sclerosis. These findings indicate small, but significant, effects of physiotherapy on balance in people with multiple sclerosis who have a mild to moderate level of disability. However, evidence for severely disabled people is lacking, and further research is needed.

Information spread of emergency events: path searching on social networks.

PubMed

Dai, Weihui; Hu, Hongzhi; Wu, Tunan; Dai, Yonghui

2014-01-01

Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning.

Resource-constrained scheduling with hard due windows and rejection penalties

NASA Astrophysics Data System (ADS)

Garcia, Christopher

2016-09-01

This work studies a scheduling problem where each job must be either accepted and scheduled to complete within its specified due window, or rejected altogether. Each job has a certain processing time and contributes a certain profit if accepted or penalty cost if rejected. There is a set of renewable resources, and no resource limit can be exceeded at any time. Each job requires a certain amount of each resource when processed, and the objective is to maximize total profit. A mixed-integer programming formulation and three approximation algorithms are presented: a priority rule heuristic, an algorithm based on the metaheuristic for randomized priority search and an evolutionary algorithm. Computational experiments comparing these four solution methods were performed on a set of generated benchmark problems covering a wide range of problem characteristics. The evolutionary algorithm outperformed the other methods in most cases, often significantly, and never significantly underperformed any method.

A systematic review of treatments for constipation in children and young adults undergoing cancer treatment.

PubMed

Phillips, Robert S; Gibson, Faith

2008-11-01

Constipation is a common problem in children and young people with cancer. Treatment of this complication is subject to wide variation in practice. We undertook a systematic review of randomized controlled trials to build a rational approach to prophylaxis and treatment of this complication. Randomized controlled trials of any intervention (pharmacologic, physical, complementary, or alternative) to prevent or treat constipation were to be included if they included children and young people 16 years old and younger who were undergoing treatment for malignancy. Of the 1336 abstracts retrieved from the searches, only 3 papers were identified for further assessment, and no studies were suitable for inclusion. There are no good data on which to base the management of constipation in children and young people with cancer. This is not to say that we do not know if laxatives work-they are clearly effective. Our ignorance is of the comparative value of different agents. The practical problems with undertaking specific trials of supportive care measures are large, and integration of such questions into treatment trials is essential.

Design optimization of steel frames using an enhanced firefly algorithm

NASA Astrophysics Data System (ADS)

Carbas, Serdar

2016-12-01

Mathematical modelling of real-world-sized steel frames under the Load and Resistance Factor Design-American Institute of Steel Construction (LRFD-AISC) steel design code provisions, where the steel profiles for the members are selected from a table of steel sections, turns out to be a discrete nonlinear programming problem. Finding the optimum design of such design optimization problems using classical optimization techniques is difficult. Metaheuristic algorithms provide an alternative way of solving such problems. The firefly algorithm (FFA) belongs to the swarm intelligence group of metaheuristics. The standard FFA has the drawback of being caught up in local optima in large-sized steel frame design problems. This study attempts to enhance the performance of the FFA by suggesting two new expressions for the attractiveness and randomness parameters of the algorithm. Two real-world-sized design examples are designed by the enhanced FFA and its performance is compared with standard FFA as well as with particle swarm and cuckoo search algorithms.

Manipulating Tabu List to Handle Machine Breakdowns in Job Shop Scheduling Problems

NASA Astrophysics Data System (ADS)

Nababan, Erna Budhiarti; SalimSitompul, Opim

2011-06-01

Machine breakdowns in a production schedule may occur on a random basis that make the well-known hard combinatorial problem of Job Shop Scheduling Problems (JSSP) becomes more complex. One of popular techniques used to solve the combinatorial problems is Tabu Search. In this technique, moves that will be not allowed to be revisited are retained in a tabu list in order to avoid in gaining solutions that have been obtained previously. In this paper, we propose an algorithm to employ a second tabu list to keep broken machines, in addition to the tabu list that keeps the moves. The period of how long the broken machines will be kept on the list is categorized using fuzzy membership function. Our technique are tested to the benchmark data of JSSP available on the OR library. From the experiment, we found that our algorithm is promising to help a decision maker to face the event of machine breakdowns.

A New TS Algorithm for Solving Low-Carbon Logistics Vehicle Routing Problem with Split Deliveries by Backpack—From a Green Operation Perspective

PubMed Central

Fu, Zhuo; Wang, Jiangtao

2018-01-01

In order to promote the development of low-carbon logistics and economize logistics distribution costs, the vehicle routing problem with split deliveries by backpack is studied. With the help of the model of classical capacitated vehicle routing problem, in this study, a form of discrete split deliveries was designed in which the customer demand can be split only by backpack. A double-objective mathematical model and the corresponding adaptive tabu search (TS) algorithm were constructed for solving this problem. By embedding the adaptive penalty mechanism, and adopting the random neighborhood selection strategy and reinitialization principle, the global optimization ability of the new algorithm was enhanced. Comparisons with the results in the literature show the effectiveness of the proposed algorithm. The proposed method can save the costs of low-carbon logistics and reduce carbon emissions, which is conducive to the sustainable development of low-carbon logistics. PMID:29747469

On the inherent competition between valid and spurious inductive inferences in Boolean data

NASA Astrophysics Data System (ADS)

Andrecut, M.

Inductive inference is the process of extracting general rules from specific observations. This problem also arises in the analysis of biological networks, such as genetic regulatory networks, where the interactions are complex and the observations are incomplete. A typical task in these problems is to extract general interaction rules as combinations of Boolean covariates, that explain a measured response variable. The inductive inference process can be considered as an incompletely specified Boolean function synthesis problem. This incompleteness of the problem will also generate spurious inferences, which are a serious threat to valid inductive inference rules. Using random Boolean data as a null model, here we attempt to measure the competition between valid and spurious inductive inference rules from a given data set. We formulate two greedy search algorithms, which synthesize a given Boolean response variable in a sparse disjunct normal form, and respectively a sparse generalized algebraic normal form of the variables from the observation data, and we evaluate numerically their performance.

The marriage problem and the fate of bachelors

NASA Astrophysics Data System (ADS)

Nieuwenhuizen, Th. M.

In the marriage problem, a variant of the bi-parted matching problem, each member has a “wish-list” expressing his/her preference for all possible partners; this list consists of random, positive real numbers drawn from a certain distribution. One searches the lowest cost for the society, at the risk of breaking up pairs in the course of time. Minimization of a global cost function (Hamiltonian) is performed with statistical mechanics techniques at a finite fictitious temperature. The problem is generalized to include bachelors, needed in particular when the groups have different size, and polygamy. Exact solutions are found for the optimal solution ( T=0). The entropy is found to vanish quadratically in T. Also, other evidence is found that the replica symmetric solution is exact, implying at most a polynomial degeneracy of the optimal solution. Whether bachelors occur or not, depends not only on their intrinsic qualities, or lack thereof, but also on global aspects of the chance for pair formation in society.

E-therapy for mental health problems: a systematic review.

PubMed

Postel, Marloes G; de Haan, Hein A; De Jong, Cor A J

2008-09-01

The widespread availability of the Internet offers opportunities for improving access to therapy for people with mental health problems. There is a seemingly infinite supply of Internet-based interventions available on the World Wide Web. The aim of the present study is to systematically assess the methodological quality of randomized controlled trials (RCTs) concerning e-therapy for mental health problems. Two reviewers independently assessed the methodological quality of the RCTs, based on a list of criteria for the methodological quality assessment as recommended by the Cochrane Back Review Group. The search yielded 14 papers that reported RCTs concerning e-therapy for mental-health problems. The methodological quality of studies included in this review was generally low. It is concluded that e-therapy may turn out to be an appropriate therapeutic entity, but the evidence needs to be more convincing. Recommendations are made concerning the method of reporting RCTs and the need to add some content items to an e-therapy study.

Computer use, internet access, and online health searching among Harlem adults.

PubMed

Cohall, Alwyn T; Nye, Andrea; Moon-Howard, Joyce; Kukafka, Rita; Dye, Bonnie; Vaughan, Roger D; Northridge, Mary E

2011-01-01

Computer use, Internet access, and online searching for health information were assessed toward enhancing Internet use for health promotion. Cross-sectional random digit dial landline phone survey. Eight zip codes that comprised Central Harlem/Hamilton Heights and East Harlem in New York City. Adults 18 years and older (N=646). Demographic characteristics, computer use, Internet access, and online searching for health information. Frequencies for categorical variables and means and standard deviations for continuous variables were calculated and compared with analogous findings reported in national surveys from similar time periods. Among Harlem adults, ever computer use and current Internet use were 77% and 52%, respectively. High-speed home Internet connections were somewhat lower for Harlem adults than for U.S. adults overall (43% vs. 68%). Current Internet users in Harlem were more likely to be younger, white vs. black or Hispanic, better educated, and in better self-reported health than non-current users (p<.01). Of those who reported searching online for health information, 74% sought information on medical problems and thought that information found on the Internet affected the way they eat (47%) or exercise (44%). Many Harlem adults currently use the Internet to search for health information. High-speed connections and culturally relevant materials may facilitate health information searching for underserved groups. Copyright © 2011 by American Journal of Health Promotion, Inc.

Hierarchical random walks in trace fossils and the origin of optimal search behavior

PubMed Central

Sims, David W.; Reynolds, Andrew M.; Humphries, Nicolas E.; Southall, Emily J.; Wearmouth, Victoria J.; Metcalfe, Brett; Twitchett, Richard J.

2014-01-01

Efficient searching is crucial for timely location of food and other resources. Recent studies show that diverse living animals use a theoretically optimal scale-free random search for sparse resources known as a Lévy walk, but little is known of the origins and evolution of foraging behavior and the search strategies of extinct organisms. Here, using simulations of self-avoiding trace fossil trails, we show that randomly introduced strophotaxis (U-turns)—initiated by obstructions such as self-trail avoidance or innate cueing—leads to random looping patterns with clustering across increasing scales that is consistent with the presence of Lévy walks. This predicts that optimal Lévy searches may emerge from simple behaviors observed in fossil trails. We then analyzed fossilized trails of benthic marine organisms by using a novel path analysis technique and find the first evidence, to our knowledge, of Lévy-like search strategies in extinct animals. Our results show that simple search behaviors of extinct animals in heterogeneous environments give rise to hierarchically nested Brownian walk clusters that converge to optimal Lévy patterns. Primary productivity collapse and large-scale food scarcity characterizing mass extinctions evident in the fossil record may have triggered adaptation of optimal Lévy-like searches. The findings suggest that Lévy-like behavior has been used by foragers since at least the Eocene but may have a more ancient origin, which might explain recent widespread observations of such patterns among modern taxa. PMID:25024221

Formative evaluation of a patient-specific clinical knowledge summarization tool

PubMed Central

Del Fiol, Guilherme; Mostafa, Javed; Pu, Dongqiuye; Medlin, Richard; Slager, Stacey; Jonnalagadda, Siddhartha R.; Weir, Charlene R.

2015-01-01

Objective To iteratively design a prototype of a computerized clinical knowledge summarization (CKS) tool aimed at helping clinicians finding answers to their clinical questions; and to conduct a formative assessment of the usability, usefulness, efficiency, and impact of the CKS prototype on physicians’ perceived decision quality compared with standard search of UpToDate and PubMed. Materials and methods Mixed-methods observations of the interactions of 10 physicians with the CKS prototype vs. standard search in an effort to solve clinical problems posed as case vignettes. Results The CKS tool automatically summarizes patient-specific and actionable clinical recommendations from PubMed (high quality randomized controlled trials and systematic reviews) and UpToDate. Two thirds of the study participants completed 15 out of 17 usability tasks. The median time to task completion was less than 10 s for 12 of the 17 tasks. The difference in search time between the CKS and standard search was not significant (median = 4.9 vs. 4.5 min). Physician’s perceived decision quality was significantly higher with the CKS than with manual search (mean = 16.6 vs. 14.4; p = 0.036). Conclusions The CKS prototype was well-accepted by physicians both in terms of usability and usefulness. Physicians perceived better decision quality with the CKS prototype compared to standard search of PubMed and UpToDate within a similar search time. Due to the formative nature of this study and a small sample size, conclusions regarding efficiency and efficacy are exploratory. PMID:26612774

Formative evaluation of a patient-specific clinical knowledge summarization tool.

PubMed

Del Fiol, Guilherme; Mostafa, Javed; Pu, Dongqiuye; Medlin, Richard; Slager, Stacey; Jonnalagadda, Siddhartha R; Weir, Charlene R

2016-02-01

To iteratively design a prototype of a computerized clinical knowledge summarization (CKS) tool aimed at helping clinicians finding answers to their clinical questions; and to conduct a formative assessment of the usability, usefulness, efficiency, and impact of the CKS prototype on physicians' perceived decision quality compared with standard search of UpToDate and PubMed. Mixed-methods observations of the interactions of 10 physicians with the CKS prototype vs. standard search in an effort to solve clinical problems posed as case vignettes. The CKS tool automatically summarizes patient-specific and actionable clinical recommendations from PubMed (high quality randomized controlled trials and systematic reviews) and UpToDate. Two thirds of the study participants completed 15 out of 17 usability tasks. The median time to task completion was less than 10s for 12 of the 17 tasks. The difference in search time between the CKS and standard search was not significant (median=4.9 vs. 4.5m in). Physician's perceived decision quality was significantly higher with the CKS than with manual search (mean=16.6 vs. 14.4; p=0.036). The CKS prototype was well-accepted by physicians both in terms of usability and usefulness. Physicians perceived better decision quality with the CKS prototype compared to standard search of PubMed and UpToDate within a similar search time. Due to the formative nature of this study and a small sample size, conclusions regarding efficiency and efficacy are exploratory. Published by Elsevier Ireland Ltd.

Effective hybrid teaching-learning-based optimization algorithm for balancing two-sided assembly lines with multiple constraints

NASA Astrophysics Data System (ADS)

Tang, Qiuhua; Li, Zixiang; Zhang, Liping; Floudas, C. A.; Cao, Xiaojun

2015-09-01

Due to the NP-hardness of the two-sided assembly line balancing (TALB) problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints (TALB-MC). Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization (TLBO) algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost function. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search (VNS) is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm (LAHC) for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.

Solving search problems by strongly simulating quantum circuits

PubMed Central

Johnson, T. H.; Biamonte, J. D.; Clark, S. R.; Jaksch, D.

2013-01-01

Simulating quantum circuits using classical computers lets us analyse the inner workings of quantum algorithms. The most complete type of simulation, strong simulation, is believed to be generally inefficient. Nevertheless, several efficient strong simulation techniques are known for restricted families of quantum circuits and we develop an additional technique in this article. Further, we show that strong simulation algorithms perform another fundamental task: solving search problems. Efficient strong simulation techniques allow solutions to a class of search problems to be counted and found efficiently. This enhances the utility of strong simulation methods, known or yet to be discovered, and extends the class of search problems known to be efficiently simulable. Relating strong simulation to search problems also bounds the computational power of efficiently strongly simulable circuits; if they could solve all problems in P this would imply that all problems in NP and #P could be solved in polynomial time. PMID:23390585

A multiobjective hybrid genetic algorithm for the capacitated multipoint network design problem.

PubMed

Lo, C C; Chang, W H

2000-01-01

The capacitated multipoint network design problem (CMNDP) is NP-complete. In this paper, a hybrid genetic algorithm for CMNDP is proposed. The multiobjective hybrid genetic algorithm (MOHGA) differs from other genetic algorithms (GAs) mainly in its selection procedure. The concept of subpopulation is used in MOHGA. Four subpopulations are generated according to the elitism reservation strategy, the shifting Prufer vector, the stochastic universal sampling, and the complete random method, respectively. Mixing these four subpopulations produces the next generation population. The MOHGA can effectively search the feasible solution space due to population diversity. The MOHGA has been applied to CMNDP. By examining computational and analytical results, we notice that the MOHGA can find most nondominated solutions and is much more effective and efficient than other multiobjective GAs.

Learning Problem-Solving Rules as Search through a Hypothesis Space

ERIC Educational Resources Information Center

Lee, Hee Seung; Betts, Shawn; Anderson, John R.

2016-01-01

Learning to solve a class of problems can be characterized as a search through a space of hypotheses about the rules for solving these problems. A series of four experiments studied how different learning conditions affected the search among hypotheses about the solution rule for a simple computational problem. Experiment 1 showed that a problem…

Web-Based Undergraduate Chemistry Problem-Solving: The Interplay of Task Performance, Domain Knowledge and Web-Searching Strategies

ERIC Educational Resources Information Center

She, Hsiao-Ching; Cheng, Meng-Tzu; Li, Ta-Wei; Wang, Chia-Yu; Chiu, Hsin-Tien; Lee, Pei-Zon; Chou, Wen-Chi; Chuang, Ming-Hua

2012-01-01

This study investigates the effect of Web-based Chemistry Problem-Solving, with the attributes of Web-searching and problem-solving scaffolds, on undergraduate students' problem-solving task performance. In addition, the nature and extent of Web-searching strategies students used and its correlation with task performance and domain knowledge also…

Nonlinear inversion of potential-field data using a hybrid-encoding genetic algorithm

USGS Publications Warehouse

Chen, C.; Xia, J.; Liu, J.; Feng, G.

2006-01-01

Using a genetic algorithm to solve an inverse problem of complex nonlinear geophysical equations is advantageous because it does not require computer gradients of models or "good" initial models. The multi-point search of a genetic algorithm makes it easier to find the globally optimal solution while avoiding falling into a local extremum. As is the case in other optimization approaches, the search efficiency for a genetic algorithm is vital in finding desired solutions successfully in a multi-dimensional model space. A binary-encoding genetic algorithm is hardly ever used to resolve an optimization problem such as a simple geophysical inversion with only three unknowns. The encoding mechanism, genetic operators, and population size of the genetic algorithm greatly affect search processes in the evolution. It is clear that improved operators and proper population size promote the convergence. Nevertheless, not all genetic operations perform perfectly while searching under either a uniform binary or a decimal encoding system. With the binary encoding mechanism, the crossover scheme may produce more new individuals than with the decimal encoding. On the other hand, the mutation scheme in a decimal encoding system will create new genes larger in scope than those in the binary encoding. This paper discusses approaches of exploiting the search potential of genetic operations in the two encoding systems and presents an approach with a hybrid-encoding mechanism, multi-point crossover, and dynamic population size for geophysical inversion. We present a method that is based on the routine in which the mutation operation is conducted in the decimal code and multi-point crossover operation in the binary code. The mix-encoding algorithm is called the hybrid-encoding genetic algorithm (HEGA). HEGA provides better genes with a higher probability by a mutation operator and improves genetic algorithms in resolving complicated geophysical inverse problems. Another significant result is that final solution is determined by the average model derived from multiple trials instead of one computation due to the randomness in a genetic algorithm procedure. These advantages were demonstrated by synthetic and real-world examples of inversion of potential-field data. ?? 2005 Elsevier Ltd. All rights reserved.

Evolutionary Dynamic Multiobjective Optimization Via Kalman Filter Prediction.

PubMed

Muruganantham, Arrchana; Tan, Kay Chen; Vadakkepat, Prahlad

2016-12-01

Evolutionary algorithms are effective in solving static multiobjective optimization problems resulting in the emergence of a number of state-of-the-art multiobjective evolutionary algorithms (MOEAs). Nevertheless, the interest in applying them to solve dynamic multiobjective optimization problems has only been tepid. Benchmark problems, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. One or more objectives may change with time in dynamic optimization problems. The optimization algorithm must be able to track the moving optima efficiently. A prediction model can learn the patterns from past experience and predict future changes. In this paper, a new dynamic MOEA using Kalman filter (KF) predictions in decision space is proposed to solve the aforementioned problems. The predictions help to guide the search toward the changed optima, thereby accelerating convergence. A scoring scheme is devised to hybridize the KF prediction with a random reinitialization method. Experimental results and performance comparisons with other state-of-the-art algorithms demonstrate that the proposed algorithm is capable of significantly improving the dynamic optimization performance.

Decentralized Bayesian search using approximate dynamic programming methods.

PubMed

Zhao, Yijia; Patek, Stephen D; Beling, Peter A

2008-08-01

We consider decentralized Bayesian search problems that involve a team of multiple autonomous agents searching for targets on a network of search points operating under the following constraints: 1) interagent communication is limited; 2) the agents do not have the opportunity to agree in advance on how to resolve equivalent but incompatible strategies; and 3) each agent lacks the ability to control or predict with certainty the actions of the other agents. We formulate the multiagent search-path-planning problem as a decentralized optimal control problem and introduce approximate dynamic heuristics that can be implemented in a decentralized fashion. After establishing some analytical properties of the heuristics, we present computational results for a search problem involving two agents on a 5 x 5 grid.

Markovian Search Games in Heterogeneous Spaces

DOE Office of Scientific and Technical Information (OSTI.GOV)

Griffin, Christopher H

2009-01-01

We consider how to search for a mobile evader in a large heterogeneous region when sensors are used for detection. Sensors are modeled using probability of detection. Due to environmental effects, this probability will not be constant over the entire region. We map this problem to a graph search problem and, even though deterministic graph search is NP-complete, we derive a tractable, optimal, probabilistic search strategy. We do this by defining the problem as a differential game played on a Markov chain. We prove that this strategy is optimal in the sense of Nash. Simulations of an example problem illustratemore » our approach and verify our claims.« less

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Li, Yongjun; Cheng, Weixing; Yu, Li Hua

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given “elite” status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitnessmore » of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. Furthermore, the machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.« less

A predictive machine learning approach for microstructure optimization and materials design

NASA Astrophysics Data System (ADS)

Liu, Ruoqian; Kumar, Abhishek; Chen, Zhengzhang; Agrawal, Ankit; Sundararaghavan, Veera; Choudhary, Alok

2015-06-01

This paper addresses an important materials engineering question: How can one identify the complete space (or as much of it as possible) of microstructures that are theoretically predicted to yield the desired combination of properties demanded by a selected application? We present a problem involving design of magnetoelastic Fe-Ga alloy microstructure for enhanced elastic, plastic and magnetostrictive properties. While theoretical models for computing properties given the microstructure are known for this alloy, inversion of these relationships to obtain microstructures that lead to desired properties is challenging, primarily due to the high dimensionality of microstructure space, multi-objective design requirement and non-uniqueness of solutions. These challenges render traditional search-based optimization methods incompetent in terms of both searching efficiency and result optimality. In this paper, a route to address these challenges using a machine learning methodology is proposed. A systematic framework consisting of random data generation, feature selection and classification algorithms is developed. Experiments with five design problems that involve identification of microstructures that satisfy both linear and nonlinear property constraints show that our framework outperforms traditional optimization methods with the average running time reduced by as much as 80% and with optimality that would not be achieved otherwise.

Drivers’ Visual Behavior-Guided RRT Motion Planner for Autonomous On-Road Driving

PubMed Central

Du, Mingbo; Mei, Tao; Liang, Huawei; Chen, Jiajia; Huang, Rulin; Zhao, Pan

2016-01-01

This paper describes a real-time motion planner based on the drivers’ visual behavior-guided rapidly exploring random tree (RRT) approach, which is applicable to on-road driving of autonomous vehicles. The primary novelty is in the use of the guidance of drivers’ visual search behavior in the framework of RRT motion planner. RRT is an incremental sampling-based method that is widely used to solve the robotic motion planning problems. However, RRT is often unreliable in a number of practical applications such as autonomous vehicles used for on-road driving because of the unnatural trajectory, useless sampling, and slow exploration. To address these problems, we present an interesting RRT algorithm that introduces an effective guided sampling strategy based on the drivers’ visual search behavior on road and a continuous-curvature smooth method based on B-spline. The proposed algorithm is implemented on a real autonomous vehicle and verified against several different traffic scenarios. A large number of the experimental results demonstrate that our algorithm is feasible and efficient for on-road autonomous driving. Furthermore, the comparative test and statistical analyses illustrate that its excellent performance is superior to other previous algorithms. PMID:26784203

Drivers' Visual Behavior-Guided RRT Motion Planner for Autonomous On-Road Driving.

PubMed

Du, Mingbo; Mei, Tao; Liang, Huawei; Chen, Jiajia; Huang, Rulin; Zhao, Pan

2016-01-15

This paper describes a real-time motion planner based on the drivers' visual behavior-guided rapidly exploring random tree (RRT) approach, which is applicable to on-road driving of autonomous vehicles. The primary novelty is in the use of the guidance of drivers' visual search behavior in the framework of RRT motion planner. RRT is an incremental sampling-based method that is widely used to solve the robotic motion planning problems. However, RRT is often unreliable in a number of practical applications such as autonomous vehicles used for on-road driving because of the unnatural trajectory, useless sampling, and slow exploration. To address these problems, we present an interesting RRT algorithm that introduces an effective guided sampling strategy based on the drivers' visual search behavior on road and a continuous-curvature smooth method based on B-spline. The proposed algorithm is implemented on a real autonomous vehicle and verified against several different traffic scenarios. A large number of the experimental results demonstrate that our algorithm is feasible and efficient for on-road autonomous driving. Furthermore, the comparative test and statistical analyses illustrate that its excellent performance is superior to other previous algorithms.

Two-dimensional triangular lattice and its application to lithium-intercalated layered compounds

NASA Astrophysics Data System (ADS)

Decerqueira, R. O.

1982-08-01

Good rechargeable batteries are being searched for use in electric vehicles and in energy storage during off-peak consumption periods and from solar sources. The interest in lithium intercalation compounds has been recently enhanced by the search for such batteries. The process of intercalation of lithium in several transition metal dichalcogenides can provide an emf of several volts. The progress achieved in the last decade in the investigation of these intercalates has been facilitated by the availability of the dichalcogenides as single crystals and by their chemical stability. The transition-metal dichalcogenides and their Li-intercalates are studied, with emphasis on the Li/su xTa/sub yTi/sub l-y/S2 series. The interactions between the Li atoms and the applicability of a lattice gas model to the problem of ordering of these atoms is discussed. A formulation is presented of the cluster-variation aproximation to the lattice gas problem. The single-site and the nearest-neighbor triangle basic clusters are considered as models for Li/sub x TiS2. Also a theory is presented for the effects of a random distribution of different species of host atoms, as in Ta/sub y/Ti/sub l-y/S2.

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

NASA Astrophysics Data System (ADS)

Li, Yongjun; Cheng, Weixing; Yu, Li Hua; Rainer, Robert

2018-05-01

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given "elite" status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitness of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. The machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

DOE PAGES

Li, Yongjun; Cheng, Weixing; Yu, Li Hua; ...

2018-05-29

With the aid of machine learning techniques, the genetic algorithm has been enhanced and applied to the multi-objective optimization problem presented by the dynamic aperture of the National Synchrotron Light Source II (NSLS-II) Storage Ring. During the evolution processes employed by the genetic algorithm, the population is classified into different clusters in the search space. The clusters with top average fitness are given “elite” status. Intervention on the population is implemented by repopulating some potentially competitive candidates based on the experience learned from the accumulated data. These candidates replace randomly selected candidates among the original data pool. The average fitnessmore » of the population is therefore improved while diversity is not lost. Maintaining diversity ensures that the optimization is global rather than local. The quality of the population increases and produces more competitive descendants accelerating the evolution process significantly. When identifying the distribution of optimal candidates, they appear to be located in isolated islands within the search space. Some of these optimal candidates have been experimentally confirmed at the NSLS-II storage ring. Furthermore, the machine learning techniques that exploit the genetic algorithm can also be used in other population-based optimization problems such as particle swarm algorithm.« less

Information Spread of Emergency Events: Path Searching on Social Networks

PubMed Central

Hu, Hongzhi; Wu, Tunan

2014-01-01

Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning. PMID:24600323

Economic Evaluation of Occupational Safety and Health Interventions From the Employer Perspective

PubMed Central

Grimani, Aikaterini; Bergström, Gunnar; Casallas, Martha Isabel Riaño; Aboagye, Emmanuel; Jensen, Irene; Lohela-Karlsson, Malin

2018-01-01

Objectives: The aim of this systematic review was to evaluate the cost-effectiveness of occupational safety and health interventions from the employer perspective. Methods: A comprehensive literature search (2005 to 2016) in five electronic databases was conducted. Pre-2005 studies were identified from the reference lists of previous studies and systematic reviews, which have similar objective to those of this search. Results: A total of 19 randomized controlled trials and quasi-experimental studies were included, targeting diverse health problems in a number of settings. Few studies included organizational-level interventions. When viewed in relation to the methodological quality and the sufficiency of economic evidence, five of 11 cost-effective occupational safety and health (OSH) interventions appear to be promising. Conclusion: The present systematic review highlights the need for high-quality economic evidence to evaluate the cost-effectiveness of OSH interventions, especially at organizational-level, in all areas of worker health. PMID:29112631

Expedite random structure searching using objects from Wyckoff positions

NASA Astrophysics Data System (ADS)

Wang, Shu-Wei; Hsing, Cheng-Rong; Wei, Ching-Ming

2018-02-01

Random structure searching has been proved to be a powerful approach to search and find the global minimum and the metastable structures. A true random sampling is in principle needed yet it would be highly time-consuming and/or practically impossible to find the global minimum for the complicated systems in their high-dimensional configuration space. Thus the implementations of reasonable constraints, such as adopting system symmetries to reduce the independent dimension in structural space and/or imposing chemical information to reach and relax into low-energy regions, are the most essential issues in the approach. In this paper, we propose the concept of "object" which is either an atom or composed of a set of atoms (such as molecules or carbonates) carrying a symmetry defined by one of the Wyckoff positions of space group and through this process it allows the searching of global minimum for a complicated system to be confined in a greatly reduced structural space and becomes accessible in practice. We examined several representative materials, including Cd3As2 crystal, solid methanol, high-pressure carbonates (FeCO3), and Si(111)-7 × 7 reconstructed surface, to demonstrate the power and the advantages of using "object" concept in random structure searching.

Fitness landscapes, heuristics and technological paradigms: A critique on random search models in evolutionary economics

NASA Astrophysics Data System (ADS)

Frenken, Koen

2001-06-01

The biological evolution of complex organisms, in which the functioning of genes is interdependent, has been analyzed as "hill-climbing" on NK fitness landscapes through random mutation and natural selection. In evolutionary economics, NK fitness landscapes have been used to simulate the evolution of complex technological systems containing elements that are interdependent in their functioning. In these models, economic agents randomly search for new technological design by trial-and-error and run the risk of ending up in sub-optimal solutions due to interdependencies between the elements in a complex system. These models of random search are legitimate for reasons of modeling simplicity, but remain limited as these models ignore the fact that agents can apply heuristics. A specific heuristic is one that sequentially optimises functions according to their ranking by users of the system. To model this heuristic, a generalized NK-model is developed. In this model, core elements that influence many functions can be distinguished from peripheral elements that affect few functions. The concept of paradigmatic search can then be analytically defined as search that leaves core elements in tact while concentrating on improving functions by mutation of peripheral elements.

Problem Solving Interventions for Diabetes Self-management and Control: A Systematic Review of the Literature

PubMed Central

Fitzpatrick, Stephanie L.; Schumann, Kristina P.; Hill-Briggs, Felicia

2013-01-01

Aims Problem solving is deemed a core skill for patient diabetes self-management education. The purpose of this systematic review is to examine the published literature on the effect of problem-solving interventions on diabetes self-management and disease control. Data Sources We searched PubMed and PsychINFO electronic databases for English language articles published between November 2006 and September 2012. Reference lists from included studies were reviewed to capture additional studies. Study Selection Studies reporting problem-solving intervention or problem solving as an intervention component for diabetes self-management training and disease control were included. Twenty-four studies met inclusion criteria. Data Extraction Study design, sample characteristics, measures, and results were reviewed. Data Synthesis Sixteen intervention studies (11 adult, 5 children/adolescents) were randomized controlled trials, and 8 intervention studies (6 adult, 2 children/adolescents) were quasi-experimental designs. Conclusions Studies varied greatly in their approaches to problem-solving use in patient education. To date, 36% of adult problem-solving interventions and 42% of children/adolescent problem-solving interventions have demonstrated significant improvement in HbA1c, while psychosocial outcomes have been more promising. The next phase of problem-solving intervention research should employ intervention characteristics found to have sufficient potency and intensity to reach therapeutic levels needed to demonstrate change. PMID:23312614

Do Patients’ Symptoms and Interpersonal Problems Improve in Psychotherapeutic Hospital Treatment in Germany? - A Systematic Review and Meta-Analysis

PubMed Central

Liebherz, Sarah; Rabung, Sven

2014-01-01

Background In Germany, inpatient psychotherapy plays a unique role in the treatment of patients with common mental disorders of higher severity. In addition to psychiatric inpatient services, psychotherapeutic hospital treatment and psychosomatic rehabilitation are offered as independent inpatient treatment options. This meta-analysis aims to provide systematic evidence for psychotherapeutic hospital treatment in Germany regarding its effects on symptomatic and interpersonal impairment. Methodology Relevant papers were identified by electronic database search and hand search. Randomized controlled trials as well as naturalistic prospective studies (including post-therapy and follow-up assessments) evaluating psychotherapeutic hospital treatment of mentally ill adults in Germany were included. Outcomes were required to be quantified by either the Symptom-Checklist (SCL-90-R or short versions) or the Inventory of Interpersonal Problems (IIP-64 or short versions). Effect sizes (Hedges’ g) were combined using random effect models. Principal Findings Sixty-seven papers representing 59 studies fulfilled inclusion criteria. Meta-analysis yielded a medium within-group effect size for symptom change at discharge (g = 0.72; 95% CI 0.68–0.76), with a small reduction to follow-up (g = 0.61; 95% CI 0.55–0.68). Regarding interpersonal problems, a small effect size was found at discharge (g = 0.35; 95% CI 0.29–0.41), which increased to follow-up (g = 0.48; 95% CI 0.36–0.60). While higher impairment at intake was associated with a larger effect size in both measures, longer treatment duration was related to lower effect sizes in SCL GSI and to larger effect sizes in IIP Total. Conclusions Psychotherapeutic hospital treatment may be considered an effective treatment. In accordance with Howard’s phase model of psychotherapy outcome, the present study demonstrated that symptom distress changes more quickly and strongly than interpersonal problems. Preliminary analyses show impairment at intake and treatment duration to be the strongest outcome predictors. Further analyses regarding this relationship are required. PMID:25141289

Superstitious Beliefs and Problem Gambling Among Thai Lottery Gamblers: The Mediation Effects of Number Search and Gambling Intensity.

PubMed

Pravichai, Sunisa; Ariyabuddhiphongs, Vanchai

2015-12-01

Thai lottery gamblers won prizes after betting on numbers they obtained from newspaper stories. We hypothesized that Thai lottery gamblers' superstitious beliefs were related to their problem gambling through the mediation of number search and gambling intensity. In a study among 380 Thai lottery gamblers, superstitious beliefs were operationally defined as the beliefs in events or objects that seemed to reveal numbers, number search as an attempt to identify numbers to bet, gambling intensity as the frequency and amounts of lottery gambling, and problem gambling as the symptoms of problems relating to lottery gambling. Results support the hypotheses. There is a statistically significant indirect relationship between Thai lottery gamblers' superstitious beliefs and their problem gambling through the mediation of number search and gambling intensity. Thai lottery gamblers need to be reminded that their superstitious beliefs and number search are precursors of their problem gambling.

Ordinal optimization and its application to complex deterministic problems

NASA Astrophysics Data System (ADS)

Yang, Mike Shang-Yu

1998-10-01

We present in this thesis a new perspective to approach a general class of optimization problems characterized by large deterministic complexities. Many problems of real-world concerns today lack analyzable structures and almost always involve high level of difficulties and complexities in the evaluation process. Advances in computer technology allow us to build computer models to simulate the evaluation process through numerical means, but the burden of high complexities remains to tax the simulation with an exorbitant computing cost for each evaluation. Such a resource requirement makes local fine-tuning of a known design difficult under most circumstances, let alone global optimization. Kolmogorov equivalence of complexity and randomness in computation theory is introduced to resolve this difficulty by converting the complex deterministic model to a stochastic pseudo-model composed of a simple deterministic component and a white-noise like stochastic term. The resulting randomness is then dealt with by a noise-robust approach called Ordinal Optimization. Ordinal Optimization utilizes Goal Softening and Ordinal Comparison to achieve an efficient and quantifiable selection of designs in the initial search process. The approach is substantiated by a case study in the turbine blade manufacturing process. The problem involves the optimization of the manufacturing process of the integrally bladed rotor in the turbine engines of U.S. Air Force fighter jets. The intertwining interactions among the material, thermomechanical, and geometrical changes makes the current FEM approach prohibitively uneconomical in the optimization process. The generalized OO approach to complex deterministic problems is applied here with great success. Empirical results indicate a saving of nearly 95% in the computing cost.

Graph drawing using tabu search coupled with path relinking.

PubMed

Dib, Fadi K; Rodgers, Peter

2018-01-01

Graph drawing, or the automatic layout of graphs, is a challenging problem. There are several search based methods for graph drawing which are based on optimizing an objective function which is formed from a weighted sum of multiple criteria. In this paper, we propose a new neighbourhood search method which uses a tabu search coupled with path relinking to optimize such objective functions for general graph layouts with undirected straight lines. To our knowledge, before our work, neither of these methods have been previously used in general multi-criteria graph drawing. Tabu search uses a memory list to speed up searching by avoiding previously tested solutions, while the path relinking method generates new solutions by exploring paths that connect high quality solutions. We use path relinking periodically within the tabu search procedure to speed up the identification of good solutions. We have evaluated our new method against the commonly used neighbourhood search optimization techniques: hill climbing and simulated annealing. Our evaluation examines the quality of the graph layout (objective function's value) and the speed of layout in terms of the number of evaluated solutions required to draw a graph. We also examine the relative scalability of each method. Our experimental results were applied to both random graphs and a real-world dataset. We show that our method outperforms both hill climbing and simulated annealing by producing a better layout in a lower number of evaluated solutions. In addition, we demonstrate that our method has greater scalability as it can layout larger graphs than the state-of-the-art neighbourhood search methods. Finally, we show that similar results can be produced in a real world setting by testing our method against a standard public graph dataset.

Graph drawing using tabu search coupled with path relinking

PubMed Central

Rodgers, Peter

2018-01-01

Graph drawing, or the automatic layout of graphs, is a challenging problem. There are several search based methods for graph drawing which are based on optimizing an objective function which is formed from a weighted sum of multiple criteria. In this paper, we propose a new neighbourhood search method which uses a tabu search coupled with path relinking to optimize such objective functions for general graph layouts with undirected straight lines. To our knowledge, before our work, neither of these methods have been previously used in general multi-criteria graph drawing. Tabu search uses a memory list to speed up searching by avoiding previously tested solutions, while the path relinking method generates new solutions by exploring paths that connect high quality solutions. We use path relinking periodically within the tabu search procedure to speed up the identification of good solutions. We have evaluated our new method against the commonly used neighbourhood search optimization techniques: hill climbing and simulated annealing. Our evaluation examines the quality of the graph layout (objective function’s value) and the speed of layout in terms of the number of evaluated solutions required to draw a graph. We also examine the relative scalability of each method. Our experimental results were applied to both random graphs and a real-world dataset. We show that our method outperforms both hill climbing and simulated annealing by producing a better layout in a lower number of evaluated solutions. In addition, we demonstrate that our method has greater scalability as it can layout larger graphs than the state-of-the-art neighbourhood search methods. Finally, we show that similar results can be produced in a real world setting by testing our method against a standard public graph dataset. PMID:29746576

Hybridization of decomposition and local search for multiobjective optimization.

PubMed

Ke, Liangjun; Zhang, Qingfu; Battiti, Roberto

2014-10-01

Combining ideas from evolutionary algorithms, decomposition approaches, and Pareto local search, this paper suggests a simple yet efficient memetic algorithm for combinatorial multiobjective optimization problems: memetic algorithm based on decomposition (MOMAD). It decomposes a combinatorial multiobjective problem into a number of single objective optimization problems using an aggregation method. MOMAD evolves three populations: 1) population P(L) for recording the current solution to each subproblem; 2) population P(P) for storing starting solutions for Pareto local search; and 3) an external population P(E) for maintaining all the nondominated solutions found so far during the search. A problem-specific single objective heuristic can be applied to these subproblems to initialize the three populations. At each generation, a Pareto local search method is first applied to search a neighborhood of each solution in P(P) to update P(L) and P(E). Then a single objective local search is applied to each perturbed solution in P(L) for improving P(L) and P(E), and reinitializing P(P). The procedure is repeated until a stopping condition is met. MOMAD provides a generic hybrid multiobjective algorithmic framework in which problem specific knowledge, well developed single objective local search and heuristics and Pareto local search methods can be hybridized. It is a population based iterative method and thus an anytime algorithm. Extensive experiments have been conducted in this paper to study MOMAD and compare it with some other state-of-the-art algorithms on the multiobjective traveling salesman problem and the multiobjective knapsack problem. The experimental results show that our proposed algorithm outperforms or performs similarly to the best so far heuristics on these two problems.

Enhancing access to reports of randomized trials published world-wide – the contribution of EMBASE records to the Cochrane Central Register of Controlled Trials (CENTRAL) in The Cochrane Library

PubMed Central

Lefebvre, Carol; Eisinga, Anne; McDonald, Steve; Paul, Nina

2008-01-01

Background Randomized trials are essential in assessing the effects of healthcare interventions and are a key component in systematic reviews of effectiveness. Searching for reports of randomized trials in databases is problematic due to the absence of appropriate indexing terms until the 1990s and inconsistent application of these indexing terms thereafter. Objectives The objectives of this study are to devise a search strategy for identifying reports of randomized trials in EMBASE which are not already indexed as trials in MEDLINE and to make these reports easily accessible by including them in the Cochrane Central Register of Controlled Trials (CENTRAL) in The Cochrane Library, with the permission of Elsevier, the publishers of EMBASE. Methods A highly sensitive search strategy was designed for EMBASE based on free-text and thesaurus terms which occurred frequently in the titles, abstracts, EMTREE terms (or some combination of these) of reports of trials indexed in EMBASE. This search strategy was run against EMBASE from 1980 to 2005 (1974 to 2005 for four of the terms) and records retrieved by the search, which were not already indexed as randomized trials in MEDLINE, were downloaded from EMBASE, printed and read. An analysis of the language of publication was conducted for the reports of trials published in 2005 (the most recent year completed at the time of this study). Results Twenty-two search terms were used (including nine which were later rejected due to poor cumulative precision). More than a third of a million records were downloaded and scanned and approximately 80,000 reports of trials were identified which were not already indexed as randomized trials in MEDLINE. These are now easily identifiable in CENTRAL, in The Cochrane Library. Cumulative sensitivity ranged from 0.1% to 60% and cumulative precision ranged from 8% to 61%. The truncated term 'random$' identified 60% of the total number of reports of trials but only 35% of the more than 130,000 records retrieved by this term were reports of trials. The language analysis for the sample year 2005 indicated that of the 18,427 reports indexed as randomized trials in MEDLINE, 959 (5%) were in languages other than English. The EMBASE search identified an additional 658 reports in languages other than English, of which the highest number were in Chinese (320). Conclusion The results of the search to date have greatly increased access to reports of trials in EMBASE, especially in some languages other than English. The search strategy used was subjectively derived from a small 'gold standard' set of test records and was not validated in an independent test set. We intend to design an objectively-derived validated search strategy using logistic regression based on the frequency of occurrence of terms in the approximately 80,000 reports of randomized trials identified compared with the frequency of these terms across the entire EMBASE database. PMID:18826567

Problem-solving skills in high school biology: The effectiveness of the IMMEX problem-solving assessment software

NASA Astrophysics Data System (ADS)

Palacio-Cayetano, Joycelin

"Problem-solving through reflective thinking should be both the method and valuable outcome of science instruction in America's schools" proclaimed John Dewey (Gabel, 1995). If the development of problem-solving is a primary goal of science education, more problem-solving opportunities must be an integral part of K-16 education. To examine the effective use of technology in developing and assessing problem-solving skills, a problem-solving authoring, learning, and assessment software, the UCLA IMMEX Program-Interactive Multimedia Exercises-was investigated. This study was a twenty-week quasi-experimental study that was implemented as a control-group time series design among 120 tenth grade students. Both the experimental group (n = 60) and the control group (n = 60) participated in a problem-based learning curriculum; however, the experimental group received regular intensive experiences with IMMEX problem-solving and the control group did not. Problem-solving pretest and posttest were administered to all students. The instruments used were a 35-item Processes of Biological Inquiry Test and an IMMEX problem-solving assessment test, True Roots. Students who participated in the IMMEX Program achieved significant (p <.05) gains in problem-solving skills on both problem-solving assessment instruments. This study provided evidence that IMMEX software is highly efficient in evaluating salient elements of problem-solving. Outputs of students' problem-solving strategies revealed that unsuccessful problem solvers primarily used the following four strategies: (1) no data search strategy, students simply guessed; (2) limited data search strategy leading to insufficient data and premature closing; (3) irrelevant data search strategy, students focus in areas bearing no substantive data; and (4) extensive data search strategy with inadequate integration and analysis. On the contrary, successful problem solvers used the following strategies; (1) focused search strategy coupled with the ability to fill in knowledge gaps by accessing the appropriate resources; (2) targeted search strategy coupled with high level of analytical and integration skills; and (3) focused search strategy coupled with superior discrimination, analytical, and integration skills. The strategies of students who were successful and unsuccessful solving IMMEX problems were consistent with those of expert and novice problem solvers identified in the literature on problem-solving.

Generalizing Backtrack-Free Search: A Framework for Search-Free Constraint Satisfaction

NASA Technical Reports Server (NTRS)

Jonsson, Ari K.; Frank, Jeremy

2000-01-01

Tractable classes of constraint satisfaction problems are of great importance in artificial intelligence. Identifying and taking advantage of such classes can significantly speed up constraint problem solving. In addition, tractable classes are utilized in applications where strict worst-case performance guarantees are required, such as constraint-based plan execution. In this work, we present a formal framework for search-free (backtrack-free) constraint satisfaction. The framework is based on general procedures, rather than specific propagation techniques, and thus generalizes existing techniques in this area. We also relate search-free problem solving to the notion of decision sets and use the result to provide a constructive criterion that is sufficient to guarantee search-free problem solving.

Cooperative mobile agents search using beehive partitioned structure and Tabu Random search algorithm

NASA Astrophysics Data System (ADS)

Ramazani, Saba; Jackson, Delvin L.; Selmic, Rastko R.

2013-05-01

In search and surveillance operations, deploying a team of mobile agents provides a robust solution that has multiple advantages over using a single agent in efficiency and minimizing exploration time. This paper addresses the challenge of identifying a target in a given environment when using a team of mobile agents by proposing a novel method of mapping and movement of agent teams in a cooperative manner. The approach consists of two parts. First, the region is partitioned into a hexagonal beehive structure in order to provide equidistant movements in every direction and to allow for more natural and flexible environment mapping. Additionally, in search environments that are partitioned into hexagons, mobile agents have an efficient travel path while performing searches due to this partitioning approach. Second, we use a team of mobile agents that move in a cooperative manner and utilize the Tabu Random algorithm to search for the target. Due to the ever-increasing use of robotics and Unmanned Aerial Vehicle (UAV) platforms, the field of cooperative multi-agent search has developed many applications recently that would benefit from the use of the approach presented in this work, including: search and rescue operations, surveillance, data collection, and border patrol. In this paper, the increased efficiency of the Tabu Random Search algorithm method in combination with hexagonal partitioning is simulated, analyzed, and advantages of this approach are presented and discussed.

Stride search: A general algorithm for storm detection in high resolution climate data

DOE PAGES

Bosler, Peter Andrew; Roesler, Erika Louise; Taylor, Mark A.; ...

2015-09-08

This article discusses the problem of identifying extreme climate events such as intense storms within large climate data sets. The basic storm detection algorithm is reviewed, which splits the problem into two parts: a spatial search followed by a temporal correlation problem. Two specific implementations of the spatial search algorithm are compared. The commonly used grid point search algorithm is reviewed, and a new algorithm called Stride Search is introduced. Stride Search is designed to work at all latitudes, while grid point searches may fail in polar regions. Results from the two algorithms are compared for the application of tropicalmore » cyclone detection, and shown to produce similar results for the same set of storm identification criteria. The time required for both algorithms to search the same data set is compared. Furthermore, Stride Search's ability to search extreme latitudes is demonstrated for the case of polar low detection.« less

A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem

PubMed Central

Zamli, Kamal Z.; Din, Fakhrud; Bures, Miroslav

2018-01-01

The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima/maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (Lévy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima/maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95% confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95% confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90% confidence level. PMID:29771918

A hybrid Q-learning sine-cosine-based strategy for addressing the combinatorial test suite minimization problem.

PubMed

Zamli, Kamal Z; Din, Fakhrud; Ahmed, Bestoun S; Bures, Miroslav

2018-01-01

The sine-cosine algorithm (SCA) is a new population-based meta-heuristic algorithm. In addition to exploiting sine and cosine functions to perform local and global searches (hence the name sine-cosine), the SCA introduces several random and adaptive parameters to facilitate the search process. Although it shows promising results, the search process of the SCA is vulnerable to local minima/maxima due to the adoption of a fixed switch probability and the bounded magnitude of the sine and cosine functions (from -1 to 1). In this paper, we propose a new hybrid Q-learning sine-cosine- based strategy, called the Q-learning sine-cosine algorithm (QLSCA). Within the QLSCA, we eliminate the switching probability. Instead, we rely on the Q-learning algorithm (based on the penalty and reward mechanism) to dynamically identify the best operation during runtime. Additionally, we integrate two new operations (Lévy flight motion and crossover) into the QLSCA to facilitate jumping out of local minima/maxima and enhance the solution diversity. To assess its performance, we adopt the QLSCA for the combinatorial test suite minimization problem. Experimental results reveal that the QLSCA is statistically superior with regard to test suite size reduction compared to recent state-of-the-art strategies, including the original SCA, the particle swarm test generator (PSTG), adaptive particle swarm optimization (APSO) and the cuckoo search strategy (CS) at the 95% confidence level. However, concerning the comparison with discrete particle swarm optimization (DPSO), there is no significant difference in performance at the 95% confidence level. On a positive note, the QLSCA statistically outperforms the DPSO in certain configurations at the 90% confidence level.

Derivation and validation of the automated search algorithms to identify cognitive impairment and dementia in electronic health records.

PubMed

Amra, Sakusic; O'Horo, John C; Singh, Tarun D; Wilson, Gregory A; Kashyap, Rahul; Petersen, Ronald; Roberts, Rosebud O; Fryer, John D; Rabinstein, Alejandro A; Gajic, Ognjen

2017-02-01

Long-term cognitive impairment is a common and important problem in survivors of critical illness. We developed electronic search algorithms to identify cognitive impairment and dementia from the electronic medical records (EMRs) that provide opportunity for big data analysis. Eligible patients met 2 criteria. First, they had a formal cognitive evaluation by The Mayo Clinic Study of Aging. Second, they were hospitalized in intensive care unit at our institution between 2006 and 2014. The "criterion standard" for diagnosis was formal cognitive evaluation supplemented by input from an expert neurologist. Using all available EMR data, we developed and improved our algorithms in the derivation cohort and validated them in the independent validation cohort. Of 993 participants who underwent formal cognitive testing and were hospitalized in intensive care unit, we selected 151 participants at random to form the derivation and validation cohorts. The automated electronic search algorithm for cognitive impairment was 94.3% sensitive and 93.0% specific. The search algorithms for dementia achieved respective sensitivity and specificity of 97% and 99%. EMR search algorithms significantly outperformed International Classification of Diseases codes. Automated EMR data extractions for cognitive impairment and dementia are reliable and accurate and can serve as acceptable and efficient alternatives to time-consuming manual data review. Copyright © 2016 Elsevier Inc. All rights reserved.

A bio-inspired swarm robot coordination algorithm for multiple target searching

NASA Astrophysics Data System (ADS)

Meng, Yan; Gan, Jing; Desai, Sachi

2008-04-01

The coordination of a multi-robot system searching for multi targets is challenging under dynamic environment since the multi-robot system demands group coherence (agents need to have the incentive to work together faithfully) and group competence (agents need to know how to work together well). In our previous proposed bio-inspired coordination method, Local Interaction through Virtual Stigmergy (LIVS), one problem is the considerable randomness of the robot movement during coordination, which may lead to more power consumption and longer searching time. To address these issues, an adaptive LIVS (ALIVS) method is proposed in this paper, which not only considers the travel cost and target weight, but also predicting the target/robot ratio and potential robot redundancy with respect to the detected targets. Furthermore, a dynamic weight adjustment is also applied to improve the searching performance. This new method a truly distributed method where each robot makes its own decision based on its local sensing information and the information from its neighbors. Basically, each robot only communicates with its neighbors through a virtual stigmergy mechanism and makes its local movement decision based on a Particle Swarm Optimization (PSO) algorithm. The proposed ALIVS algorithm has been implemented on the embodied robot simulator, Player/Stage, in a searching target. The simulation results demonstrate the efficiency and robustness in a power-efficient manner with the real-world constraints.

Young adult international adoptees' search for birth parents.

PubMed

Tieman, Wendy; van der Ende, Jan; Verhulst, Frank C

2008-10-01

This study examines international adoptees and factors associated with searching for birth parents. A total of 1,417 international adoptees in The Netherlands, aged 24 to 30 years, were divided into 4 groups: uninterested nonsearchers, interested nonsearchers, searchers, and reunited searchers. In total, 32% of adoptees had searched. Although the majority of searchers were well-adjusted, they had more problems--mainly internalizing problems--than uninterested nonsearchers. These problems, however, were not caused by the search itself. It is concluded that searching is the product of natural curiosity influenced by external factors such as the divorce of adoptive parents and the options for searching. Copyright 2008 APA, all rights reserved.

Online Help for Problem Gambling among Chinese Youths

ERIC Educational Resources Information Center

Lee, Chang Boon Patrick

2011-01-01

The objectives of this study were to determine the perceptions and accessibility of online help for problem gambling among Chinese youths. A group of undergraduates participated in a survey cum laboratory exercise to search for help for problem gambling in Macao, Hong Kong, and China. Online search engines were used. During the search process,…

Progress in low-resolution ab initio phasing with CrowdPhase

DOE PAGES

Jorda, Julien; Sawaya, Michael R.; Yeates, Todd O.

2016-03-01

Ab initio phasing by direct computational methods in low-resolution X-ray crystallography is a long-standing challenge. A common approach is to consider it as two subproblems: sampling of phase space and identification of the correct solution. While the former is amenable to a myriad of search algorithms, devising a reliable target function for the latter problem remains an open question. Here, recent developments in CrowdPhase, a collaborative online game powered by a genetic algorithm that evolves an initial population of individuals with random genetic make-up ( i.e. random phases) each expressing a phenotype in the form of an electron-density map, aremore » presented. Success relies on the ability of human players to visually evaluate the quality of these maps and, following a Darwinian survival-of-the-fittest concept, direct the search towards optimal solutions. While an initial study demonstrated the feasibility of the approach, some important crystallographic issues were overlooked for the sake of simplicity. To address these, the new CrowdPhase includes consideration of space-group symmetry, a method for handling missing amplitudes, the use of a map correlation coefficient as a quality metric and a solvent-flattening step. Lastly, performances of this installment are discussed for two low-resolution test cases based on bona fide diffraction data.« less

Progress in low-resolution ab initio phasing with CrowdPhase

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jorda, Julien; Sawaya, Michael R.; Yeates, Todd O.

Ab initio phasing by direct computational methods in low-resolution X-ray crystallography is a long-standing challenge. A common approach is to consider it as two subproblems: sampling of phase space and identification of the correct solution. While the former is amenable to a myriad of search algorithms, devising a reliable target function for the latter problem remains an open question. Here, recent developments in CrowdPhase, a collaborative online game powered by a genetic algorithm that evolves an initial population of individuals with random genetic make-up ( i.e. random phases) each expressing a phenotype in the form of an electron-density map, aremore » presented. Success relies on the ability of human players to visually evaluate the quality of these maps and, following a Darwinian survival-of-the-fittest concept, direct the search towards optimal solutions. While an initial study demonstrated the feasibility of the approach, some important crystallographic issues were overlooked for the sake of simplicity. To address these, the new CrowdPhase includes consideration of space-group symmetry, a method for handling missing amplitudes, the use of a map correlation coefficient as a quality metric and a solvent-flattening step. Lastly, performances of this installment are discussed for two low-resolution test cases based on bona fide diffraction data.« less

When the lowest energy does not induce native structures: parallel minimization of multi-energy values by hybridizing searching intelligences.

PubMed

Lü, Qiang; Xia, Xiao-Yan; Chen, Rong; Miao, Da-Jun; Chen, Sha-Sha; Quan, Li-Jun; Li, Hai-Ou

2012-01-01

Protein structure prediction (PSP), which is usually modeled as a computational optimization problem, remains one of the biggest challenges in computational biology. PSP encounters two difficult obstacles: the inaccurate energy function problem and the searching problem. Even if the lowest energy has been luckily found by the searching procedure, the correct protein structures are not guaranteed to obtain. A general parallel metaheuristic approach is presented to tackle the above two problems. Multi-energy functions are employed to simultaneously guide the parallel searching threads. Searching trajectories are in fact controlled by the parameters of heuristic algorithms. The parallel approach allows the parameters to be perturbed during the searching threads are running in parallel, while each thread is searching the lowest energy value determined by an individual energy function. By hybridizing the intelligences of parallel ant colonies and Monte Carlo Metropolis search, this paper demonstrates an implementation of our parallel approach for PSP. 16 classical instances were tested to show that the parallel approach is competitive for solving PSP problem. This parallel approach combines various sources of both searching intelligences and energy functions, and thus predicts protein conformations with good quality jointly determined by all the parallel searching threads and energy functions. It provides a framework to combine different searching intelligence embedded in heuristic algorithms. It also constructs a container to hybridize different not-so-accurate objective functions which are usually derived from the domain expertise.

When the Lowest Energy Does Not Induce Native Structures: Parallel Minimization of Multi-Energy Values by Hybridizing Searching Intelligences

PubMed Central

Lü, Qiang; Xia, Xiao-Yan; Chen, Rong; Miao, Da-Jun; Chen, Sha-Sha; Quan, Li-Jun; Li, Hai-Ou

2012-01-01

Background Protein structure prediction (PSP), which is usually modeled as a computational optimization problem, remains one of the biggest challenges in computational biology. PSP encounters two difficult obstacles: the inaccurate energy function problem and the searching problem. Even if the lowest energy has been luckily found by the searching procedure, the correct protein structures are not guaranteed to obtain. Results A general parallel metaheuristic approach is presented to tackle the above two problems. Multi-energy functions are employed to simultaneously guide the parallel searching threads. Searching trajectories are in fact controlled by the parameters of heuristic algorithms. The parallel approach allows the parameters to be perturbed during the searching threads are running in parallel, while each thread is searching the lowest energy value determined by an individual energy function. By hybridizing the intelligences of parallel ant colonies and Monte Carlo Metropolis search, this paper demonstrates an implementation of our parallel approach for PSP. 16 classical instances were tested to show that the parallel approach is competitive for solving PSP problem. Conclusions This parallel approach combines various sources of both searching intelligences and energy functions, and thus predicts protein conformations with good quality jointly determined by all the parallel searching threads and energy functions. It provides a framework to combine different searching intelligence embedded in heuristic algorithms. It also constructs a container to hybridize different not-so-accurate objective functions which are usually derived from the domain expertise. PMID:23028708

Adaptiveness in monotone pseudo-Boolean optimization and stochastic neural computation.

PubMed

Grossi, Giuliano

2009-08-01

Hopfield neural network (HNN) is a nonlinear computational model successfully applied in finding near-optimal solutions of several difficult combinatorial problems. In many cases, the network energy function is obtained through a learning procedure so that its minima are states falling into a proper subspace (feasible region) of the search space. However, because of the network nonlinearity, a number of undesirable local energy minima emerge from the learning procedure, significantly effecting the network performance. In the neural model analyzed here, we combine both a penalty and a stochastic process in order to enhance the performance of a binary HNN. The penalty strategy allows us to gradually lead the search towards states representing feasible solutions, so avoiding oscillatory behaviors or asymptotically instable convergence. Presence of stochastic dynamics potentially prevents the network to fall into shallow local minima of the energy function, i.e., quite far from global optimum. Hence, for a given fixed network topology, the desired final distribution on the states can be reached by carefully modulating such process. The model uses pseudo-Boolean functions both to express problem constraints and cost function; a combination of these two functions is then interpreted as energy of the neural network. A wide variety of NP-hard problems fall in the class of problems that can be solved by the model at hand, particularly those having a monotonic quadratic pseudo-Boolean function as constraint function. That is, functions easily derived by closed algebraic expressions representing the constraint structure and easy (polynomial time) to maximize. We show the asymptotic convergence properties of this model characterizing its state space distribution at thermal equilibrium in terms of Markov chain and give evidence of its ability to find high quality solutions on benchmarks and randomly generated instances of two specific problems taken from the computational graph theory.

The constitutionality of random drug and alcohol testing of students in secondary schools.

PubMed

Estrin, Irene; Sher, Leo

2006-01-01

Adolescent drug and alcohol use is a major public health problem. Multiple studies indicate that substance use is a risk factor for physical and mental disorders in adolescents. Secondary schools and the communities they serve have been facing a long-standing problem of substance abuse. American adolescents have become quite accustomed to drug prevention being a part of their curriculum. However, some policy decisions made by school administrators have been legally challenged. In 1989, Vernonia School District serving a small community in Oregon, instituted a random drug testing policy of its athletes. In 1991, the parents of a seventh grader refused to give their consent for random drug testing. The seventh grader was denied participation in the sport and sued the School District arguing that the school policy violated the Fourth and Fourteenth Amendments to the United States Constitution, and Article I, Section 9 of the Oregon Constitution. In 1995, on appeal to the Supreme Court of the United States, the School District won the case. The Vernonia School District versus Acton case became a landmark case, but random drug and alcohol testing in secondary schools has been a subject of multiple court cases. The authors discuss three of them. Both Federal and State Courts have recognized that a secondary school environment in itself represents "a special need," for which suspicionless searches are sometimes necessary to maintain order, safety, and discipline. Drug and alcohol testing programs in secondary schools may still be challenged on its legality. Therefore, examining court sanctioned programs and their long-term efficacy statistics is recommended.

Geometric Modeling of Inclusions as Ellipsoids

NASA Technical Reports Server (NTRS)

Bonacuse, Peter J.

2008-01-01

Nonmetallic inclusions in gas turbine disk alloys can have a significant detrimental impact on fatigue life. Because large inclusions that lead to anomalously low lives occur infrequently, probabilistic approaches can be utilized to avoid the excessively conservative assumption of lifing to a large inclusion in a high stress location. A prerequisite to modeling the impact of inclusions on the fatigue life distribution is a characterization of the inclusion occurrence rate and size distribution. To help facilitate this process, a geometric simulation of the inclusions was devised. To make the simulation problem tractable, the irregularly sized and shaped inclusions were modeled as arbitrarily oriented, three independent dimensioned, ellipsoids. Random orientation of the ellipsoid is accomplished through a series of three orthogonal rotations of axes. In this report, a set of mathematical models for the following parameters are described: the intercepted area of a randomly sectioned ellipsoid, the dimensions and orientation of the intercepted ellipse, the area of a randomly oriented sectioned ellipse, the depth and width of a randomly oriented sectioned ellipse, and the projected area of a randomly oriented ellipsoid. These parameters are necessary to determine an inclusion s potential to develop a propagating fatigue crack. Without these mathematical models, computationally expensive search algorithms would be required to compute these parameters.

Charting epilepsy by searching for intelligence in network space with the help of evolving autonomous agents.

PubMed

Ohayon, Elan L; Kalitzin, Stiliyan; Suffczynski, Piotr; Jin, Frank Y; Tsang, Paul W; Borrett, Donald S; Burnham, W McIntyre; Kwan, Hon C

2004-01-01

The problem of demarcating neural network space is formidable. A simple fully connected recurrent network of five units (binary activations, synaptic weight resolution of 10) has 3.2 *10(26) possible initial states. The problem increases drastically with scaling. Here we consider three complementary approaches to help direct the exploration to distinguish epileptic from healthy networks. [1] First, we perform a gross mapping of the space of five-unit continuous recurrent networks using randomized weights and initial activations. The majority of weight patterns (>70%) were found to result in neural assemblies exhibiting periodic limit-cycle oscillatory behavior. [2] Next we examine the activation space of non-periodic networks demonstrating that the emergence of paroxysmal activity does not require changes in connectivity. [3] The next challenge is to focus the search of network space to identify networks with more complex dynamics. Here we rely on a major available indicator critical to clinical assessment but largely ignored by epilepsy modelers, namely: behavioral states. To this end, we connected the above network layout to an external robot in which interactive states were evolved. The first random generation showed a distribution in line with approach [1]. That is, the predominate phenotypes were fixed-point or oscillatory with seizure-like motor output. As evolution progressed the profile changed markedly. Within 20 generations the entire population was able to navigate a simple environment with all individuals exhibiting multiply-stable behaviors with no cases of default locked limit-cycle oscillatory motor behavior. The resultant population may thus afford us a view of the architectural principles demarcating healthy biological networks from the pathological. The approach has an advantage over other epilepsy modeling techniques in providing a way to clarify whether observed dynamics or suggested therapies are pointing to computational viability or dead space.

Probabilistic track coverage in cooperative sensor networks.

PubMed

Ferrari, Silvia; Zhang, Guoxian; Wettergren, Thomas A

2010-12-01

The quality of service of a network performing cooperative track detection is represented by the probability of obtaining multiple elementary detections over time along a target track. Recently, two different lines of research, namely, distributed-search theory and geometric transversals, have been used in the literature for deriving the probability of track detection as a function of random and deterministic sensors' positions, respectively. In this paper, we prove that these two approaches are equivalent under the same problem formulation. Also, we present a new performance function that is derived by extending the geometric-transversal approach to the case of random sensors' positions using Poisson flats. As a result, a unified approach for addressing track detection in both deterministic and probabilistic sensor networks is obtained. The new performance function is validated through numerical simulations and is shown to bring about considerable computational savings for both deterministic and probabilistic sensor networks.

Demonstrating the suitability of genetic algorithms for driving microbial ecosystems in desirable directions.

PubMed

Vandecasteele, Frederik P J; Hess, Thomas F; Crawford, Ronald L

2007-07-01

The functioning of natural microbial ecosystems is determined by biotic interactions, which are in turn influenced by abiotic environmental conditions. Direct experimental manipulation of such conditions can be used to purposefully drive ecosystems toward exhibiting desirable functions. When a set of environmental conditions can be manipulated to be present at a discrete number of levels, finding the right combination of conditions to obtain the optimal desired effect becomes a typical combinatorial optimisation problem. Genetic algorithms are a class of robust and flexible search and optimisation techniques from the field of computer science that may be very suitable for such a task. To verify this idea, datasets containing growth levels of the total microbial community of four different natural microbial ecosystems in response to all possible combinations of a set of five chemical supplements were obtained. Subsequently, the ability of a genetic algorithm to search this parameter space for combinations of supplements driving the microbial communities to high levels of growth was compared to that of a random search, a local search, and a hill-climbing algorithm, three intuitive alternative optimisation approaches. The results indicate that a genetic algorithm is very suitable for driving microbial ecosystems in desirable directions, which opens opportunities for both fundamental ecological research and industrial applications.

Data-driven indexing mechanism for the recognition of polyhedral objects

NASA Astrophysics Data System (ADS)

McLean, Stewart; Horan, Peter; Caelli, Terry M.

1992-02-01

This paper is concerned with the problem of searching large model databases. To date, most object recognition systems have concentrated on the problem of matching using simple searching algorithms. This is quite acceptable when the number of object models is small. However, in the future, general purpose computer vision systems will be required to recognize hundreds or perhaps thousands of objects and, in such circumstances, efficient searching algorithms will be needed. The problem of searching a large model database is one which must be addressed if future computer vision systems are to be at all effective. In this paper we present a method we call data-driven feature-indexed hypothesis generation as one solution to the problem of searching large model databases.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bosler, Peter Andrew; Roesler, Erika Louise; Taylor, Mark A.

This article discusses the problem of identifying extreme climate events such as intense storms within large climate data sets. The basic storm detection algorithm is reviewed, which splits the problem into two parts: a spatial search followed by a temporal correlation problem. Two specific implementations of the spatial search algorithm are compared. The commonly used grid point search algorithm is reviewed, and a new algorithm called Stride Search is introduced. Stride Search is designed to work at all latitudes, while grid point searches may fail in polar regions. Results from the two algorithms are compared for the application of tropicalmore » cyclone detection, and shown to produce similar results for the same set of storm identification criteria. The time required for both algorithms to search the same data set is compared. Furthermore, Stride Search's ability to search extreme latitudes is demonstrated for the case of polar low detection.« less

Optimal random search for a single hidden target.

PubMed

Snider, Joseph

2011-01-01

A single target is hidden at a location chosen from a predetermined probability distribution. Then, a searcher must find a second probability distribution from which random search points are sampled such that the target is found in the minimum number of trials. Here it will be shown that if the searcher must get very close to the target to find it, then the best search distribution is proportional to the square root of the target distribution regardless of dimension. For a Gaussian target distribution, the optimum search distribution is approximately a Gaussian with a standard deviation that varies inversely with how close the searcher must be to the target to find it. For a network where the searcher randomly samples nodes and looks for the fixed target along edges, the optimum is either to sample a node with probability proportional to the square root of the out-degree plus 1 or not to do so at all.

Vocational interventions for unemployed: effects on work participation and mental distress. A systematic review.

PubMed

Audhoe, Selwin S; Hoving, Jan L; Sluiter, Judith K; Frings-Dresen, Monique H W

2010-03-01

Unemployment is a growing problem worldwide. Unemployment or job loss is one of the most stressful of life events and can lead to diminished social status, disturbed social role patterns, financial debt, reduced self-esteem and feelings of guilt. The purpose of this review was to determine the effectiveness of vocational interventions on work participation and mental distress for unemployed adults and to provide an overview of the characteristics of these interventions. Medline, EMBASE and PsycINFO were systematically searched for studies published between 1990 and August 2008. Intervention studies aimed at work participation and helping with mental distress for the unemployed were included. Methodological quality of the included studies was assessed. Six articles based on five intervention studies, of which two randomized controlled trials, fulfilled all inclusion criteria. The methodological quality of the studies ranged from good to poor. All five interventions applied group training techniques aimed at promoting re-employment and/or improving mental health. The duration of the interventions varied from 1 week to 6 months. The interventions focused on acquiring job-search skills, maintaining paid work, personal development and preparedness against setbacks during the job-search process. Only one intervention study (randomized controlled trial) reported a significant effect on re-employment. Based on our review, we conclude that there is weak evidence to support the use of vocational interventions to improve work participation and limited evidence to reduce mental distress for the unemployed. We recommend further development and evaluation of return to work intervention strategies for unemployed individuals.

Proteomics Versus Clinical Data and Stochastic Local Search Based Feature Selection for Acute Myeloid Leukemia Patients' Classification.

PubMed

Chebouba, Lokmane; Boughaci, Dalila; Guziolowski, Carito

2018-06-04

The use of data issued from high throughput technologies in drug target problems is widely widespread during the last decades. This study proposes a meta-heuristic framework using stochastic local search (SLS) combined with random forest (RF) where the aim is to specify the most important genes and proteins leading to the best classification of Acute Myeloid Leukemia (AML) patients. First we use a stochastic local search meta-heuristic as a feature selection technique to select the most significant proteins to be used in the classification task step. Then we apply RF to classify new patients into their corresponding classes. The evaluation technique is to run the RF classifier on the training data to get a model. Then, we apply this model on the test data to find the appropriate class. We use as metrics the balanced accuracy (BAC) and the area under the receiver operating characteristic curve (AUROC) to measure the performance of our model. The proposed method is evaluated on the dataset issued from DREAM 9 challenge. The comparison is done with a pure random forest (without feature selection), and with the two best ranked results of the DREAM 9 challenge. We used three types of data: only clinical data, only proteomics data, and finally clinical and proteomics data combined. The numerical results show that the highest scores are obtained when using clinical data alone, and the lowest is obtained when using proteomics data alone. Further, our method succeeds in finding promising results compared to the methods presented in the DREAM challenge.

Home visits during pregnancy and after birth for women with an alcohol or drug problem.

PubMed

Turnbull, Catherine; Osborn, David A

2012-01-18

One potential method of improving outcome for pregnant or postpartum women with a drug or alcohol problem is with home visits. To determine the effects of home visits during pregnancy and/or after birth for women with a drug or alcohol problem. We searched the Cochrane Pregnancy and Childbirth Group's Trials Register (30 November 2011), CENTRAL (The Cochrane Library 2011, Issue 4 of 4), MEDLINE (1966 to 30 November 2011), EMBASE (1980 to 30 November 2011), CINAHL (1982 to 30 November 2011) and PsycINFO (1974 to 30 November 2011) supplemented by searches of citations from previous reviews and trials and contact with experts. Studies using random or quasi-random allocation of pregnant or postpartum women with a drug or alcohol problem to home visits. Trials enrolling high-risk women of whom more than 50% were reported to use drugs or alcohol were also eligible. Review authors performed assessments of trials independently. We performed statistical analyses using fixed-effect and random-effects models where appropriate. Seven studies (reporting 803 mother-infant pairs) compared home visits mostly after birth with no home visits. Visitors included community health nurses, paediatric nurses, trained counsellors, paraprofessional advocates, midwives and lay African-American women. Several studies had significant methodological limitations. There was no significant difference in continued illicit drug use (three studies, 384 women; risk ratio (RR) 1.05, 95% confidence interval (CI) 0.89 to 1.24), continued alcohol use (three studies, 379 women; RR 1.18, 95% CI 0.96 to 1.46), failure to enrol in a drug treatment program (two studies, 211 women; RR 0.45, 95% CI 0.10 to 1.94), not breastfeeding at six months (two studies, 260 infants; RR 0.95, 95% CI 0.83 to 1.10), incomplete six-month infant vaccination schedule (two studies, 260 infants; RR 1.09, 95% CI 0.91 to 1.32), the Bayley Mental Development Index (three studies, 199 infants; mean difference 2.89, 95% CI -1.17 to 6.95) or Psychomotor Index (MD 3.14, 95% CI -0.03 to 6.32), child behavioural problems (RR 0.46, 95% CI 0.21 to 1.01), infants not in care of biological mother (two studies, 254 infants; RR 0.83, 95% CI 0.50 to 1.39), non-accidental injury and non-voluntary foster care (two studies, 254 infants; RR 0.16, 95% CI 0.02 to 1.23) or infant death (three studies, 288 infants; RR 0.70, 95% CI 0.12 to 4.16). Individual studies reported a significant reduction in involvement with child protective services (RR 0.38, 95% CI 0.20 to 0.74) and failure to use postpartum contraception (RR 0.41, 95% CI 0.20 to 0.82). There is insufficient evidence to recommend the routine use of home visits for pregnant or postpartum women with a drug or alcohol problem. Further large, high-quality trials are needed.

Optimal design of earth-moving machine elements with cusp catastrophe theory application

NASA Astrophysics Data System (ADS)

Pitukhin, A. V.; Skobtsov, I. G.

2017-10-01

This paper deals with the optimal design problem solution for the operator of an earth-moving machine with a roll-over protective structure (ROPS) in terms of the catastrophe theory. A brief description of the catastrophe theory is presented, the cusp catastrophe is considered, control parameters are viewed as Gaussian stochastic quantities in the first part of the paper. The statement of optimal design problem is given in the second part of the paper. It includes the choice of the objective function and independent design variables, establishment of system limits. The objective function is determined as mean total cost that includes initial cost and cost of failure according to the cusp catastrophe probability. Algorithm of random search method with an interval reduction subject to side and functional constraints is given in the last part of the paper. The way of optimal design problem solution can be applied to choose rational ROPS parameters, which will increase safety and reduce production and exploitation expenses.

Classification of Medical Datasets Using SVMs with Hybrid Evolutionary Algorithms Based on Endocrine-Based Particle Swarm Optimization and Artificial Bee Colony Algorithms.

PubMed

Lin, Kuan-Cheng; Hsieh, Yi-Hsiu

2015-10-01

The classification and analysis of data is an important issue in today's research. Selecting a suitable set of features makes it possible to classify an enormous quantity of data quickly and efficiently. Feature selection is generally viewed as a problem of feature subset selection, such as combination optimization problems. Evolutionary algorithms using random search methods have proven highly effective in obtaining solutions to problems of optimization in a diversity of applications. In this study, we developed a hybrid evolutionary algorithm based on endocrine-based particle swarm optimization (EPSO) and artificial bee colony (ABC) algorithms in conjunction with a support vector machine (SVM) for the selection of optimal feature subsets for the classification of datasets. The results of experiments using specific UCI medical datasets demonstrate that the accuracy of the proposed hybrid evolutionary algorithm is superior to that of basic PSO, EPSO and ABC algorithms, with regard to classification accuracy using subsets with a reduced number of features.

Optimizing Multi-Product Multi-Constraint Inventory Control Systems with Stochastic Replenishments

NASA Astrophysics Data System (ADS)

Allah Taleizadeh, Ata; Aryanezhad, Mir-Bahador; Niaki, Seyed Taghi Akhavan

Multi-periodic inventory control problems are mainly studied employing two assumptions. The first is the continuous review, where depending on the inventory level orders can happen at any time and the other is the periodic review, where orders can only happen at the beginning of each period. In this study, we relax these assumptions and assume that the periodic replenishments are stochastic in nature. Furthermore, we assume that the periods between two replenishments are independent and identically random variables. For the problem at hand, the decision variables are of integer-type and there are two kinds of space and service level constraints for each product. We develop a model of the problem in which a combination of back-order and lost-sales are considered for the shortages. Then, we show that the model is of an integer-nonlinear-programming type and in order to solve it, a search algorithm can be utilized. We employ a simulated annealing approach and provide a numerical example to demonstrate the applicability of the proposed methodology.

A comparison of fitness-case sampling methods for genetic programming

NASA Astrophysics Data System (ADS)

Martínez, Yuliana; Naredo, Enrique; Trujillo, Leonardo; Legrand, Pierrick; López, Uriel

2017-11-01

Genetic programming (GP) is an evolutionary computation paradigm for automatic program induction. GP has produced impressive results but it still needs to overcome some practical limitations, particularly its high computational cost, overfitting and excessive code growth. Recently, many researchers have proposed fitness-case sampling methods to overcome some of these problems, with mixed results in several limited tests. This paper presents an extensive comparative study of four fitness-case sampling methods, namely: Interleaved Sampling, Random Interleaved Sampling, Lexicase Selection and Keep-Worst Interleaved Sampling. The algorithms are compared on 11 symbolic regression problems and 11 supervised classification problems, using 10 synthetic benchmarks and 12 real-world data-sets. They are evaluated based on test performance, overfitting and average program size, comparing them with a standard GP search. Comparisons are carried out using non-parametric multigroup tests and post hoc pairwise statistical tests. The experimental results suggest that fitness-case sampling methods are particularly useful for difficult real-world symbolic regression problems, improving performance, reducing overfitting and limiting code growth. On the other hand, it seems that fitness-case sampling cannot improve upon GP performance when considering supervised binary classification.

Optimizing event selection with the random grid search

NASA Astrophysics Data System (ADS)

Bhat, Pushpalatha C.; Prosper, Harrison B.; Sekmen, Sezen; Stewart, Chip

2018-07-01

The random grid search (RGS) is a simple, but efficient, stochastic algorithm to find optimal cuts that was developed in the context of the search for the top quark at Fermilab in the mid-1990s. The algorithm, and associated code, have been enhanced recently with the introduction of two new cut types, one of which has been successfully used in searches for supersymmetry at the Large Hadron Collider. The RGS optimization algorithm is described along with the recent developments, which are illustrated with two examples from particle physics. One explores the optimization of the selection of vector boson fusion events in the four-lepton decay mode of the Higgs boson and the other optimizes SUSY searches using boosted objects and the razor variables.

Robust local search for spacecraft operations using adaptive noise

NASA Technical Reports Server (NTRS)

Fukunaga, Alex S.; Rabideau, Gregg; Chien, Steve

2004-01-01

Randomization is a standard technique for improving the performance of local search algorithms for constraint satisfaction. However, it is well-known that local search algorithms are constraints satisfaction. However, it is well-known that local search algorithms are to the noise values selected. We investigate the use of an adaptive noise mechanism in an iterative repair-based planner/scheduler for spacecraft operations. Preliminary results indicate that adaptive noise makes the use of randomized repair moves safe and robust; that is, using adaptive noise makes it possible to consistently achieve, performance comparable with the best tuned noise setting without the need for manually tuning the noise parameter.

Evaluation of internet search trends of some common oral problems, 2004 to 2014.

PubMed

Harorli, O T; Harorli, H

2014-09-01

Internet search trend volumes can provide free, fast and pertinent information about peoples' online interests. No study has yet been conducted on internet search trends in dentistry. This study aims to investigate ten years' data on internet search volumes regarding some oral problems: "toothache", "tooth decay", "gum disease", "wisdom teeth" and "oral cancer". The study also aims to investigate the most common geographic search locations and to examine related searches. Worldwide intermet search trend data over a period of 532 weeks (4 January 2004 and 15 March 2014) retrieved from the Google Trends web site was interrogated for each search term to identify search trends, regional interests, and related searches. The search volumes for the terms "toothache "and "wisdom teeth" increased over the decade while "tooth decay", "gum disease", and "oral cancer" showed slight changes. Each term was most commonly searched in different counties: "toothache", Philippines; "tooth decay", Singapore; "Gum Disease", Ireland; "Wisdom Teeth", United States; and "Oral cancer", India. Related searches were mainly focused on symptoms and remedies of these problems. Regional and time-related variations in search volumes may provide dental professionals with readily- and freely-available pertinent information on populations' internet searches regarding dental complaints.

Time to Treatment Initiation in People With Alzheimer Disease: A Meta-Analysis of Randomized Controlled Trials.

PubMed

Tsoi, Kelvin K F; Hirai, Hoyee W; Chan, Joyce Y C; Kwok, Timothy C Y

2016-01-01

Alzheimer disease (AD) is a global health problem which afflicts millions of old age population worldwide. Acetylcholinesterase inhibitors and memantine are recognized drug treatments with limited clinical efficacy. It is uncertain if earlier initiation of these drugs will result in better outcomes in the longer term. To evaluate the benefit of early treatment among people with AD. Prospective randomized controlled trials were systematically searched from the OVID databases. The trials were eligible if study participants diagnosed with AD and were randomized to have early or late treatment. Any clinical assessment scales on cognitive function, physical function, behavioral problems, and the overall clinical status were the primary outcomes, and any reported adverse events were the secondary outcomes. Ten randomized trials were identified between 2000 and 2010. A total of 3092 participants with AD with mean age 75.8 years were randomly assigned to receive early treatment or treatment delayed by placebo intervention for around 6 months. Compared with late treatment, early AD drug treatment showed no significant benefit on cognitive function [mean difference (MD) of Alzheimer's Disease Assessment Scale- Cognitive Subscale = -0.49, 95% CI = -1.67 to 0.69], physical function (MD of Alzheimer's Disease Cooperative Study Activities of Daily Living Inventory = 0.47, 95% CI = -1.44 to 2.39), behavioral problems (MD of Neuropsychiatric Inventory = -0.26, 95% CI = -2.70 to 2.18), and clinical status (MD of Clinician's Interview-Based Impression of Change plus Caregiver Input = 0.02, 95% CI = -0.23 to 0.27). Nausea was the most common adverse events in acetylcholinesterase inhibitor users, while memantine did not result in more side effects than the placebo group. For both drugs, early treatment had comparable adverse events when compared with late treatment. Earlier AD drug treatment by around 6 months did not result in significant difference in cognitive function, physical function, behavioral problems, and clinical status. This study included relative high proportion of early AD with the follow-up less than 2 years. Future studies can be conducted to further investigate the long-term findings. Copyright © 2016 AMDA – The Society for Post-Acute and Long-Term Care Medicine. Published by Elsevier Inc. All rights reserved.

Investigating the enhanced Best Performance Algorithm for Annual Crop Planning problem based on economic factors.

PubMed

Adewumi, Aderemi Oluyinka; Chetty, Sivashan

2017-01-01

The Annual Crop Planning (ACP) problem was a recently introduced problem in the literature. This study further expounds on this problem by presenting a new mathematical formulation, which is based on market economic factors. To determine solutions, a new local search metaheuristic algorithm is investigated which is called the enhanced Best Performance Algorithm (eBPA). eBPA's results are compared against two well-known local search metaheuristic algorithms; these include Tabu Search and Simulated Annealing. The results show the potential of the eBPA for continuous optimization problems.

The Impact of Random Metal Detector Searches on Contraband Possession and Feelings of Safety at School

ERIC Educational Resources Information Center

Bhatt, Rachana; Davis, Tomeka

2018-01-01

Weapons at school pose a danger to students as well as faculty. Educational administrators have attempted to reduce their prevalence by implementing random weapons searches in schools. This article examines the effectiveness of this approach using data from two geographically adjacent school districts in Florida (Miami-Dade and Broward). In the…

An Innovative Multi-Agent Search-and-Rescue Path Planning Approach

DTIC Science & Technology

2015-03-09

search problems from search theory and artificial intelligence /distributed robotic control, and pursuit-evasion problem perspectives may be found in...Dissanayake, “Probabilistic search for a moving target in an indoor environment”, In Proc. IEEE/RSJ Int. Conf. Intelligent Robots and Systems, 2006, pp...3393-3398. [7] H. Lau, and G. Dissanayake, “Optimal search for multiple targets in a built environment”, In Proc. IEEE/RSJ Int. Conf. Intelligent

Does the patient‐held record improve continuity and related outcomes in cancer care: a systematic review

PubMed Central

Gysels, Marjolein; Richardson, Alison; Higginson, Irene J.

2006-01-01

Abstract Objectives  To assess the effectiveness of the patient‐held record (PHR) in cancer care. Background  Patients with cancer may receive care from different services resulting in gaps. A PHR could provide continuity and patient involvement in care. Search strategy  Relevant literature was identified through five electronic databases (Medline, Embase, Cinahl, CCTR and CDSR) and hand searches. Inclusion criteria  Patient‐held records in cancer care with the purpose of improving communication and information exchange between and within different levels of care and to promote continuity of care and patients’ involvement in their own care. Data extraction and synthesis  Data extraction recorded characteristics of intervention, type of study and factors that contributed to methodological quality of individual studies. Data were then contrasted by setting, objectives, population, study design, outcome measures and changes in outcome, including knowledge, satisfaction, anxiety and depression. Methodological quality of randomized control trials and non‐experimental studies were assessed with separate standard grading scales. Main results and conclusions  Seven randomized control trials and six non‐experimental studies were identified. Evaluations of the PHR have reached equivocal findings. Randomized trials found an absence of effect, non‐experimental evaluations shed light on the conditions for its successful use. Most patients welcomed introduction of a PHR. Main problems related to its suitability for different patient groups and the lack of agreement between patients and health professionals regarding its function. Further research is required to determine the conditions under which the PHR can realize its potential as a tool to promote continuity of care and patient participation. PMID:17324196

The Lévy flight paradigm: random search patterns and mechanisms.

PubMed

Reynolds, A M; Rhodes, C J

2009-04-01

Over recent years there has been an accumulation of evidence from a variety of experimental, theoretical, and field studies that many organisms use a movement strategy approximated by Lévy flights when they are searching for resources. Lévy flights are random movements that can maximize the efficiency of resource searches in uncertain environments. This is a highly significant finding because it suggests that Lévy flights provide a rigorous mathematical basis for separating out evolved, innate behaviors from environmental influences. We discuss recent developments in random-search theory, as well as the many different experimental and data collection initiatives that have investigated search strategies. Methods for trajectory construction and robust data analysis procedures are presented. The key to prediction and understanding does, however, lie in the elucidation of mechanisms underlying the observed patterns. We discuss candidate neurological, olfactory, and learning mechanisms for the emergence of Lévy flight patterns in some organisms, and note that convergence of behaviors along such different evolutionary pathways is not surprising given the energetic efficiencies that Lévy flight movement patterns confer.

Experimental Matching of Instances to Heuristics for Constraint Satisfaction Problems.

PubMed

Moreno-Scott, Jorge Humberto; Ortiz-Bayliss, José Carlos; Terashima-Marín, Hugo; Conant-Pablos, Santiago Enrique

2016-01-01

Constraint satisfaction problems are of special interest for the artificial intelligence and operations research community due to their many applications. Although heuristics involved in solving these problems have largely been studied in the past, little is known about the relation between instances and the respective performance of the heuristics used to solve them. This paper focuses on both the exploration of the instance space to identify relations between instances and good performing heuristics and how to use such relations to improve the search. Firstly, the document describes a methodology to explore the instance space of constraint satisfaction problems and evaluate the corresponding performance of six variable ordering heuristics for such instances in order to find regions on the instance space where some heuristics outperform the others. Analyzing such regions favors the understanding of how these heuristics work and contribute to their improvement. Secondly, we use the information gathered from the first stage to predict the most suitable heuristic to use according to the features of the instance currently being solved. This approach proved to be competitive when compared against the heuristics applied in isolation on both randomly generated and structured instances of constraint satisfaction problems.

Experimental Matching of Instances to Heuristics for Constraint Satisfaction Problems

PubMed Central

Moreno-Scott, Jorge Humberto; Ortiz-Bayliss, José Carlos; Terashima-Marín, Hugo; Conant-Pablos, Santiago Enrique

2016-01-01

Constraint satisfaction problems are of special interest for the artificial intelligence and operations research community due to their many applications. Although heuristics involved in solving these problems have largely been studied in the past, little is known about the relation between instances and the respective performance of the heuristics used to solve them. This paper focuses on both the exploration of the instance space to identify relations between instances and good performing heuristics and how to use such relations to improve the search. Firstly, the document describes a methodology to explore the instance space of constraint satisfaction problems and evaluate the corresponding performance of six variable ordering heuristics for such instances in order to find regions on the instance space where some heuristics outperform the others. Analyzing such regions favors the understanding of how these heuristics work and contribute to their improvement. Secondly, we use the information gathered from the first stage to predict the most suitable heuristic to use according to the features of the instance currently being solved. This approach proved to be competitive when compared against the heuristics applied in isolation on both randomly generated and structured instances of constraint satisfaction problems. PMID:26949383

Auto-biometric for M-mode echocardiography

NASA Astrophysics Data System (ADS)

Zhang, Wei; Park, Jinhyong; Zhou, S. Kevin

2010-03-01

In this paper we present a system for fast and accurate detection of anatomical structures (calipers) in M-mode images. The task is challenging because of dramatic variations in their appearances. We propose to solve the problem in a progressive manner, which ensures both robustness and efficiency. It first obtains rough caliper localization using the intensity profile image. Then run a constrained search for accurate caliper positions. Markov Random Field (MRF) and warping image detectors are used for jointly considering appearance information and the geometric relationship between calipers. Extensive experiments show that our system achieves more accurate results and uses less time in comparison with previously reported work.

Mental Health Problems in a School Setting in Children and Adolescents.

PubMed

Schulte-Körne, Gerd

2016-03-18

10-20% of children and adolescents have a mental health problem of some type. Manifestations such as attention deficits, cognitive disturbances, lack of motivation, and negative mood all adversely affect scholastic development. It is often unclear what factors associated with school affect children's mental development and what preventive measures and interventions at school might be effective. This review is based on systematic reviews, meta-analyses, and randomized and non-randomized controlled trials that were retrieved by a selective search in the PubMed, PsycInfo, and Google Scholar databases. The prevalence of hyperkinetic disorder is 1-6%. Its main manifestations are motor hyperactivity, an attention deficit, and impulsive behavior. Learning disorders such as dyscalculia and dyslexia affect 4-6% of children each, while 4-5% of children and adolescents suffer from depression, which is twice as prevalent in girls as in boys. Mental health problems increase the risk of repeating a grade, truancy, and dropping out of school. The risk of developing an internalizing or externalizing mental health problem can be lessened by changes in the school environment and by the implementation of evidencebased school programs. Physicians, in collaboration with school social workers and psychologists, should help teachers recognize and contend with mental health problems among the children and adolescents whom they teach, to enable the timely detection of stress factors at school and the initiation of the necessary measures and aids. In particular, the school-entrance examination and screening for risk factors at school can make a positive contribution. Evidence-based preventive programs should be implemented in schools, and beneficial changes of the school environment should be a further goal.

The random fractional matching problem

NASA Astrophysics Data System (ADS)

Lucibello, Carlo; Malatesta, Enrico M.; Parisi, Giorgio; Sicuro, Gabriele

2018-05-01

We consider two formulations of the random-link fractional matching problem, a relaxed version of the more standard random-link (integer) matching problem. In one formulation, we allow each node to be linked to itself in the optimal matching configuration. In the other one, on the contrary, such a link is forbidden. Both problems have the same asymptotic average optimal cost of the random-link matching problem on the complete graph. Using a replica approach and previous results of Wästlund (2010 Acta Mathematica 204 91–150), we analytically derive the finite-size corrections to the asymptotic optimal cost. We compare our results with numerical simulations and we discuss the main differences between random-link fractional matching problems and the random-link matching problem.

Constraint Optimization Literature Review

DTIC Science & Technology

2015-11-01

COPs. 15. SUBJECT TERMS high-performance computing, mobile ad hoc network, optimization, constraint, satisfaction 16. SECURITY CLASSIFICATION OF: 17...Optimization Problems 1 2.1 Constraint Satisfaction Problems 1 2.2 Constraint Optimization Problems 3 3. Constraint Optimization Algorithms 9 3.1...Constraint Satisfaction Algorithms 9 3.1.1 Brute-Force search 9 3.1.2 Constraint Propagation 10 3.1.3 Depth-First Search 13 3.1.4 Local Search 18

Optimizing event selection with the random grid search

DOE PAGES

Bhat, Pushpalatha C.; Prosper, Harrison B.; Sekmen, Sezen; ...

2018-02-27

In this paper, the random grid search (RGS) is a simple, but efficient, stochastic algorithm to find optimal cuts that was developed in the context of the search for the top quark at Fermilab in the mid-1990s. The algorithm, and associated code, have been enhanced recently with the introduction of two new cut types, one of which has been successfully used in searches for supersymmetry at the Large Hadron Collider. The RGS optimization algorithm is described along with the recent developments, which are illustrated with two examples from particle physics. One explores the optimization of the selection of vector bosonmore » fusion events in the four-lepton decay mode of the Higgs boson and the other optimizes SUSY searches using boosted objects and the razor variables.« less

Optimizing Event Selection with the Random Grid Search

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bhat, Pushpalatha C.; Prosper, Harrison B.; Sekmen, Sezen

2017-06-29

The random grid search (RGS) is a simple, but efficient, stochastic algorithm to find optimal cuts that was developed in the context of the search for the top quark at Fermilab in the mid-1990s. The algorithm, and associated code, have been enhanced recently with the introduction of two new cut types, one of which has been successfully used in searches for supersymmetry at the Large Hadron Collider. The RGS optimization algorithm is described along with the recent developments, which are illustrated with two examples from particle physics. One explores the optimization of the selection of vector boson fusion events inmore » the four-lepton decay mode of the Higgs boson and the other optimizes SUSY searches using boosted objects and the razor variables.« less

Optimizing event selection with the random grid search

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bhat, Pushpalatha C.; Prosper, Harrison B.; Sekmen, Sezen

In this paper, the random grid search (RGS) is a simple, but efficient, stochastic algorithm to find optimal cuts that was developed in the context of the search for the top quark at Fermilab in the mid-1990s. The algorithm, and associated code, have been enhanced recently with the introduction of two new cut types, one of which has been successfully used in searches for supersymmetry at the Large Hadron Collider. The RGS optimization algorithm is described along with the recent developments, which are illustrated with two examples from particle physics. One explores the optimization of the selection of vector bosonmore » fusion events in the four-lepton decay mode of the Higgs boson and the other optimizes SUSY searches using boosted objects and the razor variables.« less

Application of tabu search to deterministic and stochastic optimization problems

NASA Astrophysics Data System (ADS)

Gurtuna, Ozgur

During the past two decades, advances in computer science and operations research have resulted in many new optimization methods for tackling complex decision-making problems. One such method, tabu search, forms the basis of this thesis. Tabu search is a very versatile optimization heuristic that can be used for solving many different types of optimization problems. Another research area, real options, has also gained considerable momentum during the last two decades. Real options analysis is emerging as a robust and powerful method for tackling decision-making problems under uncertainty. Although the theoretical foundations of real options are well-established and significant progress has been made in the theory side, applications are lagging behind. A strong emphasis on practical applications and a multidisciplinary approach form the basic rationale of this thesis. The fundamental concepts and ideas behind tabu search and real options are investigated in order to provide a concise overview of the theory supporting both of these two fields. This theoretical overview feeds into the design and development of algorithms that are used to solve three different problems. The first problem examined is a deterministic one: finding the optimal servicing tours that minimize energy and/or duration of missions for servicing satellites around Earth's orbit. Due to the nature of the space environment, this problem is modeled as a time-dependent, moving-target optimization problem. Two solution methods are developed: an exhaustive method for smaller problem instances, and a method based on tabu search for larger ones. The second and third problems are related to decision-making under uncertainty. In the second problem, tabu search and real options are investigated together within the context of a stochastic optimization problem: option valuation. By merging tabu search and Monte Carlo simulation, a new method for studying options, Tabu Search Monte Carlo (TSMC) method, is developed. The theoretical underpinnings of the TSMC method and the flow of the algorithm are explained. Its performance is compared to other existing methods for financial option valuation. In the third, and final, problem, TSMC method is used to determine the conditions of feasibility for hybrid electric vehicles and fuel cell vehicles. There are many uncertainties related to the technologies and markets associated with new generation passenger vehicles. These uncertainties are analyzed in order to determine the conditions in which new generation vehicles can compete with established technologies.

Benzodiazepines and related drugs for insomnia in palliative care.

PubMed

Hirst, A; Sloan, R

2002-01-01

Insomnia, a subjective complaint of poor sleep and associated impairment in daytime function, is a common problem. Currently, benzodiazepines are the most used pharmacological treatment for this complaint. They are considered helpful for occasional short-term use up to four weeks but longer term use is not advised due to potential problems regarding tolerance, dosing escalation, psychological addiction and physical dependence. There is no consensus on their utility in patients with progressive incurable conditions who may require assistance with sleep for many weeks as their condition deteriorates. To assess the effectiveness and safety of benzodiazepines or benzodiazepine receptor agonists such as Zolpidem, Zopiclone and Zaleplon for insomnia in palliative care. Several electronic databases were searched including Cochrane PaPaS Group specialized register, Cochrane Library Issue 4, 2001, MEDLINE, EMBASE, BNI plus, CINAHL, BIOLOGICAL ABSTRACTS, PSYCINFO, CANCERLIT, HEALTHSTAR, WEB OF SCIENCE, SIGLE, Dissertation Abstracts, ZETOC and the MetaRegister of ongoing trials. These were searched from 1960 to 2001 or as much of this range as possible. Additional articles were sought by handsearching reference lists in standard textbooks and reviews in the field and by contacting academic centres in palliative care and pharmaceutical companies. There were no language restrictions. Studies considered for inclusion were randomized controlled trials of adult patients in any setting, receiving palliative care or suffering an incurable progressive medical condition. (For example, cancers, AIDS, Motor Neurone Disease, Multiple Sclerosis, Parkinson's Disease, Chronic Obstructive Pulmonary Disease). There had to be an explicit complaint of insomnia in study participants, diagnosed by any of the three main classification systems (DSM-IV (APA 1994), ICSD (AASD 1990) or ICD (WHO 1992)), or as described in the study if it involved a subjective complaint of poor sleep. Studies had to compare a benzodiazepine or Zolpidem or Zopiclone or Zaleplon with placebo or active control for the treatment of insomnia. Any duration of therapy were considered. Abstracts were independently inspected by both reviewers, full papers were obtained where necessary. Where there was uncertainty advice was sought by a third (PW). Data extraction and quality assessments were undertaken independently by both reviewers. No randomized controlled trials were identified meeting the a priori inclusion criteria. Thirty-seven studies were considered but all were excluded from the review. Despite a comprehensive search no evidence from randomized controlled trials was identified. It was not possible to draw any conclusions regarding the use of benzodiazepines in palliative care.

LETTER TO THE EDITOR: Constant-time solution to the global optimization problem using Brüschweiler's ensemble search algorithm

NASA Astrophysics Data System (ADS)

Protopopescu, V.; D'Helon, C.; Barhen, J.

2003-06-01

A constant-time solution of the continuous global optimization problem (GOP) is obtained by using an ensemble algorithm. We show that under certain assumptions, the solution can be guaranteed by mapping the GOP onto a discrete unsorted search problem, whereupon Brüschweiler's ensemble search algorithm is applied. For adequate sensitivities of the measurement technique, the query complexity of the ensemble search algorithm depends linearly on the size of the function's domain. Advantages and limitations of an eventual NMR implementation are discussed.

Random deflections of a string on an elastic foundation.

NASA Technical Reports Server (NTRS)

Sanders, J. L., Jr.

1972-01-01

The paper is concerned with the problem of a taut string on a random elastic foundation subjected to random loads. The boundary value problem is transformed into an initial value problem by the method of invariant imbedding. Fokker-Planck equations for the random initial value problem are formulated and solved in some special cases. The analysis leads to a complete characterization of the random deflection function.

Online games: a novel approach to explore how partial information influences human random searches

NASA Astrophysics Data System (ADS)

Martínez-García, Ricardo; Calabrese, Justin M.; López, Cristóbal

2017-01-01

Many natural processes rely on optimizing the success ratio of a search process. We use an experimental setup consisting of a simple online game in which players have to find a target hidden on a board, to investigate how the rounds are influenced by the detection of cues. We focus on the search duration and the statistics of the trajectories traced on the board. The experimental data are explained by a family of random-walk-based models and probabilistic analytical approximations. If no initial information is given to the players, the search is optimized for cues that cover an intermediate spatial scale. In addition, initial information about the extension of the cues results, in general, in faster searches. Finally, strategies used by informed players turn into non-stationary processes in which the length of e ach displacement evolves to show a well-defined characteristic scale that is not found in non-informed searches.

Online games: a novel approach to explore how partial information influences human random searches.

PubMed

Martínez-García, Ricardo; Calabrese, Justin M; López, Cristóbal

2017-01-06

Many natural processes rely on optimizing the success ratio of a search process. We use an experimental setup consisting of a simple online game in which players have to find a target hidden on a board, to investigate how the rounds are influenced by the detection of cues. We focus on the search duration and the statistics of the trajectories traced on the board. The experimental data are explained by a family of random-walk-based models and probabilistic analytical approximations. If no initial information is given to the players, the search is optimized for cues that cover an intermediate spatial scale. In addition, initial information about the extension of the cues results, in general, in faster searches. Finally, strategies used by informed players turn into non-stationary processes in which the length of e ach displacement evolves to show a well-defined characteristic scale that is not found in non-informed searches.

Self Esteem, Information Search and Problem Solving Efficiency.

DTIC Science & Technology

1979-05-01

Weiss (1977, 1978) has shown that low self esteem workers are more likely to model the role behaviors and work values of superiors than are high self ...task where search is functional. Results showed that, as expected, low self esteem subjects searched for more information, search was functional and low ...situation. He has also argued that high self esteem individuals search for less information on problem solving tasks and are therefore less likely to

A predictive machine learning approach for microstructure optimization and materials design

DOE PAGES

Liu, Ruoqian; Kumar, Abhishek; Chen, Zhengzhang; ...

2015-06-23

This paper addresses an important materials engineering question: How can one identify the complete space (or as much of it as possible) of microstructures that are theoretically predicted to yield the desired combination of properties demanded by a selected application? We present a problem involving design of magnetoelastic Fe-Ga alloy microstructure for enhanced elastic, plastic and magnetostrictive properties. While theoretical models for computing properties given the microstructure are known for this alloy, inversion of these relationships to obtain microstructures that lead to desired properties is challenging, primarily due to the high dimensionality of microstructure space, multi-objective design requirement and non-uniquenessmore » of solutions. These challenges render traditional search-based optimization methods incompetent in terms of both searching efficiency and result optimality. In this paper, a route to address these challenges using a machine learning methodology is proposed. A systematic framework consisting of random data generation, feature selection and classification algorithms is developed. In conclusion, experiments with five design problems that involve identification of microstructures that satisfy both linear and nonlinear property constraints show that our framework outperforms traditional optimization methods with the average running time reduced by as much as 80% and with optimality that would not be achieved otherwise.« less

Investigating the enhanced Best Performance Algorithm for Annual Crop Planning problem based on economic factors

PubMed Central

2017-01-01

The Annual Crop Planning (ACP) problem was a recently introduced problem in the literature. This study further expounds on this problem by presenting a new mathematical formulation, which is based on market economic factors. To determine solutions, a new local search metaheuristic algorithm is investigated which is called the enhanced Best Performance Algorithm (eBPA). eBPA’s results are compared against two well-known local search metaheuristic algorithms; these include Tabu Search and Simulated Annealing. The results show the potential of the eBPA for continuous optimization problems. PMID:28792495

Lévy-taxis: a novel search strategy for finding odor plumes in turbulent flow-dominated environments

NASA Astrophysics Data System (ADS)

Pasternak, Zohar; Bartumeus, Frederic; Grasso, Frank W.

2009-10-01

Locating chemical plumes in aquatic or terrestrial environments is important for many economic, conservation, security and health related human activities. The localization process is composed mainly of two phases: finding the chemical plume and then tracking it to its source. Plume tracking has been the subject of considerable study whereas plume finding has received little attention. We address here the latter issue, where the searching agent must find the plume in a region often many times larger than the plume and devoid of the relevant chemical cues. The probability of detecting the plume not only depends on the movements of the searching agent but also on the fluid mechanical regime, shaping plume intermittency in space and time; this is a basic, general problem when exploring for ephemeral resources (e.g. moving and/or concealing targets). Here we present a bio-inspired search strategy named Lévy-taxis that, under certain conditions, located odor plumes significantly faster and with a better success rate than other search strategies such as Lévy walks (LW), correlated random walks (CRW) and systematic zig-zag. These results are based on computer simulations which contain, for the first time ever, digitalized real-world water flow and chemical plume instead of their theoretical model approximations. Combining elements of LW and CRW, Lévy-taxis is particularly efficient for searching in flow-dominated environments: it adaptively controls the stochastic search pattern using environmental information (i.e. flow) that is available throughout the course of the search and shows correlation with the source providing the cues. This strategy finds natural application in real-world search missions, both by humans and autonomous robots, since it accomodates the stochastic nature of chemical mixing in turbulent flows. In addition, it may prove useful in the field of behavioral ecology, explaining and predicting the movement patterns of various animals searching for food or mates.

Solving large scale traveling salesman problems by chaotic neurodynamics.

PubMed

Hasegawa, Mikio; Ikeguch, Tohru; Aihara, Kazuyuki

2002-03-01

We propose a novel approach for solving large scale traveling salesman problems (TSPs) by chaotic dynamics. First, we realize the tabu search on a neural network, by utilizing the refractory effects as the tabu effects. Then, we extend it to a chaotic neural network version. We propose two types of chaotic searching methods, which are based on two different tabu searches. While the first one requires neurons of the order of n2 for an n-city TSP, the second one requires only n neurons. Moreover, an automatic parameter tuning method of our chaotic neural network is presented for easy application to various problems. Last, we show that our method with n neurons is applicable to large TSPs such as an 85,900-city problem and exhibits better performance than the conventional stochastic searches and the tabu searches.

NASA Taxonomies for Searching Problem Reports and FMEAs

NASA Technical Reports Server (NTRS)

Malin, Jane T.; Throop, David R.

2006-01-01

Many types of hazard and risk analyses are used during the life cycle of complex systems, including Failure Modes and Effects Analysis (FMEA), Hazard Analysis, Fault Tree and Event Tree Analysis, Probabilistic Risk Assessment, Reliability Analysis and analysis of Problem Reporting and Corrective Action (PRACA) databases. The success of these methods depends on the availability of input data and the analysts knowledge. Standard nomenclature can increase the reusability of hazard, risk and problem data. When nomenclature in the source texts is not standard, taxonomies with mapping words (sets of rough synonyms) can be combined with semantic search to identify items and tag them with metadata based on a rich standard nomenclature. Semantic search uses word meanings in the context of parsed phrases to find matches. The NASA taxonomies provide the word meanings. Spacecraft taxonomies and ontologies (generalization hierarchies with attributes and relationships, based on terms meanings) are being developed for types of subsystems, functions, entities, hazards and failures. The ontologies are broad and general, covering hardware, software and human systems. Semantic search of Space Station texts was used to validate and extend the taxonomies. The taxonomies have also been used to extract system connectivity (interaction) models and functions from requirements text. Now the Reconciler semantic search tool and the taxonomies are being applied to improve search in the Space Shuttle PRACA database, to discover recurring patterns of failure. Usual methods of string search and keyword search fall short because the entries are terse and have numerous shortcuts (irregular abbreviations, nonstandard acronyms, cryptic codes) and modifier words cannot be used in sentence context to refine the search. The limited and fixed FMEA categories associated with the entries do not make the fine distinctions needed in the search. The approach assigns PRACA report titles to problem classes in the taxonomy. Each ontology class includes mapping words - near-synonyms naming different manifestations of that problem class. The mapping words for Problems, Entities and Functions are converted to a canonical form plus any of a small set of modifier words (e.g. non-uniformity NOT + UNIFORM.) The report titles are parsed as sentences if possible, or treated as a flat sequence of word tokens if parsing fails. When canonical forms in the title match mapping words, the PRACA entry is associated with the corresponding Problem, Entity or Function in the ontology. The user can search for types of failures associated with types of equipment, clustering by type of problem (e.g., all bearings found with problems of being uneven: rough, irregular, gritty ). The results could also be used for tagging PRACA report entries with rich metadata. This approach could also be applied to searching and tagging failure modes, failure effects and mitigations in FMEAs. In the pilot work, parsing 52K+ truncated titles (the test cases that were available), has resulted in identification of both a type of equipment and type of problem in about 75% of the cases. The results are displayed in a manner analogous to Google search results. The effort has also led to the enrichment of the taxonomy, adding some new categories and many new mapping words. Further work would make enhancements that have been identified for improving the clustering and further reducing the false alarm rate. (In searching for recurring problems, good clustering is more important than reducing false alarms). Searching complete PRACA reports should lead to immediate improvement.

Electrode channel selection based on backtracking search optimization in motor imagery brain-computer interfaces.

PubMed

Dai, Shengfa; Wei, Qingguo

2017-01-01

Common spatial pattern algorithm is widely used to estimate spatial filters in motor imagery based brain-computer interfaces. However, use of a large number of channels will make common spatial pattern tend to over-fitting and the classification of electroencephalographic signals time-consuming. To overcome these problems, it is necessary to choose an optimal subset of the whole channels to save computational time and improve the classification accuracy. In this paper, a novel method named backtracking search optimization algorithm is proposed to automatically select the optimal channel set for common spatial pattern. Each individual in the population is a N-dimensional vector, with each component representing one channel. A population of binary codes generate randomly in the beginning, and then channels are selected according to the evolution of these codes. The number and positions of 1's in the code denote the number and positions of chosen channels. The objective function of backtracking search optimization algorithm is defined as the combination of classification error rate and relative number of channels. Experimental results suggest that higher classification accuracy can be achieved with much fewer channels compared to standard common spatial pattern with whole channels.

Benefits and Harms of Sick Leave: Lack of Randomized, Controlled Trials

ERIC Educational Resources Information Center

Axelsson, Inge; Marnetoft, Sven-Uno

2010-01-01

The aim of this study was to try to identify those randomized controlled trials that compare sick leave with no sick leave or a different duration or degree of sick leave. A comprehensive, systematic, electronic search of Clinical Evidence, the Cochrane Library and PubMed, and a manual search of the Campbell Library and a journal supplement was…

Optimal Quantum Spatial Search on Random Temporal Networks

NASA Astrophysics Data System (ADS)

Chakraborty, Shantanav; Novo, Leonardo; Di Giorgio, Serena; Omar, Yasser

2017-12-01

To investigate the performance of quantum information tasks on networks whose topology changes in time, we study the spatial search algorithm by continuous time quantum walk to find a marked node on a random temporal network. We consider a network of n nodes constituted by a time-ordered sequence of Erdös-Rényi random graphs G (n ,p ), where p is the probability that any two given nodes are connected: After every time interval τ , a new graph G (n ,p ) replaces the previous one. We prove analytically that, for any given p , there is always a range of values of τ for which the running time of the algorithm is optimal, i.e., O (√{n }), even when search on the individual static graphs constituting the temporal network is suboptimal. On the other hand, there are regimes of τ where the algorithm is suboptimal even when each of the underlying static graphs are sufficiently connected to perform optimal search on them. From this first study of quantum spatial search on a time-dependent network, it emerges that the nontrivial interplay between temporality and connectivity is key to the algorithmic performance. Moreover, our work can be extended to establish high-fidelity qubit transfer between any two nodes of the network. Overall, our findings show that one can exploit temporality to achieve optimal quantum information tasks on dynamical random networks.

A Hybrid Symbiotic Organisms Search Algorithm with Variable Neighbourhood Search for Solving Symmetric and Asymmetric Traveling Salesman Problem

NASA Astrophysics Data System (ADS)

Umam, M. I. H.; Santosa, B.

2018-04-01

Combinatorial optimization has been frequently used to solve both problems in science, engineering, and commercial applications. One combinatorial problems in the field of transportation is to find a shortest travel route that can be taken from the initial point of departure to point of destination, as well as minimizing travel costs and travel time. When the distance from one (initial) node to another (destination) node is the same with the distance to travel back from destination to initial, this problems known to the Traveling Salesman Problem (TSP), otherwise it call as an Asymmetric Traveling Salesman Problem (ATSP). The most recent optimization techniques is Symbiotic Organisms Search (SOS). This paper discuss how to hybrid the SOS algorithm with variable neighborhoods search (SOS-VNS) that can be applied to solve the ATSP problem. The proposed mechanism to add the variable neighborhoods search as a local search is to generate the better initial solution and then we modify the phase of parasites with adapting mechanism of mutation. After modification, the performance of the algorithm SOS-VNS is evaluated with several data sets and then the results is compared with the best known solution and some algorithm such PSO algorithm and SOS original algorithm. The SOS-VNS algorithm shows better results based on convergence, divergence and computing time.

Guided particle swarm optimization method to solve general nonlinear optimization problems

NASA Astrophysics Data System (ADS)

Abdelhalim, Alyaa; Nakata, Kazuhide; El-Alem, Mahmoud; Eltawil, Amr

2018-04-01

The development of hybrid algorithms is becoming an important topic in the global optimization research area. This article proposes a new technique in hybridizing the particle swarm optimization (PSO) algorithm and the Nelder-Mead (NM) simplex search algorithm to solve general nonlinear unconstrained optimization problems. Unlike traditional hybrid methods, the proposed method hybridizes the NM algorithm inside the PSO to improve the velocities and positions of the particles iteratively. The new hybridization considers the PSO algorithm and NM algorithm as one heuristic, not in a sequential or hierarchical manner. The NM algorithm is applied to improve the initial random solution of the PSO algorithm and iteratively in every step to improve the overall performance of the method. The performance of the proposed method was tested over 20 optimization test functions with varying dimensions. Comprehensive comparisons with other methods in the literature indicate that the proposed solution method is promising and competitive.

Evolutionary engineering for industrial microbiology.

PubMed

Vanee, Niti; Fisher, Adam B; Fong, Stephen S

2012-01-01

Superficially, evolutionary engineering is a paradoxical field that balances competing interests. In natural settings, evolution iteratively selects and enriches subpopulations that are best adapted to a particular ecological niche using random processes such as genetic mutation. In engineering desired approaches utilize rational prospective design to address targeted problems. When considering details of evolutionary and engineering processes, more commonality can be found. Engineering relies on detailed knowledge of the problem parameters and design properties in order to predict design outcomes that would be an optimized solution. When detailed knowledge of a system is lacking, engineers often employ algorithmic search strategies to identify empirical solutions. Evolution epitomizes this iterative optimization by continuously diversifying design options from a parental design, and then selecting the progeny designs that represent satisfactory solutions. In this chapter, the technique of applying the natural principles of evolution to engineer microbes for industrial applications is discussed to highlight the challenges and principles of evolutionary engineering.

Relabeling exchange method (REM) for learning in neural networks

NASA Astrophysics Data System (ADS)

Wu, Wen; Mammone, Richard J.

1994-02-01

The supervised training of neural networks require the use of output labels which are usually arbitrarily assigned. In this paper it is shown that there is a significant difference in the rms error of learning when `optimal' label assignment schemes are used. We have investigated two efficient random search algorithms to solve the relabeling problem: the simulated annealing and the genetic algorithm. However, we found them to be computationally expensive. Therefore we shall introduce a new heuristic algorithm called the Relabeling Exchange Method (REM) which is computationally more attractive and produces optimal performance. REM has been used to organize the optimal structure for multi-layered perceptrons and neural tree networks. The method is a general one and can be implemented as a modification to standard training algorithms. The motivation of the new relabeling strategy is based on the present interpretation of dyslexia as an encoding problem.

Solving the flexible job shop problem by hybrid metaheuristics-based multiagent model

NASA Astrophysics Data System (ADS)

Nouri, Houssem Eddine; Belkahla Driss, Olfa; Ghédira, Khaled

2018-03-01

The flexible job shop scheduling problem (FJSP) is a generalization of the classical job shop scheduling problem that allows to process operations on one machine out of a set of alternative machines. The FJSP is an NP-hard problem consisting of two sub-problems, which are the assignment and the scheduling problems. In this paper, we propose how to solve the FJSP by hybrid metaheuristics-based clustered holonic multiagent model. First, a neighborhood-based genetic algorithm (NGA) is applied by a scheduler agent for a global exploration of the search space. Second, a local search technique is used by a set of cluster agents to guide the research in promising regions of the search space and to improve the quality of the NGA final population. The efficiency of our approach is explained by the flexible selection of the promising parts of the search space by the clustering operator after the genetic algorithm process, and by applying the intensification technique of the tabu search allowing to restart the search from a set of elite solutions to attain new dominant scheduling solutions. Computational results are presented using four sets of well-known benchmark literature instances. New upper bounds are found, showing the effectiveness of the presented approach.

Improved understanding of the searching behavior of ant colony optimization algorithms applied to the water distribution design problem

NASA Astrophysics Data System (ADS)

Zecchin, A. C.; Simpson, A. R.; Maier, H. R.; Marchi, A.; Nixon, J. B.

2012-09-01

Evolutionary algorithms (EAs) have been applied successfully to many water resource problems, such as system design, management decision formulation, and model calibration. The performance of an EA with respect to a particular problem type is dependent on how effectively its internal operators balance the exploitation/exploration trade-off to iteratively find solutions of an increasing quality. For a given problem, different algorithms are observed to produce a variety of different final performances, but there have been surprisingly few investigations into characterizing how the different internal mechanisms alter the algorithm's searching behavior, in both the objective and decision space, to arrive at this final performance. This paper presents metrics for analyzing the searching behavior of ant colony optimization algorithms, a particular type of EA, for the optimal water distribution system design problem, which is a classical NP-hard problem in civil engineering. Using the proposed metrics, behavior is characterized in terms of three different attributes: (1) the effectiveness of the search in improving its solution quality and entering into optimal or near-optimal regions of the search space, (2) the extent to which the algorithm explores as it converges to solutions, and (3) the searching behavior with respect to the feasible and infeasible regions. A range of case studies is considered, where a number of ant colony optimization variants are applied to a selection of water distribution system optimization problems. The results demonstrate the utility of the proposed metrics to give greater insight into how the internal operators affect each algorithm's searching behavior.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bosler, Peter A.; Roesler, Erika L.; Taylor, Mark A.

This study discusses the problem of identifying extreme climate events such as intense storms within large climate data sets. The basic storm detection algorithm is reviewed, which splits the problem into two parts: a spatial search followed by a temporal correlation problem. Two specific implementations of the spatial search algorithm are compared: the commonly used grid point search algorithm is reviewed, and a new algorithm called Stride Search is introduced. The Stride Search algorithm is defined independently of the spatial discretization associated with a particular data set. Results from the two algorithms are compared for the application of tropical cyclonemore » detection, and shown to produce similar results for the same set of storm identification criteria. Differences between the two algorithms arise for some storms due to their different definition of search regions in physical space. The physical space associated with each Stride Search region is constant, regardless of data resolution or latitude, and Stride Search is therefore capable of searching all regions of the globe in the same manner. Stride Search's ability to search high latitudes is demonstrated for the case of polar low detection. Wall clock time required for Stride Search is shown to be smaller than a grid point search of the same data, and the relative speed up associated with Stride Search increases as resolution increases.« less

What's the Right Answer? Team Problem-Solving in Environments of Uncertainty

ERIC Educational Resources Information Center

Jameson, Daphne A.

2009-01-01

Whether in the workplace or the classroom, many teams approach problem-solving as a search for certainty--even though certainty rarely exists in business. This search for the one right answer to a problem creates unrealistic expectations and often undermines teams' effectiveness. To help teams manage their problem-solving process and communication…

Deconstructing Nowicki and Smutnickis i-TSAB tabu search algorithm for the job-shop scheduling problem.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Whitley, L. Darrell; Watson, Jean-Paul; Howe, Adele E.

Over the last decade and a half, tabu search algorithms for machine scheduling have gained a near-mythical reputation by consistently equaling or establishing state-of-the-art performance levels on a range of academic and real-world problems. Yet, despite these successes, remarkably little research has been devoted to developing an understanding of why tabu search is so effective on this problem class. In this paper, we report results that provide significant progress in this direction. We consider Nowicki and Smutnicki's i-TSAB tabu search algorithm, which represents the current state-of-the-art for the makespan-minimization form of the classical jobshop scheduling problem. Via a series ofmore » controlled experiments, we identify those components of i-TSAB that enable it to achieve state-of-the-art performance levels. In doing so, we expose a number of misconceptions regarding the behavior and/or benefits of tabu search and other local search metaheuristics for the job-shop problem. Our results also serve to focus future research, by identifying those specific directions that are most likely to yield further improvements in performance.« less

Self-Adaptive Stepsize Search Applied to Optimal Structural Design

NASA Astrophysics Data System (ADS)

Nolle, L.; Bland, J. A.

Structural engineering often involves the design of space frames that are required to resist predefined external forces without exhibiting plastic deformation. The weight of the structure and hence the weight of its constituent members has to be as low as possible for economical reasons without violating any of the load constraints. Design spaces are usually vast and the computational costs for analyzing a single design are usually high. Therefore, not every possible design can be evaluated for real-world problems. In this work, a standard structural design problem, the 25-bar problem, has been solved using self-adaptive stepsize search (SASS), a relatively new search heuristic. This algorithm has only one control parameter and therefore overcomes the drawback of modern search heuristics, i.e. the need to first find a set of optimum control parameter settings for the problem at hand. In this work, SASS outperforms simulated-annealing, genetic algorithms, tabu search and ant colony optimization.

"Simulated molecular evolution" or computer-generated artifacts?

PubMed

Darius, F; Rojas, R

1994-11-01

1. The authors define a function with value 1 for the positive examples and 0 for the negative ones. They fit a continuous function but do not deal at all with the error margin of the fit, which is almost as large as the function values they compute. 2. The term "quality" for the value of the fitted function gives the impression that some biological significance is associated with values of the fitted function strictly between 0 and 1, but there is no justification for this kind of interpretation and finding the point where the fit achieves its maximum does not make sense. 3. By neglecting the error margin the authors try to optimize the fitted function using differences in the second, third, fourth, and even fifth decimal place which have no statistical significance. 4. Even if such a fit could profit from more data points, the authors should first prove that the region of interest has some kind of smoothness, that is, that a continuous fit makes any sense at all. 5. "Simulated molecular evolution" is a misnomer. We are dealing here with random search. Since the margin of error is so large, the fitted function does not provide statistically significant information about the points in search space where strings with cleavage sites could be found. This implies that the method is a highly unreliable stochastic search in the space of strings, even if the neural network is capable of learning some simple correlations. 6. Classical statistical methods are for these kind of problems with so few data points clearly superior to the neural networks used as a "black box" by the authors, which in the way they are structured provide a model with an error margin as large as the numbers being computed.7. And finally, even if someone would provide us with a function which separates strings with cleavage sites from strings without them perfectly, so-called simulated molecular evolution would not be better than random selection.Since a perfect fit would only produce exactly ones or zeros,starting a search in a region of space where all strings in the neighborhood get the value zero would not provide any kind of directional information for new iterations. We would just skip from one point to the other in a typical random walk manner.

Solution to the satisfiability problem using a complete Grover search with trapped ions

NASA Astrophysics Data System (ADS)

Yang, Wan-Li; Wei, Hua; Zhou, Fei; Chang, Weng-Long; Feng, Mang

2009-07-01

The main idea in the original Grover search (1997 Phys. Rev. Lett. 79 325) is to single out a target state containing the solution to a search problem by amplifying the amplitude of the state, following the Oracle's job, i.e., a black box giving us information about the target state. We design quantum circuits to accomplish a complete Grover search involving both the Oracle's job and the amplification of the target state, which are employed to solve satisfiability (SAT) problems. We explore how to carry out the quantum circuits with currently available ion-trap quantum computing technology.

A Statistical Method to Distinguish Functional Brain Networks

PubMed Central

Fujita, André; Vidal, Maciel C.; Takahashi, Daniel Y.

2017-01-01

One major problem in neuroscience is the comparison of functional brain networks of different populations, e.g., distinguishing the networks of controls and patients. Traditional algorithms are based on search for isomorphism between networks, assuming that they are deterministic. However, biological networks present randomness that cannot be well modeled by those algorithms. For instance, functional brain networks of distinct subjects of the same population can be different due to individual characteristics. Moreover, networks of subjects from different populations can be generated through the same stochastic process. Thus, a better hypothesis is that networks are generated by random processes. In this case, subjects from the same group are samples from the same random process, whereas subjects from different groups are generated by distinct processes. Using this idea, we developed a statistical test called ANOGVA to test whether two or more populations of graphs are generated by the same random graph model. Our simulations' results demonstrate that we can precisely control the rate of false positives and that the test is powerful to discriminate random graphs generated by different models and parameters. The method also showed to be robust for unbalanced data. As an example, we applied ANOGVA to an fMRI dataset composed of controls and patients diagnosed with autism or Asperger. ANOGVA identified the cerebellar functional sub-network as statistically different between controls and autism (p < 0.001). PMID:28261045

A Statistical Method to Distinguish Functional Brain Networks.

PubMed

Fujita, André; Vidal, Maciel C; Takahashi, Daniel Y

2017-01-01

One major problem in neuroscience is the comparison of functional brain networks of different populations, e.g., distinguishing the networks of controls and patients. Traditional algorithms are based on search for isomorphism between networks, assuming that they are deterministic. However, biological networks present randomness that cannot be well modeled by those algorithms. For instance, functional brain networks of distinct subjects of the same population can be different due to individual characteristics. Moreover, networks of subjects from different populations can be generated through the same stochastic process. Thus, a better hypothesis is that networks are generated by random processes. In this case, subjects from the same group are samples from the same random process, whereas subjects from different groups are generated by distinct processes. Using this idea, we developed a statistical test called ANOGVA to test whether two or more populations of graphs are generated by the same random graph model. Our simulations' results demonstrate that we can precisely control the rate of false positives and that the test is powerful to discriminate random graphs generated by different models and parameters. The method also showed to be robust for unbalanced data. As an example, we applied ANOGVA to an fMRI dataset composed of controls and patients diagnosed with autism or Asperger. ANOGVA identified the cerebellar functional sub-network as statistically different between controls and autism ( p < 0.001).

Online Information Search Performance and Search Strategies in a Health Problem-Solving Scenario.

PubMed

Sharit, Joseph; Taha, Jessica; Berkowsky, Ronald W; Profita, Halley; Czaja, Sara J

2015-01-01

Although access to Internet health information can be beneficial, solving complex health-related problems online is challenging for many individuals. In this study, we investigated the performance of a sample of 60 adults ages 18 to 85 years in using the Internet to resolve a relatively complex health information problem. The impact of age, Internet experience, and cognitive abilities on measures of search time, amount of search, and search accuracy was examined, and a model of Internet information seeking was developed to guide the characterization of participants' search strategies. Internet experience was found to have no impact on performance measures. Older participants exhibited longer search times and lower amounts of search but similar search accuracy performance as their younger counterparts. Overall, greater search accuracy was related to an increased amount of search but not to increased search duration and was primarily attributable to higher cognitive abilities, such as processing speed, reasoning ability, and executive function. There was a tendency for those who were younger, had greater Internet experience, and had higher cognitive abilities to use a bottom-up (i.e., analytic) search strategy, although use of a top-down (i.e., browsing) strategy was not necessarily unsuccessful. Implications of the findings for future studies and design interventions are discussed.

Online Information Search Performance and Search Strategies in a Health Problem-Solving Scenario

PubMed Central

Sharit, Joseph; Taha, Jessica; Berkowsky, Ronald W.; Profita, Halley; Czaja, Sara J.

2017-01-01

Although access to Internet health information can be beneficial, solving complex health-related problems online is challenging for many individuals. In this study, we investigated the performance of a sample of 60 adults ages 18 to 85 years in using the Internet to resolve a relatively complex health information problem. The impact of age, Internet experience, and cognitive abilities on measures of search time, amount of search, and search accuracy was examined, and a model of Internet information seeking was developed to guide the characterization of participants’ search strategies. Internet experience was found to have no impact on performance measures. Older participants exhibited longer search times and lower amounts of search but similar search accuracy performance as their younger counterparts. Overall, greater search accuracy was related to an increased amount of search but not to increased search duration and was primarily attributable to higher cognitive abilities, such as processing speed, reasoning ability, and executive function. There was a tendency for those who were younger, had greater Internet experience, and had higher cognitive abilities to use a bottom-up (i.e., analytic) search strategy, although use of a top-down (i.e., browsing) strategy was not necessarily unsuccessful. Implications of the findings for future studies and design interventions are discussed. PMID:29056885

SPLICER - A GENETIC ALGORITHM TOOL FOR SEARCH AND OPTIMIZATION, VERSION 1.0 (MACINTOSH VERSION)

NASA Technical Reports Server (NTRS)

Wang, L.

1994-01-01

SPLICER is a genetic algorithm tool which can be used to solve search and optimization problems. Genetic algorithms are adaptive search procedures (i.e. problem solving methods) based loosely on the processes of natural selection and Darwinian "survival of the fittest." SPLICER provides the underlying framework and structure for building a genetic algorithm application. These algorithms apply genetically-inspired operators to populations of potential solutions in an iterative fashion, creating new populations while searching for an optimal or near-optimal solution to the problem at hand. SPLICER 1.0 was created using a modular architecture that includes a Genetic Algorithm Kernel, interchangeable Representation Libraries, Fitness Modules and User Interface Libraries, and well-defined interfaces between these components. The architecture supports portability, flexibility, and extensibility. SPLICER comes with all source code and several examples. For instance, a "traveling salesperson" example searches for the minimum distance through a number of cities visiting each city only once. Stand-alone SPLICER applications can be used without any programming knowledge. However, to fully utilize SPLICER within new problem domains, familiarity with C language programming is essential. SPLICER's genetic algorithm (GA) kernel was developed independent of representation (i.e. problem encoding), fitness function or user interface type. The GA kernel comprises all functions necessary for the manipulation of populations. These functions include the creation of populations and population members, the iterative population model, fitness scaling, parent selection and sampling, and the generation of population statistics. In addition, miscellaneous functions are included in the kernel (e.g., random number generators). Different problem-encoding schemes and functions are defined and stored in interchangeable representation libraries. This allows the GA kernel to be used with any representation scheme. The SPLICER tool provides representation libraries for binary strings and for permutations. These libraries contain functions for the definition, creation, and decoding of genetic strings, as well as multiple crossover and mutation operators. Furthermore, the SPLICER tool defines the appropriate interfaces to allow users to create new representation libraries. Fitness modules are the only component of the SPLICER system a user will normally need to create or alter to solve a particular problem. Fitness functions are defined and stored in interchangeable fitness modules which must be created using C language. Within a fitness module, a user can create a fitness (or scoring) function, set the initial values for various SPLICER control parameters (e.g., population size), create a function which graphically displays the best solutions as they are found, and provide descriptive information about the problem. The tool comes with several example fitness modules, while the process of developing a fitness module is fully discussed in the accompanying documentation. The user interface is event-driven and provides graphic output in windows. SPLICER is written in Think C for Apple Macintosh computers running System 6.0.3 or later and Sun series workstations running SunOS. The UNIX version is easily ported to other UNIX platforms and requires MIT's X Window System, Version 11 Revision 4 or 5, MIT's Athena Widget Set, and the Xw Widget Set. Example executables and source code are included for each machine version. The standard distribution media for the Macintosh version is a set of three 3.5 inch Macintosh format diskettes. The standard distribution medium for the UNIX version is a .25 inch streaming magnetic tape cartridge in UNIX tar format. For the UNIX version, alternate distribution media and formats are available upon request. SPLICER was developed in 1991.

MAP: an iterative experimental design methodology for the optimization of catalytic search space structure modeling.

PubMed

Baumes, Laurent A

2006-01-01

One of the main problems in high-throughput research for materials is still the design of experiments. At early stages of discovery programs, purely exploratory methodologies coupled with fast screening tools should be employed. This should lead to opportunities to find unexpected catalytic results and identify the "groups" of catalyst outputs, providing well-defined boundaries for future optimizations. However, very few new papers deal with strategies that guide exploratory studies. Mostly, traditional designs, homogeneous covering, or simple random samplings are exploited. Typical catalytic output distributions exhibit unbalanced datasets for which an efficient learning is hardly carried out, and interesting but rare classes are usually unrecognized. Here is suggested a new iterative algorithm for the characterization of the search space structure, working independently of learning processes. It enhances recognition rates by transferring catalysts to be screened from "performance-stable" space zones to "unsteady" ones which necessitate more experiments to be well-modeled. The evaluation of new algorithm attempts through benchmarks is compulsory due to the lack of past proofs about their efficiency. The method is detailed and thoroughly tested with mathematical functions exhibiting different levels of complexity. The strategy is not only empirically evaluated, the effect or efficiency of sampling on future Machine Learning performances is also quantified. The minimum sample size required by the algorithm for being statistically discriminated from simple random sampling is investigated.

Feature Selection for Chemical Sensor Arrays Using Mutual Information

PubMed Central

Wang, X. Rosalind; Lizier, Joseph T.; Nowotny, Thomas; Berna, Amalia Z.; Prokopenko, Mikhail; Trowell, Stephen C.

2014-01-01

We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays. PMID:24595058

A global sampling approach to designing and reengineering RNA secondary structures.

PubMed

Levin, Alex; Lis, Mieszko; Ponty, Yann; O'Donnell, Charles W; Devadas, Srinivas; Berger, Bonnie; Waldispühl, Jérôme

2012-11-01

The development of algorithms for designing artificial RNA sequences that fold into specific secondary structures has many potential biomedical and synthetic biology applications. To date, this problem remains computationally difficult, and current strategies to address it resort to heuristics and stochastic search techniques. The most popular methods consist of two steps: First a random seed sequence is generated; next, this seed is progressively modified (i.e. mutated) to adopt the desired folding properties. Although computationally inexpensive, this approach raises several questions such as (i) the influence of the seed; and (ii) the efficiency of single-path directed searches that may be affected by energy barriers in the mutational landscape. In this article, we present RNA-ensign, a novel paradigm for RNA design. Instead of taking a progressive adaptive walk driven by local search criteria, we use an efficient global sampling algorithm to examine large regions of the mutational landscape under structural and thermodynamical constraints until a solution is found. When considering the influence of the seeds and the target secondary structures, our results show that, compared to single-path directed searches, our approach is more robust, succeeds more often and generates more thermodynamically stable sequences. An ensemble approach to RNA design is thus well worth pursuing as a complement to existing approaches. RNA-ensign is available at http://csb.cs.mcgill.ca/RNAensign.

A global sampling approach to designing and reengineering RNA secondary structures

PubMed Central

Levin, Alex; Lis, Mieszko; Ponty, Yann; O’Donnell, Charles W.; Devadas, Srinivas; Berger, Bonnie; Waldispühl, Jérôme

2012-01-01

The development of algorithms for designing artificial RNA sequences that fold into specific secondary structures has many potential biomedical and synthetic biology applications. To date, this problem remains computationally difficult, and current strategies to address it resort to heuristics and stochastic search techniques. The most popular methods consist of two steps: First a random seed sequence is generated; next, this seed is progressively modified (i.e. mutated) to adopt the desired folding properties. Although computationally inexpensive, this approach raises several questions such as (i) the influence of the seed; and (ii) the efficiency of single-path directed searches that may be affected by energy barriers in the mutational landscape. In this article, we present RNA-ensign, a novel paradigm for RNA design. Instead of taking a progressive adaptive walk driven by local search criteria, we use an efficient global sampling algorithm to examine large regions of the mutational landscape under structural and thermodynamical constraints until a solution is found. When considering the influence of the seeds and the target secondary structures, our results show that, compared to single-path directed searches, our approach is more robust, succeeds more often and generates more thermodynamically stable sequences. An ensemble approach to RNA design is thus well worth pursuing as a complement to existing approaches. RNA-ensign is available at http://csb.cs.mcgill.ca/RNAensign. PMID:22941632

Stride search: A general algorithm for storm detection in high-resolution climate data

DOE PAGES

Bosler, Peter A.; Roesler, Erika L.; Taylor, Mark A.; ...

2016-04-13

This study discusses the problem of identifying extreme climate events such as intense storms within large climate data sets. The basic storm detection algorithm is reviewed, which splits the problem into two parts: a spatial search followed by a temporal correlation problem. Two specific implementations of the spatial search algorithm are compared: the commonly used grid point search algorithm is reviewed, and a new algorithm called Stride Search is introduced. The Stride Search algorithm is defined independently of the spatial discretization associated with a particular data set. Results from the two algorithms are compared for the application of tropical cyclonemore » detection, and shown to produce similar results for the same set of storm identification criteria. Differences between the two algorithms arise for some storms due to their different definition of search regions in physical space. The physical space associated with each Stride Search region is constant, regardless of data resolution or latitude, and Stride Search is therefore capable of searching all regions of the globe in the same manner. Stride Search's ability to search high latitudes is demonstrated for the case of polar low detection. Wall clock time required for Stride Search is shown to be smaller than a grid point search of the same data, and the relative speed up associated with Stride Search increases as resolution increases.« less

Searching the Polish Medical Bibliography (Polska Bibliografia Lekarska) for trials.

PubMed

Almerie, Muhammad Qutayba; Matar, Hosam El-Din; Jones, Vicki; Kumar, Ajit; Wright, Judith; Wlostowska, Ewa; Adams, Clive E

2007-12-01

The Polish Medical Bibliography (Polska Bibliografia Lekarska) contains 350 000 records dating from 1979. These records from the fields of medicine, nursing, dentistry, health care systems and preclinical sciences are from nearly 300 biomedical journals published in Poland. We systematically searched the Polish Medical Bibliography Part II (1996-2006) CD-ROM (July 2006) using both English and Polish phrases for randomized trials, manually checked results and, for the trials identified in this way, sought these on medline and embase. Systematic searching identified records of 680 randomized trials from all areas of health care. Nearly 40% of these were not found on either medline or embase. The Polish Medical Bibliography should be of interest to health care information specialists concerned with comprehensive searches for trials.

Evaluating random search strategies in three mammals from distinct feeding guilds.

PubMed

Auger-Méthé, Marie; Derocher, Andrew E; DeMars, Craig A; Plank, Michael J; Codling, Edward A; Lewis, Mark A

2016-09-01

Searching allows animals to find food, mates, shelter and other resources essential for survival and reproduction and is thus among the most important activities performed by animals. Theory predicts that animals will use random search strategies in highly variable and unpredictable environments. Two prominent models have been suggested for animals searching in sparse and heterogeneous environments: (i) the Lévy walk and (ii) the composite correlated random walk (CCRW) and its associated area-restricted search behaviour. Until recently, it was difficult to differentiate between the movement patterns of these two strategies. Using a new method that assesses whether movement patterns are consistent with these two strategies and two other common random search strategies, we investigated the movement behaviour of three species inhabiting sparse northern environments: woodland caribou (Rangifer tarandus caribou), barren-ground grizzly bear (Ursus arctos) and polar bear (Ursus maritimus). These three species vary widely in their diets and thus allow us to contrast the movement patterns of animals from different feeding guilds. Our results showed that although more traditional methods would have found evidence for the Lévy walk for some individuals, a comparison of the Lévy walk to CCRWs showed stronger support for the latter. While a CCRW was the best model for most individuals, there was a range of support for its absolute fit. A CCRW was sufficient to explain the movement of nearly half of herbivorous caribou and a quarter of omnivorous grizzly bears, but was insufficient to explain the movement of all carnivorous polar bears. Strong evidence for CCRW movement patterns suggests that many individuals may use a multiphasic movement strategy rather than one-behaviour strategies such as the Lévy walk. The fact that the best model was insufficient to describe the movement paths of many individuals suggests that some animals living in sparse environments may use strategies that are more complicated than those described by the standard random search models. Thus, our results indicate a need to develop movement models that incorporate factors such as the perceptual and cognitive capacities of animals. © 2016 The Authors. Journal of Animal Ecology © 2016 British Ecological Society.

Prevalence and risk of violence and the physical, mental, and sexual health problems associated with human trafficking: systematic review.

PubMed

Oram, Siân; Stöckl, Heidi; Busza, Joanna; Howard, Louise M; Zimmerman, Cathy

2012-01-01

There is very limited evidence on the health consequences of human trafficking. This systematic review reports on studies investigating the prevalence and risk of violence while trafficked and the prevalence and risk of physical, mental, and sexual health problems, including HIV, among trafficked people. We conducted a systematic review comprising a search of Medline, PubMed, PsycINFO, EMBASE, and Web of Science, hand searches of reference lists of included articles, citation tracking, and expert recommendations. We included peer-reviewed papers reporting on the prevalence or risk of violence while trafficked and/or on the prevalence or risk of any measure of physical, mental, or sexual health among trafficked people. Two reviewers independently screened papers for eligibility and appraised the quality of included studies. The search identified 19 eligible studies, all of which reported on trafficked women and girls only and focused primarily on trafficking for sexual exploitation. The review suggests a high prevalence of violence and of mental distress among women and girls trafficked for sexual exploitation. The random effects pooled prevalence of diagnosed HIV was 31.9% (95% CI 21.3%-42.4%) in studies of women accessing post-trafficking support in India and Nepal, but the estimate was associated with high heterogeneity (I² = 83.7%). Infection prevalence may be related as much to prevalence rates in women's areas of origin or exploitation as to the characteristics of their experience. Findings are limited by the methodological weaknesses of primary studies and their poor comparability and generalisability. Although limited, existing evidence suggests that trafficking for sexual exploitation is associated with violence and a range of serious health problems. Further research is needed on the health of trafficked men, individuals trafficked for other forms of exploitation, and effective health intervention approaches.

Prevalence and Risk of Violence and the Physical, Mental, and Sexual Health Problems Associated with Human Trafficking: Systematic Review

PubMed Central

Oram, Siân; Stöckl, Heidi; Busza, Joanna; Howard, Louise M.; Zimmerman, Cathy

2012-01-01

Background There is very limited evidence on the health consequences of human trafficking. This systematic review reports on studies investigating the prevalence and risk of violence while trafficked and the prevalence and risk of physical, mental, and sexual health problems, including HIV, among trafficked people. Methods and Findings We conducted a systematic review comprising a search of Medline, PubMed, PsycINFO, EMBASE, and Web of Science, hand searches of reference lists of included articles, citation tracking, and expert recommendations. We included peer-reviewed papers reporting on the prevalence or risk of violence while trafficked and/or on the prevalence or risk of any measure of physical, mental, or sexual health among trafficked people. Two reviewers independently screened papers for eligibility and appraised the quality of included studies. The search identified 19 eligible studies, all of which reported on trafficked women and girls only and focused primarily on trafficking for sexual exploitation. The review suggests a high prevalence of violence and of mental distress among women and girls trafficked for sexual exploitation. The random effects pooled prevalence of diagnosed HIV was 31.9% (95% CI 21.3%–42.4%) in studies of women accessing post-trafficking support in India and Nepal, but the estimate was associated with high heterogeneity (I 2 = 83.7%). Infection prevalence may be related as much to prevalence rates in women's areas of origin or exploitation as to the characteristics of their experience. Findings are limited by the methodological weaknesses of primary studies and their poor comparability and generalisability. Conclusions Although limited, existing evidence suggests that trafficking for sexual exploitation is associated with violence and a range of serious health problems. Further research is needed on the health of trafficked men, individuals trafficked for other forms of exploitation, and effective health intervention approaches. Please see later in the article for the Editors' Summary PMID:22666182

Application of Monte Carlo techniques to optimization of high-energy beam transport in a stochastic environment

NASA Technical Reports Server (NTRS)

Parrish, R. V.; Dieudonne, J. E.; Filippas, T. A.

1971-01-01

An algorithm employing a modified sequential random perturbation, or creeping random search, was applied to the problem of optimizing the parameters of a high-energy beam transport system. The stochastic solution of the mathematical model for first-order magnetic-field expansion allows the inclusion of state-variable constraints, and the inclusion of parameter constraints allowed by the method of algorithm application eliminates the possibility of infeasible solutions. The mathematical model and the algorithm were programmed for a real-time simulation facility; thus, two important features are provided to the beam designer: (1) a strong degree of man-machine communication (even to the extent of bypassing the algorithm and applying analog-matching techniques), and (2) extensive graphics for displaying information concerning both algorithm operation and transport-system behavior. Chromatic aberration was also included in the mathematical model and in the optimization process. Results presented show this method as yielding better solutions (in terms of resolutions) to the particular problem than those of a standard analog program as well as demonstrating flexibility, in terms of elements, constraints, and chromatic aberration, allowed by user interaction with both the algorithm and the stochastic model. Example of slit usage and a limited comparison of predicted results and actual results obtained with a 600 MeV cyclotron are given.

Sleep disturbance in adults with cancer: a systematic review of evidence for best practices in assessment and management for clinical practice.

PubMed

Howell, D; Oliver, T K; Keller-Olaman, S; Davidson, J R; Garland, S; Samuels, C; Savard, J; Harris, C; Aubin, M; Olson, K; Sussman, J; MacFarlane, J; Taylor, C

2014-04-01

Sleep disturbance is prevalent in cancer with detrimental effects on health outcomes. Sleep problems are seldom identified or addressed in cancer practice. The purpose of this review was to identify the evidence base for the assessment and management of cancer-related sleep disturbance (insomnia and insomnia syndrome) for oncology practice. The search of the health literature included grey literature data sources and empirical databases from June 2004 to June 2012. The evidence was reviewed by a Canadian Sleep Expert Panel, comprised of nurses, psychologists, primary care physicians, oncologists, physicians specialized in sleep disturbances, researchers and guideline methodologists to develop clinical practice recommendations for pan-Canadian use reported in a separate paper. Three clinical practice guidelines and 12 randomized, controlled trials were identified as the main source of evidence. Additional guidelines and systematic reviews were also reviewed for evidence-based recommendations on the assessment and management of insomnia not necessarily in cancer. A need to routinely screen for sleep disturbances was identified and the randomized, controlled trial (RCT) evidence suggests benefits for cognitive behavioural therapy for improving sleep quality in cancer. Sleep disturbance is a prevalent problem in cancer that needs greater recognition in clinical practice and in future research.

Decision making in family medicine: randomized trial of the effects of the InfoClinique and Trip database search engines.

PubMed

Labrecque, Michel; Ratté, Stéphane; Frémont, Pierre; Cauchon, Michel; Ouellet, Jérôme; Hogg, William; McGowan, Jessie; Gagnon, Marie-Pierre; Njoya, Merlin; Légaré, France

2013-10-01

To compare the ability of users of 2 medical search engines, InfoClinique and the Trip database, to provide correct answers to clinical questions and to explore the perceived effects of the tools on the clinical decision-making process. Randomized trial. Three family medicine units of the family medicine program of the Faculty of Medicine at Laval University in Quebec city, Que. Fifteen second-year family medicine residents. Residents generated 30 structured questions about therapy or preventive treatment (2 questions per resident) based on clinical encounters. Using an Internet platform designed for the trial, each resident answered 20 of these questions (their own 2, plus 18 of the questions formulated by other residents, selected randomly) before and after searching for information with 1 of the 2 search engines. For each question, 5 residents were randomly assigned to begin their search with InfoClinique and 5 with the Trip database. The ability of residents to provide correct answers to clinical questions using the search engines, as determined by third-party evaluation. After answering each question, participants completed a questionnaire to assess their perception of the engine's effect on the decision-making process in clinical practice. Of 300 possible pairs of answers (1 answer before and 1 after the initial search), 254 (85%) were produced by 14 residents. Of these, 132 (52%) and 122 (48%) pairs of answers concerned questions that had been assigned an initial search with InfoClinique and the Trip database, respectively. Both engines produced an important and similar absolute increase in the proportion of correct answers after searching (26% to 62% for InfoClinique, for an increase of 36%; 24% to 63% for the Trip database, for an increase of 39%; P = .68). For all 30 clinical questions, at least 1 resident produced the correct answer after searching with either search engine. The mean (SD) time of the initial search for each question was 23.5 (7.6) minutes with InfoClinique and 22.3 (7.8) minutes with the Trip database (P = .30). Participants' perceptions of each engine's effect on the decision-making process were very positive and similar for both search engines. Family medicine residents' ability to provide correct answers to clinical questions increased dramatically and similarly with the use of both InfoClinique and the Trip database. These tools have strong potential to increase the quality of medical care.

Subject Access Problems of Different Types of OPAC Users Or, the Double Challenge.

ERIC Educational Resources Information Center

Cochrane, Pauline A.

1989-01-01

Reviews the problems of users of online public access catalogs and argues that it is necessary to think of all searching problems as systems problems rather than user failures, and to concentrate research in the area of systems enhancements. A list of improved tools needed for subject searching in online catalogs is identified. (CLB)

Multivariate classification with random forests for gravitational wave searches of black hole binary coalescence

NASA Astrophysics Data System (ADS)

Baker, Paul T.; Caudill, Sarah; Hodge, Kari A.; Talukder, Dipongkar; Capano, Collin; Cornish, Neil J.

2015-03-01

Searches for gravitational waves produced by coalescing black hole binaries with total masses ≳25 M⊙ use matched filtering with templates of short duration. Non-Gaussian noise bursts in gravitational wave detector data can mimic short signals and limit the sensitivity of these searches. Previous searches have relied on empirically designed statistics incorporating signal-to-noise ratio and signal-based vetoes to separate gravitational wave candidates from noise candidates. We report on sensitivity improvements achieved using a multivariate candidate ranking statistic derived from a supervised machine learning algorithm. We apply the random forest of bagged decision trees technique to two separate searches in the high mass (≳25 M⊙ ) parameter space. For a search which is sensitive to gravitational waves from the inspiral, merger, and ringdown of binary black holes with total mass between 25 M⊙ and 100 M⊙ , we find sensitive volume improvements as high as 70±13%-109±11% when compared to the previously used ranking statistic. For a ringdown-only search which is sensitive to gravitational waves from the resultant perturbed intermediate mass black hole with mass roughly between 10 M⊙ and 600 M⊙ , we find sensitive volume improvements as high as 61±4%-241±12% when compared to the previously used ranking statistic. We also report how sensitivity improvements can differ depending on mass regime, mass ratio, and available data quality information. Finally, we describe the techniques used to tune and train the random forest classifier that can be generalized to its use in other searches for gravitational waves.

In Search of Insight.

ERIC Educational Resources Information Center

Kaplan, Craig A.; Simon, Herbert A.

1990-01-01

Attaining the insight needed to solve the Mutilated Checkerboard problem, which requires discovery of an effective problem representation (EPR), is described. Performance on insight problems can be predicted from the availability of generators and constraints in the search for an EPR. Data for 23 undergraduates were analyzed. (TJH)

Searching for Survivors through Random Human-Body Movement Outdoors by Continuous-Wave Radar Array

PubMed Central

Liu, Miao; Li, Zhao; Liang, Fulai; Jing, Xijing; Lu, Guohua; Wang, Jianqi

2016-01-01

It is a major challenge to search for survivors after chemical or nuclear leakage or explosions. At present, biological radar can be used to achieve this goal by detecting the survivor’s respiration signal. However, owing to the random posture of an injured person at a rescue site, the radar wave may directly irradiate the person’s head or feet, in which it is difficult to detect the respiration signal. This paper describes a multichannel-based antenna array technology, which forms an omnidirectional detection system via 24-GHz Doppler biological radar, to address the random positioning relative to the antenna of an object to be detected. Furthermore, since the survivors often have random body movement such as struggling and twitching, the slight movements of the body caused by breathing are obscured by these movements. Therefore, a method is proposed to identify random human-body movement by utilizing multichannel information to calculate the background variance of the environment in combination with a constant-false-alarm-rate detector. The conducted outdoor experiments indicate that the system can realize the omnidirectional detection of random human-body movement and distinguish body movement from environmental interference such as movement of leaves and grass. The methods proposed in this paper will be a promising way to search for survivors outdoors. PMID:27073860

Searching for Survivors through Random Human-Body Movement Outdoors by Continuous-Wave Radar Array.

PubMed

Li, Chuantao; Chen, Fuming; Qi, Fugui; Liu, Miao; Li, Zhao; Liang, Fulai; Jing, Xijing; Lu, Guohua; Wang, Jianqi

2016-01-01

It is a major challenge to search for survivors after chemical or nuclear leakage or explosions. At present, biological radar can be used to achieve this goal by detecting the survivor's respiration signal. However, owing to the random posture of an injured person at a rescue site, the radar wave may directly irradiate the person's head or feet, in which it is difficult to detect the respiration signal. This paper describes a multichannel-based antenna array technology, which forms an omnidirectional detection system via 24-GHz Doppler biological radar, to address the random positioning relative to the antenna of an object to be detected. Furthermore, since the survivors often have random body movement such as struggling and twitching, the slight movements of the body caused by breathing are obscured by these movements. Therefore, a method is proposed to identify random human-body movement by utilizing multichannel information to calculate the background variance of the environment in combination with a constant-false-alarm-rate detector. The conducted outdoor experiments indicate that the system can realize the omnidirectional detection of random human-body movement and distinguish body movement from environmental interference such as movement of leaves and grass. The methods proposed in this paper will be a promising way to search for survivors outdoors.

A capacitated vehicle routing problem with order available time in e-commerce industry

NASA Astrophysics Data System (ADS)

Liu, Ling; Li, Kunpeng; Liu, Zhixue

2017-03-01

In this article, a variant of the well-known capacitated vehicle routing problem (CVRP) called the capacitated vehicle routing problem with order available time (CVRPOAT) is considered, which is observed in the operations of the current e-commerce industry. In this problem, the orders are not available for delivery at the beginning of the planning period. CVRPOAT takes all the assumptions of CVRP, except the order available time, which is determined by the precedent order picking and packing stage in the warehouse of the online grocer. The objective is to minimize the sum of vehicle completion times. An efficient tabu search algorithm is presented to tackle the problem. Moreover, a Lagrangian relaxation algorithm is developed to obtain the lower bounds of reasonably sized problems. Based on the test instances derived from benchmark data, the proposed tabu search algorithm is compared with a published related genetic algorithm, as well as the derived lower bounds. Also, the tabu search algorithm is compared with the current operation strategy of the online grocer. Computational results indicate that the gap between the lower bounds and the results of the tabu search algorithm is small and the tabu search algorithm is superior to the genetic algorithm. Moreover, the CVRPOAT formulation together with the tabu search algorithm performs much better than the current operation strategy of the online grocer.

A Memetic Algorithm for Global Optimization of Multimodal Nonseparable Problems.

PubMed

Zhang, Geng; Li, Yangmin

2016-06-01

It is a big challenging issue of avoiding falling into local optimum especially when facing high-dimensional nonseparable problems where the interdependencies among vector elements are unknown. In order to improve the performance of optimization algorithm, a novel memetic algorithm (MA) called cooperative particle swarm optimizer-modified harmony search (CPSO-MHS) is proposed in this paper, where the CPSO is used for local search and the MHS for global search. The CPSO, as a local search method, uses 1-D swarm to search each dimension separately and thus converges fast. Besides, it can obtain global optimum elements according to our experimental results and analyses. MHS implements the global search by recombining different vector elements and extracting global optimum elements. The interaction between local search and global search creates a set of local search zones, where global optimum elements reside within the search space. The CPSO-MHS algorithm is tested and compared with seven other optimization algorithms on a set of 28 standard benchmarks. Meanwhile, some MAs are also compared according to the results derived directly from their corresponding references. The experimental results demonstrate a good performance of the proposed CPSO-MHS algorithm in solving multimodal nonseparable problems.

Optimal search strategies of space-time coupled random walkers with finite lifetimes

NASA Astrophysics Data System (ADS)

Campos, D.; Abad, E.; Méndez, V.; Yuste, S. B.; Lindenberg, K.

2015-05-01

We present a simple paradigm for detection of an immobile target by a space-time coupled random walker with a finite lifetime. The motion of the walker is characterized by linear displacements at a fixed speed and exponentially distributed duration, interrupted by random changes in the direction of motion and resumption of motion in the new direction with the same speed. We call these walkers "mortal creepers." A mortal creeper may die at any time during its motion according to an exponential decay law characterized by a finite mean death rate ωm. While still alive, the creeper has a finite mean frequency ω of change of the direction of motion. In particular, we consider the efficiency of the target search process, characterized by the probability that the creeper will eventually detect the target. Analytic results confirmed by numerical results show that there is an ωm-dependent optimal frequency ω =ωopt that maximizes the probability of eventual target detection. We work primarily in one-dimensional (d =1 ) domains and examine the role of initial conditions and of finite domain sizes. Numerical results in d =2 domains confirm the existence of an optimal frequency of change of direction, thereby suggesting that the observed effects are robust to changes in dimensionality. In the d =1 case, explicit expressions for the probability of target detection in the long time limit are given. In the case of an infinite domain, we compute the detection probability for arbitrary times and study its early- and late-time behavior. We further consider the survival probability of the target in the presence of many independent creepers beginning their motion at the same location and at the same time. We also consider a version of the standard "target problem" in which many creepers start at random locations at the same time.

A class of stochastic optimization problems with one quadratic & several linear objective functions and extended portfolio selection model

NASA Astrophysics Data System (ADS)

Xu, Jiuping; Li, Jun

2002-09-01

In this paper a class of stochastic multiple-objective programming problems with one quadratic, several linear objective functions and linear constraints has been introduced. The former model is transformed into a deterministic multiple-objective nonlinear programming model by means of the introduction of random variables' expectation. The reference direction approach is used to deal with linear objectives and results in a linear parametric optimization formula with a single linear objective function. This objective function is combined with the quadratic function using the weighted sums. The quadratic problem is transformed into a linear (parametric) complementary problem, the basic formula for the proposed approach. The sufficient and necessary conditions for (properly, weakly) efficient solutions and some construction characteristics of (weakly) efficient solution sets are obtained. An interactive algorithm is proposed based on reference direction and weighted sums. Varying the parameter vector on the right-hand side of the model, the DM can freely search the efficient frontier with the model. An extended portfolio selection model is formed when liquidity is considered as another objective to be optimized besides expectation and risk. The interactive approach is illustrated with a practical example.

Attributing intentions to random motion engages the posterior superior temporal sulcus.

PubMed

Lee, Su Mei; Gao, Tao; McCarthy, Gregory

2014-01-01

The right posterior superior temporal sulcus (pSTS) is a neural region involved in assessing the goals and intentions underlying the motion of social agents. Recent research has identified visual cues, such as chasing, that trigger animacy detection and intention attribution. When readily available in a visual display, these cues reliably activate the pSTS. Here, using functional magnetic resonance imaging, we examined if attributing intentions to random motion would likewise engage the pSTS. Participants viewed displays of four moving circles and were instructed to search for chasing or mirror-correlated motion. On chasing trials, one circle chased another circle, invoking the percept of an intentional agent; while on correlated motion trials, one circle's motion was mirror reflected by another. On the remaining trials, all circles moved randomly. As expected, pSTS activation was greater when participants searched for chasing vs correlated motion when these cues were present in the displays. Of critical importance, pSTS activation was also greater when participants searched for chasing compared to mirror-correlated motion when the displays in both search conditions were statistically identical random motion. We conclude that pSTS activity associated with intention attribution can be invoked by top-down processes in the absence of reliable visual cues for intentionality.

A stochastic Markov chain model to describe lung cancer growth and metastasis.

PubMed

Newton, Paul K; Mason, Jeremy; Bethel, Kelly; Bazhenova, Lyudmila A; Nieva, Jorge; Kuhn, Peter

2012-01-01

A stochastic Markov chain model for metastatic progression is developed for primary lung cancer based on a network construction of metastatic sites with dynamics modeled as an ensemble of random walkers on the network. We calculate a transition matrix, with entries (transition probabilities) interpreted as random variables, and use it to construct a circular bi-directional network of primary and metastatic locations based on postmortem tissue analysis of 3827 autopsies on untreated patients documenting all primary tumor locations and metastatic sites from this population. The resulting 50 potential metastatic sites are connected by directed edges with distributed weightings, where the site connections and weightings are obtained by calculating the entries of an ensemble of transition matrices so that the steady-state distribution obtained from the long-time limit of the Markov chain dynamical system corresponds to the ensemble metastatic distribution obtained from the autopsy data set. We condition our search for a transition matrix on an initial distribution of metastatic tumors obtained from the data set. Through an iterative numerical search procedure, we adjust the entries of a sequence of approximations until a transition matrix with the correct steady-state is found (up to a numerical threshold). Since this constrained linear optimization problem is underdetermined, we characterize the statistical variance of the ensemble of transition matrices calculated using the means and variances of their singular value distributions as a diagnostic tool. We interpret the ensemble averaged transition probabilities as (approximately) normally distributed random variables. The model allows us to simulate and quantify disease progression pathways and timescales of progression from the lung position to other sites and we highlight several key findings based on the model.

Parallel coding of conjunctions in visual search.

PubMed

Found, A

1998-10-01

Two experiments investigated whether the conjunctive nature of nontarget items influenced search for a conjunction target. Each experiment consisted of two conditions. In both conditions, the target item was a red bar tilted to the right, among white tilted bars and vertical red bars. As well as color and orientation, display items also differed in terms of size. Size was irrelevant to search in that the size of the target varied randomly from trial to trial. In one condition, the size of items correlated with the other attributes of display items (e.g., all red items were big and all white items were small). In the other condition, the size of items varied randomly (i.e., some red items were small and some were big, and some white items were big and some were small). Search was more efficient in the size-correlated condition, consistent with the parallel coding of conjunctions in visual search.

An adaptive approach to the physical annealing strategy for simulated annealing

NASA Astrophysics Data System (ADS)

Hasegawa, M.

2013-02-01

A new and reasonable method for adaptive implementation of simulated annealing (SA) is studied on two types of random traveling salesman problems. The idea is based on the previous finding on the search characteristics of the threshold algorithms, that is, the primary role of the relaxation dynamics in their finite-time optimization process. It is shown that the effective temperature for optimization can be predicted from the system's behavior analogous to the stabilization phenomenon occurring in the heating process starting from a quenched solution. The subsequent slow cooling near the predicted point draws out the inherent optimizing ability of finite-time SA in more straightforward manner than the conventional adaptive approach.

Genetic Local Search for Optimum Multiuser Detection Problem in DS-CDMA Systems

NASA Astrophysics Data System (ADS)

Wang, Shaowei; Ji, Xiaoyong

Optimum multiuser detection (OMD) in direct-sequence code-division multiple access (DS-CDMA) systems is an NP-complete problem. In this paper, we present a genetic local search algorithm, which consists of an evolution strategy framework and a local improvement procedure. The evolution strategy searches the space of feasible, locally optimal solutions only. A fast iterated local search algorithm, which employs the proprietary characteristics of the OMD problem, produces local optima with great efficiency. Computer simulations show the bit error rate (BER) performance of the GLS outperforms other multiuser detectors in all cases discussed. The computation time is polynomial complexity in the number of users.

77 FR 37713 - Comment Request for Information Collection for ETA 9162, Random Audit of EUC 2008 Claimants...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-22

... Collection for ETA 9162, Random Audit of EUC 2008 Claimants, Comment Request for Extension Without Change... assessed. Currently, ETA is soliciting comments concerning the collection of data about Random Audit of... describing random audits of the work search provision of Public Law 112-96 (see Section 2141(b)). Random...

A DAG Scheduling Scheme on Heterogeneous Computing Systems Using Tuple-Based Chemical Reaction Optimization

PubMed Central

Jiang, Yuyi; Shao, Zhiqing; Guo, Yi

2014-01-01

A complex computing problem can be solved efficiently on a system with multiple computing nodes by dividing its implementation code into several parallel processing modules or tasks that can be formulated as directed acyclic graph (DAG) problems. The DAG jobs may be mapped to and scheduled on the computing nodes to minimize the total execution time. Searching an optimal DAG scheduling solution is considered to be NP-complete. This paper proposed a tuple molecular structure-based chemical reaction optimization (TMSCRO) method for DAG scheduling on heterogeneous computing systems, based on a very recently proposed metaheuristic method, chemical reaction optimization (CRO). Comparing with other CRO-based algorithms for DAG scheduling, the design of tuple reaction molecular structure and four elementary reaction operators of TMSCRO is more reasonable. TMSCRO also applies the concept of constrained critical paths (CCPs), constrained-critical-path directed acyclic graph (CCPDAG) and super molecule for accelerating convergence. In this paper, we have also conducted simulation experiments to verify the effectiveness and efficiency of TMSCRO upon a large set of randomly generated graphs and the graphs for real world problems. PMID:25143977

A DAG scheduling scheme on heterogeneous computing systems using tuple-based chemical reaction optimization.

PubMed

Jiang, Yuyi; Shao, Zhiqing; Guo, Yi

2014-01-01

A complex computing problem can be solved efficiently on a system with multiple computing nodes by dividing its implementation code into several parallel processing modules or tasks that can be formulated as directed acyclic graph (DAG) problems. The DAG jobs may be mapped to and scheduled on the computing nodes to minimize the total execution time. Searching an optimal DAG scheduling solution is considered to be NP-complete. This paper proposed a tuple molecular structure-based chemical reaction optimization (TMSCRO) method for DAG scheduling on heterogeneous computing systems, based on a very recently proposed metaheuristic method, chemical reaction optimization (CRO). Comparing with other CRO-based algorithms for DAG scheduling, the design of tuple reaction molecular structure and four elementary reaction operators of TMSCRO is more reasonable. TMSCRO also applies the concept of constrained critical paths (CCPs), constrained-critical-path directed acyclic graph (CCPDAG) and super molecule for accelerating convergence. In this paper, we have also conducted simulation experiments to verify the effectiveness and efficiency of TMSCRO upon a large set of randomly generated graphs and the graphs for real world problems.

Test-state approach to the quantum search problem

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sehrawat, Arun; Nguyen, Le Huy; Graduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore 117597

2011-05-15

The search for 'a quantum needle in a quantum haystack' is a metaphor for the problem of finding out which one of a permissible set of unitary mappings - the oracles - is implemented by a given black box. Grover's algorithm solves this problem with quadratic speedup as compared with the analogous search for 'a classical needle in a classical haystack'. Since the outcome of Grover's algorithm is probabilistic - it gives the correct answer with high probability, not with certainty - the answer requires verification. For this purpose we introduce specific test states, one for each oracle. These testmore » states can also be used to realize 'a classical search for the quantum needle' which is deterministic - it always gives a definite answer after a finite number of steps - and 3.41 times as fast as the purely classical search. Since the test-state search and Grover's algorithm look for the same quantum needle, the average number of oracle queries of the test-state search is the classical benchmark for Grover's algorithm.« less

Reverse Nearest Neighbor Search on a Protein-Protein Interaction Network to Infer Protein-Disease Associations.

PubMed

Suratanee, Apichat; Plaimas, Kitiporn

2017-01-01

The associations between proteins and diseases are crucial information for investigating pathological mechanisms. However, the number of known and reliable protein-disease associations is quite small. In this study, an analysis framework to infer associations between proteins and diseases was developed based on a large data set of a human protein-protein interaction network integrating an effective network search, namely, the reverse k -nearest neighbor (R k NN) search. The R k NN search was used to identify an impact of a protein on other proteins. Then, associations between proteins and diseases were inferred statistically. The method using the R k NN search yielded a much higher precision than a random selection, standard nearest neighbor search, or when applying the method to a random protein-protein interaction network. All protein-disease pair candidates were verified by a literature search. Supporting evidence for 596 pairs was identified. In addition, cluster analysis of these candidates revealed 10 promising groups of diseases to be further investigated experimentally. This method can be used to identify novel associations to better understand complex relationships between proteins and diseases.

Escalated convergent artificial bee colony

NASA Astrophysics Data System (ADS)

Jadon, Shimpi Singh; Bansal, Jagdish Chand; Tiwari, Ritu

2016-03-01

Artificial bee colony (ABC) optimisation algorithm is a recent, fast and easy-to-implement population-based meta heuristic for optimisation. ABC has been proved a rival algorithm with some popular swarm intelligence-based algorithms such as particle swarm optimisation, firefly algorithm and ant colony optimisation. The solution search equation of ABC is influenced by a random quantity which helps its search process in exploration at the cost of exploitation. In order to find a fast convergent behaviour of ABC while exploitation capability is maintained, in this paper basic ABC is modified in two ways. First, to improve exploitation capability, two local search strategies, namely classical unidimensional local search and levy flight random walk-based local search are incorporated with ABC. Furthermore, a new solution search strategy, namely stochastic diffusion scout search is proposed and incorporated into the scout bee phase to provide more chance to abandon solution to improve itself. Efficiency of the proposed algorithm is tested on 20 benchmark test functions of different complexities and characteristics. Results are very promising and they prove it to be a competitive algorithm in the field of swarm intelligence-based algorithms.

Brain tumor segmentation in 3D MRIs using an improved Markov random field model

NASA Astrophysics Data System (ADS)

Yousefi, Sahar; Azmi, Reza; Zahedi, Morteza

2011-10-01

Markov Random Field (MRF) models have been recently suggested for MRI brain segmentation by a large number of researchers. By employing Markovianity, which represents the local property, MRF models are able to solve a global optimization problem locally. But they still have a heavy computation burden, especially when they use stochastic relaxation schemes such as Simulated Annealing (SA). In this paper, a new 3D-MRF model is put forward to raise the speed of the convergence. Although, search procedure of SA is fairly localized and prevents from exploring the same diversity of solutions, it suffers from several limitations. In comparison, Genetic Algorithm (GA) has a good capability of global researching but it is weak in hill climbing. Our proposed algorithm combines SA and an improved GA (IGA) to optimize the solution which speeds up the computation time. What is more, this proposed algorithm outperforms the traditional 2D-MRF in quality of the solution.

Near-optimal alternative generation using modified hit-and-run sampling for non-linear, non-convex problems

NASA Astrophysics Data System (ADS)

Rosenberg, D. E.; Alafifi, A.

2016-12-01

Water resources systems analysis often focuses on finding optimal solutions. Yet an optimal solution is optimal only for the modelled issues and managers often seek near-optimal alternatives that address un-modelled objectives, preferences, limits, uncertainties, and other issues. Early on, Modelling to Generate Alternatives (MGA) formalized near-optimal as the region comprising the original problem constraints plus a new constraint that allowed performance within a specified tolerance of the optimal objective function value. MGA identified a few maximally-different alternatives from the near-optimal region. Subsequent work applied Markov Chain Monte Carlo (MCMC) sampling to generate a larger number of alternatives that span the near-optimal region of linear problems or select portions for non-linear problems. We extend the MCMC Hit-And-Run method to generate alternatives that span the full extent of the near-optimal region for non-linear, non-convex problems. First, start at a feasible hit point within the near-optimal region, then run a random distance in a random direction to a new hit point. Next, repeat until generating the desired number of alternatives. The key step at each iterate is to run a random distance along the line in the specified direction to a new hit point. If linear equity constraints exist, we construct an orthogonal basis and use a null space transformation to confine hits and runs to a lower-dimensional space. Linear inequity constraints define the convex bounds on the line that runs through the current hit point in the specified direction. We then use slice sampling to identify a new hit point along the line within bounds defined by the non-linear inequity constraints. This technique is computationally efficient compared to prior near-optimal alternative generation techniques such MGA, MCMC Metropolis-Hastings, evolutionary, or firefly algorithms because search at each iteration is confined to the hit line, the algorithm can move in one step to any point in the near-optimal region, and each iterate generates a new, feasible alternative. We use the method to generate alternatives that span the near-optimal regions of simple and more complicated water management problems and may be preferred to optimal solutions. We also discuss extensions to handle non-linear equity constraints.

Perceptions of randomized security schedules.

PubMed

Scurich, Nicholas; John, Richard S

2014-04-01

Security of infrastructure is a major concern. Traditional security schedules are unable to provide omnipresent coverage; consequently, adversaries can exploit predictable vulnerabilities to their advantage. Randomized security schedules, which randomly deploy security measures, overcome these limitations, but public perceptions of such schedules have not been examined. In this experiment, participants were asked to make a choice between attending a venue that employed a traditional (i.e., search everyone) or a random (i.e., a probability of being searched) security schedule. The absolute probability of detecting contraband was manipulated (i.e., 1/10, 1/4, 1/2) but equivalent between the two schedule types. In general, participants were indifferent to either security schedule, regardless of the probability of detection. The randomized schedule was deemed more convenient, but the traditional schedule was considered fairer and safer. There were no differences between traditional and random schedule in terms of perceived effectiveness or deterrence. Policy implications for the implementation and utilization of randomized schedules are discussed. © 2013 Society for Risk Analysis.

The pond is wider than you think! Problems encountered when searching family practice literature.

PubMed Central

Rosser, W. W.; Starkey, C.; Shaughnessy, R.

2000-01-01

OBJECTIVE: To explain differences in the results of literature searches in British general practice and North American family practice or family medicine. DESIGN: Comparative literature search. SETTING: The Department of Family and Community Medicine at the University of Toronto in Ontario. METHOD: Literature searches on MEDLINE demonstrated that certain search strategies ignored certain key words, depending on the search engine and the search terms chosen. Literature searches using the key words "general practice," "family practice," and "family medicine" combined with the topics "depression" and then "otitis media" were conducted in MEDLINE using four different Web-based search engines: Ovid, HealthGate, PubMed, and Internet Grateful Med. MAIN OUTCOME MEASURES: The number of MEDLINE references retrieved for both topics when searched with each of the three key words, "general practice," "family practice," and "family medicine" using each of the four search engines. RESULTS: For each topic, each search yielded very different articles. Some search engines did a better job of matching the term "general practice" to the terms "family medicine" and "family practice," and thus improved retrieval. The problem of language use extends to the variable use of terminology and differences in spelling between British and American English. CONCLUSION: We need to heighten awareness of literature search problems and the potential for duplication of research effort when some of the literature is ignored, and to suggest ways to overcome the deficiencies of the various search engines. Images Figure 1 Figure 2 PMID:10660792

Interventions for attention problems after pediatric traumatic brain injury: what is the evidence?

PubMed

Backeljauw, Barynia; Kurowski, Brad G

2014-09-01

To gain an understanding of the current state of the evidence for management of attention problems after traumatic brain injury (TBI) in children, determine gaps in the literature, and make recommendations for future research. TYPE: Focused systematic review. PubMed/Medline and PsychINFO databases were searched for relevant articles published in English during the last 20 years. Keywords included "attention" "attention deficit and disruptive behavior disorders," and "brain injuries." Studies were limited to children. Titles were examined first and eliminated based on lack of relevancy to attention problems after brain injury in children. This was followed by an abstract and full text review. Article quality was determined based on the US Preventative Services Task Force recommendations for evidence grading. Four pharmacologic and 10 cognitive therapy intervention studies were identified. These studies varied in level of evidence quality but were primarily nonrandomized or cohort studies. There are studies that demonstrate benefits of varying pharmacologic and cognitive therapies for the management of attention problems after TBI. However, there is a paucity of evidence available to definitively guide management of attention problems after pediatric TBI. Larger randomized, controlled trials and multicenter studies are needed to elucidate optimal treatment strategies in this population. Copyright © 2014 American Academy of Physical Medicine and Rehabilitation. Published by Elsevier Inc. All rights reserved.

Rate and Causes of Discharge against Medical Advice in Iranian Hospitals: A Systematic Review and Meta-Analysis

PubMed Central

MOHSENI, Mohammad; ALIKHANI, Mahtab; TOURANI, Sogand; AZAMI-AGHDASH, Saber; ROYANI, Sanaz; MORADI-JOO, Mohammad

2015-01-01

Background: Discharge against Medical Advice (DAMA) is a problem for hospitals which may result in increasing readmissions, morbidities, inabilities, deaths and health care costs. This study, aimed to investigate the rate and causes of DAMA in Iranian hospitals. Methods: A systematic review and meta-analysis study was conducted in 2014. Required data were collected through searching for key words included: “Discharge Against Medical Advice”, “Leaving against medical advice”, “causes*”, “hospital” and their Persian equivalents, over databases including PubMed, OVID, Google Scholar, Embase, Scopus, Magiran, scientific information database (SID). The reference lists of the articles, certain relevant journals and web sites in this field were also searched. Results: Out of 913 articles initially retrieved, finally 17 articles were incorporated into the study. There were 244858 individuals studied in the articles. Using a random effects model, the rate of DAMA in Iranian hospitals was estimated at 7.9% (6.3%–9.8%). While the highest rate of DAMA was associated with patients in departments of psychiatry (12%), the lowest rate was related to patients in departments of pediatrics (3.7). DAMA was in men more than women (P<0.05) Patient’s perception of feeling of wellbeing, financial problems, family problems, the lack of attention from physicians and nurses, inappropriate behavior with patients by hospital team and the lack of timely care were mentioned as main causes for DAMA. Conclusion: The rate of DAMA in Iranian hospitals is relatively high. Thus effective initiatives in this area are required. PMID:26576368

Are self-directed parenting interventions sufficient for externalising behaviour problems in childhood? A systematic review and meta-analysis.

PubMed

Tarver, Joanne; Daley, David; Lockwood, Joanna; Sayal, Kapil

2014-12-01

Externalising behaviour in childhood is a prevalent problem in the field of child and adolescent mental health. Parenting interventions are widely accepted as efficacious treatment options for reducing externalising behaviour, yet practical and psychological barriers limit their accessibility. This review aims to establish the evidence base of self-directed (SD) parenting interventions for externalising behaviour problems. Electronic searches of PubMed, Web of Knowledge, Psychinfo, Embase and CENTRAL databases and manual searches of reference lists of relevant reviews identified randomised controlled trials and cluster randomised controlled trials examining the efficacy of SD interventions compared to no-treatment or active control groups. A random-effect meta-analysis estimated pooled standard mean difference (SMD) for SD interventions on measures of externalising child behaviour. Secondary analyses examined their effect on measures of parenting behaviour, parental stress and mood and parenting efficacy. Eleven eligible trials were included in the analyses. SD interventions had a large effect on parent report of externalising child behaviour (SMD = 1.01, 95 % CI: 0.77-1.24); although this effect was not upheld by analyses of observed child behaviour. Secondary analyses revealed effects of small to moderate magnitude on measures of parenting behaviour, parental mood and stress and parenting efficacy. An analysis comparing SD interventions with therapist-led parenting interventions revealed no significant difference on parent-reported measures of externalising child behaviour. SD interventions are associated with improvements in parental perception of externalising child behaviour and parental behaviour and well-being. Future research should further investigate the relative efficacy and cost-effectiveness of SD interventions compared to therapist-led interventions.

Effects of Systemic Therapy on Mental Health of Children and Adolescents: A Meta-Analysis.

PubMed

Riedinger, Verena; Pinquart, Martin; Teubert, Daniela

2017-01-01

Systemic therapy is a frequently used form of psychotherapy for the treatment of mental disorders in children and adolescents. The present study reports the results of the first meta-analysis on the effects of systemic treatment of mental disorders and behavior problems in children and adolescents. Based on systematic search in electronic databases (PsycINFO, Psyndex, PubMed, ISI Web of Knowledge, CINAHL), k = 56 randomized, controlled trials met the inclusion criteria. We computed a random-effects meta-analysis. Systemic therapy showed small-to-medium effects in comparison with an untreated control group (posttest: k = 7, g = .59 standard deviation units, follow-up: k = 2, g = .27) and alternative treatment (posttest: k = 43, g = .32, follow-up: k = 38, g = .28). At follow-up, longer interventions produced larger effect sizes. No other moderator effects were identified. Although available randomized, controlled trials show convincing results, their effects refer to a limited number of systemic approaches and mental disorders, and also pertain to adolescents rather than younger children. Thus, more research is needed before more general conclusions about the effects of systemic therapy can be drawn.

Adding statistical regularity results in a global slowdown in visual search.

PubMed

Vaskevich, Anna; Luria, Roy

2018-05-01

Current statistical learning theories predict that embedding implicit regularities within a task should further improve online performance, beyond general practice. We challenged this assumption by contrasting performance in a visual search task containing either a consistent-mapping (regularity) condition, a random-mapping condition, or both conditions, mixed. Surprisingly, performance in a random visual search, without any regularity, was better than performance in a mixed design search that contained a beneficial regularity. This result was replicated using different stimuli and different regularities, suggesting that mixing consistent and random conditions leads to an overall slowing down of performance. Relying on the predictive-processing framework, we suggest that this global detrimental effect depends on the validity of the regularity: when its predictive value is low, as it is in the case of a mixed design, reliance on all prior information is reduced, resulting in a general slowdown. Our results suggest that our cognitive system does not maximize speed, but rather continues to gather and implement statistical information at the expense of a possible slowdown in performance. Copyright © 2018 Elsevier B.V. All rights reserved.

Applications of Artificial Intelligence (AI) and Expert Systems for Online Searching.

ERIC Educational Resources Information Center

Hawkins, Donald T.

1988-01-01

Discussion of the online searching process identifies the formulation of a search strategy as the major problem area for users of online systems. Artificial intelligence is suggested as a solution to this problem, and several expert systems for information retrieval are described. An annotated list of 24 items for further reading is included. (23…

Assessing Performance Tradeoffs in Undersea Distributed Sensor Networks

DTIC Science & Technology

2006-09-01

time. We refer to this process as track - before - detect (see [5] for a description), since the final determination of a target presence is not made until...expressions for probability of successful search and probability of false search for modeling the track - before - detect process. We then describe a numerical...random manner (randomly sampled from a uniform distribution). II. SENSOR NETWORK PERFORMANCE MODELS We model the process of track - before - detect by

Directional Bias and Pheromone for Discovery and Coverage on Networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fink, Glenn A.; Berenhaut, Kenneth S.; Oehmen, Christopher S.

2012-09-11

Natural multi-agent systems often rely on “correlated random walks” (random walks that are biased toward a current heading) to distribute their agents over a space (e.g., for foraging, search, etc.). Our contribution involves creation of a new movement and pheromone model that applies the concept of heading bias in random walks to a multi-agent, digital-ants system designed for cyber-security monitoring. We examine the relative performance effects of both pheromone and heading bias on speed of discovery of a target and search-area coverage in a two-dimensional network layout. We found that heading bias was unexpectedly helpful in reducing search time andmore » that it was more influential than pheromone for improving coverage. We conclude that while pheromone is very important for rapid discovery, heading bias can also greatly improve both performance metrics.« less

Recruiting participants for randomized controlled trials of music therapy: a practical illustration.

PubMed

Porter, Sam; McConnell, Tracey; Lynn, Fiona; McLaughlin, Katrina; Cardwell, Christopher; Holmes, Valerie

2014-01-01

Failure to recruit sufficient numbers of participants to randomized controlled trials is a common and serious problem. This problem may be additionally acute in music therapy research. To use the experience of conducting a large randomized controlled trial of music therapy for young people with emotional and behavioral difficulties to illustrate the strategies that can be used to optimize recruitment; to report on the success or otherwise of those strategies; and to draw general conclusions about the most effective approaches. Review of the methodological literature, and a narrative account and realist analysis of the recruitment process. The strategies adopted led to the achievement of the recruitment target of 250 subjects, but only with an extension to the recruitment period. In the pre-protocol stage of the research, these strategies included the engagement of non-music therapy clinical investigators, and extensive consultation with clinical stakeholders. In the protocol development and initial recruitment stages, they involved a search of systematic reviews of factors leading to under-recruitment and of interventions to promote recruitment, and the incorporation of their insights into the research protocol and practices. In the latter stages of recruitment, various stakeholders including clinicians, senior managers and participant representatives were consulted in an attempt to uncover the reasons for the low recruitment levels that the research was experiencing. The primary mechanisms to promote recruitment are education, facilitation, audit and feedback, and time allowed. The primary contextual factors affecting the effectiveness of these mechanisms are professional culture and organizational support. © the American Music Therapy Association 2014. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Multi-INT Complex Event Processing using Approximate, Incremental Graph Pattern Search

DTIC Science & Technology

2012-06-01

graph pattern search and SPARQL queries . Total execution time for 10 executions each of 5 random pattern searches in synthetic data sets...01/11 1000 10000 100000 RDF triples Time (secs) 10 20 Graph pattern algorithm SPARQL queries Initial Performance Comparisons 09/18/11 2011 Thrust Area

Search and Seizure in the Schools

ERIC Educational Resources Information Center

Staros, Kari; Williams, Charles F.

2007-01-01

The Fourth Amendment to the U.S. Constitution protects the people of the United States from unreasonable searches and seizures. On first reading, these protections seem clearly defined. The amendment was meant to protect Americans from the kinds of random searches and seizures that the colonists experienced under British colonial rule. Under…

Solving SAT Problem Based on Hybrid Differential Evolution Algorithm

NASA Astrophysics Data System (ADS)

Liu, Kunqi; Zhang, Jingmin; Liu, Gang; Kang, Lishan

Satisfiability (SAT) problem is an NP-complete problem. Based on the analysis about it, SAT problem is translated equally into an optimization problem on the minimum of objective function. A hybrid differential evolution algorithm is proposed to solve the Satisfiability problem. It makes full use of strong local search capacity of hill-climbing algorithm and strong global search capability of differential evolution algorithm, which makes up their disadvantages, improves the efficiency of algorithm and avoids the stagnation phenomenon. The experiment results show that the hybrid algorithm is efficient in solving SAT problem.

Performance analysis of parallel branch and bound search with the hypercube architecture

NASA Technical Reports Server (NTRS)

Mraz, Richard T.

1987-01-01

With the availability of commercial parallel computers, researchers are examining new classes of problems which might benefit from parallel computing. This paper presents results of an investigation of the class of search intensive problems. The specific problem discussed is the Least-Cost Branch and Bound search method of deadline job scheduling. The object-oriented design methodology was used to map the problem into a parallel solution. While the initial design was good for a prototype, the best performance resulted from fine-tuning the algorithm for a specific computer. The experiments analyze the computation time, the speed up over a VAX 11/785, and the load balance of the problem when using loosely coupled multiprocessor system based on the hypercube architecture.

Parallelization of combinatorial search when solving knapsack optimization problem on computing systems based on multicore processors

NASA Astrophysics Data System (ADS)

Rahman, P. A.

2018-05-01

This scientific paper deals with the model of the knapsack optimization problem and method of its solving based on directed combinatorial search in the boolean space. The offered by the author specialized mathematical model of decomposition of the search-zone to the separate search-spheres and the algorithm of distribution of the search-spheres to the different cores of the multi-core processor are also discussed. The paper also provides an example of decomposition of the search-zone to the several search-spheres and distribution of the search-spheres to the different cores of the quad-core processor. Finally, an offered by the author formula for estimation of the theoretical maximum of the computational acceleration, which can be achieved due to the parallelization of the search-zone to the search-spheres on the unlimited number of the processor cores, is also given.

The persistence of the attentional bias to regularities in a changing environment.

PubMed

Yu, Ru Qi; Zhao, Jiaying

2015-10-01

The environment often is stable, but some aspects may change over time. The challenge for the visual system is to discover and flexibly adapt to the changes. We examined how attention is shifted in the presence of changes in the underlying structure of the environment. In six experiments, observers viewed four simultaneous streams of objects while performing a visual search task. In the first half of each experiment, the stream in the structured location contained regularities, the shapes in the random location were randomized, and gray squares appeared in two neutral locations. In the second half, the stream in the structured or the random location may change. In the first half of all experiments, visual search was facilitated in the structured location, suggesting that attention was consistently biased toward regularities. In the second half, this bias persisted in the structured location when no change occurred (Experiment 1), when the regularities were removed (Experiment 2), or when new regularities embedded in the original or novel stimuli emerged in the previously random location (Experiments 3 and 6). However, visual search was numerically but no longer reliably faster in the structured location when the initial regularities were removed and new regularities were introduced in the previously random location (Experiment 4), or when novel random stimuli appeared in the random location (Experiment 5). This suggests that the attentional bias was weakened. Overall, the results demonstrate that the attentional bias to regularities was persistent but also sensitive to changes in the environment.

A Structural Equation Model to Analyse the Antecedents to Students' Web-Based Problem-Solving Performance

ERIC Educational Resources Information Center

Hwang, Gwo-Jen; Kuo, Fan-Ray

2015-01-01

Web-based problem-solving, a compound ability of critical thinking, creative thinking, reasoning thinking and information-searching abilities, has been recognised as an important competence for elementary school students. Some researchers have reported the possible correlations between problem-solving competence and information searching ability;…

Metallic VS2 Monolayer Polytypes as Potential Sodium-Ion Battery Anode via ab Initio Random Structure Searching.

PubMed

Putungan, Darwin Barayang; Lin, Shi-Hsin; Kuo, Jer-Lai

2016-07-27

We systematically investigated the potential of single-layer VS2 polytypes as Na-battery anode materials via density functional theory calculations. We found that sodiation tends to inhibit the 1H-to-1T structural phase transition, in contrast to lithiation-induced transition on monolayer MoS2. Thus, VS2 can have better structural stability in the cycles of charging and discharging. Diffussion of Na atom was found to be very fast on both polytypes, with very small diffusion barriers of 0.085 eV (1H) and 0.088 eV (1T). Ab initio random structure searching was performed in order to explore stable configurations of Na on VS2. Our search found that both the V top and the hexagonal center sites are preferred adsorption sites for Na, with the 1H phase showing a relatively stronger binding. Notably, our random structures search revealed that Na clusters can form as a stacked second layer at full Na concentration, which is not reported in earlier works wherein uniform, single-layer Na adsorption phases were assumed. With reasonably high specific energy capacity (232.91 and 116.45 mAh/g for 1H and 1T phases, respectively) and open-circuit voltage (1.30 and 1.42 V for 1H and 1T phases, respectively), VS2 is a promising alternative material for Na-ion battery anodes with great structural sturdiness. Finally, we have shown the capability of the ab initio random structure searching in the assessment of potential materials for energy storage applications.

Genetic evolutionary taboo search for optimal marker placement in infrared patient setup

NASA Astrophysics Data System (ADS)

Riboldi, M.; Baroni, G.; Spadea, M. F.; Tagaste, B.; Garibaldi, C.; Cambria, R.; Orecchia, R.; Pedotti, A.

2007-09-01

In infrared patient setup adequate selection of the external fiducial configuration is required for compensating inner target displacements (target registration error, TRE). Genetic algorithms (GA) and taboo search (TS) were applied in a newly designed approach to optimal marker placement: the genetic evolutionary taboo search (GETS) algorithm. In the GETS paradigm, multiple solutions are simultaneously tested in a stochastic evolutionary scheme, where taboo-based decision making and adaptive memory guide the optimization process. The GETS algorithm was tested on a group of ten prostate patients, to be compared to standard optimization and to randomly selected configurations. The changes in the optimal marker configuration, when TRE is minimized for OARs, were specifically examined. Optimal GETS configurations ensured a 26.5% mean decrease in the TRE value, versus 19.4% for conventional quasi-Newton optimization. Common features in GETS marker configurations were highlighted in the dataset of ten patients, even when multiple runs of the stochastic algorithm were performed. Including OARs in TRE minimization did not considerably affect the spatial distribution of GETS marker configurations. In conclusion, the GETS algorithm proved to be highly effective in solving the optimal marker placement problem. Further work is needed to embed site-specific deformation models in the optimization process.

Use of antidepressants in dentistry: A systematic review.

PubMed

Lino, P A; Martins, C C; Miranda, Gfpc; de Souza E Silva, M E; de Abreu, Mhng

2017-08-24

Previous research has suggested that antidepressants can be used in oral health care. The aim of this systematic review was to search for scientific evidence of the efficacy of the use of antidepressants in dentistry. The clinical question was as follows (PICO question): dentistry patients (Patients); antidepressants (Intervention); no use or placebo or other drug (Comparison); and efficacy in oral health problems (Outcome). An electronic search was conducted in seven databases, as well as a manual search without restriction regarding language and date of publication. Two independent reviewers selected studies based on eligibility criteria, extracted data and assessed methodological quality based on the PEDro scale. The PROSPERO record is number CRD42016037442. A total of 15 randomized controlled trials were associated with the use of antidepressants to control chronic or acute pain in dentistry, among other conditions such as bruxism and burning mouth syndrome. The most commonly used drug in clinical trials was amitriptyline (more than 50% of studies). Antidepressants may be effective in dentistry for acute and chronic pain, but there is a large amount of methodological heterogeneity among the evaluated studies. In summary, there is rationality for the indication of this class of medicine in dentistry in specific clinical situations. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd. All rights reserved.

Systematic reviews identify important methodological flaws in stroke rehabilitation therapy primary studies: review of reviews.

PubMed

Santaguida, Pasqualina; Oremus, Mark; Walker, Kathryn; Wishart, Laurie R; Siegel, Karen Lohmann; Raina, Parminder

2012-04-01

A "review of reviews" was undertaken to assess methodological issues in studies evaluating nondrug rehabilitation interventions in stroke patients. MEDLINE, CINAHL, PsycINFO, and the Cochrane Database of Systematic Reviews were searched from January 2000 to January 2008 within the stroke rehabilitation setting. Electronic searches were supplemented by reviews of reference lists and citations identified by experts. Eligible studies were systematic reviews; excluded citations were narrative reviews or reviews of reviews. Review characteristics and criteria for assessing methodological quality of primary studies within them were extracted. The search yielded 949 English-language citations. We included a final set of 38 systematic reviews. Cochrane reviews, which have a standardized methodology, were generally of higher methodological quality than non-Cochrane reviews. Most systematic reviews used standardized quality assessment criteria for primary studies, but not all were comprehensive. Reviews showed that primary studies had problems with randomization, allocation concealment, and blinding. Baseline comparability, adverse events, and co-intervention or contamination were not consistently assessed. Blinding of patients and providers was often not feasible and was not evaluated as a source of bias. The eligible systematic reviews identified important methodological flaws in the evaluated primary studies, suggesting the need for improvement of research methods and reporting. Copyright © 2012 Elsevier Inc. All rights reserved.

A Multiobjective Approach Applied to the Protein Structure Prediction Problem

DTIC Science & Technology

2002-03-07

like a low energy search landscape . 2.1.1 Symbolic/Formalized Problem Domain Description. Every computer representable problem can also be embodied...method [60]. 3.4 Energy Minimization Methods The energy landscape algorithms are based on the idea that a protein’s final resting conformation is...in our GA used to search the PSP problem energy landscape ). 3.5.1 Simple GA. The main routine in a sGA, after encoding the problem, builds a

Locating underwater objects. [technology transfer

NASA Technical Reports Server (NTRS)

Grice, C. F.

1974-01-01

Underwater search operations are considered to be engineering and operational problems. A process for proper definition of the problem and selection of instrumentation and operational procedures is described. An outline of underwater search instrumentation and techniques is given.

Optimization technique for problems with an inequality constraint

NASA Technical Reports Server (NTRS)

Russell, K. J.

1972-01-01

General technique uses a modified version of an existing technique termed the pattern search technique. New procedure called the parallel move strategy permits pattern search technique to be used with problems involving a constraint.

Comparison of genetic algorithm methods for fuel management optimization

DOE Office of Scientific and Technical Information (OSTI.GOV)

DeChaine, M.D.; Feltus, M.A.

1995-12-31

The CIGARO system was developed for genetic algorithm fuel management optimization. Tests are performed to find the best fuel location swap mutation operator probability and to compare genetic algorithm to a truly random search method. Tests showed the fuel swap probability should be between 0% and 10%, and a 50% definitely hampered the optimization. The genetic algorithm performed significantly better than the random search method, which did not even satisfy the peak normalized power constraint.

Model Checking with Multi-Threaded IC3 Portfolios

DTIC Science & Technology

2015-01-15

different runs varies randomly depending on the thread interleaving. The use of a portfolio of solvers to maximize the likelihood of a quick solution is...empirically show (cf. Sec. 5.2) that the predictions based on this formula have high accuracy. Note that each solver in the portfolio potentially searches...speedup of over 300. We also show that widening the proof search of ic3 by randomizing its SAT solver is not as effective as paral- lelization

Installation Restoration Program. Phase II: Stage 1 Problem Confirmation Study, Duluth International Airport, Duluth, Minnesota.

DTIC Science & Technology

1984-10-01

8 iii "i t-. Table of Contents (cont.) Section Title Page -APPENDIX A Acronyms, Definitions, Nomenclature and Units of Measure B Scope of Work, Task...Identification/Records Search Phase II - Problem Confirmation and Quantification Phase III - Technology Base Development Phase IV - Corrective Action Only...Problem Identification/Records Search Phase II - Problem Confirmation and Quantification Phase III - Technology Base Development Phase IV - Corrective

Reconfiguration and Search of Social Networks

PubMed Central

Zhang, Lianming; Peng, Aoyuan

2013-01-01

Social networks tend to exhibit some topological characteristics different from regular networks and random networks, such as shorter average path length and higher clustering coefficient, and the node degree of the majority of social networks obeys exponential distribution. Based on the topological characteristics of the real social networks, a new network model which suits to portray the structure of social networks was proposed, and the characteristic parameters of the model were calculated. To find out the relationship between two people in the social network, and using the local information of the social network and the parallel mechanism, a hybrid search strategy based on k-walker random and a high degree was proposed. Simulation results show that the strategy can significantly reduce the average number of search steps, so as to effectively improve the search speed and efficiency. PMID:24574861

RNA motif search with data-driven element ordering.

PubMed

Rampášek, Ladislav; Jimenez, Randi M; Lupták, Andrej; Vinař, Tomáš; Brejová, Broňa

2016-05-18

In this paper, we study the problem of RNA motif search in long genomic sequences. This approach uses a combination of sequence and structure constraints to uncover new distant homologs of known functional RNAs. The problem is NP-hard and is traditionally solved by backtracking algorithms. We have designed a new algorithm for RNA motif search and implemented a new motif search tool RNArobo. The tool enhances the RNAbob descriptor language, allowing insertions in helices, which enables better characterization of ribozymes and aptamers. A typical RNA motif consists of multiple elements and the running time of the algorithm is highly dependent on their ordering. By approaching the element ordering problem in a principled way, we demonstrate more than 100-fold speedup of the search for complex motifs compared to previously published tools. We have developed a new method for RNA motif search that allows for a significant speedup of the search of complex motifs that include pseudoknots. Such speed improvements are crucial at a time when the rate of DNA sequencing outpaces growth in computing. RNArobo is available at http://compbio.fmph.uniba.sk/rnarobo .

Annealing Ant Colony Optimization with Mutation Operator for Solving TSP.

PubMed

Mohsen, Abdulqader M

2016-01-01

Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality.

Binary Bees Algorithm - bioinspiration from the foraging mechanism of honeybees to optimize a multiobjective multidimensional assignment problem

NASA Astrophysics Data System (ADS)

Xu, Shuo; Ji, Ze; Truong Pham, Duc; Yu, Fan

2011-11-01

The simultaneous mission assignment and home allocation for hospital service robots studied is a Multidimensional Assignment Problem (MAP) with multiobjectives and multiconstraints. A population-based metaheuristic, the Binary Bees Algorithm (BBA), is proposed to optimize this NP-hard problem. Inspired by the foraging mechanism of honeybees, the BBA's most important feature is an explicit functional partitioning between global search and local search for exploration and exploitation, respectively. Its key parts consist of adaptive global search, three-step elitism selection (constraint handling, non-dominated solutions selection, and diversity preservation), and elites-centred local search within a Hamming neighbourhood. Two comparative experiments were conducted to investigate its single objective optimization, optimization effectiveness (indexed by the S-metric and C-metric) and optimization efficiency (indexed by computational burden and CPU time) in detail. The BBA outperformed its competitors in almost all the quantitative indices. Hence, the above overall scheme, and particularly the searching history-adapted global search strategy was validated.

Searching RNA motifs and their intermolecular contacts with constraint networks.

PubMed

Thébault, P; de Givry, S; Schiex, T; Gaspin, C

2006-09-01

Searching RNA gene occurrences in genomic sequences is a task whose importance has been renewed by the recent discovery of numerous functional RNA, often interacting with other ligands. Even if several programs exist for RNA motif search, none exists that can represent and solve the problem of searching for occurrences of RNA motifs in interaction with other molecules. We present a constraint network formulation of this problem. RNA are represented as structured motifs that can occur on more than one sequence and which are related together by possible hybridization. The implemented tool MilPat is used to search for several sRNA families in genomic sequences. Results show that MilPat allows to efficiently search for interacting motifs in large genomic sequences and offers a simple and extensible framework to solve such problems. New and known sRNA are identified as H/ACA candidates in Methanocaldococcus jannaschii. http://carlit.toulouse.inra.fr/MilPaT/MilPat.pl.

BCD Beam Search: considering suboptimal partial solutions in Bad Clade Deletion supertrees.

PubMed

Fleischauer, Markus; Böcker, Sebastian

2018-01-01

Supertree methods enable the reconstruction of large phylogenies. The supertree problem can be formalized in different ways in order to cope with contradictory information in the input. Some supertree methods are based on encoding the input trees in a matrix; other methods try to find minimum cuts in some graph. Recently, we introduced Bad Clade Deletion (BCD) supertrees which combines the graph-based computation of minimum cuts with optimizing a global objective function on the matrix representation of the input trees. The BCD supertree method has guaranteed polynomial running time and is very swift in practice. The quality of reconstructed supertrees was superior to matrix representation with parsimony (MRP) and usually on par with SuperFine for simulated data; but particularly for biological data, quality of BCD supertrees could not keep up with SuperFine supertrees. Here, we present a beam search extension for the BCD algorithm that keeps alive a constant number of partial solutions in each top-down iteration phase. The guaranteed worst-case running time of the new algorithm is still polynomial in the size of the input. We present an exact and a randomized subroutine to generate suboptimal partial solutions. Both beam search approaches consistently improve supertree quality on all evaluated datasets when keeping 25 suboptimal solutions alive. Supertree quality of the BCD Beam Search algorithm is on par with MRP and SuperFine even for biological data. This is the best performance of a polynomial-time supertree algorithm reported so far.

Contextual Cueing in Multiconjunction Visual Search Is Dependent on Color- and Configuration-Based Intertrial Contingencies

ERIC Educational Resources Information Center

Geyer, Thomas; Shi, Zhuanghua; Muller, Hermann J.

2010-01-01

Three experiments examined memory-based guidance of visual search using a modified version of the contextual-cueing paradigm (Jiang & Chun, 2001). The target, if present, was a conjunction of color and orientation, with target (and distractor) features randomly varying across trials (multiconjunction search). Under these conditions, reaction times…

Central and Peripheral Vision Loss Differentially Affects Contextual Cueing in Visual Search

ERIC Educational Resources Information Center

Geringswald, Franziska; Pollmann, Stefan

2015-01-01

Visual search for targets in repeated displays is more efficient than search for the same targets in random distractor layouts. Previous work has shown that this contextual cueing is severely impaired under central vision loss. Here, we investigated whether central vision loss, simulated with gaze-contingent displays, prevents the incidental…

Sticker charts: a method for improving adherence to treatment of chronic diseases in children.

PubMed

Luersen, Kara; Davis, Scott A; Kaplan, Sebastian G; Abel, Troy D; Winchester, Woodrow W; Feldman, Steven R

2012-01-01

Poor adherence is a common problem and may be an underlying cause of poor clinical outcomes. In pediatric populations, positive reinforcement techniques such as sticker charts may increase motivation to adhere to treatment regimens. To review the use of sticker charts to improve adherence in children with chronic disease, Medline and PsycINFO searches were conducted using the key words "positive reinforcement OR behavior therapy" and "adherence OR patient compliance" and "child." Randomized controlled retrospective cohort or single-subject-design studies were selected. Studies reporting adherence to the medical treatment of chronic disease in children using positive reinforcement techniques were included in the analysis. The systematic search was supplemented by identifying additional studies identified through the reference lists and authors of the initial articles found. Positive reinforcement techniques such as sticker charts increase adherence to medical treatment regimens. In several studies, this effect was maintained for months after the initial intervention. Better adherence correlated with better clinical outcomes in some, but not all, studies. Few studies examining the use of sticker charts were identified. Although single-subject-design studies are useful in establishing the effect of a behavioral intervention, larger randomized controlled trials would help determine the precise efficacy of sticker chart interventions. Adherence to medical treatments in children can be increased using sticker charts or other positive reinforcement techniques. This may be an effective means to encourage children with atopic dermatitis to apply their medications and improve clinical outcomes. © 2012 Wiley Periodicals, Inc.

PowerPlay: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem

PubMed Central

Schmidhuber, Jürgen

2013-01-01

Most of computer science focuses on automatically solving given computational problems. I focus on automatically inventing or discovering problems in a way inspired by the playful behavior of animals and humans, to train a more and more general problem solver from scratch in an unsupervised fashion. Consider the infinite set of all computable descriptions of tasks with possibly computable solutions. Given a general problem-solving architecture, at any given time, the novel algorithmic framework PowerPlay (Schmidhuber, 2011) searches the space of possible pairs of new tasks and modifications of the current problem solver, until it finds a more powerful problem solver that provably solves all previously learned tasks plus the new one, while the unmodified predecessor does not. Newly invented tasks may require to achieve a wow-effect by making previously learned skills more efficient such that they require less time and space. New skills may (partially) re-use previously learned skills. The greedy search of typical PowerPlay variants uses time-optimal program search to order candidate pairs of tasks and solver modifications by their conditional computational (time and space) complexity, given the stored experience so far. The new task and its corresponding task-solving skill are those first found and validated. This biases the search toward pairs that can be described compactly and validated quickly. The computational costs of validating new tasks need not grow with task repertoire size. Standard problem solver architectures of personal computers or neural networks tend to generalize by solving numerous tasks outside the self-invented training set; PowerPlay’s ongoing search for novelty keeps breaking the generalization abilities of its present solver. This is related to Gödel’s sequence of increasingly powerful formal theories based on adding formerly unprovable statements to the axioms without affecting previously provable theorems. The continually increasing repertoire of problem-solving procedures can be exploited by a parallel search for solutions to additional externally posed tasks. PowerPlay may be viewed as a greedy but practical implementation of basic principles of creativity (Schmidhuber, 2006a, 2010). A first experimental analysis can be found in separate papers (Srivastava et al., 2012a,b, 2013). PMID:23761771

Inference-Based Similarity Search in Randomized Montgomery Domains for Privacy-Preserving Biometric Identification.

PubMed

Wang, Yi; Wan, Jianwu; Guo, Jun; Cheung, Yiu-Ming; Yuen, Pong C; Yi Wang; Jianwu Wan; Jun Guo; Yiu-Ming Cheung; Yuen, Pong C; Cheung, Yiu-Ming; Guo, Jun; Yuen, Pong C; Wan, Jianwu; Wang, Yi

2018-07-01

Similarity search is essential to many important applications and often involves searching at scale on high-dimensional data based on their similarity to a query. In biometric applications, recent vulnerability studies have shown that adversarial machine learning can compromise biometric recognition systems by exploiting the biometric similarity information. Existing methods for biometric privacy protection are in general based on pairwise matching of secured biometric templates and have inherent limitations in search efficiency and scalability. In this paper, we propose an inference-based framework for privacy-preserving similarity search in Hamming space. Our approach builds on an obfuscated distance measure that can conceal Hamming distance in a dynamic interval. Such a mechanism enables us to systematically design statistically reliable methods for retrieving most likely candidates without knowing the exact distance values. We further propose to apply Montgomery multiplication for generating search indexes that can withstand adversarial similarity analysis, and show that information leakage in randomized Montgomery domains can be made negligibly small. Our experiments on public biometric datasets demonstrate that the inference-based approach can achieve a search accuracy close to the best performance possible with secure computation methods, but the associated cost is reduced by orders of magnitude compared to cryptographic primitives.

Taboo Search: An Approach to the Multiple Minima Problem

NASA Astrophysics Data System (ADS)

Cvijovic, Djurdje; Klinowski, Jacek

1995-02-01

Described here is a method, based on Glover's taboo search for discrete functions, of solving the multiple minima problem for continuous functions. As demonstrated by model calculations, the algorithm avoids entrapment in local minima and continues the search to give a near-optimal final solution. Unlike other methods of global optimization, this procedure is generally applicable, easy to implement, derivative-free, and conceptually simple.

How good is Google? The quality of otolaryngology information on the internet.

PubMed

Pusz, Max D; Brietzke, Scott E

2012-09-01

To assess the quality of the information a patient (parent) may encounter using a Google search for typical otolaryngology ailments. Cross-sectional study. Tertiary care center. A Google keyword search was performed for 10 common otolaryngology problems including ear infection, hearing loss, tonsillitis, and so on. The top 10 search results for each were critically examined using the 16-item (1-5 scale) standardized DISCERN instrument. The DISCERN instrument was developed to assess the quality and comprehensiveness of patient treatment choice literature. A total of 100 Web sites were assessed. Of these, 19 (19%) were primarily advertisements for products and were excluded from DISCERN scoring. Searches for more typically chronic otolaryngic problems (eg, tinnitus, sleep apnea, etc) resulted in more biased, advertisement-type results than those for typically acute problems (eg, ear infection, sinus infection, P = .03). The search for "sleep apnea treatment" produced the highest scoring results (mean overall DISCERN score = 3.49, range = 1.81-4.56), and the search for "hoarseness treatment" produced the lowest scores (mean = 2.49, range = 1.56-3.56). Results from major comprehensive Web sites (WebMD, EMedicinehealth.com, Wikipedia, etc.) scored higher than other Web sites (mean DISCERN score = 3.46 vs 2.48, P < .001). There is marked variability in the quality of Web site information for the treatment of common otolaryngologic problems. Searches on more chronic problems resulted in a higher proportion of biased advertisement Web sites. Larger, comprehensive Web sites generally provided better information but were less than perfect in presenting complete information on treatment options.

Hybrid foraging search: Searching for multiple instances of multiple types of target.

PubMed

Wolfe, Jeremy M; Aizenman, Avigael M; Boettcher, Sage E P; Cain, Matthew S

2016-02-01

This paper introduces the "hybrid foraging" paradigm. In typical visual search tasks, observers search for one instance of one target among distractors. In hybrid search, observers search through visual displays for one instance of any of several types of target held in memory. In foraging search, observers collect multiple instances of a single target type from visual displays. Combining these paradigms, in hybrid foraging tasks observers search visual displays for multiple instances of any of several types of target (as might be the case in searching the kitchen for dinner ingredients or an X-ray for different pathologies). In the present experiment, observers held 8-64 target objects in memory. They viewed displays of 60-105 randomly moving photographs of objects and used the computer mouse to collect multiple targets before choosing to move to the next display. Rather than selecting at random among available targets, observers tended to collect items in runs of one target type. Reaction time (RT) data indicate searching again for the same item is more efficient than searching for any other targets, held in memory. Observers were trying to maximize collection rate. As a result, and consistent with optimal foraging theory, they tended to leave 25-33% of targets uncollected when moving to the next screen/patch. The pattern of RTs shows that while observers were collecting a target item, they had already begun searching memory and the visual display for additional targets, making the hybrid foraging task a useful way to investigate the interaction of visual and memory search. Copyright © 2015 Elsevier Ltd. All rights reserved.

Hybrid foraging search: Searching for multiple instances of multiple types of target

PubMed Central

Wolfe, Jeremy M.; Aizenman, Avigael M.; Boettcher, Sage E.P.; Cain, Matthew S.

2016-01-01

This paper introduces the “hybrid foraging” paradigm. In typical visual search tasks, observers search for one instance of one target among distractors. In hybrid search, observers search through visual displays for one instance of any of several types of target held in memory. In foraging search, observers collect multiple instances of a single target type from visual displays. Combining these paradigms, in hybrid foraging tasks observers search visual displays for multiple instances of any of several types of target (as might be the case in searching the kitchen for dinner ingredients or an X-ray for different pathologies). In the present experiment, observers held 8–64 targets objects in memory. They viewed displays of 60–105 randomly moving photographs of objects and used the computer mouse to collect multiple targets before choosing to move to the next display. Rather than selecting at random among available targets, observers tended to collect items in runs of one target type. Reaction time (RT) data indicate searching again for the same item is more efficient than searching for any other targets, held in memory. Observers were trying to maximize collection rate. As a result, and consistent with optimal foraging theory, they tended to leave 25–33% of targets uncollected when moving to the next screen/patch. The pattern of RTs shows that while observers were collecting a target item, they had already begun searching memory and the visual display for additional targets, making the hybrid foraging task a useful way to investigate the interaction of visual and memory search. PMID:26731644

Puzzle of magnetic moments of Ni clusters revisited using quantum Monte Carlo method.

PubMed

Lee, Hung-Wen; Chang, Chun-Ming; Hsing, Cheng-Rong

2017-02-28

The puzzle of the magnetic moments of small nickel clusters arises from the discrepancy between values predicted using density functional theory (DFT) and experimental measurements. Traditional DFT approaches underestimate the magnetic moments of nickel clusters. Two fundamental problems are associated with this puzzle, namely, calculating the exchange-correlation interaction accurately and determining the global minimum structures of the clusters. Theoretically, the two problems can be solved using quantum Monte Carlo (QMC) calculations and the ab initio random structure searching (AIRSS) method correspondingly. Therefore, we combined the fixed-moment AIRSS and QMC methods to investigate the magnetic properties of Ni n (n = 5-9) clusters. The spin moments of the diffusion Monte Carlo (DMC) ground states are higher than those of the Perdew-Burke-Ernzerhof ground states and, in the case of Ni 8-9 , two new ground-state structures have been discovered using the DMC calculations. The predicted results are closer to the experimental findings, unlike the results predicted in previous standard DFT studies.

Stochastic Control Synthesis of Systems with Structured Uncertainty

NASA Technical Reports Server (NTRS)

Padula, Sharon L. (Technical Monitor); Crespo, Luis G.

2003-01-01

This paper presents a study on the design of robust controllers by using random variables to model structured uncertainty for both SISO and MIMO feedback systems. Once the parameter uncertainty is prescribed with probability density functions, its effects are propagated through the analysis leading to stochastic metrics for the system's output. Control designs that aim for satisfactory performances while guaranteeing robust closed loop stability are attained by solving constrained non-linear optimization problems in the frequency domain. This approach permits not only to quantify the probability of having unstable and unfavorable responses for a particular control design but also to search for controls while favoring the values of the parameters with higher chance of occurrence. In this manner, robust optimality is achieved while the characteristic conservatism of conventional robust control methods is eliminated. Examples that admit closed form expressions for the probabilistic metrics of the output are used to elucidate the nature of the problem at hand and validate the proposed formulations.

Study on probability distributions for evolution in modified extremal optimization

NASA Astrophysics Data System (ADS)

Zeng, Guo-Qiang; Lu, Yong-Zai; Mao, Wei-Jie; Chu, Jian

2010-05-01

It is widely believed that the power-law is a proper probability distribution being effectively applied for evolution in τ-EO (extremal optimization), a general-purpose stochastic local-search approach inspired by self-organized criticality, and its applications in some NP-hard problems, e.g., graph partitioning, graph coloring, spin glass, etc. In this study, we discover that the exponential distributions or hybrid ones (e.g., power-laws with exponential cutoff) being popularly used in the research of network sciences may replace the original power-laws in a modified τ-EO method called self-organized algorithm (SOA), and provide better performances than other statistical physics oriented methods, such as simulated annealing, τ-EO and SOA etc., from the experimental results on random Euclidean traveling salesman problems (TSP) and non-uniform instances. From the perspective of optimization, our results appear to demonstrate that the power-law is not the only proper probability distribution for evolution in EO-similar methods at least for TSP, the exponential and hybrid distributions may be other choices.

A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm.

PubMed

Amoshahy, Mohammad Javad; Shamsi, Mousa; Sedaaghi, Mohammad Hossein

2016-01-01

Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collective behavior of some animals. It is easy to implement and there are few parameters to adjust. The performance of PSO algorithm depends greatly on the appropriate parameter selection strategies for fine tuning its parameters. Inertia weight (IW) is one of PSO's parameters used to bring about a balance between the exploration and exploitation characteristics of PSO. This paper proposes a new nonlinear strategy for selecting inertia weight which is named Flexible Exponential Inertia Weight (FEIW) strategy because according to each problem we can construct an increasing or decreasing inertia weight strategy with suitable parameters selection. The efficacy and efficiency of PSO algorithm with FEIW strategy (FEPSO) is validated on a suite of benchmark problems with different dimensions. Also FEIW is compared with best time-varying, adaptive, constant and random inertia weights. Experimental results and statistical analysis prove that FEIW improves the search performance in terms of solution quality as well as convergence rate.

A Novel Flexible Inertia Weight Particle Swarm Optimization Algorithm

PubMed Central

Shamsi, Mousa; Sedaaghi, Mohammad Hossein

2016-01-01

Particle swarm optimization (PSO) is an evolutionary computing method based on intelligent collective behavior of some animals. It is easy to implement and there are few parameters to adjust. The performance of PSO algorithm depends greatly on the appropriate parameter selection strategies for fine tuning its parameters. Inertia weight (IW) is one of PSO’s parameters used to bring about a balance between the exploration and exploitation characteristics of PSO. This paper proposes a new nonlinear strategy for selecting inertia weight which is named Flexible Exponential Inertia Weight (FEIW) strategy because according to each problem we can construct an increasing or decreasing inertia weight strategy with suitable parameters selection. The efficacy and efficiency of PSO algorithm with FEIW strategy (FEPSO) is validated on a suite of benchmark problems with different dimensions. Also FEIW is compared with best time-varying, adaptive, constant and random inertia weights. Experimental results and statistical analysis prove that FEIW improves the search performance in terms of solution quality as well as convergence rate. PMID:27560945

Corticosteroids for ocular toxoplasmosis

PubMed Central

Jasper, Smitha; Vedula, Satyanarayana S; John, Sheeja S; Horo, Saban; Sepah, Yasir J; Nguyen, Quan Dong

2014-01-01

Background Ocular infestation with Toxoplasma gondii, a parasite, may result in inflammation in the retina, choroid, and uvea and consequently lead to complications such as glaucoma, cataract, and posterior synechiae. Objectives The objective of this systematic review was to assess the effects of adjunctive use of corticosteroids for ocular toxoplasmosis. Search methods We searched CENTRAL (which contains the Cochrane Eyes and Vision Group Trials Register) (The Cochrane Library 2012, Issue 9), Ovid MEDLINE, Ovid MEDLINE In-Process and Other Non-Indexed Citations, Ovid MEDLINE Daily, Ovid OLDMEDLINE, (January 1950 to October 2012), EMBASE (January 1980 to October 2012), Latin American and Caribbean Literature on Health Sciences (LILACS) (January 1982 to October 2012), the metaRegister of Controlled Trials (mRCT) (www.controlled-trials.com), ClinicalTrials.gov (www.clinicaltrials.gov) and the WHO International Clinical Trials Registry Platform (ICTRP) (www.who.int/ictrp/search/en). We searched the reference lists of included studies for any additional studies not identified by the electronic searches. We did not use any date or language restrictions in the electronic searches for trials. We last searched the electronic databases on 11 October 2012. Selection criteria We planned to include randomized and quasi-randomized controlled trials. Eligible trials would have enrolled participants of any age who were immunocompetent and were diagnosed with active ocular toxoplasmosis. Included trials would have compared anti-parasitic therapy plus corticosteroids versus anti-parasitic therapy alone, or different doses or times of initiation of corticosteroids. Data collection and analysis Two authors independently screened titles and abstracts retrieved from the electronic searches. We retrieved full-text articles of studies categorized as ‘unsure’ or ‘include’ after review of the abstracts. Two authors independently reviewed each full-text article. Discrepancies were resolved through discussion. Main results The electronic searches retrieved 368 titles and abstracts. We reviewed 20 full-text articles. We identified no trials eligible for inclusion in this systematic review. Authors' conclusions Although research has identified wide variation in practices regarding use of corticosteroids, our systematic review did not identify evidence from randomized controlled trials for the role of corticosteroids in the management of ocular toxoplasmosis. Several questions remain unanswered by well-conducted randomized trials in this context, including whether use of corticosteroids is more effective than use of anti-parasitic therapy alone, when corticosteroids should be initiated in the treatment regimen (early versus late course of treatment), and which dosage and duration of steroid use is best. These questions are easily amenable to research using a randomized controlled design and they are ethical due to the absence of evidence to support or discourage use of corticosteroids for this condition. The question of foremost importance, however, is whether they should be used as adjunct therapy (that is, additional) to anti-parasitic agents. PMID:23633342

Visual search for arbitrary objects in real scenes

PubMed Central

Alvarez, George A.; Rosenholtz, Ruth; Kuzmova, Yoana I.; Sherman, Ashley M.

2011-01-01

How efficient is visual search in real scenes? In searches for targets among arrays of randomly placed distractors, efficiency is often indexed by the slope of the reaction time (RT) × Set Size function. However, it may be impossible to define set size for real scenes. As an approximation, we hand-labeled 100 indoor scenes and used the number of labeled regions as a surrogate for set size. In Experiment 1, observers searched for named objects (a chair, bowl, etc.). With set size defined as the number of labeled regions, search was very efficient (~5 ms/item). When we controlled for a possible guessing strategy in Experiment 2, slopes increased somewhat (~15 ms/item), but they were much shallower than search for a random object among other distinctive objects outside of a scene setting (Exp. 3: ~40 ms/item). In Experiments 4–6, observers searched repeatedly through the same scene for different objects. Increased familiarity with scenes had modest effects on RTs, while repetition of target items had large effects (>500 ms). We propose that visual search in scenes is efficient because scene-specific forms of attentional guidance can eliminate most regions from the “functional set size” of items that could possibly be the target. PMID:21671156

Visual search for arbitrary objects in real scenes.

PubMed

Wolfe, Jeremy M; Alvarez, George A; Rosenholtz, Ruth; Kuzmova, Yoana I; Sherman, Ashley M

2011-08-01

How efficient is visual search in real scenes? In searches for targets among arrays of randomly placed distractors, efficiency is often indexed by the slope of the reaction time (RT) × Set Size function. However, it may be impossible to define set size for real scenes. As an approximation, we hand-labeled 100 indoor scenes and used the number of labeled regions as a surrogate for set size. In Experiment 1, observers searched for named objects (a chair, bowl, etc.). With set size defined as the number of labeled regions, search was very efficient (~5 ms/item). When we controlled for a possible guessing strategy in Experiment 2, slopes increased somewhat (~15 ms/item), but they were much shallower than search for a random object among other distinctive objects outside of a scene setting (Exp. 3: ~40 ms/item). In Experiments 4-6, observers searched repeatedly through the same scene for different objects. Increased familiarity with scenes had modest effects on RTs, while repetition of target items had large effects (>500 ms). We propose that visual search in scenes is efficient because scene-specific forms of attentional guidance can eliminate most regions from the "functional set size" of items that could possibly be the target.

Folksonomical P2P File Sharing Networks Using Vectorized KANSEI Information as Search Tags

NASA Astrophysics Data System (ADS)

Ohnishi, Kei; Yoshida, Kaori; Oie, Yuji

We present the concept of folksonomical peer-to-peer (P2P) file sharing networks that allow participants (peers) to freely assign structured search tags to files. These networks are similar to folksonomies in the present Web from the point of view that users assign search tags to information distributed over a network. As a concrete example, we consider an unstructured P2P network using vectorized Kansei (human sensitivity) information as structured search tags for file search. Vectorized Kansei information as search tags indicates what participants feel about their files and is assigned by the participant to each of their files. A search query also has the same form of search tags and indicates what participants want to feel about files that they will eventually obtain. A method that enables file search using vectorized Kansei information is the Kansei query-forwarding method, which probabilistically propagates a search query to peers that are likely to hold more files having search tags that are similar to the query. The similarity between the search query and the search tags is measured in terms of their dot product. The simulation experiments examine if the Kansei query-forwarding method can provide equal search performance for all peers in a network in which only the Kansei information and the tendency with respect to file collection are different among all of the peers. The simulation results show that the Kansei query forwarding method and a random-walk-based query forwarding method, for comparison, work effectively in different situations and are complementary. Furthermore, the Kansei query forwarding method is shown, through simulations, to be superior to or equal to the random-walk based one in terms of search speed.

How do teachers with self-reported voice problems differ from their peers with self-reported voice health?

PubMed

Lyberg Åhlander, Viveka; Rydell, Roland; Löfqvist, Anders

2012-07-01

This randomized case-control study compares teachers with self-reported voice problems to age-, gender-, and school-matched colleagues with self-reported voice health. The self-assessed voice function is related to factors known to influence the voice: laryngeal findings, voice quality, personality, psychosocial and coping aspects, searching for causative factors of voice problems in teachers. Subjects and controls, recruited from a teacher group in an earlier questionnaire study, underwent examinations of the larynx by high-speed imaging and kymograms; voice recordings; voice range profile; audiometry; self-assessment of voice handicap and voice function; teaching and environmental aspects; personality; coping; burnout, and work-related issues. The laryngeal and voice recordings were assessed by experienced phoniatricians and speech pathologists. The subjects with self-assessed voice problems differed from their peers with self-assessed voice health by significantly longer recovery time from voice problems and scored higher on all subscales of the Voice Handicap Index-Throat. The results show that the cause of voice dysfunction in this group of teachers with self-reported voice problems is not found in the vocal apparatus or within the individual. The individual's perception of a voice problem seems to be based on a combination of the number of symptoms and of how often the symptoms occur, along with the recovery time. The results also underline the importance of using self-assessed reports of voice dysfunction. Copyright © 2012 The Voice Foundation. Published by Mosby, Inc. All rights reserved.

Scavengers on the move: behavioural changes in foraging search patterns during the annual cycle.

PubMed

López-López, Pascual; Benavent-Corai, José; García-Ripollés, Clara; Urios, Vicente

2013-01-01

Optimal foraging theory predicts that animals will tend to maximize foraging success by optimizing search strategies. However, how organisms detect sparsely distributed food resources remains an open question. When targets are sparse and unpredictably distributed, a Lévy strategy should maximize foraging success. By contrast, when resources are abundant and regularly distributed, simple brownian random movement should be sufficient. Although very different groups of organisms exhibit Lévy motion, the shift from a Lévy to a brownian search strategy has been suggested to depend on internal and external factors such as sex, prey density, or environmental context. However, animal response at the individual level has received little attention. We used GPS satellite-telemetry data of Egyptian vultures Neophron percnopterus to examine movement patterns at the individual level during consecutive years, with particular interest in the variations in foraging search patterns during the different periods of the annual cycle (i.e. breeding vs. non-breeding). Our results show that vultures followed a brownian search strategy in their wintering sojourn in Africa, whereas they exhibited a more complex foraging search pattern at breeding grounds in Europe, including Lévy motion. Interestingly, our results showed that individuals shifted between search strategies within the same period of the annual cycle in successive years. Results could be primarily explained by the different environmental conditions in which foraging activities occur. However, the high degree of behavioural flexibility exhibited during the breeding period in contrast to the non-breeding period is challenging, suggesting that not only environmental conditions explain individuals' behaviour but also individuals' cognitive abilities (e.g., memory effects) could play an important role. Our results support the growing awareness about the role of behavioural flexibility at the individual level, adding new empirical evidence about how animals in general, and particularly scavengers, solve the problem of efficiently finding food resources.

What Is the Efficacy of Teaching Psychotherapy to Psychiatry Residents and Medical Students?

PubMed

Truong, Anh; Wu, Peter; Diez-Barroso, Ramon; Coverdale, John

2015-10-01

Because there are no formal reviews, the authors set out to identify and evaluate studies on teaching psychotherapy to psychiatry residents and medical students. PubMed, Embase, and PsycINFO were searched for papers with outcomes on teaching psychotherapy. Search terms included psychotherapy, teaching, residents, medical students, supportive, psychodynamic, cognitive, behavioral, learning, training, skills, competency, and mentalization. Nine studies were found that met inclusion criteria. There were seven studies of psychiatry residents and two of medical students. Only two of the research designs had comparison groups, and these were both randomized controlled trials, while seven of the other designs were pretest and posttest. Teaching methods, course content, and outcome measures varied widely across studies. Common methodological problems included a lack of control, low numbers of subjects as learners, and a lack of validity of the outcome measures. Only one of the studies was judged to be methodologically rigorous. These findings establish a priority for undertaking additional rigorously designed studies in evaluating the teaching of psychotherapy to psychiatry residents and medical students.

Multiphasic On/Off Pheromone Signalling in Moths as Neural Correlates of a Search Strategy

PubMed Central

Martinez, Dominique; Chaffiol, Antoine; Voges, Nicole; Gu, Yuqiao; Anton, Sylvia; Rospars, Jean-Pierre; Lucas, Philippe

2013-01-01

Insects and robots searching for odour sources in turbulent plumes face the same problem: the random nature of mixing causes fluctuations and intermittency in perception. Pheromone-tracking male moths appear to deal with discontinuous flows of information by surging upwind, upon sensing a pheromone patch, and casting crosswind, upon losing the plume. Using a combination of neurophysiological recordings, computational modelling and experiments with a cyborg, we propose a neuronal mechanism that promotes a behavioural switch between surge and casting. We show how multiphasic On/Off pheromone-sensitive neurons may guide action selection based on signalling presence or loss of the pheromone. A Hodgkin-Huxley-type neuron model with a small-conductance calcium-activated potassium (SK) channel reproduces physiological On/Off responses. Using this model as a command neuron and the antennae of tethered moths as pheromone sensors, we demonstrate the efficiency of multiphasic patterning in driving a robotic searcher toward the source. Taken together, our results suggest that multiphasic On/Off responses may mediate olfactory navigation and that SK channels may account for these responses. PMID:23613816

Multiphasic on/off pheromone signalling in moths as neural correlates of a search strategy.

PubMed

Martinez, Dominique; Chaffiol, Antoine; Voges, Nicole; Gu, Yuqiao; Anton, Sylvia; Rospars, Jean-Pierre; Lucas, Philippe

2013-01-01

Insects and robots searching for odour sources in turbulent plumes face the same problem: the random nature of mixing causes fluctuations and intermittency in perception. Pheromone-tracking male moths appear to deal with discontinuous flows of information by surging upwind, upon sensing a pheromone patch, and casting crosswind, upon losing the plume. Using a combination of neurophysiological recordings, computational modelling and experiments with a cyborg, we propose a neuronal mechanism that promotes a behavioural switch between surge and casting. We show how multiphasic On/Off pheromone-sensitive neurons may guide action selection based on signalling presence or loss of the pheromone. A Hodgkin-Huxley-type neuron model with a small-conductance calcium-activated potassium (SK) channel reproduces physiological On/Off responses. Using this model as a command neuron and the antennae of tethered moths as pheromone sensors, we demonstrate the efficiency of multiphasic patterning in driving a robotic searcher toward the source. Taken together, our results suggest that multiphasic On/Off responses may mediate olfactory navigation and that SK channels may account for these responses.

Search for function coefficient distribution in traditional Chinese medicine network

NASA Astrophysics Data System (ADS)

He, Yue; Zhang, Peipei; Sun, Anzheng; Su, Beibei; He, Da-Ren

2004-03-01

We suggest a model for a simulation on development of traditional Chinese medicine system. Suppose there are a certain number of Chinese medicines. Each of them is given randomly a "function coefficient", which has a value between 0 and 1. The larger it is the stronger is its function for solving one healthy problem and serving as an "emperor" in a prescription formulation. The smaller it is the stronger is its function for harmonizing and/or accessorizing a prescription formulation. In every step of time a new medicine is discovered. With a probability, P(m), which is determined according to our statistical investigation results, it can produce a new prescription formulation with other m-1 medicines. We assume that the probability for choosing the function coefficients of these m medicines follow a distribution function, which is everywhere smooth. A program has been set up to perform a search for this function form so that the simulation results show a best agreement to our statistical data. We believe the result function form will be helpful for an understanding on real development of traditional Chinese medicine system.

Learning dominance relations in combinatorial search problems

NASA Technical Reports Server (NTRS)

Yu, Chee-Fen; Wah, Benjamin W.

1988-01-01

Dominance relations commonly are used to prune unnecessary nodes in search graphs, but they are problem-dependent and cannot be derived by a general procedure. The authors identify machine learning of dominance relations and the applicable learning mechanisms. A study of learning dominance relations using learning by experimentation is described. This system has been able to learn dominance relations for the 0/1-knapsack problem, an inventory problem, the reliability-by-replication problem, the two-machine flow shop problem, a number of single-machine scheduling problems, and a two-machine scheduling problem. It is considered that the same methodology can be extended to learn dominance relations in general.

Coordinated Search for a Random Walk Target Motion

NASA Astrophysics Data System (ADS)

El-Hadidy, Mohamed Abd Allah; Abou-Gabal, Hamdy M.

This paper presents the cooperation between two searchers at the origin to find a Random Walk moving target on the real line. No information is not available about the target’s position all the time. Rather than finding the conditions that make the expected value of the first meeting time between one of the searchers and the target is finite, we show the existence of the optimal search strategy which minimizes this first meeting time. The effectiveness of this model is illustrated using a numerical example.

Finding paths in tree graphs with a quantum walk

NASA Astrophysics Data System (ADS)

Koch, Daniel; Hillery, Mark

2018-01-01

We analyze the potential for different types of searches using the formalism of scattering random walks on quantum computers. Given a particular type of graph consisting of nodes and connections, a "tree maze," we would like to find a selected final node as quickly as possible, faster than any classical search algorithm. We show that this can be done using a quantum random walk, both through numerical calculations as well as by using the eigenvectors and eigenvalues of the quantum system.

Clinical effectiveness of garlic (Allium sativum).

PubMed

Pittler, Max H; Ernst, Edzard

2007-11-01

The objective of this review is to update and assess the clinical evidence based on rigorous trials of the effectiveness of garlic (A. sativum). Systematic searches were carried out in Medline, Embase, Amed, the Cochrane Database of Systematic Reviews, Natural Standard, and the Natural Medicines Comprehensive Database (search date December 2006). Our own files, the bibliographies of relevant papers and the contents pages of all issues of the review journal FACT were searched for further studies. No language restrictions were imposed. To be included, trials were required to state that they were randomized and double blind. Systematic reviews and meta-analyses of garlic were included if based on the results of randomized, double-blind trials. The literature searches identified six relevant systematic reviews and meta-analysis and double-blind randomized trials (RCT) that were published subsequently. These relate to cancer, common cold, hypercholesterolemia, hypertension, peripheral arterial disease and pre-eclampsia. The evidence based on rigorous clinical trials of garlic is not convincing. For hypercholesterolemia, the reported effects are small and may therefore not be of clinical relevance. For reducing blood pressure, few studies are available and the reported effects are too small to be clinically meaningful. For all other conditions not enough data are available for clinical recommendations.

Online alcohol interventions: a systematic review.

PubMed

White, Angela; Kavanagh, David; Stallman, Helen; Klein, Britt; Kay-Lambkin, Frances; Proudfoot, Judy; Drennan, Judy; Connor, Jason; Baker, Amanda; Hines, Emily; Young, Ross

2010-12-19

There has been a significant increase in the availability of online programs for alcohol problems. A systematic review of the research evidence underpinning these programs is timely. Our objective was to review the efficacy of online interventions for alcohol misuse. Systematic searches of Medline, PsycINFO, Web of Science, and Scopus were conducted for English abstracts (excluding dissertations) published from 1998 onward. Search terms were: (1) Internet, Web*; (2) online, computer*; (3) alcohol*; and (4) E\\effect*, trial*, random* (where * denotes a wildcard). Forward and backward searches from identified papers were also conducted. Articles were included if (1) the primary intervention was delivered and accessed via the Internet, (2) the intervention focused on moderating or stopping alcohol consumption, and (3) the study was a randomized controlled trial of an alcohol-related screen, assessment, or intervention. The literature search initially yielded 31 randomized controlled trials (RCTs), 17 of which met inclusion criteria. Of these 17 studies, 12 (70.6%) were conducted with university students, and 11 (64.7%) specifically focused on at-risk, heavy, or binge drinkers. Sample sizes ranged from 40 to 3216 (median 261), with 12 (70.6%) studies predominantly involving brief personalized feedback interventions. Using published data, effect sizes could be extracted from 8 of the 17 studies. In relation to alcohol units per week or month and based on 5 RCTs where a measure of alcohol units per week or month could be extracted, differential effect sizes to posttreatment ranged from 0.02 to 0.81 (mean 0.42, median 0.54). Pre-post effect sizes for brief personalized feedback interventions ranged from 0.02 to 0.81, and in 2 multi-session modularized interventions, a pre-post effect size of 0.56 was obtained in both. Pre-post differential effect sizes for peak blood alcohol concentrations (BAC) ranged from 0.22 to 0.88, with a mean effect size of 0.66. The available evidence suggests that users can benefit from online alcohol interventions and that this approach could be particularly useful for groups less likely to access traditional alcohol-related services, such as women, young people, and at-risk users. However, caution should be exercised given the limited number of studies allowing extraction of effect sizes, the heterogeneity of outcome measures and follow-up periods, and the large proportion of student-based studies. More extensive RCTs in community samples are required to better understand the efficacy of specific online alcohol approaches, program dosage, the additive effect of telephone or face-to-face interventions, and effective strategies for their dissemination and marketing.

Online Alcohol Interventions: A Systematic Review

PubMed Central

Kavanagh, David; Stallman, Helen; Klein, Britt; Kay-Lambkin, Frances; Proudfoot, Judy; Drennan, Judy; Connor, Jason; Baker, Amanda; Hines, Emily; Young, Ross

2010-01-01

Background There has been a significant increase in the availability of online programs for alcohol problems. A systematic review of the research evidence underpinning these programs is timely. Objectives Our objective was to review the efficacy of online interventions for alcohol misuse. Systematic searches of Medline, PsycINFO, Web of Science, and Scopus were conducted for English abstracts (excluding dissertations) published from 1998 onward. Search terms were: (1) Internet, Web*; (2) online, computer*; (3) alcohol*; and (4) E\\effect*, trial*, random* (where * denotes a wildcard). Forward and backward searches from identified papers were also conducted. Articles were included if (1) the primary intervention was delivered and accessed via the Internet, (2) the intervention focused on moderating or stopping alcohol consumption, and (3) the study was a randomized controlled trial of an alcohol-related screen, assessment, or intervention. Results The literature search initially yielded 31 randomized controlled trials (RCTs), 17 of which met inclusion criteria. Of these 17 studies, 12 (70.6%) were conducted with university students, and 11 (64.7%) specifically focused on at-risk, heavy, or binge drinkers. Sample sizes ranged from 40 to 3216 (median 261), with 12 (70.6%) studies predominantly involving brief personalized feedback interventions. Using published data, effect sizes could be extracted from 8 of the 17 studies. In relation to alcohol units per week or month and based on 5 RCTs where a measure of alcohol units per week or month could be extracted, differential effect sizes to posttreatment ranged from 0.02 to 0.81 (mean 0.42, median 0.54). Pre-post effect sizes for brief personalized feedback interventions ranged from 0.02 to 0.81, and in 2 multi-session modularized interventions, a pre-post effect size of 0.56 was obtained in both. Pre-post differential effect sizes for peak blood alcohol concentrations (BAC) ranged from 0.22 to 0.88, with a mean effect size of 0.66. Conclusions The available evidence suggests that users can benefit from online alcohol interventions and that this approach could be particularly useful for groups less likely to access traditional alcohol-related services, such as women, young people, and at-risk users. However, caution should be exercised given the limited number of studies allowing extraction of effect sizes, the heterogeneity of outcome measures and follow-up periods, and the large proportion of student-based studies. More extensive RCTs in community samples are required to better understand the efficacy of specific online alcohol approaches, program dosage, the additive effect of telephone or face-to-face interventions, and effective strategies for their dissemination and marketing. PMID:21169175

Online Peer-to-Peer Support for Young People With Mental Health Problems: A Systematic Review.

PubMed

Ali, Kathina; Farrer, Louise; Gulliver, Amelia; Griffiths, Kathleen M

2015-01-01

Adolescence and early adulthood are critical periods for the development of mental disorders. Online peer-to-peer communication is popular among young people and may improve mental health by providing social support. Previous systematic reviews have targeted Internet support groups for adults with mental health problems, including depression. However, there have been no systematic reviews examining the effectiveness of online peer-to-peer support in improving the mental health of adolescents and young adults. The aim of this review was to systematically identify available evidence for the effectiveness of online peer-to peer support for young people with mental health problems. The PubMed, PsycInfo, and Cochrane databases were searched using keywords and Medical Subject Headings (MeSH) terms. Retrieved abstracts (n=3934) were double screened and coded. Studies were included if they (1) investigated an online peer-to-peer interaction, (2) the interaction discussed topics related to mental health, (3) the age range of the sample was between 12 to 25 years, and (4) the study evaluated the effectiveness of the peer-to-peer interaction. Six studies satisfied the inclusion criteria for the current review. The studies targeted a range of mental health problems including depression and anxiety (n=2), general psychological problems (n=1), eating disorders (n=1), and substance use (tobacco) (n=2). The majority of studies investigated Internet support groups (n=4), and the remaining studies focused on virtual reality chat sessions (n=2). In almost all studies (n=5), the peer support intervention was moderated by health professionals, researchers or consumers. Studies employed a range of study designs including randomized controlled trials (n=3), pre-post studies (n=2) and one randomized trial. Overall, two of the randomized controlled trials were associated with a significant positive outcome in comparison to the control group at post-intervention. In the remaining four studies, peer-to-peer support was not found to be effective. This systematic review identified an overall lack of high-quality studies examining online peer-to-peer support for young people. Given that peer support is frequently used as an adjunct to Internet interventions for a variety of mental health conditions, there is an urgent need to determine the effectiveness of peer support alone as an active intervention.

A Review of Marital Intimacy-Enhancing Interventions among Married Individuals

PubMed Central

Kardan-Souraki, Maryam; Hamzehgardeshi, Zeinab; Asadpour, Ismail; Mohammadpour, Reza Ali; Khani, Soghra

2016-01-01

Background: Lack of intimacy is currently the main concern rather than main concern of the experts in psychology and counseling. It is considered as one of the most important causes for divorce and as such to improve marital intimacy a great number of interventions have been proposed in the literature. Intimacy training and counseling make the couples take effective and successful steps to increase marital intimacy. No study has reviewed the interventions promoting marital intimacy after marriage. Thus, this review study aimed to classify the articles investigating the impact of interventional programs on marital intimacy after marriage. Search Methods: In April 2015, we performed a general search in Google Scholar search engines, and then we did an advanced search the databases of Science Direct, ProQuest, SID, Magiran, Irandoc, Pubmed, Scopus, Cochrane Library, and Psych info; Cumulative Index to Nursing and Allied Health Literature (CINAHL). Also, lists of the references of the relevant articles were reviewed for additional citations. Using Medical Subject Headings (MESH) keywords: Intervention (Clinical Trials, Non-Randomized Controlled Trials, Randomized Controlled Trials, Education), intimacy, marital (Marriage) and selected related articles to the study objective were from 1995 to April 2015. Clinical trials that evaluated one or more behavioral interventions to improve marital intimacy were reviewed in the study. Main Results: 39 trials met the inclusion criteria. Eleven interventions had follow-up, and 28 interventions lacked follow-up. The quality evidence for 22 interventions was low, for 15 interventions moderate, and for one intervention was considered high. Findings from studies were categorized in 11 categories as the intimacy promoting interventions in dimensions of emotional, psychological, physical, sexual, temporal, communicational, social and recreational, aesthetic, spiritual, intellectual intimacy, and total intimacy. Authors’ Conclusions: Improving and promoting communication, problem solving, self-disclosure and empathic response skills and sexual education and counseling in the form of cognitive-behavioral techniques and based on religious and cultural context of each society, an effective step can be taken to enhance marital intimacy and strengthen family bonds and stability. Health care providers should consider which interventions are appropriate to the couple characteristics and their relationships. PMID:27045400

Assessing Judgment Proficiency in Army Personnel

DTIC Science & Technology

2010-02-01

concepts connected to those schemata are retrieved . Searching and encoding activities are principally guided by cues resulting from the problem...representation process (Reiter-Palmon & Illies, 2004). These cues activate relevant schemata, facilitating the retrieval of concepts connected to them. But...defined problems also involves searching and encoding activities that are guided by cues resulting from the problem representation process . The use of

Distributed Efficient Similarity Search Mechanism in Wireless Sensor Networks

PubMed Central

Ahmed, Khandakar; Gregory, Mark A.

2015-01-01

The Wireless Sensor Network similarity search problem has received considerable research attention due to sensor hardware imprecision and environmental parameter variations. Most of the state-of-the-art distributed data centric storage (DCS) schemes lack optimization for similarity queries of events. In this paper, a DCS scheme with metric based similarity searching (DCSMSS) is proposed. DCSMSS takes motivation from vector distance index, called iDistance, in order to transform the issue of similarity searching into the problem of an interval search in one dimension. In addition, a sector based distance routing algorithm is used to efficiently route messages. Extensive simulation results reveal that DCSMSS is highly efficient and significantly outperforms previous approaches in processing similarity search queries. PMID:25751081

Rapid identification of anonymous subjects in large criminal databases: problems and solutions in IAFIS III/FBI subject searches

NASA Astrophysics Data System (ADS)

Kutzleb, C. D.

1997-02-01

The high incidence of recidivism (repeat offenders) in the criminal population makes the use of the IAFIS III/FBI criminal database an important tool in law enforcement. The problems and solutions employed by IAFIS III/FBI criminal subject searches are discussed for the following topics: (1) subject search selectivity and reliability; (2) the difficulty and limitations of identifying subjects whose anonymity may be a prime objective; (3) database size, search workload, and search response time; (4) techniques and advantages of normalizing the variability in an individual's name and identifying features into identifiable and discrete categories; and (5) the use of database demographics to estimate the likelihood of a match between a search subject and database subjects.

Theoretical Analysis of Local Search and Simple Evolutionary Algorithms for the Generalized Travelling Salesperson Problem.

PubMed

Pourhassan, Mojgan; Neumann, Frank

2018-06-22

The generalized travelling salesperson problem is an important NP-hard combinatorial optimization problem for which meta-heuristics, such as local search and evolutionary algorithms, have been used very successfully. Two hierarchical approaches with different neighbourhood structures, namely a Cluster-Based approach and a Node-Based approach, have been proposed by Hu and Raidl (2008) for solving this problem. In this paper, local search algorithms and simple evolutionary algorithms based on these approaches are investigated from a theoretical perspective. For local search algorithms, we point out the complementary abilities of the two approaches by presenting instances where they mutually outperform each other. Afterwards, we introduce an instance which is hard for both approaches when initialized on a particular point of the search space, but where a variable neighbourhood search combining them finds the optimal solution in polynomial time. Then we turn our attention to analysing the behaviour of simple evolutionary algorithms that use these approaches. We show that the Node-Based approach solves the hard instance of the Cluster-Based approach presented in Corus et al. (2016) in polynomial time. Furthermore, we prove an exponential lower bound on the optimization time of the Node-Based approach for a class of Euclidean instances.

Effect of Delay on Search Decisions in a Task-Oriented Reading Environment

ERIC Educational Resources Information Center

Mañá, Amelia; Vidal-Abarca, Eduardo; Salmerón, Ladislao

2017-01-01

The goal of this study was to determine the effect of setting a delay between reading a text and answering comprehension questions on "when"-to-search and "what"-to-search decisions in a task-oriented reading environment. Fifty-five eighth-grade students were randomly divided into two groups. One group read one text, answered…

The Validity of Warrantless Searches under the Occupational Safety and Health Act of 1970

ERIC Educational Resources Information Center

Shanks, Michael D.

1975-01-01

One of the most controversial federal acts providing for random administrative searches is the Occupational Safety and Health Act of 1970 (OSHA). The author reviews the search and seizure law and concludes that abandonment of Fourth Amendment rights should not be predicated on the mere convenience of even a justifiable regulatory scheme. (JT)

Boosting association rule mining in large datasets via Gibbs sampling.

PubMed

Qian, Guoqi; Rao, Calyampudi Radhakrishna; Sun, Xiaoying; Wu, Yuehua

2016-05-03

Current algorithms for association rule mining from transaction data are mostly deterministic and enumerative. They can be computationally intractable even for mining a dataset containing just a few hundred transaction items, if no action is taken to constrain the search space. In this paper, we develop a Gibbs-sampling-induced stochastic search procedure to randomly sample association rules from the itemset space, and perform rule mining from the reduced transaction dataset generated by the sample. Also a general rule importance measure is proposed to direct the stochastic search so that, as a result of the randomly generated association rules constituting an ergodic Markov chain, the overall most important rules in the itemset space can be uncovered from the reduced dataset with probability 1 in the limit. In the simulation study and a real genomic data example, we show how to boost association rule mining by an integrated use of the stochastic search and the Apriori algorithm.

Threat captures attention but does not affect learning of contextual regularities.

PubMed

Yamaguchi, Motonori; Harwood, Sarah L

2017-04-01

Some of the stimulus features that guide visual attention are abstract properties of objects such as potential threat to one's survival, whereas others are complex configurations such as visual contexts that are learned through past experiences. The present study investigated the two functions that guide visual attention, threat detection and learning of contextual regularities, in visual search. Search arrays contained images of threat and non-threat objects, and their locations were fixed on some trials but random on other trials. Although they were irrelevant to the visual search task, threat objects facilitated attention capture and impaired attention disengagement. Search time improved for fixed configurations more than for random configurations, reflecting learning of visual contexts. Nevertheless, threat detection had little influence on learning of the contextual regularities. The results suggest that factors guiding visual attention are different from factors that influence learning to guide visual attention.

Efficiency improvement by navigated safety inspection involving visual clutter based on the random search model.

PubMed

Sun, Xinlu; Chong, Heap-Yih; Liao, Pin-Chao

2018-06-25

Navigated inspection seeks to improve hazard identification (HI) accuracy. With tight inspection schedule, HI also requires efficiency. However, lacking quantification of HI efficiency, navigated inspection strategies cannot be comprehensively assessed. This work aims to determine inspection efficiency in navigated safety inspection, controlling for the HI accuracy. Based on a cognitive method of the random search model (RSM), an experiment was conducted to observe the HI efficiency in navigation, for a variety of visual clutter (VC) scenarios, while using eye-tracking devices to record the search process and analyze the search performance. The results show that the RSM is an appropriate instrument, and VC serves as a hazard classifier for navigation inspection in improving inspection efficiency. This suggests a new and effective solution for addressing the low accuracy and efficiency of manual inspection through navigated inspection involving VC and the RSM. It also provides insights into the inspectors' safety inspection ability.

The dynamics of search, impasse, and representational change provide a coherent explanation of difficulty in the nine-dot problem.

PubMed

Öllinger, Michael; Jones, Gary; Knoblich, Günther

2014-03-01

The nine-dot problem is often used to demonstrate and explain mental impasse, creativity, and out of the box thinking. The present study investigated the interplay of a restricted initial search space, the likelihood of invoking a representational change, and the subsequent constraining of an unrestricted search space. In three experimental conditions, participants worked on different versions of the nine-dot problem that hinted at removing particular sources of difficulty from the standard problem. The hints were incremental such that the first suggested a possible route for a solution attempt; the second additionally indicated the dot at which lines meet on the solution path; and the final condition also provided non-dot locations that appear in the solution path. The results showed that in the experimental conditions, representational change is encountered more quickly and problems are solved more often than for the control group. We propose a cognitive model that focuses on general problem-solving heuristics and representational change to explain problem difficulty.

The application of similar image retrieval in electronic commerce.

PubMed

Hu, YuPing; Yin, Hua; Han, Dezhi; Yu, Fei

2014-01-01

Traditional online shopping platform (OSP), which searches product information by keywords, faces three problems: indirect search mode, large search space, and inaccuracy in search results. For solving these problems, we discuss and research the application of similar image retrieval in electronic commerce. Aiming at improving the network customers' experience and providing merchants with the accuracy of advertising, we design a reasonable and extensive electronic commerce application system, which includes three subsystems: image search display subsystem, image search subsystem, and product information collecting subsystem. This system can provide seamless connection between information platform and OSP, on which consumers can automatically and directly search similar images according to the pictures from information platform. At the same time, it can be used to provide accuracy of internet marketing for enterprises. The experiment shows the efficiency of constructing the system.

The Application of Similar Image Retrieval in Electronic Commerce

PubMed Central

Hu, YuPing; Yin, Hua; Han, Dezhi; Yu, Fei

2014-01-01

Traditional online shopping platform (OSP), which searches product information by keywords, faces three problems: indirect search mode, large search space, and inaccuracy in search results. For solving these problems, we discuss and research the application of similar image retrieval in electronic commerce. Aiming at improving the network customers' experience and providing merchants with the accuracy of advertising, we design a reasonable and extensive electronic commerce application system, which includes three subsystems: image search display subsystem, image search subsystem, and product information collecting subsystem. This system can provide seamless connection between information platform and OSP, on which consumers can automatically and directly search similar images according to the pictures from information platform. At the same time, it can be used to provide accuracy of internet marketing for enterprises. The experiment shows the efficiency of constructing the system. PMID:24883411

Quantum algorithm for energy matching in hard optimization problems

NASA Astrophysics Data System (ADS)

Baldwin, C. L.; Laumann, C. R.

2018-06-01

We consider the ability of local quantum dynamics to solve the "energy-matching" problem: given an instance of a classical optimization problem and a low-energy state, find another macroscopically distinct low-energy state. Energy matching is difficult in rugged optimization landscapes, as the given state provides little information about the distant topography. Here, we show that the introduction of quantum dynamics can provide a speedup over classical algorithms in a large class of hard optimization problems. Tunneling allows the system to explore the optimization landscape while approximately conserving the classical energy, even in the presence of large barriers. Specifically, we study energy matching in the random p -spin model of spin-glass theory. Using perturbation theory and exact diagonalization, we show that introducing a transverse field leads to three sharp dynamical phases, only one of which solves the matching problem: (1) a small-field "trapped" phase, in which tunneling is too weak for the system to escape the vicinity of the initial state; (2) a large-field "excited" phase, in which the field excites the system into high-energy states, effectively forgetting the initial energy; and (3) the intermediate "tunneling" phase, in which the system succeeds at energy matching. The rate at which distant states are found in the tunneling phase, although exponentially slow in system size, is exponentially faster than classical search algorithms.

[A survey of the best bibliographic searching system in occupational medicine and discussion of its implementation].

PubMed

Inoue, J

1991-12-01

When occupational health personnel, especially occupational physicians search bibliographies, they usually have to search bibliographies by themselves. Also, if a library is not available because of the location of their work place, they might have to rely on online databases. Although there are many commercial databases in the world, people who seldom use them, will have problems with on-line searching, such as user-computer interface, keywords, and so on. The present study surveyed the best bibliographic searching system in the field of occupational medicine by questionnaire through the use of DIALOG OnDisc MEDLINE as a commercial database. In order to ascertain the problems involved in determining the best bibliographic searching system, a prototype bibliographic searching system was constructed and then evaluated. Finally, solutions for the problems were discussed. These led to the following conclusions: to construct the best bibliographic searching system at the present time, 1) a concept of micro-to-mainframe links (MML) is needed for the computer hardware network; 2) multi-lingual font standards and an excellent common user-computer interface are needed for the computer software; 3) a short course and education of database management systems, and support of personal information processing for retrieved data are necessary for the practical use of the system.

Localization Transition Induced by Learning in Random Searches

NASA Astrophysics Data System (ADS)

Falcón-Cortés, Andrea; Boyer, Denis; Giuggioli, Luca; Majumdar, Satya N.

2017-10-01

We solve an adaptive search model where a random walker or Lévy flight stochastically resets to previously visited sites on a d -dimensional lattice containing one trapping site. Because of reinforcement, a phase transition occurs when the resetting rate crosses a threshold above which nondiffusive stationary states emerge, localized around the inhomogeneity. The threshold depends on the trapping strength and on the walker's return probability in the memoryless case. The transition belongs to the same class as the self-consistent theory of Anderson localization. These results show that similarly to many living organisms and unlike the well-studied Markovian walks, non-Markov movement processes can allow agents to learn about their environment and promise to bring adaptive solutions in search tasks.

Is the Process the Problem? Impact of Selection Methods on Reported Job Satisfaction among Academic Department Chairs

ERIC Educational Resources Information Center

Fairchild, Julie E.

2013-01-01

The problem of low job satisfaction (JS) among academic department chairs (ADC) may result from the selection process. ADC searches seldom comply with best practices for hiring or are predictive of a good fit. Formal searches are seldom used. Some incumbents did not want the job. Research into the history, nature, and problems of the position…

Intention-to-treat analysis and accounting for missing data in orthopaedic randomized clinical trials.

PubMed

Herman, Amir; Botser, Itamar Busheri; Tenenbaum, Shay; Chechick, Ahron

2009-09-01

The intention-to-treat principle implies that all patients who are randomized in a clinical trial should be analyzed according to their original allocation. This means that patients crossing over to another treatment group and patients lost to follow-up should be included in the analysis as a part of their original group. This principle is important for preserving the randomization scheme, which is the basis for correct inference in any randomized trial. In this study, we examined the use of the intention-to-treat principle in recently published orthopaedic clinical trials. We surveyed eight leading orthopaedic journals for randomized clinical trials published between January 2005 and August 2008. We determined whether the intention-to-treat principle was implemented and, if so, how it was used in each trial. Specifically, we ascertained which methods were used to account for missing data. Our search yielded 274 randomized clinical trials, and the intention-to-treat principle was used in ninety-six (35%) of them. There were significant differences among the journals with regard to the use of the intention-to-treat principle. The relative number of trials in which the principle was used increased each year. The authors adhered to the strict definition of the intention-to-treat principle in forty-five of the ninety-six studies in which it was claimed that this principle had been used. In forty-four randomized trials, patients who had been lost to follow-up were excluded from the final analysis; this practice was most notable in studies of surgical interventions. The most popular method of adjusting for missing data was the "last observation carried forward" technique. In most of the randomized clinical trials published in the orthopaedic literature, the investigators did not adhere to the stringent use of the intention-to-treat principle, with the most conspicuous problem being a lack of accounting for patients lost to follow-up. This omission might introduce bias to orthopaedic randomized clinical trials and their analysis.

Combining local search with co-evolution in a remarkably simple way

DOE Office of Scientific and Technical Information (OSTI.GOV)

Boettcher, S.; Percus, A.

2000-05-01

The authors explore a new general-purpose heuristic for finding high-quality solutions to hard optimization problem. The method, called extremal optimization, is inspired by self-organized criticality, a concept introduced to describe emergent complexity in physical systems. In contrast to genetic algorithms, which operate on an entire gene-pool of possible solutions, extremal optimization successively replaces extremely undesirable elements of a single sub-optimal solution with new, random ones. Large fluctuations, or avalanches, ensue that efficiently explore many local optima. Drawing upon models used to simulate far-from-equilibrium dynamics, extremal optimization complements heuristics inspired by equilibrium statistical physics, such as simulated annealing. With only onemore » adjustable parameter, its performance has proved competitive with more elaborate methods, especially near phase transitions. Phase transitions are found in many combinatorial optimization problems, and have been conjectured to occur in the region of parameter space containing the hardest instances. We demonstrate how extremal optimization can be implemented for a variety of hard optimization problems. We believe that this will be a useful tool in the investigation of phase transitions in combinatorial optimization, thereby helping to elucidate the origin of computational complexity.« less

Co-state initialization for the minimum-time low-thrust trajectory optimization

NASA Astrophysics Data System (ADS)

Taheri, Ehsan; Li, Nan I.; Kolmanovsky, Ilya

2017-05-01

This paper presents an approach for co-state initialization which is a critical step in solving minimum-time low-thrust trajectory optimization problems using indirect optimal control numerical methods. Indirect methods used in determining the optimal space trajectories typically result in two-point boundary-value problems and are solved by single- or multiple-shooting numerical methods. Accurate initialization of the co-state variables facilitates the numerical convergence of iterative boundary value problem solvers. In this paper, we propose a method which exploits the trajectory generated by the so-called pseudo-equinoctial and three-dimensional finite Fourier series shape-based methods to estimate the initial values of the co-states. The performance of the approach for two interplanetary rendezvous missions from Earth to Mars and from Earth to asteroid Dionysus is compared against three other approaches which, respectively, exploit random initialization of co-states, adjoint-control transformation and a standard genetic algorithm. The results indicate that by using our proposed approach the percent of the converged cases is higher for trajectories with higher number of revolutions while the computation time is lower. These features are advantageous for broad trajectory search in the preliminary phase of mission designs.

How to cluster in parallel with neural networks

NASA Technical Reports Server (NTRS)

Kamgar-Parsi, Behzad; Gualtieri, J. A.; Devaney, Judy E.; Kamgar-Parsi, Behrooz

1988-01-01

Partitioning a set of N patterns in a d-dimensional metric space into K clusters - in a way that those in a given cluster are more similar to each other than the rest - is a problem of interest in astrophysics, image analysis and other fields. As there are approximately K(N)/K (factorial) possible ways of partitioning the patterns among K clusters, finding the best solution is beyond exhaustive search when N is large. Researchers show that this problem can be formulated as an optimization problem for which very good, but not necessarily optimal solutions can be found by using a neural network. To do this the network must start from many randomly selected initial states. The network is simulated on the MPP (a 128 x 128 SIMD array machine), where researchers use the massive parallelism not only in solving the differential equations that govern the evolution of the network, but also by starting the network from many initial states at once, thus obtaining many solutions in one run. Researchers obtain speedups of two to three orders of magnitude over serial implementations and the promise through Analog VLSI implementations of speedups comensurate with human perceptual abilities.

A Group Theoretic Approach to Metaheuristic Local Search for Partitioning Problems

DTIC Science & Technology

2005-05-01

Tabu Search. Mathematical and Computer Modeling 39: 599-616. 107 Daskin , M.S., E. Stern. 1981. A Hierarchical Objective Set Covering Model for EMS... A Group Theoretic Approach to Metaheuristic Local Search for Partitioning Problems by Gary W. Kinney Jr., B.G.S., M.S. Dissertation Presented to the...DISTRIBUTION STATEMENT A Approved for Public Release Distribution Unlimited The University of Texas at Austin May, 2005 20050504 002 REPORT

Metal-backed versus all-polyethylene tibial components in primary total knee arthroplasty

PubMed Central

2011-01-01

Background and purpose The choice of either all-polyethylene (AP) tibial components or metal-backed (MB) tibial components in total knee arthroplasty (TKA) remains controversial. We therefore performed a meta-analysis and systematic review of randomized controlled trials that have evaluated MB and AP tibial components in primary TKA. Methods The search strategy included a computerized literature search (Medline, EMBASE, Scopus, and the Cochrane Central Register of Controlled Trials) and a manual search of major orthopedic journals. A meta-analysis and systematic review of randomized or quasi-randomized trials that compared the performance of tibial components in primary TKA was performed using a fixed or random effects model. We assessed the methodological quality of studies using Detsky quality scale. Results 9 randomized controlled trials (RCTs) published between 2000 and 2009 met the inclusion quality standards for the systematic review. The mean standardized Detsky score was 14 (SD 3). We found that the frequency of radiolucent lines in the MB group was significantly higher than that in the AP group. There were no statistically significant differences between the MB and AP tibial components regarding component positioning, knee score, knee range of motion, quality of life, and postoperative complications. Interpretation Based on evidence obtained from this study, the AP tibial component was comparable with or better than the MB tibial component in TKA. However, high-quality RCTs are required to validate the results. PMID:21895503

Performance comparison of a new hybrid conjugate gradient method under exact and inexact line searches

NASA Astrophysics Data System (ADS)

Ghani, N. H. A.; Mohamed, N. S.; Zull, N.; Shoid, S.; Rivaie, M.; Mamat, M.

2017-09-01

Conjugate gradient (CG) method is one of iterative techniques prominently used in solving unconstrained optimization problems due to its simplicity, low memory storage, and good convergence analysis. This paper presents a new hybrid conjugate gradient method, named NRM1 method. The method is analyzed under the exact and inexact line searches in given conditions. Theoretically, proofs show that the NRM1 method satisfies the sufficient descent condition with both line searches. The computational result indicates that NRM1 method is capable in solving the standard unconstrained optimization problems used. On the other hand, the NRM1 method performs better under inexact line search compared with exact line search.

Annealing Ant Colony Optimization with Mutation Operator for Solving TSP

PubMed Central

2016-01-01

Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality. PMID:27999590

Randomized algorithms for high quality treatment planning in volumetric modulated arc therapy

NASA Astrophysics Data System (ADS)

Yang, Yu; Dong, Bin; Wen, Zaiwen

2017-02-01

In recent years, volumetric modulated arc therapy (VMAT) has been becoming a more and more important radiation technique widely used in clinical application for cancer treatment. One of the key problems in VMAT is treatment plan optimization, which is complicated due to the constraints imposed by the involved equipments. In this paper, we consider a model with four major constraints: the bound on the beam intensity, an upper bound on the rate of the change of the beam intensity, the moving speed of leaves of the multi-leaf collimator (MLC) and its directional-convexity. We solve the model by a two-stage algorithm: performing minimization with respect to the shapes of the aperture and the beam intensities alternatively. Specifically, the shapes of the aperture are obtained by a greedy algorithm whose performance is enhanced by random sampling in the leaf pairs with a decremental rate. The beam intensity is optimized using a gradient projection method with non-monotonic line search. We further improve the proposed algorithm by an incremental random importance sampling of the voxels to reduce the computational cost of the energy functional. Numerical simulations on two clinical cancer date sets demonstrate that our method is highly competitive to the state-of-the-art algorithms in terms of both computational time and quality of treatment planning.

When do I quit? The search termination problem in visual search.

PubMed

Wolfe, Jeremy M

2012-01-01

In visual search tasks, observers look for targets in displays or scenes containing distracting, non-target items. Most of the research on this topic has concerned the finding of those targets. Search termination is a less thoroughly studied topic. When is it time to abandon the current search? The answer is fairly straight forward when the one and only target has been found (There are my keys.). The problem is more vexed if nothing has been found (When is it time to stop looking for a weapon at the airport checkpoint?) or when the number of targets is unknown (Have we found all the tumors?). This chapter reviews the development of ideas about quitting time in visual search and offers an outline of our current theory.

Sundanese ancient manuscripts search engine using probability approach

NASA Astrophysics Data System (ADS)

Suryani, Mira; Hadi, Setiawan; Paulus, Erick; Nurma Yulita, Intan; Supriatna, Asep K.

2017-10-01

Today, Information and Communication Technology (ICT) has become a regular thing for every aspect of live include cultural and heritage aspect. Sundanese ancient manuscripts as Sundanese heritage are in damage condition and also the information that containing on it. So in order to preserve the information in Sundanese ancient manuscripts and make them easier to search, a search engine has been developed. The search engine must has good computing ability. In order to get the best computation in developed search engine, three types of probabilistic approaches: Bayesian Networks Model, Divergence from Randomness with PL2 distribution, and DFR-PL2F as derivative form DFR-PL2 have been compared in this study. The three probabilistic approaches supported by index of documents and three different weighting methods: term occurrence, term frequency, and TF-IDF. The experiment involved 12 Sundanese ancient manuscripts. From 12 manuscripts there are 474 distinct terms. The developed search engine tested by 50 random queries for three types of query. The experiment results showed that for the single query and multiple query, the best searching performance given by the combination of PL2F approach and TF-IDF weighting method. The performance has been evaluated using average time responds with value about 0.08 second and Mean Average Precision (MAP) about 0.33.

Technology and Teaching: Searching under Cups for Clues about Memory--An Online Demonstration

ERIC Educational Resources Information Center

Kahan, Todd A.; Mathis, Katherine M.

2007-01-01

An online demonstration, designed to enhance comprehension of Sternberg's (1966) short-term memory scanning task, involved rapidly searching under virtual cups for a ball. We randomly assigned students to 1 of 3 groups, all of whom read the same textbook description of Sternberg's work: A demonstration group used 3 search methods to look for balls…

Intersections of discrimination due to unemployment and mental health problems: the role of double stigma for job- and help-seeking behaviors.

PubMed

Staiger, Tobias; Waldmann, Tamara; Oexle, Nathalie; Wigand, Moritz; Rüsch, Nicolas

2018-05-21

The everyday lives of unemployed people with mental health problems can be affected by multiple discrimination, but studies about double stigma-an overlap of identities and experiences of discrimination-in this group are lacking. We therefore studied multiple discrimination among unemployed people with mental health problems and its consequences for job- and help-seeking behaviors. Everyday discrimination and attributions of discrimination to unemployment and/or to mental health problems were examined among 301 unemployed individuals with mental health problems. Job search self-efficacy, barriers to care, and perceived need for treatment were compared among four subgroups, depending on attributions of experienced discrimination to unemployment and to mental health problems (group i); neither to unemployment nor to mental health problems (group ii); mainly to unemployment (group iii); or mainly to mental health problems (group iv). In multiple regressions among all participants, higher levels of discrimination predicted reduced job search self-efficacy and higher barriers to care; and attributions of discrimination to unemployment were associated with increased barriers to care. In ANOVAs for subgroup comparisons, group i participants, who attributed discrimination to both unemployment and mental health problems, reported lower job search self-efficacy, more perceived stigma-related barriers to care and more need for treatment than group iii participants, as well as more stigma-related barriers to care than group iv. Multiple discrimination may affect job search and help-seeking among unemployed individuals with mental health problems. Interventions to reduce public stigma and to improve coping with multiple discrimination for this group should be developed.

Walking the Filament of Feasibility: Global Optimization of Highly-Constrained, Multi-Modal Interplanetary Trajectories Using a Novel Stochastic Search Technique

NASA Technical Reports Server (NTRS)

Englander, Arnold C.; Englander, Jacob A.

2017-01-01

Interplanetary trajectory optimization problems are highly complex and are characterized by a large number of decision variables and equality and inequality constraints as well as many locally optimal solutions. Stochastic global search techniques, coupled with a large-scale NLP solver, have been shown to solve such problems but are inadequately robust when the problem constraints become very complex. In this work, we present a novel search algorithm that takes advantage of the fact that equality constraints effectively collapse the solution space to lower dimensionality. This new approach walks the filament'' of feasibility to efficiently find the global optimal solution.

Fundamental resource-allocating model in colleges and universities based on Immune Clone Algorithms

NASA Astrophysics Data System (ADS)

Ye, Mengdie

2017-05-01

In this thesis we will seek the combination of antibodies and antigens converted from the optimal course arrangement and make an analogy with Immune Clone Algorithms. According to the character of the Algorithms, we apply clone, clone gene and clone selection to arrange courses. Clone operator can combine evolutionary search and random search, global search and local search. By cloning and clone mutating candidate solutions, we can find the global optimal solution quickly.

Treatment outcomes of pre-surgical infant orthopedics in patients with non-syndromic cleft lip and/or palate: A systematic review and meta-analysis of randomized controlled trials

PubMed Central

2017-01-01

Background Non-syndromic clefts lip and/or palate (CL/P) defects may have manifold significant and detrimental consequences for the affected individuals and their family environment. Although the use of pre-surgical infant orthopedics (PSIO) was introduced as a means to improve management and treatment outcomes, there still remains a controversy. Objective To investigate the effectiveness of PSIO in patients with non-syndromic CL/P and evaluate the quality of the available evidence. Search methods Search without restrictions, together with hand searching, until May 2016. Selection criteria Randomized clinical trials investigating the effects of pre-surgical infant orthopedic appliances. Data collection and analysis Following study retrieval and selection, data extraction and individual study risk of bias assessment using the Cochrane Risk of Bias Tool took place. The overall quality of the available evidence was assessed with the Grades of Recommendation, Assessment, Development and Evaluation approach. Results Finally 20 papers (3 unique trials) were identified, involving a total of 118 patients with unilateral complete CL/P and 16 with cleft of the soft and at least two thirds of the hard palate. Eight publications were considered as being of low, four of unclear and eight of high risk of bias. In general, the investigated appliances did not present significant effects when compared to each other or to no treatment in terms of feeding and general body growth, facial esthetics, cephalometric variables, maxillary dentoalveolar variables and dental arch relationships, speech and language evaluation, caregiver-reported outcomes, economic evaluation, as well as, adverse effects and problems. Overall, the quality of the available evidence was considered low. Conclusions The aforementioned findings could provide initial guidance in the clinical setting. However, given the multitude of parameters, which may have affected the results, good practice would suggest further research, in order to reach more robust relevant recommendations for management decisions in individual cases. PMID:28742129

Constraint-Based Local Search for Constrained Optimum Paths Problems

NASA Astrophysics Data System (ADS)

Pham, Quang Dung; Deville, Yves; van Hentenryck, Pascal

Constrained Optimum Path (COP) problems arise in many real-life applications and are ubiquitous in communication networks. They have been traditionally approached by dedicated algorithms, which are often hard to extend with side constraints and to apply widely. This paper proposes a constraint-based local search (CBLS) framework for COP applications, bringing the compositionality, reuse, and extensibility at the core of CBLS and CP systems. The modeling contribution is the ability to express compositional models for various COP applications at a high level of abstraction, while cleanly separating the model and the search procedure. The main technical contribution is a connected neighborhood based on rooted spanning trees to find high-quality solutions to COP problems. The framework, implemented in COMET, is applied to Resource Constrained Shortest Path (RCSP) problems (with and without side constraints) and to the edge-disjoint paths problem (EDP). Computational results show the potential significance of the approach.

Machine learning search for variable stars

NASA Astrophysics Data System (ADS)

Pashchenko, Ilya N.; Sokolovsky, Kirill V.; Gavras, Panagiotis

2018-04-01

Photometric variability detection is often considered as a hypothesis testing problem: an object is variable if the null hypothesis that its brightness is constant can be ruled out given the measurements and their uncertainties. The practical applicability of this approach is limited by uncorrected systematic errors. We propose a new variability detection technique sensitive to a wide range of variability types while being robust to outliers and underestimated measurement uncertainties. We consider variability detection as a classification problem that can be approached with machine learning. Logistic Regression (LR), Support Vector Machines (SVM), k Nearest Neighbours (kNN), Neural Nets (NN), Random Forests (RF), and Stochastic Gradient Boosting classifier (SGB) are applied to 18 features (variability indices) quantifying scatter and/or correlation between points in a light curve. We use a subset of Optical Gravitational Lensing Experiment phase two (OGLE-II) Large Magellanic Cloud (LMC) photometry (30 265 light curves) that was searched for variability using traditional methods (168 known variable objects) as the training set and then apply the NN to a new test set of 31 798 OGLE-II LMC light curves. Among 205 candidates selected in the test set, 178 are real variables, while 13 low-amplitude variables are new discoveries. The machine learning classifiers considered are found to be more efficient (select more variables and fewer false candidates) compared to traditional techniques using individual variability indices or their linear combination. The NN, SGB, SVM, and RF show a higher efficiency compared to LR and kNN.

An efficient hybrid approach for multiobjective optimization of water distribution systems

NASA Astrophysics Data System (ADS)

Zheng, Feifei; Simpson, Angus R.; Zecchin, Aaron C.

2014-05-01

An efficient hybrid approach for the design of water distribution systems (WDSs) with multiple objectives is described in this paper. The objectives are the minimization of the network cost and maximization of the network resilience. A self-adaptive multiobjective differential evolution (SAMODE) algorithm has been developed, in which control parameters are automatically adapted by means of evolution instead of the presetting of fine-tuned parameter values. In the proposed method, a graph algorithm is first used to decompose a looped WDS into a shortest-distance tree (T) or forest, and chords (Ω). The original two-objective optimization problem is then approximated by a series of single-objective optimization problems of the T to be solved by nonlinear programming (NLP), thereby providing an approximate Pareto optimal front for the original whole network. Finally, the solutions at the approximate front are used to seed the SAMODE algorithm to find an improved front for the original entire network. The proposed approach is compared with two other conventional full-search optimization methods (the SAMODE algorithm and the NSGA-II) that seed the initial population with purely random solutions based on three case studies: a benchmark network and two real-world networks with multiple demand loading cases. Results show that (i) the proposed NLP-SAMODE method consistently generates better-quality Pareto fronts than the full-search methods with significantly improved efficiency; and (ii) the proposed SAMODE algorithm (no parameter tuning) exhibits better performance than the NSGA-II with calibrated parameter values in efficiently offering optimal fronts.

Effectiveness of information and communication technologies interventions to increase mental health literacy: A systematic review.

PubMed

Tay, Jing Ling; Tay, Yi Fen; Klainin-Yobas, Piyanee

2018-06-13

Most mental health conditions affect adolescent and young adults. The onset of many mental disorders occurs in the young age. This is a critical period to implement interventions to enhance mental health literacy (MHL) and to prevent the occurrence of mental health problems. This systematic review examined the effectiveness of information and communication technologies interventions on MHL (recognition of conditions, stigma and help-seeking). The authors searched for both published and unpublished studies. Nineteen studies were included with 9 randomized controlled trials and 10 quasi-experimental studies. Informational interventions were useful to enhance MHL of less-known disorders such as anxiety disorder and anorexia, but not depression. Interventions that were effective in enhancing depression MHL comprised active component such as videos or quizzes. Interventions that successfully elevated MHL also reduced stigma. Elevated MHL levels did not improve help-seeking, and reduction in stigma levels did not enhance help-seeking behaviours. Future good quality, large-scale, multi-sites randomized controlled trials are necessary to evaluate MHL interventions. © 2018 John Wiley & Sons Australia, Ltd.

Corticosteroids as adjuvant therapy for ocular toxoplasmosis.

PubMed

Jasper, Smitha; Vedula, Satyanarayana S; John, Sheeja S; Horo, Saban; Sepah, Yasir J; Nguyen, Quan Dong

2013-04-30

Ocular infestation with Toxoplasma gondii, a parasite, may result in inflammation in the retina, choroid, and uvea and consequently lead to complications such as glaucoma, cataract, and posterior synechiae. The objective of this systematic review was to assess the effects of adjunctive use of corticosteroids for ocular toxoplasmosis. We searched CENTRAL (which contains the Cochrane Eyes and Vision Group Trials Register) (The Cochrane Library 2012, Issue 9), Ovid MEDLINE, Ovid MEDLINE In-Process and Other Non-Indexed Citations, Ovid MEDLINE Daily, Ovid OLDMEDLINE, (January 1950 to October 2012), EMBASE (January 1980 to October 2012), Latin American and Caribbean Literature on Health Sciences (LILACS) (January 1982 to October 2012), the metaRegister of Controlled Trials (mRCT) (www.controlled-trials.com), ClinicalTrials.gov (www.clinicaltrials.gov) and the WHO International Clinical Trials Registry Platform (ICTRP) (www.who.int/ictrp/search/en). We searched the reference lists of included studies for any additional studies not identified by the electronic searches. We did not use any date or language restrictions in the electronic searches for trials. We last searched the electronic databases on 11 October 2012. We planned to include randomized and quasi-randomized controlled trials. Eligible trials would have enrolled participants of any age who were immunocompetent and were diagnosed with active ocular toxoplasmosis. Included trials would have compared anti-parasitic therapy plus corticosteroids versus anti-parasitic therapy alone, or different doses or times of initiation of corticosteroids. Two authors independently screened titles and abstracts retrieved from the electronic searches. We retrieved full-text articles of studies categorized as 'unsure' or 'include' after review of the abstracts. Two authors independently reviewed each full-text article. Discrepancies were resolved through discussion. The electronic searches retrieved 368 titles and abstracts. We reviewed 20 full-text articles. We identified no trials eligible for inclusion in this systematic review. Although research has identified wide variation in practices regarding use of corticosteroids, our systematic review did not identify evidence from randomized controlled trials for the role of corticosteroids in the management of ocular toxoplasmosis. Several questions remain unanswered by well-conducted randomized trials in this context, including whether use of corticosteroids is more effective than use of anti-parasitic therapy alone, when corticosteroids should be initiated in the treatment regimen (early versus late course of treatment), and which dosage and duration of steroid use is best. These questions are easily amenable to research using a randomized controlled design and they are ethical due to the absence of evidence to support or discourage use of corticosteroids for this condition. The question of foremost importance, however, is whether they should be used as adjunct therapy (that is, additional) to anti-parasitic agents.

Music education for improving reading skills in children and adolescents with dyslexia.

PubMed

Cogo-Moreira, Hugo; Andriolo, Régis B; Yazigi, Latife; Ploubidis, George B; Brandão de Ávila, Clara Regina; Mari, Jair J

2012-08-15

Dyslexia (or developmental dyslexia or specific reading disability) is a specific learning disorder that has a neurobiological origin. It is marked by difficulties with accurate or fluent recognition of words and poor spelling in people who have average or above average intelligence and these difficulties cannot be attributed to another cause, for example, poor vision, hearing difficulty, or lack of socio-environmental opportunities, motivation, or adequate instruction. Studies have correlated reading skills with musical abilities. It has been hypothesized that musical training may be able to remediate timing difficulties, improve pitch perception, or increase spatial awareness, thereby having a positive effect on skills needed in the development of language and literacy. To study the effectiveness of music education on reading skills (that is, oral reading skills, reading comprehension, reading fluency, phonological awareness, and spelling) in children and adolescents with dyslexia. We searched the following electronic databases in June 2012: CENTRAL (2012, Issue 5), MEDLINE (1948 to May Week 4 2012 ), EMBASE (1980 to 2012 Week 22), CINAHL (searched 7 June 2012), LILACS (searched 7 June 2012), PsycINFO (1887 to May Week 5 2012), ERIC (searched 7 June 2012), Arts and Humanities Citation Index (1970 to 6 June 2012), Conference Proceedings Citation Index - Social Sciences and Humanities (1990 to 6 June 2012), and WorldCat (searched 7 June 2012). We also searched the WHO International Clinical Trials Registry Platform (ICTRP) and reference lists of studies. We did not apply any date or language limits. We planned to include randomized controlled trials. We looked for studies that included at least one of our primary outcomes. The primary outcomes were related to the main domain of the reading: oral reading skills, reading comprehension, reading fluency, phonological awareness, and spelling, measured through validated instruments. The secondary outcomes were self esteem and academic achievement. Two authors (HCM and RBA) independently screened all titles and abstracts identified through the search strategy to determine their eligibility. For our analysis we had planned to use mean difference for continuous data, with 95% confidence intervals, and to use the random-effects statistical model when the effect estimates of two or more studies could be combined in a meta-analysis. We retrieved 851 references via the search strategy. No randomized controlled trials testing music education for the improvement of reading skills in children with dyslexia could be included in this review. There is no evidence available from randomized controlled trials on which to base a judgment about the effectiveness of music education for the improvement of reading skills in children and adolescents with dyslexia. This uncertainty warrants further research via randomized controlled trials, involving a interdisciplinary team: musicians, hearing and speech therapists, psychologists, and physicians.

A self-adaptive memeplexes robust search scheme for solving stochastic demands vehicle routing problem

NASA Astrophysics Data System (ADS)

Chen, Xianshun; Feng, Liang; Ong, Yew Soon

2012-07-01

In this article, we proposed a self-adaptive memeplex robust search (SAMRS) for finding robust and reliable solutions that are less sensitive to stochastic behaviours of customer demands and have low probability of route failures, respectively, in vehicle routing problem with stochastic demands (VRPSD). In particular, the contribution of this article is three-fold. First, the proposed SAMRS employs the robust solution search scheme (RS 3) as an approximation of the computationally intensive Monte Carlo simulation, thus reducing the computation cost of fitness evaluation in VRPSD, while directing the search towards robust and reliable solutions. Furthermore, a self-adaptive individual learning based on the conceptual modelling of memeplex is introduced in the SAMRS. Finally, SAMRS incorporates a gene-meme co-evolution model with genetic and memetic representation to effectively manage the search for solutions in VRPSD. Extensive experimental results are then presented for benchmark problems to demonstrate that the proposed SAMRS serves as an efficable means of generating high-quality robust and reliable solutions in VRPSD.

The benefits of adaptive parametrization in multi-objective Tabu Search optimization

NASA Astrophysics Data System (ADS)

Ghisu, Tiziano; Parks, Geoffrey T.; Jaeggi, Daniel M.; Jarrett, Jerome P.; Clarkson, P. John

2010-10-01

In real-world optimization problems, large design spaces and conflicting objectives are often combined with a large number of constraints, resulting in a highly multi-modal, challenging, fragmented landscape. The local search at the heart of Tabu Search, while being one of its strengths in highly constrained optimization problems, requires a large number of evaluations per optimization step. In this work, a modification of the pattern search algorithm is proposed: this modification, based on a Principal Components' Analysis of the approximation set, allows both a re-alignment of the search directions, thereby creating a more effective parametrization, and also an informed reduction of the size of the design space itself. These changes make the optimization process more computationally efficient and more effective - higher quality solutions are identified in fewer iterations. These advantages are demonstrated on a number of standard analytical test functions (from the ZDT and DTLZ families) and on a real-world problem (the optimization of an axial compressor preliminary design).

Active Solution Space and Search on Job-shop Scheduling Problem

NASA Astrophysics Data System (ADS)

Watanabe, Masato; Ida, Kenichi; Gen, Mitsuo

In this paper we propose a new searching method of Genetic Algorithm for Job-shop scheduling problem (JSP). The coding method that represent job number in order to decide a priority to arrange a job to Gannt Chart (called the ordinal representation with a priority) in JSP, an active schedule is created by using left shift. We define an active solution at first. It is solution which can create an active schedule without using left shift, and set of its defined an active solution space. Next, we propose an algorithm named Genetic Algorithm with active solution space search (GA-asol) which can create an active solution while solution is evaluated, in order to search the active solution space effectively. We applied it for some benchmark problems to compare with other method. The experimental results show good performance.

Learning Problem-Solving Rules as Search Through a Hypothesis Space.

PubMed

Lee, Hee Seung; Betts, Shawn; Anderson, John R

2016-07-01

Learning to solve a class of problems can be characterized as a search through a space of hypotheses about the rules for solving these problems. A series of four experiments studied how different learning conditions affected the search among hypotheses about the solution rule for a simple computational problem. Experiment 1 showed that a problem property such as computational difficulty of the rules biased the search process and so affected learning. Experiment 2 examined the impact of examples as instructional tools and found that their effectiveness was determined by whether they uniquely pointed to the correct rule. Experiment 3 compared verbal directions with examples and found that both could guide search. The final experiment tried to improve learning by using more explicit verbal directions or by adding scaffolding to the example. While both manipulations improved learning, learning still took the form of a search through a hypothesis space of possible rules. We describe a model that embodies two assumptions: (1) the instruction can bias the rules participants hypothesize rather than directly be encoded into a rule; (2) participants do not have memory for past wrong hypotheses and are likely to retry them. These assumptions are realized in a Markov model that fits all the data by estimating two sets of probabilities. First, the learning condition induced one set of Start probabilities of trying various rules. Second, should this first hypothesis prove wrong, the learning condition induced a second set of Choice probabilities of considering various rules. These findings broaden our understanding of effective instruction and provide implications for instructional design. Copyright © 2015 Cognitive Science Society, Inc.

Boosting Stochastic Problem Solvers Through Online Self-Analysis of Performance

DTIC Science & Technology

2003-07-21

Boosting Stochastic Problem Solvers Through Online Self-Analysis of Performance Vincent A. Cicirello CMU-RI-TR-03-27 Submitted in partial fulfillment...AND SUBTITLE Boosting Stochastic Problem Solvers Through Online Self-Analysis of Performance 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM...lead to the development of a search control framework, called QD-BEACON that uses online -generated statistical models of search performance to

Search Problems in Mission Planning and Navigation of Autonomous Aircraft. M.S. Thesis

NASA Technical Reports Server (NTRS)

Krozel, James A.

1988-01-01

An architecture for the control of an autonomous aircraft is presented. The architecture is a hierarchical system representing an anthropomorphic breakdown of the control problem into planner, navigator, and pilot systems. The planner system determines high level global plans from overall mission objectives. This abstract mission planning is investigated by focusing on the Traveling Salesman Problem with variations on local and global constraints. Tree search techniques are applied including the breadth first, depth first, and best first algorithms. The minimum-column and row entries for the Traveling Salesman Problem cost matrix provides a powerful heuristic to guide these search techniques. Mission planning subgoals are directed from the planner to the navigator for planning routes in mountainous terrain with threats. Terrain/threat information is abstracted into a graph of possible paths for which graph searches are performed. It is shown that paths can be well represented by a search graph based on the Voronoi diagram of points representing the vertices of mountain boundaries. A comparison of Dijkstra's dynamic programming algorithm and the A* graph search algorithm from artificial intelligence/operations research is performed for several navigation path planning examples. These examples illustrate paths that minimize a combination of distance and exposure to threats. Finally, the pilot system synthesizes the flight trajectory by creating the control commands to fly the aircraft.

The medication appropriateness index at 20: where it started, where it has been, and where it may be going.

PubMed

Hanlon, Joseph T; Schmader, Kenneth E

2013-11-01

Potentially inappropriate prescribing for older adults is a major public health concern. While there are multiple measures of potentially inappropriate prescribing, the medication appropriateness index (MAI) is one of the most common implicit approaches published in the scientific literature. The objective of this narrative review is to describe findings regarding the MAI's reliability, comparison of the MAI with other quality measures of potentially inappropriate prescribing, its predictive validity with important health outcomes, and its responsiveness to change within the framework of randomized controlled trials. A search restricted to English-language literature involving humans aged 65+ years from January 1992 to June 2013 was conducted using MEDLINE and EMBASE databases using the search term 'medication appropriateness index'. A manual search of the reference lists from identified articles and the authors' article files, book chapters, and recent reviews was conducted to identify additional articles. A total of 26 articles were identified for inclusion in this narrative review. The main findings were that the MAI has acceptable inter- and intra-rater reliability, it more frequently detects potentially inappropriate prescribing than a commonly used set of explicit criteria, it predicts adverse health outcomes, and it is able to demonstrate the positive impact of interventions to improve this public health problem. We conclude that the MAI may serve as a valuable tool for measuring potentially inappropriate prescribing in older adults.

Scalable Nearest Neighbor Algorithms for High Dimensional Data.

PubMed

Muja, Marius; Lowe, David G

2014-11-01

For many computer vision and machine learning problems, large training sets are key for good performance. However, the most computationally expensive part of many computer vision and machine learning algorithms consists of finding nearest neighbor matches to high dimensional vectors that represent the training data. We propose new algorithms for approximate nearest neighbor matching and evaluate and compare them with previous algorithms. For matching high dimensional features, we find two algorithms to be the most efficient: the randomized k-d forest and a new algorithm proposed in this paper, the priority search k-means tree. We also propose a new algorithm for matching binary features by searching multiple hierarchical clustering trees and show it outperforms methods typically used in the literature. We show that the optimal nearest neighbor algorithm and its parameters depend on the data set characteristics and describe an automated configuration procedure for finding the best algorithm to search a particular data set. In order to scale to very large data sets that would otherwise not fit in the memory of a single machine, we propose a distributed nearest neighbor matching framework that can be used with any of the algorithms described in the paper. All this research has been released as an open source library called fast library for approximate nearest neighbors (FLANN), which has been incorporated into OpenCV and is now one of the most popular libraries for nearest neighbor matching.

Constipation and Outcomes of Cecostomy.

PubMed

Arya, Shruti; Gupta, Nancy; Gupta, Rahul; Aggarwal, Arun

Constipation, defined as delay or difficulty in defecation, present for 2 or more weeks, is a common problem encountered by both primary and specialty medical providers. There are no randomized controlled trials on the use of antegrade enemas in the pediatric population. Most published papers are based on the experience at a particular center. The aim of this article is to describe the pathophysiology of constipation, review the contribution of colonic manometry to the diagnosis of constipation, summarize the advancements in the management of constipation through the use of antegrade enemas, and study the outcomes of cecostomy at different centers. This study is a comprehensive literature review generated by computerized search of literature, supplemented by review of monographs and textbooks in pathology, gastroenterology, and surgery. Literature search was performed using the publications from 1997 to 2012. The search included publications of all types presenting or reviewing data on cecostomy. The antegrade continence enema is a therapeutic option for defecation disorders when maximal conventional therapy is not successful. Symptoms of defecation disorders in children with different underlying etiologies improve significantly after a cecostomy is created. In addition, there is a benefit on the patients' physical activity, healthcare utilization, and general well-being. Based on the review of published literature it seems that antegrade enemas are a successful therapeutic option in children with severe constipation and/or fecal incontinence. With the advent of cecostomy buttons, patient compliance and the overall cosmetic appearance have improved.

Biclustering of gene expression data using reactive greedy randomized adaptive search procedure.

PubMed

Dharan, Smitha; Nair, Achuthsankar S

2009-01-30

Biclustering algorithms belong to a distinct class of clustering algorithms that perform simultaneous clustering of both rows and columns of the gene expression matrix and can be a very useful analysis tool when some genes have multiple functions and experimental conditions are diverse. Cheng and Church have introduced a measure called mean squared residue score to evaluate the quality of a bicluster and has become one of the most popular measures to search for biclusters. In this paper, we review basic concepts of the metaheuristics Greedy Randomized Adaptive Search Procedure (GRASP)-construction and local search phases and propose a new method which is a variant of GRASP called Reactive Greedy Randomized Adaptive Search Procedure (Reactive GRASP) to detect significant biclusters from large microarray datasets. The method has two major steps. First, high quality bicluster seeds are generated by means of k-means clustering. In the second step, these seeds are grown using the Reactive GRASP, in which the basic parameter that defines the restrictiveness of the candidate list is self-adjusted, depending on the quality of the solutions found previously. We performed statistical and biological validations of the biclusters obtained and evaluated the method against the results of basic GRASP and as well as with the classic work of Cheng and Church. The experimental results indicate that the Reactive GRASP approach outperforms the basic GRASP algorithm and Cheng and Church approach. The Reactive GRASP approach for the detection of significant biclusters is robust and does not require calibration efforts.

Search Path Mapping: A Versatile Approach for Visualizing Problem-Solving Behavior.

ERIC Educational Resources Information Center

Stevens, Ronald H.

1991-01-01

Computer-based problem-solving examinations in immunology generate graphic representations of students' search paths, allowing evaluation of how organized and focused their knowledge is, how well their organization relates to critical concepts in immunology, where major misconceptions exist, and whether proper knowledge links exist between content…

Spectrum-to-Spectrum Searching Using a Proteome-wide Spectral Library*

PubMed Central

Yen, Chia-Yu; Houel, Stephane; Ahn, Natalie G.; Old, William M.

2011-01-01

The unambiguous assignment of tandem mass spectra (MS/MS) to peptide sequences remains a key unsolved problem in proteomics. Spectral library search strategies have emerged as a promising alternative for peptide identification, in which MS/MS spectra are directly compared against a reference library of confidently assigned spectra. Two problems relate to library size. First, reference spectral libraries are limited to rediscovery of previously identified peptides and are not applicable to new peptides, because of their incomplete coverage of the human proteome. Second, problems arise when searching a spectral library the size of the entire human proteome. We observed that traditional dot product scoring methods do not scale well with spectral library size, showing reduction in sensitivity when library size is increased. We show that this problem can be addressed by optimizing scoring metrics for spectrum-to-spectrum searches with large spectral libraries. MS/MS spectra for the 1.3 million predicted tryptic peptides in the human proteome are simulated using a kinetic fragmentation model (MassAnalyzer version2.1) to create a proteome-wide simulated spectral library. Searches of the simulated library increase MS/MS assignments by 24% compared with Mascot, when using probabilistic and rank based scoring methods. The proteome-wide coverage of the simulated library leads to 11% increase in unique peptide assignments, compared with parallel searches of a reference spectral library. Further improvement is attained when reference spectra and simulated spectra are combined into a hybrid spectral library, yielding 52% increased MS/MS assignments compared with Mascot searches. Our study demonstrates the advantages of using probabilistic and rank based scores to improve performance of spectrum-to-spectrum search strategies. PMID:21532008

A complexity theory model in science education problem solving: random walks for working memory and mental capacity.

PubMed

Stamovlasis, Dimitrios; Tsaparlis, Georgios

2003-07-01

The present study examines the role of limited human channel capacity from a science education perspective. A model of science problem solving has been previously validated by applying concepts and tools of complexity theory (the working memory, random walk method). The method correlated the subjects' rank-order achievement scores in organic-synthesis chemistry problems with the subjects' working memory capacity. In this work, we apply the same nonlinear approach to a different data set, taken from chemical-equilibrium problem solving. In contrast to the organic-synthesis problems, these problems are algorithmic, require numerical calculations, and have a complex logical structure. As a result, these problems cause deviations from the model, and affect the pattern observed with the nonlinear method. In addition to Baddeley's working memory capacity, the Pascual-Leone's mental (M-) capacity is examined by the same random-walk method. As the complexity of the problem increases, the fractal dimension of the working memory random walk demonstrates a sudden drop, while the fractal dimension of the M-capacity random walk decreases in a linear fashion. A review of the basic features of the two capacities and their relation is included. The method and findings have consequences for problem solving not only in chemistry and science education, but also in other disciplines.

Screening for Reading Problems: The Utility of SEARCH.

ERIC Educational Resources Information Center

Morrison, Delmont; And Others

1988-01-01

The accuracy of SEARCH for identifying children at risk for developing learning disabilities was evaluated with 1,107 kindergarten children. Children identified as at risk were of average intelligence. SEARCH scores were significantly correlated with sequential and simultaneous information processing skills. SEARCH predicted adequacy of…

Graph pyramids as models of human problem solving

NASA Astrophysics Data System (ADS)

Pizlo, Zygmunt; Li, Zheng

2004-05-01

Prior theories have assumed that human problem solving involves estimating distances among states and performing search through the problem space. The role of mental representation in those theories was minimal. Results of our recent experiments suggest that humans are able to solve some difficult problems quickly and accurately. Specifically, in solving these problems humans do not seem to rely on distances or on search. It is quite clear that producing good solutions without performing search requires a very effective mental representation. In this paper we concentrate on studying the nature of this representation. Our theory takes the form of a graph pyramid. To verify the psychological plausibility of this theory we tested subjects in a Euclidean Traveling Salesman Problem in the presence of obstacles. The role of the number and size of obstacles was tested for problems with 6-50 cities. We analyzed the effect of experimental conditions on solution time per city and on solution error. The main result is that time per city is systematically affected only by the size of obstacles, but not by their number, or by the number of cities.

Discovering Motifs in Biological Sequences Using the Micron Automata Processor.

PubMed

Roy, Indranil; Aluru, Srinivas

2016-01-01

Finding approximately conserved sequences, called motifs, across multiple DNA or protein sequences is an important problem in computational biology. In this paper, we consider the (l, d) motif search problem of identifying one or more motifs of length l present in at least q of the n given sequences, with each occurrence differing from the motif in at most d substitutions. The problem is known to be NP-complete, and the largest solved instance reported to date is (26,11). We propose a novel algorithm for the (l,d) motif search problem using streaming execution over a large set of non-deterministic finite automata (NFA). This solution is designed to take advantage of the micron automata processor, a new technology close to deployment that can simultaneously execute multiple NFA in parallel. We demonstrate the capability for solving much larger instances of the (l, d) motif search problem using the resources available within a single automata processor board, by estimating run-times for problem instances (39,18) and (40,17). The paper serves as a useful guide to solving problems using this new accelerator technology.

Behavioral interventions in attention-deficit/hyperactivity disorder: a meta-analysis of randomized controlled trials across multiple outcome domains.

PubMed

Daley, David; van der Oord, Saskia; Ferrin, Maite; Danckaerts, Marina; Doepfner, Manfred; Cortese, Samuele; Sonuga-Barke, Edmund J S

2014-08-01

Behavioral interventions are recommended as attention-deficit/hyperactivity disorder (ADHD) treatments. However, a recent meta-analysis found no effects on core ADHD symptoms when raters were probably blind to treatment allocation. The present analysis is extended to a broader range of child and parent outcomes. A systematic search in PubMed, Ovid, Web of Knowledge, ERIC, and CINAHAL databases (up to February 5, 2013) identified published randomized controlled trials measuring a range of patient and parent outcomes for children and adolescents diagnosed with ADHD (or who met validated cutoffs on rating scales). Thirty-two of 2,057 nonduplicate screened records were analyzed. For assessments made by individuals closest to the treatment setting (usually unblinded), there were significant improvements in parenting quality (standardized mean difference [SMD] for positive parenting 0.68; SMD for negative parenting 0.57), parenting self-concept (SMD 0.37), and child ADHD (SMD 0.35), conduct problems (SMD 0.26), social skills (SMD 0.47), and academic performance (SMD 0.28). With probably blinded assessments, significant effects persisted for parenting (SMD for positive parenting 0.63; SMD for negative parenting 0.43) and conduct problems (SMD 0.31). In contrast to the lack of blinded evidence of ADHD symptom decrease, behavioral interventions have positive effects on a range of other outcomes when used with patients with ADHD. There is blinded evidence that they improve parenting and decrease childhood conduct problems. These effects also may feed through into a more positive parenting self-concept but not improved parent mental well-being. Copyright © 2014 American Academy of Child and Adolescent Psychiatry. Published by Elsevier Inc. All rights reserved.

Facilitating the 3D Indoor Search and Rescue Problem: An Overview of the Problem and an Ant Colony Solution Approach

NASA Astrophysics Data System (ADS)

Tashakkori, H.; Rajabifard, A.; Kalantari, M.

2016-10-01

Search and rescue procedures for indoor environments are quite complicated due to the fact that much of the indoor information is unavailable to rescuers before physical entrance to the incident scene. Thus, decision making regarding the number of crew required and the way they should be dispatched in the building considering the various access points and complexities in the buildings in order to cover the search area in minimum time is dependent on prior knowledge and experience of the emergency commanders. Hence, this paper introduces the Search and Rescue Problem (SRP) which aims at finding best search and rescue routes that minimize the overall search time in the buildings. 3D BIM-oriented indoor GIS is integrated in the indoor route graph to find accurate routes based on the building geometric and semantic information. An Ant Colony Based Algorithm is presented that finds the number of first responders required and their individual routes to search all rooms and points of interest inside the building to minimize the overall time spent by all rescuers inside the disaster area. The evaluation of the proposed model for a case study building shows a significant improve in search and rescue time which will lead to a higher chance of saving lives and less exposure of emergency crew to danger.

Gradient gravitational search: An efficient metaheuristic algorithm for global optimization.

PubMed

Dash, Tirtharaj; Sahu, Prabhat K

2015-05-30

The adaptation of novel techniques developed in the field of computational chemistry to solve the concerned problems for large and flexible molecules is taking the center stage with regard to efficient algorithm, computational cost and accuracy. In this article, the gradient-based gravitational search (GGS) algorithm, using analytical gradients for a fast minimization to the next local minimum has been reported. Its efficiency as metaheuristic approach has also been compared with Gradient Tabu Search and others like: Gravitational Search, Cuckoo Search, and Back Tracking Search algorithms for global optimization. Moreover, the GGS approach has also been applied to computational chemistry problems for finding the minimal value potential energy of two-dimensional and three-dimensional off-lattice protein models. The simulation results reveal the relative stability and physical accuracy of protein models with efficient computational cost. © 2015 Wiley Periodicals, Inc.

Using Computer-Generated Random Numbers to Calculate the Lifetime of a Comet.

ERIC Educational Resources Information Center

Danesh, Iraj

1991-01-01

An educational technique to calculate the lifetime of a comet using software-generated random numbers is introduced to undergraduate physiques and astronomy students. Discussed are the generation and eligibility of the required random numbers, background literature related to the problem, and the solution to the problem using random numbers.…

A new neural network model for solving random interval linear programming problems.

PubMed

Arjmandzadeh, Ziba; Safi, Mohammadreza; Nazemi, Alireza

2017-05-01

This paper presents a neural network model for solving random interval linear programming problems. The original problem involving random interval variable coefficients is first transformed into an equivalent convex second order cone programming problem. A neural network model is then constructed for solving the obtained convex second order cone problem. Employing Lyapunov function approach, it is also shown that the proposed neural network model is stable in the sense of Lyapunov and it is globally convergent to an exact satisfactory solution of the original problem. Several illustrative examples are solved in support of this technique. Copyright © 2017 Elsevier Ltd. All rights reserved.

Urineschool: A Study of the Impact of the Earls Decision on High School Random Drug Testing Policies.

ERIC Educational Resources Information Center

Conlon, Cynthia Kelly

2003-01-01

Examines impact of Supreme Court's 2002 decision in "Board of Education v. Earls" on high school random drug-testing policies and practices. Court held that random drug-testing policy at Tecumseh, Oklahoma, school district did not violate students' Fourth Amendment right against unreasonable searches. (Contains 46 references.) (PKP)

Otitis media with effusion after radiotherapy of the head and neck: a systematic review.

PubMed

Christensen, J G; Wessel, I; Gothelf, A B; Homøe, P

2018-04-26

Otitis media (OM) and associated hearing problems may be side effects to radiotherapy of the head and neck region and affect patient quality of life. The condition is associated with the tumor location. To perform a systematic review concerning the present knowledge of the risk of OM after radiotherapy of the head and neck. A comprehensive search of PubMed and Embase was carried out between 1 October 2015 and 6 February 2017. The search strategy followed the PRISMA guideline for systematic reviews. Of 597 articles 11 fulfilled the inclusion criteria. Seven were retrospective and four prospective. There were no randomized controlled trials. Eight studies concerned nasopharyngeal cancer. One study concerned cancer of the parotid gland and two studies concerned other locations of head and neck cancer. Meta-analysis could not be done due to heterogeneity between the studies. The incidence of OM varied considerably (range 8-29%). The incidence of OM is high after radiotherapy of cancer of the upper head and neck area and the Eustachian tube (ET) irradiation dosage seems associated with development of OM, but the literature is poor. Research is needed to designate patients at risk of developing OM after radiotherapy. Preferably through analysis of dosage relationships between the ET and middle ear, and development of OM. Reporting of OM should be per ear and follow standardized protocols of middle ear assessment before and after radiotherapy. Furthermore, there is a need to find new ways to prevent and treat radiation-induced OME, preferably through randomized controlled trials.

Object recognition in images via a factor graph model

NASA Astrophysics Data System (ADS)

He, Yong; Wang, Long; Wu, Zhaolin; Zhang, Haisu

2018-04-01

Object recognition in images suffered from huge search space and uncertain object profile. Recently, the Bag-of- Words methods are utilized to solve these problems, especially the 2-dimension CRF(Conditional Random Field) model. In this paper we suggest the method based on a general and flexible fact graph model, which can catch the long-range correlation in Bag-of-Words by constructing a network learning framework contrasted from lattice in CRF. Furthermore, we explore a parameter learning algorithm based on the gradient descent and Loopy Sum-Product algorithms for the factor graph model. Experimental results on Graz 02 dataset show that, the recognition performance of our method in precision and recall is better than a state-of-art method and the original CRF model, demonstrating the effectiveness of the proposed method.

Committee-Based Active Learning for Surrogate-Assisted Particle Swarm Optimization of Expensive Problems.

PubMed

Wang, Handing; Jin, Yaochu; Doherty, John

2017-09-01

Function evaluations (FEs) of many real-world optimization problems are time or resource consuming, posing a serious challenge to the application of evolutionary algorithms (EAs) to solve these problems. To address this challenge, the research on surrogate-assisted EAs has attracted increasing attention from both academia and industry over the past decades. However, most existing surrogate-assisted EAs (SAEAs) either still require thousands of expensive FEs to obtain acceptable solutions, or are only applied to very low-dimensional problems. In this paper, a novel surrogate-assisted particle swarm optimization (PSO) inspired from committee-based active learning (CAL) is proposed. In the proposed algorithm, a global model management strategy inspired from CAL is developed, which searches for the best and most uncertain solutions according to a surrogate ensemble using a PSO algorithm and evaluates these solutions using the expensive objective function. In addition, a local surrogate model is built around the best solution obtained so far. Then, a PSO algorithm searches on the local surrogate to find its optimum and evaluates it. The evolutionary search using the global model management strategy switches to the local search once no further improvement can be observed, and vice versa. This iterative search process continues until the computational budget is exhausted. Experimental results comparing the proposed algorithm with a few state-of-the-art SAEAs on both benchmark problems up to 30 decision variables as well as an airfoil design problem demonstrate that the proposed algorithm is able to achieve better or competitive solutions with a limited budget of hundreds of exact FEs.

Interventions for reducing fear of childbirth: A systematic review and meta-analysis of clinical trials.

PubMed

MoghaddamHosseini, Vahideh; Nazarzadeh, Milad; Jahanfar, Shayesteh

2017-11-07

Fear of childbirth is a problematic mental health issue during pregnancy. But, effective interventions to reduce this problem are not well understood. To examine effective interventions for reducing fear of childbirth. The Cochrane Central Register of Controlled Trials, PubMed, Embase and PsycINFO were searched since inception till September 2017 without any restriction. Randomised controlled trials and quasi-randomised controlled trials comparing interventions for treatment of fear of childbirth were included. The standardized mean differences were pooled using random and fixed effect models. The heterogeneity was determined using the Cochran's test and I 2 index and was further explored in meta-regression model and subgroup analyses. Ten studies inclusive of 3984 participants were included in the meta-analysis (2 quasi-randomized and 8 randomized clinical trials). Eight studies investigated education and two studies investigated hypnosis-based intervention. The pooled standardized mean differences of fear for the education intervention and hypnosis group in comparison with control group were -0.46 (95% CI -0.73 to -0.19) and -0.22 (95% CI -0.34 to -0.10), respectively. Both types of interventions were effective in reducing fear of childbirth; however our pooled results revealed that educational interventions may reduce fear with double the effect of hypnosis. Further large scale randomized clinical trials and individual patient data meta-analysis are warranted for assessing the association. Copyright © 2017 Australian College of Midwives. Published by Elsevier Ltd. All rights reserved.

Using Advanced Tabu Search Approaches to Perform Enhanced Air Mobility Command Operational Airlift Analyses

DTIC Science & Technology

2009-02-28

2, No. 2, 2007, pp. 156-172. 21. Lambert, G.,J.W. Barnes, and D. Van Veldhuizen ,, "A Tabu Search Approach to the Strategic Airlift Problem...Industrial Engineering, accepted, to appear 2009, pp 1 -86, published by Taylor and Francis/CRC Press. 27. Roesener, A., J. W. Barnes, J. Moore, D. Van ... Veldhuizen , "An Advanced Tabu Search Approach To The Static Airlift Loading Problem," Military Operations Research, 2007, (in second review). 28. Burks

The effects of aquatic therapy on mobility of individuals with neurological diseases: a systematic review.

PubMed

Marinho-Buzelli, Andresa R; Bonnyman, Alison M; Verrier, Mary C

2015-08-01

To summarize evidence on the effects of aquatic therapy on mobility in individuals with neurological diseases. MEDLINE, EMBASE, PsycInfo, CENTRAL, CINAHL, SPORTDiscus, PEDro, PsycBITE and OT Seeker were searched from inception to 15 September 2014. Hand-searching of reference lists was performed in the selected studies. The search included randomized controlled trials and quasi-experimental studies that investigated the use of aquatic therapy and its effect on mobility of adults with neurological diseases. One reviewer screened titles and abstracts of retrieved studies from the search strategy. Two reviewers independently examined the full texts and conducted the study selection, data extraction and quality assessment. A narrative synthesis of data was applied to summarize information from included studies. The Downs and Black Scale was used to assess methodological quality. A total of 116 articles were obtained for full text eligibility. Twenty studies met the specified inclusion criteria: four Randomized Controlled Trials (RCTs), four non-randomized studies and 12 before-and-after tests. Two RCTs (30 patients with stroke in the aquatic therapy groups), three non-randomized studies and three before-and-after studies showed "fair" evidence that aquatic therapy increases dynamic balance in participants with some neurological disorders. One RCT (seven patients with stroke in the aquatic therapy group) and two before-and-after tests (20 patients with multiple sclerosis) demonstrated "fair" evidence on improvement of gait speed after aquatic therapy. Our synthesis showed "fair" evidence supporting the use of aquatic therapy to improve dynamic balance and gait speed in adults with certain neurological conditions. © The Author(s) 2014.

Observations on SOFIA Observation Scheduling: Search and Inference in the Face of Discrete and Continuous Constraints

NASA Technical Reports Server (NTRS)

Frank, Jeremy; Gross, Michael; Kuerklu, Elif

2003-01-01

We did cool stuff to reduce the number of IVPs and BVPs needed to schedule SOFIA by restricting the problem. The restriction costs us little in terms of the value of the flight plans we can build. The restriction allowed us to reformulate part of the search problem as a zero-finding problem. The result is a simplified planning model and significant savings in computation time.

Neurally and Ocularly Informed Graph-Based Models for Searching 3D Environments

DTIC Science & Technology

2014-06-03

hBCI = hybrid brain–computer interface, TAG = transductive annotation by graph, CV = computer vision, TSP = traveling salesman problem . are navigated...environment that are most likely to contain objects that the subject would like to visit. 2.9. Route planning A traveling salesman problem (TSP) solver...fixations in a visual search task using fixation-related potentials J. Vis. 13 Croes G 1958 A method for solving traveling - salesman problems Oper. Res

Teaching Non-Recursive Binary Searching: Establishing a Conceptual Framework.

ERIC Educational Resources Information Center

Magel, E. Terry

1989-01-01

Discusses problems associated with teaching non-recursive binary searching in computer language classes, and describes a teacher-directed dialog based on dictionary use that helps students use their previous searching experiences to conceptualize the binary search process. Algorithmic development is discussed and appropriate classroom discussion…

The Concept of the "Imploded Boolean Search": A Case Study with Undergraduate Chemistry Students

ERIC Educational Resources Information Center

Tomaszewski, Robert

2016-01-01

Critical thinking and analytical problem-solving skills in research involves using different search strategies. A proposed concept for an "Imploded Boolean Search" combines three unique identifiable field types to perform a search: keyword(s), numerical value(s), and a chemical structure or reaction. The object of this type of search is…

Sedative techniques for endoscopic retrograde cholangiopancreatography.

PubMed

Garewal, Davinder; Powell, Steve; Milan, Stephen J; Nordmeyer, Jonas; Waikar, Pallavi

2012-06-13

Endoscopic retrograde cholangiopancreatography (ERCP) is an uncomfortable therapeutic procedure that cannot be performed without adequate sedation or general anaesthesia. A considerable number of ERCPs are performed annually in the UK (at least 48,000) and many more worldwide. The primary objective of our review was to evaluate and compare the efficacy and safety of sedative or anaesthetic techniques used to facilitate the procedure of ERCP in adult (age > 18 years) patients. We searched the Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library 2010, Issue 8); MEDLINE (1950 to September 2011); EMBASE (1950 to September 2011); CINAHL, Web of Science and LILACS (all to September 2011). We searched for additional studies drawn from reference lists of retrieved trial materials and review articles and conference proceedings. We considered all randomized or quasi-randomized controlled studies where the main procedures performed were ERCPs. The three interventions we searched for were (1) conscious sedation (using midazolam plus opioid) versus deep sedation (using propofol); (2) conscious sedation versus general anaesthesia; and (3) deep sedation versus general anaesthesia. We considered all studies regardless of which healthcare professional administered the sedation. We reviewed 124 papers and identified four randomized trials (with a total of 510 participants) that compared the use of conscious sedation using midazolam and meperidine with deep sedation using propofol in patients undergoing ERCP procedures. All sedation was administered by non-anaesthetic personnel. Due to the clinical heterogeneity of the studies we decided to review the papers from a narrative perspective as opposed to a full meta-analysis. Our primary outcome measures included mortality, major complications and inability to complete the procedure due to sedation-related problems. Secondary outcomes encompassed sedation efficacy and recovery. No immediate mortality was reported. There was no significant difference in serious cardio-respiratory complications suffered by patients in either sedation group. Failure to complete the procedure due to sedation-related problems was reported in one study. Three studies found faster and better recovery in patients receiving propofol for their ERCP procedures. Study protocols regarding use of supplemental oxygen, intravenous fluid administration and capnography monitoring varied considerably. The studies showed either moderate or high risk of bias. Results from individual studies suggested that patients have a better recovery profile after propofol sedation for ERCP procedures than after midazolam and meperidine sedation. As there was no difference between the two sedation techniques as regards safety, propofol sedation is probably preferred for patients undergoing ERCP procedures. However, in all of the studies that were identified only non-anaesthesia personnel were involved in administering the sedation. It would be helpful if further research was conducted where anaesthesia personnel were involved in the administration of sedation for ERCP procedures. This would clarify the extent to which anaesthesia personnel should be involved in the administration of propofol sedation.

Heat Transfer Search Algorithm for Non-convex Economic Dispatch Problems

NASA Astrophysics Data System (ADS)

Hazra, Abhik; Das, Saborni; Basu, Mousumi

2018-06-01

This paper presents Heat Transfer Search (HTS) algorithm for the non-linear economic dispatch problem. HTS algorithm is based on the law of thermodynamics and heat transfer. The proficiency of the suggested technique has been disclosed on three dissimilar complicated economic dispatch problems with valve point effect; prohibited operating zone; and multiple fuels with valve point effect. Test results acquired from the suggested technique for the economic dispatch problem have been fitted to that acquired from other stated evolutionary techniques. It has been observed that the suggested HTS carry out superior solutions.

Heat Transfer Search Algorithm for Non-convex Economic Dispatch Problems

NASA Astrophysics Data System (ADS)

Hazra, Abhik; Das, Saborni; Basu, Mousumi

2018-03-01

This paper presents Heat Transfer Search (HTS) algorithm for the non-linear economic dispatch problem. HTS algorithm is based on the law of thermodynamics and heat transfer. The proficiency of the suggested technique has been disclosed on three dissimilar complicated economic dispatch problems with valve point effect; prohibited operating zone; and multiple fuels with valve point effect. Test results acquired from the suggested technique for the economic dispatch problem have been fitted to that acquired from other stated evolutionary techniques. It has been observed that the suggested HTS carry out superior solutions.

On local search for bi-objective knapsack problems.

PubMed

Liefooghe, Arnaud; Paquete, Luís; Figueira, José Rui

2013-01-01

In this article, a local search approach is proposed for three variants of the bi-objective binary knapsack problem, with the aim of maximizing the total profit and minimizing the total weight. First, an experimental study on a given structural property of connectedness of the efficient set is conducted. Based on this property, a local search algorithm is proposed and its performance is compared to exact algorithms in terms of runtime and quality metrics. The experimental results indicate that this simple local search algorithm is able to find a representative set of optimal solutions in most of the cases, and in much less time than exact algorithms.

An almost-parameter-free harmony search algorithm for groundwater pollution source identification.

PubMed

Jiang, Simin; Zhang, Yali; Wang, Pei; Zheng, Maohui

2013-01-01

The spatiotemporal characterization of unknown sources of groundwater pollution is frequently encountered in environmental problems. This study adopts a simulation-optimization approach that combines a contaminant transport simulation model with a heuristic harmony search algorithm to identify unknown pollution sources. In the proposed methodology, an almost-parameter-free harmony search algorithm is developed. The performance of this methodology is evaluated on an illustrative groundwater pollution source identification problem, and the identified results indicate that the proposed almost-parameter-free harmony search algorithm-based optimization model can give satisfactory estimations, even when the irregular geometry, erroneous monitoring data, and prior information shortage of potential locations are considered.

Modified harmony search

NASA Astrophysics Data System (ADS)

Mohamed, Najihah; Lutfi Amri Ramli, Ahmad; Majid, Ahmad Abd; Piah, Abd Rahni Mt

2017-09-01

A metaheuristic algorithm, called Harmony Search is quite highly applied in optimizing parameters in many areas. HS is a derivative-free real parameter optimization algorithm, and draws an inspiration from the musical improvisation process of searching for a perfect state of harmony. Propose in this paper Modified Harmony Search for solving optimization problems, which employs a concept from genetic algorithm method and particle swarm optimization for generating new solution vectors that enhances the performance of HS algorithm. The performances of MHS and HS are investigated on ten benchmark optimization problems in order to make a comparison to reflect the efficiency of the MHS in terms of final accuracy, convergence speed and robustness.

A Multi-Level Model of Information Seeking in the Clinical Domain

PubMed Central

Hung, Peter W.; Johnson, Stephen B.; Kaufman, David R.; Mendonça, Eneida A.

2008-01-01

Objective: Clinicians often have difficulty translating information needs into effective search strategies to find appropriate answers. Information retrieval systems employing an intelligent search agent that generates adaptive search strategies based on human search expertise could be helpful in meeting clinician information needs. A prerequisite for creating such systems is an information seeking model that facilitates the representation of human search expertise. The purpose of developing such a model is to provide guidance to information seeking system development and to shape an empirical research program. Design: The information seeking process was modeled as a complex problem-solving activity. After considering how similarly complex activities had been modeled in other domains, we determined that modeling context-initiated information seeking across multiple problem spaces allows the abstraction of search knowledge into functionally consistent layers. The knowledge layers were identified in the information science literature and validated through our observations of searches performed by health science librarians. Results: A hierarchical multi-level model of context-initiated information seeking is proposed. Each level represents (1) a problem space that is traversed during the online search process, and (2) a distinct layer of knowledge that is required to execute a successful search. Grand strategy determines what information resources will be searched, for what purpose, and in what order. The strategy level represents an overall approach for searching a single resource. Tactics are individual moves made to further a strategy. Operations are mappings of abstract intentions to information resource-specific concrete input. Assessment is the basis of interaction within the strategic hierarchy, influencing the direction of the search. Conclusion: The described multi-level model provides a framework for future research and the foundation for development of an automated information retrieval system that uses an intelligent search agent to bridge clinician information needs and human search expertise. PMID:18006383

An improved stochastic fractal search algorithm for 3D protein structure prediction.

PubMed

Zhou, Changjun; Sun, Chuan; Wang, Bin; Wang, Xiaojun

2018-05-03

Protein structure prediction (PSP) is a significant area for biological information research, disease treatment, and drug development and so on. In this paper, three-dimensional structures of proteins are predicted based on the known amino acid sequences, and the structure prediction problem is transformed into a typical NP problem by an AB off-lattice model. This work applies a novel improved Stochastic Fractal Search algorithm (ISFS) to solve the problem. The Stochastic Fractal Search algorithm (SFS) is an effective evolutionary algorithm that performs well in exploring the search space but falls into local minimums sometimes. In order to avoid the weakness, Lvy flight and internal feedback information are introduced in ISFS. In the experimental process, simulations are conducted by ISFS algorithm on Fibonacci sequences and real peptide sequences. Experimental results prove that the ISFS performs more efficiently and robust in terms of finding the global minimum and avoiding getting stuck in local minimums.

Ant Colony Optimization With Local Search for Dynamic Traveling Salesman Problems.

PubMed

Mavrovouniotis, Michalis; Muller, Felipe M; Yang, Shengxiang

2016-06-13

For a dynamic traveling salesman problem (DTSP), the weights (or traveling times) between two cities (or nodes) may be subject to changes. Ant colony optimization (ACO) algorithms have proved to be powerful methods to tackle such problems due to their adaptation capabilities. It has been shown that the integration of local search operators can significantly improve the performance of ACO. In this paper, a memetic ACO algorithm, where a local search operator (called unstring and string) is integrated into ACO, is proposed to address DTSPs. The best solution from ACO is passed to the local search operator, which removes and inserts cities in such a way that improves the solution quality. The proposed memetic ACO algorithm is designed to address both symmetric and asymmetric DTSPs. The experimental results show the efficiency of the proposed memetic algorithm for addressing DTSPs in comparison with other state-of-the-art algorithms.

Modified reactive tabu search for the symmetric traveling salesman problems

NASA Astrophysics Data System (ADS)

Lim, Yai-Fung; Hong, Pei-Yee; Ramli, Razamin; Khalid, Ruzelan

2013-09-01

Reactive tabu search (RTS) is an improved method of tabu search (TS) and it dynamically adjusts tabu list size based on how the search is performed. RTS can avoid disadvantage of TS which is in the parameter tuning in tabu list size. In this paper, we proposed a modified RTS approach for solving symmetric traveling salesman problems (TSP). The tabu list size of the proposed algorithm depends on the number of iterations when the solutions do not override the aspiration level to achieve a good balance between diversification and intensification. The proposed algorithm was tested on seven chosen benchmarked problems of symmetric TSP. The performance of the proposed algorithm is compared with that of the TS by using empirical testing, benchmark solution and simple probabilistic analysis in order to validate the quality of solution. The computational results and comparisons show that the proposed algorithm provides a better quality solution than that of the TS.

Development of Solution Algorithm and Sensitivity Analysis for Random Fuzzy Portfolio Selection Model

NASA Astrophysics Data System (ADS)

Hasuike, Takashi; Katagiri, Hideki

2010-10-01

This paper focuses on the proposition of a portfolio selection problem considering an investor's subjectivity and the sensitivity analysis for the change of subjectivity. Since this proposed problem is formulated as a random fuzzy programming problem due to both randomness and subjectivity presented by fuzzy numbers, it is not well-defined. Therefore, introducing Sharpe ratio which is one of important performance measures of portfolio models, the main problem is transformed into the standard fuzzy programming problem. Furthermore, using the sensitivity analysis for fuzziness, the analytical optimal portfolio with the sensitivity factor is obtained.

The Prevention Program for Externalizing Problem Behavior (PEP) Improves Child Behavior by Reducing Negative Parenting: Analysis of Mediating Processes in a Randomized Controlled Trial

ERIC Educational Resources Information Center

Hanisch, Charlotte; Hautmann, Christopher; Plück, Julia; Eichelberger, Ilka; Döpfner, Manfred

2014-01-01

Background: Our indicated Prevention program for preschool children with Externalizing Problem behavior (PEP) demonstrated improved parenting and child problem behavior in a randomized controlled efficacy trial and in a study with an effectiveness design. The aim of the present analysis of data from the randomized controlled trial was to identify…

Orthodontic treatment in periodontitis‐susceptible subjects: a systematic literature review

PubMed Central

Lindsten, Rune; Slotte, Christer; Bjerklin, Krister

2016-01-01

Abstract The aim is to evaluate the literature for clinical scientific data on possible effects of orthodontic treatment on periodontal status in periodontitis‐susceptible subjects. A systematic literature review was performed on studies in English using PubMed, MEDLINE, and Cochrane Library central databases (1965‐2014). By manually searching reference lists of selected studies, we identified additional articles; then we searched these publications: Journal of Periodontology, Periodontology 2000, Journal of Clinical Periodontology, American Journal of Orthodontics and Dentofacial Orthopedics, Angle Orthodontist, International Journal of Periodontics & Restorative Dentistry, and European Journal of Orthodontics. Search terms included randomized clinical trials, controlled clinical trials, prospective and retrospective clinical studies, case series >5 patients, periodontitis, orthodontics, alveolar bone loss, tooth migration, tooth movement, orthodontic extrusion, and orthodontic intrusion. Only studies on orthodontic treatment in periodontally compromised dentitions were included. One randomized controlled clinical trial, one controlled clinical trial, and 12 clinical studies were included. No evidence currently exists from controlled studies and randomized controlled clinical trials, which shows that orthodontic treatment improves or aggravates the status of periodontally compromised dentitions. PMID:29744163

Stochastic reduced order models for inverse problems under uncertainty

PubMed Central

Warner, James E.; Aquino, Wilkins; Grigoriu, Mircea D.

2014-01-01

This work presents a novel methodology for solving inverse problems under uncertainty using stochastic reduced order models (SROMs). Given statistical information about an observed state variable in a system, unknown parameters are estimated probabilistically through the solution of a model-constrained, stochastic optimization problem. The point of departure and crux of the proposed framework is the representation of a random quantity using a SROM - a low dimensional, discrete approximation to a continuous random element that permits e cient and non-intrusive stochastic computations. Characterizing the uncertainties with SROMs transforms the stochastic optimization problem into a deterministic one. The non-intrusive nature of SROMs facilitates e cient gradient computations for random vector unknowns and relies entirely on calls to existing deterministic solvers. Furthermore, the method is naturally extended to handle multiple sources of uncertainty in cases where state variable data, system parameters, and boundary conditions are all considered random. The new and widely-applicable SROM framework is formulated for a general stochastic optimization problem in terms of an abstract objective function and constraining model. For demonstration purposes, however, we study its performance in the specific case of inverse identification of random material parameters in elastodynamics. We demonstrate the ability to efficiently recover random shear moduli given material displacement statistics as input data. We also show that the approach remains effective for the case where the loading in the problem is random as well. PMID:25558115

Attention and Multistep Problem Solving in 24-Month-Old Children

ERIC Educational Resources Information Center

Carrico, Renee L.

2013-01-01

The current study examined the role of increased attentional load in 24 month-old children's multistep problem-solving behavior. Children solved an object-based nonspatial working-memory search task, to which a motor component of varying difficulty was added. Significant disruptions in search performance were observed with the introduction of the…