Learning based particle filtering object tracking for visible-light systems.

Science.gov (United States)

Sun, Wei

2015-10-01

We propose a novel object tracking framework based on online learning scheme that can work robustly in challenging scenarios. Firstly, a learning-based particle filter is proposed with color and edge-based features. We train a. support vector machine (SVM) classifier with object and background information and map the outputs into probabilities, then the weight of particles in a particle filter can be calculated by the probabilistic outputs to estimate the state of the object. Secondly, the tracking loop starts with Lucas-Kanade (LK) affine template matching and follows by learning-based particle filter tracking. Lucas-Kanade method estimates errors and updates object template in the positive samples dataset, and learning-based particle filter tracker will start if the LK tracker loses the object. Finally, SVM classifier evaluates every tracked appearance to update the training set or restart the tracking loop if necessary. Experimental results show that our method is robust to challenging light, scale and pose changing, and test on eButton image sequence also achieves satisfactory tracking performance.

Model of Random Polygon Particles for Concrete and Mesh Automatic Subdivision

Institute of Scientific and Technical Information of China (English)

无

2001-01-01

In order to study the constitutive behavior of concrete in mesoscopic level, a new method is proposed in this paper. This method uses random polygon particles to simulate full grading broken aggregates of concrete. Based on computational geometry, we carry out the automatic generation of the triangle finite element mesh for the model of random polygon particles of concrete. The finite element mesh generated in this paper is also applicable to many other numerical methods.

Propagation of a Strong Shock Over a Random Bed of Spherical Particles

Energy Technology Data Exchange (ETDEWEB)

Mehta, Y. [Univ. of Florida, Gainesville, FL (United States); Neal, C. [Univ. of Florida, Gainesville, FL (United States); Salari, K. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Jackson, T. L. [Univ. of Florida, Gainesville, FL (United States); Balachandar, S. [Univ. of Florida, Gainesville, FL (United States); Thakur, S. [Univ. of Florida, Gainesville, FL (United States)

2017-04-11

Propagation of a strong shock through a bed of particles results in complex wave dynamics such as a reflected shock, a transmitted shock, and highly unsteady flow inside the particle bed. In this paper we present three-dimensional numerical simulations of shock propagation in air over a random bed of particles. We assume the flow is inviscid and governed by the Euler equations of gas dynamics. Simulations are carried out by varying the volume fraction of the particle bed at a fixed shock Mach number. We compute the unsteady inviscid streamwise and transverse drag coefficients as a function of time for each particle in the random bed as a function of volume fraction. We show that (i) there are significant variations in the peak drag for the particles in the bed, (ii) the mean peak drag as a function of streamwise distance through the bed decreases with a slope that increases as the volume fraction increases, and (iii) the deviation from the mean peak drag does not correlate with local volume fraction. We also present the local Mach number and pressure contours for the different volume fractions to explain the various observed complex physical mechanisms occurring during the shock-particle interactions. Since the shock interaction with the random bed of particles leads to transmitted and reflected waves, we compute the average flow properties to characterize the strength of the transmitted and reflected shock waves and quantify the energy dissipation inside the particle bed. Finally, to better understand the complex wave dynamics in a random bed, we consider a simpler approximation of a planar shock propagating in a duct with a sudden area change. We obtain Riemann solutions to this problem, which are used to compare with fully resolved numerical simulations.

Randomized Prediction Games for Adversarial Machine Learning.

Science.gov (United States)

Rota Bulo, Samuel; Biggio, Battista; Pillai, Ignazio; Pelillo, Marcello; Roli, Fabio

In spam and malware detection, attackers exploit randomization to obfuscate malicious data and increase their chances of evading detection at test time, e.g., malware code is typically obfuscated using random strings or byte sequences to hide known exploits. Interestingly, randomization has also been proposed to improve security of learning algorithms against evasion attacks, as it results in hiding information about the classifier to the attacker. Recent work has proposed game-theoretical formulations to learn secure classifiers, by simulating different evasion attacks and modifying the classification function accordingly. However, both the classification function and the simulated data manipulations have been modeled in a deterministic manner, without accounting for any form of randomization. In this paper, we overcome this limitation by proposing a randomized prediction game, namely, a noncooperative game-theoretic formulation in which the classifier and the attacker make randomized strategy selections according to some probability distribution defined over the respective strategy set. We show that our approach allows one to improve the tradeoff between attack detection and false alarms with respect to the state-of-the-art secure classifiers, even against attacks that are different from those hypothesized during design, on application examples including handwritten digit recognition, spam, and malware detection.In spam and malware detection, attackers exploit randomization to obfuscate malicious data and increase their chances of evading detection at test time, e.g., malware code is typically obfuscated using random strings or byte sequences to hide known exploits. Interestingly, randomization has also been proposed to improve security of learning algorithms against evasion attacks, as it results in hiding information about the classifier to the attacker. Recent work has proposed game-theoretical formulations to learn secure classifiers, by simulating different

Negative permeability from random particle composites

Energy Technology Data Exchange (ETDEWEB)

Hussain, Shahid, E-mail: shussain2@qinetiq.com

2017-04-15

Artificial media, such as those composed of periodically-spaced wires for negative permittivity and split ring resonators for negative permeability have been extensively investigated for negative refractive index (NRI) applications (Smith et al., 2004; Pendry et al., 1999) [1,2]. This paper presents an alternative method for producing negative permeability: granular (or particulate) composites incorporating magnetic fillers. Artificial media, such as split-ring resonators, are designed to produce a magnetic resonance feature, which results in negative permeability over a narrow frequency range about the resonance frequency. The position of the feature is dependent upon the size of the inclusion. The material in this case is anisotropic, such that the feature is only observable when the materials are orientated in a specific direction relative to the applied field. A similar resonance can be generated in magnetic granular (particulate) materials: ferromagnetic resonance from the natural spin resonance of particles. Although the theoretical resonance profiles in granular composites shows the permeability dipping to negative values, this is rarely observed experimentally due to resonance damping effects. Results are presented for iron in spherical form and in flake form, dispersed in insulating host matrices. The two particle shapes show different permeability performance, with the magnetic flakes producing a negative contribution. This is attributed to the stronger coupling with the magnetic field resulting from the high aspect ratio of the flakes. The accompanying ferromagnetic resonance is strong enough to overcome the effects of damping and produce negative permeability. The size of random particle composites is not dictated by the wavelength of the applied field, so the materials are potentially much thinner than other, more traditional artificial composites at microwave frequencies. - Highlights: • Negative permeability from random particle composites is

Inertial-particle dynamics in turbulent flows: caustics, concentration fluctuations and random uncorrelated motion

International Nuclear Information System (INIS)

Gustavsson, K; Mehlig, B; Meneguz, E; Reeks, M

2012-01-01

We have performed numerical simulations of inertial particles in random model flows in the white-noise limit (at zero Kubo number, Ku = 0) and at finite Kubo numbers. Our results for the moments of relative inertial-particle velocities are in good agreement with recent theoretical results (Gustavsson and Mehlig 2011a) based on the formation of phase-space singularities in the inertial-particle dynamics (caustics). We discuss the relation between three recent approaches describing the dynamics and spatial distribution of inertial particles suspended in turbulent flows: caustic formation, real-space singularities of the deformation tensor and random uncorrelated motion. We discuss how the phase- and real-space singularities are related. Their formation is well understood in terms of a local theory. We summarise the implications for random uncorrelated motion. (paper)

Advances in Bayesian Model Based Clustering Using Particle Learning

Energy Technology Data Exchange (ETDEWEB)

Merl, D M

2009-11-19

Recent work by Carvalho, Johannes, Lopes and Polson and Carvalho, Lopes, Polson and Taddy introduced a sequential Monte Carlo (SMC) alternative to traditional iterative Monte Carlo strategies (e.g. MCMC and EM) for Bayesian inference for a large class of dynamic models. The basis of SMC techniques involves representing the underlying inference problem as one of state space estimation, thus giving way to inference via particle filtering. The key insight of Carvalho et al was to construct the sequence of filtering distributions so as to make use of the posterior predictive distribution of the observable, a distribution usually only accessible in certain Bayesian settings. Access to this distribution allows a reversal of the usual propagate and resample steps characteristic of many SMC methods, thereby alleviating to a large extent many problems associated with particle degeneration. Furthermore, Carvalho et al point out that for many conjugate models the posterior distribution of the static variables can be parametrized in terms of [recursively defined] sufficient statistics of the previously observed data. For models where such sufficient statistics exist, particle learning as it is being called, is especially well suited for the analysis of streaming data do to the relative invariance of its algorithmic complexity with the number of data observations. Through a particle learning approach, a statistical model can be fit to data as the data is arriving, allowing at any instant during the observation process direct quantification of uncertainty surrounding underlying model parameters. Here we describe the use of a particle learning approach for fitting a standard Bayesian semiparametric mixture model as described in Carvalho, Lopes, Polson and Taddy. In Section 2 we briefly review the previously presented particle learning algorithm for the case of a Dirichlet process mixture of multivariate normals. In Section 3 we describe several novel extensions to the original

An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions

Science.gov (United States)

Shang, Junliang; Sun, Yan; Li, Shengjun; Liu, Jin-Xing; Zheng, Chun-Hou; Zhang, Junying

2015-01-01

SNP-SNP interactions have been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases. Though many works have been done for the detection of SNP-SNP interactions, the algorithmic development is still ongoing. In this study, an improved opposition-based learning particle swarm optimization (IOBLPSO) is proposed for the detection of SNP-SNP interactions. Highlights of IOBLPSO are the introduction of three strategies, namely, opposition-based learning, dynamic inertia weight, and a postprocedure. Opposition-based learning not only enhances the global explorative ability, but also avoids premature convergence. Dynamic inertia weight allows particles to cover a wider search space when the considered SNP is likely to be a random one and converges on promising regions of the search space while capturing a highly suspected SNP. The postprocedure is used to carry out a deep search in highly suspected SNP sets. Experiments of IOBLPSO are performed on both simulation data sets and a real data set of age-related macular degeneration, results of which demonstrate that IOBLPSO is promising in detecting SNP-SNP interactions. IOBLPSO might be an alternative to existing methods for detecting SNP-SNP interactions. PMID:26236727

Random ray-tracing and graphic analysing of charged particle trajectories

International Nuclear Information System (INIS)

Lin Xiaomei; Mao Naifeng; Chen Jingxian

1990-01-01

In order to describe the optical properties of a charged particle beam realistically, the random sampling of initial conditions of particles in ray-tracing is discussed. The emission surface of particles may be a plane, a cylindrical surface or a spherical surface. The distribution functions may be expressed analytically or numerically. In order to analyse the properties of the charged particle beam systematically by use of the results from ray-tracing efficiently, the graphic processing and analysing of particle trajectories are also discussed, including the spline function fitting of trajectories, the graphic drafting of trajectories and beam envelopes, the determining of image dimensions and the correspinding positions, and also the graphic drafting of particle distributions on arbitrary cross sections

Wear and friction behaviour of soft particles filled random direction short GFRP composites

International Nuclear Information System (INIS)

Srivastava, V.K.; Wahne, S.

2007-01-01

The random direction short E-glass fibre reinforced epoxy resin composites filled with the particles of mica and tricalcium phosphate (TCP) were prepared by hand lay-up method. The wear and friction behaviour of random direction short E-glass fibre reinforced epoxy resin (GFRP) composites sliding against AISI-1045 steel in a pin-on-disc configuration were evaluated on a TR-20LE wear and friction tester. The microhardness, density, tensile strength and compressive strength of the filled and unfilled mica as well as TCP particles were determined. The morphology of the worn surfaces of the unfilled and filled random E-glass fibre composites and the transfer films were analyzed with the scanning electron microscope. It was found that the particles as the fillers contributed significantly to improve the mechanical properties and wear resistance of the E-glass fibre. This was because the particulates as the fillers contributed to enhance the bonding strength between the fibre and the epoxy resin. Moreover, the wear and friction properties of the random E-glass fibre composites were reduced by increasing filler weight of particles

Randomly dispersed particle fuel model in the PSG Monte Carlo neutron transport code

International Nuclear Information System (INIS)

Leppaenen, J.

2007-01-01

High-temperature gas-cooled reactor fuels are composed of thousands of microscopic fuel particles, randomly dispersed in a graphite matrix. The modelling of such geometry is complicated, especially using continuous-energy Monte Carlo codes, which are unable to apply any deterministic corrections in the calculation. This paper presents the geometry routine developed for modelling randomly dispersed particle fuels using the PSG Monte Carlo reactor physics code. The model is based on the delta-tracking method, and it takes into account the spatial self-shielding effects and the random dispersion of the fuel particles. The calculation routine is validated by comparing the results to reference MCNP4C calculations using uranium and plutonium based fuels. (authors)

Evolving Random Forest for Preference Learning

DEFF Research Database (Denmark)

Abou-Zleikha, Mohamed; Shaker, Noor

2015-01-01

This paper introduces a novel approach for pairwise preference learning through a combination of an evolutionary method and random forest. Grammatical evolution is used to describe the structure of the trees in the Random Forest (RF) and to handle the process of evolution. Evolved random forests ...... obtained for predicting pairwise self-reports of users for the three emotional states engagement, frustration and challenge show very promising results that are comparable and in some cases superior to those obtained from state-of-the-art methods....

Diffusion of charged particles in strong large-scale random and regular magnetic fields

International Nuclear Information System (INIS)

Mel'nikov, Yu.P.

2000-01-01

The nonlinear collision integral for the Green's function averaged over a random magnetic field is transformed using an iteration procedure taking account of the strong random scattering of particles on the correlation length of the random magnetic field. Under this transformation the regular magnetic field is assumed to be uniform at distances of the order of the correlation length. The single-particle Green's functions of the scattered particles in the presence of a regular magnetic field are investigated. The transport coefficients are calculated taking account of the broadening of the cyclotron and Cherenkov resonances as a result of strong random scattering. The mean-free path lengths parallel and perpendicular to the regular magnetic field are found for a power-law spectrum of the random field. The analytical results obtained are compared with the experimental data on the transport ranges of solar and galactic cosmic rays in the interplanetary magnetic field. As a result, the conditions for the propagation of cosmic rays in the interplanetary space and a more accurate idea of the structure of the interplanetary magnetic field are determined

Learning of couplings for random asymmetric kinetic Ising models revisited: random correlation matrices and learning curves

International Nuclear Information System (INIS)

Bachschmid-Romano, Ludovica; Opper, Manfred

2015-01-01

We study analytically the performance of a recently proposed algorithm for learning the couplings of a random asymmetric kinetic Ising model from finite length trajectories of the spin dynamics. Our analysis shows the importance of the nontrivial equal time correlations between spins induced by the dynamics for the speed of learning. These correlations become more important as the spin’s stochasticity is decreased. We also analyse the deviation of the estimation error (paper)

Accurate Quasiparticle Spectra from the T-Matrix Self-Energy and the Particle-Particle Random Phase Approximation.

Science.gov (United States)

Zhang, Du; Su, Neil Qiang; Yang, Weitao

2017-07-20

The GW self-energy, especially G 0 W 0 based on the particle-hole random phase approximation (phRPA), is widely used to study quasiparticle (QP) energies. Motivated by the desirable features of the particle-particle (pp) RPA compared to the conventional phRPA, we explore the pp counterpart of GW, that is, the T-matrix self-energy, formulated with the eigenvectors and eigenvalues of the ppRPA matrix. We demonstrate the accuracy of the T-matrix method for molecular QP energies, highlighting the importance of the pp channel for calculating QP spectra.

Symbiosis-Based Alternative Learning Multi-Swarm Particle Swarm Optimization.

Science.gov (United States)

Niu, Ben; Huang, Huali; Tan, Lijing; Duan, Qiqi

2017-01-01

Inspired by the ideas from the mutual cooperation of symbiosis in natural ecosystem, this paper proposes a new variant of PSO, named Symbiosis-based Alternative Learning Multi-swarm Particle Swarm Optimization (SALMPSO). A learning probability to select one exemplar out of the center positions, the local best position, and the historical best position including the experience of internal and external multiple swarms, is used to keep the diversity of the population. Two different levels of social interaction within and between multiple swarms are proposed. In the search process, particles not only exchange social experience with others that are from their own sub-swarms, but also are influenced by the experience of particles from other fellow sub-swarms. According to the different exemplars and learning strategy, this model is instantiated as four variants of SALMPSO and a set of 15 test functions are conducted to compare with some variants of PSO including 10, 30 and 50 dimensions, respectively. Experimental results demonstrate that the alternative learning strategy in each SALMPSO version can exhibit better performance in terms of the convergence speed and optimal values on most multimodal functions in our simulation.

Research on machine learning framework based on random forest algorithm

Science.gov (United States)

Ren, Qiong; Cheng, Hui; Han, Hai

2017-03-01

With the continuous development of machine learning, industry and academia have released a lot of machine learning frameworks based on distributed computing platform, and have been widely used. However, the existing framework of machine learning is limited by the limitations of machine learning algorithm itself, such as the choice of parameters and the interference of noises, the high using threshold and so on. This paper introduces the research background of machine learning framework, and combined with the commonly used random forest algorithm in machine learning classification algorithm, puts forward the research objectives and content, proposes an improved adaptive random forest algorithm (referred to as ARF), and on the basis of ARF, designs and implements the machine learning framework.

Particle swarm optimization with random keys applied to the nuclear reactor reload problem

Energy Technology Data Exchange (ETDEWEB)

Meneses, Anderson Alvarenga de Moura [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE). Programa de Engenharia Nuclear; Fundacao Educacional de Macae (FUNEMAC), RJ (Brazil). Faculdade Professor Miguel Angelo da Silva Santos; Machado, Marcelo Dornellas; Medeiros, Jose Antonio Carlos Canedo; Schirru, Roberto [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia (COPPE). Programa de Engenharia Nuclear]. E-mails: ameneses@con.ufrj.br; marcelo@lmp.ufrj.br; canedo@lmp.ufrj.br; schirru@lmp.ufrj.br

2007-07-01

In 1995, Kennedy and Eberhart presented the Particle Swarm Optimization (PSO), an Artificial Intelligence metaheuristic technique to optimize non-linear continuous functions. The concept of Swarm Intelligence is based on the socials aspects of intelligence, it means, the ability of individuals to learn with their own experience in a group as well as to take advantage of the performance of other individuals. Some PSO models for discrete search spaces have been developed for combinatorial optimization, although none of them presented satisfactory results to optimize a combinatorial problem as the nuclear reactor fuel reloading problem (NRFRP). In this sense, we developed the Particle Swarm Optimization with Random Keys (PSORK) in previous research to solve Combinatorial Problems. Experiences demonstrated that PSORK performed comparable to or better than other techniques. Thus, PSORK metaheuristic is being applied in optimization studies of the NRFRP for Angra 1 Nuclear Power Plant. Results will be compared with Genetic Algorithms and the manual method provided by a specialist. In this experience, the problem is being modeled for an eight-core symmetry and three-dimensional geometry, aiming at the minimization of the Nuclear Enthalpy Power Peaking Factor as well as the maximization of the cycle length. (author)

Particle swarm optimization with random keys applied to the nuclear reactor reload problem

International Nuclear Information System (INIS)

Meneses, Anderson Alvarenga de Moura; Fundacao Educacional de Macae; Machado, Marcelo Dornellas; Medeiros, Jose Antonio Carlos Canedo; Schirru, Roberto

2007-01-01

In 1995, Kennedy and Eberhart presented the Particle Swarm Optimization (PSO), an Artificial Intelligence metaheuristic technique to optimize non-linear continuous functions. The concept of Swarm Intelligence is based on the socials aspects of intelligence, it means, the ability of individuals to learn with their own experience in a group as well as to take advantage of the performance of other individuals. Some PSO models for discrete search spaces have been developed for combinatorial optimization, although none of them presented satisfactory results to optimize a combinatorial problem as the nuclear reactor fuel reloading problem (NRFRP). In this sense, we developed the Particle Swarm Optimization with Random Keys (PSORK) in previous research to solve Combinatorial Problems. Experiences demonstrated that PSORK performed comparable to or better than other techniques. Thus, PSORK metaheuristic is being applied in optimization studies of the NRFRP for Angra 1 Nuclear Power Plant. Results will be compared with Genetic Algorithms and the manual method provided by a specialist. In this experience, the problem is being modeled for an eight-core symmetry and three-dimensional geometry, aiming at the minimization of the Nuclear Enthalpy Power Peaking Factor as well as the maximization of the cycle length. (author)

Strong Shock Propagating Over A Random Bed of Spherical Particles

Science.gov (United States)

Mehta, Yash; Salari, Kambiz; Jackson, Thomas L.; Balachandar, S.; Thakur, Siddharth

2017-11-01

The study of shock interaction with particles has been largely motivated because of its wide-ranging applications. The complex interaction between the compressible flow features, such as shock wave and expansion fan, and the dispersed phase makes this multi-phase flow very difficult to predict and control. In this talk we will be presenting results on fully resolved inviscid simulations of shock interaction with random bed of particles. One of the fascinating observations from these simulations are the flow field fluctuations due to the presence of randomly distributed particles. Rigorous averaging (Favre averaging) of the governing equations results in Reynolds stress like term, which can be classified as pseudo turbulence in this case. We have computed this ``Reynolds stress'' term along with individual fluctuations and the turbulent kinetic energy. Average pressure was also computed to characterize the strength of the transmitted and the reflected waves. This work was supported by the U.S. Department of Energy, National Nuclear Security Administration, Advanced Simulation and Computing Program, as a Cooperative Agreement under the Predictive Science Academic Alliance Program.

Depinning of interacting particles in random media

Science.gov (United States)

Zapperi, Stefano; Andrade, José S., Jr.; Mendes Filho, Josué

2000-06-01

We study the overdamped motion of interacting particles in a random medium using the model introduced by Pla and Nori [Phys. Rev. Lett. 67, 919 (1991)]. We investigate the associated depinning transition by numerical integration of the equation of motion and show evidence that the model is in the same universality class of a driven elastic chain on a rough substrate. We discuss the implications of these results for flux line motion in type-II superconductors.

Leveraging Random Number Generation for Mastery of Learning in Teaching Quantitative Research Courses via an E-Learning Method

Science.gov (United States)

Boonsathorn, Wasita; Charoen, Danuvasin; Dryver, Arthur L.

2014-01-01

E-Learning brings access to a powerful but often overlooked teaching tool: random number generation. Using random number generation, a practically infinite number of quantitative problem-solution sets can be created. In addition, within the e-learning context, in the spirit of the mastery of learning, it is possible to assign online quantitative…

Demagnetization factor for a powder of randomly packed spherical particles

DEFF Research Database (Denmark)

Bjørk, Rasmus; Bahl, Christian R.H.

2013-01-01

The demagnetization factors for randomly packed spherical particle powders with different porosities, sample aspect ratios, and monodisperse, normal, and log-normal particle size distributions have been calculated using a numerical model. For a relative permeability of 2, comparable to room...... temperature Gd, the calculated demagnetization factor is close to the theoretical value. The normalized standard deviation of the magnetization in the powder was 6.0%-6.7%. The demagnetization factor decreased significantly, while the standard deviation of the magnetization increased, for increasing relative...

Patterns of particle distribution in multiparticle systems by random walks with memory enhancement and decay

Science.gov (United States)

Tan, Zhi-Jie; Zou, Xian-Wu; Huang, Sheng-You; Zhang, Wei; Jin, Zhun-Zhi

2002-07-01

We investigate the pattern of particle distribution and its evolution with time in multiparticle systems using the model of random walks with memory enhancement and decay. This model describes some biological intelligent walks. With decrease in the memory decay exponent α, the distribution of particles changes from a random dispersive pattern to a locally dense one, and then returns to the random one. Correspondingly, the fractal dimension Df,p characterizing the distribution of particle positions increases from a low value to a maximum and then decreases to the low one again. This is determined by the degree of overlap of regions consisting of sites with remanent information. The second moment of the density ρ(2) was introduced to investigate the inhomogeneity of the particle distribution. The dependence of ρ(2) on α is similar to that of Df,p on α. ρ(2) increases with time as a power law in the process of adjusting the particle distribution, and then ρ(2) tends to a stable equilibrium value.

Oncology E-Learning for Undergraduate. A Prospective Randomized Controlled Trial.

Science.gov (United States)

da Costa Vieira, René Aloisio; Lopes, Ana Helena; Sarri, Almir José; Benedetti, Zuleica Caulada; de Oliveira, Cleyton Zanardo

2017-06-01

The e-learning education is a promising method, but there are few prospective randomized publications in oncology. The purpose of this study was to assess the level of retention of information in oncology from undergraduate students of physiotherapy. A prospective, controlled, randomized, crossover study, 72 undergraduate students of physiotherapy, from the second to fourth years, were randomized to perform a course of physiotherapy in oncology (PHO) using traditional classroom or e-learning. Students were offered the same content of the subject. The teacher in the traditional classroom model and the e-learning students used the Articulate® software. The course tackled the main issues related to PHO, and it was divided into six modules, 18 lessons, evaluated by 126 questions. A diagnosis evaluation was performed previous to the course and after every module. The sample consisted of 67 students, allocated in groups A (n = 35) and B (n = 32), and the distribution was homogeneous between the groups. Evaluating the correct answers, we observed a limited score in the pre-test (average grade 44.6 %), which has significant (p e-learning, a fact that encourages the use of e-learning in oncology. REBECU1111-1142-1963.

Random neural Q-learning for obstacle avoidance of a mobile robot in unknown environments

Directory of Open Access Journals (Sweden)

Jing Yang

2016-07-01

Full Text Available The article presents a random neural Q-learning strategy for the obstacle avoidance problem of an autonomous mobile robot in unknown environments. In the proposed strategy, two independent modules, namely, avoidance without considering the target and goal-seeking without considering obstacles, are first trained using the proposed random neural Q-learning algorithm to obtain their best control policies. Then, the two trained modules are combined based on a switching function to realize the obstacle avoidance in unknown environments. For the proposed random neural Q-learning algorithm, a single-hidden layer feedforward network is used to approximate the Q-function to estimate the Q-value. The parameters of the single-hidden layer feedforward network are modified using the recently proposed neural algorithm named the online sequential version of extreme learning machine, where the parameters of the hidden nodes are assigned randomly and the sample data can come one by one. However, different from the original online sequential version of extreme learning machine algorithm, the initial output weights are estimated subjected to quadratic inequality constraint to improve the convergence speed. Finally, the simulation results demonstrate that the proposed random neural Q-learning strategy can successfully solve the obstacle avoidance problem. Also, the higher learning efficiency and better generalization ability are achieved by the proposed random neural Q-learning algorithm compared with the Q-learning based on the back-propagation method.

Learning Random Numbers: A Matlab Anomaly

Czech Academy of Sciences Publication Activity Database

Savický, Petr; Robnik-Šikonja, M.

2008-01-01

Roč. 22, č. 3 (2008), s. 254-265 ISSN 0883-9514 R&D Projects: GA AV ČR 1ET100300517 Institutional research plan: CEZ:AV0Z10300504 Keywords : random number s * machine learning * classification * attribute evaluation * regression Subject RIV: BA - General Mathematics Impact factor: 0.795, year: 2008

Pseudo-random number generation for Brownian Dynamics and Dissipative Particle Dynamics simulations on GPU devices

International Nuclear Information System (INIS)

Phillips, Carolyn L.; Anderson, Joshua A.; Glotzer, Sharon C.

2011-01-01

Highlights: → Molecular Dynamics codes implemented on GPUs have achieved two-order of magnitude computational accelerations. → Brownian Dynamics and Dissipative Particle Dynamics simulations require a large number of random numbers per time step. → We introduce a method for generating small batches of pseudorandom numbers distributed over many threads of calculations. → With this method, Dissipative Particle Dynamics is implemented on a GPU device without requiring thread-to-thread communication. - Abstract: Brownian Dynamics (BD), also known as Langevin Dynamics, and Dissipative Particle Dynamics (DPD) are implicit solvent methods commonly used in models of soft matter and biomolecular systems. The interaction of the numerous solvent particles with larger particles is coarse-grained as a Langevin thermostat is applied to individual particles or to particle pairs. The Langevin thermostat requires a pseudo-random number generator (PRNG) to generate the stochastic force applied to each particle or pair of neighboring particles during each time step in the integration of Newton's equations of motion. In a Single-Instruction-Multiple-Thread (SIMT) GPU parallel computing environment, small batches of random numbers must be generated over thousands of threads and millions of kernel calls. In this communication we introduce a one-PRNG-per-kernel-call-per-thread scheme, in which a micro-stream of pseudorandom numbers is generated in each thread and kernel call. These high quality, statistically robust micro-streams require no global memory for state storage, are more computationally efficient than other PRNG schemes in memory-bound kernels, and uniquely enable the DPD simulation method without requiring communication between threads.

Homogenization of metasurfaces formed by random resonant particles in periodical lattices

DEFF Research Database (Denmark)

Andryieuski, Andrei; Lavrinenko, Andrei; Petrov, Mihail

2016-01-01

In this paper we suggest a simple analytical method for description of electromagnetic properties of a geometrically regular two-dimensional subwavelength arrays (metasurfaces) formed by particles with randomly fluctuating polarizabilities. We propose an analytical homogenization method applicable...

Spherical particle Brownian motion in viscous medium as non-Markovian random process

International Nuclear Information System (INIS)

Morozov, Andrey N.; Skripkin, Alexey V.

2011-01-01

The Brownian motion of a spherical particle in an infinite medium is described by the conventional methods and integral transforms considering the entrainment of surrounding particles of the medium by the Brownian particle. It is demonstrated that fluctuations of the Brownian particle velocity represent a non-Markovian random process. The features of Brownian motion in short time intervals and in small displacements are considered. -- Highlights: → Description of Brownian motion considering the entrainment of medium is developed. → We find the equations for statistical characteristics of impulse fluctuations. → Brownian motion at small time intervals is considered. → Theoretical results and experimental data are compared.

Does peer learning or higher levels of e-learning improve learning abilities? A randomized controlled trial.

Science.gov (United States)

Worm, Bjarne Skjødt; Jensen, Kenneth

2013-01-01

Background and aims The fast development of e-learning and social forums demands us to update our understanding of e-learning and peer learning. We aimed to investigate if higher, pre-defined levels of e-learning or social interaction in web forums improved students' learning ability. Methods One hundred and twenty Danish medical students were randomized to six groups all with 20 students (eCases level 1, eCases level 2, eCases level 2+, eTextbook level 1, eTextbook level 2, and eTextbook level 2+). All students participated in a pre-test, Group 1 participated in an interactive case-based e-learning program, while Group 2 was presented with textbook material electronically. The 2+ groups were able to discuss the material between themselves in a web forum. The subject was head injury and associated treatment and observation guidelines in the emergency room. Following the e-learning, all students completed a post-test. Pre- and post-tests both consisted of 25 questions randomly chosen from a pool of 50 different questions. Results All students concluded the study with comparable pre-test results. Students at Level 2 (in both groups) improved statistically significant compared to students at level 1 (p>0.05). There was no statistically significant difference between level 2 and level 2+. However, level 2+ was associated with statistically significant greater student's satisfaction than the rest of the students (p>0.05). Conclusions This study applies a new way of comparing different types of e-learning using a pre-defined level division and the possibility of peer learning. Our findings show that higher levels of e-learning does in fact provide better results when compared with the same type of e-learning at lower levels. While social interaction in web forums increase student satisfaction, learning ability does not seem to change. Both findings are relevant when designing new e-learning materials.

Does peer learning or higher levels of e-learning improve learning abilities? A randomized controlled trial

Science.gov (United States)

Worm, Bjarne Skjødt; Jensen, Kenneth

2013-01-01

Background and aims The fast development of e-learning and social forums demands us to update our understanding of e-learning and peer learning. We aimed to investigate if higher, pre-defined levels of e-learning or social interaction in web forums improved students’ learning ability. Methods One hundred and twenty Danish medical students were randomized to six groups all with 20 students (eCases level 1, eCases level 2, eCases level 2+, eTextbook level 1, eTextbook level 2, and eTextbook level 2+). All students participated in a pre-test, Group 1 participated in an interactive case-based e-learning program, while Group 2 was presented with textbook material electronically. The 2+ groups were able to discuss the material between themselves in a web forum. The subject was head injury and associated treatment and observation guidelines in the emergency room. Following the e-learning, all students completed a post-test. Pre- and post-tests both consisted of 25 questions randomly chosen from a pool of 50 different questions. Results All students concluded the study with comparable pre-test results. Students at Level 2 (in both groups) improved statistically significant compared to students at level 1 (p>0.05). There was no statistically significant difference between level 2 and level 2+. However, level 2+ was associated with statistically significant greater student's satisfaction than the rest of the students (p>0.05). Conclusions This study applies a new way of comparing different types of e-learning using a pre-defined level division and the possibility of peer learning. Our findings show that higher levels of e-learning does in fact provide better results when compared with the same type of e-learning at lower levels. While social interaction in web forums increase student satisfaction, learning ability does not seem to change. Both findings are relevant when designing new e-learning materials. PMID:24229729

Does peer learning or higher levels of e-learning improve learning abilities? A randomized controlled trial

Directory of Open Access Journals (Sweden)

Bjarne Skjødt Worm

2013-11-01

Full Text Available Background and aims : The fast development of e-learning and social forums demands us to update our understanding of e-learning and peer learning. We aimed to investigate if higher, pre-defined levels of e-learning or social interaction in web forums improved students’ learning ability. Methods : One hundred and twenty Danish medical students were randomized to six groups all with 20 students (eCases level 1, eCases level 2, eCases level 2+, eTextbook level 1, eTextbook level 2, and eTextbook level 2+. All students participated in a pre-test, Group 1 participated in an interactive case-based e-learning program, while Group 2 was presented with textbook material electronically. The 2+ groups were able to discuss the material between themselves in a web forum. The subject was head injury and associated treatment and observation guidelines in the emergency room. Following the e-learning, all students completed a post-test. Pre- and post-tests both consisted of 25 questions randomly chosen from a pool of 50 different questions. Results : All students concluded the study with comparable pre-test results. Students at Level 2 (in both groups improved statistically significant compared to students at level 1 (p>0.05. There was no statistically significant difference between level 2 and level 2+. However, level 2+ was associated with statistically significant greater student's satisfaction than the rest of the students (p>0.05. Conclusions : This study applies a new way of comparing different types of e-learning using a pre-defined level division and the possibility of peer learning. Our findings show that higher levels of e-learning does in fact provide better results when compared with the same type of e-learning at lower levels. While social interaction in web forums increase student satisfaction, learning ability does not seem to change. Both findings are relevant when designing new e-learning materials.

Tensor hypercontracted ppRPA: Reducing the cost of the particle-particle random phase approximation from O(r 6) to O(r 4)

International Nuclear Information System (INIS)

Shenvi, Neil; Yang, Yang; Yang, Weitao; Aggelen, Helen van

2014-01-01

In recent years, interest in the random-phase approximation (RPA) has grown rapidly. At the same time, tensor hypercontraction has emerged as an intriguing method to reduce the computational cost of electronic structure algorithms. In this paper, we combine the particle-particle random phase approximation with tensor hypercontraction to produce the tensor-hypercontracted particle-particle RPA (THC-ppRPA) algorithm. Unlike previous implementations of ppRPA which scale as O(r 6 ), the THC-ppRPA algorithm scales asymptotically as only O(r 4 ), albeit with a much larger prefactor than the traditional algorithm. We apply THC-ppRPA to several model systems and show that it yields the same results as traditional ppRPA to within mH accuracy. Our method opens the door to the development of post-Kohn Sham functionals based on ppRPA without the excessive asymptotic cost of traditional ppRPA implementations

A Particle Swarm Optimization Variant with an Inner Variable Learning Strategy

Directory of Open Access Journals (Sweden)

Guohua Wu

2014-01-01

Full Text Available Although Particle Swarm Optimization (PSO has demonstrated competitive performance in solving global optimization problems, it exhibits some limitations when dealing with optimization problems with high dimensionality and complex landscape. In this paper, we integrate some problem-oriented knowledge into the design of a certain PSO variant. The resulting novel PSO algorithm with an inner variable learning strategy (PSO-IVL is particularly efficient for optimizing functions with symmetric variables. Symmetric variables of the optimized function have to satisfy a certain quantitative relation. Based on this knowledge, the inner variable learning (IVL strategy helps the particle to inspect the relation among its inner variables, determine the exemplar variable for all other variables, and then make each variable learn from the exemplar variable in terms of their quantitative relations. In addition, we design a new trap detection and jumping out strategy to help particles escape from local optima. The trap detection operation is employed at the level of individual particles whereas the trap jumping out strategy is adaptive in its nature. Experimental simulations completed for some representative optimization functions demonstrate the excellent performance of PSO-IVL. The effectiveness of the PSO-IVL stresses a usefulness of augmenting evolutionary algorithms by problem-oriented domain knowledge.

Comparing Exploration Strategies for Q-learning in Random Stochastic Mazes

NARCIS (Netherlands)

Tijsma, Arryon; Drugan, Madalina; Wiering, Marco

2016-01-01

Balancing the ratio between exploration and exploitation is an important problem in reinforcement learning. This paper evaluates four different exploration strategies combined with Q-learning using random stochastic mazes to investigate their performances. We will compare: UCB-1, softmax,

Effect of magnetic helicity upon rectilinear propagation of charged particles in random magnetic fields

Science.gov (United States)

Earl, James A.

1992-01-01

When charged particles spiral along a large constant magnetic field, their trajectories are scattered by any random field components that are superposed on the guiding field. If the random field configuration embodies helicity, the scattering is asymmetrical with respect to a plane perpendicular to the guiding field, for particles moving into the forward hemisphere are scattered at different rates from those moving into the backward hemisphere. This asymmetry gives rise to new terms in the transport equations that describe propagation of charged particles. Helicity has virtually no impact on qualitative features of the diffusive mode of propagation. However, characteristic velocities of the coherent modes that appear after a highly anisotropic injection exhibit an asymmetry related to helicity. Explicit formulas, which embody the effects of helicity, are given for the anisotropies, the coefficient diffusion, and the coherent velocities. Predictions derived from these expressions are in good agreement with Monte Carlo simulations of particle transport, but the simulations reveal certain phenomena whose explanation calls for further analytical work.

Pseudo-random number generation for Brownian Dynamics and Dissipative Particle Dynamics simulations on GPU devices

Science.gov (United States)

Phillips, Carolyn L.; Anderson, Joshua A.; Glotzer, Sharon C.

2011-08-01

Brownian Dynamics (BD), also known as Langevin Dynamics, and Dissipative Particle Dynamics (DPD) are implicit solvent methods commonly used in models of soft matter and biomolecular systems. The interaction of the numerous solvent particles with larger particles is coarse-grained as a Langevin thermostat is applied to individual particles or to particle pairs. The Langevin thermostat requires a pseudo-random number generator (PRNG) to generate the stochastic force applied to each particle or pair of neighboring particles during each time step in the integration of Newton's equations of motion. In a Single-Instruction-Multiple-Thread (SIMT) GPU parallel computing environment, small batches of random numbers must be generated over thousands of threads and millions of kernel calls. In this communication we introduce a one-PRNG-per-kernel-call-per-thread scheme, in which a micro-stream of pseudorandom numbers is generated in each thread and kernel call. These high quality, statistically robust micro-streams require no global memory for state storage, are more computationally efficient than other PRNG schemes in memory-bound kernels, and uniquely enable the DPD simulation method without requiring communication between threads.

Toward single-mode random lasing within a submicrometre-sized spherical ZnO particle film

International Nuclear Information System (INIS)

Niyuki, Ryo; Fujiwara, Hideki; Sasaki, Keiji; Ishikawa, Yoshie; Koshizaki, Naoto; Tsuji, Takeshi

2016-01-01

We had recently reported unique random laser action such as quasi-single-mode and low-threshold lasing from a submicrometre-sized spherical ZnO nanoparticle film with polymer particles as defects. The present study demonstrates a novel approach to realize single-mode random lasing by adjusting the sizes of the defect particles. From the dependence of random lasing properties on defect size, we find that the average number of lasing peaks can be modified by the defect size, while other lasing properties such as lasing wavelengths and thresholds remain unchanged. These results suggest that lasing wavelengths and thresholds are determined by the resonant properties of the surrounding scatterers, while the defect size stochastically determines the number of lasing peaks. Therefore, if we optimize the sizes of the defects and scatterers, we can intentionally induce single-mode lasing even in a random structure (Fujiwara et al 2013 Appl. Phys. Lett. 102 061110). (paper)

Random set particle filter for bearings-only multitarget tracking

Science.gov (United States)

Vihola, Matti

2005-05-01

The random set approach to multitarget tracking is a theoretically sound framework that covers joint estimation of the number of targets and the state of the targets. This paper describes a particle filter implementation of the random set multitarget filter. The contribution of this paper to the random set tracking framework is the formulation of a measurement model where each sensor report is assumed to contain at most one measurement. The implemented filter was tested in synthetic bearings-only tracking scenarios containing up to two targets in the presence of false alarms and missed measurements. The estimated target state consisted of 2D position and velocity components. The filter was capable to track the targets fairly well despite of the missing measurements and the relatively high false alarm rates. In addition, the filter showed robustness against wrong parameter values of false alarm rates. The results that were obtained during the limited tests of the filter show that the random set framework has potential for challenging tracking situations. On the other hand, the computational burden of the described implementation is quite high and increases approximately linearly with respect to the expected number of targets.

Remediating radium contaminated legacy sites: Advances made through machine learning in routine monitoring of “hot” particles

Energy Technology Data Exchange (ETDEWEB)

Varley, Adam, E-mail: a.l.varley@stir.ac.uk [Department of Biological and Environmental Sciences, University of Stirling, Stirling FK9 4LA (United Kingdom); Tyler, Andrew, E-mail: a.n.tyler@stir.ac.uk [Department of Biological and Environmental Sciences, University of Stirling, Stirling FK9 4LA (United Kingdom); Smith, Leslie, E-mail: l.s.smith@cs.stir.ac.uk [Department of Computing Science and Mathematics, University of Stirling, Stirling FK9 4LA (United Kingdom); Dale, Paul, E-mail: paul.dale@sepa.org.uk [Scottish Environmental Protection Agency, Radioactive Substances, Strathallan House, Castle Business Park, Stirling FK9 4TZ (United Kingdom); Davies, Mike, E-mail: Mike.Davies@nuvia.co.uk [Nuvia Limited, The Library, Eight Street, Harwell Oxford, Didcot, Oxfordshire OX11 0RL (United Kingdom)

2015-07-15

The extensive use of radium during the 20th century for industrial, military and pharmaceutical purposes has led to a large number of contaminated legacy sites across Europe and North America. Sites that pose a high risk to the general public can present expensive and long-term remediation projects. Often the most pragmatic remediation approach is through routine monitoring operating gamma-ray detectors to identify, in real-time, the signal from the most hazardous heterogeneous contamination (hot particles); thus facilitating their removal and safe disposal. However, current detection systems do not fully utilise all spectral information resulting in low detection rates and ultimately an increased risk to the human health. The aim of this study was to establish an optimised detector-algorithm combination. To achieve this, field data was collected using two handheld detectors (sodium iodide and lanthanum bromide) and a number of Monte Carlo simulated hot particles were randomly injected into the field data. This allowed for the detection rate of conventional deterministic (gross counts) and machine learning (neural networks and support vector machines) algorithms to be assessed. The results demonstrated that a Neural Network operated on a sodium iodide detector provided the best detection capability. Compared to deterministic approaches, this optimised detection system could detect a hot particle on average 10 cm deeper into the soil column or with half of the activity at the same depth. It was also found that noise presented by internal contamination restricted lanthanum bromide for this application. - Highlights: • Land contaminated with radium is hazardous to human health. • Routine monitoring permits identification and removal of radioactive hot particles. • Current alarm algorithms do not provide reliable hot particle detection. • Spectral processing using Machine Learning significantly improves detection.

Remediating radium contaminated legacy sites: Advances made through machine learning in routine monitoring of “hot” particles

International Nuclear Information System (INIS)

Varley, Adam; Tyler, Andrew; Smith, Leslie; Dale, Paul; Davies, Mike

2015-01-01

The extensive use of radium during the 20th century for industrial, military and pharmaceutical purposes has led to a large number of contaminated legacy sites across Europe and North America. Sites that pose a high risk to the general public can present expensive and long-term remediation projects. Often the most pragmatic remediation approach is through routine monitoring operating gamma-ray detectors to identify, in real-time, the signal from the most hazardous heterogeneous contamination (hot particles); thus facilitating their removal and safe disposal. However, current detection systems do not fully utilise all spectral information resulting in low detection rates and ultimately an increased risk to the human health. The aim of this study was to establish an optimised detector-algorithm combination. To achieve this, field data was collected using two handheld detectors (sodium iodide and lanthanum bromide) and a number of Monte Carlo simulated hot particles were randomly injected into the field data. This allowed for the detection rate of conventional deterministic (gross counts) and machine learning (neural networks and support vector machines) algorithms to be assessed. The results demonstrated that a Neural Network operated on a sodium iodide detector provided the best detection capability. Compared to deterministic approaches, this optimised detection system could detect a hot particle on average 10 cm deeper into the soil column or with half of the activity at the same depth. It was also found that noise presented by internal contamination restricted lanthanum bromide for this application. - Highlights: • Land contaminated with radium is hazardous to human health. • Routine monitoring permits identification and removal of radioactive hot particles. • Current alarm algorithms do not provide reliable hot particle detection. • Spectral processing using Machine Learning significantly improves detection

Exclusive many-particle diffusion in disordered media and correlation functions for random vertex models

International Nuclear Information System (INIS)

Schuetz, G.; Sandow, S.

1993-05-01

We consider systems of particles hopping stochastically on d-dimensional lattices with space-dependent probabilities. We map the master equation in a Fock space where the dynamics are given by a quantum Hamiltonian (continuous time) or a transfer matrix resp. (discrete time). We show that under certain conditions the time-dependent two-point density correlation function in N-particle steady state can be computed from the probability distribution of a single particle moving in the same environment. Focussing on exclusion models where the lattice site can be occupied by at most one particle we discuss as an example for such a stochastic process a generalized Heisenberg antiferromagnet where the strength of the spin-spin coupling in space-dependent. In discrete time one obtains for one dimensional systems the diagonal-to-diagonal transfer matrix of the two dimensional six vertex model with space dependent vertex weights. For a random distribution of the vertex weights one obtains a version of the random barrier model describing diffusion of particles in disordered media. We derive exact expressions for the average two-point density correlation function in the presence of weak, correlated disorder. (authors)

Tensor hypercontracted ppRPA: Reducing the cost of the particle-particle random phase approximation from O(r {sup 6}) to O(r {sup 4})

Energy Technology Data Exchange (ETDEWEB)

Shenvi, Neil; Yang, Yang; Yang, Weitao [Department of Chemistry, Duke University, Durham, NC 27708 (United States); Aggelen, Helen van [Department of Chemistry, Princeton University, Princeton, New Jersey 08544 (United States)

2014-07-14

In recent years, interest in the random-phase approximation (RPA) has grown rapidly. At the same time, tensor hypercontraction has emerged as an intriguing method to reduce the computational cost of electronic structure algorithms. In this paper, we combine the particle-particle random phase approximation with tensor hypercontraction to produce the tensor-hypercontracted particle-particle RPA (THC-ppRPA) algorithm. Unlike previous implementations of ppRPA which scale as O(r{sup 6}), the THC-ppRPA algorithm scales asymptotically as only O(r{sup 4}), albeit with a much larger prefactor than the traditional algorithm. We apply THC-ppRPA to several model systems and show that it yields the same results as traditional ppRPA to within mH accuracy. Our method opens the door to the development of post-Kohn Sham functionals based on ppRPA without the excessive asymptotic cost of traditional ppRPA implementations.

Analysis in nuclear power accident emergency based on random network and particle swarm optimization

International Nuclear Information System (INIS)

Gong Dichen; Fang Fang; Ding Weicheng; Chen Zhi

2014-01-01

The GERT random network model of nuclear power accident emergency was built in this paper, and the intelligent computation was combined with the random network based on the analysis of Fukushima nuclear accident in Japan. The emergency process was divided into the series link and parallel link, and the parallel link was the part of series link. The overall allocation of resources was firstly optimized, and then the parallel link was analyzed. The effect of the resources for emergency used in different links was analyzed, and it was put forward that the corresponding particle velocity vector was limited under the condition of limited emergency resources. The resource-constrained particle swarm optimization was obtained by using velocity projection matrix to correct the motion of particles. The optimized allocation of resources in emergency process was obtained and the time consumption of nuclear power accident emergency was reduced. (authors)

Generative Learning Objects Instantiated with Random Numbers Based Expressions

Directory of Open Access Journals (Sweden)

Ciprian Bogdan Chirila

2015-12-01

Full Text Available The development of interactive e-learning content requires special skills like programming techniques, web integration, graphic design etc. Generally, online educators do not possess such skills and their e-learning products tend to be static like presentation slides and textbooks. In this paper we propose a new interactive model of generative learning objects as a compromise betweenstatic, dull materials and dynamic, complex software e-learning materials developed by specialized teams. We find that random numbers based automatic initialization learning objects increases content diversity, interactivity thus enabling learners’ engagement. The resulted learning object model is at a limited level of complexity related to special e-learning software, intuitive and capable of increasing learners’ interactivity, engagement and motivation through dynamic content. The approach was applied successfully on several computer programing disciplines.

Random-subset fitting of digital holograms for fast three-dimensional particle tracking [invited].

Science.gov (United States)

Dimiduk, Thomas G; Perry, Rebecca W; Fung, Jerome; Manoharan, Vinothan N

2014-09-20

Fitting scattering solutions to time series of digital holograms is a precise way to measure three-dimensional dynamics of microscale objects such as colloidal particles. However, this inverse-problem approach is computationally expensive. We show that the computational time can be reduced by an order of magnitude or more by fitting to a random subset of the pixels in a hologram. We demonstrate our algorithm on experimentally measured holograms of micrometer-scale colloidal particles, and we show that 20-fold increases in speed, relative to fitting full frames, can be attained while introducing errors in the particle positions of 10 nm or less. The method is straightforward to implement and works for any scattering model. It also enables a parallelization strategy wherein random-subset fitting is used to quickly determine initial guesses that are subsequently used to fit full frames in parallel. This approach may prove particularly useful for studying rare events, such as nucleation, that can only be captured with high frame rates over long times.

Randomized Algorithms for Scalable Machine Learning

OpenAIRE

Kleiner, Ariel Jacob

2012-01-01

Many existing procedures in machine learning and statistics are computationally intractable in the setting of large-scale data. As a result, the advent of rapidly increasing dataset sizes, which should be a boon yielding improved statistical performance, instead severely blunts the usefulness of a variety of existing inferential methods. In this work, we use randomness to ameliorate this lack of scalability by reducing complex, computationally difficult inferential problems to larger sets o...

Benchmark tests and spin adaptation for the particle-particle random phase approximation

Energy Technology Data Exchange (ETDEWEB)

Yang, Yang; Steinmann, Stephan N.; Peng, Degao [Department of Chemistry, Duke University, Durham, North Carolina 27708 (United States); Aggelen, Helen van, E-mail: Helen.VanAggelen@UGent.be [Department of Chemistry, Duke University, Durham, North Carolina 27708 (United States); Department of Inorganic and Physical Chemistry, Ghent University, 9000 Ghent (Belgium); Yang, Weitao, E-mail: Weitao.Yang@duke.edu [Department of Chemistry and Department of Physics, Duke University, Durham, North Carolina 27708 (United States)

2013-11-07

The particle-particle random phase approximation (pp-RPA) provides an approximation to the correlation energy in density functional theory via the adiabatic connection [H. van Aggelen, Y. Yang, and W. Yang, Phys. Rev. A 88, 030501 (2013)]. It has virtually no delocalization error nor static correlation error for single-bond systems. However, with its formal O(N{sup 6}) scaling, the pp-RPA is computationally expensive. In this paper, we implement a spin-separated and spin-adapted pp-RPA algorithm, which reduces the computational cost by a substantial factor. We then perform benchmark tests on the G2/97 enthalpies of formation database, DBH24 reaction barrier database, and four test sets for non-bonded interactions (HB6/04, CT7/04, DI6/04, and WI9/04). For the G2/97 database, the pp-RPA gives a significantly smaller mean absolute error (8.3 kcal/mol) than the direct particle-hole RPA (ph-RPA) (22.7 kcal/mol). Furthermore, the error in the pp-RPA is nearly constant with the number of atoms in a molecule, while the error in the ph-RPA increases. For chemical reactions involving typical organic closed-shell molecules, pp- and ph-RPA both give accurate reaction energies. Similarly, both RPAs perform well for reaction barriers and nonbonded interactions. These results suggest that the pp-RPA gives reliable energies in chemical applications. The adiabatic connection formalism based on pairing matrix fluctuation is therefore expected to lead to widely applicable and accurate density functionals.

Monte Carlo learning/biasing experiment with intelligent random numbers

International Nuclear Information System (INIS)

Booth, T.E.

1985-01-01

A Monte Carlo learning and biasing technique is described that does its learning and biasing in the random number space rather than the physical phase-space. The technique is probably applicable to all linear Monte Carlo problems, but no proof is provided here. Instead, the technique is illustrated with a simple Monte Carlo transport problem. Problems encountered, problems solved, and speculations about future progress are discussed. 12 refs

A new logistic dynamic particle swarm optimization algorithm based on random topology.

Science.gov (United States)

Ni, Qingjian; Deng, Jianming

2013-01-01

Population topology of particle swarm optimization (PSO) will directly affect the dissemination of optimal information during the evolutionary process and will have a significant impact on the performance of PSO. Classic static population topologies are usually used in PSO, such as fully connected topology, ring topology, star topology, and square topology. In this paper, the performance of PSO with the proposed random topologies is analyzed, and the relationship between population topology and the performance of PSO is also explored from the perspective of graph theory characteristics in population topologies. Further, in a relatively new PSO variant which named logistic dynamic particle optimization, an extensive simulation study is presented to discuss the effectiveness of the random topology and the design strategies of population topology. Finally, the experimental data are analyzed and discussed. And about the design and use of population topology on PSO, some useful conclusions are proposed which can provide a basis for further discussion and research.

Active Learning Not Associated with Student Learning in a Random Sample of College Biology Courses

Science.gov (United States)

Andrews, T. M.; Leonard, M. J.; Colgrove, C. A.; Kalinowski, S. T.

2011-01-01

Previous research has suggested that adding active learning to traditional college science lectures substantially improves student learning. However, this research predominantly studied courses taught by science education researchers, who are likely to have exceptional teaching expertise. The present study investigated introductory biology courses randomly selected from a list of prominent colleges and universities to include instructors representing a broader population. We examined the relationship between active learning and student learning in the subject area of natural selection. We found no association between student learning gains and the use of active-learning instruction. Although active learning has the potential to substantially improve student learning, this research suggests that active learning, as used by typical college biology instructors, is not associated with greater learning gains. We contend that most instructors lack the rich and nuanced understanding of teaching and learning that science education researchers have developed. Therefore, active learning as designed and implemented by typical college biology instructors may superficially resemble active learning used by education researchers, but lacks the constructivist elements necessary for improving learning. PMID:22135373

Machine learning based global particle indentification algorithms at LHCb experiment

CERN Multimedia

Derkach, Denis; Likhomanenko, Tatiana; Rogozhnikov, Aleksei; Ratnikov, Fedor

2017-01-01

One of the most important aspects of data processing at LHC experiments is the particle identification (PID) algorithm. In LHCb, several different sub-detector systems provide PID information: the Ring Imaging CHerenkov (RICH) detector, the hadronic and electromagnetic calorimeters, and the muon chambers. To improve charged particle identification, several neural networks including a deep architecture and gradient boosting have been applied to data. These new approaches provide higher identification efficiencies than existing implementations for all charged particle types. It is also necessary to achieve a flat dependency between efficiencies and spectator variables such as particle momentum, in order to reduce systematic uncertainties during later stages of data analysis. For this purpose, "flat” algorithms that guarantee the flatness property for efficiencies have also been developed. This talk presents this new approach based on machine learning and its performance.

Comparative study of random and uniform models for the distribution of TRISO particles in HTR-10 fuel elements

International Nuclear Information System (INIS)

Rosales, J.; Perez, J.; Garcia, C.; Munnoz, A.; Lira, C. A. B. O.

2015-01-01

TRISO particles are the specific features of HTR-10 and generally HTGR reactors. Their heterogeneity and random arrangement in graphite matrix of these reactors create a significant modeling challenge. In the simulation of spherical fuel elements using MCNPX are usually created repetitive structures using uniform distribution models. The use of these repetitive structures introduces two major approaches: the non-randomness of the TRISO particles inside the pebbles and the intersection of the pebble surface with the TRISO particles. These approaches could affect significantly the multiplicative properties of the core. In order to study the influence of these approaches in the multiplicative properties was estimated the K inf value in one pebble with white boundary conditions using 4 different configurations regarding the distribution of the TRISO particles inside the pebble: uniform hexagonal model, cubic uniform model, cubic uniform without the effect of cutting and a random distribution model. It was studied the impact these models on core scale solving the problem B1, from the Benchmark Problems presented in a Coordinated Research Program of the IAEA. (Author)

DeepPicker: A deep learning approach for fully automated particle picking in cryo-EM.

Science.gov (United States)

Wang, Feng; Gong, Huichao; Liu, Gaochao; Li, Meijing; Yan, Chuangye; Xia, Tian; Li, Xueming; Zeng, Jianyang

2016-09-01

Particle picking is a time-consuming step in single-particle analysis and often requires significant interventions from users, which has become a bottleneck for future automated electron cryo-microscopy (cryo-EM). Here we report a deep learning framework, called DeepPicker, to address this problem and fill the current gaps toward a fully automated cryo-EM pipeline. DeepPicker employs a novel cross-molecule training strategy to capture common features of particles from previously-analyzed micrographs, and thus does not require any human intervention during particle picking. Tests on the recently-published cryo-EM data of three complexes have demonstrated that our deep learning based scheme can successfully accomplish the human-level particle picking process and identify a sufficient number of particles that are comparable to those picked manually by human experts. These results indicate that DeepPicker can provide a practically useful tool to significantly reduce the time and manual effort spent in single-particle analysis and thus greatly facilitate high-resolution cryo-EM structure determination. DeepPicker is released as an open-source program, which can be downloaded from https://github.com/nejyeah/DeepPicker-python. Copyright © 2016 Elsevier Inc. All rights reserved.

Randomized random walk on a random walk

International Nuclear Information System (INIS)

Lee, P.A.

1983-06-01

This paper discusses generalizations of the model introduced by Kehr and Kunter of the random walk of a particle on a one-dimensional chain which in turn has been constructed by a random walk procedure. The superimposed random walk is randomised in time according to the occurrences of a stochastic point process. The probability of finding the particle in a particular position at a certain instant is obtained explicitly in the transform domain. It is found that the asymptotic behaviour for large time of the mean-square displacement of the particle depends critically on the assumed structure of the basic random walk, giving a diffusion-like term for an asymmetric walk or a square root law if the walk is symmetric. Many results are obtained in closed form for the Poisson process case, and these agree with those given previously by Kehr and Kunter. (author)

A New Class of Particle Filters for Random Dynamic Systems with Unknown Statistics

Directory of Open Access Journals (Sweden)

Joaquín Míguez

2004-11-01

Full Text Available In recent years, particle filtering has become a powerful tool for tracking signals and time-varying parameters of random dynamic systems. These methods require a mathematical representation of the dynamics of the system evolution, together with assumptions of probabilistic models. In this paper, we present a new class of particle filtering methods that do not assume explicit mathematical forms of the probability distributions of the noise in the system. As a consequence, the proposed techniques are simpler, more robust, and more flexible than standard particle filters. Apart from the theoretical development of specific methods in the new class, we provide computer simulation results that demonstrate the performance of the algorithms in the problem of autonomous positioning of a vehicle in a 2-dimensional space.

Multiswarm comprehensive learning particle swarm optimization for solving multiobjective optimization problems.

Science.gov (United States)

Yu, Xiang; Zhang, Xueqing

2017-01-01

Comprehensive learning particle swarm optimization (CLPSO) is a powerful state-of-the-art single-objective metaheuristic. Extending from CLPSO, this paper proposes multiswarm CLPSO (MSCLPSO) for multiobjective optimization. MSCLPSO involves multiple swarms, with each swarm associated with a separate original objective. Each particle's personal best position is determined just according to the corresponding single objective. Elitists are stored externally. MSCLPSO differs from existing multiobjective particle swarm optimizers in three aspects. First, each swarm focuses on optimizing the associated objective using CLPSO, without learning from the elitists or any other swarm. Second, mutation is applied to the elitists and the mutation strategy appropriately exploits the personal best positions and elitists. Third, a modified differential evolution (DE) strategy is applied to some extreme and least crowded elitists. The DE strategy updates an elitist based on the differences of the elitists. The personal best positions carry useful information about the Pareto set, and the mutation and DE strategies help MSCLPSO discover the true Pareto front. Experiments conducted on various benchmark problems demonstrate that MSCLPSO can find nondominated solutions distributed reasonably over the true Pareto front in a single run.

Localization of a polymer in random media: Relation to the localization of a quantum particle

International Nuclear Information System (INIS)

Shiferaw, Yohannes; Goldschmidt, Yadin Y.

2001-01-01

In this paper we consider in detail the connection between the problem of a polymer in a random medium and that of a quantum particle in a random potential. We are interested in a system of finite volume where the polymer is known to be localized inside a low minimum of the potential. We show how the end-to-end distance of a polymer that is free to move can be obtained from the density of states of the quantum particle using extreme value statistics. We give a physical interpretation to the recently discovered one-step replica-symmetry-breaking solution for the polymer [Phys. Rev. E 61, 1729 (2000)] in terms of the statistics of localized tail states. Numerical solutions of the variational equations for chains of different length are performed and compared with quenched averages computed directly by using the eigenfunctions and eigenenergies of the Schro''dinger equation for a particle in a one-dimensional random potential. The quantities investigated are the radius of gyration of a free Gaussian chain, its mean square distance from the origin and the end-to-end distance of a tethered chain. The probability distribution for the position of the chain is also investigated. The glassiness of the system is explained and is estimated from the variance of the measured quantities

A Novel Path Planning for Robots Based on Rapidly-Exploring Random Tree and Particle Swarm Optimizer Algorithm

Directory of Open Access Journals (Sweden)

Zhou Feng

2013-09-01

Full Text Available A based on Rapidly-exploring Random Tree(RRT and Particle Swarm Optimizer (PSO for path planning of the robot is proposed.First the grid method is built to describe the working space of the mobile robot,then the Rapidly-exploring Random Tree algorithm is used to obtain the global navigation path,and the Particle Swarm Optimizer algorithm is adopted to get the better path.Computer experiment results demonstrate that this novel algorithm can plan an optimal path rapidly in a cluttered environment.The successful obstacle avoidance is achieved,and the model is robust and performs reliably.

Effect of particle size distribution on permeability in the randomly packed porous media

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Markicevic, Bojan

2017-11-01

An answer of how porous medium heterogeneity influences the medium permeability is still inconclusive, where both increase and decrease in the permeability value are reported. A numerical procedure is used to generate a randomly packed porous material consisting of spherical particles. Six different particle size distributions are used including mono-, bi- and three-disperse particles, as well as uniform, normal and log-normal particle size distribution with the maximum to minimum particle size ratio ranging from three to eight for different distributions. In all six cases, the average particle size is kept the same. For all media generated, the stochastic homogeneity is checked from distribution of three coordinates of particle centers, where uniform distribution of x-, y- and z- positions is found. The medium surface area remains essentially constant except for bi-modal distribution in which medium area decreases, while no changes in the porosity are observed (around 0.36). The fluid flow is solved in such domain, and after checking for the pressure axial linearity, the permeability is calculated from the Darcy law. The permeability comparison reveals that the permeability of the mono-disperse medium is smallest, and the permeability of all poly-disperse samples is less than ten percent higher. For bi-modal particles, the permeability is for a quarter higher compared to the other media which can be explained by volumetric contribution of larger particles and larger passages for fluid flow to take place.

Shear elastic modulus of magnetic gels with random distribution of magnetizable particles

Science.gov (United States)

Iskakova, L. Yu; Zubarev, A. Yu

2017-04-01

Magnetic gels present new type of composite materials with rich set of uniquie physical properties, which find active applications in many industrial and bio-medical technologies. We present results of mathematically strict theoretical study of elastic modulus of these systems with randomly distributed magnetizable particles in an elastic medium. The results show that an external magnetic field can pronouncedly increase the shear modulus of these composites.

Instanton Approach to the Langevin Motion of a Particle in a Random Potential

International Nuclear Information System (INIS)

Lopatin, A. V.; Vinokur, V. M.

2001-01-01

We develop an instanton approach to the nonequilibrium dynamics in one-dimensional random environments. The long time behavior is controlled by rare fluctuations of the disorder potential and, accordingly, by the tail of the distribution function for the time a particle needs to propagate along the system (the delay time). The proposed method allows us to find the tail of the delay time distribution function and delay time moments, providing thus an exact description of the long time dynamics. We analyze arbitrary environments covering different types of glassy dynamics: dynamics in a short-range random field, creep, and Sinai's motion

First-order reversal curves of single domain particles: diluted random assemblages and chains

Science.gov (United States)

Egli, R.

2009-04-01

Exact magnetic models can be used to calculate first-order reversal curves (FORC) of single domain (SD) particle assemblages, as shown by Newell [2005] for the case of isolated Stoner-Wohlfarth particles. After overcoming experimental difficulties, a FORC diagram sharing many similarities to Newell's model has been measured on a lake sediment sample (see A.P. Chen et al., "Quantification of magnetofossils using first-order reversal curves", EGU General Assembly 2009, Abstracts Vol. 11, EGU2009-10719). This sample contains abundant magnetofossils, as shown by coercivity analysis and electron microscopy, therefore suggesting that well dispersed, intact magnetosome chains are the main SD carriers. Subtle differences between the reversible and the irreversible contributions of the measured FORC distribution suggest that magnetosome chains might not be correctly described by the Stoner-Wohlfarth model. To better understand the hysteresis properties of such chains, a simple magnetic model has been implemented, taking dipole-dipole interactions between particles within the same chain into account. The model results depend on the magnetosome elongation, the number of magnetosomes in a chain, and the gap between them. If the chain axis is subparallel to the applied field, the magnetic moment reverses by a pseudo-fanning mode, which is replaced by a pseudo-coherent rotation mode at greater angles. These reversal modes are intrinsically different from coherent rotation assumed Stoner-Wohlfarth model, resulting in FORC diagrams with a smaller reversible component. On the other hand, isolated authigenic SD particles can precipitate in the sediment matrix, as it might occur for pedogenic magnetite. In this case, an assembly of randomly located particles provides a possible model for the resulting FORC diagram. If the concentration of the particles is small, each particle is affected by a random interaction field whose statistical distribution can be calculated from first

Mental health first aid training by e-learning: a randomized controlled trial.

Science.gov (United States)

Jorm, Anthony F; Kitchener, Betty A; Fischer, Julie-Anne; Cvetkovski, Stefan

2010-12-01

Mental Health First Aid training is a course for the public that teaches how to give initial help to a person developing a mental health problem or in a mental health crisis. The present study evaluated the effects of Mental Health First Aid training delivered by e-learning on knowledge about mental disorders, stigmatizing attitudes and helping behaviour. A randomized controlled trial was carried out with 262 members of the Australian public. Participants were randomly assigned to complete an e-learning CD, read a Mental Health First Aid manual or be in a waiting list control group. The effects of the interventions were evaluated using online questionnaires pre- and post-training and at 6-months follow up. The questionnaires covered mental health knowledge, stigmatizing attitudes, confidence in providing help to others, actions taken to implement mental health first aid and participant mental health. Both e-learning and the printed manual increased aspects of knowledge, reduced stigma and increased confidence compared to waiting list. E-learning also improved first aid actions taken more than waiting list, and was superior to the printed manual in reducing stigma and disability due to mental ill health. Mental Health First Aid information received by either e-learning or printed manual had positive effects, but e-learning was better at reducing stigma.

An Analysis of Spherical Particles Distribution Randomly Packed in a Medium for the Monte Carlo Implicit Modeling

Energy Technology Data Exchange (ETDEWEB)

Lee, Jae Yong; Kim, Song Hyun; Shin, Chang Ho; Kim, Jong Kyung [Hanyang Univ., Seoul (Korea, Republic of)

2014-05-15

In this study, as a preliminary study to develop an implicit method having high accuracy, the distribution characteristics of spherical particles were evaluated by using explicit modeling techniques in various volume packing fractions. This study was performed to evaluate implicitly simulated distribution of randomly packed spheres in a medium. At first, an explicit modeling method to simulate random packed spheres in a hexahedron medium was proposed. The distributed characteristics of l{sub p} and r{sub p}, which are used in the particle position sampling, was estimated. It is analyzed that the use of the direct exponential distribution, which is generally used in the implicit modeling, can cause the distribution bias of the spheres. It is expected that the findings in this study can be utilized for improving the accuracy in using the implicit method. Spherical particles, which are randomly distributed in medium, are utilized for the radiation shields, fusion reactor blanket, fuels of VHTR reactors. Due to the difficulty on the simulation of the stochastic distribution, Monte Carlo (MC) method has been mainly considered as the tool for the analysis of the particle transport. For the MC modeling of the spherical particles, three methods are known; repeated structure, explicit modeling, and implicit modeling. Implicit method (called as the track length sampling method) is a modeling method that is the sampling based modeling technique of each spherical geometry (or track length of the sphere) during the MC simulation. Implicit modeling method has advantages in high computational efficiency and user convenience. However, it is noted that the implicit method has lower modeling accuracy in various finite mediums.

Collaborative Random Faces-Guided Encoders for Pose-Invariant Face Representation Learning.

Science.gov (United States)

Shao, Ming; Zhang, Yizhe; Fu, Yun

2018-04-01

Learning discriminant face representation for pose-invariant face recognition has been identified as a critical issue in visual learning systems. The challenge lies in the drastic changes of facial appearances between the test face and the registered face. To that end, we propose a high-level feature learning framework called "collaborative random faces (RFs)-guided encoders" toward this problem. The contributions of this paper are three fold. First, we propose a novel supervised autoencoder that is able to capture the high-level identity feature despite of pose variations. Second, we enrich the identity features by replacing the target values of conventional autoencoders with random signals (RFs in this paper), which are unique for each subject under different poses. Third, we further improve the performance of the framework by incorporating deep convolutional neural network facial descriptors and linking discriminative identity features from different RFs for the augmented identity features. Finally, we conduct face identification experiments on Multi-PIE database, and face verification experiments on labeled faces in the wild and YouTube Face databases, where face recognition rate and verification accuracy with Receiver Operating Characteristic curves are rendered. In addition, discussions of model parameters and connections with the existing methods are provided. These experiments demonstrate that our learning system works fairly well on handling pose variations.

Exchange-correlation energy from pairing matrix fluctuation and the particle-particle random phase approximation.

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van Aggelen, Helen; Yang, Yang; Yang, Weitao

2014-05-14

Despite their unmatched success for many applications, commonly used local, semi-local, and hybrid density functionals still face challenges when it comes to describing long-range interactions, static correlation, and electron delocalization. Density functionals of both the occupied and virtual orbitals are able to address these problems. The particle-hole (ph-) Random Phase Approximation (RPA), a functional of occupied and virtual orbitals, has recently known a revival within the density functional theory community. Following up on an idea introduced in our recent communication [H. van Aggelen, Y. Yang, and W. Yang, Phys. Rev. A 88, 030501 (2013)], we formulate more general adiabatic connections for the correlation energy in terms of pairing matrix fluctuations described by the particle-particle (pp-) propagator. With numerical examples of the pp-RPA, the lowest-order approximation to the pp-propagator, we illustrate the potential of density functional approximations based on pairing matrix fluctuations. The pp-RPA is size-extensive, self-interaction free, fully anti-symmetric, describes the strong static correlation limit in H2, and eliminates delocalization errors in H2(+) and other single-bond systems. It gives surprisingly good non-bonded interaction energies--competitive with the ph-RPA--with the correct R(-6) asymptotic decay as a function of the separation R, which we argue is mainly attributable to its correct second-order energy term. While the pp-RPA tends to underestimate absolute correlation energies, it gives good relative energies: much better atomization energies than the ph-RPA, as it has no tendency to underbind, and reaction energies of similar quality. The adiabatic connection in terms of pairing matrix fluctuation paves the way for promising new density functional approximations.

Exchange-correlation energy from pairing matrix fluctuation and the particle-particle random phase approximation

International Nuclear Information System (INIS)

Aggelen, Helen van; Yang, Yang; Yang, Weitao

2014-01-01

Despite their unmatched success for many applications, commonly used local, semi-local, and hybrid density functionals still face challenges when it comes to describing long-range interactions, static correlation, and electron delocalization. Density functionals of both the occupied and virtual orbitals are able to address these problems. The particle-hole (ph-) Random Phase Approximation (RPA), a functional of occupied and virtual orbitals, has recently known a revival within the density functional theory community. Following up on an idea introduced in our recent communication [H. van Aggelen, Y. Yang, and W. Yang, Phys. Rev. A 88, 030501 (2013)], we formulate more general adiabatic connections for the correlation energy in terms of pairing matrix fluctuations described by the particle-particle (pp-) propagator. With numerical examples of the pp-RPA, the lowest-order approximation to the pp-propagator, we illustrate the potential of density functional approximations based on pairing matrix fluctuations. The pp-RPA is size-extensive, self-interaction free, fully anti-symmetric, describes the strong static correlation limit in H 2 , and eliminates delocalization errors in H 2 + and other single-bond systems. It gives surprisingly good non-bonded interaction energies – competitive with the ph-RPA – with the correct R −6 asymptotic decay as a function of the separation R, which we argue is mainly attributable to its correct second-order energy term. While the pp-RPA tends to underestimate absolute correlation energies, it gives good relative energies: much better atomization energies than the ph-RPA, as it has no tendency to underbind, and reaction energies of similar quality. The adiabatic connection in terms of pairing matrix fluctuation paves the way for promising new density functional approximations

An Interactive Computer Model for Improved Student Understanding of Random Particle Motion and Osmosis

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Kottonau, Johannes

2011-01-01

Effectively teaching the concepts of osmosis to college-level students is a major obstacle in biological education. Therefore, a novel computer model is presented that allows students to observe the random nature of particle motion simultaneously with the seemingly directed net flow of water across a semipermeable membrane during osmotic…

Medical Dataset Classification: A Machine Learning Paradigm Integrating Particle Swarm Optimization with Extreme Learning Machine Classifier

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C. V. Subbulakshmi

2015-01-01

Full Text Available Medical data classification is a prime data mining problem being discussed about for a decade that has attracted several researchers around the world. Most classifiers are designed so as to learn from the data itself using a training process, because complete expert knowledge to determine classifier parameters is impracticable. This paper proposes a hybrid methodology based on machine learning paradigm. This paradigm integrates the successful exploration mechanism called self-regulated learning capability of the particle swarm optimization (PSO algorithm with the extreme learning machine (ELM classifier. As a recent off-line learning method, ELM is a single-hidden layer feedforward neural network (FFNN, proved to be an excellent classifier with large number of hidden layer neurons. In this research, PSO is used to determine the optimum set of parameters for the ELM, thus reducing the number of hidden layer neurons, and it further improves the network generalization performance. The proposed method is experimented on five benchmarked datasets of the UCI Machine Learning Repository for handling medical dataset classification. Simulation results show that the proposed approach is able to achieve good generalization performance, compared to the results of other classifiers.

Effects of hydrodynamic interaction on random adhesive loose packings of micron-sized particles

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Liu Wenwei

2017-01-01

Full Text Available Random loose packings of monodisperse spherical micron-sized particles under a uniform flow field are investigated via an adhesive discrete-element method with the two-way coupling between the particles and the fluid. Characterized by a dimensionless adhesion parameter, the packing fraction follows the similar law to that without fluid, but results in larger values due to the hydrodynamic compression. The total pressure drop through the packed bed shows a critical behaviour at the packing fraction of ϕ ≈ 0.22 in the present study. The normalized permeability of the packed bed for different parameters increases with the increase of porosities and is also in consistent with the Kozeny-Carman equation.

Vector solution for the mean electromagnetic fields in a layer of random particles

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Lang, R. H.; Seker, S. S.; Levine, D. M.

1986-01-01

The mean electromagnetic fields are found in a layer of randomly oriented particles lying over a half space. A matrix-dyadic formulation of Maxwell's equations is employed in conjunction with the Foldy-Lax approximation to obtain equations for the mean fields. A two variable perturbation procedure, valid in the limit of small fractional volume, is then used to derive uncoupled equations for the slowly varying amplitudes of the mean wave. These equations are solved to obtain explicit expressions for the mean electromagnetic fields in the slab region in the general case of arbitrarily oriented particles and arbitrary polarization of the incident radiation. Numerical examples are given for the application to remote sensing of vegetation.

Particle identification at LHCb: new calibration techniques and machine learning classification algorithms

CERN Multimedia

CERN. Geneva

2018-01-01

Particle identification (PID) plays a crucial role in LHCb analyses. Combining information from LHCb subdetectors allows one to distinguish between various species of long-lived charged and neutral particles. PID performance directly affects the sensitivity of most LHCb measurements. Advanced multivariate approaches are used at LHCb to obtain the best PID performance and control systematic uncertainties. This talk highlights recent developments in PID that use innovative machine learning techniques, as well as novel data-driven approaches which ensure that PID performance is well reproduced in simulation.

E-learning in pediatric basic life support: a randomized controlled non-inferiority study.

Science.gov (United States)

Krogh, Lise Qvirin; Bjørnshave, Katrine; Vestergaard, Lone Due; Sharma, Maja Bendtsen; Rasmussen, Stinne Eika; Nielsen, Henrik Vendelbo; Thim, Troels; Løfgren, Bo

2015-05-01

Dissemination of pediatric basic life support (PBLS) skills is recommended. E-learning is accessible and cost-effective, but it is currently unknown whether laypersons can learn PBLS through e-learning. The hypothesis of this study was to investigate whether e-learning PBLS is non-inferior to instructor-led training. Participants were recruited among child-minders and parents of children aged 0-6 years. Participants were randomized to either 2-h instructor-led training or e-learning using an e-learning program (duration 17 min) including an inflatable manikin. After training, participants were assessed in a simulated pediatric cardiac arrest scenario. Tests were video recorded and PBLS skills were assessed independently by two assessors blinded to training method. Primary outcome was the pass rate of the PBLS test (≥8 of 15 skills adequately performed) with a pre-specified non-inferiority margin of 20%. In total 160 participants were randomized 1:1. E-learning was non-inferior to instructor-led training (difference in pass rate -4%; 95% CI -9:0.5). Pass rates were 100% among instructor-led trained (n=67) and 96% among e-learned (n=71). E-learners median time spent on the e-learning program was 30 min (range: 15-120 min) and the median number of log-ons was 2 (range: 1-5). After the study, all participants felt that their skills had improved. E-learning PBLS is non-inferior to instructor-led training among child-minders and parents with children aged 0-6 years, although the pass rate was 4% (95% CI -9:0.5) lower with e-learning. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines.

Science.gov (United States)

Neftci, Emre O; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP) rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F) neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines

Directory of Open Access Journals (Sweden)

Emre O. Neftci

2017-06-01

Full Text Available An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP rule, often relies on the immediate availability of network-wide information stored with high-precision memory during learning, and precise operations that are difficult to realize in neuromorphic hardware. Remarkably, recent work showed that exact backpropagated gradients are not essential for learning deep representations. Building on these results, we demonstrate an event-driven random BP (eRBP rule that uses an error-modulated synaptic plasticity for learning deep representations. Using a two-compartment Leaky Integrate & Fire (I&F neuron, the rule requires only one addition and two comparisons for each synaptic weight, making it very suitable for implementation in digital or mixed-signal neuromorphic hardware. Our results show that using eRBP, deep representations are rapidly learned, achieving classification accuracies on permutation invariant datasets comparable to those obtained in artificial neural network simulations on GPUs, while being robust to neural and synaptic state quantizations during learning.

Random walk on random walks

NARCIS (Netherlands)

Hilário, M.; Hollander, den W.Th.F.; Sidoravicius, V.; Soares dos Santos, R.; Teixeira, A.

2014-01-01

In this paper we study a random walk in a one-dimensional dynamic random environment consisting of a collection of independent particles performing simple symmetric random walks in a Poisson equilibrium with density ¿¿(0,8). At each step the random walk performs a nearest-neighbour jump, moving to

Mental Imagery as Facilitator to Lexical Learning-Blocked and Random Trials

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Subhash Bhatnagar

2014-04-01

Full Text Available Developing an effective treatment plan that promotes learning-generalization beyond treated stimuli remains a challenging task in language rehabilitation. Many specific treatments have been used to document therapeutic gains in learned lexical behaviors and now learning generalizations beyond practiced stimuli are being forged (Boyle, 2004 and Kiran & Thompson, 2003.Accordingly, generalization beyond practiced structures still remains an exciting therapeutic strategy. As major elements of cognitive processing, the perceptual representations embedded within mental imagery (MI, have long been recognized for their healing potential (Thomas, 2008, and training mindfulness. MI is also known to modulate the brain’s neural-circuitry in new learning (Davidson, 2000. It also acts as a mean to access memories and passes undistorted through mental resistances (Singer 1974. Purpose We integrated blocked and random presentations of MI with our treatment of anomia with three goals in mind: (1 to evaluate whether activation of the neural circuitry through controlled MI facilitated word finding skills; (2 to determine if the blocked or random modes of MI presentation facilitate learning equally or not; and, (3 to evaluate if the effects of evoked MI generalize to untrained lexical items. Subject The participating subject was a three-year post-onset, right-handed, 68-year old University-educated male with chronic aphasia secondary to a MRI confirmed large left temporal-parietal infarct in addition to an earlier left frontal infarct. These strokes resulted in moderately impaired comprehension and verbal expression. He made gains in both aspects of language following two years of SLP treatment. However, he continued to exhibit moderate to severe word finding (Goodglass & Kaplan, 1983. Methods We incorporated MI with random and blocked presentations in ABA format to explore the learning and generalization of trained mental representations that comprised of both

Pressure Prediction of Coal Slurry Transportation Pipeline Based on Particle Swarm Optimization Kernel Function Extreme Learning Machine

Directory of Open Access Journals (Sweden)

Xue-cun Yang

2015-01-01

Full Text Available For coal slurry pipeline blockage prediction problem, through the analysis of actual scene, it is determined that the pressure prediction from each measuring point is the premise of pipeline blockage prediction. Kernel function of support vector machine is introduced into extreme learning machine, the parameters are optimized by particle swarm algorithm, and blockage prediction method based on particle swarm optimization kernel function extreme learning machine (PSOKELM is put forward. The actual test data from HuangLing coal gangue power plant are used for simulation experiments and compared with support vector machine prediction model optimized by particle swarm algorithm (PSOSVM and kernel function extreme learning machine prediction model (KELM. The results prove that mean square error (MSE for the prediction model based on PSOKELM is 0.0038 and the correlation coefficient is 0.9955, which is superior to prediction model based on PSOSVM in speed and accuracy and superior to KELM prediction model in accuracy.

Learning of Multimodal Representations With Random Walks on the Click Graph.

Science.gov (United States)

Wu, Fei; Lu, Xinyan; Song, Jun; Yan, Shuicheng; Zhang, Zhongfei Mark; Rui, Yong; Zhuang, Yueting

2016-02-01

In multimedia information retrieval, most classic approaches tend to represent different modalities of media in the same feature space. With the click data collected from the users' searching behavior, existing approaches take either one-to-one paired data (text-image pairs) or ranking examples (text-query-image and/or image-query-text ranking lists) as training examples, which do not make full use of the click data, particularly the implicit connections among the data objects. In this paper, we treat the click data as a large click graph, in which vertices are images/text queries and edges indicate the clicks between an image and a query. We consider learning a multimodal representation from the perspective of encoding the explicit/implicit relevance relationship between the vertices in the click graph. By minimizing both the truncated random walk loss as well as the distance between the learned representation of vertices and their corresponding deep neural network output, the proposed model which is named multimodal random walk neural network (MRW-NN) can be applied to not only learn robust representation of the existing multimodal data in the click graph, but also deal with the unseen queries and images to support cross-modal retrieval. We evaluate the latent representation learned by MRW-NN on a public large-scale click log data set Clickture and further show that MRW-NN achieves much better cross-modal retrieval performance on the unseen queries/images than the other state-of-the-art methods.

Random geometry capability in RMC code for explicit analysis of polytype particle/pebble and applications to HTR-10 benchmark

International Nuclear Information System (INIS)

Liu, Shichang; Li, Zeguang; Wang, Kan; Cheng, Quan; She, Ding

2018-01-01

Highlights: •A new random geometry was developed in RMC for mixed and polytype particle/pebble. •This capability was applied to the full core calculations of HTR-10 benchmark. •Reactivity, temperature coefficient and control rod worth of HTR-10 were compared. •This method can explicitly model different packing fraction of different pebbles. •Monte Carlo code with this method can simulate polytype particle/pebble type reactor. -- Abstract: With the increasing demands of high fidelity neutronics analysis and the development of computer technology, Monte Carlo method is becoming more and more attractive in accurate simulation of pebble bed High Temperature gas-cooled Reactor (HTR), owing to its advantages of the flexible geometry modeling and the use of continuous-energy nuclear cross sections. For the double-heterogeneous geometry of pebble bed, traditional Monte Carlo codes can treat it by explicit geometry description. However, packing methods such as Random Sequential Addition (RSA) can only produce a sphere packing up to 38% volume packing fraction, while Discrete Element Method (DEM) is troublesome and also time consuming. Moreover, traditional Monte Carlo codes are difficult and inconvenient to simulate the mixed and polytype particles or pebbles. A new random geometry method was developed in Monte Carlo code RMC to simulate the particle transport in polytype particle/pebble in double heterogeneous geometry systems. This method was verified by some test cases, and applied to the full core calculations of HTR-10 benchmark. The reactivity, temperature coefficient and control rod worth of HTR-10 were compared for full core and initial core in helium and air atmosphere respectively, and the results agree well with the benchmark results and experimental results. This work would provide an efficient tool for the innovative design of pebble bed, prism HTRs and molten salt reactors with polytype particles or pebbles using Monte Carlo method.

Hyperuniformity, quasi-long-range correlations, and void-space constraints in maximally random jammed particle packings. I. Polydisperse spheres.

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Zachary, Chase E; Jiao, Yang; Torquato, Salvatore

2011-05-01

Hyperuniform many-particle distributions possess a local number variance that grows more slowly than the volume of an observation window, implying that the local density is effectively homogeneous beyond a few characteristic length scales. Previous work on maximally random strictly jammed sphere packings in three dimensions has shown that these systems are hyperuniform and possess unusual quasi-long-range pair correlations decaying as r(-4), resulting in anomalous logarithmic growth in the number variance. However, recent work on maximally random jammed sphere packings with a size distribution has suggested that such quasi-long-range correlations and hyperuniformity are not universal among jammed hard-particle systems. In this paper, we show that such systems are indeed hyperuniform with signature quasi-long-range correlations by characterizing the more general local-volume-fraction fluctuations. We argue that the regularity of the void space induced by the constraints of saturation and strict jamming overcomes the local inhomogeneity of the disk centers to induce hyperuniformity in the medium with a linear small-wave-number nonanalytic behavior in the spectral density, resulting in quasi-long-range spatial correlations scaling with r(-(d+1)) in d Euclidean space dimensions. A numerical and analytical analysis of the pore-size distribution for a binary maximally random jammed system in addition to a local characterization of the n-particle loops governing the void space surrounding the inclusions is presented in support of our argument. This paper is the first part of a series of two papers considering the relationships among hyperuniformity, jamming, and regularity of the void space in hard-particle packings.

Game-Based Learning as a Vehicle to Teach First Aid Content: A Randomized Experiment

Science.gov (United States)

Charlier, Nathalie; De Fraine, Bieke

2013-01-01

Background: Knowledge of first aid (FA), which constitutes lifesaving treatments for injuries or illnesses, is important for every individual. In this study, we have set up a group-randomized controlled trial to assess the effectiveness of a board game for learning FA. Methods: Four class groups (120 students) were randomly assigned to 2…

Hebbian Learning in a Random Network Captures Selectivity Properties of the Prefrontal Cortex

Science.gov (United States)

Lindsay, Grace W.

2017-01-01

Complex cognitive behaviors, such as context-switching and rule-following, are thought to be supported by the prefrontal cortex (PFC). Neural activity in the PFC must thus be specialized to specific tasks while retaining flexibility. Nonlinear “mixed” selectivity is an important neurophysiological trait for enabling complex and context-dependent behaviors. Here we investigate (1) the extent to which the PFC exhibits computationally relevant properties, such as mixed selectivity, and (2) how such properties could arise via circuit mechanisms. We show that PFC cells recorded from male and female rhesus macaques during a complex task show a moderate level of specialization and structure that is not replicated by a model wherein cells receive random feedforward inputs. While random connectivity can be effective at generating mixed selectivity, the data show significantly more mixed selectivity than predicted by a model with otherwise matched parameters. A simple Hebbian learning rule applied to the random connectivity, however, increases mixed selectivity and enables the model to match the data more accurately. To explain how learning achieves this, we provide analysis along with a clear geometric interpretation of the impact of learning on selectivity. After learning, the model also matches the data on measures of noise, response density, clustering, and the distribution of selectivities. Of two styles of Hebbian learning tested, the simpler and more biologically plausible option better matches the data. These modeling results provide clues about how neural properties important for cognition can arise in a circuit and make clear experimental predictions regarding how various measures of selectivity would evolve during animal training. SIGNIFICANCE STATEMENT The prefrontal cortex is a brain region believed to support the ability of animals to engage in complex behavior. How neurons in this area respond to stimuli—and in particular, to combinations of stimuli (�

Deep learning the quantum phase transitions in random two-dimensional electron systems

International Nuclear Information System (INIS)

Ohtsuki, Tomoki; Ohtsuki, Tomi

2016-01-01

Random electron systems show rich phases such as Anderson insulator, diffusive metal, quantum Hall and quantum anomalous Hall insulators, Weyl semimetal, as well as strong/weak topological insulators. Eigenfunctions of each matter phase have specific features, but owing to the random nature of systems, determining the matter phase from eigenfunctions is difficult. Here, we propose the deep learning algorithm to capture the features of eigenfunctions. Localization-delocalization transition, as well as disordered Chern insulator-Anderson insulator transition, is discussed. (author)

A Random-Walk-Model for heavy metal particles in natural waters; Ein Random-Walk-Modell fuer Schwermetallpartikel in natuerlichen Gewaessern

Energy Technology Data Exchange (ETDEWEB)

Wollschlaeger, A.

1996-12-31

The presented particle tracking model is for the numerical calculation of heavy metal transport in natural waters. The Navier-Stokes-Equations are solved with the Finite-Element-Method. The advective movement of the particles is interpolated from the velocities on the discrete mesh. The influence of turbulence is simulated with a Random-Walk-Model where particles are distributed due to a given probability function. Both parts are added and lead to the new particle position. The characteristics of the heavy metals are assigned to the particules as their attributes. Dissolved heavy metals are transported only by the flow. Heavy metals which are bound to particulate matter have an additional settling velocity. The sorption and the remobilization processes are approximated through a probability law which maintains the proportionality ratio between dissolved heavy metals and those which are bound to particulate matter. At the bed heavy metals bound to particulate matter are subjected to deposition and erosion processes. The model treats these processes by considering the absorption intensity of the heavy metals to the bottom sediments. Calculations of the Weser estuary show that the particle tracking model allows the simulation of the heavy metal behaviour even under complex flow conditions. (orig.) [Deutsch] Das vorgestellte Partikelmodell dient zur numerischen Berechnung des Schwermetalltransports in natuerlichen Gewaessern. Die Navier-Stokes-Gleichungen werden mit der Methode der Finiten Elemente geloest. Die advektive Bewegung der Teilchen ergibt sich aus der Interpolation der Geschwindigkeiten auf dem diskreten Netz. Der Einfluss der Turbulenz wird mit einem Random-Walk-Modell simuliert, bei dem sich die Partikel anhand einer vorgegebenen Wahrscheinlichkeitsfunktion verteilen. Beide Bewegungsanteile werden zusammengefasst und ergeben die neue Partikelposition. Die Eigenschaften der Schwermetalle werden den Partikeln als Attribute zugeordnet. Geloeste Schwermetalle

Extremely Randomized Machine Learning Methods for Compound Activity Prediction

Directory of Open Access Journals (Sweden)

Wojciech M. Czarnecki

2015-11-01

Full Text Available Speed, a relatively low requirement for computational resources and high effectiveness of the evaluation of the bioactivity of compounds have caused a rapid growth of interest in the application of machine learning methods to virtual screening tasks. However, due to the growth of the amount of data also in cheminformatics and related fields, the aim of research has shifted not only towards the development of algorithms of high predictive power but also towards the simplification of previously existing methods to obtain results more quickly. In the study, we tested two approaches belonging to the group of so-called ‘extremely randomized methods’—Extreme Entropy Machine and Extremely Randomized Trees—for their ability to properly identify compounds that have activity towards particular protein targets. These methods were compared with their ‘non-extreme’ competitors, i.e., Support Vector Machine and Random Forest. The extreme approaches were not only found out to improve the efficiency of the classification of bioactive compounds, but they were also proved to be less computationally complex, requiring fewer steps to perform an optimization procedure.

Learning basic life support (BLS) with tablet PCs in reciprocal learning at school: are videos superior to pictures? A randomized controlled trial.

Science.gov (United States)

Iserbyt, Peter; Charlier, Nathalie; Mols, Liesbet

2014-06-01

It is often assumed that animations (i.e., videos) will lead to higher learning compared to static media (i.e., pictures) because they provide a more realistic demonstration of the learning task. To investigate whether learning basic life support (BLS) and cardiopulmonary resuscitation (CPR) from video produce higher learning outcomes compared to pictures in reciprocal learning. A randomized controlled trial. A total of 128 students (mean age: 17 years) constituting eight intact classes from a secondary school learned BLS in reciprocal roles of doer and helper with tablet PCs. Student pairs in each class were randomized over a Picture and a Video group. In the Picture group, students learned BLS by means of pictures combined with written instructions. In the Video group, BLS was learned through videos with on-screen instructions. Informational equivalence was assured since instructions in both groups comprised exactly the same words. BLS assessment occurred unannounced, three weeks following intervention. Analysis of variance demonstrated no significant differences in chest compression depths between the Picture group (M=42 mm, 95% CI=40-45) and the Video group (M=39 mm, 95% CI=36-42). In the Picture group significantly higher percentages of chest compressions with correct hand placement were achieved (M=67%, CI=58-77) compared to the Video group (M=53%, CI=43-63), P=.03, η(p)(2)=.03. No other significant differences were found. Results do not support the assumption that videos are superior to pictures for learning BLS and CPR in reciprocal learning. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Assessing the Effectiveness of Case-Based Collaborative Learning via Randomized Controlled Trial.

Science.gov (United States)

Krupat, Edward; Richards, Jeremy B; Sullivan, Amy M; Fleenor, Thomas J; Schwartzstein, Richard M

2016-05-01

Case-based collaborative learning (CBCL) is a novel small-group approach that borrows from team-based learning principles and incorporates elements of problem-based learning (PBL) and case-based learning. CBCL includes a preclass readiness assurance process and case-based in-class activities in which students respond to focused, open-ended questions individually, discuss their answers in groups of 4, and then reach consensus in larger groups of 16. This study introduces CBCL and assesses its effectiveness in one course at Harvard Medical School. In a 2013 randomized controlled trial, 64 medical and dental student volunteers were assigned randomly to one of four 8-person PBL tutorial groups (control; n = 32) or one of two 16-person CBCL tutorial groups (experimental condition; n = 32) as part of a required first-year physiology course. Outcomes for the PBL and CBCL groups were compared using final exam scores, student responses to a postcourse survey, and behavioral coding of portions of video-recorded class sessions. Overall, the course final exam scores for CBCL and PBL students were not significantly different. However, CBCL students whose mean exam performance in prior courses was below the participant median scored significantly higher than their PBL counterparts on the physiology course final exam. The most common adjectives students used to describe CBCL were "engaging," "fun," and "thought-provoking." Coding of observed behaviors indicated that individual affect was significantly higher in the CBCL groups than in the PBL groups. CBCL is a viable, engaging, active learning method. It may particularly benefit students with lower academic performance.

Hyperuniformity, quasi-long-range correlations, and void-space constraints in maximally random jammed particle packings. II. Anisotropy in particle shape.

Science.gov (United States)

Zachary, Chase E; Jiao, Yang; Torquato, Salvatore

2011-05-01

We extend the results from the first part of this series of two papers by examining hyperuniformity in heterogeneous media composed of impenetrable anisotropic inclusions. Specifically, we consider maximally random jammed (MRJ) packings of hard ellipses and superdisks and show that these systems both possess vanishing infinite-wavelength local-volume-fraction fluctuations and quasi-long-range pair correlations scaling as r(-(d+1)) in d Euclidean dimensions. Our results suggest a strong generalization of a conjecture by Torquato and Stillinger [Phys. Rev. E 68, 041113 (2003)], namely, that all strictly jammed saturated packings of hard particles, including those with size and shape distributions, are hyperuniform with signature quasi-long-range correlations. We show that our arguments concerning the constrained distribution of the void space in MRJ packings directly extend to hard-ellipse and superdisk packings, thereby providing a direct structural explanation for the appearance of hyperuniformity and quasi-long-range correlations in these systems. Additionally, we examine general heterogeneous media with anisotropic inclusions and show unexpectedly that one can decorate a periodic point pattern to obtain a hard-particle system that is not hyperuniform with respect to local-volume-fraction fluctuations. This apparent discrepancy can also be rationalized by appealing to the irregular distribution of the void space arising from the anisotropic shapes of the particles. Our work suggests the intriguing possibility that the MRJ states of hard particles share certain universal features independent of the local properties of the packings, including the packing fraction and average contact number per particle.

rFerns: An Implementation of the Random Ferns Method for General-Purpose Machine Learning

Directory of Open Access Journals (Sweden)

Miron B. Kursa

2014-11-01

Full Text Available Random ferns is a very simple yet powerful classification method originally introduced for specific computer vision tasks. In this paper, I show that this algorithm may be considered as a constrained decision tree ensemble and use this interpretation to introduce a series of modifications which enable the use of random ferns in general machine learning problems. Moreover, I extend the method with an internal error approximation and an attribute importance measure based on corresponding features of the random forest algorithm. I also present the R package rFerns containing an efficient implementation of this modified version of random ferns.

Effect of improving the usability of an e-learning resource: a randomized trial.

Science.gov (United States)

Davids, Mogamat Razeen; Chikte, Usuf M E; Halperin, Mitchell L

2014-06-01

Optimizing the usability of e-learning materials is necessary to reduce extraneous cognitive load and maximize their potential educational impact. However, this is often neglected, especially when time and other resources are limited. We conducted a randomized trial to investigate whether a usability evaluation of our multimedia e-learning resource, followed by fixing of all problems identified, would translate into improvements in usability parameters and learning by medical residents. Two iterations of our e-learning resource [version 1 (V1) and version 2 (V2)] were compared. V1 was the first fully functional version and V2 was the revised version after all identified usability problems were addressed. Residents in internal medicine and anesthesiology were randomly assigned to one of the versions. Usability was evaluated by having participants complete a user satisfaction questionnaire and by recording and analyzing their interactions with the application. The effect on learning was assessed by questions designed to test the retention and transfer of knowledge. Participants reported high levels of satisfaction with both versions, with good ratings on the System Usability Scale and adjective rating scale. In contrast, analysis of video recordings revealed significant differences in the occurrence of serious usability problems between the two versions, in particular in the interactive HandsOn case with its treatment simulation, where there was a median of five serious problem instances (range: 0-50) recorded per participant for V1 and zero instances (range: 0-1) for V2 (P e-learning resource resulted in significant improvements in usability. This is likely to translate into improved motivation and willingness to engage with the learning material. In this population of relatively high-knowledge participants, learning scores were similar across the two versions. Copyright © 2014 The American Physiological Society.

Production of complex particles in low energy spallation and in fragmentation reactions by in-medium random clusterization

International Nuclear Information System (INIS)

Lacroix, D.; Durand, D.

2005-09-01

Rules for in-medium complex particle production in nuclear reactions are proposed. These rules have been implemented in two models to simulate nucleon-nucleus and nucleus-nucleus reactions around the Fermi energy. Our work emphasizes the effect of randomness in cluster formation, the importance of the nucleonic Fermi motion as well as the role of conservation laws. The concepts of total available phase-space and explored phase-space under constraint imposed by the reaction are clarified. The compatibility of experimental observations with a random clusterization is illustrated in a schematic scenario of a proton-nucleus collision. The role of randomness under constraint is also illustrated in the nucleus-nucleus case. (authors)

Effects of mobile phone-based app learning compared to computer-based web learning on nursing students: pilot randomized controlled trial.

Science.gov (United States)

Lee, Myung Kyung

2015-04-01

This study aimed to determine the effect of mobile-based discussion versus computer-based discussion on self-directed learning readiness, academic motivation, learner-interface interaction, and flow state. This randomized controlled trial was conducted at one university. Eighty-six nursing students who were able to use a computer, had home Internet access, and used a mobile phone were recruited. Participants were randomly assigned to either the mobile phone app-based discussion group (n = 45) or a computer web-based discussion group (n = 41). The effect was measured at before and after an online discussion via self-reported surveys that addressed academic motivation, self-directed learning readiness, time distortion, learner-learner interaction, learner-interface interaction, and flow state. The change in extrinsic motivation on identified regulation in the academic motivation (p = 0.011) as well as independence and ability to use basic study (p = 0.047) and positive orientation to the future in self-directed learning readiness (p = 0.021) from pre-intervention to post-intervention was significantly more positive in the mobile phone app-based group compared to the computer web-based discussion group. Interaction between learner and interface (p = 0.002), having clear goals (p = 0.012), and giving and receiving unambiguous feedback (p = 0.049) in flow state was significantly higher in the mobile phone app-based discussion group than it was in the computer web-based discussion group at post-test. The mobile phone might offer more valuable learning opportunities for discussion teaching and learning methods in terms of self-directed learning readiness, academic motivation, learner-interface interaction, and the flow state of the learning process compared to the computer.

Emergent dynamics of Cucker-Smale particles under the effects of random communication and incompressible fluids

Science.gov (United States)

Ha, Seung-Yeal; Xiao, Qinghua; Zhang, Xiongtao

2018-04-01

We study the dynamics of infinitely many Cucker-Smale (C-S) flocking particles under the interplay of random communication and incompressible fluids. For the dynamics of an ensemble of flocking particles, we use the kinetic Cucker-Smale-Fokker-Planck (CS-FP) equation with a degenerate diffusion, whereas for the fluid component, we use the incompressible Navier-Stokes (N-S) equations. These two subsystems are coupled via the drag force. For this coupled model, we present the global existence of weak and strong solutions in Rd (d = 2 , 3). Under the extra regularity assumptions of the initial data, the unique solvability of strong solutions is also established in R2. In a large coupling regime and periodic spatial domain T2 : =R2 /Z2, we show that the velocities of C-S particles and fluids are asymptotically aligned to two constant velocities which may be different.

Random synaptic feedback weights support error backpropagation for deep learning

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Lillicrap, Timothy P.; Cownden, Daniel; Tweed, Douglas B.; Akerman, Colin J.

2016-01-01

The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame by multiplying error signals with all the synaptic weights on each neuron's axon and further downstream. However, this involves a precise, symmetric backward connectivity pattern, which is thought to be impossible in the brain. Here we demonstrate that this strong architectural constraint is not required for effective error propagation. We present a surprisingly simple mechanism that assigns blame by multiplying errors by even random synaptic weights. This mechanism can transmit teaching signals across multiple layers of neurons and performs as effectively as backpropagation on a variety of tasks. Our results help reopen questions about how the brain could use error signals and dispel long-held assumptions about algorithmic constraints on learning. PMID:27824044

Learning by random walks in the weight space of the Ising perceptron

International Nuclear Information System (INIS)

Huang, Haiping; Zhou, Haijun

2010-01-01

Several variants of a stochastic local search process for constructing the synaptic weights of an Ising perceptron are studied. In this process, binary patterns are sequentially presented to the Ising perceptron and are then learned as the synaptic weight configuration is modified through a chain of single- or double-weight flips within the compatible weight configuration space of the earlier learned patterns. This process is able to reach a storage capacity of α≈0.63 for pattern length N = 101 and α≈0.41 for N = 1001. If in addition a relearning process is exploited, the learning performance is further improved to a storage capacity of α≈0.80 for N = 101 and α≈0.42 for N = 1001. We found that, for a given learning task, the solutions constructed by the random walk learning process are separated by a typical Hamming distance, which decreases with the constraint density α of the learning task; at a fixed value of α, the width of the Hamming distance distribution decreases with N

Implementation of Chaotic Gaussian Particle Swarm Optimization for Optimize Learning-to-Rank Software Defect Prediction Model Construction

Science.gov (United States)

Buchari, M. A.; Mardiyanto, S.; Hendradjaya, B.

2018-03-01

Finding the existence of software defect as early as possible is the purpose of research about software defect prediction. Software defect prediction activity is required to not only state the existence of defects, but also to be able to give a list of priorities which modules require a more intensive test. Therefore, the allocation of test resources can be managed efficiently. Learning to rank is one of the approach that can provide defect module ranking data for the purposes of software testing. In this study, we propose a meta-heuristic chaotic Gaussian particle swarm optimization to improve the accuracy of learning to rank software defect prediction approach. We have used 11 public benchmark data sets as experimental data. Our overall results has demonstrated that the prediction models construct using Chaotic Gaussian Particle Swarm Optimization gets better accuracy on 5 data sets, ties in 5 data sets and gets worse in 1 data sets. Thus, we conclude that the application of Chaotic Gaussian Particle Swarm Optimization in Learning-to-Rank approach can improve the accuracy of the defect module ranking in data sets that have high-dimensional features.

Remote-online case-based learning: A comparison of remote-online and face-to-face, case-based learning - a randomized controlled trial.

Science.gov (United States)

Nicklen, Peter; Keating, Jenny L; Paynter, Sophie; Storr, Michael; Maloney, Stephen

2016-01-01

Case-based learning (CBL) is an educational approach where students work in small, collaborative groups to solve problems. Computer assisted learning (CAL) is the implementation of computer technology in education. The purpose of this study was to compare the effects of a remote-online CBL (RO-CBL) with traditional face-to-face CBL on learning the outcomes of undergraduate physiotherapy students. Participants were randomized to either the control (face-to-face CBL) or to the CAL intervention (RO-CBL). The entire 3rd year physiotherapy cohort (n = 41) at Monash University, Victoria, Australia, were invited to participate in the randomized controlled trial. Outcomes included a postintervention multiple-choice test evaluating the knowledge gained from the CBL, a self-assessment of learning based on examinable learning objectives and student satisfaction with the CBL. In addition, a focus group was conducted investigating perceptions and responses to the online format. Thirty-eight students (control n = 19, intervention n = 19) participated in two CBL sessions and completed the outcome assessments. CBL median scores for the postintervention multiple-choice test were comparable (Wilcoxon rank sum P = 0.61) (median/10 [range] intervention group: 9 [8-10] control group: 10 [7-10]). Of the 15 examinable learning objectives, eight were significantly in favor of the control group, suggesting a greater perceived depth of learning. Eighty-four percent of students (16/19) disagreed with the statement "I enjoyed the method of CBL delivery." Key themes identified from the focus group included risks associated with the implementation of, challenges of communicating in, and flexibility offered, by web-based programs. RO-CBL appears to provide students with a comparable learning experience to traditional CBL. Procedural and infrastructure factors need to be addressed in future studies to counter student dissatisfaction and decreased perceived depth of learning.

Improving EEG signal peak detection using feature weight learning ...

Indian Academy of Sciences (India)

Therefore, we aimed to develop a general procedure for eye event-related applications based on feature weight learning (FWL), through the use of a neural network with random weights (NNRW) as the classifier. The FWL is performed using a particle swarm optimization algorithm, applied to the well-studied Dumpala, Acir, ...

The design of instructional tools affects secondary school students' learning of cardiopulmonary resuscitation (CPR) in reciprocal peer learning: a randomized controlled trial.

Science.gov (United States)

Iserbyt, Peter; Byra, Mark

2013-11-01

Research investigating design effects of instructional tools for learning Basic Life Support (BLS) is almost non-existent. To demonstrate the design of instructional tools matter. The effect of spatial contiguity, a design principle stating that people learn more deeply when words and corresponding pictures are placed close (i.e., integrated) rather than far from each other on a page was investigated on task cards for learning Cardiopulmonary Resuscitation (CPR) during reciprocal peer learning. A randomized controlled trial. A total of 111 students (mean age: 13 years) constituting six intact classes learned BLS through reciprocal learning with task cards. Task cards combine a picture of the skill with written instructions about how to perform it. In each class, students were randomly assigned to the experimental group or the control. In the control, written instructions were placed under the picture on the task cards. In the experimental group, written instructions were placed close to the corresponding part of the picture on the task cards reflecting application of the spatial contiguity principle. One-way analysis of variance found significantly better performances in the experimental group for ventilation volumes (P=.03, ηp2=.10) and flow rates (P=.02, ηp2=.10). For chest compression depth, compression frequency, compressions with correct hand placement, and duty cycles no significant differences were found. This study shows that the design of instructional tools (i.e., task cards) affects student learning. Research-based design of learning tools can enhance BLS and CPR education. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

Wordless intervention for epilepsy in learning disabilities (WIELD): study protocol for a randomized controlled feasibility trial.

Science.gov (United States)

Durand, Marie-Anne; Gates, Bob; Parkes, Georgina; Zia, Asif; Friedli, Karin; Barton, Garry; Ring, Howard; Oostendorp, Linda; Wellsted, David

2014-11-20

Epilepsy is the most common neurological problem that affects people with learning disabilities. The high seizure frequency, resistance to treatments, associated skills deficit and co-morbidities make the management of epilepsy particularly challenging for people with learning disabilities. The Books Beyond Words booklet for epilepsy uses images to help people with learning disabilities manage their condition and improve quality of life. Our aim is to conduct a randomized controlled feasibility trial exploring key methodological, design and acceptability issues, in order to subsequently undertake a large-scale randomized controlled trial of the Books Beyond Words booklet for epilepsy. We will use a two-arm, single-centre randomized controlled feasibility design, over a 20-month period, across five epilepsy clinics in Hertfordshire, United Kingdom. We will recruit 40 eligible adults with learning disabilities and a confirmed diagnosis of epilepsy and will randomize them to use either the Books Beyond Words booklet plus usual care (intervention group) or to receive routine information and services (control group). We will collect quantitative data about the number of eligible participants, number of recruited participants, demographic data, discontinuation rates, variability of the primary outcome measure (quality of life: Epilepsy and Learning Disabilities Quality of Life scale), seizure severity, seizure control, intervention's patterns of use, use of other epilepsy-related information, resource use and the EQ-5D-5L health questionnaire. We will also gather qualitative data about the feasibility and acceptability of the study procedures and the Books Beyond Words booklet. Ethical approval for this study was granted on 28 April 2014, by the Wales Research Ethics Committee 5. Recruitment began on 1 July 2014. The outcomes of this feasibility study will be used to inform the design and methodology of a definitive study, adequately powered to determine the impact of

Wave propagation and scattering in random media

CERN Document Server

Ishimaru, Akira

1978-01-01

Wave Propagation and Scattering in Random Media, Volume 2, presents the fundamental formulations of wave propagation and scattering in random media in a unified and systematic manner. The topics covered in this book may be grouped into three categories: waves in random scatterers, waves in random continua, and rough surface scattering. Random scatterers are random distributions of many particles. Examples are rain, fog, smog, hail, ocean particles, red blood cells, polymers, and other particles in a state of Brownian motion. Random continua are the media whose characteristics vary randomly an

A Grouping Particle Swarm Optimizer with Personal-Best-Position Guidance for Large Scale Optimization.

Science.gov (United States)

Guo, Weian; Si, Chengyong; Xue, Yu; Mao, Yanfen; Wang, Lei; Wu, Qidi

2017-05-04

Particle Swarm Optimization (PSO) is a popular algorithm which is widely investigated and well implemented in many areas. However, the canonical PSO does not perform well in population diversity maintenance so that usually leads to a premature convergence or local optima. To address this issue, we propose a variant of PSO named Grouping PSO with Personal- Best-Position (Pbest) Guidance (GPSO-PG) which maintains the population diversity by preserving the diversity of exemplars. On one hand, we adopt uniform random allocation strategy to assign particles into different groups and in each group the losers will learn from the winner. On the other hand, we employ personal historical best position of each particle in social learning rather than the current global best particle. In this way, the exemplars diversity increases and the effect from the global best particle is eliminated. We test the proposed algorithm to the benchmarks in CEC 2008 and CEC 2010, which concern the large scale optimization problems (LSOPs). By comparing several current peer algorithms, GPSO-PG exhibits a competitive performance to maintain population diversity and obtains a satisfactory performance to the problems.

Random Walk Particle Tracking For Multiphase Heat Transfer

Science.gov (United States)

Lattanzi, Aaron; Yin, Xiaolong; Hrenya, Christine

2017-11-01

As computing capabilities have advanced, direct numerical simulation (DNS) has become a highly effective tool for quantitatively predicting the heat transfer within multiphase flows. Here we utilize a hybrid DNS framework that couples the lattice Boltzmann method (LBM) to the random walk particle tracking (RWPT) algorithm. The main challenge of such a hybrid is that discontinuous fields pose a significant challenge to the RWPT framework and special attention must be given to the handling of interfaces. We derive a method for addressing discontinuities in the diffusivity field, arising at the interface between two phases. Analytical means are utilized to develop an interfacial tracer balance and modify the RWPT algorithm. By expanding the modulus of the stochastic (diffusive) step and only allowing a subset of the tracers within the high diffusivity medium to undergo a diffusive step, the correct equilibrium state can be restored (globally homogeneous tracer distribution). The new RWPT algorithm is implemented within the SUSP3D code and verified against a variety of systems: effective diffusivity of a static gas-solids mixture, hot sphere in unbounded diffusion, cooling sphere in unbounded diffusion, and uniform flow past a hot sphere.

Second Graders' Emerging Particle Models of Matter in the Context of Learning through Model-Based Inquiry

Science.gov (United States)

Samarapungavan, Ala; Bryan, Lynn; Wills, Jamison

2017-01-01

In this paper, we present a study of second graders' learning about the nature of matter in the context of content-rich, model-based inquiry instruction. The goal of instruction was to help students learn to use simple particle models to explain states of matter and phase changes. We examined changes in students' ideas about matter, the coherence…

Video- or text-based e-learning when teaching clinical procedures? A randomized controlled trial.

Science.gov (United States)

Buch, Steen Vigh; Treschow, Frederik Philip; Svendsen, Jesper Brink; Worm, Bjarne Skjødt

2014-01-01

This study investigated the effectiveness of two different levels of e-learning when teaching clinical skills to medical students. Sixty medical students were included and randomized into two comparable groups. The groups were given either a video- or text/picture-based e-learning module and subsequently underwent both theoretical and practical examination. A follow-up test was performed 1 month later. The students in the video group performed better than the illustrated text-based group in the practical examination, both in the primary test (Pvideo group performed better on the follow-up test (P=0.04). Video-based e-learning is superior to illustrated text-based e-learning when teaching certain practical clinical skills.

Time distributions of solar energetic particle events: Are SEPEs really random?

Science.gov (United States)

Jiggens, P. T. A.; Gabriel, S. B.

2009-10-01

Solar energetic particle events (SEPEs) can exhibit flux increases of several orders of magnitude over background levels and have always been considered to be random in nature in statistical models with no dependence of any one event on the occurrence of previous events. We examine whether this assumption of randomness in time is correct. Engineering modeling of SEPEs is important to enable reliable and efficient design of both Earth-orbiting and interplanetary spacecraft and future manned missions to Mars and the Moon. All existing engineering models assume that the frequency of SEPEs follows a Poisson process. We present analysis of the event waiting times using alternative distributions described by Lévy and time-dependent Poisson processes and compared these with the usual Poisson distribution. The results show significant deviation from a Poisson process and indicate that the underlying physical processes might be more closely related to a Lévy-type process, suggesting that there is some inherent “memory” in the system. Inherent Poisson assumptions of stationarity and event independence are investigated, and it appears that they do not hold and can be dependent upon the event definition used. SEPEs appear to have some memory indicating that events are not completely random with activity levels varying even during solar active periods and are characterized by clusters of events. This could have significant ramifications for engineering models of the SEP environment, and it is recommended that current statistical engineering models of the SEP environment should be modified to incorporate long-term event dependency and short-term system memory.

A Randomized Crossover Design to Assess Learning Impact and Student Preference for Active and Passive Online Learning Modules.

Science.gov (United States)

Prunuske, Amy J; Henn, Lisa; Brearley, Ann M; Prunuske, Jacob

Medical education increasingly involves online learning experiences to facilitate the standardization of curriculum across time and space. In class, delivering material by lecture is less effective at promoting student learning than engaging students in active learning experience and it is unclear whether this difference also exists online. We sought to evaluate medical student preferences for online lecture or online active learning formats and the impact of format on short- and long-term learning gains. Students participated online in either lecture or constructivist learning activities in a first year neurologic sciences course at a US medical school. In 2012, students selected which format to complete and in 2013, students were randomly assigned in a crossover fashion to the modules. In the first iteration, students strongly preferred the lecture modules and valued being told "what they need to know" rather than figuring it out independently. In the crossover iteration, learning gains and knowledge retention were found to be equivalent regardless of format, and students uniformly demonstrated a strong preference for the lecture format, which also on average took less time to complete. When given a choice for online modules, students prefer passive lecture rather than completing constructivist activities, and in the time-limited environment of medical school, this choice results in similar performance on multiple-choice examinations with less time invested. Instructors need to look more carefully at whether assessments and learning strategies are helping students to obtain self-directed learning skills and to consider strategies to help students learn to value active learning in an online environment.

Bayesian structure learning for Markov Random Fields with a spike and slab prior

NARCIS (Netherlands)

Chen, Y.; Welling, M.; de Freitas, N.; Murphy, K.

2012-01-01

In recent years a number of methods have been developed for automatically learning the (sparse) connectivity structure of Markov Random Fields. These methods are mostly based on L1-regularized optimization which has a number of disadvantages such as the inability to assess model uncertainty and

Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Features

Science.gov (United States)

Bouboulis, Pantelis; Chouvardas, Symeon; Theodoridis, Sergios

2018-04-01

We present a novel diffusion scheme for online kernel-based learning over networks. So far, a major drawback of any online learning algorithm, operating in a reproducing kernel Hilbert space (RKHS), is the need for updating a growing number of parameters as time iterations evolve. Besides complexity, this leads to an increased need of communication resources, in a distributed setting. In contrast, the proposed method approximates the solution as a fixed-size vector (of larger dimension than the input space) using Random Fourier Features. This paves the way to use standard linear combine-then-adapt techniques. To the best of our knowledge, this is the first time that a complete protocol for distributed online learning in RKHS is presented. Conditions for asymptotic convergence and boundness of the networkwise regret are also provided. The simulated tests illustrate the performance of the proposed scheme.

AUTOCLASSIFICATION OF THE VARIABLE 3XMM SOURCES USING THE RANDOM FOREST MACHINE LEARNING ALGORITHM

International Nuclear Information System (INIS)

Farrell, Sean A.; Murphy, Tara; Lo, Kitty K.

2015-01-01

In the current era of large surveys and massive data sets, autoclassification of astrophysical sources using intelligent algorithms is becoming increasingly important. In this paper we present the catalog of variable sources in the Third XMM-Newton Serendipitous Source catalog (3XMM) autoclassified using the Random Forest machine learning algorithm. We used a sample of manually classified variable sources from the second data release of the XMM-Newton catalogs (2XMMi-DR2) to train the classifier, obtaining an accuracy of ∼92%. We also evaluated the effectiveness of identifying spurious detections using a sample of spurious sources, achieving an accuracy of ∼95%. Manual investigation of a random sample of classified sources confirmed these accuracy levels and showed that the Random Forest machine learning algorithm is highly effective at automatically classifying 3XMM sources. Here we present the catalog of classified 3XMM variable sources. We also present three previously unidentified unusual sources that were flagged as outlier sources by the algorithm: a new candidate supergiant fast X-ray transient, a 400 s X-ray pulsar, and an eclipsing 5 hr binary system coincident with a known Cepheid.

Mediating Parent Learning to Promote Social Communication for Toddlers with Autism: Effects from a Randomized Controlled Trial

Science.gov (United States)

Schertz, Hannah H.; Odom, Samuel L.; Baggett, Kathleen M.; Sideris, John H.

2018-01-01

A randomized controlled trial was conducted to evaluate effects of the Joint Attention Mediated Learning (JAML) intervention. Toddlers with autism spectrum disorders (ASD) aged 16-30 months (n = 144) were randomized to intervention and community control conditions. Parents, who participated in 32 weekly home-based sessions, followed a mediated…

Gamma/hadron segregation for a ground based imaging atmospheric Cherenkov telescope using machine learning methods: Random Forest leads

International Nuclear Information System (INIS)

Sharma Mradul; Koul Maharaj Krishna; Mitra Abhas; Nayak Jitadeepa; Bose Smarajit

2014-01-01

A detailed case study of γ-hadron segregation for a ground based atmospheric Cherenkov telescope is presented. We have evaluated and compared various supervised machine learning methods such as the Random Forest method, Artificial Neural Network, Linear Discriminant method, Naive Bayes Classifiers, Support Vector Machines as well as the conventional dynamic supercut method by simulating triggering events with the Monte Carlo method and applied the results to a Cherenkov telescope. It is demonstrated that the Random Forest method is the most sensitive machine learning method for γ-hadron segregation. (research papers)

Partial splenic embolization using polyvinyl alcohol particles for hypersplenism in cirrhosis: A prospective randomized study

Energy Technology Data Exchange (ETDEWEB)

Zhu Kangshun [Department of Radiology, Third Affiliated Hospital, Sun Yat-sen University, 600 Tianhe Road, Guangzhou, Guangdong Province 510630 (China)], E-mail: zhksh010@163.com; Meng Xiaochun [Department of Radiology, Third Affiliated Hospital, Sun Yat-sen University, 600 Tianhe Road, Guangzhou, Guangdong Province 510630 (China)], E-mail: mengxiaochun1972@163.com; Li Zhengran [Department of Radiology, Third Affiliated Hospital, Sun Yat-sen University, 600 Tianhe Road, Guangzhou, Guangdong Province 510630 (China)], E-mail: andyreede@21cn.com; Huang Mingsheng [Department of Radiology, Third Affiliated Hospital, Sun Yat-sen University, 600 Tianhe Road, Guangzhou, Guangdong Province 510630 (China)], E-mail: laom502@tom.com; Guan Shouhai [Department of Radiology, Third Affiliated Hospital, Sun Yat-sen University, 600 Tianhe Road, Guangzhou, Guangdong Province 510630 (China)], E-mail: guanshouhai@163.com; Jiang Zaibo [Department of Radiology, Third Affiliated Hospital, Sun Yat-sen University, 600 Tianhe Road, Guangzhou, Guangdong Province 510630 (China)], E-mail: jiangzaibo@yahoo.com.cn; Shan Hong [Department of Radiology, Third Affiliated Hospital, Sun Yat-sen University, 600 Tianhe Road, Guangzhou, Guangdong Province 510630 (China)], E-mail: gzshsums@public.guangzhou.gd.cn

2008-04-15

Purpose: To prospectively evaluate the efficacy and safety of partial splenic embolization (PSE) using polyvinyl alcohol (PVA) particles for hypersplenism in cirrhosis, as compared to PSE using gelfoam particles. Materials and methods: PSE was performed in 60 consecutive patients with hypersplenism caused by cirrhosis. The patients were randomly assigned into 2 groups: gelfoam group, 32 patients received PSE using gelfoam particles as the embolic material; PVA group, 28 patients received PSE using PVA particles. The follow-up contents included peripheral blood cell counts (leukocyte, platelet and red blood cell) and complications associated with PSE. Results: Prior to PSE, there was no significant difference between the two groups in sex, age, Child-Pugh grade, the extent of embolization and peripheral blood cell counts. After PSE, no matter in which group, leukocyte and platelet counts kept significantly higher than pre-PSE during the 3-year follow-up period (P < .0001), but the post-PSE improvement of leukocyte and platelet counts was significantly better in PVA group than in gelfoam group (P < .05). Red blood cell counts showed no remarkable changes after PSE (P > .05). Severe complications occurred in 8 patients (25.0%) in gelfoam group and 6 patients (21.4%) in PVA group (P > .05), but the degree of abdominal pain was higher in the latter than in the former (P < .05). Among 17 patients who received more than 70% embolization of spleen, 10 (58.8%) developed severe complications, while among 43 patients who received 70% or less embolization of spleen, only four (9.3%) had severe complications. This difference was statistically significant (P < .05). Conclusion: PVA particles could be used as the embolic material in PSE; in comparison with PSE using gelfoam particles, PSE using PVA particles can achieve even better efficacy in alleviating hypersplenism, but the extent of embolization should be strictly limited to not more than 70% of splenic volume.

Food2Learn: Randomized control trial investigating influence of krill oil supplementation on learning, cognition, and behaviour in healthy adolescents. Design presentation

NARCIS (Netherlands)

Van der Wurff, Inge; Von Schacky, Clemens; Berge, Kjetil; Kirschner, Paul A.; De Groot, Renate

2014-01-01

Food2Learn is a double blind randomized controlled trial which looks at the influence of Krill oil (rich in LCPUFA) on the cognitive performance, academic performance and mental well-being of student of lower vocational schools.

Anomalous particle diffusion and Levy random walk of magnetic field lines in three dimensional solar wind turbulence

International Nuclear Information System (INIS)

Zimbardo, G.

2005-01-01

Plasma transport in the presence of turbulence depends on a variety of parameters like the fluctuation level ? B/B0, the ratio between the particle Larmor radius and the turbulence correlation lengths, and the turbulence anisotropy. In this presentation, we review the results of numerical simulations of plasma and magnetic field line transport in the case of anisotropic magnetic turbulence, for parameter values close to those of the solar wind. We assume a uniform background magnetic field B0 = B0ez and a Fourier representation for magnetic fluctuations, with wavectors forming any angle with respect to B0. The energy density spectrum is a power law, and in k space the constant amplitude surfaces are ellipsoids, described by the correlation lengths lx, ly, lz, which quantify the anisotropy of turbulence. For magnetic field lines, we find that transport perpendicular to the background field depends on the Kubo number R = ? B B0 lz lx . For small Kubo numbers, R ? 1, we find anomalous, non Gaussian transport regimes (both sub and superdiffusive) which can be described as a Levy random walk. Increasing the Kubo number, i.e., the fluctuation level ? B/B0 and/or the ratio lz/lx, we find first a quasilinear and then a percolative regime, both corresponding to Gaussian diffusion. For particles, we find that transport parallel and perpendicular to the background magnetic field heavily depends on the turbulence anisotropy and on the particle Larmor radius. For turbulence levels typical of the solar wind, ? B/B0 ? 0.5 ?1, when the ratio between the particle Larmor radius and the turbulence correlation lengths is small, anomalous regimes are found in the case lz/lx ? 1, with Levy random walk (superdiffusion) along the magnetic field and subdiffusion in the perpendicular directions. Conversely, for lz/lx > 1 normal, Gaussian diffusion is found. Increasing the ratio between the particle Larmor radius and the turbulence correlation lengths, the parallel superdiffusion is

Particle physics

International Nuclear Information System (INIS)

Kamal, Anwar

2014-01-01

Provides step-by-step derivations. Contains numerous tables and diagrams. Supports learning and teaching with numerous worked examples, questions and problems with answers. Sketches also the historical development of the subject. This textbook teaches particle physics very didactically. It supports learning and teaching with numerous worked examples, questions and problems with answers. Numerous tables and diagrams lead to a better understanding of the explanations. The content of the book covers all important topics of particle physics: Elementary particles are classified from the point of view of the four fundamental interactions. The nomenclature used in particle physics is explained. The discoveries and properties of known elementary particles and resonances are given. The particles considered are positrons, muon, pions, anti-protons, strange particles, neutrino and hadrons. The conservation laws governing the interactions of elementary particles are given. The concepts of parity, spin, charge conjugation, time reversal and gauge invariance are explained. The quark theory is introduced to explain the hadron structure and strong interactions. The solar neutrino problem is considered. Weak interactions are classified into various types, and the selection rules are stated. Non-conservation of parity and the universality of the weak interactions are discussed. Neutral and charged currents, discovery of W and Z bosons and the early universe form important topics of the electroweak interactions. The principles of high energy accelerators including colliders are elaborately explained. Additionally, in the book detectors used in nuclear and particle physics are described. This book is on the upper undergraduate level.

Particulate morphology mathematics applied to particle assemblies

CERN Document Server

Gotoh, Keishi

2012-01-01

Encompassing over fifty years of research, Professor Gotoh addresses the correlation function of spatial structures and the statistical geometry of random particle assemblies. In this book morphological study is formed into random particle assemblies to which various mathematics are applied such as correlation function, radial distribution function and statistical geometry. This leads to the general comparison between the thermodynamic state such as gases and liquids and the random particle assemblies. Although structures of molecular configurations change at every moment due to thermal vibration, liquids can be regarded as random packing of particles. Similarly, gaseous states correspond to particle dispersion. If physical and chemical properties are taken away from the subject, the remainder is the structure itself. Hence, the structural study is ubiquitous and of fundamental importance. This book will prove useful to chemical engineers working on powder technology as well as mathematicians interested in le...

Evolution of the concentration PDF in random environments modeled by global random walk

Science.gov (United States)

Suciu, Nicolae; Vamos, Calin; Attinger, Sabine; Knabner, Peter

2013-04-01

The evolution of the probability density function (PDF) of concentrations of chemical species transported in random environments is often modeled by ensembles of notional particles. The particles move in physical space along stochastic-Lagrangian trajectories governed by Ito equations, with drift coefficients given by the local values of the resolved velocity field and diffusion coefficients obtained by stochastic or space-filtering upscaling procedures. A general model for the sub-grid mixing also can be formulated as a system of Ito equations solving for trajectories in the composition space. The PDF is finally estimated by the number of particles in space-concentration control volumes. In spite of their efficiency, Lagrangian approaches suffer from two severe limitations. Since the particle trajectories are constructed sequentially, the demanded computing resources increase linearly with the number of particles. Moreover, the need to gather particles at the center of computational cells to perform the mixing step and to estimate statistical parameters, as well as the interpolation of various terms to particle positions, inevitably produce numerical diffusion in either particle-mesh or grid-free particle methods. To overcome these limitations, we introduce a global random walk method to solve the system of Ito equations in physical and composition spaces, which models the evolution of the random concentration's PDF. The algorithm consists of a superposition on a regular lattice of many weak Euler schemes for the set of Ito equations. Since all particles starting from a site of the space-concentration lattice are spread in a single numerical procedure, one obtains PDF estimates at the lattice sites at computational costs comparable with those for solving the system of Ito equations associated to a single particle. The new method avoids the limitations concerning the number of particles in Lagrangian approaches, completely removes the numerical diffusion, and

Multilevel and quasi-Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media.

Science.gov (United States)

Crevillén-García, D; Power, H

2017-08-01

In this study, we apply four Monte Carlo simulation methods, namely, Monte Carlo, quasi-Monte Carlo, multilevel Monte Carlo and multilevel quasi-Monte Carlo to the problem of uncertainty quantification in the estimation of the average travel time during the transport of particles through random heterogeneous porous media. We apply the four methodologies to a model problem where the only input parameter, the hydraulic conductivity, is modelled as a log-Gaussian random field by using direct Karhunen-Loéve decompositions. The random terms in such expansions represent the coefficients in the equations. Numerical calculations demonstrating the effectiveness of each of the methods are presented. A comparison of the computational cost incurred by each of the methods for three different tolerances is provided. The accuracy of the approaches is quantified via the mean square error.

Multilevel and quasi-Monte Carlo methods for uncertainty quantification in particle travel times through random heterogeneous porous media

Science.gov (United States)

Crevillén-García, D.; Power, H.

2017-08-01

In this study, we apply four Monte Carlo simulation methods, namely, Monte Carlo, quasi-Monte Carlo, multilevel Monte Carlo and multilevel quasi-Monte Carlo to the problem of uncertainty quantification in the estimation of the average travel time during the transport of particles through random heterogeneous porous media. We apply the four methodologies to a model problem where the only input parameter, the hydraulic conductivity, is modelled as a log-Gaussian random field by using direct Karhunen-Loéve decompositions. The random terms in such expansions represent the coefficients in the equations. Numerical calculations demonstrating the effectiveness of each of the methods are presented. A comparison of the computational cost incurred by each of the methods for three different tolerances is provided. The accuracy of the approaches is quantified via the mean square error.

Charged Particle Diffusion in Isotropic Random Magnetic Fields

Energy Technology Data Exchange (ETDEWEB)

Subedi, P.; Matthaeus, W. H.; Chuychai, P.; Parashar, T. N.; Chhiber, R. [Department of Physics and Astronomy, University of Delaware, Newark, Delaware 19716 (United States); Sonsrettee, W. [Faculty of Engineering and Technology, Panyapiwat Institute of Management, Nonthaburi 11120 (Thailand); Blasi, P. [INAF/Osservatorio Astrofisico di Arcetri, Largo E. Fermi, 5—I-50125 Firenze (Italy); Ruffolo, D. [Department of Physics, Faculty of Science, Mahidol University, Bangkok 10400 (Thailand); Montgomery, D. [Department of Physics and Astronomy, Dartmouth College, Hanover, NH 03755 (United States); Dmitruk, P. [Departamento de Física Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires Ciudad Universitaria, 1428 Buenos Aires (Argentina); Wan, M. [Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen, Guangdong 518055 (China)

2017-03-10

The investigation of the diffusive transport of charged particles in a turbulent magnetic field remains a subject of considerable interest. Research has most frequently concentrated on determining the diffusion coefficient in the presence of a mean magnetic field. Here we consider the diffusion of charged particles in fully three-dimensional isotropic turbulent magnetic fields with no mean field, which may be pertinent to many astrophysical situations. We identify different ranges of particle energy depending upon the ratio of Larmor radius to the characteristic outer length scale of turbulence. Two different theoretical models are proposed to calculate the diffusion coefficient, each applicable to a distinct range of particle energies. The theoretical results are compared to those from computer simulations, showing good agreement.

Classification of Phishing Email Using Random Forest Machine Learning Technique

Directory of Open Access Journals (Sweden)

Andronicus A. Akinyelu

2014-01-01

Full Text Available Phishing is one of the major challenges faced by the world of e-commerce today. Thanks to phishing attacks, billions of dollars have been lost by many companies and individuals. In 2012, an online report put the loss due to phishing attack at about $1.5 billion. This global impact of phishing attacks will continue to be on the increase and thus requires more efficient phishing detection techniques to curb the menace. This paper investigates and reports the use of random forest machine learning algorithm in classification of phishing attacks, with the major objective of developing an improved phishing email classifier with better prediction accuracy and fewer numbers of features. From a dataset consisting of 2000 phishing and ham emails, a set of prominent phishing email features (identified from the literature were extracted and used by the machine learning algorithm with a resulting classification accuracy of 99.7% and low false negative (FN and false positive (FP rates.

Game-based learning as a vehicle to teach first aid content: a randomized experiment.

Science.gov (United States)

Charlier, Nathalie; De Fraine, Bieke

2013-07-01

Knowledge of first aid (FA), which constitutes lifesaving treatments for injuries or illnesses, is important for every individual. In this study, we have set up a group-randomized controlled trial to assess the effectiveness of a board game for learning FA. Four class groups (120 students) were randomly assigned to 2 conditions, a board game or a traditional lecture method (control condition). The effect of the learning environment on students' achievement was examined through a paper-and-pencil test of FA knowledge. Two months after the intervention, the participants took a retention test and completed a questionnaire assessing enjoyment, interest, and motivation. An analysis of pre- and post-test knowledge scores showed that both conditions produced significant increases in knowledge. The lecture was significantly more effective in increasing knowledge, as compared to the board game. Participants indicated that they liked the game condition more than their fellow participants in the traditional lecture condition. These results suggest that traditional lectures are more effective in increasing student knowledge, whereas educational games are more effective for student enjoyment. From this case study we recommend alteration or a combination of these teaching methods to make learning both effective and enjoyable. © 2013, American School Health Association.

Random packing of colloids and granular matter

NARCIS (Netherlands)

Wouterse, A.

2008-01-01

This thesis deals with the random packing of colloids and granular matter. A random packing is a stable disordered collection of touching particles, without long-range positional and orientational order. Experimental random packings of particles with the same shape but made of different materials

Biochemical systems identification by a random drift particle swarm optimization approach

Science.gov (United States)

2014-01-01

Background Finding an efficient method to solve the parameter estimation problem (inverse problem) for nonlinear biochemical dynamical systems could help promote the functional understanding at the system level for signalling pathways. The problem is stated as a data-driven nonlinear regression problem, which is converted into a nonlinear programming problem with many nonlinear differential and algebraic constraints. Due to the typical ill conditioning and multimodality nature of the problem, it is in general difficult for gradient-based local optimization methods to obtain satisfactory solutions. To surmount this limitation, many stochastic optimization methods have been employed to find the global solution of the problem. Results This paper presents an effective search strategy for a particle swarm optimization (PSO) algorithm that enhances the ability of the algorithm for estimating the parameters of complex dynamic biochemical pathways. The proposed algorithm is a new variant of random drift particle swarm optimization (RDPSO), which is used to solve the above mentioned inverse problem and compared with other well known stochastic optimization methods. Two case studies on estimating the parameters of two nonlinear biochemical dynamic models have been taken as benchmarks, under both the noise-free and noisy simulation data scenarios. Conclusions The experimental results show that the novel variant of RDPSO algorithm is able to successfully solve the problem and obtain solutions of better quality than other global optimization methods used for finding the solution to the inverse problems in this study. PMID:25078435

An Improved Ensemble of Random Vector Functional Link Networks Based on Particle Swarm Optimization with Double Optimization Strategy.

Science.gov (United States)

Ling, Qing-Hua; Song, Yu-Qing; Han, Fei; Yang, Dan; Huang, De-Shuang

2016-01-01

For ensemble learning, how to select and combine the candidate classifiers are two key issues which influence the performance of the ensemble system dramatically. Random vector functional link networks (RVFL) without direct input-to-output links is one of suitable base-classifiers for ensemble systems because of its fast learning speed, simple structure and good generalization performance. In this paper, to obtain a more compact ensemble system with improved convergence performance, an improved ensemble of RVFL based on attractive and repulsive particle swarm optimization (ARPSO) with double optimization strategy is proposed. In the proposed method, ARPSO is applied to select and combine the candidate RVFL. As for using ARPSO to select the optimal base RVFL, ARPSO considers both the convergence accuracy on the validation data and the diversity of the candidate ensemble system to build the RVFL ensembles. In the process of combining RVFL, the ensemble weights corresponding to the base RVFL are initialized by the minimum norm least-square method and then further optimized by ARPSO. Finally, a few redundant RVFL is pruned, and thus the more compact ensemble of RVFL is obtained. Moreover, in this paper, theoretical analysis and justification on how to prune the base classifiers on classification problem is presented, and a simple and practically feasible strategy for pruning redundant base classifiers on both classification and regression problems is proposed. Since the double optimization is performed on the basis of the single optimization, the ensemble of RVFL built by the proposed method outperforms that built by some single optimization methods. Experiment results on function approximation and classification problems verify that the proposed method could improve its convergence accuracy as well as reduce the complexity of the ensemble system.

Does reviewing lead to better learning and decision making? Answers from a randomized stock market experiment.

Science.gov (United States)

Wessa, Patrick; Holliday, Ian E

2012-01-01

The literature is not univocal about the effects of Peer Review (PR) within the context of constructivist learning. Due to the predominant focus on using PR as an assessment tool, rather than a constructivist learning activity, and because most studies implicitly assume that the benefits of PR are limited to the reviewee, little is known about the effects upon students who are required to review their peers. Much of the theoretical debate in the literature is focused on explaining how and why constructivist learning is beneficial. At the same time these discussions are marked by an underlying presupposition of a causal relationship between reviewing and deep learning. The purpose of the study is to investigate whether the writing of PR feedback causes students to benefit in terms of: perceived utility about statistics, actual use of statistics, better understanding of statistical concepts and associated methods, changed attitudes towards market risks, and outcomes of decisions that were made. We conducted a randomized experiment, assigning students randomly to receive PR or non-PR treatments and used two cohorts with a different time span. The paper discusses the experimental design and all the software components that we used to support the learning process: Reproducible Computing technology which allows students to reproduce or re-use statistical results from peers, Collaborative PR, and an AI-enhanced Stock Market Engine. The results establish that the writing of PR feedback messages causes students to experience benefits in terms of Behavior, Non-Rote Learning, and Attitudes, provided the sequence of PR activities are maintained for a period that is sufficiently long.

Parallel Monte Carlo Particle Transport and the Quality of Random Number Generators: How Good is Good Enough?

International Nuclear Information System (INIS)

Procassini, R J; Beck, B R

2004-01-01

It might be assumed that use of a ''high-quality'' random number generator (RNG), producing a sequence of ''pseudo random'' numbers with a ''long'' repetition period, is crucial for producing unbiased results in Monte Carlo particle transport simulations. While several theoretical and empirical tests have been devised to check the quality (randomness and period) of an RNG, for many applications it is not clear what level of RNG quality is required to produce unbiased results. This paper explores the issue of RNG quality in the context of parallel, Monte Carlo transport simulations in order to determine how ''good'' is ''good enough''. This study employs the MERCURY Monte Carlo code, which incorporates the CNPRNG library for the generation of pseudo-random numbers via linear congruential generator (LCG) algorithms. The paper outlines the usage of random numbers during parallel MERCURY simulations, and then describes the source and criticality transport simulations which comprise the empirical basis of this study. A series of calculations for each test problem in which the quality of the RNG (period of the LCG) is varied provides the empirical basis for determining the minimum repetition period which may be employed without producing a bias in the mean integrated results

Effects of errorless skill learning in people with mild-to-moderate or severe dementia: a randomized controlled pilot study.

NARCIS (Netherlands)

Kessels, R.P.C.; Hensken, L.M.

2009-01-01

This pilot study examines whether learning without errors is advantageous compared to trial-and-error learning in people with dementia using a procedural task and a randomized case-control design. A sample of 60 people was recruited, consisting of 20 patients with severe dementia, 20 patients with

Effects of errorless skill learning in people with mild-to-moderate or severe dementia: A randomized controlled pilot study

NARCIS (Netherlands)

Kessels, R.P.C.; Olde Hensken, L.M.G.

2009-01-01

This pilot study examines whether learning without errors is advantageous compared to trial-and-error learning in people with dementia using a procedural task and a randomized case-control design. A sample of 60 people was recruited, consisting of 20 patients with severe dementia, 20 patients with

Exploration of probability distribution of velocities of saltating sand particles based on the stochastic particle-bed collisions

International Nuclear Information System (INIS)

Zheng Xiaojing; Xie Li; Zhou Youhe

2005-01-01

The wind-blown sand saltating movement is mainly categorized into two mechanical processes, that is, the interaction between the moving sand particles and the wind in the saltation layer, and the collisions of incident particles with sand bed, and the latter produces a lift-off velocity of a sand particle moving into saltation. In this Letter a methodology of phenomenological analysis is presented to get probability density (distribution) function (pdf) of the lift-off velocity of sand particles from sand bed based on the stochastic particle-bed collision. After the sand particles are dealt with by uniform circular disks and a 2D collision between an incident particle and the granular bed is employed, we get the analytical formulas of lift-off velocity of ejected and rebound particles in saltation, which are functions of some random parameters such as angle and magnitude of incident velocity of the impacting particles, impact and contact angles between the collision particles, and creeping velocity of sand particles, etc. By introducing the probability density functions (pdf's) of these parameters in communion with all possible patterns of sand bed and all possible particle-bed collisions, and using the essential arithmetic of multi-dimension random variables' pdf, the pdf's of lift-off velocities are deduced out and expressed by the pdf's of the random parameters in the collisions. The numerical results of the distributions of lift-off velocities display that they agree well with experimental ones

Development of an E-learning System for the Endoscopic Diagnosis of Early Gastric Cancer: An International Multicenter Randomized Controlled Trial.

Science.gov (United States)

Yao, K; Uedo, N; Muto, M; Ishikawa, H; Cardona, H J; Filho, E C Castro; Pittayanon, R; Olano, C; Yao, F; Parra-Blanco, A; Ho, S H; Avendano, A G; Piscoya, A; Fedorov, E; Bialek, A P; Mitrakov, A; Caro, L; Gonen, C; Dolwani, S; Farca, A; Cuaresma, L F; Bonilla, J J; Kasetsermwiriya, W; Ragunath, K; Kim, S E; Marini, M; Li, H; Cimmino, D G; Piskorz, M M; Iacopini, F; So, J B; Yamazaki, K; Kim, G H; Ang, T L; Milhomem-Cardoso, D M; Waldbaum, C A; Carvajal, W A Piedra; Hayward, C M; Singh, R; Banerjee, R; Anagnostopoulos, G K; Takahashi, Y

2016-07-01

In many countries, gastric cancer is not diagnosed until an advanced stage. An Internet-based e-learning system to improve the ability of endoscopists to diagnose gastric cancer at an early stage was developed and was evaluated for its effectiveness. The study was designed as a randomized controlled trial. After receiving a pre-test, participants were randomly allocated to either an e-learning or non-e-learning group. Only those in the e-learning group gained access to the e-learning system. Two months after the pre-test, both groups received a post-test. The primary endpoint was the difference between the two groups regarding the rate of improvement of their test results. 515 endoscopists from 35 countries were assessed for eligibility, and 332 were enrolled in the study, with 166 allocated to each group. Of these, 151 participants in the e-learning group and 144 in the non-e-learning group were included in the analysis. The mean improvement rate (standard deviation) in the e-learning and non-e-learning groups was 1·24 (0·26) and 1·00 (0·16), respectively (Pe-learning system to expand knowledge and provide invaluable experience regarding the endoscopic detection of early gastric cancer (R000012039). Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.

Machine-learning techniques for family demography: an application of random forests to the analysis of divorce determinants in Germany

OpenAIRE

Arpino, Bruno; Le Moglie, Marco; Mencarini, Letizia

2018-01-01

Demographers often analyze the determinants of life-course events with parametric regression-type approaches. Here, we present a class of nonparametric approaches, broadly defined as machine learning (ML) techniques, and discuss advantages and disadvantages of a popular type known as random forest. We argue that random forests can be useful either as a substitute, or a complement, to more standard parametric regression modeling. Our discussion of random forests is intuitive and...

Emergent dynamics of Cucker-Smale flocking particles in a random environment

Science.gov (United States)

Ha, Seung-Yeal; Jeong, Jiin; Noh, Se Eun; Xiao, Qinghua; Zhang, Xiongtao

2017-02-01

We present a new kinetic Cucker-Smale-Fokker-Planck (CS-FP) type equation with a degenerate diffusion, which describes the dynamics for an ensemble of infinitely many Cucker-Smale particles in a random environment. The asymptotic dynamics of the CS-FP equation exhibits a threshold-like phenomenon depending on the relative strength between the coupling strength and the noise strength. In the small coupling regime, the noise effect becomes dominant, which induces the velocity variance to increase to infinity exponentially fast. In contrast, the velocity alignment effect is strong in the large coupling regime, and the velocity variance tends to zero exponentially fast. We present the global existence of classical solutions to the CS-FP equation for a sufficiently smooth initial datum without smallness in its size. For the kinetic CS-FP equation with a metric dependent communication weight, we provide a uniform-in-time mean-field limit from the stochastic CS-model to the kinetic CS-FP equation without convergence rate.

Diffusion and superdiffusion of a particle in a random potential with finite correlation time

International Nuclear Information System (INIS)

Lebedev, N.; Maass, P.; Feng, S.

1995-01-01

We study theoretically the long time asymptotic of a quantum particle moving in a random time-dependent potential with finite correlation time, in d=1. By applying a new unitary numerical scheme we first show the minor importance of quantum interference and then derive an effective Langevin-type equation for the corresponding clasical problem in the limit of weak potential. We find that on intermediate time scales E kin (t)∼t 2/5 , while the true long time asymptotic is determined by a new friction term, which gives rise to a stationary power law velocity distribution, multifractality of the velocity moments, and a slowing down of the superdiffusive behavior

Virtual reality training versus blended learning of laparoscopic cholecystectomy:a randomized controlled trial with laparoscopic novices

OpenAIRE

Nickel, Felix; Brzoska, Julia Anja; Gondan, Matthias; Rangnick, Henriette Maria; Chu, Jackson; Kenngott, Hannes Götz; Linke, Georg Richard; Kadmon, Martina; Fischer, Lars; Müller-Stich, Beat Peter

2015-01-01

Objective: This study compared virtual reality (VR) training with low cost blended learning (BL) in a structured training program. Background: Training of laparoscopic skills outside the operating room is mandatory to reduce operative times and risks. Methods: Laparoscopy-naïve medical students were randomized in two groups stratified for gender. The BL group (n = 42) used E-learning for laparoscopic cholecystectomy (LC) and practiced basic skills with box trainers. The VR group (n = 42) trai...

PARTICLE FILTERING WITH SEQUENTIAL PARAMETER LEARNING FOR NONLINEAR BOLD fMRI SIGNALS.

Science.gov (United States)

Xia, Jing; Wang, Michelle Yongmei

Analyzing the blood oxygenation level dependent (BOLD) effect in the functional magnetic resonance imaging (fMRI) is typically based on recent ground-breaking time series analysis techniques. This work represents a significant improvement over existing approaches to system identification using nonlinear hemodynamic models. It is important for three reasons. First, instead of using linearized approximations of the dynamics, we present a nonlinear filtering based on the sequential Monte Carlo method to capture the inherent nonlinearities in the physiological system. Second, we simultaneously estimate the hidden physiological states and the system parameters through particle filtering with sequential parameter learning to fully take advantage of the dynamic information of the BOLD signals. Third, during the unknown static parameter learning, we employ the low-dimensional sufficient statistics for efficiency and avoiding potential degeneration of the parameters. The performance of the proposed method is validated using both the simulated data and real BOLD fMRI data.

Modeling Transport in Fractured Porous Media with the Random-Walk Particle Method: The Transient Activity Range and the Particle-Transfer Probability

International Nuclear Information System (INIS)

Lehua Pan; G.S. Bodvarsson

2001-01-01

Multiscale features of transport processes in fractured porous media make numerical modeling a difficult task, both in conceptualization and computation. Modeling the mass transfer through the fracture-matrix interface is one of the critical issues in the simulation of transport in a fractured porous medium. Because conventional dual-continuum-based numerical methods are unable to capture the transient features of the diffusion depth into the matrix (unless they assume a passive matrix medium), such methods will overestimate the transport of tracers through the fractures, especially for the cases with large fracture spacing, resulting in artificial early breakthroughs. We have developed a new method for calculating the particle-transfer probability that can capture the transient features of diffusion depth into the matrix within the framework of the dual-continuum random-walk particle method (RWPM) by introducing a new concept of activity range of a particle within the matrix. Unlike the multiple-continuum approach, the new dual-continuum RWPM does not require using additional grid blocks to represent the matrix. It does not assume a passive matrix medium and can be applied to the cases where global water flow exists in both continua. The new method has been verified against analytical solutions for transport in the fracture-matrix systems with various fracture spacing. The calculations of the breakthrough curves of radionuclides from a potential repository to the water table in Yucca Mountain demonstrate the effectiveness of the new method for simulating 3-D, mountain-scale transport in a heterogeneous, fractured porous medium under variably saturated conditions

Randomizing Roaches: Exploring the "Bugs" of Randomization in Experimental Design

Science.gov (United States)

Wagler, Amy; Wagler, Ron

2014-01-01

Understanding the roles of random selection and random assignment in experimental design is a central learning objective in most introductory statistics courses. This article describes an activity, appropriate for a high school or introductory statistics course, designed to teach the concepts, values and pitfalls of random selection and assignment…

eLearning course may shorten the duration of mechanical restraint among psychiatric inpatients: a cluster-randomized trial.

Science.gov (United States)

Kontio, Raija; Pitkänen, Anneli; Joffe, Grigori; Katajisto, Jouko; Välimäki, Maritta

2014-10-01

The management of psychiatric inpatients exhibiting severely disturbed and aggressive behaviour is an important educational topic. Well structured, IT-based educational programmes (eLearning) often ensure quality and may make training more affordable and accessible. The aim of this study was to explore the impact of an eLearning course for personnel on the rates and duration of seclusion and mechanical restraint among psychiatric inpatients. In a cluster-randomized intervention trial, the nursing personnel on 10 wards were randomly assigned to eLearning (intervention) or training-as-usual (control) groups. The eLearning course comprised six modules with specific topics (legal and ethical issues, behaviour-related factors, therapeutic relationship and self-awareness, teamwork and integrating knowledge with practice) and specific learning methods. The rates (incidents per 1000 occupied bed days) and durations of the coercion incidents were examined before and after the course. A total of 1283 coercion incidents (1143 seclusions [89%] and 140 incidents involving the use of mechanical restraints [11%]) were recorded on the study wards during the data collection period. On the intervention wards, there were no statistically significant changes in the rates of seclusion and mechanical restraint. However, the duration of incidents involving mechanical restraints shortened from 36.0 to 4.0 h (median) (P eLearning course, the duration of incidents involving the use of mechanical restraints decreased. However, more studies are needed to ensure that the content of the course focuses on the most important factors associated with the seclusion-related elements. The eLearning course deserves further development and further studies. The duration of coercion incidents merits attention in future research.

Diffusion of oriented particles in porous media

Energy Technology Data Exchange (ETDEWEB)

Haber, René [Institut für Physik, Technische Universität Chemnitz, D-09107 Chemnitz (Germany); Centre for Nonlinear Studies, Institute of Cybernetics at Tallinn University of Technology, Akadeemia tee 21, 12618 Tallinn (Estonia); Prehl, Janett [Institut für Physik, Technische Universität Chemnitz, D-09107 Chemnitz (Germany); Herrmann, Heiko [Centre for Nonlinear Studies, Institute of Cybernetics at Tallinn University of Technology, Akadeemia tee 21, 12618 Tallinn (Estonia); Hoffmann, Karl Heinz, E-mail: hoffmann@physik.tu-chemnitz.de [Institut für Physik, Technische Universität Chemnitz, D-09107 Chemnitz (Germany)

2013-11-29

Diffusion of particles in porous media often shows subdiffusive behavior. Here, we analyze the dynamics of particles exhibiting an orientation. The features we focus on are geometrical restrictions and the dynamical consequences of the interactions between the local surrounding structure and the particle orientation. This interaction can lead to particles getting temporarily stuck in parts of the structure. Modeling this interaction by a particular random walk dynamics on fractal structures we find that the random walk dimension is not affected while the diffusion constant shows a variety of interesting and surprising features.

Diffusion of oriented particles in porous media

International Nuclear Information System (INIS)

Haber, René; Prehl, Janett; Herrmann, Heiko; Hoffmann, Karl Heinz

2013-01-01

Diffusion of particles in porous media often shows subdiffusive behavior. Here, we analyze the dynamics of particles exhibiting an orientation. The features we focus on are geometrical restrictions and the dynamical consequences of the interactions between the local surrounding structure and the particle orientation. This interaction can lead to particles getting temporarily stuck in parts of the structure. Modeling this interaction by a particular random walk dynamics on fractal structures we find that the random walk dimension is not affected while the diffusion constant shows a variety of interesting and surprising features.

The effect of adaptive versus static practicing on student learning - evidence from a randomized field experiment

NARCIS (Netherlands)

van Klaveren, Chris; Vonk, Sebastiaan; Cornelisz, Ilja

2017-01-01

Schools and governments are increasingly investing in adaptive practice software. To date, the evidence whether adaptivity improves learning outcomes is limited and mixed. A large-scale randomized control trial is conducted in Dutch secondary schools to evaluate the effectiveness of an adaptive

Trapping, percolation, and anomalous diffusion of particles in a two-dimensional random field

International Nuclear Information System (INIS)

Avellaneda, M.; Apelian, C.; Elliott, F. Jr.

1993-01-01

The authors analyze the advection of particles in a velocity field with Hamiltonian H(x,y) = bar V 1 y-bar V 2 x + W 1 (y) - W 2 (x), where W i , i=1,2, are random functions with stationary, independent increments. In the absence of molecular diffusion, the particle dynamics are sensitive to the streamline topology, which depends on the mean-to-fluctuations ratio p=max(|bar V 1 |/bar U;|bar V 2 |/bar U), with bar U = [|W' 1 | 2 ] 1/2 = rms fluctuations. The model is exactly solvable for p≥1 and well suited for Monte Carlo simulations for all p. Statistics are considered of streamlines for p=0, deriving power laws for the escape probability and the length of escaping trajectories for a box of size L much-gt 1. Also obtained is a characterization of the statistical topography of the Hamiltonian. The large-scale transport is studied of advected particles with p > 0. For 0 -v/2 [x(t) - (x(t))] and t -v/2 [y(t) - (y(t))]. The large-scale motions are Fickian (v=1) or superdiffusive (v=3/2) with a non-Gaussian coarse-grained probability, according to the direction of the mean velocity relative to the underlying lattice. These results are obtained analytically for p≥1 and extended to the regime 0 1 , bar V 2 ) for which stagnation regions in the flow exist. The results are compared with existing predictions on the topology of streamlines based on percolation theory and with mean-field calculations of effective diffusivities. 29 refs., 15 figs., 7 tabs

E-learning as a complement to presential teaching of blindness prevention: a randomized clinical trial

Directory of Open Access Journals (Sweden)

Rodrigo Pessoa Cavalcanti Lira

2013-02-01

Full Text Available OBJECTIVE: To investigate if E-learning material improves the basal student knowledge level before attending the presential class of blindness prevention (BP and if helps to fix this information one-month after the class. METHODS: Fourth-year medical students were randomly assigned to have a presential class of BP (Traditional group = TG or to have a presential class of BP plus an additional E-learning material (E-learning group = ELG. This material was e-mailed one week before the presential class. The students were submitted to a multiple-choice test (with three options each with seven questions immediately before the presential class, immediately after the class, and one-month later. The three tests had the same questions; however, the answers options were distributed in different sequences. The primary outcome was immediate pretest score. The secondary outcomes were immediate posttest score and one-month posttest score. RESULTS: Among the 120 fourth-year medical students, a random sample of 34 students was assigned to the TG and 34 students was assigned to the ELG. The two groups showed similar immediate posttest score (TG=6.8 and ELG=6.9; P<.754, but the differences at the immediate pretest score (TG=3.6 and ELG=4.7; P<.001, and at the one-month posttest score, were significant (TG=6.1 and ELG=6.8; P<.001. CONCLUSIONS: The pretest and the one-month posttest results suggested that the E-learning material acts as an effective complementary tool of the presential class of blindness prevention.

TU-G-BRB-00: Clinical Trials in Proton and Particle Therapy

International Nuclear Information System (INIS)

2015-01-01

Proton therapy, in particular, and ion therapy, just beginning, are becoming an increasing focus of attention in clinical radiation oncology and medical physics. Both modalities have been criticized of lacking convincing evidence from randomized trials proving their efficacy, justifying the higher costs involved in these therapies. This session will provide an overview of the current status of clinical trials in proton therapy, including recent developments in ion therapy. As alluded to in the introductory talk by Dr. Schulte, opinions are diverging widely as to the usefulness and need for clinical trials in particle therapy and the challenge of equipoise. The lectures will highlight some of the challenges that surround clinical trials in particle therapy. One, presented by Dr. Choy from UT Southwestern, is that new technology and even different types of particles such as helium and carbon ions are introduced into this environment, increasing the phase space of clinical variables. The other is the issue of medical physics quality assurance with physical phantoms, presented by Mrs. Taylor from IROC Houston, which is more challenging because 3D and 4D image guidance and active delivery techniques are in relatively early stages of development. The role of digital phantoms in developing clinical treatment planning protocols and as a QA tool will also be highlighted by Dr. Lee from NCI. The symposium will be rounded off by a panel discussion among the Symposium speakers, arguing pro or con the need and readiness for clinical trials in proton and ion therapy. Learning Objectives: To get an update on the current status of clinical trials allowing or mandating proton therapy. Learn about the status of planned clinical trials in the U.S. and worldwide involving ion therapy. Discuss the challenges in the design and QA of clinical trials in particle therapy. Learn about existing and future physical and computational anthropomorphic phantoms for charged particle clinical trial

Evaluation of an e-learning system for diagnosis of gastric lesions using magnifying narrow-band imaging: a multicenter randomized controlled study.

Science.gov (United States)

Nakanishi, Hiroyoshi; Doyama, Hisashi; Ishikawa, Hideki; Uedo, Noriya; Gotoda, Takuji; Kato, Mototsugu; Nagao, Shigeaki; Nagami, Yasuaki; Aoyagi, Hiroyuki; Imagawa, Atsushi; Kodaira, Junichi; Mitsui, Shinya; Kobayashi, Nozomu; Muto, Manabu; Takatori, Hajime; Abe, Takashi; Tsujii, Masahiko; Watari, Jiro; Ishiyama, Shuhei; Oda, Ichiro; Ono, Hiroyuki; Kaneko, Kazuhiro; Yokoi, Chizu; Ueo, Tetsuya; Uchita, Kunihisa; Matsumoto, Kenshi; Kanesaka, Takashi; Morita, Yoshinori; Katsuki, Shinichi; Nishikawa, Jun; Inamura, Katsuhisa; Kinjo, Tetsu; Yamamoto, Katsumi; Yoshimura, Daisuke; Araki, Hiroshi; Kashida, Hiroshi; Hosokawa, Ayumu; Mori, Hirohito; Yamashita, Haruhiro; Motohashi, Osamu; Kobayashi, Kazuhiko; Hirayama, Michiaki; Kobayashi, Hiroyuki; Endo, Masaki; Yamano, Hiroo; Murakami, Kazunari; Koike, Tomoyuki; Hirasawa, Kingo; Miyaoka, Youichi; Hamamoto, Hidetaka; Hikichi, Takuto; Hanabata, Norihiro; Shimoda, Ryo; Hori, Shinichiro; Sato, Tadashi; Kodashima, Shinya; Okada, Hiroyuki; Mannami, Tomohiko; Yamamoto, Shojiro; Niwa, Yasumasa; Yashima, Kazuo; Tanabe, Satoshi; Satoh, Hiro; Sasaki, Fumisato; Yamazato, Tetsuro; Ikeda, Yoshiou; Nishisaki, Hogara; Nakagawa, Masahiro; Matsuda, Akio; Tamura, Fumio; Nishiyama, Hitoshi; Arita, Keiko; Kawasaki, Keisuke; Hoppo, Kazushige; Oka, Masashi; Ishihara, Shinichi; Mukasa, Michita; Minamino, Hiroaki; Yao, Kenshi

2017-10-01

Background and study aim  Magnifying narrow-band imaging (M-NBI) is useful for the accurate diagnosis of early gastric cancer (EGC). However, acquiring skill at M-NBI diagnosis takes substantial effort. An Internet-based e-learning system to teach endoscopic diagnosis of EGC using M-NBI has been developed. This study evaluated its effectiveness. Participants and methods  This study was designed as a multicenter randomized controlled trial. We recruited endoscopists as participants from all over Japan. After completing Test 1, which consisted of M-NBI images of 40 gastric lesions, participants were randomly assigned to the e-learning or non-e-learning groups. Only the e-learning group was allowed to access the e-learning system. After the e-learning period, both groups received Test 2. The analysis set was participants who scored e-learning group and 197 in the non-e-learning group). After the e-learning period, all 395 completed Test 2. The analysis sets were e-learning group: n = 184; and non-e-learning group: n = 184. The mean Test 1 score was 59.9 % for the e-learning group and 61.7 % for the non-e-learning group. The change in accuracy in Test 2 was significantly higher in the e-learning group than in the non-e-learning group (7.4 points vs. 0.14 points, respectively; P  e-learning system in improving practitioners' capabilities to diagnose EGC using M-NBI.Trial registered at University Hospital Medical Information Network Clinical Trials Registry (UMIN000008569). © Georg Thieme Verlag KG Stuttgart · New York.

Biking with Particles: Junior Triathletes' Learning about Drafting through Exploring Agent-Based Models and Inventing New Tactics

Science.gov (United States)

Hirsh, Alon; Levy, Sharona T.

2013-01-01

The present research addresses a curious finding: how learning physical principles enhanced athletes' biking performance but not their conceptual understanding. The study involves a model-based triathlon training program, Biking with Particles, concerning aerodynamics of biking in groups (drafting). A conceptual framework highlights several…

Probabilistic homogenization of random composite with ellipsoidal particle reinforcement by the iterative stochastic finite element method

Science.gov (United States)

Sokołowski, Damian; Kamiński, Marcin

2018-01-01

This study proposes a framework for determination of basic probabilistic characteristics of the orthotropic homogenized elastic properties of the periodic composite reinforced with ellipsoidal particles and a high stiffness contrast between the reinforcement and the matrix. Homogenization problem, solved by the Iterative Stochastic Finite Element Method (ISFEM) is implemented according to the stochastic perturbation, Monte Carlo simulation and semi-analytical techniques with the use of cubic Representative Volume Element (RVE) of this composite containing single particle. The given input Gaussian random variable is Young modulus of the matrix, while 3D homogenization scheme is based on numerical determination of the strain energy of the RVE under uniform unit stretches carried out in the FEM system ABAQUS. The entire series of several deterministic solutions with varying Young modulus of the matrix serves for the Weighted Least Squares Method (WLSM) recovery of polynomial response functions finally used in stochastic Taylor expansions inherent for the ISFEM. A numerical example consists of the High Density Polyurethane (HDPU) reinforced with the Carbon Black particle. It is numerically investigated (1) if the resulting homogenized characteristics are also Gaussian and (2) how the uncertainty in matrix Young modulus affects the effective stiffness tensor components and their PDF (Probability Density Function).

A pedestrian's view on interacting particle systems, KPZ universality and random matrices

International Nuclear Information System (INIS)

Kriecherbauer, Thomas; Krug, Joachim

2010-01-01

These notes are based on lectures delivered by the authors at a Langeoog seminar of SFB/TR12 Symmetries and Universality in Mesoscopic Systems to a mixed audience of mathematicians and theoretical physicists. After a brief outline of the basic physical concepts of equilibrium and nonequilibrium states, the one-dimensional simple exclusion process is introduced as a paradigmatic nonequilibrium interacting particle system. The stationary measure on the ring is derived and the idea of the hydrodynamic limit is sketched. We then introduce the phenomenological Kardar-Parisi-Zhang (KPZ) equation and explain the associated universality conjecture for surface fluctuations in growth models. This is followed by a detailed exposition of a seminal paper of Johansson [59] that relates the current fluctuations of the totally asymmetric simple exclusion process (TASEP) to the Tracy-Widom distribution of random matrix theory. The implications of this result are discussed within the framework of the KPZ conjecture. (topical review)

Pediatric emergency medicine asynchronous e-learning: a multicenter randomized controlled Solomon four-group study.

Science.gov (United States)

Chang, Todd P; Pham, Phung K; Sobolewski, Brad; Doughty, Cara B; Jamal, Nazreen; Kwan, Karen Y; Little, Kim; Brenkert, Timothy E; Mathison, David J

2014-08-01

Asynchronous e-learning allows for targeted teaching, particularly advantageous when bedside and didactic education is insufficient. An asynchronous e-learning curriculum has not been studied across multiple centers in the context of a clinical rotation. We hypothesize that an asynchronous e-learning curriculum during the pediatric emergency medicine (EM) rotation improves medical knowledge among residents and students across multiple participating centers. Trainees on pediatric EM rotations at four large pediatric centers from 2012 to 2013 were randomized in a Solomon four-group design. The experimental arms received an asynchronous e-learning curriculum consisting of nine Web-based, interactive, peer-reviewed Flash/HTML5 modules. Postrotation testing and in-training examination (ITE) scores quantified improvements in knowledge. A 2 × 2 analysis of covariance (ANCOVA) tested interaction and main effects, and Pearson's correlation tested associations between module usage, scores, and ITE scores. A total of 256 of 458 participants completed all study elements; 104 had access to asynchronous e-learning modules, and 152 were controls who used the current education standards. No pretest sensitization was found (p = 0.75). Use of asynchronous e-learning modules was associated with an improvement in posttest scores (p effect (partial η(2) = 0.19). Posttest scores correlated with ITE scores (r(2) = 0.14, p e-learning is an effective educational tool to improve knowledge in a clinical rotation. Web-based asynchronous e-learning is a promising modality to standardize education among multiple institutions with common curricula, particularly in clinical rotations where scheduling difficulties, seasonality, and variable experiences limit in-hospital learning. © 2014 by the Society for Academic Emergency Medicine.

Strategies for processing diffraction data from randomly oriented particles

International Nuclear Information System (INIS)

Elser, Veit

2011-01-01

The high intensity of free-electron X-ray light sources may enable structure determinations of viruses or even individual proteins without the encumbrance of first forming crystals. This note compares two schemes of non-crystalline diffraction data collection that have been proposed: serial single-shot data from individual particles, and averaged cross-correlation data from particle ensembles. The information content of these schemes is easily compared and we show that the single-shot approach, although experimentally more challenging, is always superior in this respect. In fact, for 3D structure determination a constraint counting argument shows that the cross-correlation scheme suffers from data deficiency. -- Research Highlights: →We compare two data collection schemes for imaging single particles with x-rays. →Cross-correlation data suffers an information deficit relative to single-shot data. →We recognize John Spence for his many contributions to single particle imaging.

TU-G-BRB-04: Digital Phantoms for Developing Protocols in Particle Therapy

International Nuclear Information System (INIS)

Lee, C.

2015-01-01

Proton therapy, in particular, and ion therapy, just beginning, are becoming an increasing focus of attention in clinical radiation oncology and medical physics. Both modalities have been criticized of lacking convincing evidence from randomized trials proving their efficacy, justifying the higher costs involved in these therapies. This session will provide an overview of the current status of clinical trials in proton therapy, including recent developments in ion therapy. As alluded to in the introductory talk by Dr. Schulte, opinions are diverging widely as to the usefulness and need for clinical trials in particle therapy and the challenge of equipoise. The lectures will highlight some of the challenges that surround clinical trials in particle therapy. One, presented by Dr. Choy from UT Southwestern, is that new technology and even different types of particles such as helium and carbon ions are introduced into this environment, increasing the phase space of clinical variables. The other is the issue of medical physics quality assurance with physical phantoms, presented by Mrs. Taylor from IROC Houston, which is more challenging because 3D and 4D image guidance and active delivery techniques are in relatively early stages of development. The role of digital phantoms in developing clinical treatment planning protocols and as a QA tool will also be highlighted by Dr. Lee from NCI. The symposium will be rounded off by a panel discussion among the Symposium speakers, arguing pro or con the need and readiness for clinical trials in proton and ion therapy. Learning Objectives: To get an update on the current status of clinical trials allowing or mandating proton therapy. Learn about the status of planned clinical trials in the U.S. and worldwide involving ion therapy. Discuss the challenges in the design and QA of clinical trials in particle therapy. Learn about existing and future physical and computational anthropomorphic phantoms for charged particle clinical trial

A Mock Randomized Controlled Trial With Audience Response Technology for Teaching and Learning Epidemiology.

Science.gov (United States)

Baker, Philip R A; Francis, Daniel P; Cathcart, Abby

2017-04-01

The study's objective was to apply and assess an active learning approach to epidemiology and critical appraisal. Active learning comprised a mock, randomized controlled trial (RCT) conducted with learners in 3 countries. The mock trial consisted of blindly eating red Smarties candy (intervention) compared to yellow Smarties (control) to determine whether red Smarties increase happiness. Audience response devices were employed with the 3-fold purposes to produce outcome data for analysis of the effects of red Smarties, identify baseline and subsequent changes in participant's knowledge and confidence in understanding of RCTs, and assess the teaching approach. Of those attending, 82% (117 of 143 learners) participated in the trial component. Participating in the mock trial was a positive experience, and the use of the technology aided learning. The trial produced data that learners analyzed in "real time" during the class. The mock RCT is a fun and engaging approach to teaching RCTs and helping students to develop skills in critical appraisal.

E-Learning in Urology: Implementation of the Learning and Teaching Platform CASUS® - Do Virtual Patients Lead to Improved Learning Outcomes? A Randomized Study among Students.

Science.gov (United States)

Schneider, Anna-Teresa; Albers, Peter; Müller-Mattheis, Volker

2015-01-01

E-learning is playing an increasing role in medical education, supporting a problem-based and practical oriented education without putting patients at risk and compensating for the decrease in instructor-centered teaching. Not much research has been done concerning learning effects and reaction on behalf of the students. We created computer-based cases for four important diagnoses in urology using the authoring system CASUS®. Fourth-year medical school students were randomized into two groups: (1) the CASUS® group, using the online cases for preparation, and (2) the book group, using a textbook. A multiple-choice test referring to the prepared topic had to be completed at the beginning of each lecture and the results were analyzed. Evaluation of the students concerning the acceptance of the program was done at the end of the semester. Members of the CASUS® group scored significantly higher with an average of 20% better test results than students using textbooks for preparation. Evaluation regarding the program showed a highly positive rating. Limitations include the small study population and the possibly biased test performance of the students. Computerized patient cases facilitate practice-oriented teaching and result in an interesting and engaging learning model with improved learning outcomes. © 2015 S. Karger AG, Basel.

Radiation Transport in Random Media With Large Fluctuations

Science.gov (United States)

Olson, Aaron; Prinja, Anil; Franke, Brian

2017-09-01

Neutral particle transport in media exhibiting large and complex material property spatial variation is modeled by representing cross sections as lognormal random functions of space and generated through a nonlinear memory-less transformation of a Gaussian process with covariance uniquely determined by the covariance of the cross section. A Karhunen-Loève decomposition of the Gaussian process is implemented to effciently generate realizations of the random cross sections and Woodcock Monte Carlo used to transport particles on each realization and generate benchmark solutions for the mean and variance of the particle flux as well as probability densities of the particle reflectance and transmittance. A computationally effcient stochastic collocation method is implemented to directly compute the statistical moments such as the mean and variance, while a polynomial chaos expansion in conjunction with stochastic collocation provides a convenient surrogate model that also produces probability densities of output quantities of interest. Extensive numerical testing demonstrates that use of stochastic reduced-order modeling provides an accurate and cost-effective alternative to random sampling for particle transport in random media.

Progressive learning in endoscopy simulation training improves clinical performance: a blinded randomized trial.

Science.gov (United States)

Grover, Samir C; Scaffidi, Michael A; Khan, Rishad; Garg, Ankit; Al-Mazroui, Ahmed; Alomani, Tareq; Yu, Jeffrey J; Plener, Ian S; Al-Awamy, Mohamed; Yong, Elaine L; Cino, Maria; Ravindran, Nikila C; Zasowski, Mark; Grantcharov, Teodor P; Walsh, Catharine M

2017-11-01

A structured comprehensive curriculum (SCC) that uses simulation-based training (SBT) can improve clinical colonoscopy performance. This curriculum may be enhanced through the application of progressive learning, a training strategy centered on incrementally challenging learners. We aimed to determine whether a progressive learning-based curriculum (PLC) would lead to superior clinical performance compared with an SCC. This was a single-blinded randomized controlled trial conducted at a single academic center. Thirty-seven novice endoscopists were recruited and randomized to either a PLC (n = 18) or to an SCC (n = 19). The PLC comprised 6 hours of SBT, which progressed in complexity and difficulty. The SCC included 6 hours of SBT, with cases of random order of difficulty. Both groups received expert feedback and 4 hours of didactic teaching. Participants were assessed at baseline, immediately after training, and 4 to 6 weeks after training. The primary outcome was participants' performance during their first 2 clinical colonoscopies, as assessed by using the Joint Advisory Group Direct Observation of Procedural Skills assessment tool (JAG DOPS). Secondary outcomes were differences in endoscopic knowledge, technical and communication skills, and global performance in the simulated setting. The PLC group outperformed the SCC group during first and second clinical colonoscopies, measured by JAG DOPS (P PLC group had superior technical and communication skills and global performance in the simulated setting (P  .05). Our findings demonstrate the superiority of a PLC for endoscopic simulation, compared with an SCC. Challenging trainees progressively is a simple, theory-based approach to simulation whereby the performance of clinical colonoscopies can be improved. (Clinical trial registration number: NCT02000180.). Copyright © 2017 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.

Randomness at the root of things 1: Random walks

Science.gov (United States)

Ogborn, Jon; Collins, Simon; Brown, Mick

2003-09-01

This is the first of a pair of articles about randomness in physics. In this article, we use some variations on the idea of a `random walk' to consider first the path of a particle in Brownian motion, and then the random variation to be expected in radioactive decay. The arguments are set in the context of the general importance of randomness both in physics and in everyday life. We think that the ideas could usefully form part of students' A-level work on random decay and quantum phenomena, as well as being good for their general education. In the second article we offer a novel and simple approach to Poisson sequences.

A tale of two "forests": random forest machine learning AIDS tropical forest carbon mapping.

Science.gov (United States)

Mascaro, Joseph; Asner, Gregory P; Knapp, David E; Kennedy-Bowdoin, Ty; Martin, Roberta E; Anderson, Christopher; Higgins, Mark; Chadwick, K Dana

2014-01-01

Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus). The Random Forest machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such as regionally stratified sampling and upscaling, and it has rarely been employed with spatial data. Here, we evaluated the performance of Random Forest in upscaling airborne LiDAR (Light Detection and Ranging)-based carbon estimates compared to the stratification approach over a 16-million hectare focal area of the Western Amazon. We considered two runs of Random Forest, both with and without spatial contextual modeling by including--in the latter case--x, and y position directly in the model. In each case, we set aside 8 million hectares (i.e., half of the focal area) for validation; this rigorous test of Random Forest went above and beyond the internal validation normally compiled by the algorithm (i.e., called "out-of-bag"), which proved insufficient for this spatial application. In this heterogeneous region of Northern Peru, the model with spatial context was the best preforming run of Random Forest, and explained 59% of LiDAR-based carbon estimates within the validation area, compared to 37% for stratification or 43% by Random Forest without spatial context. With the 60% improvement in explained variation, RMSE against validation LiDAR samples improved from 33 to 26 Mg C ha(-1) when using Random Forest with spatial context. Our results suggest that spatial context should be considered when using Random Forest, and that doing so may result in substantially improved carbon stock modeling for purposes of climate change mitigation.

A tale of two "forests": random forest machine learning AIDS tropical forest carbon mapping.

Directory of Open Access Journals (Sweden)

Joseph Mascaro

Full Text Available Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus. The Random Forest machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such as regionally stratified sampling and upscaling, and it has rarely been employed with spatial data. Here, we evaluated the performance of Random Forest in upscaling airborne LiDAR (Light Detection and Ranging-based carbon estimates compared to the stratification approach over a 16-million hectare focal area of the Western Amazon. We considered two runs of Random Forest, both with and without spatial contextual modeling by including--in the latter case--x, and y position directly in the model. In each case, we set aside 8 million hectares (i.e., half of the focal area for validation; this rigorous test of Random Forest went above and beyond the internal validation normally compiled by the algorithm (i.e., called "out-of-bag", which proved insufficient for this spatial application. In this heterogeneous region of Northern Peru, the model with spatial context was the best preforming run of Random Forest, and explained 59% of LiDAR-based carbon estimates within the validation area, compared to 37% for stratification or 43% by Random Forest without spatial context. With the 60% improvement in explained variation, RMSE against validation LiDAR samples improved from 33 to 26 Mg C ha(-1 when using Random Forest with spatial context. Our results suggest that spatial context should be considered when using Random Forest, and that doing so may result in substantially improved carbon stock modeling for purposes of climate change mitigation.

Image Segmentation using a Refined Comprehensive Learning Particle Swarm Optimizer for Maximum Tsallis Entropy Thresholding

OpenAIRE

L. Jubair Ahmed; A. Ebenezer Jeyakumar

2013-01-01

Thresholding is one of the most important techniques for performing image segmentation. In this paper to compute optimum thresholds for Maximum Tsallis entropy thresholding (MTET) model, a new hybrid algorithm is proposed by integrating the Comprehensive Learning Particle Swarm Optimizer (CPSO) with the Powell’s Conjugate Gradient (PCG) method. Here the CPSO will act as the main optimizer for searching the near-optimal thresholds while the PCG method will be used to fine tune the best solutio...

Steady- and transient-state analysis of fully ceramic microencapsulated fuel with randomly dispersed tristructural isotropic particles via two-temperature homogenized model-I: Theory and method

International Nuclear Information System (INIS)

Lee, Yoon Hee; Cho, Bum Hee; Cho, Nam Zin

2016-01-01

As a type of accident-tolerant fuel, fully ceramic microencapsulated (FCM) fuel was proposed after the Fukushima accident in Japan. The FCM fuel consists of tristructural isotropic particles randomly dispersed in a silicon carbide (SiC) matrix. For a fuel element with such high heterogeneity, we have proposed a two-temperature homogenized model using the particle transport Monte Carlo method for the heat conduction problem. This model distinguishes between fuel-kernel and SiC matrix temperatures. Moreover, the obtained temperature profiles are more realistic than those of other models. In Part I of the paper, homogenized parameters for the FCM fuel in which tristructural isotropic particles are randomly dispersed in the fine lattice stochastic structure are obtained by (1) matching steady-state analytic solutions of the model with the results of particle transport Monte Carlo method for heat conduction problems, and (2) preserving total enthalpies in fuel kernels and SiC matrix. The homogenized parameters have two desirable properties: (1) they are insensitive to boundary conditions such as coolant bulk temperatures and thickness of cladding, and (2) they are independent of operating power density. By performing the Monte Carlo calculations with the temperature-dependent thermal properties of the constituent materials of the FCM fuel, temperature-dependent homogenized parameters are obtained

Effects of particles on macrophage migration

International Nuclear Information System (INIS)

Mueller, H.L.; Robinson, B.; Muggenburg, B.A.; Guilmette, R.A.

1988-01-01

Random and directed migration of cells lavaged from the lungs of rats and dogs were studied in vitro using formyl-methionyl-leucyl-phenylalanine (FMLP) as a chemo attractant. Rats and dogs were intratracheally instilled with either saline (controls) or about 10 9 fluorescent polystyrene microspheres, and lungs were lavaged after 1 or 7 days. More rat cells than dog cells migrated at 1 day after both particle and saline instillations; at 7 days, only cells lavaged from rats after particle instillations showed enhanced migration abilities. FMLP caused a 1.5 to 2-fold enhancement compared to random migration in cell numbers counted on the lower sides of chemotactic membranes. Cells with low particle numbers were more likely to migrate than those with high numbers of ingested particles. (author)

Microwave single-scattering properties of randomly oriented soft-ice hydrometeors

Directory of Open Access Journals (Sweden)

D. Casella

2008-11-01

Full Text Available Large ice hydrometeors are usually present in intense convective clouds and may significantly affect the upwelling radiances that are measured by satellite-borne microwave radiometers – especially, at millimeter-wavelength frequencies. Thus, interpretation of these measurements (e.g., for precipitation retrieval requires knowledge of the single scattering properties of ice particles. On the other hand, shape and internal structure of these particles (especially, the larger ones is very complex and variable, and therefore it is necessary to resort to simplifying assumptions in order to compute their single-scattering parameters.

In this study, we use the discrete dipole approximation (DDA to compute the absorption and scattering efficiencies and the asymmetry factor of two kinds of quasi-spherical and non-homogeneous soft-ice particles in the frequency range 50–183 GHz. Particles of the first kind are modeled as quasi-spherical ice particles having randomly distributed spherical air inclusions. Particles of the second kind are modeled as random aggregates of ice spheres having random radii. In both cases, particle densities and dimensions are coherent with the snow hydrometeor category that is utilized by the University of Wisconsin – Non-hydrostatic Modeling System (UW-NMS cloud-mesoscale model. Then, we compare our single-scattering results for randomly-oriented soft-ice hydrometeors with corresponding ones that make use of: a effective-medium equivalent spheres, b solid-ice equivalent spheres, and c randomly-oriented aggregates of ice cylinders. Finally, we extend to our particles the scattering formulas that have been developed by other authors for randomly-oriented aggregates of ice cylinders.

Efficacy of the LiSN & Learn auditory training software: randomized blinded controlled study

Directory of Open Access Journals (Sweden)

Sharon Cameron

2012-09-01

Full Text Available Children with a spatial processing disorder (SPD require a more favorable signal-to-noise ratio in the classroom because they have difficulty perceiving sound source location cues. Previous research has shown that a novel training program - LiSN & Learn - employing spatialized sound, overcomes this deficit. Here we investigate whether improvements in spatial processing ability are specific to the LiSN & Learn training program. Participants were ten children (aged between 6;0 [years;months] and 9;9 with normal peripheral hearing who were diagnosed as having SPD using the Listening in Spatialized Noise - Sentences test (LiSN-S. In a blinded controlled study, the participants were randomly allocated to train with either the LiSN & Learn or another auditory training program - Earobics - for approximately 15 min per day for twelve weeks. There was a significant improvement post-training on the conditions of the LiSN-S that evaluate spatial processing ability for the LiSN & Learn group (P=0.03 to 0.0008, η 2=0.75 to 0.95, n=5, but not for the Earobics group (P=0.5 to 0.7, η 2=0.1 to 0.04, n=5. Results from questionnaires completed by the participants and their parents and teachers revealed improvements in real-world listening performance post-training were greater in the LiSN & Learn group than the Earobics group. LiSN & Learn training improved binaural processing ability in children with SPD, enhancing their ability to understand speech in noise. Exposure to non-spatialized auditory training does not produce similar outcomes, emphasizing the importance of deficit-specific remediation.

Efficacy of the LiSN & Learn Auditory Training Software: randomized blinded controlled study

Directory of Open Access Journals (Sweden)

Sharon Cameron

2012-01-01

Full Text Available Background: Children with a spatial processing disorder (SPD require a more favorable signal-to-noise ratio in the classroom because they have difficulty perceiving sound source location cues. Previous research has shown that a novel training program - LiSN & Learn - employing spatialized sound, overcomes this deficit. Here we investigate whether improvements in spatial processing ability are specific to the LiSN & Learn training program. Materials and methods: Participants were ten children (aged between 6;0 [years;months] and 9;9 with normal peripheral hearing who were diagnosed as having SPD using the Listening in Spatialized Noise – Sentences Test (LISN-S. In a blinded controlled study, the participants were randomly allocated to train with either the LiSN & Learn or another auditory training program – Earobics - for approximately 15 minutes per day for twelve weeks. Results: There was a significant improvement post-training on the conditions of the LiSN-S that evaluate spatial processing ability for the LiSN & Learn group (p=0.03 to 0.0008, η2=0.75 to 0.95, n=5, but not for the Earobics group (p=0.5 to 0.7, η2=0.1 to 0.04, n=5. Results from questionnaires completed by the participants and their parents and teachers revealed improvements in real-world listening performance post-training were greater in the LiSN & Learn group than the Earobics group. Conclusions: LiSN & Learn training improved binaural processing ability in children with SPD, enhancing their ability to understand speech in noise. Exposure to non-spatialized auditory training does not produce similar outcomes, emphasizing the importance of deficit-specific remediation.

Video- or text-based e-learning when teaching clinical procedures? A randomized controlled trial

Directory of Open Access Journals (Sweden)

Buch SV

2014-08-01

Full Text Available Steen Vigh Buch,1 Frederik Philip Treschow,2 Jesper Brink Svendsen,3 Bjarne Skjødt Worm4 1Department of Vascular Surgery, Rigshospitalet, Copenhagen, Denmark; 2Department of Anesthesia and Intensive Care, Herlev Hospital, Copenhagen, Denmark; 3Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; 4Department of Anesthesia and Intensive Care, Bispebjerg Hospital, Copenhagen, Denmark Background and aims: This study investigated the effectiveness of two different levels of e-learning when teaching clinical skills to medical students. Materials and methods: Sixty medical students were included and randomized into two comparable groups. The groups were given either a video- or text/picture-based e-learning module and subsequently underwent both theoretical and practical examination. A follow-up test was performed 1 month later. Results: The students in the video group performed better than the illustrated text-based group in the practical examination, both in the primary test (P<0.001 and in the follow-up test (P<0.01. Regarding theoretical knowledge, no differences were found between the groups on the primary test, though the video group performed better on the follow-up test (P=0.04. Conclusion: Video-based e-learning is superior to illustrated text-based e-learning when teaching certain practical clinical skills. Keywords: e-learning, video versus text, medicine, clinical skills

Discovery Mondays - 'Particle tracks: Seeing the invisible'

CERN Multimedia

2007-01-01

Simulation of particle tracks in the CMS detector. How can you 'see' something as infinitesimal and fleeting as an elementary particle that defeats even the most powerful microscope? Well, physicists have detectors to snoop on them. Unlike biologists looking at bacteria, physicists don't see the particles themselves. They study their impact on sensitive materials as they pass through them at ultra high speed, a bit like seeing plane vapour trails in a clear sky. At the next Discovery Monday you will be able to find out about the different methods used at CERN to detect particles. There will be demonstrations of the cloud chamber, where particles leave tell-tale evidence of their passage in tracks of droplets. You will also learn about past and current particle track detection techniques and how the tracks are reconstructed into magnificent composite images. Don't miss this opportunity to learn about the various ways of 'seeing' particles. The event will be conducted in French. Come along to the Microcosm ...

Using random walk models to simulate the vertical distribution of particles in a turbulent water column

DEFF Research Database (Denmark)

Visser, Andre

1997-01-01

Random walk simulation has the potential to be an extremely powerful tool in the investigation of turbulence in environmental processes. However, care must be taken in applying such simulations to the motion of particles in turbulent marine systems where turbulent diffusivity is commonly spatially...... are incorrect, and a simple technique that can properly simulate turbulent diffusion in the marine environment is discussed...... non-uniform. The problems associated with this nonuniformity are far from negligible and have been recognised for quite some time. However, incorrect implementations continue to appear in the Literature. In this note computer simulations are presented to illustrate how and why these implementations...

Long-term Results of the UCSF-LBNL Randomized Trial: Charged Particle With Helium Ion Versus Iodine-125 Plaque Therapy for Choroidal and Ciliary Body Melanoma

Energy Technology Data Exchange (ETDEWEB)

Mishra, Kavita K., E-mail: Kavita.mishra@ucsf.edu [Department of Radiation Oncology, University of California-San Francisco, San Francisco, California (United States); Quivey, Jeanne M.; Daftari, Inder K. [Department of Radiation Oncology, University of California-San Francisco, San Francisco, California (United States); Lawrence Berkeley National Laboratory, Berkeley, California (United States); Weinberg, Vivian [Department of Radiation Oncology, University of California-San Francisco, San Francisco, California (United States); Cole, Tia B. [The Tumori Foundation, San Francisco, California (United States); Patel, Kishan [Department of Radiation Oncology, University of California-San Francisco, San Francisco, California (United States); Castro, Joseph R.; Phillips, Theodore L. [Department of Radiation Oncology, University of California-San Francisco, San Francisco, California (United States); Lawrence Berkeley National Laboratory, Berkeley, California (United States); Char, Devron H. [The Tumori Foundation, San Francisco, California (United States); Department of Ophthalmology, University of California-San Francisco, San Francisco, California (United States); Department of Ophthalmology, Stanford University, Palo Alto, California (United States)

2015-06-01

Purpose: Relevant clinical data are needed given the increasing national interest in charged particle radiation therapy (CPT) programs. Here we report long-term outcomes from the only randomized, stratified trial comparing CPT with iodine-125 plaque therapy for choroidal and ciliary body melanoma. Methods and Materials: From 1985 to 1991, 184 patients met eligibility criteria and were randomized to receive particle (86 patients) or plaque therapy (98 patients). Patients were stratified by tumor diameter, thickness, distance to disc/fovea, anterior extension, and visual acuity. Tumors close to the optic disc were included. Local tumor control, as well as eye preservation, metastases due to melanoma, and survival were evaluated. Results: Median follow-up times for particle and plaque arm patients were 14.6 years and 12.3 years, respectively (P=.22), and for those alive at last follow-up, 18.5 and 16.5 years, respectively (P=.81). Local control (LC) for particle versus plaque treatment was 100% versus 84% at 5 years, and 98% versus 79% at 12 years, respectively (log rank: P=.0006). If patients with tumors close to the disc (<2 mm) were excluded, CPT still resulted in significantly improved LC: 100% versus 90% at 5 years and 98% versus 86% at 12 years, respectively (log rank: P=.048). Enucleation rate was lower after CPT: 11% versus 22% at 5 years and 17% versus 37% at 12 years, respectively (log rank: P=.01). Using Cox regression model, likelihood ratio test, treatment was the most important predictor of LC (P=.0002) and eye preservation (P=.01). CPT was a significant predictor of prolonged disease-free survival (log rank: P=.001). Conclusions: Particle therapy resulted in significantly improved local control, eye preservation, and disease-free survival as confirmed by long-term outcomes from the only randomized study available to date comparing radiation modalities in choroidal and ciliary body melanoma.

Vacuum instability in a random electric field

International Nuclear Information System (INIS)

Krive, I.V.; Pastur, L.A.

1984-01-01

The reaction of the vacuum on an intense spatially homogeneous random electric field is investigated. It is shown that a stochastic electric field always causes a breakdown of the boson vacuum, and the number of pairs of particles which are created by the electric field increases exponentially in time. For the choice of potential field in the form of a dichotomic random process we find in explicit form the dependence of the average number of pairs of particles on the time of the action of the source of the stochastic field. For the fermion vacuum the average number of pairs of particles which are created by the field in the lowest order of perturbation theory in the amplitude of the random field is independent of time

Mobile-Based Video Learning Outcomes in Clinical Nursing Skill Education: A Randomized Controlled Trial.

Science.gov (United States)

Lee, Nam-Ju; Chae, Sun-Mi; Kim, Haejin; Lee, Ji-Hye; Min, Hyojin Jennifer; Park, Da-Eun

2016-01-01

Mobile devices are a regular part of daily life among the younger generations. Thus, now is the time to apply mobile device use to nursing education. The purpose of this study was to identify the effects of a mobile-based video clip on learning motivation, competence, and class satisfaction in nursing students using a randomized controlled trial with a pretest and posttest design. A total of 71 nursing students participated in this study: 36 in the intervention group and 35 in the control group. A video clip of how to perform a urinary catheterization was developed, and the intervention group was able to download it to their own mobile devices for unlimited viewing throughout 1 week. All of the students participated in a practice laboratory to learn urinary catheterization and were blindly tested for their performance skills after participation in the laboratory. The intervention group showed significantly higher levels of learning motivation and class satisfaction than did the control. Of the fundamental nursing competencies, the intervention group was more confident in practicing catheterization than their counterparts. Our findings suggest that video clips using mobile devices are useful tools that educate student nurses on relevant clinical skills and improve learning outcomes.

Steady- and Transient-State Analyses of Fully Ceramic Microencapsulated Fuel with Randomly Dispersed Tristructural Isotropic Particles via Two-Temperature Homogenized Model—I: Theory and Method

Directory of Open Access Journals (Sweden)

Yoonhee Lee

2016-06-01

Full Text Available As a type of accident-tolerant fuel, fully ceramic microencapsulated (FCM fuel was proposed after the Fukushima accident in Japan. The FCM fuel consists of tristructural isotropic particles randomly dispersed in a silicon carbide (SiC matrix. For a fuel element with such high heterogeneity, we have proposed a two-temperature homogenized model using the particle transport Monte Carlo method for the heat conduction problem. This model distinguishes between fuel-kernel and SiC matrix temperatures. Moreover, the obtained temperature profiles are more realistic than those of other models. In Part I of the paper, homogenized parameters for the FCM fuel in which tristructural isotropic particles are randomly dispersed in the fine lattice stochastic structure are obtained by (1 matching steady-state analytic solutions of the model with the results of particle transport Monte Carlo method for heat conduction problems, and (2 preserving total enthalpies in fuel kernels and SiC matrix. The homogenized parameters have two desirable properties: (1 they are insensitive to boundary conditions such as coolant bulk temperatures and thickness of cladding, and (2 they are independent of operating power density. By performing the Monte Carlo calculations with the temperature-dependent thermal properties of the constituent materials of the FCM fuel, temperature-dependent homogenized parameters are obtained.

Random forest learning of ultrasonic statistical physics and object spaces for lesion detection in 2D sonomammography

Science.gov (United States)

Sheet, Debdoot; Karamalis, Athanasios; Kraft, Silvan; Noël, Peter B.; Vag, Tibor; Sadhu, Anup; Katouzian, Amin; Navab, Nassir; Chatterjee, Jyotirmoy; Ray, Ajoy K.

2013-03-01

Breast cancer is the most common form of cancer in women. Early diagnosis can significantly improve lifeexpectancy and allow different treatment options. Clinicians favor 2D ultrasonography for breast tissue abnormality screening due to high sensitivity and specificity compared to competing technologies. However, inter- and intra-observer variability in visual assessment and reporting of lesions often handicaps its performance. Existing Computer Assisted Diagnosis (CAD) systems though being able to detect solid lesions are often restricted in performance. These restrictions are inability to (1) detect lesion of multiple sizes and shapes, and (2) differentiate between hypo-echoic lesions from their posterior acoustic shadowing. In this work we present a completely automatic system for detection and segmentation of breast lesions in 2D ultrasound images. We employ random forests for learning of tissue specific primal to discriminate breast lesions from surrounding normal tissues. This enables it to detect lesions of multiple shapes and sizes, as well as discriminate between hypo-echoic lesion from associated posterior acoustic shadowing. The primal comprises of (i) multiscale estimated ultrasonic statistical physics and (ii) scale-space characteristics. The random forest learns lesion vs. background primal from a database of 2D ultrasound images with labeled lesions. For segmentation, the posterior probabilities of lesion pixels estimated by the learnt random forest are hard thresholded to provide a random walks segmentation stage with starting seeds. Our method achieves detection with 99.19% accuracy and segmentation with mean contour-to-contour error < 3 pixels on a set of 40 images with 49 lesions.

Modeling and Compensation of Random Drift of MEMS Gyroscopes Based on Least Squares Support Vector Machine Optimized by Chaotic Particle Swarm Optimization.

Science.gov (United States)

Xing, Haifeng; Hou, Bo; Lin, Zhihui; Guo, Meifeng

2017-10-13

MEMS (Micro Electro Mechanical System) gyroscopes have been widely applied to various fields, but MEMS gyroscope random drift has nonlinear and non-stationary characteristics. It has attracted much attention to model and compensate the random drift because it can improve the precision of inertial devices. This paper has proposed to use wavelet filtering to reduce noise in the original data of MEMS gyroscopes, then reconstruct the random drift data with PSR (phase space reconstruction), and establish the model for the reconstructed data by LSSVM (least squares support vector machine), of which the parameters were optimized using CPSO (chaotic particle swarm optimization). Comparing the effect of modeling the MEMS gyroscope random drift with BP-ANN (back propagation artificial neural network) and the proposed method, the results showed that the latter had a better prediction accuracy. Using the compensation of three groups of MEMS gyroscope random drift data, the standard deviation of three groups of experimental data dropped from 0.00354°/s, 0.00412°/s, and 0.00328°/s to 0.00065°/s, 0.00072°/s and 0.00061°/s, respectively, which demonstrated that the proposed method can reduce the influence of MEMS gyroscope random drift and verified the effectiveness of this method for modeling MEMS gyroscope random drift.

Machine Learning

CERN Multimedia

CERN. Geneva

2017-01-01

Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses on developing algorithms to identify patterns and regularities in data, and using these learned patterns to make predictions on new observations. Boosted by its industrial and commercial applications, the field of machine learning is quickly evolving and expanding. Recent advances have seen great success in the realms of computer vision, natural language processing, and broadly in data science. Many of these techniques have already been applied in particle physics, for instance for particle identification, detector monitoring, and the optimization of computer resources. Modern machine learning approaches, such as deep learning, are only just beginning to be applied to the analysis of High Energy Physics data to approach more and more complex problems. These classes will review the framework behind machine learning and discuss recent developments in the field.

Thermophoresis as persistent random walk

International Nuclear Information System (INIS)

Plyukhin, A.V.

2009-01-01

In a simple model of a continuous random walk a particle moves in one dimension with the velocity fluctuating between +v and -v. If v is associated with the thermal velocity of a Brownian particle and allowed to be position dependent, the model accounts readily for the particle's drift along the temperature gradient and recovers basic results of the conventional thermophoresis theory.

Convergence of a random walk method for the Burgers equation

International Nuclear Information System (INIS)

Roberts, S.

1985-10-01

In this paper we consider a random walk algorithm for the solution of Burgers' equation. The algorithm uses the method of fractional steps. The non-linear advection term of the equation is solved by advecting ''fluid'' particles in a velocity field induced by the particles. The diffusion term of the equation is approximated by adding an appropriate random perturbation to the positions of the particles. Though the algorithm is inefficient as a method for solving Burgers' equation, it does model a similar method, the random vortex method, which has been used extensively to solve the incompressible Navier-Stokes equations. The purpose of this paper is to demonstrate the strong convergence of our random walk method and so provide a model for the proof of convergence for more complex random walk algorithms; for instance, the random vortex method without boundaries

Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation

Science.gov (United States)

Janahiraman, Tiagrajah V.; Ahmad, Nooraziah; Hani Nordin, Farah

2018-04-01

The CNC machine is controlled by manipulating cutting parameters that could directly influence the process performance. Many optimization methods has been applied to obtain the optimal cutting parameters for the desired performance function. Nonetheless, the industry still uses the traditional technique to obtain those values. Lack of knowledge on optimization techniques is the main reason for this issue to be prolonged. Therefore, the simple yet easy to implement, Optimal Cutting Parameters Selection System is introduced to help the manufacturer to easily understand and determine the best optimal parameters for their turning operation. This new system consists of two stages which are modelling and optimization. In modelling of input-output and in-process parameters, the hybrid of Extreme Learning Machine and Particle Swarm Optimization is applied. This modelling technique tend to converge faster than other artificial intelligent technique and give accurate result. For the optimization stage, again the Particle Swarm Optimization is used to get the optimal cutting parameters based on the performance function preferred by the manufacturer. Overall, the system can reduce the gap between academic world and the industry by introducing a simple yet easy to implement optimization technique. This novel optimization technique can give accurate result besides being the fastest technique.

Algorithmic learning in a random world

CERN Document Server

Vovk, Vladimir; Shafer, Glenn

2005-01-01

A new scientific monograph developing significant new algorithmic foundations in machine learning theory. Researchers and postgraduates in CS, statistics, and A.I. will find the book an authoritative and formal presentation of some of the most promising theoretical developments in machine learning.

Effects of Flipped Learning Using Online Materials in a Surgical Nursing Practicum: A Pilot Stratified Group-Randomized Trial.

Science.gov (United States)

Lee, Myung Kyung; Park, Bu Kyung

2018-01-01

This study examined the effect of flipped learning in comparison to traditional learning in a surgical nursing practicum. The subjects of this study were 102 nursing students in their third year of university who were scheduled to complete a clinical nursing practicum in an operating room or surgical unit. Participants were randomly assigned to either a flipped learning group (n = 51) or a traditional learning group (n = 51) for the 1-week, 45-hour clinical nursing practicum. The flipped-learning group completed independent e-learning lessons on surgical nursing and received a brief orientation prior to the commencement of the practicum, while the traditional-learning group received a face-to-face orientation and on-site instruction. After the completion of the practicum, both groups completed a case study and a conference. The student's self-efficacy, self-leadership, and problem-solving skills in clinical practice were measured both before and after the one-week surgical nursing practicum. Participants' independent goal setting and evaluation of beliefs and assumptions for the subscales of self-leadership and problem-solving skills were compared for the flipped learning group and the traditional learning group. The results showed greater improvement on these indicators for the flipped learning group in comparison to the traditional learning group. The flipped learning method might offer more effective e-learning opportunities in terms of self-leadership and problem-solving than the traditional learning method in surgical nursing practicums.

Numerical investigation of compaction of deformable particles with bonded-particle model

Directory of Open Access Journals (Sweden)

Dosta Maksym

2017-01-01

Full Text Available In this contribution, a novel approach developed for the microscale modelling of particles which undergo large deformations is presented. The proposed method is based on the bonded-particle model (BPM and multi-stage strategy to adjust material and model parameters. By the BPM, modelled objects are represented as agglomerates which consist of smaller ideally spherical particles and are connected with cylindrical solid bonds. Each bond is considered as a separate object and in each time step the forces and moments acting in them are calculated. The developed approach has been applied to simulate the compaction of elastomeric rubber particles as single particles or in a random packing. To describe the complex mechanical behaviour of the particles, the solid bonds were modelled as ideally elastic beams. The functional parameters of solid bonds as well as material parameters of bonds and primary particles were estimated based on the experimental data for rubber spheres. Obtained results for acting force and for particle deformations during uniaxial compression are in good agreement with experimental data at higher strains.

Friend or Foe? Flipped Classroom for Undergraduate Electrocardiogram Learning: a Randomized Controlled Study.

Science.gov (United States)

Rui, Zeng; Lian-Rui, Xiang; Rong-Zheng, Yue; Jing, Zeng; Xue-Hong, Wan; Chuan, Zuo

2017-03-07

Interpreting an electrocardiogram (ECG) is not only one of the most important parts of clinical diagnostics but also one of the most difficult topics to teach and learn. In order to enable medical students to master ECG interpretation skills in a limited teaching period, the flipped teaching method has been recommended by previous research to improve teaching effect on undergraduate ECG learning. A randomized controlled trial for ECG learning was conducted, involving 181 junior-year medical undergraduates using a flipped classroom as an experimental intervention, compared with Lecture-Based Learning (LBL) as a control group. All participants took an examination one week after the intervention by analysing 20 ECGs from actual clinical cases and submitting their ECG reports. A self-administered questionnaire was also used to evaluate the students' attitudes, total learning time, and conditions under each teaching method. The students in the experimental group scored significantly higher than the control group (8.72 ± 1.01 vs 8.03 ± 1.01, t = 4.549, P = 0.000) on ECG interpretation. The vast majority of the students in the flipped classroom group held positive attitudes toward the flipped classroom method and also supported LBL. There was no significant difference (4.07 ± 0.96 vs 4.16 ± 0.89, Z = - 0.948, P = 0.343) between the groups. Prior to class, the students in the flipped class group devoted significantly more time than those in the control group (42.33 ± 22.19 vs 30.55 ± 10.15, t = 4.586, P = 0.000), whereas after class, the time spent by the two groups were not significantly different (56.50 ± 46.80 vs 54.62 ± 31.77, t = 0.317, P = 0.752). Flipped classroom teaching can improve medical students' interest in learning and their self-learning abilities. It is an effective teaching model that needs to be further studied and promoted.

Effectiveness of applying flipped learning to clinical nursing practicums for nursing students in Korea: A randomized controlled trial.

Science.gov (United States)

Kim, Hyun Sook; Kim, Mi Young; Cho, Mi-Kyoung; Jang, Sun Joo

2017-10-01

The purpose of this study was to develop flipped learning models for clinical practicums and compare their effectiveness regarding learner motivation toward learning, satisfaction, and confidence in performing core nursing skills among undergraduate nursing students in Korea. This study was a randomized clinical trial designed to compare the effectiveness of 2 flipped learning models. Data were collected for 3 days from October 21 to 23, 2015 before the clinical practicum was implemented and for 2 weeks from October 26 to December 18, 2015 during the practicum period. The confidence of the students in performing core nursing skills was likely to increase after they engaged in the clinical practicum in both study groups. However, while learner confidence and motivation were not affected by the type of flipped learning, learner satisfaction did differ between the 2 groups. The findings indicate that applying flipped learning allows students to conduct individualized learning with a diversity of clinical cases at their own level of understanding and at their own pace before they participate in real-world practicums. © 2017 John Wiley & Sons Australia, Ltd.

Development of random geometry capability in RMC code for stochastic media analysis

International Nuclear Information System (INIS)

Liu, Shichang; She, Ding; Liang, Jin-gang; Wang, Kan

2015-01-01

Highlights: • Monte Carlo method plays an important role in modeling of particle transport in random media. • Three stochastic geometry modeling methods have been developed in RMC. • The stochastic effects of the randomly dispersed fuel particles are analyzed. • Investigation of accuracy and efficiency of three methods has been carried out. • All the methods are effective, and explicit modeling is regarded as the best choice. - Abstract: Simulation of particle transport in random media poses a challenge for traditional deterministic transport methods, due to the significant effects of spatial and energy self-shielding. Monte Carlo method plays an important role in accurate simulation of random media, owing to its flexible geometry modeling and the use of continuous-energy nuclear cross sections. Three stochastic geometry modeling methods including Random Lattice Method, Chord Length Sampling and explicit modeling approach with mesh acceleration technique, have been developed in RMC to simulate the particle transport in the dispersed fuels. The verifications of the accuracy and the investigations of the calculation efficiency have been carried out. The stochastic effects of the randomly dispersed fuel particles are also analyzed. The results show that all three stochastic geometry modeling methods can account for the effects of the random dispersion of fuel particles, and the explicit modeling method can be regarded as the best choice

MMRW-BOOKS, Legacy books on slowing down, thermalization, particle transport theory, random processes in reactors

International Nuclear Information System (INIS)

Williams, M.M.R.

2007-01-01

Description: Prof. M.M..R Williams has now released three of his legacy books for free distribution: 1 - M.M.R. Williams: The Slowing Down and Thermalization of Neutrons, North-Holland Publishing Company - Amsterdam, 582 pages, 1966. Content: Part I - The Thermal Energy Region: 1. Introduction and Historical Review, 2. The Scattering Kernel, 3. Neutron Thermalization in an Infinite Homogeneous Medium, 4. Neutron Thermalization in Finite Media, 5. The Spatial Dependence of the Energy Spectrum, 6. Reactor Cell Calculations, 7. Synthetic Scattering Kernels. Part II - The Slowing Down Region: 8. Scattering Kernels in the Slowing Down Region, 9. Neutron Slowing Down in an Infinite Homogeneous Medium, 10.Neutron Slowing Down and Diffusion. 2 - M.M.R. Williams: Mathematical Methods in Particle Transport Theory, Butterworths, London, 430 pages, 1971. Content: 1 The General Problem of Particle Transport, 2 The Boltzmann Equation for Gas Atoms and Neutrons, 3 Boundary Conditions, 4 Scattering Kernels, 5 Some Basic Problems in Neutron Transport and Rarefied Gas Dynamics, 6 The Integral Form of the Transport Equation in Plane, Spherical and Cylindrical Geometries, 7 Exact Solutions of Model Problems, 8 Eigenvalue Problems in Transport Theory, 9 Collision Probability Methods, 10 Variational Methods, 11 Polynomial Approximations. 3 - M.M.R. Williams: Random Processes in Nuclear Reactors, Pergamon Press Oxford New York Toronto Sydney, 243 pages, 1974. Content: 1. Historical Survey and General Discussion, 2. Introductory Mathematical Treatment, 3. Applications of the General Theory, 4. Practical Applications of the Probability Distribution, 5. The Langevin Technique, 6. Point Model Power Reactor Noise, 7. The Spatial Variation of Reactor Noise, 8. Random Phenomena in Heterogeneous Reactor Systems, 9. Associated Fluctuation Problems, Appendix: Noise Equivalent Sources. Note to the user: Prof. M.M.R Williams owns the copyright of these books and he authorises the OECD/NEA Data Bank

Dissipative particle dynamics simulation of flow generated by two rotating concentric cylinders: II. Lateral dissipative and random forces

International Nuclear Information System (INIS)

Filipovic, N; Haber, S; Kojic, M; Tsuda, A

2008-01-01

Traditional DPD methods address dissipative and random forces exerted along the line connecting neighbouring particles. Espanol (1998 Phys. Rev. E 57 2930-48) suggested adding dissipative and random force components in a direction perpendicular to this line. This paper focuses on the advantages and disadvantages of such an addition as compared with the traditional DPD method. Our benchmark system comprises fluid initially at rest occupying the space between two concentric cylinders rotating with various angular velocities. The effect of the lateral force components on the time evolution of the simulated velocity profile was also compared with that of the known analytical solution. The results show that (i) the solution accuracy at steady state has improved and the error has been reduced by at least 30% (in one case by 75%), (ii) the DPD time to reach steady state has been halved, (iii) the CPU time has increased by only 30%, and (iv) no significant differences exist in density and temperature distributions

A new fundamental model of moving particle for reinterpreting Schroedinger equation

International Nuclear Information System (INIS)

Umar, Muhamad Darwis

2012-01-01

The study of Schrödinger equation based on a hypothesis that every particle must move randomly in a quantum-sized volume has been done. In addition to random motion, every particle can do relative motion through the movement of its quantum-sized volume. On the other way these motions can coincide. In this proposed model, the random motion is one kind of intrinsic properties of the particle. The every change of both speed of randomly intrinsic motion and or the velocity of translational motion of a quantum-sized volume will represent a transition between two states, and the change of speed of randomly intrinsic motion will generate diffusion process or Brownian motion perspectives. Diffusion process can take place in backward and forward processes and will represent a dissipative system. To derive Schrödinger equation from our hypothesis we use time operator introduced by Nelson. From a fundamental analysis, we find out that, naturally, we should view the means of Newton’s Law F(vector sign) = ma(vector sign) as no an external force, but it is just to describe both the presence of intrinsic random motion and the change of the particle energy.

Performance evaluation of different types of particle representation procedures of Particle Swarm Optimization in Job-shop Scheduling Problems

Science.gov (United States)

Izah Anuar, Nurul; Saptari, Adi

2016-02-01

This paper addresses the types of particle representation (encoding) procedures in a population-based stochastic optimization technique in solving scheduling problems known in the job-shop manufacturing environment. It intends to evaluate and compare the performance of different particle representation procedures in Particle Swarm Optimization (PSO) in the case of solving Job-shop Scheduling Problems (JSP). Particle representation procedures refer to the mapping between the particle position in PSO and the scheduling solution in JSP. It is an important step to be carried out so that each particle in PSO can represent a schedule in JSP. Three procedures such as Operation and Particle Position Sequence (OPPS), random keys representation and random-key encoding scheme are used in this study. These procedures have been tested on FT06 and FT10 benchmark problems available in the OR-Library, where the objective function is to minimize the makespan by the use of MATLAB software. Based on the experimental results, it is discovered that OPPS gives the best performance in solving both benchmark problems. The contribution of this paper is the fact that it demonstrates to the practitioners involved in complex scheduling problems that different particle representation procedures can have significant effects on the performance of PSO in solving JSP.

Machine Learning and Infrared Thermography for Fiber Orientation Assessment on Randomly-Oriented Strands Parts.

Science.gov (United States)

Fernandes, Henrique; Zhang, Hai; Figueiredo, Alisson; Malheiros, Fernando; Ignacio, Luis Henrique; Sfarra, Stefano; Ibarra-Castanedo, Clemente; Guimaraes, Gilmar; Maldague, Xavier

2018-01-19

The use of fiber reinforced materials such as randomly-oriented strands has grown in recent years, especially for manufacturing of aerospace composite structures. This growth is mainly due to their advantageous properties: they are lighter and more resistant to corrosion when compared to metals and are more easily shaped than continuous fiber composites. The resistance and stiffness of these materials are directly related to their fiber orientation. Thus, efficient approaches to assess their fiber orientation are in demand. In this paper, a non-destructive evaluation method is applied to assess the fiber orientation on laminates reinforced with randomly-oriented strands. More specifically, a method called pulsed thermal ellipsometry combined with an artificial neural network, a machine learning technique, is used in order to estimate the fiber orientation on the surface of inspected parts. Results showed that the method can be potentially used to inspect large areas with good accuracy and speed.

Effects of mobile augmented reality learning compared to textbook learning on medical students: randomized controlled pilot study.

Science.gov (United States)

Albrecht, Urs-Vito; Folta-Schoofs, Kristian; Behrends, Marianne; von Jan, Ute

2013-08-20

By adding new levels of experience, mobile Augmented Reality (mAR) can significantly increase the attractiveness of mobile learning applications in medical education. To compare the impact of the heightened realism of a self-developed mAR blended learning environment (mARble) on learners to textbook material, especially for ethically sensitive subjects such as forensic medicine, while taking into account basic psychological aspects (usability and higher level of emotional involvement) as well as learning outcomes (increased learning efficiency). A prestudy was conducted based on a convenience sample of 10 third-year medical students. The initial emotional status was captured using the "Profile of Mood States" questionnaire (POMS, German variation); previous knowledge about forensic medicine was determined using a 10-item single-choice (SC) test. During the 30-minute learning period, the students were randomized into two groups: the first group consisted of pairs of students, each equipped with one iPhone with a preinstalled copy of mARble, while the second group was provided with textbook material. Subsequently, both groups were asked to once again complete the POMS questionnaire and SC test to measure changes in emotional state and knowledge gain. Usability as well as pragmatic and hedonic qualities of the learning material was captured using AttrakDiff2 questionnaires. Data evaluation was conducted anonymously. Descriptive statistics for the score in total and the subgroups were calculated before and after the intervention. The scores of both groups were tested against each other using paired and unpaired signed-rank tests. An item analysis was performed for the SC test to objectify difficulty and selectivity. Statistically significant, the mARble group (6/10) showed greater knowledge gain than the control group (4/10) (Wilcoxon z=2.232, P=.03). The item analysis of the SC test showed a difficulty of P=0.768 (s=0.09) and a selectivity of RPB=0.2. For m

Open-closed-loop iterative learning control for a class of nonlinear systems with random data dropouts

Science.gov (United States)

Cheng, X. Y.; Wang, H. B.; Jia, Y. L.; Dong, YH

2018-05-01

In this paper, an open-closed-loop iterative learning control (ILC) algorithm is constructed for a class of nonlinear systems subjecting to random data dropouts. The ILC algorithm is implemented by a networked control system (NCS), where only the off-line data is transmitted by network while the real-time data is delivered in the point-to-point way. Thus, there are two controllers rather than one in the control system, which makes better use of the saved and current information and thereby improves the performance achieved by open-loop control alone. During the transfer of off-line data between the nonlinear plant and the remote controller data dropout occurs randomly and the data dropout rate is modeled as a binary Bernoulli random variable. Both measurement and control data dropouts are taken into consideration simultaneously. The convergence criterion is derived based on rigorous analysis. Finally, the simulation results verify the effectiveness of the proposed method.

Electroless plating apparatus for discrete microsized particles

International Nuclear Information System (INIS)

Mayer, A.

1978-01-01

Method and apparatus are disclosed for producing very uniform coatings of a desired material on discrete microsized particles by electroless techniques. Agglomeration or bridging of the particles during the deposition process is prevented by imparting a sufficiently random motion to the particles that they are not in contact with each other for a time sufficient for such to occur

Quincke random walkers

Science.gov (United States)

Pradillo, Gerardo; Heintz, Aneesh; Vlahovska, Petia

2017-11-01

The spontaneous rotation of a sphere in an applied uniform DC electric field (Quincke effect) has been utilized to engineer self-propelled particles: if the sphere is initially resting on a surface, it rolls. The Quincke rollers have been widely used as a model system to study collective behavior in ``active'' suspensions. If the applied field is DC, an isolated Quincke roller follows a straight line trajectory. In this talk, we discuss the design of a Quincke roller that executes a random-walk-like behavior. We utilize AC field - upon reversal of the field direction a fluctuation in the axis of rotation (which is degenerate in the plane perpendicular to the field and parallel to the surface) introduces randomness in the direction of motion. The MSD of an isolated Quincke walker depends on frequency, amplitude, and waveform of the electric field. Experiment and theory are compared. We also investigate the collective behavior of Quincke walkers,the transport of inert particles in a bath of Quincke walkers, and the spontaneous motion of a drop containing Quincke active particle. supported by NSF Grant CBET 1437545.

Angular Distribution of Particles Emerging from a Diffusive Region and its Implications for the Fleck-Canfield Random Walk Algorithm for Implicit Monte Carlo Radiation Transport

CERN Document Server

Cooper, M A

2000-01-01

We present various approximations for the angular distribution of particles emerging from an optically thick, purely isotropically scattering region into a vacuum. Our motivation is to use such a distribution for the Fleck-Canfield random walk method [1] for implicit Monte Carlo (IMC) [2] radiation transport problems. We demonstrate that the cosine distribution recommended in the original random walk paper [1] is a poor approximation to the angular distribution predicted by transport theory. Then we examine other approximations that more closely match the transport angular distribution.

Diffusion of massive particles around an Abelian-Higgs string

Science.gov (United States)

Saha, Abhisek; Sanyal, Soma

2018-03-01

We study the diffusion of massive particles in the space time of an Abelian Higgs string. The particles in the early universe plasma execute Brownian motion. This motion of the particles is modeled as a two dimensional random walk in the plane of the Abelian Higgs string. The particles move randomly in the space time of the string according to their geodesic equations. We observe that for certain values of their energy and angular momentum, an overdensity of particles is observed close to the string. We find that the string parameters determine the distribution of the particles. We make an estimate of the density fluctuation generated around the string as a function of the deficit angle. Though the thickness of the string is small, the length is large and the overdensity close to the string may have cosmological consequences in the early universe.

Quantum random number generator

Science.gov (United States)

Soubusta, Jan; Haderka, Ondrej; Hendrych, Martin

2001-03-01

Since reflection or transmission of a quantum particle on a beamsplitter is inherently random quantum process, a device built on this principle does not suffer from drawbacks of neither pseudo-random computer generators or classical noise sources. Nevertheless, a number of physical conditions necessary for high quality random numbers generation must be satisfied. Luckily, in quantum optics realization they can be well controlled. We present an easy random number generator based on the division of weak light pulses on a beamsplitter. The randomness of the generated bit stream is supported by passing the data through series of 15 statistical test. The device generates at a rate of 109.7 kbit/s.

Variational Infinite Hidden Conditional Random Fields

NARCIS (Netherlands)

Bousmalis, Konstantinos; Zafeiriou, Stefanos; Morency, Louis-Philippe; Pantic, Maja; Ghahramani, Zoubin

2015-01-01

Hidden conditional random fields (HCRFs) are discriminative latent variable models which have been shown to successfully learn the hidden structure of a given classification problem. An Infinite hidden conditional random field is a hidden conditional random field with a countably infinite number of

Exits in order: How crowding affects particle lifetimes

Energy Technology Data Exchange (ETDEWEB)

Penington, Catherine J.; Simpson, Matthew J. [School of Mathematical Sciences, Queensland University of Technology, Brisbane (Australia); Baker, Ruth E. [Mathematical Institute, University of Oxford, Radcliffe Observatory Quarter, Woodstock Road, Oxford (United Kingdom)

2016-06-28

Diffusive processes are often represented using stochastic random walk frameworks. The amount of time taken for an individual in a random walk to intersect with an absorbing boundary is a fundamental property that is often referred to as the particle lifetime, or the first passage time. The mean lifetime of particles in a random walk model of diffusion is related to the amount of time required for the diffusive process to reach a steady state. Mathematical analysis describing the mean lifetime of particles in a standard model of diffusion without crowding is well known. However, the lifetime of agents in a random walk with crowding has received much less attention. Since many applications of diffusion in biology and biophysics include crowding effects, here we study a discrete model of diffusion that incorporates crowding. Using simulations, we show that crowding has a dramatic effect on agent lifetimes, and we derive an approximate expression for the mean agent lifetime that includes crowding effects. Our expression matches simulation results very well, and highlights the importance of crowding effects that are sometimes overlooked.

Implementation of Treat-to-Target in Rheumatoid Arthritis Through a Learning Collaborative: Results of a Randomized Controlled Trial.

Science.gov (United States)

Solomon, Daniel H; Losina, Elena; Lu, Bing; Zak, Agnes; Corrigan, Cassandra; Lee, Sara B; Agosti, Jenifer; Bitton, Asaf; Harrold, Leslie R; Pincus, Theodore; Radner, Helga; Yu, Zhi; Smolen, Josef S; Fraenkel, Liana; Katz, Jeffrey N

2017-07-01

Treat-to-target (TTT) is an accepted paradigm for the management of rheumatoid arthritis (RA), but some evidence suggests poor adherence. The purpose of this study was to test the effects of a group-based multisite improvement learning collaborative on adherence to TTT. We conducted a cluster-randomized quality-improvement trial with waitlist control across 11 rheumatology sites in the US. The intervention entailed a 9-month group-based learning collaborative that incorporated rapid-cycle improvement methods. A composite TTT implementation score was calculated as the percentage of 4 required items documented in the visit notes for each patient at 2 time points, as evaluated by trained staff. The mean change in the implementation score for TTT across all patients for the intervention sites was compared with that for the control sites after accounting for intracluster correlation using linear mixed models. Five sites with a total of 23 participating rheumatology providers were randomized to intervention and 6 sites with 23 participating rheumatology providers were randomized to the waitlist control. The intervention included 320 patients, and the control included 321 patients. At baseline, the mean TTT implementation score was 11% in both arms; after the 9-month intervention, the mean TTT implementation score was 57% in the intervention group and 25% in the control group (change in score of 46% for intervention and 14% for control; P = 0.004). We did not observe excessive use of resources or excessive occurrence of adverse events in the intervention arm. A learning collaborative resulted in substantial improvements in adherence to TTT for the management of RA. This study supports the use of an educational collaborative to improve quality. © 2017, American College of Rheumatology.

Electrolytic plating apparatus for discrete microsized particles

International Nuclear Information System (INIS)

Mayer, A.

1976-01-01

Method and apparatus are disclosed for electrolytically producing very uniform coatings of a desired material on discrete microsized particles. Agglomeration or bridging of the particles during the deposition process is prevented by imparting a sufficiently random motion to the particles that they are not in contact with a powered cathode for a time sufficient for such to occur. 4 claims, 2 figures

Anomalous particle diffusion and Levy random walk of magnetic field lines in three-dimensional solar wind turbulence

International Nuclear Information System (INIS)

Zimbardo, Gaetano

2005-01-01

Plasma transport in the presence of turbulence depends on a variety of parameters such as the fluctuation level, δB/B 0 , the ratio between the particle Larmor radius and the turbulence correlation length, and the turbulence anisotropy. In this paper, we present the results of numerical simulations of plasma and magnetic field line transport in the case of anisotropic magnetic turbulence, for parameter values close to those of the solar wind. We assume a uniform background magnetic field B 0 = B 0 e z and a Fourier representation for magnetic fluctuations, which includes wavectors oblique with respect to B 0 . The energy density spectrum is a power law, and in k space it is described by the correlation lengths l x , l y , l z , which quantify the anisotropy of turbulence. For magnetic field lines, transport perpendicular to the background field depends on the Kubo number R (δB/B 0 ) (l z /l x ). For small Kubo numbers, R 0 , or the ratio l z /l x , we find first a quasilinear regime and then a percolative regime, both corresponding to Gaussian diffusion. For particles, we find that transport parallel and perpendicular to the background magnetic field depends heavily on the turbulence anisotropy and on the particle Larmor radius. For turbulence levels typical of the solar wind, δB/B 0 ≅ 0.5-1, when the ratio between the particle Larmor radius and the turbulence correlation lengths is small, anomalous regimes are found in the case l z /l x ≤ 1, with a Levy random walk (superdiffusion) along the magnetic field and subdiffusion in the perpendicular directions. Conversely, for l z /l x > 1 normal Gaussian diffusion is found. A possible expression for generalized double diffusion is discussed

A pedestrian's view on interacting particle systems, KPZ universality and random matrices

Energy Technology Data Exchange (ETDEWEB)

Kriecherbauer, Thomas [Fakultaet fuer Mathematik, Ruhr-Universitaet Bochum (Germany); Krug, Joachim, E-mail: thomas.kriecherbauer@ruhr-uni-bochum.d, E-mail: krug@thp.uni-koeln.d [Institut fuer Theoretische Physik, Universitaet zu Koeln (Germany)

2010-10-08

These notes are based on lectures delivered by the authors at a Langeoog seminar of SFB/TR12 Symmetries and Universality in Mesoscopic Systems to a mixed audience of mathematicians and theoretical physicists. After a brief outline of the basic physical concepts of equilibrium and nonequilibrium states, the one-dimensional simple exclusion process is introduced as a paradigmatic nonequilibrium interacting particle system. The stationary measure on the ring is derived and the idea of the hydrodynamic limit is sketched. We then introduce the phenomenological Kardar-Parisi-Zhang (KPZ) equation and explain the associated universality conjecture for surface fluctuations in growth models. This is followed by a detailed exposition of a seminal paper of Johansson [59] that relates the current fluctuations of the totally asymmetric simple exclusion process (TASEP) to the Tracy-Widom distribution of random matrix theory. The implications of this result are discussed within the framework of the KPZ conjecture. (topical review)

Machine Learning and Infrared Thermography for Fiber Orientation Assessment on Randomly-Oriented Strands Parts

Science.gov (United States)

Maldague, Xavier

2018-01-01

The use of fiber reinforced materials such as randomly-oriented strands has grown in recent years, especially for manufacturing of aerospace composite structures. This growth is mainly due to their advantageous properties: they are lighter and more resistant to corrosion when compared to metals and are more easily shaped than continuous fiber composites. The resistance and stiffness of these materials are directly related to their fiber orientation. Thus, efficient approaches to assess their fiber orientation are in demand. In this paper, a non-destructive evaluation method is applied to assess the fiber orientation on laminates reinforced with randomly-oriented strands. More specifically, a method called pulsed thermal ellipsometry combined with an artificial neural network, a machine learning technique, is used in order to estimate the fiber orientation on the surface of inspected parts. Results showed that the method can be potentially used to inspect large areas with good accuracy and speed. PMID:29351240

eEduHeart I: A Multicenter, Randomized, Controlled Trial Investigating the Effectiveness of a Cardiac Web-Based eLearning Platform - Rationale and Study Design.

Science.gov (United States)

Frederix, Ines; Vandenberk, Thijs; Janssen, Leen; Geurden, Anne; Vandervoort, Pieter; Dendale, Paul

Cardiac telerehabilitation includes, in its most comprehensive format, telemonitoring, telecoaching, social interaction, and eLearning. The specific role of eLearning, however, was seldom assessed. The aim of eEduHeart I is to investigate the medium-term effectiveness of the addition of a cardiac web-based eLearing platform to conventional cardiac care. In this prospective, multicenter randomized, controlled trial, 1,000 patients with coronary artery disease will be randomized 1:1 to an intervention group (receiving 1-month unrestricted access to the cardiac eLearning platform in addition to conventional cardiac care) or to conventional cardiac care alone. The primary endpoint is health-related quality of life, assessed by the HeartQoL questionnaire at the 1- and 3-month follow-ups. Secondary endpoints include pathology-specific knowledge and self-reported eLearning platform user experience. Data on the eLearning platform usage will be gathered through web logging during the study period. eEduHeart I will be one of the first studies to report on the added value of eLearning. If the intervention is proven effective, current cardiac telerehabilitation programs can be augmented by including eLearning, too. The platform can then be used as a model for other chronic diseases in which patient education plays a key role. © 2016 S. Karger AG, Basel.

Improving Pediatric Basic Life Support Performance Through Blended Learning With Web-Based Virtual Patients: Randomized Controlled Trial.

Science.gov (United States)

Lehmann, Ronny; Thiessen, Christiane; Frick, Barbara; Bosse, Hans Martin; Nikendei, Christoph; Hoffmann, Georg Friedrich; Tönshoff, Burkhard; Huwendiek, Sören

2015-07-02

E-learning and blended learning approaches gain more and more popularity in emergency medicine curricula. So far, little data is available on the impact of such approaches on procedural learning and skill acquisition and their comparison with traditional approaches. This study investigated the impact of a blended learning approach, including Web-based virtual patients (VPs) and standard pediatric basic life support (PBLS) training, on procedural knowledge, objective performance, and self-assessment. A total of 57 medical students were randomly assigned to an intervention group (n=30) and a control group (n=27). Both groups received paper handouts in preparation of simulation-based PBLS training. The intervention group additionally completed two Web-based VPs with embedded video clips. Measurements were taken at randomization (t0), after the preparation period (t1), and after hands-on training (t2). Clinical decision-making skills and procedural knowledge were assessed at t0 and t1. PBLS performance was scored regarding adherence to the correct algorithm, conformance to temporal demands, and the quality of procedural steps at t1 and t2. Participants' self-assessments were recorded in all three measurements. Procedural knowledge of the intervention group was significantly superior to that of the control group at t1. At t2, the intervention group showed significantly better adherence to the algorithm and temporal demands, and better procedural quality of PBLS in objective measures than did the control group. These aspects differed between the groups even at t1 (after VPs, prior to practical training). Self-assessments differed significantly only at t1 in favor of the intervention group. Training with VPs combined with hands-on training improves PBLS performance as judged by objective measures.

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.

Directory of Open Access Journals (Sweden)

Jiayi Wu

Full Text Available Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM. We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization.

Massively parallel unsupervised single-particle cryo-EM data clustering via statistical manifold learning.

Science.gov (United States)

Wu, Jiayi; Ma, Yong-Bei; Congdon, Charles; Brett, Bevin; Chen, Shuobing; Xu, Yaofang; Ouyang, Qi; Mao, Youdong

2017-01-01

Structural heterogeneity in single-particle cryo-electron microscopy (cryo-EM) data represents a major challenge for high-resolution structure determination. Unsupervised classification may serve as the first step in the assessment of structural heterogeneity. However, traditional algorithms for unsupervised classification, such as K-means clustering and maximum likelihood optimization, may classify images into wrong classes with decreasing signal-to-noise-ratio (SNR) in the image data, yet demand increased computational costs. Overcoming these limitations requires further development of clustering algorithms for high-performance cryo-EM data processing. Here we introduce an unsupervised single-particle clustering algorithm derived from a statistical manifold learning framework called generative topographic mapping (GTM). We show that unsupervised GTM clustering improves classification accuracy by about 40% in the absence of input references for data with lower SNRs. Applications to several experimental datasets suggest that our algorithm can detect subtle structural differences among classes via a hierarchical clustering strategy. After code optimization over a high-performance computing (HPC) environment, our software implementation was able to generate thousands of reference-free class averages within hours in a massively parallel fashion, which allows a significant improvement on ab initio 3D reconstruction and assists in the computational purification of homogeneous datasets for high-resolution visualization.

Immersive and interactive virtual reality to improve learning and retention of neuroanatomy in medical students: a randomized controlled study.

Science.gov (United States)

Ekstrand, Chelsea; Jamal, Ali; Nguyen, Ron; Kudryk, Annalise; Mann, Jennifer; Mendez, Ivar

2018-02-23

Spatial 3-dimensional understanding of the brain is essential to learning neuroanatomy, and 3-dimensional learning techniques have been proposed as tools to enhance neuroanatomy training. The aim of this study was to examine the impact of immersive virtual-reality neuroanatomy training and compare it to traditional paper-based methods. In this randomized controlled study, participants consisted of first- or second-year medical students from the University of Saskatchewan recruited via email and posters displayed throughout the medical school. Participants were randomly assigned to the virtual-reality group or the paper-based group and studied the spatial relations between neural structures for 12 minutes after performing a neuroanatomy baseline test, with both test and control questions. A postintervention test was administered immediately after the study period and 5-9 days later. Satisfaction measures were obtained. Of the 66 participants randomly assigned to the study groups, 64 were included in the final analysis, 31 in the virtual-reality group and 33 in the paper-based group. The 2 groups performed comparably on the baseline questions and showed significant performance improvement on the test questions following study. There were no significant differences between groups for the control questions, the postintervention test questions or the 7-day postintervention test questions. Satisfaction survey results indicated that neurophobia was decreased. Results from this study provide evidence that training in neuroanatomy in an immersive and interactive virtual-reality environment may be an effective neuroanatomy learning tool that warrants further study. They also suggest that integration of virtual-reality into neuroanatomy training may improve knowledge retention, increase study motivation and decrease neurophobia. Copyright 2018, Joule Inc. or its licensors.

Effects of Mobile Augmented Reality Learning Compared to Textbook Learning on Medical Students: Randomized Controlled Pilot Study

Science.gov (United States)

2013-01-01

Background By adding new levels of experience, mobile Augmented Reality (mAR) can significantly increase the attractiveness of mobile learning applications in medical education. Objective To compare the impact of the heightened realism of a self-developed mAR blended learning environment (mARble) on learners to textbook material, especially for ethically sensitive subjects such as forensic medicine, while taking into account basic psychological aspects (usability and higher level of emotional involvement) as well as learning outcomes (increased learning efficiency). Methods A prestudy was conducted based on a convenience sample of 10 third-year medical students. The initial emotional status was captured using the “Profile of Mood States” questionnaire (POMS, German variation); previous knowledge about forensic medicine was determined using a 10-item single-choice (SC) test. During the 30-minute learning period, the students were randomized into two groups: the first group consisted of pairs of students, each equipped with one iPhone with a preinstalled copy of mARble, while the second group was provided with textbook material. Subsequently, both groups were asked to once again complete the POMS questionnaire and SC test to measure changes in emotional state and knowledge gain. Usability as well as pragmatic and hedonic qualities of the learning material was captured using AttrakDiff2 questionnaires. Data evaluation was conducted anonymously. Descriptive statistics for the score in total and the subgroups were calculated before and after the intervention. The scores of both groups were tested against each other using paired and unpaired signed-rank tests. An item analysis was performed for the SC test to objectify difficulty and selectivity. Results Statistically significant, the mARble group (6/10) showed greater knowledge gain than the control group (4/10) (Wilcoxon z=2.232, P=.03). The item analysis of the SC test showed a difficulty of P=0.768 (s=0.09) and a

Cognitive Style and Mobile E-Learning in Emergent Otorhinolaryngology-Head and Neck Surgery Disorders for Millennial Undergraduate Medical Students: Randomized Controlled Trial.

Science.gov (United States)

Lee, Li-Ang; Chao, Yi-Ping; Huang, Chung-Guei; Fang, Ji-Tseng; Wang, Shu-Ling; Chuang, Cheng-Keng; Kang, Chung-Jan; Hsin, Li-Jen; Lin, Wan-Ni; Fang, Tuan-Jen; Li, Hsueh-Yu

2018-02-13

Electronic learning (e-learning) through mobile technology represents a novel way to teach emergent otorhinolaryngology-head and neck surgery (ORL-HNS) disorders to undergraduate medical students. Whether a cognitive style of education combined with learning modules can impact learning outcomes and satisfaction in millennial medical students is unknown. The aim of this study was to assess the impact of cognitive styles and learning modules using mobile e-learning on knowledge gain, competence gain, and satisfaction for emergent ORL-HNS disorders. This randomized controlled trial included 60 undergraduate medical students who were novices in ORL-HNS at an academic teaching hospital. The cognitive style of the participants was assessed using the group embedded figures test. The students were randomly assigned (1:1) to a novel interactive multimedia (IM) group and conventional Microsoft PowerPoint show (PPS) group matched by age, sex, and cognitive style. The content for the gamified IM module was derived from and corresponded to the textbook-based learning material of the PPS module (video lectures). The participants were unblinded and used fully automated courseware containing the IM or PPS module on a 7-inch tablet for 100 min. Knowledge and competence were assessed using multiple-choice questions and multimedia situation tests, respectively. Each participant also rated their global satisfaction. All of the participants (median age 23 years, range 22-26 years; 36 males and 24 females) received the intended intervention after randomization. Overall, the participants had significant gains in knowledge (median 50%, interquartile range [IQR]=17%-80%, Plearning modules (IM or PPS) had significant effects on both knowledge gain (both adjusted Plearning is an effective modality to improve knowledge of emergent ORL-HNS in millennial undergraduate medical students. Our findings suggest the necessity of developing various modules for undergraduate medical students with

Random walk of passive tracers among randomly moving obstacles.

Science.gov (United States)

Gori, Matteo; Donato, Irene; Floriani, Elena; Nardecchia, Ilaria; Pettini, Marco

2016-04-14

This study is mainly motivated by the need of understanding how the diffusion behavior of a biomolecule (or even of a larger object) is affected by other moving macromolecules, organelles, and so on, inside a living cell, whence the possibility of understanding whether or not a randomly walking biomolecule is also subject to a long-range force field driving it to its target. By means of the Continuous Time Random Walk (CTRW) technique the topic of random walk in random environment is here considered in the case of a passively diffusing particle among randomly moving and interacting obstacles. The relevant physical quantity which is worked out is the diffusion coefficient of the passive tracer which is computed as a function of the average inter-obstacles distance. The results reported here suggest that if a biomolecule, let us call it a test molecule, moves towards its target in the presence of other independently interacting molecules, its motion can be considerably slowed down.

Influence of case-based e-learning on students' performance in point-of-care ultrasound courses: a randomized trial.

Science.gov (United States)

Hempel, Dorothea; Sinnathurai, Sivajini; Haunhorst, Stephanie; Seibel, Armin; Michels, Guido; Heringer, Frank; Recker, Florian; Breitkreutz, Raoul

2016-08-01

Theoretical knowledge, visual perception, and sensorimotor skills are key elements in ultrasound education. Classroom-based presentations are used routinely to teach theoretical knowledge, whereas visual perception and sensorimotor skills typically require hands-on training (HT). We aimed to compare the effect of classroom-based lectures versus a case-based e-learning (based on clinical cases only) on the hands-on performance of trainees during an emergency ultrasound course. This is a randomized, controlled, parallel-group study. Sixty-two medical students were randomized into two groups [group 1 (G1) and group 2 (G2)]. G1 (n=29) was subjected to a precourse e-learning, based on 14 short screencasts (each 5 min), an on-site discussion (60 min), and a standardized HT session on the day of the course. G2 (n=31) received classroom-based presentations on the day of the course before an identical HT session. Both groups completed a multiple-choice (MC) pretest (test A), a practical postcourse test (objective structured clinical exam), and MC tests directly after the HT (test B) and 1 day after the course (test C). The Mann-Whitney U-test was used for statistical analysis. G1 performed markedly better in test A (median 84.2, 25%; 75% percentile: 68.5; 92.2) compared with G2 (65.8; 53.8; 80.4), who had not participated in case-based e-learning (P=0.0009). No differences were found in the objective structured clinical exam, test B, and test C. e-learning exclusively based on clinical cases is an effective method of education in preparation for HT sessions and can reduce attendance time in ultrasound courses.

Large scale particle simulations in a virtual memory computer

International Nuclear Information System (INIS)

Gray, P.C.; Million, R.; Wagner, J.S.; Tajima, T.

1983-01-01

Virtual memory computers are capable of executing large-scale particle simulations even when the memory requirements exceeds the computer core size. The required address space is automatically mapped onto slow disc memory the the operating system. When the simulation size is very large, frequent random accesses to slow memory occur during the charge accumulation and particle pushing processes. Assesses to slow memory significantly reduce the excecution rate of the simulation. We demonstrate in this paper that with the proper choice of sorting algorithm, a nominal amount of sorting to keep physically adjacent particles near particles with neighboring array indices can reduce random access to slow memory, increase the efficiency of the I/O system, and hence, reduce the required computing time. (orig.)

Large-scale particle simulations in a virtual-memory computer

International Nuclear Information System (INIS)

Gray, P.C.; Wagner, J.S.; Tajima, T.; Million, R.

1982-08-01

Virtual memory computers are capable of executing large-scale particle simulations even when the memory requirements exceed the computer core size. The required address space is automatically mapped onto slow disc memory by the operating system. When the simulation size is very large, frequent random accesses to slow memory occur during the charge accumulation and particle pushing processes. Accesses to slow memory significantly reduce the execution rate of the simulation. We demonstrate in this paper that with the proper choice of sorting algorithm, a nominal amount of sorting to keep physically adjacent particles near particles with neighboring array indices can reduce random access to slow memory, increase the efficiency of the I/O system, and hence, reduce the required computing time

Multiple Kernel Learning with Random Effects for Predicting Longitudinal Outcomes and Data Integration

Science.gov (United States)

Chen, Tianle; Zeng, Donglin

2015-01-01

Summary Predicting disease risk and progression is one of the main goals in many clinical research studies. Cohort studies on the natural history and etiology of chronic diseases span years and data are collected at multiple visits. Although kernel-based statistical learning methods are proven to be powerful for a wide range of disease prediction problems, these methods are only well studied for independent data but not for longitudinal data. It is thus important to develop time-sensitive prediction rules that make use of the longitudinal nature of the data. In this paper, we develop a novel statistical learning method for longitudinal data by introducing subject-specific short-term and long-term latent effects through a designed kernel to account for within-subject correlation of longitudinal measurements. Since the presence of multiple sources of data is increasingly common, we embed our method in a multiple kernel learning framework and propose a regularized multiple kernel statistical learning with random effects to construct effective nonparametric prediction rules. Our method allows easy integration of various heterogeneous data sources and takes advantage of correlation among longitudinal measures to increase prediction power. We use different kernels for each data source taking advantage of the distinctive feature of each data modality, and then optimally combine data across modalities. We apply the developed methods to two large epidemiological studies, one on Huntington's disease and the other on Alzheimer's Disease (Alzheimer's Disease Neuroimaging Initiative, ADNI) where we explore a unique opportunity to combine imaging and genetic data to study prediction of mild cognitive impairment, and show a substantial gain in performance while accounting for the longitudinal aspect of the data. PMID:26177419

Jet-images — deep learning edition

Energy Technology Data Exchange (ETDEWEB)

Oliveira, Luke de [Institute for Computational and Mathematical Engineering, Stanford University,Huang Building 475 Via Ortega, Stanford, CA 94305 (United States); Kagan, Michael [SLAC National Accelerator Laboratory, Stanford University,2575 Sand Hill Rd, Menlo Park, CA 94025 (United States); Mackey, Lester [Department of Statistics, Stanford University,390 Serra Mall, Stanford, CA 94305 (United States); Nachman, Benjamin; Schwartzman, Ariel [SLAC National Accelerator Laboratory, Stanford University,2575 Sand Hill Rd, Menlo Park, CA 94025 (United States)

2016-07-13

Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons. Modern deep learning algorithms trained on jet images can out-perform standard physically-motivated feature driven approaches to jet tagging. We develop techniques for visualizing how these features are learned by the network and what additional information is used to improve performance. This interplay between physically-motivated feature driven tools and supervised learning algorithms is general and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.

Jet-images — deep learning edition

International Nuclear Information System (INIS)

Oliveira, Luke de; Kagan, Michael; Mackey, Lester; Nachman, Benjamin; Schwartzman, Ariel

2016-01-01

Building on the notion of a particle physics detector as a camera and the collimated streams of high energy particles, or jets, it measures as an image, we investigate the potential of machine learning techniques based on deep learning architectures to identify highly boosted W bosons. Modern deep learning algorithms trained on jet images can out-perform standard physically-motivated feature driven approaches to jet tagging. We develop techniques for visualizing how these features are learned by the network and what additional information is used to improve performance. This interplay between physically-motivated feature driven tools and supervised learning algorithms is general and can be used to significantly increase the sensitivity to discover new particles and new forces, and gain a deeper understanding of the physics within jets.

Virus Particle Detection by Convolutional Neural Network in Transmission Electron Microscopy Images.

Science.gov (United States)

Ito, Eisuke; Sato, Takaaki; Sano, Daisuke; Utagawa, Etsuko; Kato, Tsuyoshi

2018-06-01

A new computational method for the detection of virus particles in transmission electron microscopy (TEM) images is presented. Our approach is to use a convolutional neural network that transforms a TEM image to a probabilistic map that indicates where virus particles exist in the image. Our proposed approach automatically and simultaneously learns both discriminative features and classifier for virus particle detection by machine learning, in contrast to existing methods that are based on handcrafted features that yield many false positives and require several postprocessing steps. The detection performance of the proposed method was assessed against a dataset of TEM images containing feline calicivirus particles and compared with several existing detection methods, and the state-of-the-art performance of the developed method for detecting virus was demonstrated. Since our method is based on supervised learning that requires both the input images and their corresponding annotations, it is basically used for detection of already-known viruses. However, the method is highly flexible, and the convolutional networks can adapt themselves to any virus particles by learning automatically from an annotated dataset.

Spin, statistics, and geometry of random walks

International Nuclear Information System (INIS)

Jaroszewicz, T.; Kurzepa, P.S.

1991-01-01

The authors develop and unify two complementary descriptions of propagation of spinning particles: the directed random walk representation and the spin factor approach. Working in an arbitrary number of dimensions D, they first represent the Dirac propagator in terms of a directed random walk. They then derive the general and explicit form of the gauge connection describing parallel transport of spin and investigate the resulting quantum-mechanical problem of a particle moving on a sphere in the field of a nonabelian SO(D-1) monopole. This construction, generalizing Polyakov's results, enables them to prove the equivalence of the random walk and path-integral (spin factor) representation. As an alternative, they construct and discuss various Wess-Zumino-Witten forms of the spin factor. They clarify the role played by the coupling between the particle's spin and translational degrees of freedom in establishing the geometrical properties of particle's paths in spacetime. To this end, they carefully define and evaluate Hausdorff dimensions of bosonic and fermionic sample paths, in the covariant as well as nonrelativistic formulations. Finally, as an application of the developed formalism, they give an intuitive spacetime interpretation of chiral anomalies in terms of the geometry of fermion trajectories

The Particle Beam Optics Interactive Computer Laboratory

International Nuclear Information System (INIS)

Gillespie, George H.; Hill, Barrey W.; Brown, Nathan A.; Babcock, R. Chris; Martono, Hendy; Carey, David C.

1997-01-01

The Particle Beam Optics Interactive Computer Laboratory (PBO Lab) is an educational software concept to aid students and professionals in learning about charged particle beams and particle beam optical systems. The PBO Lab is being developed as a cross-platform application and includes four key elements. The first is a graphic user interface shell that provides for a highly interactive learning session. The second is a knowledge database containing information on electric and magnetic optics transport elements. The knowledge database provides interactive tutorials on the fundamental physics of charged particle optics and on the technology used in particle optics hardware. The third element is a graphical construction kit that provides tools for students to interactively and visually construct optical beamlines. The final element is a set of charged particle optics computational engines that compute trajectories, transport beam envelopes, fit parameters to optical constraints and carry out similar calculations for the student designed beamlines. The primary computational engine is provided by the third-order TRANSPORT code. Augmenting TRANSPORT is the multiple ray tracing program TURTLE and a first-order matrix program that includes a space charge model and support for calculating single particle trajectories in the presence of the beam space charge. This paper describes progress on the development of the PBO Lab

Acceptance of technology-enhanced learning for a theoretical radiological science course: a randomized controlled trial.

Science.gov (United States)

Nkenke, Emeka; Vairaktaris, Elefterios; Bauersachs, Anne; Eitner, Stephan; Budach, Alexander; Knipfer, Christoph; Stelzle, Florian

2012-03-30

Technology-enhanced learning (TEL) gives a view to improved education. However, there is a need to clarify how TEL can be used effectively. The study compared students' attitudes and opinions towards a traditional face-to-face course on theoretical radiological science and a TEL course where students could combine face-to-face lectures and e-learning modules at their best convenience. 42 third-year dental students were randomly assigned to the traditional face-to-face group and the TEL group. Both groups completed questionnaires before the beginning and after completion of the course on attitudes and opinions towards a traditional face-to-face lectures and technology-enhanced learning. After completion of the course both groups also filled in the validated German-language TRIL (Trierer Inventar zur Lehrevaluation) questionnaire for the evaluation of courses given at universities. Both groups had a positive attitude towards e-learning that did not change over time. The TEL group attended significantly less face-to-face lectures than the traditional group. However, both groups stated that face-to-face lectures were the basis for education in a theoretical radiological science course. The members of the TEL group rated e-mail reminders significantly more important when they filled in the questionnaire on attitudes and opinions towards a traditional face-to-face lectures and technology-enhanced learning for the second time after completion of the course. The members of the technology-enhanced learning group were significantly less confident in passing the exam compared to the members of the traditional group. However, examination results did not differ significantly for traditional and the TEL group. It seems that technology-enhanced learning in a theoretical radiological science course has the potential to reduce the need for face-to-face lectures. At the same time examination results are not impaired. However, technology-enhanced learning cannot completely replace

Acceptance of technology-enhanced learning for a theoretical radiological science course: a randomized controlled trial

Directory of Open Access Journals (Sweden)

Nkenke Emeka

2012-03-01

Full Text Available Abstract Background Technology-enhanced learning (TEL gives a view to improved education. However, there is a need to clarify how TEL can be used effectively. The study compared students' attitudes and opinions towards a traditional face-to-face course on theoretical radiological science and a TEL course where students could combine face-to-face lectures and e-learning modules at their best convenience. Methods 42 third-year dental students were randomly assigned to the traditional face-to-face group and the TEL group. Both groups completed questionnaires before the beginning and after completion of the course on attitudes and opinions towards a traditional face-to-face lectures and technology-enhanced learning. After completion of the course both groups also filled in the validated German-language TRIL (Trierer Inventar zur Lehrevaluation questionnaire for the evaluation of courses given at universities. Results Both groups had a positive attitude towards e-learning that did not change over time. The TEL group attended significantly less face-to-face lectures than the traditional group. However, both groups stated that face-to-face lectures were the basis for education in a theoretical radiological science course. The members of the TEL group rated e-mail reminders significantly more important when they filled in the questionnaire on attitudes and opinions towards a traditional face-to-face lectures and technology-enhanced learning for the second time after completion of the course. The members of the technology-enhanced learning group were significantly less confident in passing the exam compared to the members of the traditional group. However, examination results did not differ significantly for traditional and the TEL group. Conclusions It seems that technology-enhanced learning in a theoretical radiological science course has the potential to reduce the need for face-to-face lectures. At the same time examination results are not impaired

Acceptance of technology-enhanced learning for a theoretical radiological science course: a randomized controlled trial

Science.gov (United States)

2012-01-01

Background Technology-enhanced learning (TEL) gives a view to improved education. However, there is a need to clarify how TEL can be used effectively. The study compared students' attitudes and opinions towards a traditional face-to-face course on theoretical radiological science and a TEL course where students could combine face-to-face lectures and e-learning modules at their best convenience. Methods 42 third-year dental students were randomly assigned to the traditional face-to-face group and the TEL group. Both groups completed questionnaires before the beginning and after completion of the course on attitudes and opinions towards a traditional face-to-face lectures and technology-enhanced learning. After completion of the course both groups also filled in the validated German-language TRIL (Trierer Inventar zur Lehrevaluation) questionnaire for the evaluation of courses given at universities. Results Both groups had a positive attitude towards e-learning that did not change over time. The TEL group attended significantly less face-to-face lectures than the traditional group. However, both groups stated that face-to-face lectures were the basis for education in a theoretical radiological science course. The members of the TEL group rated e-mail reminders significantly more important when they filled in the questionnaire on attitudes and opinions towards a traditional face-to-face lectures and technology-enhanced learning for the second time after completion of the course. The members of the technology-enhanced learning group were significantly less confident in passing the exam compared to the members of the traditional group. However, examination results did not differ significantly for traditional and the TEL group. Conclusions It seems that technology-enhanced learning in a theoretical radiological science course has the potential to reduce the need for face-to-face lectures. At the same time examination results are not impaired. However, technology

Promising Therapeutics with Natural Bioactive Compounds for Improving Learning and Memory — A Review of Randomized Trials

Directory of Open Access Journals (Sweden)

Jin-Yong Choi

2012-09-01

Full Text Available Cognitive disorders can be associated with brain trauma, neurodegenerative disease or as a part of physiological aging. Aging in humans is generally associated with deterioration of cognitive performance and, in particular, learning and memory. Different therapeutic approaches are available to treat cognitive impairment during physiological aging and neurodegenerative or psychiatric disorders. Traditional herbal medicine and numerous plants, either directly as supplements or indirectly in the form of food, improve brain functions including memory and attention. More than a hundred herbal medicinal plants have been traditionally used for learning and memory improvement, but only a few have been tested in randomized clinical trials. Here, we will enumerate those medicinal plants that show positive effects on various cognitive functions in learning and memory clinical trials. Moreover, besides natural products that show promising effects in clinical trials, we briefly discuss medicinal plants that have promising experimental data or initial clinical data and might have potential to reach a clinical trial in the near future.

Ultrasound simulator-assisted teaching of cardiac anatomy to preclinical anatomy students: A pilot randomized trial of a three-hour learning exposure.

Science.gov (United States)

Canty, David Jeffrey; Hayes, Jenny A; Story, David Andrew; Royse, Colin Forbes

2015-01-01

Ultrasound simulation allows students to virtually explore internal anatomy by producing accurate, moving, color, three-dimensional rendered slices from any angle or approach leaving the organs and their relationships intact without requirement for consumables. The aim was to determine the feasibility and efficacy of self-directed learning of cardiac anatomy with an ultrasound simulator compared to cadavers and plastic models. After a single cardiac anatomy lecture, fifty university anatomy students participated in a three-hour supervised self-directed learning exposure in groups of five, randomized to an ultrasound simulator or human cadaveric specimens and plastic models. Pre- and post-tests were conducted using pictorial and non-pictorial multiple-choice questions (MCQs). Simulator students completed a survey on their experience. Four simulator and seven cadaver group students did not attend after randomization. Simulator use in groups of five students was feasible and feedback from participants was very positive. Baseline test scores were similar (P = 0.9) between groups. After the learning intervention, there was no difference between groups in change in total test score (P = 0.37), whether they were pictorial (P = 0.6) or non-pictorial (P = 0.21). In both groups there was an increase in total test scores (simulator +19.8 ±12.4%% and cadaver: +16.4% ± 10.2, P human cadaveric prosections for learning cardiac anatomy. © 2014 American Association of Anatomists.

Three-dimensional printing model improves morphological understanding in acetabular fracture learning: A multicenter, randomized, controlled study.

Directory of Open Access Journals (Sweden)

Zhenfei Huang

Full Text Available Conventional education results in unsatisfactory morphological understanding of acetabular fractures due to lack of three-dimensional (3D details and tactile feedback of real fractures. Virtual reality (VR and 3D printing (3DP techniques are widely applied in teaching. The purpose of this study was to identify the effect of physical model (PM, VR and 3DP models in education of morphological understanding of acetabular fractures. 141 students were invited to participate in this study. Participants were equally and randomly assigned to the PM, VR and 3DP learning groups. Three-level objective tests were conducted to evaluate learning, including identifying anatomical landmarks, describing fracture lines, identifying classification, and inferring fracture mechanism. Four subjective questions were asked to evaluate the usability and value of instructional materials. Generally, the 3DP group showed a clear advantage over the PM and VR groups in objective tests, while there was no significant difference between the PM and VR groups. 3DP was considered to be the most valuable learning tool for understanding acetabular fractures. The findings demonstrate that 3DP modelling of real fractures is an effective learning instrument that can be used to understand the morphology of acetabular fractures and promote subjective interest.

Particle methods: An introduction with applications

Directory of Open Access Journals (Sweden)

Moral Piere Del

2014-01-01

Full Text Available Interacting particle methods are increasingly used to sample from complex high-dimensional distributions. They have found a wide range of applications in applied probability, Bayesian statistics and information engineering. Understanding rigorously these new Monte Carlo simulation tools leads to fascinating mathematics related to Feynman-Kac path integral theory and their interacting particle interpretations. In these lecture notes, we provide a pedagogical introduction to the stochastic modeling and the theoretical analysis of these particle algorithms. We also illustrate these methods through several applications including random walk confinements, particle absorption models, nonlinear filtering, stochastic optimization, combinatorial counting and directed polymer models.

A mathematical analysis of the effects of Hebbian learning rules on the dynamics and structure of discrete-time random recurrent neural networks.

Science.gov (United States)

Siri, Benoît; Berry, Hugues; Cessac, Bruno; Delord, Bruno; Quoy, Mathias

2008-12-01

We present a mathematical analysis of the effects of Hebbian learning in random recurrent neural networks, with a generic Hebbian learning rule, including passive forgetting and different timescales, for neuronal activity and learning dynamics. Previous numerical work has reported that Hebbian learning drives the system from chaos to a steady state through a sequence of bifurcations. Here, we interpret these results mathematically and show that these effects, involving a complex coupling between neuronal dynamics and synaptic graph structure, can be analyzed using Jacobian matrices, which introduce both a structural and a dynamical point of view on neural network evolution. Furthermore, we show that sensitivity to a learned pattern is maximal when the largest Lyapunov exponent is close to 0. We discuss how neural networks may take advantage of this regime of high functional interest.

Making ATLAS Data from CERN Accessible to the General Public: The Development and Evaluation of a Learning Resource in Experimental Particle Physics

CERN Document Server

AUTHOR|(CDS)2243922; Ekelin, Svea Magdalena; Lund-Jensen, Bengt; Christiansen, Iben

2017-08-15

In 2016, the ATLAS experiment at CERN released data from 100 trillion proton-proton collisions to the general public. In connection to this release the ATLAS Outreach group has developed several tools for visualizing and analyzing the data, one of which is a Histogram analyzer. The focus of this project is to bridge the gap between the general public's knowledge in physics and what is needed to use this Histogram analyzer. The project consists of both the development and an evaluation of a learning resource that explains experimental particle physics for a general public audience. The learning resource is a website making use of analogies and two perspectives on learning: Variation Theory and Cognitive Load Theory. The evaluation of the website was done using a survey with 10 respondents and it focused on whether analogies and the perspectives on learning helped their understanding. In general the respondents found the analogies to be helpful for their learning, and to some degree they found the explanations ...

Cognitive Style and Mobile E-Learning in Emergent Otorhinolaryngology-Head and Neck Surgery Disorders for Millennial Undergraduate Medical Students: Randomized Controlled Trial

Science.gov (United States)

Chao, Yi-Ping; Huang, Chung-Guei; Fang, Ji-Tseng; Wang, Shu-Ling; Chuang, Cheng-Keng; Kang, Chung-Jan; Hsin, Li-Jen; Lin, Wan-Ni; Fang, Tuan-Jen; Li, Hsueh-Yu

2018-01-01

Background Electronic learning (e-learning) through mobile technology represents a novel way to teach emergent otorhinolaryngology-head and neck surgery (ORL-HNS) disorders to undergraduate medical students. Whether a cognitive style of education combined with learning modules can impact learning outcomes and satisfaction in millennial medical students is unknown. Objective The aim of this study was to assess the impact of cognitive styles and learning modules using mobile e-learning on knowledge gain, competence gain, and satisfaction for emergent ORL-HNS disorders. Methods This randomized controlled trial included 60 undergraduate medical students who were novices in ORL-HNS at an academic teaching hospital. The cognitive style of the participants was assessed using the group embedded figures test. The students were randomly assigned (1:1) to a novel interactive multimedia (IM) group and conventional Microsoft PowerPoint show (PPS) group matched by age, sex, and cognitive style. The content for the gamified IM module was derived from and corresponded to the textbook-based learning material of the PPS module (video lectures). The participants were unblinded and used fully automated courseware containing the IM or PPS module on a 7-inch tablet for 100 min. Knowledge and competence were assessed using multiple-choice questions and multimedia situation tests, respectively. Each participant also rated their global satisfaction. Results All of the participants (median age 23 years, range 22-26 years; 36 males and 24 females) received the intended intervention after randomization. Overall, the participants had significant gains in knowledge (median 50%, interquartile range [IQR]=17%-80%, P<.001) and competence (median 13%, IQR=0%-33%, P=.006). There were no significant differences in knowledge gain (40%, IQR=13%-76% vs 60%, IQR=20%-100%, P=.42) and competence gain (0%, IQR= −21% to 38% vs 25%, IQR=0%-33%, P=.16) between the IM and PPS groups. However, the IM group had

Continuous-time random walks on networks with vertex- and time-dependent forcing.

Science.gov (United States)

Angstmann, C N; Donnelly, I C; Henry, B I; Langlands, T A M

2013-08-01

We have investigated the transport of particles moving as random walks on the vertices of a network, subject to vertex- and time-dependent forcing. We have derived the generalized master equations for this transport using continuous time random walks, characterized by jump and waiting time densities, as the underlying stochastic process. The forcing is incorporated through a vertex- and time-dependent bias in the jump densities governing the random walking particles. As a particular case, we consider particle forcing proportional to the concentration of particles on adjacent vertices, analogous to self-chemotactic attraction in a spatial continuum. Our algebraic and numerical studies of this system reveal an interesting pair-aggregation pattern formation in which the steady state is composed of a high concentration of particles on a small number of isolated pairs of adjacent vertices. The steady states do not exhibit this pair aggregation if the transport is random on the vertices, i.e., without forcing. The manifestation of pair aggregation on a transport network may thus be a signature of self-chemotactic-like forcing.

On contact numbers in random rod packings

NARCIS (Netherlands)

Wouterse, A.; Luding, Stefan; Philipse, A.P.

2009-01-01

Random packings of non-spherical granular particles are simulated by combining mechanical contraction and molecular dynamics, to determine contact numbers as a function of density. Particle shapes are varied from spheres to thin rods. The observed contact numbers (and packing densities) agree well

Small-scale gradients of charged particles in the heliospheric magnetic field

International Nuclear Information System (INIS)

Guo, Fan; Giacalone, Joe

2014-01-01

Using numerical simulations of charged-particles propagating in the heliospheric magnetic field, we study small-scale gradients, or 'dropouts,' in the intensity of solar energetic particles seen at 1 AU. We use two turbulence models, the foot-point random motion model and the two-component model, to generate fluctuating magnetic fields similar to spacecraft observations at 1 AU. The turbulence models include a Kolmogorov-like magnetic field power spectrum containing a broad range of spatial scales from those that lead to large-scale field-line random walk to small scales leading to resonant pitch-angle scattering of energetic particles. We release energetic protons (20 keV-10 MeV) from a spatially compact and instantaneous source. The trajectories of energetic charged particles in turbulent magnetic fields are numerically integrated. Spacecraft observations are mimicked by collecting particles in small windows when they pass the windows at a distance of 1 AU. We show that small-scale gradients in the intensity of energetic particles and velocity dispersions observed by spacecraft can be reproduced using the foot-point random motion model. However, no dropouts are seen in simulations using the two-component magnetic turbulence model. We also show that particle scattering in the solar wind magnetic field needs to be infrequent for intensity dropouts to form.

The Effects of Math Video Games on Learning: A Randomized Evaluation Study with Innovative Impact Estimation Techniques. CRESST Report 841

Science.gov (United States)

Chung, Gregory K. W. K.; Choi, Kilchan; Baker, Eva L.; Cai, Li

2014-01-01

A large-scale randomized controlled trial tested the effects of researcher-developed learning games on a transfer measure of fractions knowledge. The measure contained items similar to standardized assessments. Thirty treatment and 29 control classrooms (~1500 students, 9 districts, 26 schools) participated in the study. Students in treatment…

Effect of e-learning program on risk assessment and pressure ulcer classification - A randomized study.

Science.gov (United States)

Bredesen, Ida Marie; Bjøro, Karen; Gunningberg, Lena; Hofoss, Dag

2016-05-01

Pressure ulcers (PUs) are a problem in health care. Staff competency is paramount to PU prevention. Education is essential to increase skills in pressure ulcer classification and risk assessment. Currently, no pressure ulcer learning programs are available in Norwegian. Develop and test an e-learning program for assessment of pressure ulcer risk and pressure ulcer classification. Forty-four nurses working in acute care hospital wards or nursing homes participated and were assigned randomly into two groups: an e-learning program group (intervention) and a traditional classroom lecture group (control). Data was collected immediately before and after training, and again after three months. The study was conducted at one nursing home and two hospitals between May and December 2012. Accuracy of risk assessment (five patient cases) and pressure ulcer classification (40 photos [normal skin, pressure ulcer categories I-IV] split in two sets) were measured by comparing nurse evaluations in each of the two groups to a pre-established standard based on ratings by experts in pressure ulcer classification and risk assessment. Inter-rater reliability was measured by exact percent agreement and multi-rater Fleiss kappa. A Mann-Whitney U test was used for continuous sum score variables. An e-learning program did not improve Braden subscale scoring. For pressure ulcer classification, however, the intervention group scored significantly higher than the control group on several of the categories in post-test immediately after training. However, after three months there were no significant differences in classification skills between the groups. An e-learning program appears to have a greater effect on the accuracy of pressure ulcer classification than classroom teaching in the short term. For proficiency in Braden scoring, no significant effect of educational methods on learning results was detected. Copyright © 2016 Elsevier Ltd. All rights reserved.

Random walk through fractal environments

International Nuclear Information System (INIS)

Isliker, H.; Vlahos, L.

2003-01-01

We analyze random walk through fractal environments, embedded in three-dimensional, permeable space. Particles travel freely and are scattered off into random directions when they hit the fractal. The statistical distribution of the flight increments (i.e., of the displacements between two consecutive hittings) is analytically derived from a common, practical definition of fractal dimension, and it turns out to approximate quite well a power-law in the case where the dimension D F of the fractal is less than 2, there is though, always a finite rate of unaffected escape. Random walks through fractal sets with D F ≤2 can thus be considered as defective Levy walks. The distribution of jump increments for D F >2 is decaying exponentially. The diffusive behavior of the random walk is analyzed in the frame of continuous time random walk, which we generalize to include the case of defective distributions of walk increments. It is shown that the particles undergo anomalous, enhanced diffusion for D F F >2 is normal for large times, enhanced though for small and intermediate times. In particular, it follows that fractals generated by a particular class of self-organized criticality models give rise to enhanced diffusion. The analytical results are illustrated by Monte Carlo simulations

Lévy particles

DEFF Research Database (Denmark)

Hansen, Linda Vadgård; Thorarinsdottir, Thordis Linda; Gneiting, Tilmann

to a von Mises–Fisher density, or uniform on a spherical cap, the correlation function of the associated random field admits a closed form expression. Using a Gaussian basis, the fractal or Hausdorff dimension of the surface of the Lévy particle reflects the decay of the correlation function at the origin...

Effect of Methods of Learning and Self Regulated Learning toward Outcomes of Learning Social Studies

Science.gov (United States)

Tjalla, Awaluddin; Sofiah, Evi

2015-01-01

This research aims to reveal the influence of learning methods and self-regulated learning on students learning scores for Social Studies object. The research was done in Islamic Junior High School (MTs Manba'ul Ulum), Batuceper City Tangerang using quasi-experimental method. The research employed simple random technique to 28 students. Data were…

Bell inequalities for random fields

Energy Technology Data Exchange (ETDEWEB)

Morgan, Peter [Physics Department, Yale University, CT 06520 (United States)

2006-06-09

The assumptions required for the derivation of Bell inequalities are not satisfied for random field models in which there are any thermal or quantum fluctuations, in contrast to the general satisfaction of the assumptions for classical two point particle models. Classical random field models that explicitly include the effects of quantum fluctuations on measurement are possible for experiments that violate Bell inequalities.

Bell inequalities for random fields

OpenAIRE

Morgan, Peter

2004-01-01

The assumptions required for the derivation of Bell inequalities are not usually satisfied for random fields in which there are any thermal or quantum fluctuations, in contrast to the general satisfaction of the assumptions for classical two point particle models. Classical random field models that explicitly include the effects of quantum fluctuations on measurement are possible for experiments that violate Bell inequalities.

The Particle Beam Optics Interactive Computer Laboratory

International Nuclear Information System (INIS)

Gillespie, G.H.; Hill, B.W.; Brown, N.A.; Babcock, R.C.; Martono, H.; Carey, D.C.

1997-01-01

The Particle Beam Optics Interactive Computer Laboratory (PBO Lab) is an educational software concept to aid students and professionals in learning about charged particle beams and particle beam optical systems. The PBO Lab is being developed as a cross-platform application and includes four key elements. The first is a graphic user interface shell that provides for a highly interactive learning session. The second is a knowledge database containing information on electric and magnetic optics transport elements. The knowledge database provides interactive tutorials on the fundamental physics of charged particle optics and on the technology used in particle optics hardware. The third element is a graphical construction kit that provides tools for students to interactively and visually construct optical beamlines. The final element is a set of charged particle optics computational engines that compute trajectories, transport beam envelopes, fit parameters to optical constraints and carry out similar calculations for the student designed beamlines. The primary computational engine is provided by the third-order TRANSPORT code. Augmenting TRANSPORT is the multiple ray tracing program TURTLE and a first-order matrix program that includes a space charge model and support for calculating single particle trajectories in the presence of the beam space charge. This paper describes progress on the development of the PBO Lab. copyright 1997 American Institute of Physics

Suppression of thermal noise in a non-Markovian random velocity field

International Nuclear Information System (INIS)

Ueda, Masahiko

2016-01-01

We study the diffusion of Brownian particles in a Gaussian random velocity field with short memory. By extending the derivation of an effective Fokker–Planck equation for the Lanvegin equation with weakly colored noise to a random velocity-field problem, we find that the effect of thermal noise on particles is suppressed by the existence of memory. We also find that the renormalization effect for the relative diffusion of two particles is stronger than that for single-particle diffusion. The results are compared with those of molecular dynamics simulations. (paper: classical statistical mechanics, equilibrium and non-equilibrium)

Role of stochastic fluctuations in the charge on macroscopic particles in dusty plasmas

International Nuclear Information System (INIS)

Vaulina, O.S.; Nefedov, A.P.; Petrov, O.F.; Khrapak, S.A.

1999-01-01

The currents which charge a macroscopic particle placed in a plasma consist of discrete charges; hence, the charge can undergo random fluctuations about its equilibrium value. These random fluctuations can be described by a simple model which, if the mechanisms for charging of macroscopic particles are known, makes it possible to determine the dependence of the temporal and amplitude characteristics of the fluctuations on the plasma parameters. This model can be used to study the effect of charge fluctuations on the dynamics of the macroscopic particles. The case of so-called plasma-dust crystals (i.e., highly ordered structures which develop because of strong interactions among macroscopic particles) in laboratory gaseous discharge plasmas is considered as an example. The molecular dynamics method shows that, under certain conditions, random fluctuations in the charge can effectively heat a system of macroscopic particles, thereby impeding the ordering process

The discovery of subatomic particles

International Nuclear Information System (INIS)

Weinberg, S.

1984-01-01

This book developed from a course for students with no prior training in mathematics of physics to learn about the achievements of 20th century physics and classical physics. It covers the discovery of fundamental particles of ordinary atoms: the electron, the proton, and the neutron. The general outline is historical and it is for readers unfamiliar with classical physics who wish to understand the ideas and experiments that make up the history of 20th century physics. Contents include: A world of particles, the discovery of the electron, the atomic scale, the nucleus, more particles

Network-based stochastic competitive learning approach to disambiguation in collaborative networks

Science.gov (United States)

Christiano Silva, Thiago; Raphael Amancio, Diego

2013-03-01

Many patterns have been uncovered in complex systems through the application of concepts and methodologies of complex networks. Unfortunately, the validity and accuracy of the unveiled patterns are strongly dependent on the amount of unavoidable noise pervading the data, such as the presence of homonymous individuals in social networks. In the current paper, we investigate the problem of name disambiguation in collaborative networks, a task that plays a fundamental role on a myriad of scientific contexts. In special, we use an unsupervised technique which relies on a particle competition mechanism in a networked environment to detect the clusters. It has been shown that, in this kind of environment, the learning process can be improved because the network representation of data can capture topological features of the input data set. Specifically, in the proposed disambiguating model, a set of particles is randomly spawned into the nodes constituting the network. As time progresses, the particles employ a movement strategy composed of a probabilistic convex mixture of random and preferential walking policies. In the former, the walking rule exclusively depends on the topology of the network and is responsible for the exploratory behavior of the particles. In the latter, the walking rule depends both on the topology and the domination levels that the particles impose on the neighboring nodes. This type of behavior compels the particles to perform a defensive strategy, because it will force them to revisit nodes that are already dominated by them, rather than exploring rival territories. Computer simulations conducted on the networks extracted from the arXiv repository of preprint papers and also from other databases reveal the effectiveness of the model, which turned out to be more accurate than traditional clustering methods.

Comparative Study of Learning Using E-Learning and Printed Materials on Independent Learning and Creativity

Science.gov (United States)

Wahyu Utami, Niken; Aziz Saefudin, Abdul

2018-01-01

This study aims to determine: 1) differences in students taking independent learning by using e-learning and the students who attend the learning by using the print instructional materials ; 2) differences in the creativity of students who follow learning with e-learning and the students who attend the learning by using the print instructional materials ; 3) differences in learning independence and creativity of students attend learning with e-learning and the students who attend lessons using printed teaching materials in the subject of Mathematics Instructional Media Development. This study was a quasi-experimental research design using only posttest control design. The study population was all students who take courses in Learning Mathematics Media Development, Academic Year 2014/2015 100 students and used a random sample (random sampling) is 60 students. To test the hypothesis used multivariate analysis of variance or multivariable analysis of variance (MANOVA) of the track. The results of this study indicate that 1) There is a difference in student learning independence following study using the e-learning and the students who attend lessons using printed teaching materials in the lecture PMPM ( F = 4.177, p = 0.046 0.05) ; No difference learning independence and creativity of students attend learning by using e-learning and the students who attend the learning using printed teaching materials in the lecture PMPM (F = 2.452, p = 0.095 > 0.05). Based on these studies suggested that the learning using e -learning can be used to develop student creativity, while learning to use e -learning and teaching materials can be printed to use to develop students’ independence.

A Support Vector Learning-Based Particle Filter Scheme for Target Localization in Communication-Constrained Underwater Acoustic Sensor Networks.

Science.gov (United States)

Li, Xinbin; Zhang, Chenglin; Yan, Lei; Han, Song; Guan, Xinping

2017-12-21

Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid "particle degeneracy" problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network.

Social media to supplement point-of-care ultrasound courses: the "sandwich e-learning" approach. A randomized trial.

Science.gov (United States)

Hempel, Dorothea; Haunhorst, Stephanie; Sinnathurai, Sivajini; Seibel, Armin; Recker, Florian; Heringer, Frank; Michels, Guido; Breitkreutz, Raoul

2016-12-01

Point-of-care ultrasound (POC-US) is gaining importance in almost all specialties. E-learning has been used to teach theoretical knowledge and pattern recognition. As social media are universally available, they can be utilized for educational purposes. We wanted to evaluate the utility of the sandwich e-learning approach defined as a pre-course e-learning and a post-course learning activity using Facebook after a one-day point-of-care ultrasound (POC-US) course and its effect on the retention of knowledge. A total of 62 medial students were recruited for this study and randomly assigned to one of four groups. All groups received an identical hands-on training and performed several tests during the study period. The hands-on training was performed in groups of five students per instructor with the students scanning each other. Group 1 had access to pre-course e-learning, but not to post-course e-learning. Instead of a pre-course e-learning, group 2 listened to presentations at the day of the course (classroom teaching) and had access to the post-course learning activity using Facebook. Group 3 had access to both pre- and post-course e-learning (sandwich e-learning) activities, while group 4 listened classroom presentations only (classroom teaching only). Therefore only groups 2 and 3 had access to post-course learning via Facebook by joining a secured group. Posts containing ultrasound pictures and videos were published to this group. The students were asked to "like" the posts to monitor attendance. Knowledge retention was assessed 6 weeks after the course. After 6 weeks, group 3 achieved comparable results when compared to group 2 (82.2 % + -8.2 vs. 84.3 + -8.02) (p = 0.3). Students who participated in the post-course activity were more satisfied with the overall course than students without post-course learning (5.5 vs. 5.3 on a range from 1 to 6). In this study, the sandwich e-learning approach led to equal rates of knowledge retention compared to

Snake representation of a superprocess in random environment

OpenAIRE

Mytnik, Leonid; Xiong, Jie; Zeitouni, Ofer

2011-01-01

We consider (discrete time) branching particles in a random environment which is i.i.d. in time and possibly spatially correlated. We prove a representation of the limit process by means of a Brownian snake in random environment.

Document page structure learning for fixed-layout e-books using conditional random fields

Science.gov (United States)

Tao, Xin; Tang, Zhi; Xu, Canhui

2013-12-01

In this paper, a model is proposed to learn logical structure of fixed-layout document pages by combining support vector machine (SVM) and conditional random fields (CRF). Features related to each logical label and their dependencies are extracted from various original Portable Document Format (PDF) attributes. Both local evidence and contextual dependencies are integrated in the proposed model so as to achieve better logical labeling performance. With the merits of SVM as local discriminative classifier and CRF modeling contextual correlations of adjacent fragments, it is capable of resolving the ambiguities of semantic labels. The experimental results show that CRF based models with both tree and chain graph structures outperform the SVM model with an increase of macro-averaged F1 by about 10%.

Impact of eLearning course on nurses' professional competence in seclusion and restraint practices: a randomized controlled study (ISRCTN32869544).

Science.gov (United States)

Kontio, R; Lahti, M; Pitkänen, A; Joffe, G; Putkonen, H; Hätönen, H; Katajisto, J; Välimäki, M

2011-11-01

Education on the care of aggressive and disturbed patients is fragmentary. eLearning could ensure the quality of such education, but data on its impact on professional competence in psychiatry are lacking. The aim of this study was to explore the impact of ePsychNurse.Net, an eLearning course, on psychiatric nurses' professional competence in seclusion and restraint and on their job satisfaction and general self-efficacy. In a randomized controlled study, 12 wards were randomly assigned to ePsychNurse.Net (intervention) or education as usual (control). Baseline and 3-month follow-up data on nurses' knowledge of coercion-related legislation, physical restraint and seclusion, their attitudes towards physical restraint and seclusion, job satisfaction and general self-efficacy were analysed for 158 completers. Knowledge (primary outcome) of coercion-related legislation improved in the intervention group, while knowledge of physical restraint improved and knowledge of seclusion remained unchanged in both groups. General self-efficacy improved in the intervention group also attitude to seclusion in the control group. In between-group comparison, attitudes to seclusion (one of secondary outcomes) favoured the control group. Although the ePsychNurse.Net demonstrated only slight advantages over conventional learning, it may be worth further development with, e.g. flexible time schedule and individualized content. © 2011 Blackwell Publishing.

Bridging Weighted Rules and Graph Random Walks for Statistical Relational Models

Directory of Open Access Journals (Sweden)

Seyed Mehran Kazemi

2018-02-01

Full Text Available The aim of statistical relational learning is to learn statistical models from relational or graph-structured data. Three main statistical relational learning paradigms include weighted rule learning, random walks on graphs, and tensor factorization. These paradigms have been mostly developed and studied in isolation for many years, with few works attempting at understanding the relationship among them or combining them. In this article, we study the relationship between the path ranking algorithm (PRA, one of the most well-known relational learning methods in the graph random walk paradigm, and relational logistic regression (RLR, one of the recent developments in weighted rule learning. We provide a simple way to normalize relations and prove that relational logistic regression using normalized relations generalizes the path ranking algorithm. This result provides a better understanding of relational learning, especially for the weighted rule learning and graph random walk paradigms. It opens up the possibility of using the more flexible RLR rules within PRA models and even generalizing both by including normalized and unnormalized relations in the same model.

Wave-particle interaction in the Faraday waves.

Science.gov (United States)

Francois, N; Xia, H; Punzmann, H; Shats, M

2015-10-01

Wave motion in disordered Faraday waves is analysed in terms of oscillons or quasi-particles. The motion of these oscillons is measured using particle tracking tools and it is compared with the motion of fluid particles on the water surface. Both the real floating particles and the oscillons, representing the collective fluid motion, show Brownian-type dispersion exhibiting ballistic and diffusive mean squared displacement at short and long times, respectively. While the floating particles motion has been previously explained in the context of two-dimensional turbulence driven by Faraday waves, no theoretical description exists for the random walk type motion of oscillons. It is found that the r.m.s velocity ⟨μ̃(osc)⟩(rms) of oscillons is directly related to the turbulent r.m.s. velocity ⟨μ̃⟩(rms) of the fluid particles in a broad range of vertical accelerations. The measured ⟨μ̃(osc)⟩(rms) accurately explains the broadening of the frequency spectra of the surface elevation observed in disordered Faraday waves. These results suggest that 2D turbulence is the driving force behind both the randomization of the oscillons motion and the resulting broadening of the wave frequency spectra. The coupling between wave motion and hydrodynamic turbulence demonstrated here offers new perspectives for predicting complex fluid transport from the knowledge of wave field spectra and vice versa.

Spatial learning and memory deficits induced by exposure to iron-56-particle radiation

Science.gov (United States)

Shukitt-Hale, B.; Casadesus, G.; McEwen, J. J.; Rabin, B. M.; Joseph, J. A.

2000-01-01

It has previously been shown that exposing rats to particles of high energy and charge (HZE) disrupts the functioning of the dopaminergic system and behaviors mediated by this system, such as motor performance and an amphetamine-induced conditioned taste aversion; these adverse behavioral and neuronal effects are similar to those seen in aged animals. Because cognition declines with age, spatial learning and memory were assessed in the Morris water maze 1 month after whole-body irradiation with 1.5 Gy of 1 GeV/nucleon high-energy (56)Fe particles, to test the cognitive behavioral consequences of radiation exposure. Irradiated rats demonstrated cognitive impairment compared to the control group as seen in their increased latencies to find the hidden platform, particularly on the reversal day when the platform was moved to the opposite quadrant. Also, the irradiated group used nonspatial strategies during the probe trials (swim with no platform), i.e. less time spent in the platform quadrant, fewer crossings of and less time spent in the previous platform location, and longer latencies to the previous platform location. These findings are similar to those seen in aged rats, suggesting that an increased release of reactive oxygen species may be responsible for the induction of radiation- and age-related cognitive deficits. If these decrements in behavior also occur in humans, they may impair the ability of astronauts to perform critical tasks during long-term space travel beyond the magnetosphere.

The impact of blended teaching on knowledge, satisfaction, and self-directed learning in nursing undergraduates: a randomized, controlled trial.

Science.gov (United States)

Gagnon, Marie-Pierre; Gagnon, Johanne; Desmartis, Marie; Njoya, Merlin

2013-01-01

This study aimed to assess the effectiveness of a blended-teaching intervention using Internet-based tutorials coupled with traditional lectures in an introduction to research undergraduate nursing course. Effects of the intervention were compared with conventional, face-to-face classroom teaching on three outcomes: knowledge, satisfaction, and self-learning readiness. A two-group, randomized, controlled design was used, involving 112 participants. Descriptive statistics and analysis of covariance (ANCOVA) were performed. The teaching method was found to have no direct impact on knowledge acquisition, satisfaction, and self-learning readiness. However, motivation and teaching method had an interaction effect on knowledge acquisition by students. Among less motivated students, those in the intervention group performed better than those who received traditional training. These findings suggest that this blended-teaching method could better suit some students, depending on their degree of motivation and level of self-directed learning readiness.

Particle-associated flagellates: swimming patterns, colonization rates, and grazing on attached bacteria

DEFF Research Database (Denmark)

Kiørboe, Thomas; Grossart, H.P.; Ploug, H.

2004-01-01

Some pelagic flagellates colonize particles, such as marine snow, where they graze on bacteria and thus impact the dynamics of the attached microbial communities. Particle colonization is governed by motility. Swimming patterns of 2 particle-associated flagellates, Bodo designis and Spumella sp......., are very different, the former swimming slowly in an erratic, random pattern, and the latter faster and along smooth helixes of variable amplitude and frequency. At spatial scales exceeding ca. 50 mum, the motility of B. designis can be described as a random walk and modeled as diffusion. Spumella sp...

Robust visual tracking via multi-task sparse learning

KAUST Repository

Zhang, Tianzhu

2012-06-01

In this paper, we formulate object tracking in a particle filter framework as a multi-task sparse learning problem, which we denote as Multi-Task Tracking (MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in MTT. By employing popular sparsity-inducing p, q mixed norms (p D; 1), we regularize the representation problem to enforce joint sparsity and learn the particle representations together. As compared to previous methods that handle particles independently, our results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity. Interestingly, we show that the popular L 1 tracker [15] is a special case of our MTT formulation (denoted as the L 11 tracker) when p q 1. The learning problem can be efficiently solved using an Accelerated Proximal Gradient (APG) method that yields a sequence of closed form updates. As such, MTT is computationally attractive. We test our proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that MTT methods consistently outperform state-of-the-art trackers. © 2012 IEEE.

Collective rotations of active particles interacting with obstacles

Science.gov (United States)

Mokhtari, Zahra; Aspelmeier, Timo; Zippelius, Annette

2017-10-01

We consider active particles in a heterogeneous medium, modeled by static, random obstacles. In accordance with the known tendency of active particles to cluster, we observe accumulation and crystallization of active particles around the obstacles which serve as nucleation sites. In the limit of high activity, the crystals start to rotate spontaneously, resembling a rotating rigid body. We trace the occurrence of these oscillations to the enhanced attraction of particles whose orientation points along the rotational velocity as compared to those whose orientation points in the opposite direction.

Single particle dynamics of many-body systems described by Vlasov-Fokker-Planck equations

International Nuclear Information System (INIS)

Frank, T.D.

2003-01-01

Using Langevin equations we describe the random walk of single particles that belong to particle systems satisfying Vlasov-Fokker-Planck equations. In doing so, we show that Haissinski distributions of bunched particles in electron storage rings can be derived from a particle dynamics model

Comparison of three problem-based learning conditions (real patients, digital and paper) with lecture-based learning in a dermatology course: a prospective randomized study from China.

Science.gov (United States)

Li, Jie; Li, Qing Ling; Li, Ji; Chen, Ming Liang; Xie, Hong Fu; Li, Ya Ping; Chen, Xiang

2013-01-01

The precise effect and the quality of different cases used in dermatology problem-based learning (PBL) curricula are yet unclear. To prospectively compare the impact of real patients, digital, paper PBL (PPBL) and traditional lecture-based learning (LBL) on academic results and student perceptions. A total of 120 students were randomly allocated into either real-patients PBL (RPBL) group studied via real-patient cases, digital PBL (DPBL) group studied via digital-form cases, PPBL group studied via paper-form cases, or conventional group who received didactic lectures. Academic results were assessed through review of written examination, objective structured clinical examination and student performance scores. A five-point Likert scale questionnaire was used to evaluate student perceptions. Compared to those receiving lectures only, all PBL participants had better results for written examination, clinical examination and overall performance. Students in RPBL group exhibited better overall performance than those in the other two PBL groups. Real-patient cases were more effective in helping develop students' self-directed learning skills, improving their confidence in future patient encounters and encouraging them to learn more about the discussed condition, compared to digital and paper cases. Both real patient and digital triggers are helpful in improving students' clinical problem-handling skills. However, real patients provide greater benefits to students.

Does competition work as a motivating factor in e-learning? A randomized controlled trial.

Directory of Open Access Journals (Sweden)

Bjarne Skjødt Worm

Full Text Available BACKGROUND AND AIMS: Examinations today are often computerized and the primary motivation and curriculum is often based on the examinations. This study aims to test if competition widgets in e-learning quiz modules improve post-test and follow-up test results and self-evaluation. The secondary aim is to evaluate improvements during the training period comparing test-results and number of tests taken. METHODS: Two groups were randomly assigned to either a quiz-module with competition widgets or a module without. Pre-, post- and follow up test-results were recorded. Time used within the modules was measured and students reported time studying. Students were able to choose questions from former examinations in the quiz-module. RESULTS: Students from the competing group were significantly better at both post-and follow-up-test and had a significantly better overall learning efficiency than those from the non-competing group. They were also significantly better at guessing their post-test results. CONCLUSION: Quiz modules with competition widgets motivate students to become more active during the module and stimulate better total efficiency. They also generate improved self-awareness regarding post-test-results.

Random walk and the heat equation

CERN Document Server

Lawler, Gregory F

2010-01-01

The heat equation can be derived by averaging over a very large number of particles. Traditionally, the resulting PDE is studied as a deterministic equation, an approach that has brought many significant results and a deep understanding of the equation and its solutions. By studying the heat equation by considering the individual random particles, however, one gains further intuition into the problem. While this is now standard for many researchers, this approach is generally not presented at the undergraduate level. In this book, Lawler introduces the heat equation and the closely related notion of harmonic functions from a probabilistic perspective. The theme of the first two chapters of the book is the relationship between random walks and the heat equation. The first chapter discusses the discrete case, random walk and the heat equation on the integer lattice; and the second chapter discusses the continuous case, Brownian motion and the usual heat equation. Relationships are shown between the two. For exa...

First-passage exponents of multiple random walks

International Nuclear Information System (INIS)

Ben-Naim, E; Krapivsky, P L

2010-01-01

We investigate first-passage statistics of an ensemble of N noninteracting random walks on a line. Starting from a configuration in which all particles are located in the positive half-line, we study S n (t), the probability that the nth rightmost particle remains in the positive half-line up to time t. This quantity decays algebraically, S n (t)∼t -β n , in the long-time limit. Interestingly, there is a family of nontrivial first-passage exponents, β 1 2 N-1 ; the only exception is the two-particle case where β 1 = 1/3. In the N → ∞ limit, however, the exponents attain a scaling form, β n (N) → β(z) with z=(n-N/2)/√N. We also demonstrate that the smallest exponent decays exponentially with N. We deduce these results from first-passage kinetics of a random walk in an N-dimensional cone and confirm them using numerical simulations. Additionally, we investigate the family of exponents that characterizes leadership statistics of multiple random walks and find that in this case, the cone provides an excellent approximation.

Development of self-learning Monte Carlo technique for more efficient modeling of nuclear logging measurements

International Nuclear Information System (INIS)

Zazula, J.M.

1988-01-01

The self-learning Monte Carlo technique has been implemented to the commonly used general purpose neutron transport code MORSE, in order to enhance sampling of the particle histories that contribute to a detector response. The parameters of all the biasing techniques available in MORSE, i.e. of splitting, Russian roulette, source and collision outgoing energy importance sampling, path length transformation and additional biasing of the source angular distribution are optimized. The learning process is iteratively performed after each batch of particles, by retrieving the data concerning the subset of histories that passed the detector region and energy range in the previous batches. This procedure has been tested on two sample problems in nuclear geophysics, where an unoptimized Monte Carlo calculation is particularly inefficient. The results are encouraging, although the presented method does not directly minimize the variance and the convergence of our algorithm is restricted by the statistics of successful histories from previous random walk. Further applications for modeling of the nuclear logging measurements seem to be promising. 11 refs., 2 figs., 3 tabs. (author)

Repeated testing improves achievement in a blended learning approach for risk competence training of medical students: results of a randomized controlled trial.

Science.gov (United States)

Spreckelsen, C; Juenger, J

2017-09-26

Adequate estimation and communication of risks is a critical competence of physicians. Due to an evident lack of these competences, effective training addressing risk competence during medical education is needed. Test-enhanced learning has been shown to produce marked effects on achievements. This study aimed to investigate the effect of repeated tests implemented on top of a blended learning program for risk competence. We introduced a blended-learning curriculum for risk estimation and risk communication based on a set of operationalized learning objectives, which was integrated into a mandatory course "Evidence-based Medicine" for third-year students. A randomized controlled trial addressed the effect of repeated testing on achievement as measured by the students' pre- and post-training score (nine multiple-choice items). Basic numeracy and statistical literacy were assessed at baseline. Analysis relied on descriptive statistics (histograms, box plots, scatter plots, and summary of descriptive measures), bootstrapped confidence intervals, analysis of covariance (ANCOVA), and effect sizes (Cohen's d, r) based on adjusted means and standard deviations. All of the 114 students enrolled in the course consented to take part in the study and were assigned to either the intervention or control group (both: n = 57) by balanced randomization. Five participants dropped out due to non-compliance (control: 4, intervention: 1). Both groups profited considerably from the program in general (Cohen's d for overall pre vs. post scores: 2.61). Repeated testing yielded an additional positive effect: while the covariate (baseline score) exhibits no relation to the post-intervention score, F(1, 106) = 2.88, p > .05, there was a significant effect of the intervention (repeated tests scenario) on learning achievement, F(1106) = 12.72, p blended learning approach can be improved significantly by implementing a test-enhanced learning design, namely repeated testing. As

Orthopaedic resident preparedness for closed reduction and pinning of pediatric supracondylar fractures is improved by e-learning: a multisite randomized controlled study.

Science.gov (United States)

Hearty, Thomas; Maizels, Max; Pring, Maya; Mazur, John; Liu, Raymond; Sarwark, John; Janicki, Joseph

2013-09-04

There is a need to provide more efficient surgical training methods for orthopaedic residents. E-learning could possibly increase resident surgical preparedness, confidence, and comfort for surgery. Using closed reduction and pinning of pediatric supracondylar humeral fractures as the index case, we hypothesized that e-learning could increase resident knowledge acquisition for case preparation in the operating room. An e-learning surgical training module was created on the Computer Enhanced Visual Learning platform. The module provides a detailed and focused road map of the procedure utilizing a multimedia format. A multisite prospective randomized controlled study design compared residents who used a textbook for case preparation (control group) with residents who used the same textbook plus completed the e-learning module (test group). All subjects completed a sixty-question test on the theory and methods of the case. After completion of the test, the control group then completed the module as well. All subjects were surveyed on their opinion regarding the effectiveness of the module after performing an actual surgical case. Twenty-eight subjects with no previous experience in this surgery were enrolled at four academic centers. Subjects were randomized into two equal groups. The test group scored significantly better (p < 0.001) and demonstrated competence on the test compared with the control group; the mean correct test score (and standard deviation) was 90.9% ± 6.8% for the test group and 73.5% ± 6.4% for the control group. All residents surveyed (n = 27) agreed that the module is a useful supplement to traditional methods for case preparation and twenty-two of twenty-seven residents agreed that it reduced their anxiety during the case and improved their attention to surgical detail. E-learning using the Computer Enhanced Visual Learning platform significantly improved preparedness, confidence, and comfort with percutaneous closed reduction and pinning of a

De Broglie's Wavefunction and Wave-Particle Dualism

International Nuclear Information System (INIS)

Leydolt, Hans J.

2005-01-01

A different approach to wave mechanics is presented in accordance with de Broglie's original hypothesis applying the case of photons to all material particles. It derives propagating matter waves conceptually from a theory of evolution, and not by the formal setting of eigenvalue equations. The quality of explanation is at issue, and not the mere description of phenomena. A monoenergetic particle transport along two directions is described by a partial differential equation and by a random walk model. The dual description is applied to the particle picture and the wave picture; it leads with identical initial values to identical causal and timelike solutions in either picture. The partial differential equations are a Telegrapher equation in 1-d space and its analytic continuation, a modified Klein Gordon equation; the solutions represent a density distribution and an amplitude profile, respectively. However, only the corresponding random walk models - equivalent to Feynman's integral over all paths - derive the solutions as path-end distributions with the additional information about the flight direction. This allows following up momentum dissipation along two directions by means of two particle beams and their profiles. Thereby the total number of particles is divided up onto the two beams according to linear or squared fractions depending on the beam configuration putting up a 1-d or a 2-d space. This aspect escapes conventional descriptions but serves to describe the transport in the particle or in the wave picture in dependence on the knowledge/ignorance of the particle's flight direction

Airborne Particles: What We Have Learned About Their Role in Climate from Remote Sensing, and Prospects for Future Advances

Science.gov (United States)

Kahn, Ralph A.

2013-01-01

Desert dust, wildfire smoke, volcanic ash, biogenic and urban pollution particles, all affect the regional-scale climate of Earth in places and at times; some have global-scale impacts on the column radiation balance, cloud properties, atmospheric stability structure, and circulation patterns. Remote sensing has played a central role in identifying the sources and transports of airborne particles, mapping their three-dimensional distribution and variability, quantifying their amount, and constraining aerosol air mass type. The measurements obtained from remote sensing have strengths and limitations, and their value for characterizing Earths environment is enhanced immensely when they are combined with direct, in situ observations, and used to constrain aerosol transport and climate models. A similar approach has been taken to study the role particles play in determining the climate of Mars, though based on far fewer observations. This presentation will focus what we have learned from remote sensing about the impacts aerosol have on Earths climate; a few points about how aerosols affect the climate of Mars will also be introduced, in the context of how we might assess aerosol-climate impacts more generally on other worlds.

Elementary particle physics in early physics education

CERN Document Server

Wiener, Gerfried

2017-01-01

Current physics education research is faced with the important question of how best to introduce elementary particle physics in the classroom early on. Therefore, a learning unit on the subatomic structure of matter was developed, which aims to introduce 12-year-olds to elementary particles and fundamental interactions. This unit was iteratively evaluated and developed by means of a design-based research project with grade-6 students. In addition, dedicated professional development programmes were set up to instruct high school teachers about the learning unit and enable them to investigate its didactical feasibility. Overall, the doctoral research project led to successful results and showed the topic of elementary particle physics to be a viable candidate for introducing modern physics in the classroom. Furthermore, thanks to the design-based research methodology, the respective findings have implications for both physics education and physics education research, which will be presented during the PhD defen...

New machine learning tools for predictive vegetation mapping after climate change: Bagging and Random Forest perform better than Regression Tree Analysis

Science.gov (United States)

L.R. Iverson; A.M. Prasad; A. Liaw

2004-01-01

More and better machine learning tools are becoming available for landscape ecologists to aid in understanding species-environment relationships and to map probable species occurrence now and potentially into the future. To thal end, we evaluated three statistical models: Regression Tree Analybib (RTA), Bagging Trees (BT) and Random Forest (RF) for their utility in...

Determinants of Teachers' Attitudes towards E- Learning in Tanzanian Higher Learning Institutions

Science.gov (United States)

Kisanga, Dalton H.

2016-01-01

This survey research study presents the findings on determinants of teachers' attitudes towards e-learning in Tanzanian higher learning institutions. The study involved 258 teachers from 4 higher learning institutions obtained through stratified, simple random sampling. Questionnaires and documentary review were used in data collection. Data were…

Particle swarm genetic algorithm and its application

International Nuclear Information System (INIS)

Liu Chengxiang; Yan Changxiang; Wang Jianjun; Liu Zhenhai

2012-01-01

To solve the problems of slow convergence speed and tendency to fall into the local optimum of the standard particle swarm optimization while dealing with nonlinear constraint optimization problem, a particle swarm genetic algorithm is designed. The proposed algorithm adopts feasibility principle handles constraint conditions and avoids the difficulty of penalty function method in selecting punishment factor, generates initial feasible group randomly, which accelerates particle swarm convergence speed, and introduces genetic algorithm crossover and mutation strategy to avoid particle swarm falls into the local optimum Through the optimization calculation of the typical test functions, the results show that particle swarm genetic algorithm has better optimized performance. The algorithm is applied in nuclear power plant optimization, and the optimization results are significantly. (authors)

Improved identification of primary biological aerosol particles using single-particle mass spectrometry

Directory of Open Access Journals (Sweden)

M. A. Zawadowicz

2017-06-01

Full Text Available Measurements of primary biological aerosol particles (PBAP, especially at altitudes relevant to cloud formation, are scarce. Single-particle mass spectrometry (SPMS has been used to probe aerosol chemical composition from ground and aircraft for over 20 years. Here we develop a method for identifying bioaerosols (PBAP and particles containing fragments of PBAP as part of an internal mixture using SPMS. We show that identification of bioaerosol using SPMS is complicated because phosphorus-bearing mineral dust and phosphorus-rich combustion by-products such as fly ash produce mass spectra with peaks similar to those typically used as markers for bioaerosol. We have developed a methodology to differentiate and identify bioaerosol using machine learning statistical techniques applied to mass spectra of known particle types. This improved method provides far fewer false positives compared to approaches reported in the literature. The new method was then applied to two sets of ambient data collected at Storm Peak Laboratory and a forested site in Central Valley, California to show that 0.04–2 % of particles in the 200–3000 nm aerodynamic diameter range were identified as bioaerosol. In addition, 36–56 % of particles identified as biological also contained spectral features consistent with mineral dust, suggesting internal dust–biological mixtures.

Preschool Teachers Can Use a PBS KIDS Transmedia Curriculum Supplement to Support Young Children's Mathematics Learning: Results of a Randomized Controlled Trial

Science.gov (United States)

Llorente, Carlin; Pasnik, Shelley; Moorthy, Savitha; Hupert, Naomi; Rosenfeld, Deborah; Gerard, Sarah

2015-01-01

The current study, a randomized controlled trial, explores how technology and educational transmedia resources can enhance prekindergarten math teaching and learning in preschools, especially those serving children who may be at risk for academic difficulties due to economic and social disadvantages. This research is part of a multi-year summative…

The stochastic dynamics of intermittent porescale particle motion

Science.gov (United States)

Dentz, Marco; Morales, Veronica; Puyguiraud, Alexandre; Gouze, Philippe; Willmann, Matthias; Holzner, Markus

2017-04-01

Numerical and experimental data for porescale particle dynamics show intermittent patterns in Lagrangian velocities and accelerations, which manifest in long time intervals of low and short durations of high velocities [1, 2]. This phenomenon is due to the spatial persistence of particle velocities on characteristic heterogeneity length scales. In order to systematically quantify these behaviors and extract the stochastic dynamics of particle motion, we focus on the analysis of Lagrangian velocities sampled equidistantly along trajectories [3]. This method removes the intermittency observed under isochrone sampling. The space-Lagrangian velocity series can be quantified by a Markov process that is continuous in distance along streamline. It is fully parameterized in terms of the flux-weighted Eulerian velocity PDF and the characteristic pore-length. The resulting stochastic particle motion describes a continuous time random walk (CTRW). This approach allows for the process based interpretation of experimental and numerical porescale velocity, acceleration and displacement data. It provides a framework for the characterization and upscaling of particle transport and dispersion from the pore to the Darcy-scale based on the medium geometry and Eulerian flow attributes. [1] P. De Anna, T. Le Borgne, M. Dentz, A.M. Tartakovsky, D. Bolster, and P. Davy, "Flow intermittency, dispersion, and correlated continuous time random walks in porous media," Phys. Rev. Lett. 110, 184502 (2013). [2] M. Holzner, V. L. Morales, M. Willmann, and M. Dentz, "Intermittent Lagrangian velocities and accelerations in three- dimensional porous medium flow," Phys. Rev. E 92, 013015 (2015). [3] M. Dentz, P. K. Kang, A. Comolli, T. Le Borgne, and D. R. Lester, "Continuous time random walks for the evolution of Lagrangian velocities," Phys. Rev. Fluids (2016).

Perturbation theory for the effective diffusion constant in a medium of random scatterers

International Nuclear Information System (INIS)

Dean, D S; Drummond, I T; Horgan, R R; Lefevre, A

2004-01-01

We develop perturbation theory and physically motivated resummations of the perturbation theory for the problem of a tracer particle diffusing in a random medium. The random medium contains point scatterers of density ρ uniformly distributed throughout the material. The tracer is a Langevin particle subjected to the quenched random force generated by the scatterers. Via our perturbative analysis, we determine when the random potential can be approximated by a Gaussian random potential. We also develop a self-similar renormalization group approach based on thinning out the scatterers; this scheme is similar to that used with success for diffusion in Gaussian random potentials and agrees with known exact results. To assess the accuracy of this approximation scheme, its predictions are confronted with results obtained by numerical simulation

Effect of Worksheet Scaffolds on Student Learning in Problem-Based Learning

Science.gov (United States)

Choo, Serene S. Y.; Rotgans, Jerome I.; Yew, Elaine H. J.; Schmidt, Henk G.

2011-01-01

The purpose of this study was to investigate the effect of worksheets as a scaffolding tool on students' learning achievement in a problem-based learning (PBL) environment. Seventeen PBL classes (N = 241) were randomly assigned to two experimental groups--one with a worksheet provided and the other without. Students' learning of the topic at hand…

Random walk through fractal environments

OpenAIRE

Isliker, H.; Vlahos, L.

2002-01-01

We analyze random walk through fractal environments, embedded in 3-dimensional, permeable space. Particles travel freely and are scattered off into random directions when they hit the fractal. The statistical distribution of the flight increments (i.e. of the displacements between two consecutive hittings) is analytically derived from a common, practical definition of fractal dimension, and it turns out to approximate quite well a power-law in the case where the dimension D of the fractal is ...

Lack of interaction between sensing-intuitive learning styles and problem-first versus information-first instruction: a randomized crossover trial.

Science.gov (United States)

Cook, David A; Thompson, Warren G; Thomas, Kris G; Thomas, Matthew R

2009-03-01

Adaptation to learning styles has been proposed to enhance learning. We hypothesized that learners with sensing learning style would perform better using a problem-first instructional method while intuitive learners would do better using an information-first method. Randomized, controlled, crossover trial. Resident ambulatory clinics. 123 internal medicine residents. Four Web-based modules in ambulatory internal medicine were developed in both "didactic" (information first, followed by patient problem and questions) and "problem" (case and questions first, followed by information) format. Knowledge posttest, format preference, learning style (Index of Learning Styles). Knowledge scores were similar between the didactic (mean +/- standard error, 83.0 +/- 0.8) and problem (82.3 +/- 0.8) formats (p = .42; 95% confidence interval [CI] for difference, -2.3 to 0.9). There was no difference between formats in regression slopes of knowledge scores on sensing-intuitive scores (p = .63) or in analysis of knowledge scores by styles classification (sensing 82.5 +/- 1.0, intermediate 83.7 +/- 1.2, intuitive 81.0 +/- 1.5; p = .37 for main effect, p = .59 for interaction with format). Format preference was neutral (3.2 +/- 0.2 [1 strongly prefers didactic, 6 strongly prefers problem], p = .12), and there was no association between learning styles and preference (p = .44). Formats were similar in time to complete modules (43.7 +/- 2.2 vs 43.2 +/- 2.2 minutes, p = .72). Starting instruction with a problem (versus employing problems later on) may not improve learning outcomes. Sensing and intuitive learners perform similarly following problem-first and didactic-first instruction. Results may apply to other instructional media.

Dispersal of sticky particles

Science.gov (United States)

Reddy, Ramana; Kumar, Sanjeev

2007-12-01

In this paper, we show through simulations that when sticky particles are broken continually, particles are dispersed into fine dust only if they are present in a narrow range of volume fractions. The upper limit of this range is 0.20 in the 2D and 0.10 in the 3D space. An increase in the dimensionality of space reduces the upper limit nearly by a factor of two. This scaling holds for dispersal of particles in hyperdimensional space of dimensions up to ten, the maximum dimension studied in this work. The maximum values of volume fractions obtained are significantly lower than those required for close packing and random packing of discs in 2D and spheres in 3D space. These values are also smaller than those required for critical phenomena of cluster percolation. The results obtained are attributed to merger cascades of sticky particles, triggered by breakup events. A simple theory that incorporates this cascade is developed to quantitatively explain the observed scaling of the upper limit with the dimensionality of space. The theory also captures the dynamics of the dispersal process in the corresponding range of particle volume fractions. The theory suggests that cascades of order one and two predominantly decide the upper limit for complete dispersal of particles.

Multi-Label Learning via Random Label Selection for Protein Subcellular Multi-Locations Prediction.

Science.gov (United States)

Wang, Xiao; Li, Guo-Zheng

2013-03-12

Prediction of protein subcellular localization is an important but challenging problem, particularly when proteins may simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular localization methods are only used to deal with the single-location proteins. In the past few years, only a few methods have been proposed to tackle proteins with multiple locations. However, they only adopt a simple strategy, that is, transforming the multi-location proteins to multiple proteins with single location, which doesn't take correlations among different subcellular locations into account. In this paper, a novel method named RALS (multi-label learning via RAndom Label Selection), is proposed to learn from multi-location proteins in an effective and efficient way. Through five-fold cross validation test on a benchmark dataset, we demonstrate our proposed method with consideration of label correlations obviously outperforms the baseline BR method without consideration of label correlations, indicating correlations among different subcellular locations really exist and contribute to improvement of prediction performance. Experimental results on two benchmark datasets also show that our proposed methods achieve significantly higher performance than some other state-of-the-art methods in predicting subcellular multi-locations of proteins. The prediction web server is available at http://levis.tongji.edu.cn:8080/bioinfo/MLPred-Euk/ for the public usage.

Exclusion principle and indistinguishability of identical particles in quantum mechanics

International Nuclear Information System (INIS)

Kaplan, I.G.

1975-01-01

The relation between the Pauli exclusion principle and the principle of indistiguishability of identical particles in quantum mechanics is discussed. Using the density matrix determined for states with random permutational symmetry, it has been shown that the particle indistinguishability takes place only for onedimensional representations of the permutation group whereas in all states which are degenerated over permutations the particles are distinguishable. Thus it has been shown that the exclusion principle follows from that of indistinguishability of identical particles

Residual Defect Density in Random Disks Deposits.

Science.gov (United States)

Topic, Nikola; Pöschel, Thorsten; Gallas, Jason A C

2015-08-03

We investigate the residual distribution of structural defects in very tall packings of disks deposited randomly in large channels. By performing simulations involving the sedimentation of up to 50 × 10(9) particles we find all deposits to consistently show a non-zero residual density of defects obeying a characteristic power-law as a function of the channel width. This remarkable finding corrects the widespread belief that the density of defects should vanish algebraically with growing height. A non-zero residual density of defects implies a type of long-range spatial order in the packing, as opposed to only local ordering. In addition, we find deposits of particles to involve considerably less randomness than generally presumed.

Procedures can be learned on the Web: a randomized study of ultrasound-guided vascular access training.

Science.gov (United States)

Chenkin, Jordan; Lee, Shirley; Huynh, Thien; Bandiera, Glen

2008-10-01

Web-based learning has several potential advantages over lectures, such as anytime-anywhere access, rich multimedia, and nonlinear navigation. While known to be an effective method for learning facts, few studies have examined the effectiveness of Web-based formats for learning procedural skills. The authors sought to determine whether a Web-based tutorial is at least as effective as a didactic lecture for learning ultrasound-guided vascular access (UGVA). Participating staff emergency physicians (EPs) and junior emergency medicine (EM) residents with no UGVA experience completed a precourse test and were randomized to either a Web-based or a didactic group. The Web-based group was instructed to use an online tutorial and the didactic group attended a lecture. Participants then practiced on simulators and live models without any further instruction. Following a rest period, participants completed a four-station objective structured clinical examination (OSCE), a written examination, and a postcourse questionnaire. Examination results were compared using a noninferiority data analysis with a 10% margin of difference. Twenty-one residents and EPs participated in the study. There were no significant differences in mean OSCE scores (absolute difference = -2.8%; 95% confidence interval [CI] = -9.3% to 3.8%) or written test scores (absolute difference = -1.4%; 95% CI = -7.8% to 5.0%) between the Web group and the didactic group. Both groups demonstrated similar improvements in written test scores (26.1% vs. 25.8%; p = 0.95). Ninety-one percent (10/11) of the Web group and 80% (8/10) of the didactic group participants found the teaching format to be effective (p = 0.59). Our Web-based tutorial was at least as effective as a traditional didactic lecture for teaching the knowledge and skills essential for UGVA. Participants expressed high satisfaction with this teaching technology. Web-based teaching may be a useful alternative to didactic teaching for learning procedural

Treatment of cancer with heavy charged particles

International Nuclear Information System (INIS)

Castro, J.R.

1981-01-01

The clinical radiotherapy trial has accured 243 patients irradiated with particles and 13 patients irradiated as controls in randomized studies. Of the 243 particle patients, 194 have been treated with helium ions, either solely or in combination with photon irradiation, and 49 have received all or part of their irradiation with one of the heavier particles, either carbon, neon, or argon ions. The project thus can be divided into two general phases: (1) evaluation of improved dose distribution without significant biologic advantage by use of helium ion irradiation; and (2) evaluation of improved dose distribution and enhanced biologic effect by irradiation with heavy charged particles such as carbon, neon, and argon ions

A randomized trial of two e-learning strategies for teaching substance abuse management skills to physicians.

Science.gov (United States)

Harris, John M; Sun, Huaping

2013-09-01

To compare the educational effectiveness of two virtual patient (VP)-based e-learning strategies, versus no training, in improving physicians' substance abuse management knowledge, attitudes, self-reported behaviors, and decision making. The 2011-2012 study was a posttest-only, three-arm, randomized controlled trial in 90 resident and 30 faculty physicians from five adult medicine primary care training programs. The intervention was one of two 2-hour VP-based e-learning programs, designed by national experts to teach structured screening, brief interventions, referral, and treatment skills. One used traditional problem solving with feedback (unworked example), and the other incorporated an expert demonstration first, followed by problem solving with feedback (worked example). The main outcome measure was performance on the Physicians' Competence in Substance Abuse Test (P-CSAT, maximum score = 315), a self-administered, previously validated measure of physicians' competence in managing substance abuse. The survey was completed at the outset of the study and two months later. Overall P-CSAT scores were virtually identical (202-211, P > .05) between both intervention groups and the no-training control group at both times. Average faculty P-CSAT scores (221.9, 224.6) were significantly higher (P study did not provide evidence that a brief, worked example, VP-based e-learning program or a traditional, unworked, VP-based e-learning program was superior to no training in improving physicians' substance abuse management skills. The study did provide additional evidence that the P-CSAT distinguishes between physicians who should possess different levels of substance abuse management skills.

Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

Science.gov (United States)

Abdulameer, Mohammed Hasan; Othman, Zulaiha Ali

2014-01-01

Existing face recognition methods utilize particle swarm optimizer (PSO) and opposition based particle swarm optimizer (OPSO) to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO) technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM). In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented. PMID:24790584

Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Mohammed Hasan Abdulameer

2014-01-01

Full Text Available Existing face recognition methods utilize particle swarm optimizer (PSO and opposition based particle swarm optimizer (OPSO to optimize the parameters of SVM. However, the utilization of random values in the velocity calculation decreases the performance of these techniques; that is, during the velocity computation, we normally use random values for the acceleration coefficients and this creates randomness in the solution. To address this problem, an adaptive acceleration particle swarm optimization (AAPSO technique is proposed. To evaluate our proposed method, we employ both face and iris recognition based on AAPSO with SVM (AAPSO-SVM. In the face and iris recognition systems, performance is evaluated using two human face databases, YALE and CASIA, and the UBiris dataset. In this method, we initially perform feature extraction and then recognition on the extracted features. In the recognition process, the extracted features are used for SVM training and testing. During the training and testing, the SVM parameters are optimized with the AAPSO technique, and in AAPSO, the acceleration coefficients are computed using the particle fitness values. The parameters in SVM, which are optimized by AAPSO, perform efficiently for both face and iris recognition. A comparative analysis between our proposed AAPSO-SVM and the PSO-SVM technique is presented.

Learned Helplessness in Exceptional Children.

Science.gov (United States)

Brock, Herman B.; Kowitz, Gerald T.

The research literature on learned helplessness in exceptional children is reviewed and the authors' efforts to identify and retrain learning disabled (LD) children who have characteristics typical of learned helplessness are reported. Twenty-eight elementary aged LD children viewed as "learned helpless" were randomly assigned to one of four…

Active particles

CERN Document Server

Degond, Pierre; Tadmor, Eitan

2017-01-01

This volume collects ten surveys on the modeling, simulation, and applications of active particles using methods ranging from mathematical kinetic theory to nonequilibrium statistical mechanics. The contributing authors are leading experts working in this challenging field, and each of their chapters provides a review of the most recent results in their areas and looks ahead to future research directions. The approaches to studying active matter are presented here from many different perspectives, such as individual-based models, evolutionary games, Brownian motion, and continuum theories, as well as various combinations of these. Applications covered include biological network formation and network theory; opinion formation and social systems; control theory of sparse systems; theory and applications of mean field games; population learning; dynamics of flocking systems; vehicular traffic flow; and stochastic particles and mean field approximation. Mathematicians and other members of the scientific commu...

Disentangling random thermal motion of particles and collective expansion of source from transverse momentum spectra in high energy collisions

Science.gov (United States)

Wei, Hua-Rong; Liu, Fu-Hu; Lacey, Roy A.

2016-12-01

In the framework of a multisource thermal model, we describe experimental results of the transverse momentum spectra of final-state light flavor particles produced in gold-gold (Au-Au), copper-copper (Cu-Cu), lead-lead (Pb-Pb), proton-lead (p-Pb), and proton-proton (p -p) collisions at various energies, measured by the PHENIX, STAR, ALICE, and CMS Collaborations, by using the Tsallis-standard (Tsallis form of Fermi-Dirac or Bose-Einstein), Tsallis, and two- or three-component standard distributions which can be in fact regarded as different types of ‘thermometers’ or ‘thermometric scales’ and ‘speedometers’. A central parameter in the three distributions is the effective temperature which contains information on the kinetic freeze-out temperature of the emitting source and reflects the effects of random thermal motion of particles as well as collective expansion of the source. To disentangle both effects, we extract the kinetic freeze-out temperature from the intercept of the effective temperature (T) curve as a function of particle’s rest mass (m 0) when plotting T versus m 0, and the mean transverse flow velocity from the slope of the mean transverse momentum ( ) curve as a function of mean moving mass (\\overline{m}) when plotting versus \\overline{m}.

Random maintenance policies

CERN Document Server

Nakagawa, Toshio

2014-01-01

Exploring random maintenance models, this book provides an introduction to the implementation of random maintenance, and it is one of the first books to be written on this subject.  It aims to help readers learn new techniques for applying random policies to actual reliability models, and it provides new theoretical analyses of various models including classical replacement, preventive maintenance and inspection policies. These policies are applied to scheduling problems, backup policies of database systems, maintenance policies of cumulative damage models, and reliability of random redundant systems. Reliability theory is a major concern for engineers and managers, and in light of Japan’s recent earthquake, the reliability of large-scale systems has increased in importance. This also highlights the need for a new notion of maintenance and reliability theory, and how this can practically be applied to systems. Providing an essential guide for engineers and managers specializing in reliability maintenance a...

Traffic of indistinguishable particles in complex networks

International Nuclear Information System (INIS)

Qing-Kuan, Meng; Jian-Yang, Zhu

2009-01-01

In this paper, we apply a simple walk mechanism to the study of the traffic of many indistinguishable particles in complex networks. The network with particles stands for a particle system, and every vertex in the network stands for a quantum state with the corresponding energy determined by the vertex degree. Although the particles are indistinguishable, the quantum states can be distinguished. When the many indistinguishable particles walk randomly in the system for a long enough time and the system reaches dynamic equilibrium, we find that under different restrictive conditions the particle distributions satisfy different forms, including the Bose–Einstein distribution, the Fermi–Dirac distribution and the non-Fermi distribution (as we temporarily call it). As for the Bose–Einstein distribution, we find that only if the particle density is larger than zero, with increasing particle density, do more and more particles condense in the lowest energy level. While the particle density is very low, the particle distribution transforms from the quantum statistical form to the classically statistical form, i.e., transforms from the Bose distribution or the Fermi distribution to the Boltzmann distribution. The numerical results fit well with the analytical predictions

Irregular Magnetic Fields and Energetic Particles near the Termination Shock

International Nuclear Information System (INIS)

Giacalone, J.; Jokipii, J. R.

2004-01-01

The physics of magnetic field-line meandering and the associated energetic-particle transport in the outer heliosphere is discussed. We assume that the heliospheric magnetic field, which is frozen into the solar-wind plasma, is composed of both an average and random component. The power in the random component is dominated by spatial scales that are very large (by a few orders of magnitude) compared to the shock thickness. The results from recent numerical simulations are presented. They reveal a number of characteristics which may be related to recent Voyager 1 observations of energetic particles and fields. For instance, low-energy (tens of keV) particles are seen well upstream of the shock that also have large pitch-angle anisotropies. Furthermore, low-energy particles are readily accelerated by the shock, even though their mean-free paths are very large compared to their gyroradii. When averaging over the entire system, the downstream spectra are qualitatively consistent with the theory of diffusive shock acceleration

Randomized controlled trial evaluating the temporal effects of high-intensity exercise on learning, short-term and long-term memory, and prospective memory.

Science.gov (United States)

Frith, Emily; Sng, Eveleen; Loprinzi, Paul D

2017-11-01

The broader purpose of this study was to examine the temporal effects of high-intensity exercise on learning, short-term and long-term retrospective memory and prospective memory. Among a sample of 88 young adult participants, 22 were randomized into one of four different groups: exercise before learning, control group, exercise during learning, and exercise after learning. The retrospective assessments (learning, short-term and long-term memory) were assessed using the Rey Auditory Verbal Learning Test. Long-term memory including a 20-min and 24-hr follow-up assessment. Prospective memory was assessed using a time-based procedure by having participants contact (via phone) the researchers at a follow-up time period. The exercise stimulus included a 15-min bout of progressive maximal exertion treadmill exercise. High-intensity exercise prior to memory encoding (vs. exercise during memory encoding or consolidation) was effective in enhancing long-term memory (for both 20-min and 24-h follow-up assessments). We did not observe a differential temporal effect of high-intensity exercise on short-term memory (immediate post-memory encoding), learning or prospective memory. The timing of high-intensity exercise may play an important role in facilitating long-term memory. © 2017 Federation of European Neuroscience Societies and John Wiley & Sons Ltd.

When are active Brownian particles and run-and-tumble particles equivalent? Consequences for motility-induced phase separation

OpenAIRE

Cates, M. E.; Tailleur, J.

2012-01-01

Active Brownian particles (ABPs, such as self-phoretic colloids) swim at fixed speed $v$ along a body-axis ${\\bf u}$ that rotates by slow angular diffusion. Run-and-tumble particles (RTPs, such as motile bacteria) swim with constant $\\u$ until a random tumble event suddenly decorrelates the orientation. We show that when the motility parameters depend on density $\\rho$ but not on ${\\bf u}$, the coarse-grained fluctuating hydrodynamics of interacting ABPs and RTPs can be mapped onto each other...

Randomized trial of two e-learning programs for oral health students on secondary prevention of eating disorders.

Science.gov (United States)

DeBate, Rita D; Severson, Herbert H; Cragun, Deborah; Bleck, Jennifer; Gau, Jeff; Merrell, Laura; Cantwell, Carley; Christiansen, Steve; Koerber, Anne; Tomar, Scott L; Brown, Kelli McCormack; Tedesco, Lisa A; Hendricson, William; Taris, Mark

2014-01-01

The purpose of this study was to test whether an interactive, web-based training program is more effective than an existing, flat-text, e-learning program at improving oral health students' knowledge, motivation, and self-efficacy to address signs of disordered eating behaviors with patients. Eighteen oral health classes of dental and dental hygiene students were randomized to either the Intervention (interactive program; n=259) or Alternative (existing program; n=58) conditions. Hierarchical linear modeling assessed for posttest differences between groups while controlling for baseline measures. Improvement among Intervention participants was superior to those who completed the Alternative program for three of the six outcomes: benefits/barriers, self-efficacy, and skills-based knowledge (effect sizes ranging from 0.43 to 0.87). This study thus suggests that interactive training programs may be better than flat-text e-learning programs for improving the skills-based knowledge and self-efficacy necessary for behavior change.

Local random configuration-tree theory for string repetition and facilitated dynamics of glass

Science.gov (United States)

Lam, Chi-Hang

2018-02-01

We derive a microscopic theory of glassy dynamics based on the transport of voids by micro-string motions, each of which involves particles arranged in a line hopping simultaneously displacing one another. Disorder is modeled by a random energy landscape quenched in the configuration space of distinguishable particles, but transient in the physical space as expected for glassy fluids. We study the evolution of local regions with m coupled voids. At a low temperature, energetically accessible local particle configurations can be organized into a random tree with nodes and edges denoting configurations and micro-string propagations respectively. Such trees defined in the configuration space naturally describe systems defined in two- or three-dimensional physical space. A micro-string propagation initiated by a void can facilitate similar motions by other voids via perturbing the random energy landscape, realizing path interactions between voids or equivalently string interactions. We obtain explicit expressions of the particle diffusion coefficient and a particle return probability. Under our approximation, as temperature decreases, random trees of energetically accessible configurations exhibit a sequence of percolation transitions in the configuration space, with local regions containing fewer coupled voids entering the non-percolating immobile phase first. Dynamics is dominated by coupled voids of an optimal group size, which increases as temperature decreases. Comparison with a distinguishable-particle lattice model (DPLM) of glass shows very good quantitative agreements using only two adjustable parameters related to typical energy fluctuations and the interaction range of the micro-strings.

Random electrodynamics: the theory of classical electrodynamics with classical electromagnetic zero-point radiation

International Nuclear Information System (INIS)

Boyer, T.H.

1975-01-01

The theory of classical electrodynamics with classical electromagnetic zero-point radiation is outlined here under the title random electrodynamics. The work represents a reanalysis of the bounds of validity of classical electron theory which should sharpen the understanding of the connections and distinctions between classical and quantum theories. The new theory of random electrodynamics is a classical electron theory involving Newton's equations for particle motion due to the Lorentz force, and Maxwell's equations for the electromagnetic fields with point particles as sources. However, the theory departs from the classical electron theory of Lorentz in that it adopts a new boundary condition on Maxwell's equations. It is assumed that the homogeneous boundary condition involves random classical electromagnetic radiation with a Lorentz-invariant spectrum, classical electromagnetic zero-point radiation. The implications of random electrodynamics for atomic structure, atomic spectra, and particle-interference effects are discussed on an order-of-magnitude or heuristic level. Some detailed mathematical connections and some merely heuristic connections are noted between random electrodynamics and quantum theory. (U.S.)

EHLS at School: school-age follow-up of the Early Home Learning Study cluster randomized controlled trial.

Science.gov (United States)

Westrupp, Elizabeth M; Bennett, Clair; Cullinane, Meabh; Hackworth, Naomi J; Berthelsen, Donna; Reilly, Sheena; Mensah, Fiona K; Gold, Lisa; Bennetts, Shannon K; Levickis, Penny; Nicholson, Jan M

2018-05-02

Targeted interventions during early childhood can assist families in providing strong foundations that promote children's health and wellbeing across the life course. There is growing recognition that longer follow-up times are necessary to assess intervention outcomes, as effects may change as children develop. The Early Home Learning Study, or 'EHLS', comprised two cluster randomized controlled superiority trials of a brief parenting intervention, smalltalk, aimed at supporting parents to strengthen the early childhood home learning environment of infants (6-12 months) or toddlers (12-36 months). Results showed sustained improvements in parent-child interactions and the home environment at the 32 week follow-up for the toddler but not the infant trial. The current study will therefore follow up the EHLS toddler cohort to primary school age, with the aim of addressing a gap in literature concerning long-term effects of early childhood interventions focused on improving school readiness and later developmental outcomes. 'EHLS at School' is a school-aged follow-up study of the toddler cluster randomized controlled trial (n = 1226). Data will be collected by parent-, child- and teacher-report questionnaires, recorded observations of parent-child interactions, and direct child assessment when children are aged 7.5 years old. Data linkage will provide additional data on child health and academic functioning at ages 5, 8 and 10 years. Child outcomes will be compared for families allocated to standard/usual care (control) versus those allocated to the smalltalk program (group program only or group program with additional home coaching). Findings from The Early Home Learning Study provided evidence of the benefits of the smalltalk intervention delivered via facilitated playgroups for parents of toddlers. The EHLS at School Study aims to examine the long-term outcomes of this initiative to determine whether improvements in the quality of the parent

Impact of cap-assisted colonoscopy on the learning curve and quality in colonoscopy: a randomized controlled trial.

Science.gov (United States)

Tang, Zhouwen; Zhang, Daniel S; Thrift, Aaron P; Patel, Kalpesh K

2018-03-01

Colonoscopy competency assessment in trainees traditionally has been informal. Comprehensive metrics such as the Assessment of Competency in Endoscopy (ACE) tool suggest that competency thresholds are higher than assumed. Cap-assisted colonoscopy (CAC) may improve competency, but data regarding novice trainees are lacking. We compared CAC versus standard colonoscopy (SC) performed by novice trainees in a randomized controlled trial. All colonoscopies performed by 3 gastroenterology fellows without prior experience were eligible for the study. Exclusion criteria included patient age 90 years, pregnancy, prior colon resection, diverticulitis, colon obstruction, severe hematochezia, referral for EMR, or a procedure done without patient sedation. Patients were randomized to either CAC or SC in a 1:1 fashion. The primary outcome was the independent cecal intubation rate (ICIR). Secondary outcomes were cecal intubation time, polyp detection rate, polyp miss rate, adenoma detection rate, ACE tool scores, and cumulative summation learning curves. A total of 203 colonoscopies were analyzed, 101 in CAC and 102 in SC. CAC resulted in a significantly higher cecal intubation rate, at 79.2% in CAC compared with 66.7% in SC (P = .04). Overall cecal intubation time was significantly shorter at 13.7 minutes for CAC versus 16.5 minutes for SC (P =.02). Cecal intubation time in the case of successful independent fellow intubation was not significantly different between CAC and SC (11.6 minutes vs 12.7 minutes; P = .29). Overall ACE tool motor and cognitive scores were higher with CAC. Learning curves for ICIR approached the competency threshold earlier with cap use but reached competency for only 1 fellow. The polyp detection rate, polyp miss rate, and adenoma detection rate were not significantly different between groups. CAC resulted in significant improvement in ICIR, overall ACE tool scores, and trend toward competency on learning curves when compared with SC in colonoscopy

RANDOMNUMBERS, Random Number Sequence Generated from Gas Ionisation Chamber Data

International Nuclear Information System (INIS)

Frigerio, N.A.; Sanathanan, L.P.; Morley, M.; Tyler, S.A.; Clark, N.A.; Wang, J.

1989-01-01

1 - Description of problem or function: RANDOM NUMBERS is a data collection of almost 2.7 million 31-bit random numbers generated by using a high resolution gas ionization detector chamber in conjunction with a 4096-channel multichannel analyzer to record the radioactive decay of alpha particles from a U-235 source. The signals from the decaying alpha particles were fed to the 4096-channel analyzer, and for each channel the frequency of signals registered in a 20,000-microsecond interval was recorded. The parity bits of these frequency counts, 0 for an even count and 1 for and odd count, were then assembled in sequence to form 31-bit random numbers and transcribed onto magnetic tape. This cycle was repeated to obtain the random numbers. 2 - Method of solution: The frequency distribution of counts from the device conforms to the Brockwell-Moyal distribution which takes into account the dead time of the counter. The count data were analyzed and tests for randomness on a sample indicate that the device is a highly reliable source of truly random numbers. 3 - Restrictions on the complexity of the problem: The RANDOM NUMBERS tape contains 2,669,568 31-bit numbers

Random walks of a quantum particle on a circle

International Nuclear Information System (INIS)

Fjeldsoe, N.; Midtdal, J.; Ravndal, F.

1987-07-01

When the quantum planar rotor is put on a lattice, its dynamics can be approximated by random walks on a circle. This allows for fast and accurate Monto Carlo simulations to determine the topological charge of different configurations of the system and thereby the Θ-dependency of the lowest energy levels

The CMS Masterclass and Particle Physics Outreach

Energy Technology Data Exchange (ETDEWEB)

Cecire, Kenneth [Notre Dame U.; Bardeen, Marjorie [Fermilab; McCauley, Thomas [Notre Dame U.

2014-01-01

The CMS Masterclass enables high school students to analyse authentic CMS data. Students can draw conclusions on key ratios and particle masses by combining their analyses. In particular, they can use the ratio of W^+ to W^- candidates to probe the structure of the proton, they can find the mass of the Z boson, and they can identify additional particles including, tentatively, the Higgs boson. In the United States, masterclasses are part of QuarkNet, a long-term program that enables students and teachers to use cosmic ray and particle physics data for learning with an emphasis on data from CMS.

Effect of meal composition on postprandial lipid concentrations and lipoprotein particle numbers: A randomized cross-over study.

Directory of Open Access Journals (Sweden)

Meena Shah

Full Text Available It is unclear how high-protein (HP and high-monounsaturated fat (HMF meals affect postprandial blood lipids and lipoprotein particle numbers (LPN.To compare a HP versus a HMF meal on postprandial lipid and LPN responses.Twenty-four participants (age: 36.3±15.0 years; body mass index: 23.6±2.0 kg/m2; 45.8% female were fed a HP (31.9% energy from protein and a HMF (35.2% fat and 20.7% monounsaturated fat meal in a randomized cross-over trial design. Energy and carbohydrate content were the same across meals. Blood samples were drawn in the fasting state and 3 hour postprandial state, and assessed for lipids and LPN.Repeated measures analysis showed a significant (p<0.05 treatment by time interaction effect for triglycerides (TG, the primary variable, total high-density lipoprotein particles (T-HDLP and T-HDLP minus large-buoyant high-density lipoprotein 2b (T-HDLP-LB-HDL2b. HP versus HMF condition led to significantly lower TG at 120 (geometric mean: 90.1 (95% confidence interval (CI: 76.4-106.3 vs. 146.5 (124.2-172.9 mg/dL and 180 (101.4 (83.1-123.8 vs. 148.7 (121.9-181.4 mg/dL min and higher T-HDLP at 120 (mean difference: 297.3 (95% CI: 48.6-545.9 nmol/L and 180 (291.6 (15.8-567.5 nmol/L min. The difference in T-HDLP by condition was due to the significantly higher small-dense HDLP (T-HDLP-LB-HDL2b during HP versus HMF condition at 120 (mean difference: 452.6 (95% CI: 177.4-727.9 nmol/L and 180 (496.8 (263.1-730.6 nmol/L min. Area under the curve analysis showed that HP versus HMF condition led to significantly lower TG, non-HDLP, and very-low-density lipoprotein particles (VLDLP responses but significantly less favorable responses in LB-HDL2b particles, T-HDLP-LB-HDL2b, and LB-HDL2b/T-HDLP ratio.The HP meal led to lower TG, non-HDLP, and VLDLP but less favorable LB-HDL2b, small-dense HDLP, and LB-HDL2b/T-HDLP ratio responses versus a HMF meal. Further studies are needed to confirm these findings over multiple meals.

Random elements on lattices: Review and statistical applications

Science.gov (United States)

Potocký, Rastislav; Villarroel, Claudia Navarro; Sepúlveda, Maritza; Luna, Guillermo; Stehlík, Milan

2017-07-01

We discuss important contributions to random elements on lattices. We relate to both algebraic and probabilistic properties. Several applications and concepts are discussed, e.g. positive dependence, Random walks and distributions on lattices, Super-lattices, learning. The application to Chilean Ecology is given.

Flow Navigation by Smart Microswimmers via Reinforcement Learning

Science.gov (United States)

Colabrese, Simona; Biferale, Luca; Celani, Antonio; Gustavsson, Kristian

2017-11-01

We have numerically modeled active particles which are able to acquire some limited knowledge of the fluid environment from simple mechanical cues and exert a control on their preferred steering direction. We show that those swimmers can learn effective strategies just by experience, using a reinforcement learning algorithm. As an example, we focus on smart gravitactic swimmers. These are active particles whose task is to reach the highest altitude within some time horizon, exploiting the underlying flow whenever possible. The reinforcement learning algorithm allows particles to learn effective strategies even in difficult situations when, in the absence of control, they would end up being trapped by flow structures. These strategies are highly nontrivial and cannot be easily guessed in advance. This work paves the way towards the engineering of smart microswimmers that solve difficult navigation problems. ERC AdG NewTURB 339032.

Accelerating Pseudo-Random Number Generator for MCNP on GPU

Science.gov (United States)

Gong, Chunye; Liu, Jie; Chi, Lihua; Hu, Qingfeng; Deng, Li; Gong, Zhenghu

2010-09-01

Pseudo-random number generators (PRNG) are intensively used in many stochastic algorithms in particle simulations, artificial neural networks and other scientific computation. The PRNG in Monte Carlo N-Particle Transport Code (MCNP) requires long period, high quality, flexible jump and fast enough. In this paper, we implement such a PRNG for MCNP on NVIDIA's GTX200 Graphics Processor Units (GPU) using CUDA programming model. Results shows that 3.80 to 8.10 times speedup are achieved compared with 4 to 6 cores CPUs and more than 679.18 million double precision random numbers can be generated per second on GPU.

Plastic fluctuations in empty crystals formed by cubic wireframe particles

Science.gov (United States)

McBride, John M.; Avendaño, Carlos

2018-05-01

We present a computer simulation study of the phase behavior of colloidal hard cubic frames, i.e., particles with nonconvex cubic wireframe geometry interacting purely by excluded volume. Despite the propensity of cubic wireframe particles to form cubic phases akin to their convex counterparts, these particles exhibit unusual plastic fluctuations in which a random and dynamic fraction of particles rotate around their lattice positions in the crystal lattice while the remainder of the particles remains fully ordered. We argue that this unexpected effect stems from the nonconvex geometry of the particles in which the faces of a particle can be penetrated by the vertices of the nearest neighbors even at high number densities.

Effect of Demographic Factors on E-Learning Effectiveness in a Higher Learning Institution in Malaysia

Science.gov (United States)

Islam, Md. Aminul; Rahim, Noor Asliza Abdul; Liang, Tan Chee; Momtaz, Hasina

2011-01-01

This research attempted to find out the effect of demographic factors on the effectiveness of the e-learning system in a higher learning Institution. The students from this institution were randomly selected in order to evaluate the effectiveness of learning system in student's learning process. The primary data source is the questionnaires that…

RANS modeling for particle transport and deposition in turbulent duct flows: Near wall model uncertainties

International Nuclear Information System (INIS)

Jayaraju, S.T.; Sathiah, P.; Roelofs, F.; Dehbi, A.

2015-01-01

Highlights: • Near-wall modeling uncertainties in the RANS particle transport and deposition are addressed in a turbulent duct flow. • Discrete Random Walk (DRW) model and Continuous Random Walk (CRW) model performances are tested. • Several near-wall anisotropic model accuracy is assessed. • Numerous sensitivity studies are performed to recommend a robust, well-validated near-wall model for accurate particle deposition predictions. - Abstract: Dust accumulation in the primary system of a (V)HTR is identified as one of the foremost concerns during a potential accident. Several numerical efforts have focused on the use of RANS methodology to better understand the complex phenomena of fluid–particle interaction at various flow conditions. In the present work, several uncertainties relating to the near-wall modeling of particle transport and deposition are addressed for the RANS approach. The validation analyses are performed in a fully developed turbulent duct flow setup. A standard k − ε turbulence model with enhanced wall treatment is used for modeling the turbulence. For the Lagrangian phase, the performance of a continuous random walk (CRW) model and a discrete random walk (DRW) model for the particle transport and deposition are assessed. For wall bounded flows, it is generally seen that accounting for near wall anisotropy is important to accurately predict particle deposition. The various near-wall correlations available in the literature are either derived from the DNS data or from the experimental data. A thorough investigation into various near-wall correlations and their applicability for accurate particle deposition predictions are assessed. The main outcome of the present work is a well validated turbulence model with optimal near-wall modeling which provides realistic particle deposition predictions

Bose condensation in (random traps

Directory of Open Access Journals (Sweden)

V.A. Zagrebnov

2009-01-01

Full Text Available We study a non-interacting (perfect Bose-gas in random external potentials (traps. It is shown that a generalized Bose-Einstein condensation in the random eigenstates manifests if and only if the same occurs in the one-particle kinetic-energy eigenstates, which corresponds to the generalized condensation of the free Bose-gas. Moreover, we prove that the amounts of both condensate densities are equal. This statement is relevant for justification of the Bogoliubov approximation} in the theory of disordered boson systems.

Brownian motion in short range random potentials

International Nuclear Information System (INIS)

Romero, A.H.; Romero, A.H.; Sancho, J.M.

1998-01-01

A numerical study of Brownian motion of noninteracting particles in random potentials is presented. The dynamics are modeled by Langevin equations in the high friction limit. The random potentials are Gaussian distributed and short ranged. The simulations are performed in one and two dimensions. Different dynamical regimes are found and explained. Effective subdiffusive exponents are obtained and commented on. copyright 1998 The American Physical Society

A multiple scattering theory for EM wave propagation in a dense random medium

Science.gov (United States)

Karam, M. A.; Fung, A. K.; Wong, K. W.

1985-01-01

For a dense medium of randomly distributed scatterers an integral formulation for the total coherent field has been developed. This formulation accounts for the multiple scattering of electromagnetic waves including both the twoand three-particle terms. It is shown that under the Markovian assumption the total coherent field and the effective field have the same effective wave number. As an illustration of this theory, the effective wave number and the extinction coefficient are derived in terms of the polarizability tensor and the pair distribution function for randomly distributed small spherical scatterers. It is found that the contribution of the three-particle term increases with the particle size, the volume fraction, the frequency and the permittivity of the particle. This increase is more significant with frequency and particle size than with other parameters.

Quantum walks of two interacting particles on percolation graphs

Science.gov (United States)

Siloi, Ilaria; Benedetti, Claudia; Piccinini, Enrico; Paris, Matteo G. A.; Bordone, Paolo

2017-10-01

We address the dynamics of two indistinguishable interacting particles moving on a dynamical percolation graph, i.e., a graph where the edges are independent random telegraph processes whose values jump between 0 and 1, thus mimicking percolation. The interplay between the particle interaction strength, initial state and the percolation rate determine different dynamical regimes for the walkers. We show that, whenever the walkers are initially localised within the interaction range, fast noise enhances the particle spread compared to the noiseless case.

Microcomputer-Assisted Discoveries: Generate Your Own Random Numbers.

Science.gov (United States)

Kimberling, Clark

1984-01-01

Having students try to generate their own random numbers can lead to much discovery learning as one tries to create 'patternlessness' from formulas. Developing an equidistribution test and runs test, plus other ideas for generating random numbers, is discussed, with computer programs given. (MNS)

Learning Performance Enhancement Using Computer-Assisted Language Learning by Collaborative Learning Groups

Directory of Open Access Journals (Sweden)

Ya-huei Wang

2017-08-01

Full Text Available This study attempted to test whether the use of computer-assisted language learning (CALL and innovative collaborative learning could be more effective than the use of traditional collaborative learning in improving students’ English proficiencies. A true experimental design was used in the study. Four randomly-assigned groups participated in the study: a traditional collaborative learning group (TCLG, 34 students, an innovative collaborative learning group (ICLG, 31 students, a CALL traditional collaborative learning group (CALLTCLG, 32 students, and a CALL innovative collaborative learning group (CALLICLG, 31 students. TOEIC (Test of English for International Communication listening, reading, speaking, and writing pre-test and post-test assessments were given to all students at an interval of sixteen weeks. Multivariate analysis of covariance (MANCOVA, multivariate analysis of variance (MANOVA, and analysis of variance (ANOVA were used to analyze the data. The results revealed that students who used CALL had significantly better learning performance than those who did not. Students in innovative collaborative learning had significantly better learning performances than those in traditional collaborative learning. Additionally, students using CALL innovative collaborative learning had better learning performances than those in CALL collaborative learning, those in innovative collaborative learning, and those in traditional collaborative learning.

The better you feel the better you learn: do warm colours and rounded shapes enhance learning outcome in multimedia learning?

OpenAIRE

Münchow, Hannes; Mengelkamp, Christoph; Bannert, Maria

2018-01-01

The aim of the present study was to examine whether fostering positive activating affect during multimedia learning enhances learning outcome. University students were randomly assigned to either a multimedia learning environment designed to induce positive activating affect through the use of “warm” colours and rounded shapes (n=61) or an affectively neutral environment that used achromatic colours and sharp edges (n=50). Participants learned about the topic of functional neuroanatomy for 20...

Online multi-modal robust non-negative dictionary learning for visual tracking.

Science.gov (United States)

Zhang, Xiang; Guan, Naiyang; Tao, Dacheng; Qiu, Xiaogang; Luo, Zhigang

2015-01-01

Dictionary learning is a method of acquiring a collection of atoms for subsequent signal representation. Due to its excellent representation ability, dictionary learning has been widely applied in multimedia and computer vision. However, conventional dictionary learning algorithms fail to deal with multi-modal datasets. In this paper, we propose an online multi-modal robust non-negative dictionary learning (OMRNDL) algorithm to overcome this deficiency. Notably, OMRNDL casts visual tracking as a dictionary learning problem under the particle filter framework and captures the intrinsic knowledge about the target from multiple visual modalities, e.g., pixel intensity and texture information. To this end, OMRNDL adaptively learns an individual dictionary, i.e., template, for each modality from available frames, and then represents new particles over all the learned dictionaries by minimizing the fitting loss of data based on M-estimation. The resultant representation coefficient can be viewed as the common semantic representation of particles across multiple modalities, and can be utilized to track the target. OMRNDL incrementally learns the dictionary and the coefficient of each particle by using multiplicative update rules to respectively guarantee their non-negativity constraints. Experimental results on a popular challenging video benchmark validate the effectiveness of OMRNDL for visual tracking in both quantity and quality.

Genetic algorithm enhanced by machine learning in dynamic aperture optimization

Science.gov (United States)

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.

Effect of worksheet scaffolds on student learning in problem-based learning

NARCIS (Netherlands)

S.S.Y. Choo (Serene); J.I. Rotgans (Jerome); E.H.J. Yew (Elaine); H.G. Schmidt (Henk)

2011-01-01

textabstractThe purpose of this study was to investigate the effect of worksheets as a scaffolding tool on students' learning achievement in a problem-based learning (PBL) environment. Seventeen PBL classes (N = 241) were randomly assigned to two experimental groups-one with a worksheet provided and

Learning with LEGO

Science.gov (United States)

Durrani, Matin

2017-12-01

I have lost count of the number of wheezes to get people hooked on particle physics. There have been straightforward scientific accounts, personal tales of discovery, books filled with cartoons, essays and even historical vignettes. In Particle Physics Brick by Brick, science communicator Ben Still has decided to use LEGO bricks to coax readers into learning more about the subatomic world.

Selection Of Suitable Particle Size And Particle Ratio For Japanese Cucumber Cucumis Sativus L. Plants

Directory of Open Access Journals (Sweden)

Galahitigama GAH

2015-08-01

Full Text Available This study was conducted to select the best particle size of coco peat for cucumber nurseries as well as best particle ratio for optimum plant growth and development of cucumber. The experiment was carried out in International Foodstuff Company and Faculty of Agriculture University of Ruhuna Sri Lanka during 2015 to 2016. Under experiment one three types of different particle sizes were used namely fine amp88040.5mm T2 medium 3mm-0.5mm T3 and coarse 4mm T4 with normal coco peat T1 as treatments. Complete Randomized Design CRD used as experimental design with five replicates. Germination percentage number of leaves per seedling seedling height in frequent day intervals was taken as growth parameters. Analysis of variance procedure was applied to analyze the data at 5 probability level. The results revealed that medium size particle media sieve size 0.5mm -3mm of coco peat was the best particle size for cucumber nursery practice when considered the physical and chemical properties of medium particles of coco peat. In the experiment of selecting of suitable particle ratio for cucumber plants the compressed mixture of coco peat particles that contain 70 ww unsieved coco peat 20 ww coarse particles and 10 ww coconut husk chips 5 12mm has given best results for growth performances compared to other treatments and cucumber grown in this mixture has shown maximum growth and yield performances.

Brain Tumor Segmentation Based on Random Forest

Directory of Open Access Journals (Sweden)

László Lefkovits

2016-09-01

Full Text Available In this article we present a discriminative model for tumor detection from multimodal MR images. The main part of the model is built around the random forest (RF classifier. We created an optimization algorithm able to select the important features for reducing the dimensionality of data. This method is also used to find out the training parameters used in the learning phase. The algorithm is based on random feature properties for evaluating the importance of the variable, the evolution of learning errors and the proximities between instances. The detection performances obtained have been compared with the most recent systems, offering similar results.

Development and testing of high performance pseudo random number generator for Monte Carlo simulation

International Nuclear Information System (INIS)

Chakraborty, Brahmananda

2009-01-01

Random number plays an important role in any Monte Carlo simulation. The accuracy of the results depends on the quality of the sequence of random numbers employed in the simulation. These include randomness of the random numbers, uniformity of their distribution, absence of correlation and long period. In a typical Monte Carlo simulation of particle transport in a nuclear reactor core, the history of a particle from its birth in a fission event until its death by an absorption or leakage event is tracked. The geometry of the core and the surrounding materials are exactly modeled in the simulation. To track a neutron history one needs random numbers for determining inter collision distance, nature of the collision, the direction of the scattered neutron etc. Neutrons are tracked in batches. In one batch approximately 2000-5000 neutrons are tracked. The statistical accuracy of the results of the simulation depends on the total number of particles (number of particles in one batch multiplied by the number of batches) tracked. The number of histories to be generated is usually large for a typical radiation transport problem. To track a very large number of histories one needs to generate a long sequence of independent random numbers. In other words the cycle length of the random number generator (RNG) should be more than the total number of random numbers required for simulating the given transport problem. The number of bits of the machine generally limits the cycle length. For a binary machine of p bits the maximum cycle length is 2 p . To achieve higher cycle length in the same machine one has to use either register arithmetic or bit manipulation technique

Effect of Peer-Led Team Learning (PLTL) on Student Achievement, Attitude, and Self-Concept in College General Chemistry in Randomized and Quasi Experimental Designs

Science.gov (United States)

Chan, Julia Y. K.; Bauer, Christopher F.

2015-01-01

This study investigated exam achievement and affective characteristics of students in general chemistry in a fully-randomized experimental design, contrasting Peer-Led Team Learning (PLTL) participation with a control group balanced for time-on-task and study activity. This study population included two independent first-semester courses with…

Modeling pollutant transport using a meshless-lagrangian particle model

International Nuclear Information System (INIS)

Carrington, D.B.; Pepper, D.W.

2002-01-01

A combined meshless-Lagrangian particle transport model is used to predict pollutant transport over irregular terrain. The numerical model for initializing the velocity field is based on a meshless approach utilizing multiquadrics established by Kansa. The Lagrangian particle transport technique uses a random walk procedure to depict the advection and dispersion of pollutants over any type of surface, including street and city canyons

Generalized random walk algorithm for the numerical modeling of complex diffusion processes

CERN Document Server

Vamos, C; Vereecken, H

2003-01-01

A generalized form of the random walk algorithm to simulate diffusion processes is introduced. Unlike the usual approach, at a given time all the particles from a grid node are simultaneously scattered using the Bernoulli repartition. This procedure saves memory and computing time and no restrictions are imposed for the maximum number of particles to be used in simulations. We prove that for simple diffusion the method generalizes the finite difference scheme and gives the same precision for large enough number of particles. As an example, simulations of diffusion in random velocity field are performed and the main features of the stochastic mathematical model are numerically tested.

Generalized random walk algorithm for the numerical modeling of complex diffusion processes

International Nuclear Information System (INIS)

Vamos, Calin; Suciu, Nicolae; Vereecken, Harry

2003-01-01

A generalized form of the random walk algorithm to simulate diffusion processes is introduced. Unlike the usual approach, at a given time all the particles from a grid node are simultaneously scattered using the Bernoulli repartition. This procedure saves memory and computing time and no restrictions are imposed for the maximum number of particles to be used in simulations. We prove that for simple diffusion the method generalizes the finite difference scheme and gives the same precision for large enough number of particles. As an example, simulations of diffusion in random velocity field are performed and the main features of the stochastic mathematical model are numerically tested

Modeling of magnetic particle suspensions for simulations

CERN Document Server

Satoh, Akira

2017-01-01

The main objective of the book is to highlight the modeling of magnetic particles with different shapes and magnetic properties, to provide graduate students and young researchers information on the theoretical aspects and actual techniques for the treatment of magnetic particles in particle-based simulations. In simulation, we focus on the Monte Carlo, molecular dynamics, Brownian dynamics, lattice Boltzmann and stochastic rotation dynamics (multi-particle collision dynamics) methods. The latter two simulation methods can simulate both the particle motion and the ambient flow field simultaneously. In general, specialized knowledge can only be obtained in an effective manner under the supervision of an expert. The present book is written to play such a role for readers who wish to develop the skill of modeling magnetic particles and develop a computer simulation program using their own ability. This book is therefore a self-learning book for graduate students and young researchers. Armed with this knowledge,...

Enhancing interpretability of automatically extracted machine learning features: application to a RBM-Random Forest system on brain lesion segmentation.

Science.gov (United States)

Pereira, Sérgio; Meier, Raphael; McKinley, Richard; Wiest, Roland; Alves, Victor; Silva, Carlos A; Reyes, Mauricio

2018-02-01

Machine learning systems are achieving better performances at the cost of becoming increasingly complex. However, because of that, they become less interpretable, which may cause some distrust by the end-user of the system. This is especially important as these systems are pervasively being introduced to critical domains, such as the medical field. Representation Learning techniques are general methods for automatic feature computation. Nevertheless, these techniques are regarded as uninterpretable "black boxes". In this paper, we propose a methodology to enhance the interpretability of automatically extracted machine learning features. The proposed system is composed of a Restricted Boltzmann Machine for unsupervised feature learning, and a Random Forest classifier, which are combined to jointly consider existing correlations between imaging data, features, and target variables. We define two levels of interpretation: global and local. The former is devoted to understanding if the system learned the relevant relations in the data correctly, while the later is focused on predictions performed on a voxel- and patient-level. In addition, we propose a novel feature importance strategy that considers both imaging data and target variables, and we demonstrate the ability of the approach to leverage the interpretability of the obtained representation for the task at hand. We evaluated the proposed methodology in brain tumor segmentation and penumbra estimation in ischemic stroke lesions. We show the ability of the proposed methodology to unveil information regarding relationships between imaging modalities and extracted features and their usefulness for the task at hand. In both clinical scenarios, we demonstrate that the proposed methodology enhances the interpretability of automatically learned features, highlighting specific learning patterns that resemble how an expert extracts relevant data from medical images. Copyright © 2017 Elsevier B.V. All rights reserved.

Dynamical correlations for circular ensembles of random matrices

International Nuclear Information System (INIS)

Nagao, Taro; Forrester, Peter

2003-01-01

Circular Brownian motion models of random matrices were introduced by Dyson and describe the parametric eigenparameter correlations of unitary random matrices. For symmetric unitary, self-dual quaternion unitary and an analogue of antisymmetric Hermitian matrix initial conditions, Brownian dynamics toward the unitary symmetry is analyzed. The dynamical correlation functions of arbitrary number of Brownian particles at arbitrary number of times are shown to be written in the forms of quaternion determinants, similarly as in the case of Hermitian random matrix models

On the Langevin approach to particle transport

International Nuclear Information System (INIS)

Bringuier, Eric

2006-01-01

In the Langevin description of Brownian motion, the action of the surrounding medium upon the Brownian particle is split up into a systematic friction force of Stokes type and a randomly fluctuating force, alternatively termed noise. That simple description accounts for several basic features of particle transport in a medium, making it attractive to teach at the undergraduate level, but its range of applicability is limited. The limitation is illustrated here by showing that the Langevin description fails to account realistically for the transport of a charged particle in a medium under crossed electric and magnetic fields and the ensuing Hall effect. That particular failure is rooted in the concept of the friction force rather than in the accompanying random force. It is then shown that the framework of kinetic theory offers a better account of the Hall effect. It is concluded that the Langevin description is nothing but an extension of Drude's transport model subsuming diffusion, and so it inherits basic limitations from that model. This paper thus describes the interrelationship of the Langevin approach, the Drude model and kinetic theory, in a specific transport problem of physical interest

Distribution functions for fluids in random media

International Nuclear Information System (INIS)

Madden, W.G.; Glandt, E.D.

1988-01-01

A random medium is considered, composed of identifiable interactive sites or obstacles equilibrated at a high temperature and then quenched rapidly to form a rigid structure, statistically homogeneous on all but molecular length scales. The equilibrium statistical mechanics of a fluid contained inside this quenched medium is discussed. Various particle-particle and particle-obstacle correlation functions, which differ form the corresponding functions for a fully equilibrated binary mixture, are defined through an averaging process over the static ensemble of obstacle configurations and applications of topological reduction techniques. The Ornstein-Zernike equations also differ from their equilibrium counterparts

Particle swarm optimisation classical and quantum perspectives

CERN Document Server

Sun, Jun; Wu, Xiao-Jun

2016-01-01

IntroductionOptimisation Problems and Optimisation MethodsRandom Search TechniquesMetaheuristic MethodsSwarm IntelligenceParticle Swarm OptimisationOverviewMotivationsPSO Algorithm: Basic Concepts and the ProcedureParadigm: How to Use PSO to Solve Optimisation ProblemsSome Harder Examples Some Variants of Particle Swarm Optimisation Why Does the PSO Algorithm Need to Be Improved? Inertia and Constriction-Acceleration Techniques for PSOLocal Best ModelProbabilistic AlgorithmsOther Variants of PSO Quantum-Behaved Particle Swarm Optimisation OverviewMotivation: From Classical Dynamics to Quantum MechanicsQuantum Model: Fundamentals of QPSOQPSO AlgorithmSome Essential ApplicationsSome Variants of QPSOSummary Advanced Topics Behaviour Analysis of Individual ParticlesConvergence Analysis of the AlgorithmTime Complexity and Rate of ConvergenceParameter Selection and PerformanceSummaryIndustrial Applications Inverse Problems for Partial Differential EquationsInverse Problems for Non-Linear Dynamical SystemsOptimal De...

Dynamical correlations for vicious random walk with a wall

International Nuclear Information System (INIS)

Nagao, Taro

2003-01-01

A one-dimensional system of nonintersecting Brownian particles is constructed as the diffusion scaling limit of Fisher's vicious random walk model. N Brownian particles start from the origin at time t=0 and undergo mutually avoiding motion until a finite time t=T. Dynamical correlation functions among the walkers are exactly evaluated in the case with a wall at the origin. Taking an asymptotic limit N→∞, we observe discontinuous transitions in the dynamical correlations. It is further shown that the vicious walk model with a wall is equivalent to a parametric random matrix model describing the crossover between the Bogoliubov-deGennes universality classes

Correlated random walks induced by dynamical wavefunction collapse

Science.gov (United States)

Bedingham, Daniel

2015-03-01

Wavefunction collapse models modify Schrödinger's equation so that it describes the collapse of a superposition of macroscopically distinguishable states as a genuine physical process [PRA 42, 78 (1990)]. This provides a basis for the resolution of the quantum measurement problem. An additional generic consequence of the collapse mechanism is that it causes particles to exhibit a tiny random diffusive motion. Furthermore, the diffusions of two sufficiently nearby particles are positively correlated -- it is more likely that the particles diffuse in the same direction than would happen if they behaved independently [PRA 89, 032713 (2014)]. The use of this effect is proposed as an experimental test of wave function collapse models in which pairs of nanoparticles are simultaneously released from nearby traps and allowed a brief period of free fall. The random displacements of the particles are then measured. The experiment must be carried out at sufficiently low temperature and pressure for the collapse effects to dominate over the ambient environmental noise. It is argued that these constraints can be satisfied by current technologies for a large class of viable wavefunction collapse models. Work supported by the Templeton World Charity Foundation.

The effect of reminder letters on the uptake of an e-learning programme on dementia: a randomized trial in general practice

DEFF Research Database (Denmark)

Waldorff, Frans Boch; Siersma, V.; Nielsen, B.

2009-01-01

BACKGROUND AND AIMS: The aim of the present study was to evaluate whether three reminder letters mailed to GPs after dissemination of a Dementia Guideline increased the GPs' use of the corresponding e-learning programme (ELP). METHODS: Single-blinded randomized trial among all GPs in Copenhagen......, further research is needed in order to consider future implementation strategies for Internet-based Continuous Medical Education activities among not primed GPs Udgivelsesdato: 2009/12...

Steady- and transient-state analysis of fully ceramic microencapsulated fuel with randomly dispersed tristructural isotropic particles via two-temperature homogenized model-II: Applications by coupling with COREDAX

International Nuclear Information System (INIS)

Lee, Yoon Hee; Cho, Bum Hee; Cho, Nam Zin

2016-01-01

In Part I of this paper, the two-temperature homogenized model for the fully ceramic microencapsulated fuel, in which tristructural isotropic particles are randomly dispersed in a fine lattice stochastic structure, was discussed. In this model, the fuel-kernel and silicon carbide matrix temperatures are distinguished. Moreover, the obtained temperature profiles are more realistic than those obtained using other models. Using the temperature-dependent thermal conductivities of uranium nitride and the silicon carbide matrix, temperature-dependent homogenized parameters were obtained. In Part II of the paper, coupled with the COREDAX code, a reactor core loaded by fully ceramic microencapsulated fuel in which tristructural isotropic particles are randomly dispersed in the fine lattice stochastic structure is analyzed via a two-temperature homogenized model at steady and transient states. The results are compared with those from harmonic- and volumetric-average thermal conductivity models; i.e., we compare keff eigenvalues, power distributions, and temperature profiles in the hottest single channel at a steady state. At transient states, we compare total power, average energy deposition, and maximum temperatures in the hottest single channel obtained by the different thermal analysis models. The different thermal analysis models and the availability of fuel-kernel temperatures in the two-temperature homogenized model for Doppler temperature feedback lead to significant differences

E-learning or educational leaflet: does it make a difference in oral health promotion? A clustered randomized trial.

Science.gov (United States)

Al Bardaweel, Susan; Dashash, Mayssoon

2018-05-10

The early recognition of technology together with great ability to use computers and smart systems have promoted researchers to investigate the possibilities of utilizing technology for improving health care in children. The aim of this study was to compare between the traditional educational leaflets and E-applications in improving oral health knowledge, oral hygiene and gingival health in schoolchildren of Damascus city, Syria. A clustered randomized controlled trial at two public primary schools was performed. About 220 schoolchildren aged 10-11 years were included in this study and grouped into two clusters. Children in Leaflet cluster received oral health education through leaflets, while children in E-learning cluster received oral health education through an E-learning program. A questionnaire was designed to register information related to oral health knowledge and to record Plaque and Gingival indices. Questionnaire administration and clinical assessment were undertaken at baseline, 6 and at 12 weeks of oral health education. Data was analysed using one way repeated measures ANOVA, post hoc Bonferroni test and independent samples t-test. Leaflet cluster (107 participants) had statistically significant better oral health knowledge than E-learning cluster (104 participants) at 6 weeks (P E-learning cluster:100 participants). The mean knowledge gain compared to baseline was higher in Leaflet cluster than in E-learning cluster. A significant reduction in the PI means at 6 weeks and 12 weeks was observed in both clusters (P E-learning cluster at 6 weeks (P E-learning cluster at 6 weeks (P < 0.05) and 12 weeks (P < 0.05). Traditional educational leaflets are an effective tool in the improvement of both oral health knowledge as well as clinical indices of oral hygiene and care among Syrian children. Leaflets can be used in school-based oral health education for a positive outcome. Australian New Zealand Clinical Trials Registry ( ACTRN

Changes in lipids and lipoprotein particle concentrations after interruption of antiretroviral therapy

DEFF Research Database (Denmark)

Lampe, Fiona C; Duprez, Daniel A; Kuller, Lewis H

2010-01-01

The effect of interruption of antiretroviral therapy (ART) on lipoprotein particle subclasses has not been studied. We examined short-term changes in lipids and lipoprotein particles among 332 HIV-infected individuals randomized to interrupt or continue ART in the "Strategies for Management...

Alpha particle physics experiments in the Tokamak Fusion Test Reactor

International Nuclear Information System (INIS)

Zweben, S.J.; Budny, R.V.; Darrow, D.S.; Medley, S.S.; Nazikian, R.; Stratton, B.C.; Synakowski, E.J.; Taylor, G.

2000-01-01

Alpha particle physics experiments were done on TFTR during its DT run from 1993 to 1997. These experiments utilized several new alpha particle diagnostics and hundreds of DT discharges to characterize the alpha particle confinement and wave-particle interactions. In general, the results from the alpha particle diagnostics agreed with the classical single particle confinement model in MHD quiescent discharges. The alpha loss due to toroidal field ripple was identified in some cases, and the low radial diffusivity inferred for high energy alphas was consistent with orbit averaging over small scale turbulence. Finally, the observed alpha particle interactions with sawteeth, toroidal Alfven eigenmodes and ICRF waves were approximately consistent with theoretical modelling. What was learned is reviewed and what remains to be understood is identified. (author)

Learning in the machine: The symmetries of the deep learning channel.

Science.gov (United States)

Baldi, Pierre; Sadowski, Peter; Lu, Zhiqin

2017-11-01

In a physical neural system, learning rules must be local both in space and time. In order for learning to occur, non-local information must be communicated to the deep synapses through a communication channel, the deep learning channel. We identify several possible architectures for this learning channel (Bidirectional, Conjoined, Twin, Distinct) and six symmetry challenges: (1) symmetry of architectures; (2) symmetry of weights; (3) symmetry of neurons; (4) symmetry of derivatives; (5) symmetry of processing; and (6) symmetry of learning rules. Random backpropagation (RBP) addresses the second and third symmetry, and some of its variations, such as skipped RBP (SRBP) address the first and the fourth symmetry. Here we address the last two desirable symmetries showing through simulations that they can be achieved and that the learning channel is particularly robust to symmetry variations. Specifically, random backpropagation and its variations can be performed with the same non-linear neurons used in the main input-output forward channel, and the connections in the learning channel can be adapted using the same algorithm used in the forward channel, removing the need for any specialized hardware in the learning channel. Finally, we provide mathematical results in simple cases showing that the learning equations in the forward and backward channels converge to fixed points, for almost any initial conditions. In symmetric architectures, if the weights in both channels are small at initialization, adaptation in both channels leads to weights that are essentially symmetric during and after learning. Biological connections are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

A randomized controlled trial of an online, modular, active learning training program for behavioral activation for depression.

Science.gov (United States)

Puspitasari, Ajeng J; Kanter, Jonathan W; Busch, Andrew M; Leonard, Rachel; Dunsiger, Shira; Cahill, Shawn; Martell, Christopher; Koerner, Kelly

2017-08-01

This randomized-controlled trial assessed the efficacy of a trainer-led, active-learning, modular, online behavioral activation (BA) training program compared with a self-paced online BA training with the same modular content. Seventy-seven graduate students (M = 30.3 years, SD = 6.09; 76.6% female) in mental health training programs were randomly assigned to receive either the trainer-led or self-paced BA training. Both trainings consisted of 4 weekly sessions covering 4 core BA strategies. Primary outcomes were changes in BA skills as measured by an objective role-play assessment and self-reported use of BA strategies. Assessments were conducted at pre-, post-, and 6-weeks after training. A series of longitudinal mixed effect models assessed changes in BA skills and a longitudinal model implemented with generalized estimating equations assessed BA use over time. Significantly greater increases in total BA skills were found in the trainer-led training condition. The trainer-led training condition also showed greater increases in all core BA skills either at posttraining, follow-up, or both. Reported use of BA strategies with actual clients increased significantly from pre- to posttraining and maintained at follow-up in both training conditions. This trial adds to the literature on the efficacy of online training as a method to disseminate BA. Online training with an active learning, modular approach may be a promising and accessible implementation strategy. Additional strategies may need to be paired with the online BA training to assure the long-term implementation and sustainability of BA in clinical practice. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Virtual reality training versus blended learning of laparoscopic cholecystectomy: a randomized controlled trial with laparoscopic novices.

Science.gov (United States)

Nickel, Felix; Brzoska, Julia A; Gondan, Matthias; Rangnick, Henriette M; Chu, Jackson; Kenngott, Hannes G; Linke, Georg R; Kadmon, Martina; Fischer, Lars; Müller-Stich, Beat P

2015-05-01

This study compared virtual reality (VR) training with low cost-blended learning (BL) in a structured training program.Training of laparoscopic skills outside the operating room is mandatory to reduce operative times and risks.Laparoscopy-naïve medical students were randomized in 2 groups stratified for sex. The BL group (n = 42) used E-learning for laparoscopic cholecystectomy (LC) and practiced basic skills with box trainers. The VR group (n = 42) trained basic skills and LC on the LAP Mentor II (Simbionix, Cleveland, OH). Each group trained 3 × 4 hours followed by a knowledge test concerning LC. Blinded raters assessed the operative performance of cadaveric porcine LC using the Objective Structured Assessment of Technical Skills (OSATS). The LC was discontinued when it was not completed within 80 min. Students evaluated their training modality with questionnaires.The VR group completed the LC significantly faster and more often within 80 min than BL (45% v 21%, P = .02). The BL group scored higher than the VR group in the knowledge test (13.3 ± 1.3 vs 11.0 ± 1.7, P advantages of both approaches.

Identifying WIMP dark matter from particle and astroparticle data

Science.gov (United States)

Bertone, Gianfranco; Bozorgnia, Nassim; Kim, Jong Soo; Liem, Sebastian; McCabe, Christopher; Otten, Sydney; Ruiz de Austri, Roberto

2018-03-01

One of the most promising strategies to identify the nature of dark matter consists in the search for new particles at accelerators and with so-called direct detection experiments. Working within the framework of simplified models, and making use of machine learning tools to speed up statistical inference, we address the question of what we can learn about dark matter from a detection at the LHC and a forthcoming direct detection experiment. We show that with a combination of accelerator and direct detection data, it is possible to identify newly discovered particles as dark matter, by reconstructing their relic density assuming they are weakly interacting massive particles (WIMPs) thermally produced in the early Universe, and demonstrating that it is consistent with the measured dark matter abundance. An inconsistency between these two quantities would instead point either towards additional physics in the dark sector, or towards a non-standard cosmology, with a thermal history substantially different from that of the standard cosmological model.

Intrinsic speckle noise in in-line particle holography due to polydisperse and continuous particle sizes

Science.gov (United States)

Edwards, Philip J.; Hobson, Peter R.; Rodgers, G. J.

2000-08-01

In-line particle holography is subject to image deterioration due to intrinsic speckle noise. The resulting reduction in the signal to noise ratio (SNR) of the replayed image can become critical for applications such as holographic particle velocimetry (HPV) and 3D visualisation of marine plankton. Work has been done to extend the mono-disperse model relevant to HPV to include poly-disperse particle fields appropriate for the visualisation of marine plankton. Continuous and discrete particle fields are both considered. It is found that random walk statistics still apply for the poly-disperse case. The speckle field is simply the summation of the individual speckle patters due to each scatter size. Therefor the characteristic speckle parameter (which encompasses particle diameter, concentration and sample depth) is alos just the summation of the individual speckle parameters. This reduces the SNR calculation to the same form as for the mono-disperse case. For the continuous situation three distributions, power, exponential and Gaussian are discussed with the resulting SNR calcuated. The work presented here was performed as part of the Holomar project to produce a working underwater holographic camera for recording plankton.

Interacting particle systems on graphs

Science.gov (United States)

Sood, Vishal

In this dissertation, the dynamics of socially or biologically interacting populations are investigated. The individual members of the population are treated as particles that interact via links on a social or biological network represented as a graph. The effect of the structure of the graph on the properties of the interacting particle system is studied using statistical physics techniques. In the first chapter, the central concepts of graph theory and social and biological networks are presented. Next, interacting particle systems that are drawn from physics, mathematics and biology are discussed in the second chapter. In the third chapter, the random walk on a graph is studied. The mean time for a random walk to traverse between two arbitrary sites of a random graph is evaluated. Using an effective medium approximation it is found that the mean first-passage time between pairs of sites, as well as all moments of this first-passage time, are insensitive to the density of links in the graph. The inverse of the mean-first passage time varies non-monotonically with the density of links near the percolation transition of the random graph. Much of the behavior can be understood by simple heuristic arguments. Evolutionary dynamics, by which mutants overspread an otherwise uniform population on heterogeneous graphs, are studied in the fourth chapter. Such a process underlies' epidemic propagation, emergence of fads, social cooperation or invasion of an ecological niche by a new species. The first part of this chapter is devoted to neutral dynamics, in which the mutant genotype does not have a selective advantage over the resident genotype. The time to extinction of one of the two genotypes is derived. In the second part of this chapter, selective advantage or fitness is introduced such that the mutant genotype has a higher birth rate or a lower death rate. This selective advantage leads to a dynamical competition in which selection dominates for large populations

Experimental techniques in nuclear and particle physics

International Nuclear Information System (INIS)

Tavernier, Stefaan

2010-01-01

The book is based on a course in nuclear and particle physics that the author has taught over many years to physics students, students in nuclear engineering and students in biomedical engineering. It provides the basic understanding that any student or researcher using such instruments and techniques should have about the subject. After an introduction to the structure of matter at the subatomic scale, it covers the experimental aspects of nuclear and particle physics. Ideally complementing a theoretically-oriented textbook on nuclear physics and/or particle physics, it introduces the reader to the different techniques used in nuclear and particle physics to accelerate particles and to measurement techniques (detectors) in nuclear and particle physics. The main subjects treated are: interactions of subatomic particles in matter; particle accelerators; basics of different types of detectors; and nuclear electronics. The book will be of interest to undergraduates, graduates and researchers in both particle and nuclear physics. For the physicists it is a good introduction to all experimental aspects of nuclear and particle physics. Nuclear engineers will appreciate the nuclear measurement techniques, while biomedical engineers can learn about measuring ionising radiation, the use of accelerators for radiotherapy. What's more, worked examples, end-of-chapter exercises, and appendices with key constants, properties and relationships supplement the textual material. (orig.)

Effects of thermal and particle-number fluctuations on the giant isovector dipole modes for the 58Ni nucleus in the finite-temperature random-phase approximation

International Nuclear Information System (INIS)

Nguyen Dinhdang; Nguyen Zuythang

1988-01-01

Using the realistic single-particle energy spectrum obtained in the Woods-Saxon nucleon mean-field potential, we calculate the BCS pairing gap for 58 Ni as a function of temperature taking into account the thermal and particle-number fluctuations. The strength distributions of the electric dipole transitions and the centroids of the isovector giant dipole resonance (IV-GDR) are computed in the framework of the finite-temperature random-phase approximation (RPA) based on the Hamiltonian of the quasiparticle-phonon nuclear model with separate dipole forces. It is shown that the change of the pairing gap at finite temperature can noticeably influence the IV-GDR localisation in realistic nuclei. By taking both thermal and quasiparticle fluctuations in the pairing gap into account the effect of the phase transition from superfluid to normal in the temperature dependence of the IV-GDR centroid is completely smeared out. (author)

Criticality and entanglement in random quantum systems

International Nuclear Information System (INIS)

Refael, G; Moore, J E

2009-01-01

We review studies of entanglement entropy in systems with quenched randomness, concentrating on universal behavior at strongly random quantum critical points. The disorder-averaged entanglement entropy provides insight into the quantum criticality of these systems and an understanding of their relationship to non-random ('pure') quantum criticality. The entanglement near many such critical points in one dimension shows a logarithmic divergence in subsystem size, similar to that in the pure case but with a different universal coefficient. Such universal coefficients are examples of universal critical amplitudes in a random system. Possible measurements are reviewed along with the one-particle entanglement scaling at certain Anderson localization transitions. We also comment briefly on higher dimensions and challenges for the future.

Exact solution of two interacting run-and-tumble random walkers with finite tumble duration

International Nuclear Information System (INIS)

Slowman, A B; Evans, M R; Blythe, R A

2017-01-01

We study a model of interacting run-and-tumble random walkers operating under mutual hardcore exclusion on a one-dimensional lattice with periodic boundary conditions. We incorporate a finite, poisson-distributed, tumble duration so that a particle remains stationary whilst tumbling, thus generalising the persistent random walker model. We present the exact solution for the nonequilibrium stationary state of this system in the case of two random walkers. We find this to be characterised by two lengthscales, one arising from the jamming of approaching particles, and the other from one particle moving when the other is tumbling. The first of these lengthscales vanishes in a scaling limit where the continuous-space dynamics is recovered whilst the second remains finite. Thus the nonequilibrium stationary state reveals a rich structure of attractive, jammed and extended pieces. (paper)

Impact of metal-ion contaminated silica particles on gate oxide integrity

NARCIS (Netherlands)

Rink, Ingrid; Wali, F.; Knotter, D.M.

2009-01-01

The impact of metal-ion contamination (present on wafer surface before oxidation) on gate oxide integrity (GOI) is well known in literature, which is not the case for clean silica particles [1, 2]. However, it is known that particles present in ultra-pure water (UPW) decrease the random yield in

APM_GUI: analyzing particle movement on the cell membrane and determining confinement.

Science.gov (United States)

Menchón, Silvia A; Martín, Mauricio G; Dotti, Carlos G

2012-02-20

Single-particle tracking is a powerful tool for tracking individual particles with high precision. It provides useful information that allows the study of diffusion properties as well as the dynamics of movement. Changes in particle movement behavior, such as transitions between Brownian motion and temporary confinement, can reveal interesting biophysical interactions. Although useful applications exist to determine the paths of individual particles, only a few software implementations are available to analyze these data, and these implementations are generally not user-friendly and do not have a graphical interface,. Here, we present APM_GUI (Analyzing Particle Movement), which is a MatLab-implemented application with a Graphical User Interface. This user-friendly application detects confined movement considering non-random confinement when a particle remains in a region longer than a Brownian diffusant would remain. In addition, APM_GUI exports the results, which allows users to analyze this information using software that they are familiar with. APM_GUI provides an open-source tool that quantifies diffusion coefficients and determines whether trajectories have non-random confinements. It also offers a simple and user-friendly tool that can be used by individuals without programming skills.

APM_GUI: analyzing particle movement on the cell membrane and determining confinement

Directory of Open Access Journals (Sweden)

Menchón Silvia A

2012-02-01

Full Text Available Abstract Background Single-particle tracking is a powerful tool for tracking individual particles with high precision. It provides useful information that allows the study of diffusion properties as well as the dynamics of movement. Changes in particle movement behavior, such as transitions between Brownian motion and temporary confinement, can reveal interesting biophysical interactions. Although useful applications exist to determine the paths of individual particles, only a few software implementations are available to analyze these data, and these implementations are generally not user-friendly and do not have a graphical interface,. Results Here, we present APM_GUI (Analyzing Particle Movement, which is a MatLab-implemented application with a Graphical User Interface. This user-friendly application detects confined movement considering non-random confinement when a particle remains in a region longer than a Brownian diffusant would remain. In addition, APM_GUI exports the results, which allows users to analyze this information using software that they are familiar with. Conclusions APM_GUI provides an open-source tool that quantifies diffusion coefficients and determines whether trajectories have non-random confinements. It also offers a simple and user-friendly tool that can be used by individuals without programming skills.

Reciprocal learning with task cards for teaching Basic Life Support (BLS): investigating effectiveness and the effect of instructor expertise on learning outcomes. A randomized controlled trial.

Science.gov (United States)

Iserbyt, Peter; Mols, Liesbet; Charlier, Nathalie; De Meester, Sophie

2014-01-01

Basic Life Support (BLS) education in secondary schools and universities is often neglected or outsourced because teachers indicate not feeling competent to teach this content. Investigate reciprocal learning with task cards as instructional model for teaching BLS and the effect of instructor expertise in BLS on learning outcomes. There were 175 students (mean age = 18.9 years) randomized across a reciprocal/BLS instructor (RBI) group, a reciprocal/non-BLS instructor (RNI) group, and a traditional/BLS instructor group (TBI). In the RBI and RNI group, students were taught BLS through reciprocal learning with task cards. The instructor in the RBI group was certified in BLS by the European Resuscitation Council. In the TBI, students were taught BLS by a certified instructor according to the Belgian Red Cross instructional model. Student performance was assessed 1 day (intervention) and 3 weeks after intervention (retention). At retention, significantly higher BLS performances were found in the RBI group (M = 78%), p = 0.007, ES = 0.25, and the RNI group (M = 80%), p < 0.001, Effect Size (ES) = .36, compared to the TBI (M = 73%). Significantly more students remembered and performed all BLS skills in the experimental groups at intervention and retention. No differences in BLS performance were found between the reciprocal groups. Ventilation volumes and flow rates were significantly better in the TBI at intervention and retention. Reciprocal learning with task cards is a valuable model for teaching BLS when instructors are not experienced or skilled in BLS. Copyright © 2014 Elsevier Inc. All rights reserved.

Machine Learning wins the Higgs Challenge

CERN Multimedia

Abha Eli Phoboo

2014-01-01

The winner of the four-month-long Higgs Machine Learning Challenge, launched on 12 May, is Gábor Melis from Hungary, followed closely by Tim Salimans from the Netherlands and Pierre Courtiol from France. The challenge explored the potential of advanced machine learning methods to improve the significance of the Higgs discovery.   Winners of the Higgs Machine Learning Challenge: Gábor Melis and Tim Salimans (top row), Tianqi Chen and Tong He (bottom row). Participants in the Higgs Machine Learning Challenge were tasked with developing an algorithm to improve the detection of Higgs boson signal events decaying into two tau particles in a sample of simulated ATLAS data* that contains few signal and a majority of non-Higgs boson “background” events. No knowledge of particle physics was required for the challenge but skills in machine learning - the training of computers to recognise patterns in data – were essential. The Challenge, hosted by Ka...

Model-free adaptive control optimization using a chaotic particle swarm approach

Energy Technology Data Exchange (ETDEWEB)

Santos Coelho, Leandro dos [Industrial and Systems Engineering Graduate Program, LAS/PPGEPS, Pontifical Catholic University of Parana, PUCPR, Imaculada Conceicao, 1155, 80215-901 Curitiba, Parana (Brazil)], E-mail: leandro.coelho@pucpr.br; Rodrigues Coelho, Antonio Augusto [Department of Automation and Systems, Federal University of Santa Catarina, Box 476, 88040-900 Florianopolis, Santa Catarina (Brazil)], E-mail: aarc@das.ufsc.br

2009-08-30

It is well known that conventional control theories are widely suited for applications where the processes can be reasonably described in advance. However, when the plant's dynamics are hard to characterize precisely or are subject to environmental uncertainties, one may encounter difficulties in applying the conventional controller design methodologies. Despite the difficulty in achieving high control performance, the fine tuning of controller parameters is a tedious task that always requires experts with knowledge in both control theory and process information. Nowadays, more and more studies have focused on the development of adaptive control algorithms that can be directly applied to complex processes whose dynamics are poorly modeled and/or have severe nonlinearities. In this context, the design of a Model-Free Learning Adaptive Control (MFLAC) based on pseudo-gradient concepts and optimization procedure by a Particle Swarm Optimization (PSO) approach using constriction coefficient and Henon chaotic sequences (CPSOH) is presented in this paper. PSO is a stochastic global optimization technique inspired by social behavior of bird flocking. The PSO models the exploration of a problem space by a population of particles. Each particle in PSO has a randomized velocity associated to it, which moves through the space of the problem. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed CPSOH introduces chaos mapping which introduces some flexibility in particle movements in each iteration. The chaotic sequences allow also explorations at early stages and exploitations at later stages during the search procedure of CPSOH. Motivation for application of CPSOH approach is to overcome the limitation of the conventional MFLAC design, which cannot guarantee satisfactory control performance when the plant has different gains for the operational range when designed by trial-and-error by user. Numerical results of the MFLAC with

Model-free adaptive control optimization using a chaotic particle swarm approach

International Nuclear Information System (INIS)

Santos Coelho, Leandro dos; Rodrigues Coelho, Antonio Augusto

2009-01-01

It is well known that conventional control theories are widely suited for applications where the processes can be reasonably described in advance. However, when the plant's dynamics are hard to characterize precisely or are subject to environmental uncertainties, one may encounter difficulties in applying the conventional controller design methodologies. Despite the difficulty in achieving high control performance, the fine tuning of controller parameters is a tedious task that always requires experts with knowledge in both control theory and process information. Nowadays, more and more studies have focused on the development of adaptive control algorithms that can be directly applied to complex processes whose dynamics are poorly modeled and/or have severe nonlinearities. In this context, the design of a Model-Free Learning Adaptive Control (MFLAC) based on pseudo-gradient concepts and optimization procedure by a Particle Swarm Optimization (PSO) approach using constriction coefficient and Henon chaotic sequences (CPSOH) is presented in this paper. PSO is a stochastic global optimization technique inspired by social behavior of bird flocking. The PSO models the exploration of a problem space by a population of particles. Each particle in PSO has a randomized velocity associated to it, which moves through the space of the problem. Since chaotic mapping enjoys certainty, ergodicity and the stochastic property, the proposed CPSOH introduces chaos mapping which introduces some flexibility in particle movements in each iteration. The chaotic sequences allow also explorations at early stages and exploitations at later stages during the search procedure of CPSOH. Motivation for application of CPSOH approach is to overcome the limitation of the conventional MFLAC design, which cannot guarantee satisfactory control performance when the plant has different gains for the operational range when designed by trial-and-error by user. Numerical results of the MFLAC with CPSOH

Build Your Own Particle Detector

CERN Document Server

Mehlhase, Sascha; The ATLAS collaboration

2016-01-01

To support the outreach activities of Atlas institutes and to grab people's attention in science exhibitions and during public events, we have created both a very detailed model of the experiment built entirely out of about Lego bricks as well as an outreach programme using Lego bricks to get people to think about particle detectors and involve them into a conversation about particle physics in general. A large Lego model, consisting of about 9500 pieces, has been 'exported' to more than 55 Atlas institutes and has been used in numerous exhibitions to explain the proportion and composition of the experiment to the public. As part of 'Build Your Own Particle Detector' programme (byopd.org) we conducted more than 15 events,either involving a competition to design and build the 'best' particle detector from a random pile of pieces or to take part in the construction of one of the large models, as part of a full day outreach event. Recently we've added miniature models of all four LHC experiments, that will be us...

Comparing the effect of e-learning and educational booklet on the childbirth self-efficacy: a randomized controlled clinical trial.

Science.gov (United States)

Abbasi, Parastoo; Mohammad-Alizadeh Charandabi, Sakineh; Mirghafourvand, Mojgan

2018-03-01

This study aimed to compare the effect of e-learning and educational booklet on the childbirth self-efficacy (CBSE). This randomized controlled clinical trial was conducted on 153 pregnant women referred to health centers in the city of Miandoab, Iran in 2015-2016. Participants were assigned into two intervention groups (e-learning and educational booklet) and the control group. A single face-to-face session was held for intervention groups about the management of labor pain in 30-34 weeks of pregnancy and the booklet and software were provided. The CBSE questionnaire was filled out by the participants before intervention and active phase of labor at 4-5 cm dilatation of cervix. One-way ANOVA and ANCOVA test with adjusting the baseline scores were used to compare the mean score of self-efficacy among study groups respectively before and after the intervention. There was no significant difference between the three groups in terms of socio-demographic characteristics (p > 0.05). After the intervention, the mean score of the CBSE in the educational booklet group (adjusted mean difference: 113.4; confidence interval 95%: 100.7-126.1) and e-learning group (159.3; 146.5-172.0) was significantly higher than the control group. Also, the mean score of the CBSE in the e-learning group had a significant increase compared to the educational booklet group (45.9; 33.0-58.7). The results indicate that e-learning and educational booklet are effective in enhancing mothers' CBSE. Thus, the mothers are recommended to use these teaching methods.

Factorization Procedure for Harmonically Bound Brownian Particle

International Nuclear Information System (INIS)

Omolo, JK.

2006-01-01

The method of factorization to solve the problem of the one-dimensional harmonically bound Brownian particle was applied. Assuming the the rapidily fluctuating random force is Gaussian and has an infinitely short correlation time, explicit expressions for the position-position,velocity-velocity, and the position-velocity correlation functions, which are also use to write down appropriate distribution functions were used. The correlation and distribution functions for the complex quantity (amplititude) which provides the expressions for the position and velocity of the particle are calculated. Finally, Fokker-Planck equations for the joint probability distribution functions for the amplititude and it's complex conjugate as well as for the position and velocity of the particle are obtained. (author)

Efficacy of Adolescent Suicide Prevention E-Learning Modules for Gatekeepers: A Randomized Controlled Trial.

Science.gov (United States)

Ghoncheh, Rezvan; Gould, Madelyn S; Twisk, Jos Wr; Kerkhof, Ad Jfm; Koot, Hans M

2016-01-29

Face-to-face gatekeeper training can be an effective strategy in the enhancement of gatekeepers' knowledge and self-efficacy in adolescent suicide prevention. However, barriers related to access (eg, time, resources) may hamper participation in face-to-face training sessions. The transition to a Web-based setting could address obstacles associated with face-to-face gatekeeper training. Although Web-based suicide prevention training targeting adolescents exists, so far no randomized controlled trials (RCTs) have been conducted to investigate their efficacy. This RCT study investigated the efficacy of a Web-based adolescent suicide prevention program entitled Mental Health Online, which aimed to improve the knowledge and self-confidence of gatekeepers working with adolescents (12-20 years old). The program consisted of 8 short e-learning modules each capturing an important aspect of the process of early recognition, guidance, and referral of suicidal adolescents, alongside additional information on the topic of (adolescent) suicide prevention. A total of 190 gatekeepers (ages 21 to 62 years) participated in this study and were randomized to either the experimental group or waitlist control group. The intervention was not masked. Participants from both groups completed 3 Web-based assessments (pretest, posttest, and 3-month follow-up). The outcome measures of this study were actual knowledge, and participants' ratings of perceived knowledge and perceived self-confidence using questionnaires developed specifically for this study. The actual knowledge, perceived knowledge, and perceived self-confidence of gatekeepers in the experimental group improved significantly compared to those in the waitlist control group at posttest, and the effects remained significant at 3-month follow-up. The overall effect sizes were 0.76, 1.20, and 1.02, respectively, across assessments. The findings of this study indicate that Web-based suicide prevention e-learning modules can be an

Computing diffusivities from particle models out of equilibrium

Science.gov (United States)

Embacher, Peter; Dirr, Nicolas; Zimmer, Johannes; Reina, Celia

2018-04-01

A new method is proposed to numerically extract the diffusivity of a (typically nonlinear) diffusion equation from underlying stochastic particle systems. The proposed strategy requires the system to be in local equilibrium and have Gaussian fluctuations but it is otherwise allowed to undergo arbitrary out-of-equilibrium evolutions. This could be potentially relevant for particle data obtained from experimental applications. The key idea underlying the method is that finite, yet large, particle systems formally obey stochastic partial differential equations of gradient flow type satisfying a fluctuation-dissipation relation. The strategy is here applied to three classic particle models, namely independent random walkers, a zero-range process and a symmetric simple exclusion process in one space dimension, to allow the comparison with analytic solutions.

α-particle shielding of semiconductor device

International Nuclear Information System (INIS)

McKeown, P.J.A.; Perry, J.P.; Waddell, J.M.; Barker, K.D.

1981-01-01

Soft errors in semiconductor devices, e.g. random access memories, arising from the bombardment of the device by alpha particles produced by the disintegration of minute traces of uranium or thorium in the packaging materials are prevented by coating the active surface of the semiconductor chip with a thin layer, e.g. 20 to 100 microns of an organic polymeric material, this layer being of sufficient thickness to absorb the particles. Typically, the polymer is a poly-imide formed by u.v. electron-beam or thermal curing of liquid monomer applied to the chip surface. (author)

Effects of changing the random number stride in Monte Carlo calculations

International Nuclear Information System (INIS)

Hendricks, J.S.

1991-01-01

This paper reports on a common practice in Monte Carlo radiation transport codes which is to start each random walk a specified number of steps up the random number sequence from the previous one. This is called the stride in the random number sequence between source particles. It is used for correlated sampling or to provide tree-structured random numbers. A new random number generator algorithm for the major Monte Carlo code MCNP has been written to allow adjustment of the random number stride. This random number generator is machine portable. The effects of varying the stride for several sample problems are examined

Mobile Augmented Reality as a Feature for Self-Oriented, Blended Learning in Medicine: Randomized Controlled Trial

Science.gov (United States)

2017-01-01

Background Advantages of mobile Augmented Reality (mAR) application-based learning versus textbook-based learning were already shown in a previous study. However, it was unclear whether the augmented reality (AR) component was responsible for the success of the self-developed app or whether this was attributable to the novelty of using mobile technology for learning. Objective The study’s aim was to test the hypothesis whether there is no difference in learning success between learners who employed the mobile AR component and those who learned without it to determine possible effects of mAR. Also, we were interested in potential emotional effects of using this technology. Methods Forty-four medical students (male: 25, female: 19, mean age: 22.25 years, standard deviation [SD]: 3.33 years) participated in this study. Baseline emotional status was evaluated using the Profile of Mood States (POMS) questionnaire. Dermatological knowledge was ascertained using a single choice (SC) test (10 questions). The students were randomly assigned to learn 45 min with either a mobile learning method with mAR (group A) or without AR (group B). Afterwards, both groups were again asked to complete the previous questionnaires. AttrakDiff 2 questionnaires were used to evaluate the perceived usability as well as pragmatic and hedonic qualities. For capturing longer term effects, after 14 days, all participants were again asked to complete the SC questionnaire. All evaluations were anonymous, and descriptive statistics were calculated. For hypothesis testing, an unpaired signed-rank test was applied. Results For the SC tests, there were only minor differences, with both groups gaining knowledge (average improvement group A: 3.59 [SD 1.48]; group B: 3.86 [SD 1.51]). Differences between both groups were statistically insignificant (exact Mann Whitney U, U=173.5; P=.10; r=.247). However, in the follow-up SC test after 14 days, group A had retained more knowledge (average decrease of the

The Inertia Weight Updating Strategies in Particle Swarm Optimisation Based on the Beta Distribution

Directory of Open Access Journals (Sweden)

Petr Maca

2015-01-01

Full Text Available The presented paper deals with the comparison of selected random updating strategies of inertia weight in particle swarm optimisation. Six versions of particle swarm optimization were analysed on 28 benchmark functions, prepared for the Special Session on Real-Parameter Single Objective Optimisation at CEC2013. The random components of tested inertia weight were generated from Beta distribution with different values of shape parameters. The best analysed PSO version is the multiswarm PSO, which combines two strategies of updating the inertia weight. The first is driven by the temporally varying shape parameters, while the second is based on random control of shape parameters of Beta distribution.

Random walk, diffusion and mixing in simulations of scalar transport in fluid flows

International Nuclear Information System (INIS)

Klimenko, A Y

2008-01-01

Physical similarity and mathematical equivalence of continuous diffusion and particle random walk form one of the cornerstones of modern physics and the theory of stochastic processes. In many applied models used in simulation of turbulent transport and turbulent combustion, mixing between particles is used to reflect the influence of the continuous diffusion terms in the transport equations. We show that the continuous scalar transport and diffusion can be accurately specified by means of mixing between randomly walking Lagrangian particles with scalar properties and assess errors associated with this scheme. This gives an alternative formulation for the stochastic process which is selected to represent the continuous diffusion. This paper focuses on statistical errors and deals with relatively simple cases, where one-particle distributions are sufficient for a complete description of the problem.

Game-based e-learning is more effective than a conventional instructional method: a randomized controlled trial with third-year medical students.

Directory of Open Access Journals (Sweden)

Martin Boeker

Full Text Available BACKGROUND: When compared with more traditional instructional methods, Game-based e-learning (GbEl promises a higher motivation of learners by presenting contents in an interactive, rule-based and competitive way. Most recent systematic reviews and meta-analysis of studies on Game-based learning and GbEl in the medical professions have shown limited effects of these instructional methods. OBJECTIVES: To compare the effectiveness on the learning outcome of a Game-based e-learning (GbEl instruction with a conventional script-based instruction in the teaching of phase contrast microscopy urinalysis under routine training conditions of undergraduate medical students. METHODS: A randomized controlled trial was conducted with 145 medical students in their third year of training in the Department of Urology at the University Medical Center Freiburg, Germany. 82 subjects where allocated for training with an educational adventure-game (GbEl group and 69 subjects for conventional training with a written script-based approach (script group. Learning outcome was measured with a 34 item single choice test. Students' attitudes were collected by a questionnaire regarding fun with the training, motivation to continue the training and self-assessment of acquired knowledge. RESULTS: The students in the GbEl group achieved significantly better results in the cognitive knowledge test than the students in the script group: the mean score was 28.6 for the GbEl group and 26.0 for the script group of a total of 34.0 points with a Cohen's d effect size of 0.71 (ITT analysis. Attitudes towards the recent learning experience were significantly more positive with GbEl. Students reported to have more fun while learning with the game when compared to the script-based approach. CONCLUSIONS: Game-based e-learning is more effective than a script-based approach for the training of urinalysis in regard to cognitive learning outcome and has a high positive motivational impact on

Game-based e-learning is more effective than a conventional instructional method: a randomized controlled trial with third-year medical students.

Science.gov (United States)

Boeker, Martin; Andel, Peter; Vach, Werner; Frankenschmidt, Alexander

2013-01-01

When compared with more traditional instructional methods, Game-based e-learning (GbEl) promises a higher motivation of learners by presenting contents in an interactive, rule-based and competitive way. Most recent systematic reviews and meta-analysis of studies on Game-based learning and GbEl in the medical professions have shown limited effects of these instructional methods. To compare the effectiveness on the learning outcome of a Game-based e-learning (GbEl) instruction with a conventional script-based instruction in the teaching of phase contrast microscopy urinalysis under routine training conditions of undergraduate medical students. A randomized controlled trial was conducted with 145 medical students in their third year of training in the Department of Urology at the University Medical Center Freiburg, Germany. 82 subjects where allocated for training with an educational adventure-game (GbEl group) and 69 subjects for conventional training with a written script-based approach (script group). Learning outcome was measured with a 34 item single choice test. Students' attitudes were collected by a questionnaire regarding fun with the training, motivation to continue the training and self-assessment of acquired knowledge. The students in the GbEl group achieved significantly better results in the cognitive knowledge test than the students in the script group: the mean score was 28.6 for the GbEl group and 26.0 for the script group of a total of 34.0 points with a Cohen's d effect size of 0.71 (ITT analysis). Attitudes towards the recent learning experience were significantly more positive with GbEl. Students reported to have more fun while learning with the game when compared to the script-based approach. Game-based e-learning is more effective than a script-based approach for the training of urinalysis in regard to cognitive learning outcome and has a high positive motivational impact on learning. Game-based e-learning can be used as an effective teaching

Comparison of error-based and errorless learning for people with severe traumatic brain injury: study protocol for a randomized control trial.

Science.gov (United States)

Ownsworth, Tamara; Fleming, Jennifer; Tate, Robyn; Shum, David H K; Griffin, Janelle; Schmidt, Julia; Lane-Brown, Amanda; Kendall, Melissa; Chevignard, Mathilde

2013-11-05

Poor skills generalization poses a major barrier to successful outcomes of rehabilitation after traumatic brain injury (TBI). Error-based learning (EBL) is a relatively new intervention approach that aims to promote skills generalization by teaching people internal self-regulation skills, or how to anticipate, monitor and correct their own errors. This paper describes the protocol of a study that aims to compare the efficacy of EBL and errorless learning (ELL) for improving error self-regulation, behavioral competency, awareness of deficits and long-term outcomes after TBI. This randomized, controlled trial (RCT) has two arms (EBL and ELL); each arm entails 8 × 2 h training sessions conducted within the participants' homes. The first four sessions involve a meal preparation activity, and the final four sessions incorporate a multitasking errand activity. Based on a sample size estimate, 135 participants with severe TBI will be randomized into either the EBL or ELL condition. The primary outcome measure assesses error self-regulation skills on a task related to but distinct from training. Secondary outcomes include measures of self-monitoring and self-regulation, behavioral competency, awareness of deficits, role participation and supportive care needs. Assessments will be conducted at pre-intervention, post-intervention, and at 6-months post-intervention. This study seeks to determine the efficacy and long-term impact of EBL for training internal self-regulation strategies following severe TBI. In doing so, the study will advance theoretical understanding of the role of errors in task learning and skills generalization. EBL has the potential to reduce the length and costs of rehabilitation and lifestyle support because the techniques could enhance generalization success and lifelong application of strategies after TBI. ACTRN12613000585729.

Effects of a very high saturated fat diet on LDL particles in adults with atherogenic dyslipidemia: A randomized controlled trial.

Directory of Open Access Journals (Sweden)

Sally Chiu

Full Text Available Previous studies have shown that increases in LDL-cholesterol resulting from substitution of dietary saturated fat for carbohydrate or unsaturated fat are due primarily to increases in large cholesterol-enriched LDL, with minimal changes in small, dense LDL particles and apolipoprotein B. However, individuals can differ by their LDL particle distribution, and it is possible that this may influence LDL subclass response.The objective of this study was to test whether the reported effects of saturated fat apply to individuals with atherogenic dyslipidemia as characterized by a preponderance of small LDL particles (LDL phenotype B.Fifty-three phenotype B men and postmenopausal women consumed a baseline diet (55%E carbohydrate, 15%E protein, 30%E fat, 8%E saturated fat for 3 weeks, after which they were randomized to either a moderate carbohydrate, very high saturated fat diet (HSF; 39%E carbohydrate, 25%E protein, 36%E fat, 18%E saturated fat or low saturated fat diet (LSF; 37%E carbohydrate, 25%E protein, 37%E fat, 9%E saturated fat for 3 weeks.Compared to the LSF diet, consumption of the HSF diet resulted in significantly greater increases from baseline (% change; 95% CI in plasma concentrations of apolipoprotein B (HSF vs. LSF: 9.5; 3.6 to 15.7 vs. -6.8; -11.7 to -1.76; p = 0.0003 and medium (8.8; -1.3 to 20.0 vs. -7.3; -15.7 to 2.0; p = 0.03, small (6.1; -10.3 to 25.6 vs. -20.8; -32.8 to -6.7; p = 0.02, and total LDL (3.6; -3.2 to 11.0 vs. -7.9; -13.9 to -1.5; p = 0.03 particles, with no differences in change of large and very small LDL concentrations. As expected, total-cholesterol (11.0; 6.5 to 15.7 vs. -5.7; -9.4 to -1.8; p<0.0001 and LDL-cholesterol (16.7; 7.9 to 26.2 vs. -8.7; -15.4 to -1.4; p = 0.0001 also increased with increased saturated fat intake.Because medium and small LDL particles are more highly associated with cardiovascular disease than are larger LDL, the present results suggest that very high saturated fat intake may

Effects of a very high saturated fat diet on LDL particles in adults with atherogenic dyslipidemia: A randomized controlled trial.

Science.gov (United States)

Chiu, Sally; Williams, Paul T; Krauss, Ronald M

2017-01-01

Previous studies have shown that increases in LDL-cholesterol resulting from substitution of dietary saturated fat for carbohydrate or unsaturated fat are due primarily to increases in large cholesterol-enriched LDL, with minimal changes in small, dense LDL particles and apolipoprotein B. However, individuals can differ by their LDL particle distribution, and it is possible that this may influence LDL subclass response. The objective of this study was to test whether the reported effects of saturated fat apply to individuals with atherogenic dyslipidemia as characterized by a preponderance of small LDL particles (LDL phenotype B). Fifty-three phenotype B men and postmenopausal women consumed a baseline diet (55%E carbohydrate, 15%E protein, 30%E fat, 8%E saturated fat) for 3 weeks, after which they were randomized to either a moderate carbohydrate, very high saturated fat diet (HSF; 39%E carbohydrate, 25%E protein, 36%E fat, 18%E saturated fat) or low saturated fat diet (LSF; 37%E carbohydrate, 25%E protein, 37%E fat, 9%E saturated fat) for 3 weeks. Compared to the LSF diet, consumption of the HSF diet resulted in significantly greater increases from baseline (% change; 95% CI) in plasma concentrations of apolipoprotein B (HSF vs. LSF: 9.5; 3.6 to 15.7 vs. -6.8; -11.7 to -1.76; p = 0.0003) and medium (8.8; -1.3 to 20.0 vs. -7.3; -15.7 to 2.0; p = 0.03), small (6.1; -10.3 to 25.6 vs. -20.8; -32.8 to -6.7; p = 0.02), and total LDL (3.6; -3.2 to 11.0 vs. -7.9; -13.9 to -1.5; p = 0.03) particles, with no differences in change of large and very small LDL concentrations. As expected, total-cholesterol (11.0; 6.5 to 15.7 vs. -5.7; -9.4 to -1.8; pvs. -8.7; -15.4 to -1.4; p = 0.0001) also increased with increased saturated fat intake. Because medium and small LDL particles are more highly associated with cardiovascular disease than are larger LDL, the present results suggest that very high saturated fat intake may increase cardiovascular disease risk in phenotype B

WDL-RF: Predicting Bioactivities of Ligand Molecules Acting with G Protein-coupled Receptors by Combining Weighted Deep Learning and Random Forest.

Science.gov (United States)

Wu, Jiansheng; Zhang, Qiuming; Wu, Weijian; Pang, Tao; Hu, Haifeng; Chan, Wallace K B; Ke, Xiaoyan; Zhang, Yang; Wren, Jonathan

2018-02-08

Precise assessment of ligand bioactivities (including IC50, EC50, Ki, Kd, etc.) is essential for virtual screening and lead compound identification. However, not all ligands have experimentally-determined activities. In particular, many G protein-coupled receptors (GPCRs), which are the largest integral membrane protein family and represent targets of nearly 40% drugs on the market, lack published experimental data about ligand interactions. Computational methods with the ability to accurately predict the bioactivity of ligands can help efficiently address this problem. We proposed a new method, WDL-RF, using weighted deep learning and random forest, to model the bioactivity of GPCR-associated ligand molecules. The pipeline of our algorithm consists of two consecutive stages: 1) molecular fingerprint generation through a new weighted deep learning method, and 2) bioactivity calculations with a random forest model; where one uniqueness of the approach is that the model allows end-to-end learning of prediction pipelines with input ligands being of arbitrary size. The method was tested on a set of twenty-six non-redundant GPCRs that have a high number of active ligands, each with 200∼4000 ligand associations. The results from our benchmark show that WDL-RF can generate bioactivity predictions with an average root-mean square error 1.33 and correlation coefficient (r2) 0.80 compared to the experimental measurements, which are significantly more accurate than the control predictors with different molecular fingerprints and descriptors. In particular, data-driven molecular fingerprint features, as extracted from the weighted deep learning models, can help solve deficiencies stemming from the use of traditional hand-crafted features and significantly increase the efficiency of short molecular fingerprints in virtual screening. The WDL-RF web server, as well as source codes and datasets of WDL-RF, is freely available at https://zhanglab.ccmb.med.umich.edu/WDL-RF/ for

Event-Driven Random Back-Propagation: Enabling Neuromorphic Deep Learning Machines

OpenAIRE

Neftci, Emre O.; Augustine, Charles; Paul, Somnath; Detorakis, Georgios

2017-01-01

An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Gradient Back Propagation (BP) rule, often relies on the immediate availability of net...

On the retrieval of ice cloud particle shapes from POLDER measurements

International Nuclear Information System (INIS)

Sun Wenbo; Loeb, Norman G.; Yang Ping

2006-01-01

Shapes of ice crystals can significantly affect the radiative transfer in ice clouds. The angular distribution of the polarized reflectance over ice clouds strongly depends on ice crystal shapes. Although the angular-distribution features of the total or polarized reflectance over ice clouds imply a possibility of retrieving ice cloud particle shapes by use of remote sensing data, the accuracy of the retrieval must be evaluated. In this study, a technique that applies single ice crystal habit and multidirectional polarized radiance to retrieve ice cloud particle shapes is assessed. Our sensitivity studies show that the retrieved particle shapes from this algorithm can be considered good approximations to those in actual clouds in calculation of the phase matrix elements. However, this algorithm can only work well under the following conditions: (1) the retrievable must be overcast and thick ice cloud pixels, (2) the particles in the cloud must be randomly oriented, (3) the particle shapes and size distributions used in the lookup tables must be representative, and (4) the multi-angle polarized measurements must be accurate and sufficient to identify ice cloud pixels of randomly oriented particles. In practice, these conditions will exclude most of the measured cloud pixels. Additionally, because the polarized measurements are only sensitive to the upper cloud part not deeper than an optical thickness of 4, the retrieved particle shapes with the polarized radiance may only approximate those in the upper parts of the clouds. In other words, for thicker clouds with vertical inhomogeneity in particle shapes, these retrieved particle shapes cannot represent those of whole clouds. More robust algorithm is needed in accurate retrieval of ice cloud particle shapes

Density functional formulation of the random-phase approximation for inhomogeneous fluids: Application to the Gaussian core and Coulomb particles.

Science.gov (United States)

Frydel, Derek; Ma, Manman

2016-06-01

Using the adiabatic connection, we formulate the free energy in terms of the correlation function of a fictitious system, h_{λ}(r,r^{'}), in which interactions λu(r,r^{'}) are gradually switched on as λ changes from 0 to 1. The function h_{λ}(r,r^{'}) is then obtained from the inhomogeneous Ornstein-Zernike equation and the two equations constitute a general liquid-state framework for treating inhomogeneous fluids. The two equations do not yet constitute a closed set. In the present work we use the closure c_{λ}(r,r^{'})≈-λβu(r,r^{'}), known as the random-phase approximation (RPA). We demonstrate that the RPA is identical with the variational Gaussian approximation derived within the field-theoretical framework, originally derived and used for charged particles. We apply our generalized RPA approximation to the Gaussian core model and Coulomb charges.

Learning from simple ebooks, online cases or classroom teaching when acquiring complex knowledge. A randomized controlled trial in respiratory physiology and pulmonology.

Science.gov (United States)

Worm, Bjarne Skjødt

2013-01-01

E-learning is developing fast because of the rapid increased use of smartphones, tablets and portable computers. We might not think of it as e-learning, but today many new e-books are in fact very complex electronic teaching platforms. It is generally accepted that e-learning is as effective as classroom teaching methods, but little is known about its value in relaying contents of different levels of complexity to students. We set out to investigate e-learning effects on simple recall and complex problem-solving compared to classroom teaching. 63 nurses specializing in anesthesiology were evenly randomized into three groups. They were given internet-based knowledge tests before and after attending a teaching module about respiratory physiology and pulmonology. The three groups was either an e-learning group with eBook teaching material, an e-learning group with case-based teaching or a group with face-to-face case-based classroom teaching. After the module the students were required to answer a post-test. Time spent and the number of logged into the system was also measured. For simple recall, all methods were equally effective. For problem-solving, the eCase group achieved a comparable knowledge level to classroom teaching, while textbook learning was inferior to both (p<0.01). The textbook group also spent the least amount of time on acquiring knowledge (33 minutes, p<0.001), while the eCase group spent significantly more time on the subject (53 minutes, p<0.001) and logged into the system significantly more (2.8 vs 1.6, p<0.001). E-learning based cases are an effective tool for teaching complex knowledge and problem-solving ability, but future studies using higher-level e-learning are encouraged.Simple recall skills, however, do not require any particular learning method.

Learning from simple ebooks, online cases or classroom teaching when acquiring complex knowledge. A randomized controlled trial in respiratory physiology and pulmonology.

Directory of Open Access Journals (Sweden)

Bjarne Skjødt Worm

Full Text Available BACKGROUND AND AIMS: E-learning is developing fast because of the rapid increased use of smartphones, tablets and portable computers. We might not think of it as e-learning, but today many new e-books are in fact very complex electronic teaching platforms. It is generally accepted that e-learning is as effective as classroom teaching methods, but little is known about its value in relaying contents of different levels of complexity to students. We set out to investigate e-learning effects on simple recall and complex problem-solving compared to classroom teaching. METHODS: 63 nurses specializing in anesthesiology were evenly randomized into three groups. They were given internet-based knowledge tests before and after attending a teaching module about respiratory physiology and pulmonology. The three groups was either an e-learning group with eBook teaching material, an e-learning group with case-based teaching or a group with face-to-face case-based classroom teaching. After the module the students were required to answer a post-test. Time spent and the number of logged into the system was also measured. RESULTS: For simple recall, all methods were equally effective. For problem-solving, the eCase group achieved a comparable knowledge level to classroom teaching, while textbook learning was inferior to both (p<0.01. The textbook group also spent the least amount of time on acquiring knowledge (33 minutes, p<0.001, while the eCase group spent significantly more time on the subject (53 minutes, p<0.001 and logged into the system significantly more (2.8 vs 1.6, p<0.001. CONCLUSIONS: E-learning based cases are an effective tool for teaching complex knowledge and problem-solving ability, but future studies using higher-level e-learning are encouraged.Simple recall skills, however, do not require any particular learning method.

The 10 μm amorphous silicate feature of fractal aggregates and compact particles with complex shapes

NARCIS (Netherlands)

Min, M.; Dominik, C.; Hovenier, J.W.; de Koter, A.; Waters, L.B.F.M.

2006-01-01

We model the 10 μm absorption spectra of nonspherical particles composed of amorphous silicate. We consider two classes of particles, compact ones and fractal aggregates composed of homogeneous spheres. For the compact particles we consider Gaussian random spheres with various degrees of

Counting the corners of a random walk and its application to traffic flow

International Nuclear Information System (INIS)

Knorr, Florian; Schreckenberg, Michael

2012-01-01

We study a system with two types of interacting particles on a one-dimensional lattice. Particles of the first type, which we call ‘active’, are able to detect particles of the second type (called ‘passive’). By relating the problem to a discrete random walk in one dimension with a fixed number of steps we determine the fraction of active and detected particles for both open and periodic boundary conditions as well as for the case where passive particles interact with both or only one neighbors. In the random walk picture, where the two particles types stand for steps in opposite directions, passive particles are detected whenever the resulting path has a corner. For open boundary conditions, it turns out that a simple mean field approximation reproduces the exact result if the particles interact with one neighbor only. A practical application of this problem is heterogeneous traffic flow with communicating and non-communicating vehicles. In this context communicating vehicles can be thought of as active particles which can by front (and rear) sensors detect the vehicle ahead (and behind) although these vehicles do not actively share information. Therefore, we also present simulation results which show the validity of our analysis for real traffic flow. (paper)

Robust visual tracking via structured multi-task sparse learning

KAUST Repository

Zhang, Tianzhu

2012-11-09

In this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary templates that are updated dynamically, learning the representation of each particle is considered a single task in Multi-Task Tracking (MTT). By employing popular sparsity-inducing lp,q mixed norms (specifically p∈2,∞ and q=1), we regularize the representation problem to enforce joint sparsity and learn the particle representations together. As compared to previous methods that handle particles independently, our results demonstrate that mining the interdependencies between particles improves tracking performance and overall computational complexity. Interestingly, we show that the popular L1 tracker (Mei and Ling, IEEE Trans Pattern Anal Mach Intel 33(11):2259-2272, 2011) is a special case of our MTT formulation (denoted as the L11 tracker) when p=q=1. Under the MTT framework, some of the tasks (particle representations) are often more closely related and more likely to share common relevant covariates than other tasks. Therefore, we extend the MTT framework to take into account pairwise structural correlations between particles (e.g. spatial smoothness of representation) and denote the novel framework as S-MTT. The problem of learning the regularized sparse representation in MTT and S-MTT can be solved efficiently using an Accelerated Proximal Gradient (APG) method that yields a sequence of closed form updates. As such, S-MTT and MTT are computationally attractive. We test our proposed approach on challenging sequences involving heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that S-MTT is much better than MTT, and both methods consistently outperform state-of-the-art trackers. © 2012 Springer Science+Business Media New York.

Does rating the operation videos with a checklist score improve the effect of E-learning for bariatric surgical training? Study protocol for a randomized controlled trial.

Science.gov (United States)

De La Garza, Javier Rodrigo; Kowalewski, Karl-Friedrich; Friedrich, Mirco; Schmidt, Mona Wanda; Bruckner, Thomas; Kenngott, Hannes Götz; Fischer, Lars; Müller-Stich, Beat-Peter; Nickel, Felix

2017-03-21

Laparoscopic training has become an important part of surgical education. Laparoscopic Roux-en-Y gastric bypass (RYGB) is the most common bariatric procedure performed. Surgeons must be well trained prior to operating on a patient. Multimodality training is vital for bariatric surgery. E-learning with videos is a standard approach for training. The present study investigates whether scoring the operation videos with performance checklists improves learning effects and transfer to a simulated operation. This is a monocentric, two-arm, randomized controlled trial. The trainees are medical students from the University of Heidelberg in their clinical years with no prior laparoscopic experience. After a laparoscopic basic virtual reality (VR) training, 80 students are randomized into one of two arms in a 1:1 ratio to the checklist group (group A) and control group without a checklist (group B). After all students are given an introduction of the training center, VR trainer and laparoscopic instruments, they start with E-learning while watching explanations and videos of RYGB. Only group A will perform ratings with a modified Bariatric Objective Structured Assessment of Technical Skill (BOSATS) scale checklist for all videos watched. Group B watches the same videos without rating. Both groups will then perform an RYGB in the VR trainer as a primary endpoint and small bowel suturing as an additional test in the box trainer for evaluation. This study aims to assess if E-learning and rating bariatric surgical videos with a modified BOSATS checklist will improve the learning curve for medical students in an RYGB VR performance. This study may help in future laparoscopic and bariatric training courses. German Clinical Trials Register, DRKS00010493 . Registered on 20 May 2016.

Relativistic Equations for Spin Particles: What can We Learn from Noncommutativity?

International Nuclear Information System (INIS)

Dvoeglazov, V. V.

2009-01-01

We derive relativistic equations for charged and neutral spin particles. The approach for higher-spin particles is based on generalizations of the Bargmann-Wigner formalism. Next, we study, what new physical information can give the introduction of non-commutativity. Additional non-commutative parameters can provide a suitable basis for explanation of the origin of mass.

Effectiveness of adolescent suicide prevention e-learning modules that aim to improve knowledge and self-confidence of gatekeepers: study protocol for a randomized controlled trial.

Science.gov (United States)

Ghoncheh, Rezvan; Kerkhof, Ad J F M; Koot, Hans M

2014-02-08

Providing e-learning modules can be an effective strategy for enhancing gatekeepers' knowledge, self-confidence and skills in adolescent suicide prevention. The aim of this study was to test the effectiveness of an online training program called Mental Health Online which consists of eight short e-learning modules, each capturing an important aspect of the process of recognition, guidance and referral of suicidal adolescents (12-20 years). The primary outcomes of this study are participant's ratings on perceived knowledge, perceived self-confidence, and actual knowledge regarding adolescent suicidality. A randomized controlled trial will be carried out among 154 gatekeepers. After completing the first assessment (pre-test), participants will be randomly assigned to either the experimental group or the waitlist control group. One week after completing the first assessment the experimental group will have access to the website Mental Health Online containing the eight e-learning modules and additional information on adolescent suicide prevention. Participants in both conditions will be assessed 4 weeks after completing the first assessment (post-test), and 12 weeks after completing the post-test (follow-up). At post-test, participants from the experimental group are asked to complete an evaluation questionnaire on the modules. The waitlist control group will have access to the modules and additional information on the website after completing the follow-up assessment. Gatekeepers can benefit from e-learning modules on adolescent suicide prevention. This approach allows them to learn about this sensitive subject at their own pace and from any given location, as long as they have access to the Internet. Given the flexible nature of the program, each participant can compose his/her own training creating an instant customized course with the required steps in adolescent suicide prevention. Netherlands Trial Register NTR3625.

Neural Networks for Modeling and Control of Particle Accelerators

Science.gov (United States)

Edelen, A. L.; Biedron, S. G.; Chase, B. E.; Edstrom, D.; Milton, S. V.; Stabile, P.

2016-04-01

Particle accelerators are host to myriad nonlinear and complex physical phenomena. They often involve a multitude of interacting systems, are subject to tight performance demands, and should be able to run for extended periods of time with minimal interruptions. Often times, traditional control techniques cannot fully meet these requirements. One promising avenue is to introduce machine learning and sophisticated control techniques inspired by artificial intelligence, particularly in light of recent theoretical and practical advances in these fields. Within machine learning and artificial intelligence, neural networks are particularly well-suited to modeling, control, and diagnostic analysis of complex, nonlinear, and time-varying systems, as well as systems with large parameter spaces. Consequently, the use of neural network-based modeling and control techniques could be of significant benefit to particle accelerators. For the same reasons, particle accelerators are also ideal test-beds for these techniques. Many early attempts to apply neural networks to particle accelerators yielded mixed results due to the relative immaturity of the technology for such tasks. The purpose of this paper is to re-introduce neural networks to the particle accelerator community and report on some work in neural network control that is being conducted as part of a dedicated collaboration between Fermilab and Colorado State University (CSU). We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.

Adaptive sampling method in deep-penetration particle transport problem

International Nuclear Information System (INIS)

Wang Ruihong; Ji Zhicheng; Pei Lucheng

2012-01-01

Deep-penetration problem has been one of the difficult problems in shielding calculation with Monte Carlo method for several decades. In this paper, a kind of particle transport random walking system under the emission point as a sampling station is built. Then, an adaptive sampling scheme is derived for better solution with the achieved information. The main advantage of the adaptive scheme is to choose the most suitable sampling number from the emission point station to obtain the minimum value of the total cost in the process of the random walk. Further, the related importance sampling method is introduced. Its main principle is to define the importance function due to the particle state and to ensure the sampling number of the emission particle is proportional to the importance function. The numerical results show that the adaptive scheme under the emission point as a station could overcome the difficulty of underestimation of the result in some degree, and the adaptive importance sampling method gets satisfied results as well. (authors)

Learning cardiopulmonary resuscitation theory with face-to-face versus audiovisual instruction for secondary school students: a randomized controlled trial.

Science.gov (United States)

Cerezo Espinosa, Cristina; Nieto Caballero, Sergio; Juguera Rodríguez, Laura; Castejón-Mochón, José Francisco; Segura Melgarejo, Francisca; Sánchez Martínez, Carmen María; López López, Carmen Amalia; Pardo Ríos, Manuel

2018-02-01

To compare secondary students' learning of basic life support (BLS) theory and the use of an automatic external defibrillator (AED) through face-to-face classroom instruction versus educational video instruction. A total of 2225 secondary students from 15 schools were randomly assigned to one of the following 5 instructional groups: 1) face-to-face instruction with no audiovisual support, 2) face-to-face instruction with audiovisual support, 3) audiovisual instruction without face-to-face instruction, 4) audiovisual instruction with face-to-face instruction, and 5) a control group that received no instruction. The students took a test of BLS and AED theory before instruction, immediately after instruction, and 2 months later. The median (interquartile range) scores overall were 2.33 (2.17) at baseline, 5.33 (4.66) immediately after instruction (Paudiovisual instruction for learning BLS and AED theory were found in secondary school students either immediately after instruction or 2 months later.

Low-rank sparse learning for robust visual tracking

KAUST Repository

Zhang, Tianzhu

2012-01-01

In this paper, we propose a new particle-filter based tracking algorithm that exploits the relationship between particles (candidate targets). By representing particles as sparse linear combinations of dictionary templates, this algorithm capitalizes on the inherent low-rank structure of particle representations that are learned jointly. As such, it casts the tracking problem as a low-rank matrix learning problem. This low-rank sparse tracker (LRST) has a number of attractive properties. (1) Since LRST adaptively updates dictionary templates, it can handle significant changes in appearance due to variations in illumination, pose, scale, etc. (2) The linear representation in LRST explicitly incorporates background templates in the dictionary and a sparse error term, which enables LRST to address the tracking drift problem and to be robust against occlusion respectively. (3) LRST is computationally attractive, since the low-rank learning problem can be efficiently solved as a sequence of closed form update operations, which yield a time complexity that is linear in the number of particles and the template size. We evaluate the performance of LRST by applying it to a set of challenging video sequences and comparing it to 6 popular tracking methods. Our experiments show that by representing particles jointly, LRST not only outperforms the state-of-the-art in tracking accuracy but also significantly improves the time complexity of methods that use a similar sparse linear representation model for particles [1]. © 2012 Springer-Verlag.

Learning Outcomes and Affective Factors of Blended Learning of English for Library Science

Science.gov (United States)

Wentao, Chen; Jinyu, Zhang; Zhonggen, Yu

2016-01-01

English for Library Science is an essential course for students to command comprehensive scope of library knowledge. This study aims to compare the learning outcomes, gender differences and affective factors in the environments of blended and traditional learning. Around one thousand participants from one university were randomly selected to…

Quantum particle swarm approaches applied to combinatorial problems

Energy Technology Data Exchange (ETDEWEB)

Nicolau, Andressa dos S.; Schirru, Roberto; Lima, Alan M.M. de, E-mail: andressa@lmp.ufrj.br [Universidade Federal do Rio de Janeiro (UFRJ), RJ (Brazil). Programa de Engenharia Nuclear

2017-07-01

Quantum Particle Swarm Optimization (QPSO) is a global convergence algorithm that combines the classical PSO philosophy and quantum mechanics to improve performance of PSO. Different from PSO it only has the 'measurement' of the position equation for all particles. The process of 'measurement' in quantum mechanics, obey classic laws while the particle itself follows the quantum rules. QPSO works like PSO in search ability but has fewer parameters control. In order to improve the QPSO performance, some strategies have been proposed in the literature. Weighted QPSO (WQPSO) is a version of QPSO, where weight parameter is insert in the calculation of the balance between the global and local searching of the algorithm. It has been shown to perform well in finding the optimal solutions for many optimization problems. In this article random confinement was introduced in WQPSO. The WQPSO with random confinement was tested in two combinatorial problems. First, we execute the model on Travelling Salesman Problem (TSP) to find the parameters' values resulting in good solutions in general. Finally, the model was tested on Nuclear Reactor Reload Problem, and the performance was compared with QPSO standard. (author)

Quantum particle swarm approaches applied to combinatorial problems

International Nuclear Information System (INIS)

Nicolau, Andressa dos S.; Schirru, Roberto; Lima, Alan M.M. de

2017-01-01

Quantum Particle Swarm Optimization (QPSO) is a global convergence algorithm that combines the classical PSO philosophy and quantum mechanics to improve performance of PSO. Different from PSO it only has the 'measurement' of the position equation for all particles. The process of 'measurement' in quantum mechanics, obey classic laws while the particle itself follows the quantum rules. QPSO works like PSO in search ability but has fewer parameters control. In order to improve the QPSO performance, some strategies have been proposed in the literature. Weighted QPSO (WQPSO) is a version of QPSO, where weight parameter is insert in the calculation of the balance between the global and local searching of the algorithm. It has been shown to perform well in finding the optimal solutions for many optimization problems. In this article random confinement was introduced in WQPSO. The WQPSO with random confinement was tested in two combinatorial problems. First, we execute the model on Travelling Salesman Problem (TSP) to find the parameters' values resulting in good solutions in general. Finally, the model was tested on Nuclear Reactor Reload Problem, and the performance was compared with QPSO standard. (author)

An introduction to random interlacements

CERN Document Server

Drewitz, Alexander; Sapozhnikov, Artëm

2014-01-01

This book gives a self-contained introduction to the theory of random interlacements. The intended reader of the book is a graduate student with a background in probability theory who wants to learn about the fundamental results and methods of this rapidly emerging field of research. The model was introduced by Sznitman in 2007 in order to describe the local picture left by the trace of a random walk on a large discrete torus when it runs up to times proportional to the volume of the torus. Random interlacements is a new percolation model on the d-dimensional lattice. The main results covered by the book include the full proof of the local convergence of random walk trace on the torus to random interlacements and the full proof of the percolation phase transition of the vacant set of random interlacements in all dimensions. The reader will become familiar with the techniques relevant to working with the underlying Poisson Process and the method of multi-scale renormalization, which helps in overcoming the ch...

Contextual Approach with Guided Discovery Learning and Brain Based Learning in Geometry Learning

Science.gov (United States)

Kartikaningtyas, V.; Kusmayadi, T. A.; Riyadi

2017-09-01

The aim of this study was to combine the contextual approach with Guided Discovery Learning (GDL) and Brain Based Learning (BBL) in geometry learning of junior high school. Furthermore, this study analysed the effect of contextual approach with GDL and BBL in geometry learning. GDL-contextual and BBL-contextual was built from the steps of GDL and BBL that combined with the principles of contextual approach. To validate the models, it uses quasi experiment which used two experiment groups. The sample had been chosen by stratified cluster random sampling. The sample was 150 students of grade 8th in junior high school. The data were collected through the student’s mathematics achievement test that given after the treatment of each group. The data analysed by using one way ANOVA with different cell. The result shows that GDL-contextual has not different effect than BBL-contextual on mathematics achievement in geometry learning. It means both the two models could be used in mathematics learning as the innovative way in geometry learning.

The application of online transcranial random noise stimulation and perceptual learning in the improvement of visual functions in mild myopia.

Science.gov (United States)

Camilleri, Rebecca; Pavan, Andrea; Campana, Gianluca

2016-08-01

It has recently been demonstrated how perceptual learning, that is an improvement in a sensory/perceptual task upon practice, can be boosted by concurrent high-frequency transcranial random noise stimulation (tRNS). It has also been shown that perceptual learning can generalize and produce an improvement of visual functions in participants with mild refractive defects. By using three different groups of participants (single-blind study), we tested the efficacy of a short training (8 sessions) using a single Gabor contrast-detection task with concurrent hf-tRNS in comparison with the same training with sham stimulation or hf-tRNS with no concurrent training, in improving visual acuity (VA) and contrast sensitivity (CS) of individuals with uncorrected mild myopia. A short training with a contrast detection task is able to improve VA and CS only if coupled with hf-tRNS, whereas no effect on VA and marginal effects on CS are seen with the sole administration of hf-tRNS. Our results support the idea that, by boosting the rate of perceptual learning via the modulation of neuronal plasticity, hf-tRNS can be successfully used to reduce the duration of the perceptual training and/or to increase its efficacy in producing perceptual learning and generalization to improved VA and CS in individuals with uncorrected mild myopia. Copyright © 2016 Elsevier Ltd. All rights reserved.

Didactic training vs. computer-based self-learning in the prediction of diminutive colon polyp histology by trainees: a randomized controlled study.