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The classic question of whether one should walk or run in the rain to remain the least wet has inspired a myriad of solutions ranging from physically performing test runs in raining conditions to mathematically modeling human movement through rain. This manuscript approaches the classical problem by simulating movement through rainfall using MATLAB. Our simulation was generalizable to include snowfall as well. An increase in walking speed resulted in a corresponding decrease in raindrop and snowflake collisions. When raindrops or snowflakes were given a horizontal movement vector due to wind, a local minimum in collisions was achieved when moving in parallel with the same horizontal speed as the raindrop; no local minimum was detected with antiparallel movement. In general, our simulation revealed that the faster one moves, the drier one remains., Comment: 15 pages, 9 figures

In this work, we introduce an efficient generation procedure to produce synthetic multi-modal datasets of fluid simulations. The procedure can reproduce the dynamics of fluid flows and allows for exploring and learning various properties of their complex behavior, from distinct perspectives and modalities. We employ our framework to generate a set of thoughtfully designed training datasets, which attempt to span specific fluid simulation scenarios in a meaningful way. The properties of our contributions are demonstrated by evaluating recently published algorithms for the neural fluid simulation and fluid inverse rendering tasks using our benchmark datasets. Our contribution aims to fulfill the community's need for standardized training data, fostering more reproducibile and robust research., Comment: 10 pages, 7 figures

The concept of statistical complexity is studied to characterize the classical kicked top model which plays important role in the qbit systems and the chaotic properties of the entanglement. This allows us to understand this driven dynamical system by the probability distribution in phase space to make distinctions among the regular, random and structural complexity on finite simulation. We present the dependence of the kicked top and kicked rotor model through the strength excitation in the framework of statistical complexity.

In this paper the auditory model developed by Dau et al. [J. Acoust. Soc. Am. 102, 2892-2905 (1997)] was used to simulate the perceptual similarity between complex sounds. For this purpose, a central processor stage was developed and attached as a back-end module to the auditory model. As complex sounds, a set of recordings of one note played on seven different pianos was used, whose similarity has been recently measured by Osses et al. [J. Acoust. Soc. Am. 146, 1024-1035 (2019)] using a 3-AFC discrimination task in noise. The auditory model has several processing stages that are to a greater or lesser extent inspired by physiological aspects of the human normal-hearing system. A set of configurable parameters in each stage affects directly the sound (internal) representations that are further processed in the developed central processor. Therefore, a comprehensive review of the model parameters is given, indicating the configuration we chose. This includes an in-depth description of the auditory adaptation stage, the adaptation loops. Simulations of the similarity task were compared with (1) existing experimental data, where they had a moderate to high correlation, and with (2) simulations using an alternative but similar background noise to that of the experiments, which were used to obtain further information about how the participants' responses were weighted as a function of frequency.

In this proof-of-concept work, we evaluate the performance of multiple machine-learning methods as statistical emulators for use in the analysis of agent-based models (ABMs). Analysing ABM outputs can be challenging, as the relationships between input parameters can be non-linear or even chaotic even in relatively simple models, and each model run can require significant CPU time. Statistical emulation, in which a statistical model of the ABM is constructed to facilitate detailed model analyses, has been proposed as an alternative to computationally costly Monte Carlo methods. Here we compare multiple machine-learning methods for ABM emulation in order to determine the approaches best suited to emulating the complex behaviour of ABMs. Our results suggest that, in most scenarios, artificial neural networks (ANNs) and gradient-boosted trees outperform Gaussian process emulators, currently the most commonly used method for the emulation of complex computational models. ANNs produced the most accurate model replications in scenarios with high numbers of model runs, although training times were longer than the other methods. We propose that agent-based modelling would benefit from using machine-learning methods for emulation, as this can facilitate more robust sensitivity analyses for the models while also reducing CPU time consumption when calibrating and analysing the simulation.

Most of the current complex networks that are of interest to practitioners possess a certain community structure that plays an important role in understanding the properties of these networks. Moreover, many machine learning algorithms and tools that are developed for complex networks try to take advantage of the existence of communities to improve their performance or speed. As a result, there are many competing algorithms for detecting communities in large networks. Unfortunately, these algorithms are often quite sensitive and so they cannot be fine-tuned for a given, but a constantly changing, real-world network at hand. It is therefore important to test these algorithms for various scenarios that can only be done using synthetic graphs that have built-in community structure, power-law degree distribution, and other typical properties observed in complex networks. The standard and extensively used method for generating artificial networks is the LFR graph generator. Unfortunately, this model has some scalability limitations and it is challenging to analyze it theoretically. Finally, the mixing parameter $\mu$, the main parameter of the model guiding the strength of the communities, has a non-obvious interpretation and so can lead to unnaturally-defined networks. In this paper, we provide an alternative random graph model with community structure and power-law distribution for both degrees and community sizes, the Artificial Benchmark for Community Detection (ABCD). We show that the new model solves the three issues identified above and more. The conclusion is that these models produce comparable graphs but ABCD is fast, simple, and can be easily tuned to allow the user to make a smooth transition between the two extremes: pure (independent) communities and random graph with no community structure., Comment: 22 pages, 4 figures

This research considers potential dose increase in target due to cisplatin (Pt) concentration and radiation type. Cisplatin concentrations from 0.003 to 120 mM were used. Monte-Carlo simulation of Linear accelerator (Elekta Synergy) and X-ray tube (Xstrahl300) was carried out using Geant4 and PClab. As the first step of this research, we performed simulation of energy spectrum from radiotherapy units (spectrum model). The next step was the modeling of linear accelerator head and X-ray tube, and the distribution of dose in the water phantom (PDD model). At the second stage, dose changes were investigated in the presence of cisplatin in the target (CIS model). The simulation results showed that the dose escalation can be caused by photon-capture therapy (PCT). There is a dose enhancement in the volume where cisplatin is accumulated. Then higher is concentration, then higher is the effect. However, the photon energy increase from 60 to 250 kV and increase of depth of target reduces the effect of PCT due the decrease of the photoelectric effect cross-section. Should be noticed, that the orthovoltage X-rays energy, listed in the table with results shows higher dose enhancement, than the megavoltage photon beam generated from linear acceleration sources. In addition, that the dose enhancement factors (DEF) are higher in linac without flattening filter, than in linac with flattening filter., Comment: 10 pages, 20 figures, 5 tables

The method of microwave radiometry is one of the areas of medical diagnosis of breast cancer. It is based on analysis of the spatial distribution of internal and surface tissue temperatures, which are measured in the microwave (RTM) and infrared (IR) ranges. Complex mathematical and computer models describing complex physical and biological processes within biotissue increase the efficiency of this method. Physical and biological processes are related to temperature dynamics and microwave electromagnetic radiation. Verification and validation of the numerical model is a key challenge to ensure consistency with medical big data. These data are obtained by medical measurements of patients. We present an original approach to verification and validation of simulation models of physical processes in biological tissues. Our approach is based on deep analysis of medical data and we use machine learning algorithms. We have achieved impressive success for the model of dynamics of thermal processes in a breast with cancer foci. This method allows us to carry out a significant refinement of almost all parameters of the mathematical model in order to achieve the maximum possible adequacy., Comment: 15 pages, 9 figures, Creativity in Intelligent Technologies and Data Science (CIT&DS 2019), Volgograd, September, 2019

This paper presents entropy maps, an approach to describing and visualising uncertainty among alternative potential movement intentions in pedestrian simulation models. In particular, entropy maps show the instantaneous level of randomness in decisions of a pedestrian agent situated in a specific point of the simulated environment with an heatmap approach. Experimental results highlighting the relevance of this tool supporting modelers are provided and discussed., Comment: pre-print of paper presented at the The 16th International Conference on Modeling Decisions for Artificial Intelligence, Milan, Italy September 4 - 6, 2019. arXiv admin note: substantial text overlap with arXiv:1610.07901

We present an efficient implementation of the highly robust and scalable GenEO preconditioner in the high-performance PDE framework DUNE. The GenEO coarse space is constructed by combining low energy solutions of a local generalised eigenproblem using a partition of unity. In this paper we demonstrate both weak and strong scaling for the GenEO solver on over 15,000 cores by solving an industrially motivated problem with over 200 million degrees of freedom. Further, we show that for highly complex parameter distributions arising in certain real-world applications, established methods become intractable while GenEO remains fully effective. The purpose of this paper is two-fold: to demonstrate the robustness and high parallel efficiency of the solver and to document the technical details that are crucial to the efficiency of the code., Comment: Fixed error in definition of partition of unity

High levels of air pollution may seriously affect people's living environment and even endanger their lives. In order to reduce air pollution concentrations, and warn the public before the occurrence of hazardous air pollutants, it is urgent to design an accurate and reliable air pollutant forecasting model. However, most previous research have many deficiencies, such as ignoring the importance of predictive stability, and poor initial parameters and so on, which have significantly effect on the performance of air pollution prediction. Therefore, to address these issues, a novel hybrid model is proposed in this study. Specifically, a powerful data preprocessing techniques is applied to decompose the original time series into different modes from low- frequency to high- frequency. Next, a new multi-objective algorithm called MOHHO is first developed in this study, which are introduced to tune the parameters of ELM model with high forecasting accuracy and stability for air pollution series prediction, simultaneously. And the optimized ELM model is used to perform the time series prediction. Finally, a scientific and robust evaluation system including several error criteria, benchmark models, and several experiments using six air pollutant concentrations time series from three cities in China is designed to perform a compressive assessment for the presented hybrid forecasting model. Experimental results indicate that the proposed hybrid model can guarantee a more stable and higher predictive performance compared to others, whose superior prediction ability may help to develop effective plans for air pollutant emissions and prevent health problems caused by air pollution., Comment: 24 pages, 4 figures

To effectively prepare engineering students requires of formation of a system of fundamental physical knowledge together with the ability to apply them in specific productive activities, both on fundamental and on the profiled-oriented level. Accordingly the tasks of physics of high school is mastering the methodology of science knowledge and scientific way of thinking and generalize of experimental natural ability to conduct scientific research by methods of physical knowledge. In the process of teaching physics simulations simultaneously acts by scientific knowledge, is part of the content of educational material and effective means of experiment. It is possible to obtain special teaching methods of computer simulation, which include primarily computer experiment. The main methods of computer modeling learning in university are a multimedia lecture, telecommunications project and computer-oriented laboratory practice. The main tasks of teaching computer modeling in physics course are the overall development and formation of outlook of future engineers and development practical skills in computer simulation. The leading method of learning computer simulation in physics course is a project method. Those methods of learning are optimal in the context of developmental education. Computational experiment is a modeling methodology as a science, so it can be attributed to the principles of scientific methods of learning. Purposes of learning physics in university include the necessity of mastering a given set of scientific facts and methods of getting these facts. Computational experiment reflects the method of knowledge which applied in physics., Comment: 8 pages, 1 figure, 1 table, in Ukrainian

We introduce an algorithmic model of heat conduction, the thermodynamic graph. The thermodynamic graph is analogous to meshes in the finite difference method in the sense that the calculation of temperature is carried out at the vertices of the graph, and the edges indicate the direct heat exchange between the vertices. Recurrence relations of heat conduction in graph are derived without using of differential equations and based on the coefficients of thermal conductivity and heat capacity. This approach seems to be more direct and flexible from the point of view of algorithmic modeling of thermodynamic process than the derivation of difference schemes from differential equations. We introduce also the notion of entropy of thermodynamic graph. We find the maximum length of the time step at which the entropy does not decrease in the general case. As a result, this give us the accurate boundary of the model stability., Comment: 13 pages, 2 figures, in russian

In Autonomous Transit Networks some basic elements influence the throughput: network structure, maximum velocity, number of vehicles etc. Other parameters like station structure, dynamic routing or vehicle behavior on intersections play minor role. Yet in highly congested nets, when vehicles interfere in the traffic, some subtle decisions may influence overall system ridership. We tested the impact of intersection priority rules on passenger waiting time, which measures the throughput. The dependence occurred its relevance in a crowded network., Comment: 4 pages, 4 figures, 1 table

We present an easy-to-use, Python-based framework that allows a researcher to automate their computational simulations. In particular the framework facilitates assembling several long-running computations and producing various plots from the data produced by these computations. The framework makes it possible to reproduce every figure made for a publication with a single command. It also allows one to distribute the computations across a network of computers. The framework has been used to write research papers in numerical computing. This paper discusses the design of the framework, and the benefits of using it. The ideas presented are general and should help researchers organize their computations for better reproducibility.

Hepatitis C virus (HCV) infection is endemic in people who inject drugs (PWID), with prevalence estimates above 60 percent for PWID in the United States. Previous modeling studies suggest that direct acting antiviral (DAA) treatment can lower overall prevalence in this population, but treatment is often delayed until the onset of advanced liver disease (fibrosis stage 3 or later) due to cost. Lower cost interventions featuring syringe access (SA) and medically assisted treatment (MAT) for addiction are known to be less costly, but have shown mixed results in lowering HCV rates below current levels. Little is known about the potential synergistic effects of combining DAA and MAT treatment, and large-scale tests of combined interventions are rare. While simulation experiments can reveal likely long-term effects, most prior simulations have been performed on closed populations of model agents--a scenario quite different from the open, mobile populations known to most health agencies. This paper uses data from the Centers for Disease Control's National HIV Behavioral Surveillance project, IDU round 3, collected in New York City in 2012 by the New York City Department of Health and Mental Hygiene to parameterize simulations of open populations. Our results show that, in an open population, SA/MAT by itself has only small effects on HCV prevalence, while DAA treatment by itself can significantly lower both HCV and HCV-related advanced liver disease prevalence. More importantly, the simulation experiments suggest that cost effective synergistic combinations of the two strategies can dramatically reduce HCV incidence. We conclude that adopting SA/MAT implementations alongside DAA interventions can play a critical role in reducing the long-term consequences of ongoing infection.

Current metropolises largely depend on a functioning transport infrastructure and the increasing demand can only be satisfied by a well organized mass transit. One example for a crucial mass transit system is New York City's Staten Island Ferry, connecting the two boroughs of Staten Island and Manhattan with a regular passenger service. Today's demand already exceeds 2500 passengers for a single cycle during peek hours, and future projections suggest that it will further increase. One way to appraise how the system will cope with future demand is by simulation. This contribution proposes an integrated simulation approach to evaluate the system performance with respect to future demand. The simulation relies on a multiscale modeling approach where the terminal buildings are simulated by a microscopic and quantitatively valid cellular automata (CA) and the journeys of the ferries themselves are modeled by a mesoscopic queue simulation approach. Based on the simulation results recommendations with respect to the future demand are given., Comment: 96th Transportation Research Board annual meeting, Washington, January 2017

We provide spatial discretizations of nonlinear incompressible Navier-Stokes equations with inputs and outputs in the form of matrices ready to use in any numerical linear algebra package. We discuss the assembling of the system operators and the realization of boundary conditions and inputs and outputs. We describe the two benchmark problems - the driven cavity and the cylinder wake - and provide the corresponding data. The use of the data is illustrated by numerous example setups. The test cases are provided as plain PYTHON or OCTAVE/MATLAB script files for immediate replication.

The article discusses issues related to the construction of the PRT network simulator and the simulation process: the elements of PRT network structure, their representation in the simulator, the simulation process itself, animation, and automation of the experiments. An example of a simulation environment Feniks is described, elaborated within the framework of the Eco-Mobility project., Comment: 12 pages, 3 figures

The paper presents a flexibility of management of vehicles in Personal Rapid Transit (PRT) network. The algorithm used for delivering empty vehicles for waiting passengers is based on multiparameter analysis. Due to its distributed construction, the algorithm has a horizon parameter, which specifies the maximum distance between stations the communications is performed. Every decision is made basing on an information about situation (number of vehicles standing at a station, number of vehicles travelling to a station, number of passengers waiting) sent between stations, without any central data base containing traffic conditions. The simulation of the traffic in random case (typical) and in unusual case of delivering people to a social event occurring at single place is presented. It is shown that simple manipulation with horizon parameter allows to adapt the network to extremely uneven demand and destination choice., Comment: 6 pages, 1 figure, 3 tables

The transition from petascale to exascale computers is characterized by substantial changes in the computer architectures and technologies. The research community relying on computational simulations is being forced to revisit the algorithms for data generation and analysis due to various concerns, such as higher degrees of concurrency, deeper memory hierarchies, substantial I/O and communication constraints. Simulations today typically save all data to analyze later. Simulations at the exascale will require us to analyze data as it is generated and save only what is really needed for analysis, which must be performed predominately in-situ, i.e., executed sufficiently fast locally, limiting memory and disk usage, and avoiding the need to move large data across nodes. In this paper, we present a distributed method that enables in-situ data analysis for large protein folding trajectory datasets. Traditional trajectory analysis methods currently follow a centralized approach that moves the trajectory datasets to a centralized node and processes the data only after simulations have been completed. Our method, on the other hand, captures conformational information in-situ using local data only while reducing the storage space needed for the part of the trajectory under consideration. This method processes the input trajectory data in one pass, breaks from the centralized approach of traditional analysis, avoids the movement of trajectory data, and still builds the global knowledge on the formation of individual $\alpha$-helices or $\beta$-strands as trajectory frames are generated., Comment: 40 pages, 15 figures, this paper is presently in the format request of the journal to which it was submitted for publication

Computational models of complex systems are usually elaborate and sensitive to implementation details, characteristics which often affect their verification and validation. Model replication is a possible solution to this issue. It avoids biases associated with the language or toolkit used to develop the original model, not only promoting its verification and validation, but also fostering the credibility of the underlying conceptual model. However, different model implementations must be compared to assess their equivalence. The problem is, given two or more implementations of a stochastic model, how to prove that they display similar behavior? In this paper, we present a model comparison technique, which uses principal component analysis to convert simulation output into a set of linearly uncorrelated statistical measures, analyzable in a consistent, model-independent fashion. It is appropriate for ascertaining distributional equivalence of a model replication with its original implementation. Besides model-independence, this technique has three other desirable properties: a) it automatically selects output features that best explain implementation differences; b) it does not depend on the distributional properties of simulation output; and, c) it simplifies the modelers' work, as it can be used directly on simulation outputs. The proposed technique is shown to produce similar results to the manual or empirical selection of output features when applied to a well-studied reference model., Comment: The peer-reviewed version of this paper is published in Simulation Modelling Practice and Theory at http://dx.doi.org/10.1016/j.simpat.2016.12.013 . This version is typeset by the authors and differs only in pagination and typographical detail

Vehicular networks have a diverse range of applications that vary from safety, to traffic management and comfort. Vehicular communications (VC) can assist in the ecorouting of vehicles in order to reduce the overall mileage and CO2 emissions by the exchange of data among vehicle-entities. However, the trustworthiness of these data is crucial as false information can heavily affect the performance of applications. Hence, the devising of mechanisms that reassure the integrity of the exchanged data is of utmost importance. In this article we investigate how tweaked information originating from malicious nodes can affect the performance of a real time eco routing mechanism that uses DSRC communications, namely ErouVe. We also develop and evaluate defense mechanisms that exploit vehicular communications in order to filter out tweaked data. We prove that our proposed mechanisms can restore the performance of the ErouVe to near its optimal operation and can be used as a basis for protecting other similar traffic management systems., Comment: 8 pages, 8 figures

When incorrectly designed, windows can be responsible for unnecessary energy consumption in a building. This may result from its dimensions, orientation and shadowing. In a moderate climate like the Portuguese, and considering an annual thermal comfort assessment of a space, if windows are under-dimensioned or over-shadowed, they can contribute to the increase of heating needs. However, when over-dimensioned or under-shadowed, they contribute to the increase of cooling requirements. Therefore, it is important to find the optimum design that balances orientation, dimension and shadowing, contributing to minimize both the heating and cooling needs. This study presents a parametric analysis of a double glazing window in its orientation and dimension, located in the Portuguese city of Coimbra. For each window orientation and dimension, the optimum overhang depth is determined. The objective is to minimize degree-hours of thermal discomfort. Results show that overhangs are mainly a corrective mechanism to over-dimensioned openings, thus allowing that building practitioners may choose a wider range of windows dimensions., Comment: 7 pages, 2 figures, 3rd LAETA Young Researchers Meeting - 3EJIL, Coimbra, 7-8 May, 2015. arXiv admin note: text overlap with arXiv:1503.07016

The windows can be responsible for unnecessary energy consumption in a building, if incorrectly designed, shadowed or oriented. Considering an annual thermal comfort assessment of a space, if windows are over-dimensioned, they can contribute to the increase of the heating needs due to heat losses, and also to the increase of cooling needs due to over-exposure to solar radiation. When under-dimensioned, the same space may benefit from reduced heat losses through the glazing surface but does not benefit from solar radiation gains. Therefore, it is important to find the optimum design that minimizes both the heating and cooling needs. This paper presents a parametric study of window type (single, double and triple glazing), orientation and opening size, located in the city of Coimbra, Portugal. An annual and a seasonal assessment were done, in order to obtain the set of optimum values around 360 degree orientation., Comment: 7 pages, 2 figures, Proceedings of Energy for Sustainability 2015 Conference: Sustainable Cities: Designing for People and the Planet, Coimbra, 14-15 May, 2015

Ultra-intense ultra-short laser is firstly used to irradiate the capacity-coil target to generate magnetic field. The spatial structure and temporal evolution of huge magnetic fields were studied with time-gated proton radiography method. A magnetic flux density of 40T was measured by comparing the proton deflection and particle track simulations. Although the laser pulse duration is only 30fs, the generated magnetic field can last for over 100 picoseconds. The energy conversion efficiency from laser to magnetic field can reach as high as ~20%. The results indicate that tens of tesla (T) magnetic field could be produced in many ultra intense laser facilities around the world, and higher magnetic field could be produced by picosecond lasers., Comment: 5 pages, 5 figures

Response time is one of the characteristics of scheduler, happens to be a prominent attribute of any CPU scheduling algorithm. The proposed New Multi Level Feedback Queue [NMLFQ] Scheduler is compared with dynamic, real time, Dependent Activity Scheduling Algorithm (DASA) and Lockes Best Effort Scheduling Algorithm (LBESA). We abbreviated beneficial result of NMLFQ scheduler in comparison with dynamic best effort schedulers with respect to response time., Comment: 7 pages, 5 figures

Discrete pedestrian simulation models are viable alternatives to particle based approaches based on a continuous spatial representation. The effects of discretisation, however, also imply some difficulties in modelling certain phenomena that can be observed in reality. This paper focuses on the possibility to manage heterogeneity in the walking speed of the simulated population of pedestrians by modifying an existing multi-agent model extending the floor field approach. Whereas some discrete models allow pedestrians (or cars, when applied to traffic modelling) to move more than a single cell per time step, the present work proposes a maximum speed of one cell per step, but we model lower speeds by having pedestrians yielding their movement in some turns. Different classes of pedestrians are associated to different desired walking speeds and we define a stochastic mechanism ensuring that they maintain an average speed close to this threshold. In the paper we formally describe the model and we show the results of its application in benchmark scenarios. Finally, we also show how this approach can also support the definition of slopes and stairs as elements reducing the walking speed of pedestrians climbing them in a simulated scenario., Comment: Poster at the 93rd Transportation Research Board annual meeting, Washington, January 2014 - Committee number AHB45 - TRB Committee on Traffic Flow Theory and Characteristics

At computer modeling of process of training it is usually supposed that all elements of a training material are forgotten with an identical speed. But in practice that knowledge which are included in educational activity of the pupil are remembered much more strongly and forgotten more slowly then knowledge which he doesn't use. For the purpose of more exact research of didactic systems is offered the model of training, in which consider that in case increasing the number of applications of this element of a learning material: 1) duration of its use by the pupil decreases; 2) the coefficient of forgetting decreases. The computer model is considered, programs in the Pascal language are submitted, results of modeling are given and analyzed. Keywords: didactics, information and cybernetic approach, computer modeling of process of training., Comment: 4 pages, 5 figures, 3 programs (on russian). http://injoit.org/index.php/j1

The problem of Monte-Carlo method study at computer simulation lessons in a Teachers' Training Institute is reviewed in the article. The suggested technique envisages the simulation modelling of various stochastic processes. They include transmission of information via a communication link, oscillation of a pendulum in an air stream, deflection of alpha particles by Au atoms, formation of a percolating cluster, etc. (New educational technologies in higher education institution: the collection of reports of the tenth international scientific and methodical conference. Yekaterinburg, UGTU UPI, 2013)., Comment: 6 pages, 4 figures,on russian. New educational technologies in higher education institution, Yekaterinburg, 2013, pp. 1 - 7

One of the important problems of cyber pedagogy is the following: how, knowing the parameters of the student, his initial level of knowledge and the impact of the teacher to predict knowledge of student at subsequent times. Simulation method allows you to create a computer program that simulates the behavior of the 'teacher-student'-system and investigate the influence of system parameters on the results of learning., Comment: 6 pages, 2 figures, 2 programs; Psychology, sociology and pedagogy April 2013

In computer simulation of the learning process is usually assumed that all elements of the training material are assimilated equally durable. But in practice, the knowledge, which a student uses in its operations, are remembered much better. For a more precise study of didactic systems the multi component model of learning are proposed. It takes into account: 1) the transition of weak knowledge in trustworthy knowledge; 2) the difference in the rate of forgetting the trustworthy and weak knowledge. It is assumed that the rate of increase of student's knowledge is proportional to: 1) the difference between the level of the requirements of teachers and the number of learned knowledge; 2) the amount of learned knowledge, raised to some power. Examples of the use of a multi component model for the study of situations in the learning process are considered, the resulting graphs of the student's level of knowledge of the time are presented. A generalized model of learning, which allows to take into account the complexity of the various elements of the educational material are proposed. The possibility of creating a training program for the training of students of pedagogical institutes are considered., Comment: 5 pages, 2 figures, on russian

In article the following tasks on computer modeling of electric fields are analyzed: 1) calculation of distribution of potential for the field created by two parallel plates and charged bodies in the non-uniform environment; 2) calculation of distribution of potential and force lines of electric field in which are brought the cylinder, a pipe, a plate, a rectangular parallelepiped from dielectric, and also the metal cylinder; 3) calculation of distribution of potential in the one-dimensional nonuniform environment; 4) the solution of the equation of Poisson in spherical coordinates; 5) calculation of distribution of potential in cylindrical coordinates with the subsequent creation of equipotential surfaces and force lines., Comment: in Russian, 10 pages, 5 figures, 3 programs on Pascal. Modern scientific researches and innovations 2013 12 December

The principles on which can be based computer model of process of training are formulated. Are considered: 1) the unicomponent model, which is recognizing that educational information consists of equal elements; 2) the multicomponent model, which is considering that knowledge is assimilate with a various strength, and on lesson weak knowledge becomes strong; 3) the generalized multicomponent model which considers change of working capacity of the pupil and various complexity of studied elements of a training material. Typical results of imitating modeling of learning process are presented in article., Comment: 8 pages, 5 figures, on russian; Modern scientific researches and innovations 2013 oktober 10

Analyzed models of learning, which take into account that: 1) the rate of increase of student's knowledge is proportional to the difference between levels of teacher's requirements and prior knowledge; 2) if the requirements are too high, then student motivation decreases and he stops learning. Was proposed: 1) a one component model, coming from the fact that the training information consists of equal elements; 2) a two component model that takes into account that knowledge is assimilated with varying strength, 'trustworthy' knowledge forgotten much slower then 'weak'; 3) two component model, which takes into account the transition of 'weak' knowledge in 'trustworthy' knowledge. The solution of the five predictors and optimization problems of learning theory are represented., Comment: 6 pages, 3 figures, Perspestive of Science and Education, 2013, N 5, pp. 95-100 (In Russian)

Swarm robotic systems are mainly inspired by swarms of socials insects and the collective emergent behavior that arises from their cooperation at the lower lever. Despite the limited sensory ability, computational power, and communication means of each swarm member, the swarm as a group manages to achieve difficult tasks such as searching for food in terrains with obstacles that individual robots cannot achieve in isolation of the other group members. Moreover, such tasks are usually achieved without having information sharing capabilities at the swarm level or having a centralized decision making system. In this report, I survey the state of the field of applying adaptive control method to increase swarm robotic systems robustness to the failure of individual robots, and increase its efficiency in performing its task. A few techniques for the division of labor problem are briefly presented while one of them is given in more detail. A discussion of the advantages and disadvantages of this system is given and suggestions of potential improvements that can be made to the system are presented.

In our previous work there was some indication that Partition Sort could be having a more robust average case O(nlogn) complexity than the popular Quick Sort. In our first study in this paper, we reconfirm this through computer experiments for inputs from Cauchy distribution for which expectation theoretically does not exist. Additionally, the algorithm is found to be sensitive to parameters of the input probability distribution demanding further investigation on parameterized complexity. The results on this algorithm for Binomial inputs in our second study are very encouraging in that direction., Comment: 8 pages

In this paper a novel computational technique for finite discrete approximation of continuous dynamical systems suitable for a significant class of biochemical dynamical systems is introduced. The method is parameterized in order to affect the imposed level of approximation provided that with increasing parameter value the approximation converges to the original continuous system. By employing this approximation technique, we present algorithms solving the reachability problem for biochemical dynamical systems. The presented method and algorithms are evaluated on several exemplary biological models and on a real case study. This is a full version of the paper published in the proceedings of CompMod 2011., Comment: Full version of a paper accepted to COMPMOD 2011

In this article a stochastic particle system approximation to the parametric sensitivity in the Smoluchowski coagulation equation is introduced. The parametric sensitivity is the derivative of the solution to the equation with respect to some parameter, where the coagulation kernel depends on this parameter. It is proved that the particle system converges weakly to the sensitivity as the number of particles N increases. A Monte Carlo algorithm is developed and variance reduction techniques are applied. Numerical experiments are conducted for two kernels: the additive kernel and one which has been used for studying soot formation in a free molecular regime. It is shown empirically that the techniques for variance reduction are indeed very effective and that the order of convergence is O(1/N). The algorithm is then compared to an algorithm based on a finite difference approximation to the sensitivity and it is found that the variance of the sensitivity estimators are considerably lower than that for the finite difference approach. Furthermore, two methods of establishing `efficiency' are considered and the new algorithm is found to be significantly more efficient., Comment: 23 pages

We use coupling into and from the past to sample perfectly in a simple and provably fast fashion from the Vervaat family of perpetuities. The family includes the Dickman distribution, which arises both in number theory and in the analysis of the Quickselect algorithm, which was the motivation for our work., Comment: 14 pages, no figures

Several methods are currently available to simulate paths of the Brownian motion. In particular, paths of the BM can be simulated using the properties of the increments of the process like in the Euler scheme, or as the limit of a random walk or via L2 decomposition like the Kac-Siegert/Karnounen-Loeve series. In this paper we first propose a IFSM (Iterated Function Systems with Maps) operator whose fixed point is the trajectory of the BM. We then use this representation of the process to simulate its trajectories. The resulting simulated trajectories are self-affine, continuous and fractal by construction. This fact produces more realistic trajectories than other schemes in the sense that their geometry is closer to the one of the true BM's trajectories. The IFSM trajectory of the BM can then be used to generate more realistic solutions of stochastic differential equations.

This note extends the work of Foss and Tweedie (1997), who showed that availability of the classic Coupling from The Past algorithm of Propp and Wilson (1996) is essentially equivalent to uniform ergodicity for a Markov chain (see also HobertRobert, 2004). In this note we show that all geometrically ergodic chains possess dominated Coupling from The Past algorithms (not necessarily practical!) which are rather closely connected to Foster-Lyapunov criteria., Comment: 16 pages; LaTeX. Minor reformatting to reduce incidence of long lines