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Current understanding of the structure and functioning of biomembranes is impossible without determining the mechanism of formation of membrane lipid rafts. The formation of liquid-ordered and disordered phases (Lo and Ld) and lipid rafts in membranes and their simplified models is discussed. A new consideration of the processes of formation of lipid phases Lo and Ld and lipid rafts is proposed, taking into account the division of each of the glycerophospholipids into several groups. Generally accepted three-component schemes for modeling the membrane structure are critically considered. A four-component scheme is proposed, which is designed to more accurately assume the composition of lipids in the resulting Lo and Ld phases. The role of the polar head groups of phospholipids and, in particular, phosphatidylethanolamine is considered. The structure of membrane rafts and the possible absence of a clear boundary between the Lo and Ld phases are discussed. [ABSTRACT FROM AUTHOR]

This research develops a method for synthesizing linguistic models of fuzzy systems with fuzzy input and output parameters that are described by linguistic variables. Based on the proposed method, linguistic models of the Title 1000 catalytic cracking unit for heavy residues at the Shymkent oil refinery are developed, describing the dependence of the volume and quality of gasoline on the input and operating parameters of the facility, which are fuzzy. It is substantiated that the use of a fuzzy approach, which allows the use of the experience, knowledge, and intuition (intelligence) of the decision maker and subject matter experts, is the most suitable effective method for synthesizing models of complex, fuzzily described objects and processes for comparison with other methods. The main idea of the proposed work is to solve the problems of shortage and fuzziness of initial information when developing models and optimizing the operating modes of a catalytic cracking unit through the use of knowledge, experience, and intuition of experts in this field. To solve the problems of the shortage of initial quantitative information and the fuzziness of available information when developing mathematical models, it is proposed to systematically use statistical methods, expert assessment methods, and a heuristic method based on fuzzy logic. The scientific novelty of the research lies in the development of a method for synthesizing linguistic models in a fuzzy environment and an algorithm for its implementation, which makes it possible to describe the dependence of the fuzzy values of the object's output parameters on its fuzzy input and operating parameters. The proposed approach allows the formalization and synthesis of models of fuzzily described objects when other methods of model development are not applicable or do not give the expected results. The results of the work were simulated in the MATLAB Fuzzy Logic Toolbox. [ABSTRACT FROM AUTHOR]

Zn-air batteries have attracted considerable attention from researchers owing to their high theoretical energy density and the abundance of zinc on Earth. The modification of battery component materials represent a common approach to improve battery performance. The effects of cell design on cell performance are seldom investigated. In this study, we designed four battery structures as follows. Cell 1: close-proximity electrode, Cell 2: equal-area electrode, Cell 3: large zinc electrode, and Cell 4: air channel flow. The effects of four factors: (1) carbon paste, (2) natural and forced air convection, (3) anode/cathode area ratio, and (4) anode–cathode distance were also investigated. Results showed that the addition of carbon paste on the air side of 25BC increased cell power density under forced air convection. Moreover, cell performance also improved by increasing the anode/cathode ratio and by decreasing the anode–cathode distance. These four types of cells were compared based on the oxygen reduction reaction electrode area. Cell 3 displayed the highest power density. In terms of volumetric power density, the proximity cell (Cell 1) exhibited the highest power density among the cells. Therefore, this cell configuration may be suitable for portable applications. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the success rate of drugs for CNS indications at an all-time low, new approaches are needed to turn the tide of failed clinical trials. This paper reviews the history of CNS drug Discovery over the last 60 years and proposes a new paradigm based on the lessons learned. The initial wave of successful therapeutics discovered using careful clinical observations was followed by an emphasis on a phenotypic target-agnostic approach, often leading to successful drugs with a rich pharmacology. The subsequent introduction of molecular biology and the focus on a target-driven strategy has largely dominated drug discovery efforts over the last 30 years, but has not increased the probability of success, because these highly selective molecules are unlikely to address the complex pathological phenotypes of most CNS disorders. In many cases, reliance on preclinical animal models has lacked robust translational power. We argue that Quantitative Systems Pharmacology (QSP), a mechanism-based computer model of biological processes informed by preclinical knowledge and enhanced by neuroimaging and clinical data could be a new powerful knowledge generator engine and paradigm for rational polypharmacy. Progress in the academic discipline of computational neurosciences, allows one to model the effect of pathology and therapeutic interventions on neuronal circuit firing activity that can relate to clinical phenotypes, driven by complex properties of specific brain region activation states. The model is validated by optimizing the correlation between relevant emergent properties of these neuronal circuits and historical clinical and imaging datasets. A rationally designed polypharmacy target profile will be discovered using reverse engineering and sensitivity analysis. Small molecules will be identified using a combination of Artificial Intelligence methods and computational modeling, tested subsequently in heterologous cellular systems with human targets. Animal models will be used to establish target engagement and for ADME-Tox, with the QSP approach complemented by in vivo preclinical models that can be further refined to increase predictive validity. The QSP platform can also mitigate the variability in clinical trials with the concept of virtual patients. Because the QSP platform integrates knowledge from a wide variety of sources in an actionable simulation, it offers the possibility of substantially improving the success rate of CNS R&D programs while, at the same time, reducing both cost and the number of animals. [ABSTRACT FROM AUTHOR]

Computer models and simulations have provided enormous benefits to researchers in the natural and social sciences, as well as many areas of philosophy. However, to date, there has been little attempt to use computer models in the development and evaluation of metaphysical theories. This is a shame, as there are good reasons for believing that metaphysics could benefit just as much from this practice as other disciplines. In this paper I assess the possibilities and limitations of using computer models in metaphysics. I outline the way in which different kinds of model could be useful for different areas of metaphysics, and I illustrate in more detail how agent-based models specifically could be used to model two well-known theories of laws: David Lewis's "Best System Account" and David Armstrong's "Nomic Necessitation" view. Some logically possible processes cannot be simulated on a standard computing device. I finish by assessing how much of a threat this is to the prospect of metaphysical modeling in general. [ABSTRACT FROM AUTHOR]

To what extent can online social networks predict who is at risk of an infection? Many infections are transmitted through physical encounter between humans, but collecting detailed information about it can be expensive, might invade privacy, or might not even be possible. In this paper, we ask whether online social networks help predict and contain epidemic risk. Using a dataset from a popular online review service which includes over 100 thousand users and spans 4 years of activity, we build a time-varying network that is a proxy of physical encounter between its users (the encounter network) and a static network based on their reported online friendship (the friendship With computer simulations, we compare stochastic infection processes on the two networks, considering infections on the encounter network as the benchmark. First, we show that the friendship network is useful to identify the individuals at risk of infection, despite providing lower accuracy than the ideal case in which the encounters are known. This limited prediction accuracy is not only due to the static nature of the friendship network because a static version of the encounter network provides more accurate prediction of risk than the friendship network. Then, we show that periodical monitoring of the infection spreading on the encounter network allows to correct the infection predicted by a process spreading on the friendly staff ndship network, and achieves high prediction accuracy. Finally, we show that the friendship network contains valuable information to effectively contain epidemic outbreaks even when a limited budget is available for immunization. In particular, a strategy that immunizes random friends of random individuals achieves the same performance as knowing individuals’ encounters at a small additional cost, even if the infection spreads on the encounter network. [ABSTRACT FROM AUTHOR]

Copyright of Technical Diagnostics & Nondestructive Testing / Tekhnicheskaya Diagnostika I Nerazrushayushchiy Kontrol is the property of International Association Welding (Paton Publishing House) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper considers some aspects of determining stiffness properties of cross-sections of bar elements and modeling the stress-strain states of bar systems. A method is proposed to find stresses based on nonlinear "stress-strain" dependencies. When numerically determining stiffness properties of a cross-section, a nonlinear analysis of a given collection of forces was performed. Using the method for performing nonlinear analysis, which is implemented in the software package "LIRA-SAPR," the tangent and secant stiffness properties are determined. The methods proposed for determining and modeling stiffness properties allow to more precisely specify characteristics of nonlinear strain in materials and to apply them to elements of the cross-sections being designed. [ABSTRACT FROM AUTHOR]

The presence of treatment-resistant cells is an important factor that limits the efficacy of cancer therapy, and the prospect of resistance is considered the major cause of the treatment strategy. Several recent studies have employed mathematical models to elucidate the dynamics of generating resistant cancer cells and attempted to predict the probability of emerging resistant cells. The purpose of this paper is to present numerical approach to compute the number of resistant cells and the emerging probability of resistance. Stochastic model was designed and developed a method to approximately but efficiently compute the number of resistant cells and the probability of resistance. To model the progression of cancer, a discrete-state, two-dimensional Markov process whose states are the total number of cells and the number of resistant cells was employed. Then exact analysis and approximate aggregation approaches were proposed to calculate the number of resistant cells and the probability of resistance when the cell population reaches detection size. To confirm the accuracy of computed results of approximation, relative errors between exact analysis and approximation were computed. The numerical values of our approximation method were very close to those of exact analysis calculated in the range of small detection size M = 500, 100, and 1500. Then computer simulation was performed to confirm the accuracy of computed results of approximation when the detection size was M = 10000,30000,50000,100000 and 1000000. All the numerical results of approximation fell between the upper level and the lower level of 95% confidential intervals and our method took less time to compute over a broad range of cell size. The effects of parameter change on emerging probabilities of resistance were also investigated by computed values using approximation method. The results showed that the number of divisions until the cell population reached the detection size is important for emerging the probability of resistance. The next step of numerical approach is to compute the emerging probabilities of resistance under drug administration and with multiple mutation. Another effective approximation would be necessary for the analysis of the latter case. [ABSTRACT FROM AUTHOR]

The use of modeling and simulation (M&S) methodologies is growing rapidly across the psychological and social sciences. After a brief introduction to the relevance of computational methods for research on human cognition and culture, we describe the sense in which computer models and simulations can be understood, respectively, as "theories" and "predictions." Most readers of JoCC are interested in integrating micro- and macro-level theories and in pursuing empirical research that informs scientific predictions, and we argue that M&S provides a powerful new set of tools for pursuing these interests. We also point out the way in which M&S can help scholars of cognition and culture address four key desiderata for social scientific research related to the themes of clarity, falsifiability, dynamicity, and complexity. Finally, we provide an introduction to the other papers that comprise this special issue, which includes contributions on topics such as the role of M&S in interdisciplinary debates, shamanism, early Christian ritual practices, the emergence of the Axial age, and the social scientific appropriation of algorithms from massively multiplayer online games. [ABSTRACT FROM AUTHOR]

Copyright of Automatic Welding / Avtomatitcheskaia Svarka is the property of International Association Welding (Paton Publishing House) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper presents the promising directions and obtained results in the field of welding and related technologies, pursued in Belarus at SSPA «Powder Metallurgy», including friction stir welding, additive technologies, flame spraying of powder materials and electrospark coatings. Comprehensive development of works in the field of additive technologies is achieved by application of equipment, which ensures polymer and metal 3D-printing, production of metal powders and additional compaction operations. Alongside research performance, this equipment is used for product manufacturing. A unit with a system of recording the welding mode parameters was developed for conducting experimental studies and practical application of friction stir welding. Computer modeling programs (Deform, Sysweld, Ansys, etc.) are used during performance of investigations and development of technological processes and tool designs. Tool manufacture is performed in experimental production, in particular by the methods of powder metallurgy and additive technologies. New scientific and practical results were obtained on development of processes and equipment for flame deposition of coatings from powder materials on parts of various functional purposes, using high power jets (up to 125 kW). Investigations were performed and equipment was developed for forming thick-layer (up to 5000-7000 mm) electrospark coatings with application of electrode-anode vibration of 22 kHz frequency. 18 Ref., 8 Figures. [ABSTRACT FROM AUTHOR]

The aim of this paper was to determine the roughness and Vickers hardness of popular composite materials used for direct tooth fillings. The tests were carried out on the surfaces of samples previously polished with traditional silicone polishing rubbers and discs used in dentistry. The testing methodology and software used for statistical analysis is important to the validity of engineering tests. Measurement systems use sophisticated computer algorithms that are part of the equipment. Analysis of measurement data requires very complex algorithms often based on artificial intelligence. A model and procedure were also prepared to simulate the composite itself under near real-world conditions. [ABSTRACT FROM AUTHOR]

The paper presents a procedure for creating a simulation model of the horizontal seat suspension system. At first, the physical model is proposed and then the non-linear characteristics of individual system components are described mathematically. The unknown model parameters are determined by means of the specific experiments that are performed in laboratory conditions. Finally, the vibration transmissibility behavior is investigated ensuring that the created simulation model is as close to the real vibro-isolation process as possible. The results of numerical research and experimental investigation are discussed in view of their potential interest in the development of vibration isolation systems used for protecting the human body against harmful vibrations. The proposed model should support the selection process of system components that significantly affect the vibro-isolation properties of horizontal seat suspensions. [ABSTRACT FROM AUTHOR]

A new interest in the study of small molecules arises with the development of science and technology and consideration to the accumulated knowledge in genomics, proteomics and metabolomics. Now it appears no less important to explore the functional activity and roles of individual metabolites, as well as to elucidate the pathways of biological processes, their cascade reactions, in norm and pathology. We turned our attention to lactate, which is no longer considered as a "dead end" of anaerobic metabolism, produced in tissues with accelerated glycolysis. In recent years, lactate has been predominantly viewed as a metabolite that plays an important role in regulating the function of many cells (adipocytes, endothelial cells, neurons) and is involved in tissue remodeling, angiogenesis, proliferation and differentiation. The study of the effect of lactate on isolated catalytic proteins is able to reveal important and as yet unexplored roles of small molecules in intermolecular processes. We chose the enzyme glycerol-3-phosphate dehydrogenase (G3PDH), which is responsible for the switching of carbohydrate and lipid metabolism, as a potential target of its action. The study aimed to assess lactate affinity for G3PDH in order to find out the functional ability of the intermediate. The range of lactate biological activities was revealed and its protein partners were characterized by computer modeling. Using differential scanning fluorimetry and microcapillary thermophoresis, we found the G3PDH–lactate interaction and demonstrated changes in the conformational stability of the enzyme. [ABSTRACT FROM AUTHOR]

Perspective texture synthesis has great significance in many fields like video editing, scene capturing etc., due to its ability to read and control global feature information. In this paper, we present a novel example-based, specifically energy optimization-based algorithm, to synthesize perspective textures. Energy optimization technique is a pixel-based approach, so it’s time-consuming. We improve it from two aspects with the purpose of achieving faster synthesis and high quality. Firstly, we change this pixel-based technique by replacing the pixel computation with a little patch. Secondly, we present a novel technique to accelerate searching nearest neighborhoods in energy optimization. Using k- means clustering technique to build a search tree to accelerate the search. Hence, we make use of principal component analysis (PCA) technique to reduce dimensions of input vectors. The high quality results prove that our approach is feasible. Besides, our proposed algorithm needs shorter time relative to other similar methods. [ABSTRACT FROM AUTHOR]

Distributed control systems based on communication networks are becoming a common interest in the contemporary control research community. This paper presents simulation and experimental investigations for such systems. We investigate the available simulation environments or tools and introduce an appropriate experimental setup to conduct control designs in distributed networked control systems. We establish experimental setups for networked control systems on pilot-scale plants. Our results enhance the need to continue research into experimental distributed control systems. [ABSTRACT FROM PUBLISHER]

Homeostatic and adaptive control mechanisms are essential for keeping organisms structurally and functionally stable. Integral feedback is a control theoretic concept which has long been known to keep a controlled variable robustly (i.e. perturbation-independent) at a given set-point by feeding the integrated error back into the process that generates . The classical concept of homeostasis as robust regulation within narrow limits is often considered as unsatisfactory and even incompatible with many biological systems which show sustained oscillations, such as circadian rhythms and oscillatory calcium signaling. Nevertheless, there are many similarities between the biological processes which participate in oscillatory mechanisms and classical homeostatic (non-oscillatory) mechanisms. We have investigated whether biological oscillators can show robust homeostatic and adaptive behaviors, and this paper is an attempt to extend the homeostatic concept to include oscillatory conditions. Based on our previously published kinetic conditions on how to generate biochemical models with robust homeostasis we found two properties, which appear to be of general interest concerning oscillatory and homeostatic controlled biological systems. The first one is the ability of these oscillators (“oscillatory homeostats”) to keep the average level of a controlled variable at a defined set-point by involving compensatory changes in frequency and/or amplitude. The second property is the ability to keep the period/frequency of the oscillator tuned within a certain well-defined range. In this paper we highlight mechanisms that lead to these two properties. The biological applications of these findings are discussed using three examples, the homeostatic aspects during oscillatory calcium and p53 signaling, and the involvement of circadian rhythms in homeostatic regulation. [ABSTRACT FROM AUTHOR]

Long-running applications are often subject to failures. Once failures occur, it will lead to unacceptable system overheads. The checkpoint technology is used to reduce the losses in the event of a failure. For the two-level checkpoint recovery scheme used in the long-running tasks, it is unavoidable for the system to periodically transfer huge memory context to a remote stable storage. Therefore, the overheads of setting checkpoints and the re-computing time become a critical issue which directly impacts the system total overheads. Motivated by these concerns, this paper presents a new model by introducing i-checkpoints into the existing two-level checkpoint recovery scheme to deal with the more probable failures with the smaller cost and the faster speed. The proposed scheme is independent of the specific failure distribution type and can be applied to different failure distribution types. We respectively make analyses between the two-level incremental and two-level checkpoint recovery schemes with the Weibull distribution and exponential distribution, both of which fit with the actual failure distribution best. The comparison results show that the total overheads of setting checkpoints, the total re-computing time and the system total overheads in the two-level incremental checkpoint recovery scheme are all significantly smaller than those in the two-level checkpoint recovery scheme. At last, limitations of our study are discussed, and at the same time, open questions and possible future work are given. [ABSTRACT FROM AUTHOR]

The automatic detection of bilateral symmetry is a challenging task in computer vision and pattern recognition. This paper presents an approach for the detection of bilateral symmetry in digital single object images. Our method relies on the extraction of Scale Invariant Feature Transform (SIFT) based feature points, which serves as the basis for the ascertainment of the centroid of the object; the latter being the origin under the Cartesian coordinate system to be converted to the polar coordinate system in order to facilitate the selection symmetric coordinate pairs. This is followed by comparing the gradient magnitude and orientation of the corresponding points to evaluate the amount of symmetry exhibited by each pair of points. The experimental results show that our approach draw the symmetry line accurately, provided that the observed centroid point is true. [ABSTRACT FROM AUTHOR]

In today's technology-assisted society, social interactions may be expressed through a variety of techno-communication channels, including online social networks, email and mobile phones (calls, text messages). Consequently, a clear grasp of human behavior through the diverse communication media is considered a key factor in understanding the formation of the today's information society. So far, all previous research on user communication behavior has focused on a sole communication activity. In this paper we move forward another step on this research path by performing a multidimensional study of human sociality as an expression of the use of mobile phones. The paper focuses on user temporal communication behavior in the interplay between the two complementary communication media, text messages and phone calls, that represent the bi-dimensional scenario of analysis. Our study provides a theoretical framework for analyzing multidimensional bursts as the most general burst category, that includes one-dimensional bursts as the simplest case, and offers empirical evidence of their nature by following the combined phone call/text message communication patterns of approximately one million people over three-month period. This quantitative approach enables the design of a generative model rooted in the three most significant features of the multidimensional burst - the number of dimensions, prevalence and interleaving degree - able to reproduce the main media usage attitude. The other findings of the paper include a novel multidimensional burst detection algorithm and an insight analysis of the human media selection process. [ABSTRACT FROM AUTHOR]

Complex networks describe a wide range of systems in nature and society. To understand complex networks, it is crucial to investigate their community structure. In this paper, we develop an online community detection algorithm with linear time complexity for large complex networks. Our algorithm processes a network edge by edge in the order that the network is fed to the algorithm. If a new edge is added, it just updates the existing community structure in constant time, and does not need to re-compute the whole network. Therefore, it can efficiently process large networks in real time. Our algorithm optimizes expected modularity instead of modularity at each step to avoid poor performance. The experiments are carried out using 11 public data sets, and are measured by two criteria, modularity and NMI (Normalized Mutual Information). The results show that our algorithm's running time is less than the commonly used Louvain algorithm while it gives competitive performance. [ABSTRACT FROM AUTHOR]

The Bronze Age/Iron Age transition in Prehistoric Europe represents a perfect case study to test different and competing hypotheses of social dynamics and economic change in small-scale societies. The paper discusses the possibilities of modeling what could have happened in Europe between 1800 and 800 bc, in terms of spatiotemporal dynamics. The paper presents some theoretical aspects of the dynamic study of expansive phenomena and gives an overview of a computer model programmed to explain the way new burial forms expanded in Europe. The main idea is comparing classic demic diffusion models (spread of population), cultural transmission models (spread of ideas), and technological innovation diffusion model (spread of goods). We will present the fundamentals of a preliminary study towards the computational simulation of such hypothetical social mechanisms, using a dataset composed of more than 1,500 georeferenced and radiocarbon dated archaeological contexts of a period between the Early Bronze Age and the first Iron Age (1800-800 bc) from an area including the North-East of Iberian Peninsula, Southern France, Northern and Central Italy, Switzerland, Austria, and Southern Germany. [ABSTRACT FROM AUTHOR]

Recently, contagion-based (disease, information, etc.) spreading on social networks has been extensively studied. In this paper, other than traditional full interaction, we propose a partial interaction based spreading model, considering that the informed individuals would transmit information to only a certain fraction of their neighbors due to the transmission ability in real-world social networks. Simulation results on three representative networks (BA, ER, WS) indicate that the spreading efficiency is highly correlated with the network heterogeneity. In addition, a special phenomenon, namely Information Blind Areas where the network is separated by several information-unreachable clusters, will emerge from the spreading process. Furthermore, we also find that the size distribution of such information blind areas obeys power-law-like distribution, which has very similar exponent with that of site percolation. Detailed analyses show that the critical value is decreasing along with the network heterogeneity for the spreading process, which is complete the contrary to that of random selection. Moreover, the critical value in the latter process is also larger than that of the former for the same network. Those findings might shed some lights in in-depth understanding the effect of network properties on information spreading. [ABSTRACT FROM AUTHOR]

DNA microarrays and cell cycle synchronization experiments have made possible the study of the mechanisms of cell cycle regulation of Saccharomyces cerevisiae by simultaneously monitoring the expression levels of thousands of genes at specific time points. On the other hand, pattern recognition techniques can contribute to the analysis of such massive measurements, providing a model of gene expression level evolution through the cell cycle process. In this paper, we propose the use of one of such techniques –an unsupervised artificial neural network called a Self-Organizing Map (SOM)–which has been successfully applied to processes involving very noisy signals, classifying and organizing them, and assisting in the discovery of behavior patterns without requiring prior knowledge about the process under analysis. As a test bed for the use of SOMs in finding possible relationships among genes and their possible contribution in some biological processes, we selected 282 S. cerevisiae genes that have been shown through biological experiments to have an activity during the cell cycle. The expression level of these genes was analyzed in five of the most cited time series DNA microarray databases used in the study of the cell cycle of this organism. With the use of SOM, it was possible to find clusters of genes with similar behavior in the five databases along two cell cycles. This result suggested that some of these genes might be biologically related or might have a regulatory relationship, as was corroborated by comparing some of the clusters obtained with SOMs against a previously reported regulatory network that was generated using biological knowledge, such as protein-protein interactions, gene expression levels, metabolism dynamics, promoter binding, and modification, regulation and transport of proteins. The methodology described in this paper could be applied to the study of gene relationships of other biological processes in different organisms. [ABSTRACT FROM AUTHOR]

Allosteric modulators are ligands for proteins that exert their effects via a different binding site than the natural (orthosteric) ligand site and hence form a conceptually distinct class of ligands for a target of interest. Here, the physicochemical and structural features of a large set of allosteric and non-allosteric ligands from the ChEMBL database of bioactive molecules are analyzed. In general allosteric modulators are relatively smaller, more lipophilic and more rigid compounds, though large differences exist between different targets and target classes. Furthermore, there are differences in the distribution of targets that bind these allosteric modulators. Allosteric modulators are over-represented in membrane receptors, ligand-gated ion channels and nuclear receptor targets, but are underrepresented in enzymes (primarily proteases and kinases). Moreover, allosteric modulators tend to bind to their targets with a slightly lower potency (5.96 log units versus 6.66 log units, p<0.01). However, this lower absolute affinity is compensated by their lower molecular weight and more lipophilic nature, leading to similar binding efficiency and surface efficiency indices. Subsequently a series of classifier models are trained, initially target class independent models followed by finer-grained target (architecture/functional class) based models using the target hierarchy of the ChEMBL database. Applications of these insights include the selection of likely allosteric modulators from existing compound collections, the design of novel chemical libraries biased towards allosteric regulators and the selection of targets potentially likely to yield allosteric modulators on screening. All data sets used in the paper are available for download. [ABSTRACT FROM AUTHOR]

Electrochemical impedance spectroscopy (EIS) is a key method for the characterizing the ionic and electronic conductivity of materials. One of the requirements of this technique is a model to forecast conductivity in preliminary experiments. The aim of this paper is to examine the prediction of conductivity by neuro-fuzzy inference with basic experimental factors such as temperature, frequency, thickness of the film and weight percentage of salt. In order to provide the optimal sets of fuzzy logic rule bases, the grid partition fuzzy inference method was applied. The validation of the model was tested by four random data sets. To evaluate the validity of the model, eleven statistical features were examined. Statistical analysis of the results clearly shows that modeling with an adaptive neuro-fuzzy is powerful enough for the prediction of conductivity. [ABSTRACT FROM AUTHOR]

This paper examines a biologically-inspired representation technique designed for the support of sensory-motor learning in developmental robotics. An interesting feature of the many topographic neural sheets in the brain is that closely packed receptive fields must overlap in order to fully cover a spatial region. This raises interesting scientific questions with engineering implications: e.g. is overlap detrimental? does it have any benefits? This paper examines the effects and properties of overlap between elements arranged in arrays or maps. In particular we investigate how overlap affects the representation and transmission of spatial location information on and between topographic maps. Through a series of experiments we determine the conditions under which overlap offers advantages and identify useful ranges of overlap for building mappings in cognitive robotic systems. Our motivation is to understand the phenomena of overlap in order to provide guidance for application in sensory-motor learning robots. [ABSTRACT FROM AUTHOR]

Theanalysisoftrajectorydynamicsandthesolutionofoptimizationproblemsusingcomputermethods are relevant areas of research in dynamic population-migration models. In this paper, four-dimensional dynamic models describing the processes of competition and migration in ecosystems are studied. Firstly, we consider a modification of the “two competitors-two migration areas” model, which takes into account uniform intraspecific and interspecific competition in two populations as well as non-uniform bidirectional migration in both populations. Secondly, we consider a modification of the “two competitors-two migration areas” model, in which intraspecific competition is uniform and interspecific competition and bidirectional migration are non-uniform. For these two types of models, the study is carried out taking into account the variability of parameters. The problems of searching for model parameters based on the implementation of two optimality criteria are solved. The first criterion of optimality is associated with the fulfillment of such a condition for the coexistence of populations, which in mathematical form is the integral maximization of the functions product characterizing the populations densities. The second criterion of optimality involves checking the assumption of the such a four-dimensional positive vector existence, which will be a state of equilibrium. The algorithms developed on the basis of the first and second optimality criteria using the differential evolution method result in optimal sets of parameters for the studied population-migration models. The obtained sets of parameters are used to find positive equilibrium states and analyze trajectory dynamics. Using the method of constructing self-consistent one-step models and an automated stochastization procedure, the transition to the stochastic case is performed. The structural description and the possibility of analyzing two types of populationmigration stochastic models are provided by obtaining Fokker-Planck equations and Langevin equations with corresponding coefficients. Algorithms for generating trajectories of the Wiener process, multipoint distributions and modifications of the Runge-Kutta method are used. A series of computational experiments is carried out using a specialized software package whose capabilities allow for the construction and analysis of dynamic models of high dimension, taking into account the evaluation of the stochastics influence. The trajectory dynamics of two types of population-migration models are investigated, and a comparative analysis of the results is carried out both in the deterministic and stochastic cases. The results can be used in the modeling and optimization of dynamic models in natural science.

The Computer modeling is one of the best tools for the development of a real automated warehouse system. It aims to explore and define the behavior of the system and make the evaluation of its performance. In this paper is analyzed and simulated a software for the design of logistic systems. [ABSTRACT FROM AUTHOR]

Extensive research has shown that one of the benefits of programming to learn about scientific phenomena is that it facilitates learning about mechanisms underlying the phenomenon. However, using programming activities in classrooms is associated with costs such as requiring additional time to learn to program or students needing prior experience with programming. This paper presents a class of programming environments that we call quickstart: Environments with a negligible threshold for entry into programming and a modest ceiling. We posit that such environments can provide benefits of programming for learning without incurring associated costs for novice programmers. To make this claim, we present a design-based research study conducted to compare programming models of evolutionary processes with a quickstart toolkit with exploring pre-built models of the same processes. The study was conducted in six seventh grade science classes in two schools. Students in the programming condition used EvoBuild, a quickstart toolkit for programming agent-based models of evolutionary processes, to build their NetLogo models. Students in the exploration condition used pre-built NetLogo models. We demonstrate that although students came from a range of academic backgrounds without prior programming experience, and all students spent the same number of class periods on the activities including the time students took to learn programming in this environment, EvoBuild students showed greater learning about evolutionary mechanisms. We discuss the implications of this work for design research on programming environments in K-12 science education. [ABSTRACT FROM AUTHOR]

Continuous in-field robotic and automated systems are challenged by the need for a continuous supply of power. This off-grid power supply can be diesel, gasoline, propane, or alternative energy sources like solar or wind. This study investigates the required solar panel arrays and energy storage capacities for an off-grid solar-battery power supply system. This study used 22 years of historical weather data from Lexington, Kentucky, and processed it with the National Renewable Energy Laboratory's System Advisor Model to model hourly energy output from solar panel arrays of 2 kW, 3 kW, 4 kW, 5 kW, 10 kW, 15 kW, and 20 kW. This was combined with an energy storage model (simulated battery capacities of 5 kWh, 10 kWh, 15 kWh, 20 kWh, 30 kWh, 40 kWh, 50 kWh, and 60 kWh) and an energy use model (simulated daily energy demands of 3.6 kWh, 6 kWh, 12 kWh, 18 kWh, and 24 kWh) to determine which systems could operate over the entire 22 years without reaching critical minimum levels. For a 3.6 kWh daily energy demand, 32 combinations of solar panel arrays and battery capacities remained above critical levels, while this was only 12 combinations for a 6 kWh daily energy demand and no combination could support larger energy demands. An example a feasible system to support a 6 kWh energy demand is one that uses a 15 kW panel array (42 standard panels with an approximate system hardware cost of $22,650) and 30 kWh of energy storage capacity (which with lead acid batteries would cost $11,661 and require a volume of 300 L). There is a tradeoff between solar panel array size and battery capacity so other feasible systems can be realized by increasing one to make up for reductions in the other variable. Additionally, limiting system operation to March through October can reduce the size of the required panel array (to between 33% to 67% of the original size) or energy storage capacity (to between 40% to 67% of the original capacity) for a given daily energy load. The size of the solar battery system could be further decreased by adding an emergency generator that uses no more than 50 kg of propane annually. Battery capacities could be reduced by 17% to 38% compared to the original system, and the solar panel arrays could be reduced by 13% to 15% compared to the original. Specific reduction amounts depended on the specific configuration of the original system and the daily load level that had to be supported. A system that only operated from March through October with a backup generator could also support the 12 and 18 kWh daily energy demands, which could not be supported by the original system. Even with limiting operation to March through October and using an emergency generator, the minimal battery capacity was at least 2.5 times larger than the daily energy demand, and the solar panel array had to be large enough that the nominal output would provide the daily energy demand in 3 hours of full sun. However, because of the trade-off relationship, these minimums cannot be attained together. While continuous in-field robotic deployments show promise for improving agriculture, it will be critical to design with energy efficiency in mind and the off-grid power supply for wide-spread use across agricultural lands. [ABSTRACT FROM AUTHOR]

This paper reports the results of a three-stage usability test of a modeling tool designed to support learners' deep understanding of the impacts of climate change on ecosystems. The design process involved repurposing an existing modeling technology used by professional scientists into a learning tool specifically designed for middle school students. To evaluate usability, we analyzed students' task performance and task completion time as they worked on an activity with the repurposed modeling technology. In stage 1, we conducted remote testing of an early modeling prototype with urban middle school students ( n = 84). In stages 2 and 3, we used screencasting software to record students' mouse and keyboard movements during collaborative think-alouds ( n = 22) and conducted a qualitative analysis of their peer discussions. Taken together, the study findings revealed two kinds of usability issues that interfered with students' productive use of the tool: issues related to the use of data and information, and issues related to the use of the modeling technology. The study findings resulted in design improvements that led to stronger usability outcomes and higher task performance among students. In this paper, we describe our methods for usability testing, our research findings, and our design solutions for supporting students' use of the modeling technology and use of data. The paper concludes with implications for the design and study of modeling technologies for science learning. [ABSTRACT FROM AUTHOR]

Minimum squared error based classification (MSEC) method establishes a unique classification model for all the test samples. However, this classification model may be not optimal for each test sample. This paper proposes an improved MSEC (IMSEC) method, which is tailored for each test sample. The proposed method first roughly identifies the possible classes of the test sample, and then establishes a minimum squared error (MSE) model based on the training samples from these possible classes of the test sample. We apply our method to face recognition. The experimental results on several datasets show that IMSEC outperforms MSEC and the other state-of-the-art methods in terms of accuracy. [ABSTRACT FROM AUTHOR]

Flux balance analysis and constraint based modeling have been successfully used in the past to elucidate the metabolism of single cellular organisms. However, limited work has been done with multicellular organisms and even less with humans. The focus of this paper is to present a novel use of this technique by investigating human nutrition, a challenging field of study. Specifically, we present a steady state constraint based model of skeletal muscle tissue to investigate amino acid supplementation's effect on protein synthesis. We implement several in silico supplementation strategies to study whether amino acid supplementation might be beneficial for increasing muscle contractile protein synthesis. Concurrent with published data on amino acid supplementation's effect on protein synthesis in a post resistance exercise state, our results suggest that increasing bioavailability of methionine, arginine, and the branched-chain amino acids can increase the flux of contractile protein synthesis. The study also suggests that a common commercial supplement, glutamine, is not an effective supplement in the context of increasing protein synthesis and thus, muscle mass. Similar to any study in a model organism, the computational modeling of this research has some limitations. Thus, this paper introduces the prospect of using systems biology as a framework to formally investigate how supplementation and nutrition can affect human metabolism and physiology. [ABSTRACT FROM AUTHOR]

Background: Lung cancer is one of the most common forms of cancer resulting in over a million deaths per year worldwide. Typically, the problem can be approached by developing more discriminative diagnosis methods. In this paper, computer-aided diagnosis was used to facilitate the prediction of characteristics of solitary pulmonary nodules in CT of lungs to diagnose early-stage lung cancer. Methods: The synthetic minority over-sampling technique (SMOTE) was used to account for raw data in order to balance the original training data set. Curvelet-transformation textural features, together with 3 patient demographic characteristics, and 9 morphological features were used to establish a support vector machine (SVM) prediction model. Longitudinal data as the test data set was used to evaluate the classification performance of predicting early-stage lung cancer. Results: Using the SMOTE as a pre-processing procedure, the original training data was balanced with a ratio of malignant to benign cases of 1∶1. Accuracy based on cross-evaluation for the original unbalanced data and balanced data was 80% and 97%, respectively. Based on Curvelet-transformation textural features and other features, the SVM prediction model had good classification performance for early-stage lung cancer, with an area under the curve of the SVMs of 0.949 (P<0.001). Textural feature (standard deviation) showed benign cases had a higher change in the follow-up period than malignant cases. Conclusions: With textural features extracted from a Curvelet transformation and other parameters, a sensitive support vector machine prediction model can increase the rate of diagnosis for early-stage lung cancer. This scheme can be used as an auxiliary tool to differentiate between benign and malignant early-stage lung cancers in CT images. [ABSTRACT FROM AUTHOR]

Power and performance management problem in large scale computing systems like data centers has attracted a lot of interests from both enterprises and academic researchers as power saving has become more and more important in many fields. Because of the multiple objectives, multiple influential factors and hierarchical structure in the system, the problem is indeed complex and hard. In this paper, the problem will be investigated in a virtualized computing system. Specifically, it is formulated as a power optimization problem with some constraints on performance. Then, the adaptive controller based on least-square self-tuning regulator(LS-STR) is designed to track performance in the first step; and the resource solved by the controller is allocated in order to minimize the power consumption as the second step. Some simulations are designed to test the effectiveness of this method and to compare it with some other controllers. The simulation results show that the adaptive controller is generally effective: it is applicable for different performance metrics, for different workloads, and for single and multiple workloads; it can track the performance requirement effectively and save the power consumption significantly. [ABSTRACT FROM AUTHOR]

The population migration algorithm (PMA) is a simulation of a population of the intelligent algorithm. Given the prematurity and low precision of PMA, this paper introduces a local search mechanism of the leap-frog algorithm and crossover operator to improve the PMA search speed and global convergence properties. The typical test function verifies the improved algorithm through its performance. Compared with the improved population migration and other intelligential algorithms, the result shows that the convergence rate of the improved PMA is very high and its convergence is proved. [ABSTRACT FROM AUTHOR]

The problems of contemporary IT specialists’ training in terms of the high requirements to their computational thinking skills as well as the urgency of raising their motivation to mastering algorithmization and programming are discussed in the paper. It is emphasized by the authors that initial university courses should focus pre-service IT-specialists on the deep understanding of an algorithmic nature of any coding task, to realize basic characteristics of the algorithms, to understand their role in modern software development. Due to the contemporary demands, programming should rest on algorithms building and has to be a part of lager scale experiences in order to realize its full potential. One of such experiences offered by the authors in the paper is involving the students into specially arranged activity focused on efficient game algorithms creation and simulation of the tournament between the algorithms. The offered activity is elaborated based on the applying the gamification elements into the learning process. Basing on the core gamifiaction principles, there were thought over and arranged an activity involving the students into the creation of gamified products. In our case, the gamified product which the students had to develop in the process of learning of algorithmization and programming was the software platform which enables a computer simulation of the tournament between the different game algorithms which realize winning strategies. The peculiarities and the stages of the said activity are covered in details along with the description of the final software product. Analyzing the described functionality of the computer simulator of the algorithms tournaments based on the gamification ideas, we can emphasize its significant didactic facilities in the context of its using for IT-specialists training. In particular, the developed gamified product was probed in the process of other students’ mastering algorithmization and programming as well as of the schoolchildren training during summer IT schools. The prospects of the research are outlined in the lines of using the obtained results for holding the empirical research for the verification of offered activity impact on the results of IT-specialists training.

This paper explores the conditions under which nonmyocytes, when electrically coupled to myocytes, act as a passive load during the depolarization-phase. Using theoretical arguments and numerical simulations in a tissue incorporating fibroblasts, the passive load approximation is shown to be accurate at low coupling conductances (<2 nS). In this case, the effect on conduction velocity can be expressed as a function of the elevation in resting potential and the coupling only. [ABSTRACT FROM AUTHOR]