An artificial molecular machine that builds an asymmetric catalyst

Science.gov (United States)

De Bo, Guillaume; Gall, Malcolm A. Y.; Kuschel, Sonja; De Winter, Julien; Gerbaux, Pascal; Leigh, David A.

2018-05-01

Biomolecular machines perform types of complex molecular-level tasks that artificial molecular machines can aspire to. The ribosome, for example, translates information from the polymer track it traverses (messenger RNA) to the new polymer it constructs (a polypeptide)1. The sequence and number of codons read determines the sequence and number of building blocks incorporated into the biomachine-synthesized polymer. However, neither control of sequence2,3 nor the transfer of length information from one polymer to another (which to date has only been accomplished in man-made systems through template synthesis)4 is easily achieved in the synthesis of artificial macromolecules. Rotaxane-based molecular machines5-7 have been developed that successively add amino acids8-10 (including β-amino acids10) to a growing peptide chain by the action of a macrocycle moving along a mono-dispersed oligomeric track derivatized with amino-acid phenol esters. The threaded macrocycle picks up groups that block its path and links them through successive native chemical ligation reactions11 to form a peptide sequence corresponding to the order of the building blocks on the track. Here, we show that as an alternative to translating sequence information, a rotaxane molecular machine can transfer the narrow polydispersity of a leucine-ester-derivatized polystyrene chain synthesized by atom transfer radical polymerization12 to a molecular-machine-made homo-leucine oligomer. The resulting narrow-molecular-weight oligomer folds to an α-helical secondary structure13 that acts as an asymmetric catalyst for the Juliá-Colonna epoxidation14,15 of chalcones.

Molecular active plasmonics: controlling plasmon resonances with molecular machines

KAUST Repository

Zheng, Yue Bing

2009-08-26

The paper studies the molecular-level active control of localized surface plasmon resonances (LSPRs) of Au nanodisk arrays with molecular machines. Two types of molecular machines - azobenzene and rotaxane - have been demonstrated to enable the reversible tuning of the LSPRs via the controlled mechanical movements. Azobenzene molecules have the property of trans-cis photoisomerization and enable the photo-induced nematic (N)-isotropic (I) phase transition of the liquid crystals (LCs) that contain the molecules as dopant. The phase transition of the azobenzene-doped LCs causes the refractive-index difference of the LCs, resulting in the reversible peak shift of the LSPRs of the embedded Au nanodisks due to the sensitivity of the LSPRs to the disks\\' surroundings\\' refractive index. Au nanodisk array, coated with rotaxanes, switches its LSPRs reversibly when it is exposed to chemical oxidants and reductants alternatively. The correlation between the peak shift of the LSPRs and the chemically driven mechanical movement of rotaxanes is supported by control experiments and a time-dependent density functional theory (TDDFT)-based, microscopic model.

Molecular active plasmonics: controlling plasmon resonances with molecular machines

KAUST Repository

Zheng, Yue Bing; Yang, Ying-Wei; Jensen, Lasse; Fang, Lei; Juluri, Bala Krishna; Flood, Amar H.; Weiss, Paul S.; Stoddart, J. Fraser; Huang, Tony Jun

2009-01-01

The paper studies the molecular-level active control of localized surface plasmon resonances (LSPRs) of Au nanodisk arrays with molecular machines. Two types of molecular machines - azobenzene and rotaxane - have been demonstrated to enable the reversible tuning of the LSPRs via the controlled mechanical movements. Azobenzene molecules have the property of trans-cis photoisomerization and enable the photo-induced nematic (N)-isotropic (I) phase transition of the liquid crystals (LCs) that contain the molecules as dopant. The phase transition of the azobenzene-doped LCs causes the refractive-index difference of the LCs, resulting in the reversible peak shift of the LSPRs of the embedded Au nanodisks due to the sensitivity of the LSPRs to the disks' surroundings' refractive index. Au nanodisk array, coated with rotaxanes, switches its LSPRs reversibly when it is exposed to chemical oxidants and reductants alternatively. The correlation between the peak shift of the LSPRs and the chemically driven mechanical movement of rotaxanes is supported by control experiments and a time-dependent density functional theory (TDDFT)-based, microscopic model.

Machine Learning Techniques in Clinical Vision Sciences.

Science.gov (United States)

Caixinha, Miguel; Nunes, Sandrina

2017-01-01

This review presents and discusses the contribution of machine learning techniques for diagnosis and disease monitoring in the context of clinical vision science. Many ocular diseases leading to blindness can be halted or delayed when detected and treated at its earliest stages. With the recent developments in diagnostic devices, imaging and genomics, new sources of data for early disease detection and patients' management are now available. Machine learning techniques emerged in the biomedical sciences as clinical decision-support techniques to improve sensitivity and specificity of disease detection and monitoring, increasing objectively the clinical decision-making process. This manuscript presents a review in multimodal ocular disease diagnosis and monitoring based on machine learning approaches. In the first section, the technical issues related to the different machine learning approaches will be present. Machine learning techniques are used to automatically recognize complex patterns in a given dataset. These techniques allows creating homogeneous groups (unsupervised learning), or creating a classifier predicting group membership of new cases (supervised learning), when a group label is available for each case. To ensure a good performance of the machine learning techniques in a given dataset, all possible sources of bias should be removed or minimized. For that, the representativeness of the input dataset for the true population should be confirmed, the noise should be removed, the missing data should be treated and the data dimensionally (i.e., the number of parameters/features and the number of cases in the dataset) should be adjusted. The application of machine learning techniques in ocular disease diagnosis and monitoring will be presented and discussed in the second section of this manuscript. To show the clinical benefits of machine learning in clinical vision sciences, several examples will be presented in glaucoma, age-related macular degeneration

Light-driven molecular machine at ITIES

International Nuclear Information System (INIS)

Kornyshev, Alexei A; Kuimova, Marina; Kuznetsov, Alexander M; Ulstrup, Jens; Urbakh, Michael

2007-01-01

We suggest a principle of operation of a new molecular device that transforms the energy of light into repetitive mechanical motions. Such a device can also serve as a model system for the study of the effect of electric field on intramolecular electron transfer. We discuss the design of suitable molecular systems and the methods that may monitor the 'performance' of such a machine

2016 IFToMM Asian Conference on Mechanism and Machine Science (IFToMM Asian MMS 2016) & 2016 International Conference on Mechanism and Machine Science (CCMMS 2016)

CERN Document Server

Wang, Nianfeng; Huang, Yanjiang

2017-01-01

These proceedings collect the latest research results in mechanism and machine science, intended to reinforce and improve the role of mechanical systems in a variety of applications in daily life and industry. Gathering more than 120 academic papers, it addresses topics including: Computational kinematics, Machine elements, Actuators, Gearing and transmissions, Linkages and cams, Mechanism design, Dynamics of machinery, Tribology, Vehicle mechanisms, dynamics and design, Reliability, Experimental methods in mechanisms, Robotics and mechatronics, Biomechanics, Micro/nano mechanisms and machines, Medical/welfare devices, Nature and machines, Design methodology, Reconfigurable mechanisms and reconfigurable manipulators, and Origami mechanisms. This is the fourth installment in the IFToMM Asian conference series on Mechanism and Machine Science (ASIAN MMS 2016). The ASIAN MMS conference initiative was launched to provide a forum mainly for the Asian community working in Mechanism and Machine Science, in order to ...

Controlling Motion at the Nanoscale: Rise of the Molecular Machines.

Science.gov (United States)

Abendroth, John M; Bushuyev, Oleksandr S; Weiss, Paul S; Barrett, Christopher J

2015-08-25

As our understanding and control of intra- and intermolecular interactions evolve, ever more complex molecular systems are synthesized and assembled that are capable of performing work or completing sophisticated tasks at the molecular scale. Commonly referred to as molecular machines, these dynamic systems comprise an astonishingly diverse class of motifs and are designed to respond to a plethora of actuation stimuli. In this Review, we outline the conditions that distinguish simple switches and rotors from machines and draw from a variety of fields to highlight some of the most exciting recent examples of opportunities for driven molecular mechanics. Emphasis is placed on the need for controllable and hierarchical assembly of these molecular components to display measurable effects at the micro-, meso-, and macroscales. As in Nature, this strategy will lead to dramatic amplification of the work performed via the collective action of many machines organized in linear chains, on functionalized surfaces, or in three-dimensional assemblies.

Macroscopic transport by synthetic molecular machines

NARCIS (Netherlands)

Berna, J; Leigh, DA; Lubomska, M; Mendoza, SM; Perez, EM; Rudolf, P; Teobaldi, G; Zerbetto, F

Nature uses molecular motors and machines in virtually every significant biological process, but demonstrating that simpler artificial structures operating through the same gross mechanisms can be interfaced with - and perform physical tasks in - the macroscopic world represents a significant hurdle

Molecular computing: paths to chemical Turing machines.

Science.gov (United States)

Varghese, Shaji; Elemans, Johannes A A W; Rowan, Alan E; Nolte, Roeland J M

2015-11-13

To comply with the rapidly increasing demand of information storage and processing, new strategies for computing are needed. The idea of molecular computing, where basic computations occur through molecular, supramolecular, or biomolecular approaches, rather than electronically, has long captivated researchers. The prospects of using molecules and (bio)macromolecules for computing is not without precedent. Nature is replete with examples where the handling and storing of data occurs with high efficiencies, low energy costs, and high-density information encoding. The design and assembly of computers that function according to the universal approaches of computing, such as those in a Turing machine, might be realized in a chemical way in the future; this is both fascinating and extremely challenging. In this perspective, we highlight molecular and (bio)macromolecular systems that have been designed and synthesized so far with the objective of using them for computing purposes. We also present a blueprint of a molecular Turing machine, which is based on a catalytic device that glides along a polymer tape and, while moving, prints binary information on this tape in the form of oxygen atoms.

Machine learning molecular dynamics for the simulation of infrared spectra.

Science.gov (United States)

Gastegger, Michael; Behler, Jörg; Marquetand, Philipp

2017-10-01

Machine learning has emerged as an invaluable tool in many research areas. In the present work, we harness this power to predict highly accurate molecular infrared spectra with unprecedented computational efficiency. To account for vibrational anharmonic and dynamical effects - typically neglected by conventional quantum chemistry approaches - we base our machine learning strategy on ab initio molecular dynamics simulations. While these simulations are usually extremely time consuming even for small molecules, we overcome these limitations by leveraging the power of a variety of machine learning techniques, not only accelerating simulations by several orders of magnitude, but also greatly extending the size of systems that can be treated. To this end, we develop a molecular dipole moment model based on environment dependent neural network charges and combine it with the neural network potential approach of Behler and Parrinello. Contrary to the prevalent big data philosophy, we are able to obtain very accurate machine learning models for the prediction of infrared spectra based on only a few hundreds of electronic structure reference points. This is made possible through the use of molecular forces during neural network potential training and the introduction of a fully automated sampling scheme. We demonstrate the power of our machine learning approach by applying it to model the infrared spectra of a methanol molecule, n -alkanes containing up to 200 atoms and the protonated alanine tripeptide, which at the same time represents the first application of machine learning techniques to simulate the dynamics of a peptide. In all of these case studies we find an excellent agreement between the infrared spectra predicted via machine learning models and the respective theoretical and experimental spectra.

Machine learning and data science in soft materials engineering

Science.gov (United States)

Ferguson, Andrew L.

2018-01-01

In many branches of materials science it is now routine to generate data sets of such large size and dimensionality that conventional methods of analysis fail. Paradigms and tools from data science and machine learning can provide scalable approaches to identify and extract trends and patterns within voluminous data sets, perform guided traversals of high-dimensional phase spaces, and furnish data-driven strategies for inverse materials design. This topical review provides an accessible introduction to machine learning tools in the context of soft and biological materials by ‘de-jargonizing’ data science terminology, presenting a taxonomy of machine learning techniques, and surveying the mathematical underpinnings and software implementations of popular tools, including principal component analysis, independent component analysis, diffusion maps, support vector machines, and relative entropy. We present illustrative examples of machine learning applications in soft matter, including inverse design of self-assembling materials, nonlinear learning of protein folding landscapes, high-throughput antimicrobial peptide design, and data-driven materials design engines. We close with an outlook on the challenges and opportunities for the field.

Machine learning and data science in soft materials engineering.

Science.gov (United States)

Ferguson, Andrew L

2018-01-31

In many branches of materials science it is now routine to generate data sets of such large size and dimensionality that conventional methods of analysis fail. Paradigms and tools from data science and machine learning can provide scalable approaches to identify and extract trends and patterns within voluminous data sets, perform guided traversals of high-dimensional phase spaces, and furnish data-driven strategies for inverse materials design. This topical review provides an accessible introduction to machine learning tools in the context of soft and biological materials by 'de-jargonizing' data science terminology, presenting a taxonomy of machine learning techniques, and surveying the mathematical underpinnings and software implementations of popular tools, including principal component analysis, independent component analysis, diffusion maps, support vector machines, and relative entropy. We present illustrative examples of machine learning applications in soft matter, including inverse design of self-assembling materials, nonlinear learning of protein folding landscapes, high-throughput antimicrobial peptide design, and data-driven materials design engines. We close with an outlook on the challenges and opportunities for the field.

A nanoplasmonic switch based on molecular machines

KAUST Repository

Zheng, Yue Bing; Yang, Ying-Wei; Jensen, Lasse; Fang, Lei; Juluri, Bala Krishna; Weiss, Paul S.; Stoddart, J. Fraser; Huang, Tony Jun

2009-01-01

We aim to develop a molecular-machine-driven nanoplasmonic switch for its use in future nanophotonic integrated circuits (ICs) that have applications in optical communication, information processing, biological and chemical sensing. Experimental

Advances Towards Synthetic Machines at the Molecular and Nanoscale Level

Directory of Open Access Journals (Sweden)

Kristina Konstas

2010-06-01

Full Text Available The fabrication of increasingly smaller machines to the nanometer scale can be achieved by either a “top-down” or “bottom-up” approach. While the former is reaching its limits of resolution, the latter is showing promise for the assembly of molecular components, in a comparable approach to natural systems, to produce functioning ensembles in a controlled and predetermined manner. In this review we focus on recent progress in molecular systems that act as molecular machine prototypes such as switches, motors, vehicles and logic operators.

A Focus on Triazolium as a Multipurpose Molecular Station for pH-Sensitive Interlocked Crown-Ether-Based Molecular Machines.

Science.gov (United States)

Coutrot, Frédéric

2015-10-01

The control of motion of one element with respect to others in an interlocked architecture allows for different co-conformational states of a molecule. This can result in variations of physical or chemical properties. The increase of knowledge in the field of molecular interactions led to the design, the synthesis, and the study of various systems of molecular machinery in a wide range of interlocked architectures. In this field, the discovery of new molecular stations for macrocycles is an attractive way to conceive original molecular machines. In the very recent past, the triazolium moiety proved to interact with crown ethers in interlocked molecules, so that it could be used as an ideal molecular station. It also served as a molecular barrier in order to lock interlaced structures or to compartmentalize interlocked molecular machines. This review describes the recently reported examples of pH-sensitive triazolium-containing molecular machines and their peculiar features.

Solid surface vs. liquid surface: nanoarchitectonics, molecular machines, and DNA origami.

Science.gov (United States)

Ariga, Katsuhiko; Mori, Taizo; Nakanishi, Waka; Hill, Jonathan P

2017-09-13

The investigation of molecules and materials at interfaces is critical for the accumulation of new scientific insights and technological advances in the chemical and physical sciences. Immobilization on solid surfaces permits the investigation of different properties of functional molecules or materials with high sensitivity and high spatial resolution. Liquid surfaces also present important media for physicochemical innovation and insight based on their great flexibility and dynamicity, rapid diffusion of molecular components for mixing and rearrangements, as well as drastic spatial variation in the prevailing dielectric environment. Therefore, a comparative discussion of the relative merits of the properties of materials when positioned at solid or liquid surfaces would be informative regarding present-to-future developments of surface-based technologies. In this perspective article, recent research examples of nanoarchitectonics, molecular machines, DNA nanotechnology, and DNA origami are compared with respect to the type of surface used, i.e. solid surfaces vs. liquid surfaces, for future perspectives of interfacial physics and chemistry.

Contemporary machine learning: techniques for practitioners in the physical sciences

Science.gov (United States)

Spears, Brian

2017-10-01

Machine learning is the science of using computers to find relationships in data without explicitly knowing or programming those relationships in advance. Often without realizing it, we employ machine learning every day as we use our phones or drive our cars. Over the last few years, machine learning has found increasingly broad application in the physical sciences. This most often involves building a model relationship between a dependent, measurable output and an associated set of controllable, but complicated, independent inputs. The methods are applicable both to experimental observations and to databases of simulated output from large, detailed numerical simulations. In this tutorial, we will present an overview of current tools and techniques in machine learning - a jumping-off point for researchers interested in using machine learning to advance their work. We will discuss supervised learning techniques for modeling complicated functions, beginning with familiar regression schemes, then advancing to more sophisticated decision trees, modern neural networks, and deep learning methods. Next, we will cover unsupervised learning and techniques for reducing the dimensionality of input spaces and for clustering data. We'll show example applications from both magnetic and inertial confinement fusion. Along the way, we will describe methods for practitioners to help ensure that their models generalize from their training data to as-yet-unseen test data. We will finally point out some limitations to modern machine learning and speculate on some ways that practitioners from the physical sciences may be particularly suited to help. This work was performed by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Molecularization in nutritional science: a view from philosophy of science.

Science.gov (United States)

Ströhle, Alexander; Döring, Frank

2010-10-01

Over the past decade, a trend toward molecularization, which could be observed in almost all bioscientific disciplines, now appears to have also developed in nutritional science. However, molecular nutrition research gives birth to a series of questions. Therefore, we take a look at the epistemological foundation of (molecular) nutritional science. We (i) analyze the scientific status of (molecular) nutritional science and its position in the canon of other scientific disciplines, (ii) focus on the cognitive aims of nutritional science in general and (iii) on the chances and limits of molecular nutrition research in particular. By taking up the thoughts of an earlier work, we are analyzing (molecular) nutritional science from a strictly realist and emergentist-naturalist perspective. Methodologically, molecular nutrition research is bound to a microreductive research approach. We emphasize, however, that it need not be a radical microreductionism whose scientific reputation is not the best. Instead we favor moderate microreductionism, which combines reduction with integration. As mechanismic explanations are one of the primary aims of factual sciences, we consider it as the task of molecular nutrition research to find profound, i.e. molecular-mechanismic, explanations for the conditions, characteristics and changes of organisms related to the organism-nutrition environment interaction.

High-pressure microscopy for tracking dynamic properties of molecular machines.

Science.gov (United States)

Nishiyama, Masayoshi

2017-12-01

High-pressure microscopy is one of the powerful techniques to visualize the effects of hydrostatic pressures on research targets. It could be used for monitoring the pressure-induced changes in the structure and function of molecular machines in vitro and in vivo. This review focuses on the dynamic properties of the assemblies and machines, analyzed by means of high-pressure microscopy measurement. We developed a high-pressure microscope that is optimized both for the best image formation and for the stability to hydrostatic pressure up to 150 MPa. Application of pressure could change polymerization and depolymerization processes of the microtubule cytoskeleton, suggesting a modulation of the intermolecular interaction between tubulin molecules. A novel motility assay demonstrated that high hydrostatic pressure induces counterclockwise (CCW) to clockwise (CW) reversals of the Escherichia coli flagellar motor. The present techniques could be extended to study how molecular machines in complicated systems respond to mechanical stimuli. Copyright © 2017 Elsevier B.V. All rights reserved.

Molecular sciences

International Nuclear Information System (INIS)

Anon.

1975-01-01

The research in molecular sciences summarized includes photochemistry, radiation chemistry, geophysics, electromechanics, heavy-element oxidizers , heavy element chemistry collisions, atoms, organic solids. A list of publications is included

Discovery machines accelerators for science, technology, health and innovation

CERN Document Server

Australian Academy of Sciences

2016-01-01

Discovery machines: Accelerators for science, technology, health and innovation explores the science of particle accelerators, the machines that supercharge our ability to discover the secrets of nature and have opened up new tools in medicine, energy, manufacturing, and the environment as well as in pure research. Particle accelerators are now an essential ingredient in discovery science because they offer new ways to analyse the world, such as by probing objects with high energy x-rays or colliding them beams of electrons. They also have a huge—but often unnoticed—impact on all our lives; medical imaging, cancer treatment, new materials and even the chips that power our phones and computers have all been transformed by accelerators of various types. Research accelerators also provide fundamental infrastructure that encourages better collaboration between international and domestic scientists, organisations and governments.

Why Machine-Information Metaphors Are Bad for Science and Science Education

Science.gov (United States)

Pigliucci, Massimo; Boudry, Maarten

2011-01-01

Genes are often described by biologists using metaphors derived from computational science: they are thought of as carriers of information, as being the equivalent of "blueprints" for the construction of organisms. Likewise, cells are often characterized as "factories" and organisms themselves become analogous to machines. Accordingly, when the…

A comparison of machine learning and Bayesian modelling for molecular serotyping.

Science.gov (United States)

Newton, Richard; Wernisch, Lorenz

2017-08-11

Streptococcus pneumoniae is a human pathogen that is a major cause of infant mortality. Identifying the pneumococcal serotype is an important step in monitoring the impact of vaccines used to protect against disease. Genomic microarrays provide an effective method for molecular serotyping. Previously we developed an empirical Bayesian model for the classification of serotypes from a molecular serotyping array. With only few samples available, a model driven approach was the only option. In the meanwhile, several thousand samples have been made available to us, providing an opportunity to investigate serotype classification by machine learning methods, which could complement the Bayesian model. We compare the performance of the original Bayesian model with two machine learning algorithms: Gradient Boosting Machines and Random Forests. We present our results as an example of a generic strategy whereby a preliminary probabilistic model is complemented or replaced by a machine learning classifier once enough data are available. Despite the availability of thousands of serotyping arrays, a problem encountered when applying machine learning methods is the lack of training data containing mixtures of serotypes; due to the large number of possible combinations. Most of the available training data comprises samples with only a single serotype. To overcome the lack of training data we implemented an iterative analysis, creating artificial training data of serotype mixtures by combining raw data from single serotype arrays. With the enhanced training set the machine learning algorithms out perform the original Bayesian model. However, for serotypes currently lacking sufficient training data the best performing implementation was a combination of the results of the Bayesian Model and the Gradient Boosting Machine. As well as being an effective method for classifying biological data, machine learning can also be used as an efficient method for revealing subtle biological

Historical and Epistemological Reflections on the Culture of Machines around the Renaissance: How Science and Technique Work?

Directory of Open Access Journals (Sweden)

Raffaele Pisano

2014-10-01

Full Text Available This paper is divided into two parts, this being the first one. The second is entitled ‘Historical and Epistemological Reflections on the Culture of Machines around Renaissance: Machines, Machineries and Perpetual Motion’ and will be published in Acta Baltica Historiae et Philosophiae Scientiarum in 2015. Based on our recent studies, we provide here a historical and epistemological feature on the role played by machines and machineries. Ours is an epistemological thesis based on a series of historical examples to show that the relations between theoretical science and the construction of machines cannot be taken for granted, a priori. Our analysis is mainly based on the culture of machines around 15th and 17th centuries, namely the epoch of Late Renaissance and Early Modern Age. For this is the period of scientific revolution and this age offers abundant interesting material for researches into the relations of theoretical science/construction of machines as well. However, to prove our epistemological thesis, we will also exploit examples of machines built in other historical periods. Particularly, a discussion concerning the relationship between science theory and the development of science art crafts produced by non-recognized scientists in a certain historical time is presented. The main questions are: when and why did the tension between science (physics, mathematics and geometry give rise to a new scientific approach to applied discipline such as studies on machines and machineries? What kind of science was used (if at all for projecting machines and machineries? Was science at the time a necessary precondition to build a machine? In the first part we will focus on the difference between Aristotelian-Euclidean and Archimedean approaches and we will outline the heritage of these two different approaches in late medieval and Renaissance science. In the second part, we will apply our reconstructions to some historical and epistemological

Simplify and Accelerate Earth Science Data Preparation to Systemize Machine Learning

Science.gov (United States)

Kuo, K. S.; Rilee, M. L.; Oloso, A.

2017-12-01

Data preparation is the most laborious and time-consuming part of machine learning. The effort required is usually more than linearly proportional to the varieties of data used. From a system science viewpoint, useful machine learning in Earth Science likely involves diverse datasets. Thus, simplifying data preparation to ease the systemization of machine learning in Earth Science is of immense value. The technologies we have developed and applied to an array database, SciDB, are explicitly designed for the purpose, including the innovative SpatioTemporal Adaptive-Resolution Encoding (STARE), a remapping tool suite, and an efficient implementation of connected component labeling (CCL). STARE serves as a universal Earth data representation that homogenizes data varieties and facilitates spatiotemporal data placement as well as alignment, to maximize query performance on massively parallel, distributed computing resources for a major class of analysis. Moreover, it converts spatiotemporal set operations into fast and efficient integer interval operations, supporting in turn moving-object analysis. Integrative analysis requires more than overlapping spatiotemporal sets. For example, meaningful comparison of temperature fields obtained with different means and resolutions requires their transformation to the same grid. Therefore, remapping has been implemented to enable integrative analysis. Finally, Earth Science investigations are generally studies of phenomena, e.g. tropical cyclone, atmospheric river, and blizzard, through their associated events, like hurricanes Katrina and Sandy. Unfortunately, except for a few high-impact phenomena, comprehensive episodic records are lacking. Consequently, we have implemented an efficient CCL tracking algorithm, enabling event-based investigations within climate data records beyond mere event presence. In summary, we have implemented the core unifying capabilities on a Big Data technology to enable systematic machine learning in

Machine learning for the structure-energy-property landscapes of molecular crystals.

Science.gov (United States)

Musil, Félix; De, Sandip; Yang, Jack; Campbell, Joshua E; Day, Graeme M; Ceriotti, Michele

2018-02-07

Molecular crystals play an important role in several fields of science and technology. They frequently crystallize in different polymorphs with substantially different physical properties. To help guide the synthesis of candidate materials, atomic-scale modelling can be used to enumerate the stable polymorphs and to predict their properties, as well as to propose heuristic rules to rationalize the correlations between crystal structure and materials properties. Here we show how a recently-developed machine-learning (ML) framework can be used to achieve inexpensive and accurate predictions of the stability and properties of polymorphs, and a data-driven classification that is less biased and more flexible than typical heuristic rules. We discuss, as examples, the lattice energy and property landscapes of pentacene and two azapentacene isomers that are of interest as organic semiconductor materials. We show that we can estimate force field or DFT lattice energies with sub-kJ mol -1 accuracy, using only a few hundred reference configurations, and reduce by a factor of ten the computational effort needed to predict charge mobility in the crystal structures. The automatic structural classification of the polymorphs reveals a more detailed picture of molecular packing than that provided by conventional heuristics, and helps disentangle the role of hydrogen bonded and π-stacking interactions in determining molecular self-assembly. This observation demonstrates that ML is not just a black-box scheme to interpolate between reference calculations, but can also be used as a tool to gain intuitive insights into structure-property relations in molecular crystal engineering.

Machine learning of molecular properties: Locality and active learning

Science.gov (United States)

Gubaev, Konstantin; Podryabinkin, Evgeny V.; Shapeev, Alexander V.

2018-06-01

In recent years, the machine learning techniques have shown great potent1ial in various problems from a multitude of disciplines, including materials design and drug discovery. The high computational speed on the one hand and the accuracy comparable to that of density functional theory on another hand make machine learning algorithms efficient for high-throughput screening through chemical and configurational space. However, the machine learning algorithms available in the literature require large training datasets to reach the chemical accuracy and also show large errors for the so-called outliers—the out-of-sample molecules, not well-represented in the training set. In the present paper, we propose a new machine learning algorithm for predicting molecular properties that addresses these two issues: it is based on a local model of interatomic interactions providing high accuracy when trained on relatively small training sets and an active learning algorithm of optimally choosing the training set that significantly reduces the errors for the outliers. We compare our model to the other state-of-the-art algorithms from the literature on the widely used benchmark tests.

Resonance – Journal of Science Education | Indian Academy of ...

Indian Academy of Sciences (India)

2016 Nobel Prize in Chemistry: Conferring Molecular Machines as Engines of Creativity ... Science Academies' 92nd Refresher Course in Experimental Physics ... Science Academies' Refresher Course on Advances in Molecular Biology.

Molecular science for drug development and biomedicine.

Science.gov (United States)

Zhong, Wei-Zhu; Zhou, Shu-Feng

2014-11-04

With the avalanche of biological sequences generated in the postgenomic age, molecular science is facing an unprecedented challenge, i.e., how to timely utilize the huge amount of data to benefit human beings. Stimulated by such a challenge, a rapid development has taken place in molecular science, particularly in the areas associated with drug development and biomedicine, both experimental and theoretical. The current thematic issue was launched with the focus on the topic of "Molecular Science for Drug Development and Biomedicine", in hopes to further stimulate more useful techniques and findings from various approaches of molecular science for drug development and biomedicine.[...].

Internal force corrections with machine learning for quantum mechanics/molecular mechanics simulations.

Science.gov (United States)

Wu, Jingheng; Shen, Lin; Yang, Weitao

2017-10-28

Ab initio quantum mechanics/molecular mechanics (QM/MM) molecular dynamics simulation is a useful tool to calculate thermodynamic properties such as potential of mean force for chemical reactions but intensely time consuming. In this paper, we developed a new method using the internal force correction for low-level semiempirical QM/MM molecular dynamics samplings with a predefined reaction coordinate. As a correction term, the internal force was predicted with a machine learning scheme, which provides a sophisticated force field, and added to the atomic forces on the reaction coordinate related atoms at each integration step. We applied this method to two reactions in aqueous solution and reproduced potentials of mean force at the ab initio QM/MM level. The saving in computational cost is about 2 orders of magnitude. The present work reveals great potentials for machine learning in QM/MM simulations to study complex chemical processes.

Learning surface molecular structures via machine vision

Science.gov (United States)

Ziatdinov, Maxim; Maksov, Artem; Kalinin, Sergei V.

2017-08-01

Recent advances in high resolution scanning transmission electron and scanning probe microscopies have allowed researchers to perform measurements of materials structural parameters and functional properties in real space with a picometre precision. In many technologically relevant atomic and/or molecular systems, however, the information of interest is distributed spatially in a non-uniform manner and may have a complex multi-dimensional nature. One of the critical issues, therefore, lies in being able to accurately identify (`read out') all the individual building blocks in different atomic/molecular architectures, as well as more complex patterns that these blocks may form, on a scale of hundreds and thousands of individual atomic/molecular units. Here we employ machine vision to read and recognize complex molecular assemblies on surfaces. Specifically, we combine Markov random field model and convolutional neural networks to classify structural and rotational states of all individual building blocks in molecular assembly on the metallic surface visualized in high-resolution scanning tunneling microscopy measurements. We show how the obtained full decoding of the system allows us to directly construct a pair density function—a centerpiece in analysis of disorder-property relationship paradigm—as well as to analyze spatial correlations between multiple order parameters at the nanoscale, and elucidate reaction pathway involving molecular conformation changes. The method represents a significant shift in our way of analyzing atomic and/or molecular resolved microscopic images and can be applied to variety of other microscopic measurements of structural, electronic, and magnetic orders in different condensed matter systems.

Analysis of machining and machine tools

CERN Document Server

Liang, Steven Y

2016-01-01

This book delivers the fundamental science and mechanics of machining and machine tools by presenting systematic and quantitative knowledge in the form of process mechanics and physics. It gives readers a solid command of machining science and engineering, and familiarizes them with the geometry and functionality requirements of creating parts and components in today’s markets. The authors address traditional machining topics, such as: single and multiple point cutting processes grinding components accuracy and metrology shear stress in cutting cutting temperature and analysis chatter They also address non-traditional machining, such as: electrical discharge machining electrochemical machining laser and electron beam machining A chapter on biomedical machining is also included. This book is appropriate for advanced undergraduate and graduate mechani cal engineering students, manufacturing engineers, and researchers. Each chapter contains examples, exercises and their solutions, and homework problems that re...

Molecular machines in living cells. Seibutsu no bunshi kikai to sono system

Energy Technology Data Exchange (ETDEWEB)

Osawa, F. (Aichi Inst. of Tech., Nagoya (Japan))

1992-12-20

At first, flagellar motors of bacteria are reviewed as a typical example of molecular machines in living cells. A rotational motor is embedded in the cell membrane at the root of the flagellum. The driving power of the rotation is the flow of hydrogen ion from the inside to the outside of the cell. In a normal state of a bacterium, potential difference of about 0.2 V is produced by the ion pump existing in the cell membrane. A molecular motor of sliding motion of muscle attracts the attention on the relation of the input and output of the molecular motor. The mechanism of the smooth motion without fluctuation in the fluctuated environment and the fluctuated input is unknown. Next, the motion of Paramecium is discussed as an example of a system composed of a number of molecular machines. Paramecium moves to a place of a suitable temperature when placed in a water tank with temperature gradient, however, it does not stop the motion at the place of the suitable temperature and increases a probability to change the direction when leaving, that is it takes a method of indirect probabilistic control. 12 refs., 8 figs.

Molecular-Sized DNA or RNA Sequencing Machine | NCI Technology Transfer Center | TTC

Science.gov (United States)

The National Cancer Institute's Gene Regulation and Chromosome Biology Laboratory is seeking statements of capability or interest from parties interested in collaborative research to co-develop a molecular-sized DNA or RNA sequencing machine.

Structural and Conformational Chemistry from Electrochemical Molecular Machines. Replicating Biological Functions. A Review.

Science.gov (United States)

Otero, Toribio F

2017-12-14

Each constitutive chain of a conducting polymer electrode acts as a reversible multi-step electrochemical molecular motor: reversible reactions drive reversible conformational movements of the chain. The reaction-driven cooperative actuation of those molecular machines generates, or destroys, inside the film the free volume required to lodge/expel balancing counterions and solvent: reactions drive reversible film volume variations, which basic structural components are here identified and quantified from electrochemical responses. The content of the reactive dense gel (chemical molecular machines, ions and water) mimics that of the intracellular matrix in living functional cells. Reaction-driven properties (composition-dependent properties) and devices replicate biological functions and organs. An emerging technological world of soft, wet, reaction-driven, multifunctional and biomimetic devices and the concomitant zoomorphic or anthropomorphic robots is presented. © 2017 The Chemical Society of Japan & Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Synthetic small molecules as machines: a chemistry perspective

Indian Academy of Sciences (India)

PGhosh

2017-07-01

Jul 1, 2017 ... Indian Association for the Cultivation of Science. 2A & 2B Raja S. C. ... Many research groups reported in the 1950s and 1960s that their reaction .... A molecular-level machine can be defined as “an assembly of a distinct ...

Molecular Imprinting Applications in Forensic Science.

Science.gov (United States)

Yılmaz, Erkut; Garipcan, Bora; Patra, Hirak K; Uzun, Lokman

2017-03-28

Producing molecular imprinting-based materials has received increasing attention due to recognition selectivity, stability, cast effectiveness, and ease of production in various forms for a wide range of applications. The molecular imprinting technique has a variety of applications in the areas of the food industry, environmental monitoring, and medicine for diverse purposes like sample pretreatment, sensing, and separation/purification. A versatile usage, stability and recognition capabilities also make them perfect candidates for use in forensic sciences. Forensic science is a demanding area and there is a growing interest in molecularly imprinted polymers (MIPs) in this field. In this review, recent molecular imprinting applications in the related areas of forensic sciences are discussed while considering the literature of last two decades. Not only direct forensic applications but also studies of possible forensic value were taken into account like illicit drugs, banned sport drugs, effective toxins and chemical warfare agents in a review of over 100 articles. The literature was classified according to targets, material shapes, production strategies, detection method, and instrumentation. We aimed to summarize the current applications of MIPs in forensic science and put forth a projection of their potential uses as promising alternatives for benchmark competitors.

International Journal of Molecular Science 2017 Best Paper Award.

Science.gov (United States)

2017-11-02

The Editors of the International Journal of Molecular Sciences have established the Best Paper Award to recognize the most outstanding articles published in the areas of molecular biology, molecular physics and chemistry that have been published in the International Journal of Molecular Sciences. The prizes have been awarded annually since 2012 [...].

Molecular Science Computing: 2010 Greenbook

Energy Technology Data Exchange (ETDEWEB)

De Jong, Wibe A.; Cowley, David E.; Dunning, Thom H.; Vorpagel, Erich R.

2010-04-02

This 2010 Greenbook outlines the science drivers for performing integrated computational environmental molecular research at EMSL and defines the next-generation HPC capabilities that must be developed at the MSC to address this critical research. The EMSL MSC Science Panel used EMSL’s vision and science focus and white papers from current and potential future EMSL scientific user communities to define the scientific direction and resulting HPC resource requirements presented in this 2010 Greenbook.

Big Machines and Big Science: 80 Years of Accelerators at Stanford

Energy Technology Data Exchange (ETDEWEB)

Loew, Gregory

2008-12-16

Longtime SLAC physicist Greg Loew will present a trip through SLAC's origins, highlighting its scientific achievements, and provide a glimpse of the lab's future in 'Big Machines and Big Science: 80 Years of Accelerators at Stanford.'

Distinguished figures in mechanism and machine science

CERN Document Server

2014-01-01

This book is composed of chapters that focus specifically on technological developments by distinguished figures in the history of MMS (Mechanism and Machine Science).  Biographies of well-known scientists are also included to describe their efforts and experiences, and surveys of their work and achievements, and a modern interpretation of their legacy are presented. After the first two volumes, the papers in this third volume again cover a wide range within the field of the History of Mechanical Engineering with specific focus on MMS and will be of interest and motivation to the work (historical or not) of many.

Machine learning of accurate energy-conserving molecular force fields

Science.gov (United States)

Chmiela, Stefan; Tkatchenko, Alexandre; Sauceda, Huziel E.; Poltavsky, Igor; Schütt, Kristof T.; Müller, Klaus-Robert

2017-01-01

Using conservation of energy—a fundamental property of closed classical and quantum mechanical systems—we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories. The GDML implementation is able to reproduce global potential energy surfaces of intermediate-sized molecules with an accuracy of 0.3 kcal mol−1 for energies and 1 kcal mol−1 Å̊−1 for atomic forces using only 1000 conformational geometries for training. We demonstrate this accuracy for AIMD trajectories of molecules, including benzene, toluene, naphthalene, ethanol, uracil, and aspirin. The challenge of constructing conservative force fields is accomplished in our work by learning in a Hilbert space of vector-valued functions that obey the law of energy conservation. The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods. PMID:28508076

Molecular Science Computing Facility Scientific Challenges: Linking Across Scales

Energy Technology Data Exchange (ETDEWEB)

De Jong, Wibe A.; Windus, Theresa L.

2005-07-01

The purpose of this document is to define the evolving science drivers for performing environmental molecular research at the William R. Wiley Environmental Molecular Sciences Laboratory (EMSL) and to provide guidance associated with the next-generation high-performance computing center that must be developed at EMSL's Molecular Science Computing Facility (MSCF) in order to address this critical research. The MSCF is the pre-eminent computing facility?supported by the U.S. Department of Energy's (DOE's) Office of Biological and Environmental Research (BER)?tailored to provide the fastest time-to-solution for current computational challenges in chemistry and biology, as well as providing the means for broad research in the molecular and environmental sciences. The MSCF provides integral resources and expertise to emerging EMSL Scientific Grand Challenges and Collaborative Access Teams that are designed to leverage the multiple integrated research capabilities of EMSL, thereby creating a synergy between computation and experiment to address environmental molecular science challenges critical to DOE and the nation.

Committee on Atomic, Molecular, and Optical Sciences (CAMOS)

International Nuclear Information System (INIS)

1992-01-01

The Committee on Atomic, Molecular, and Optical Sciences is a standing committee under the auspices of the Board on Physics and Astronomy, Commission on Physical Sciences, Mathematics, and Applications of the National Academy of Sciences -- National Research Council. The atomic, molecular, and optical (AMO) sciences represent a broad and diverse field in which much of the research is carried out by small groups. These groups generally have not operated in concert with each other and, prior to the establishment of CAMOS, there was no single committee or organization that accepted the responsibility of monitoring the continuing development and assessing the general public health of the field as a whole. CAMOS has accepted this responsibility and currently provides a focus for the AMO community that is unique and essential. The membership of CAMOS is drawn from research laboratories in universities, industry, and government. Areas of expertise on the committee include atomic physics, molecular science, and optics. A special effort has been made to include a balanced representation from the three subfields. (A roster is attached.) CAMOS has conducted a number of studies related to the health of atomic and molecular science and is well prepared to response to requests for studies on specific issues. This report brief reviews the committee work of progress

Molecular machines operating on the nanoscale: from classical to quantum

Directory of Open Access Journals (Sweden)

Igor Goychuk

2016-03-01

Full Text Available The main physical features and operating principles of isothermal nanomachines in the microworld, common to both classical and quantum machines, are reviewed. Special attention is paid to the dual, constructive role of dissipation and thermal fluctuations, the fluctuation–dissipation theorem, heat losses and free energy transduction, thermodynamic efficiency, and thermodynamic efficiency at maximum power. Several basic models are considered and discussed to highlight generic physical features. This work examines some common fallacies that continue to plague the literature. In particular, the erroneous beliefs that one should minimize friction and lower the temperature for high performance of Brownian machines, and that the thermodynamic efficiency at maximum power cannot exceed one-half are discussed. The emerging topic of anomalous molecular motors operating subdiffusively but very efficiently in the viscoelastic environment of living cells is also discussed.

Motor proteins and molecular motors: how to operate machines at the nanoscale

International Nuclear Information System (INIS)

Kolomeisky, Anatoly B

2013-01-01

Several classes of biological molecules that transform chemical energy into mechanical work are known as motor proteins or molecular motors. These nanometer-sized machines operate in noisy stochastic isothermal environments, strongly supporting fundamental cellular processes such as the transfer of genetic information, transport, organization and functioning. In the past two decades motor proteins have become a subject of intense research efforts, aimed at uncovering the fundamental principles and mechanisms of molecular motor dynamics. In this review, we critically discuss recent progress in experimental and theoretical studies on motor proteins. Our focus is on analyzing fundamental concepts and ideas that have been utilized to explain the non-equilibrium nature and mechanisms of molecular motors. (topical review)

Large-scale theoretical calculations in molecular science - design of a large computer system for molecular science and necessary conditions for future computers

Energy Technology Data Exchange (ETDEWEB)

Kashiwagi, H [Institute for Molecular Science, Okazaki, Aichi (Japan)

1982-06-01

A large computer system was designed and established for molecular science under the leadership of molecular scientists. Features of the computer system are an automated operation system and an open self-service system. Large-scale theoretical calculations have been performed to solve many problems in molecular science, using the computer system. Necessary conditions for future computers are discussed on the basis of this experience.

Large-scale theoretical calculations in molecular science - design of a large computer system for molecular science and necessary conditions for future computers

International Nuclear Information System (INIS)

Kashiwagi, H.

1982-01-01

A large computer system was designed and established for molecular science under the leadership of molecular scientists. Features of the computer system are an automated operation system and an open self-service system. Large-scale theoretical calculations have been performed to solve many problems in molecular science, using the computer system. Necessary conditions for future computers are discussed on the basis of this experience. (orig.)

Knowledge machines digital transformations of the sciences and humanities

CERN Document Server

Meyer, Eric T

2015-01-01

In Knowledge Machines, Eric Meyer and Ralph Schroeder argue that digital technologies have fundamentally changed research practices in the sciences, social sciences, and humanities. Meyer and Schroeder show that digital tools and data, used collectively and in distributed mode -- which they term e-research -- have transformed not just the consumption of knowledge but also the production of knowledge. Digital technologies for research are reshaping how knowledge advances in disciplines that range from physics to literary analysis. Meyer and Schroeder map the rise of digital research and offer case studies from many fields, including biomedicine, social science uses of the Web, astronomy, and large-scale textual analysis in the humanities. They consider such topics as the challenges of sharing research data and of big data approaches, disciplinary differences and new forms of interdisciplinary collaboration, the shifting boundaries between researchers and their publics, and the ways that digital tools promote o...

Synthetic Molecular Machines for Active Self-Assembly: Prototype Algorithms, Designs, and Experimental Study

Science.gov (United States)

Dabby, Nadine L.

Computer science and electrical engineering have been the great success story of the twentieth century. The neat modularity and mapping of a language onto circuits has led to robots on Mars, desktop computers and smartphones. But these devices are not yet able to do some of the things that life takes for granted: repair a scratch, reproduce, regenerate, or grow exponentially fast--all while remaining functional. This thesis explores and develops algorithms, molecular implementations, and theoretical proofs in the context of "active self-assembly" of molecular systems. The long-term vision of active self-assembly is the theoretical and physical implementation of materials that are composed of reconfigurable units with the programmability and adaptability of biology's numerous molecular machines. En route to this goal, we must first find a way to overcome the memory limitations of molecular systems, and to discover the limits of complexity that can be achieved with individual molecules. One of the main thrusts in molecular programming is to use computer science as a tool for figuring out what can be achieved. While molecular systems that are Turing-complete have been demonstrated [Winfree, 1996], these systems still cannot achieve some of the feats biology has achieved. One might think that because a system is Turing-complete, capable of computing "anything," that it can do any arbitrary task. But while it can simulate any digital computational problem, there are many behaviors that are not "computations" in a classical sense, and cannot be directly implemented. Examples include exponential growth and molecular motion relative to a surface. Passive self-assembly systems cannot implement these behaviors because (a) molecular motion relative to a surface requires a source of fuel that is external to the system, and (b) passive systems are too slow to assemble exponentially-fast-growing structures. We call these behaviors "energetically incomplete" programmable

[Molecular imaging; current status and future prospects in USA].

Science.gov (United States)

Kobayashi, Hisataka

2007-02-01

The goal of this review is to introduce the definition, current status, and future prospects of the molecular imaging, which has recently been a hot topic in medicine and the biological science in USA. In vivo imaging methods to visualize the molecular events and functions in organs or animals/humans are overviewed and discussed especially in combinations of imaging modalities (machines) and contrast agents(chemicals) used in the molecular imaging. Next, the close relationship between the molecular imaging and the nanotechnology, an important part of nanomedicine, is stressed from the aspect of united multidisciplinary sciences such as physics, chemistry, biology, and medicine.

Zooniverse - Web scale citizen science with people and machines. (Invited)

Science.gov (United States)

Smith, A.; Lynn, S.; Lintott, C.; Simpson, R.

2013-12-01

The Zooniverse (zooniverse.org) began in 2007 with the launch of Galaxy Zoo, a project in which more than 175,000 people provided shape analyses of more than 1 million galaxy images sourced from the Sloan Digital Sky Survey. These galaxy 'classifications', some 60 million in total, have since been used to produce more than 50 peer-reviewed publications based not only on the original research goals of the project but also because of serendipitous discoveries made by the volunteer community. Based upon the success of Galaxy Zoo the team have gone on to develop more than 25 web-based citizen science projects, all with a strong research focus in a range of subjects from astronomy to zoology where human-based analysis still exceeds that of machine intelligence. Over the past 6 years Zooniverse projects have collected more than 300 million data analyses from over 1 million volunteers providing fantastically rich datasets for not only the individuals working to produce research from their project but also the machine learning and computer vision research communities. The Zooniverse platform has always been developed to be the 'simplest thing that works' implementing only the most rudimentary algorithms for functionality such as task allocation and user-performance metrics - simplifications necessary to scale the Zooniverse such that the core team of developers and data scientists can remain small and the cost of running the computing infrastructure relatively modest. To date these simplifications have been appropriate for the data volumes and analysis tasks being addressed. This situation however is changing: next generation telescopes such as the Large Synoptic Sky Telescope (LSST) will produce data volumes dwarfing those previously analyzed. If citizen science is to have a part to play in analyzing these next-generation datasets then the Zooniverse will need to evolve into a smarter system capable for example of modeling the abilities of users and the complexities of

Gravity Spy - Integrating LIGO detector characterization, citizen science, and machine learning

Science.gov (United States)

Zevin, Michael; Gravity Spy

2016-06-01

On September 14th 2015, the Advanced Laser Interferometer Gravitational-wave Observatory (aLIGO) made the first direct observation of gravitational waves and opened a new field of observational astronomy. However, being the most complicated and sensitve experiment ever undertaken in gravitational physics, aLIGO is susceptible to various sources of environmental and instrumental noise that hinder the search for more gravitational waves.Of particular concern are transient, non-Gaussian noise features known as glitches. Glitches can mimic true astrophysical gravitational waves, occur at a high enough frequency to be coherent between the two detectors, and generally worsen aLIGO's detection capabilities. The proper classification and charaterization of glitches is paramount in optimizing aLIGO's ability to detect gravitational waves. However, teaching computers to identify and morphologically classify these artifacts is exceedingly difficult.Human intuition has proven to be a useful tool in classifcation probelms such as this. Gravity Spy is an innovative, interdisciplinary project hosted by Zooniverse that combines aLIGO detector characterization, citizen science, machine learning, and social science. In this project, citizen scientists and computers will work together in a sybiotic relationship that leverages human pattern recognition and the ability of machine learning to process large amounts of data systematically: volunteers classify triggers from the aLIGO data steam that are constantly updated as aLIGO takes in new data, and these classifications are used to train machine learning algorithms which proceed to classify the bulk of aLIGO data and feed questionable glithces back to the users.In this talk, I will discuss the workflow and initial results of the Gravity Spy project with regard to aLIGO's future observing runs and highlight the potential of such citizen science projects in promoting nascent fields such as gravitational wave astrophysics.

Committee on Atomic, Molecular, and Optical Sciences (CAMOS)

International Nuclear Information System (INIS)

1992-01-01

The Committee on Atomic, Molecular and Optical Sciences (CAMOS) of the National Research Council (NRC) is charged with monitoring the health of the field of atomic, molecular, and optical (AMO) science in the United States. Accordingly, the Committee identifies and examines both broad and specific issues affecting the field. Regular meetings, teleconferences, briefings from agencies and the scientific community, the formation of study panels to prepare reports, and special symposia are among the mechanisms used by the CAMOS to meet its charge. This progress report presents a review of CAMOS activities from February 1, 1992 to January 31, 1993. This report also includes the status of activities associated with the CAMOS study on the field that is being conducted by the Panel on the Future of Atomic, Molecular, and Optical Sciences (FAMOS)

Mastering the non-equilibrium assembly and operation of molecular machines.

Science.gov (United States)

Pezzato, Cristian; Cheng, Chuyang; Stoddart, J Fraser; Astumian, R Dean

2017-09-18

In mechanically interlocked compounds, such as rotaxanes and catenanes, the molecules are held together by mechanical rather than chemical bonds. These compounds can be engineered to have several well-defined mechanical states by incorporating recognition sites between the different components. The rates of the transitions between the recognition sites can be controlled by introducing steric "speed bumps" or electrostatically switchable gates. A mechanism for the absorption of energy can also be included by adding photoactive, catalytically active, or redox-active recognition sites, or even charges and dipoles. At equilibrium, these Mechanically Interlocked Molecules (MIMs) undergo thermally activated transitions continuously between their different mechanical states where every transition is as likely as its microscopic reverse. External energy, for example, light, external modulation of the chemical and/or physical environment or catalysis of an exergonic reaction, drives the system away from equilibrium. The absorption of energy from these processes can be used to favour some, and suppress other, transitions so that completion of a mechanical cycle in a direction in which work is done on the environment - the requisite of a molecular machine - is more likely than completion in a direction in which work is absorbed from the environment. In this Tutorial Review, we discuss the different design principles by which molecular machines can be engineered to use different sources of energy to carry out self-organization and the performance of work in their environments.

Nanomedicine: tiny particles and machines give huge gains.

Science.gov (United States)

Tong, Sheng; Fine, Eli J; Lin, Yanni; Cradick, Thomas J; Bao, Gang

2014-02-01

Nanomedicine is an emerging field that integrates nanotechnology, biomolecular engineering, life sciences and medicine; it is expected to produce major breakthroughs in medical diagnostics and therapeutics. Nano-scale structures and devices are compatible in size with proteins and nucleic acids in living cells. Therefore, the design, characterization and application of nano-scale probes, carriers and machines may provide unprecedented opportunities for achieving a better control of biological processes, and drastic improvements in disease detection, therapy, and prevention. Recent advances in nanomedicine include the development of nanoparticle (NP)-based probes for molecular imaging, nano-carriers for drug/gene delivery, multifunctional NPs for theranostics, and molecular machines for biological and medical studies. This article provides an overview of the nanomedicine field, with an emphasis on NPs for imaging and therapy, as well as engineered nucleases for genome editing. The challenges in translating nanomedicine approaches to clinical applications are discussed.

Marine molecular biology: an emerging field of biological sciences.

Science.gov (United States)

Thakur, Narsinh L; Jain, Roopesh; Natalio, Filipe; Hamer, Bojan; Thakur, Archana N; Müller, Werner E G

2008-01-01

An appreciation of the potential applications of molecular biology is of growing importance in many areas of life sciences, including marine biology. During the past two decades, the development of sophisticated molecular technologies and instruments for biomedical research has resulted in significant advances in the biological sciences. However, the value of molecular techniques for addressing problems in marine biology has only recently begun to be cherished. It has been proven that the exploitation of molecular biological techniques will allow difficult research questions about marine organisms and ocean processes to be addressed. Marine molecular biology is a discipline, which strives to define and solve the problems regarding the sustainable exploration of marine life for human health and welfare, through the cooperation between scientists working in marine biology, molecular biology, microbiology and chemistry disciplines. Several success stories of the applications of molecular techniques in the field of marine biology are guiding further research in this area. In this review different molecular techniques are discussed, which have application in marine microbiology, marine invertebrate biology, marine ecology, marine natural products, material sciences, fisheries, conservation and bio-invasion etc. In summary, if marine biologists and molecular biologists continue to work towards strong partnership during the next decade and recognize intellectual and technological advantages and benefits of such partnership, an exciting new frontier of marine molecular biology will emerge in the future.

Propulsion of a Molecular Machine by Asymmetric Distribution of Reaction Products

Science.gov (United States)

Golestanian, Ramin; Liverpool, Tanniemola B.; Ajdari, Armand

2005-06-01

A simple model for the reaction-driven propulsion of a small device is proposed as a model for (part of) a molecular machine in aqueous media. The motion of the device is driven by an asymmetric distribution of reaction products. The propulsive velocity of the device is calculated as well as the scale of the velocity fluctuations. The effects of hydrodynamic flow as well as a number of different scenarios for the kinetics of the reaction are addressed.

Molecular Environmental Science and Synchrotron Radiation Facilities An Update of the 1995 DOE-Airlie Report on Molecular Environmental Science

Energy Technology Data Exchange (ETDEWEB)

Bargar, John R

1999-05-07

This workshop was requested by Dr. Robert Marianelli, Director of the DOE-BES Chemical Sciences Division, to update the findings of the Workshop on Molecular Environmental Sciences (MES) held at Airlie, VA, in July 1995. The Airlie Workshop Report defined the new interdisciplinary field referred to as Molecular Environmental Science (MES), reviewed the synchrotron radiation methods used in MES research, assessed the adequacy of synchrotron radiation facilities for research in this field, and summarized the beam time requirements of MES users based on a national MES user survey. The objectives of MES research are to provide information on the chemical and physical forms (speciation), spatial distribution, and reactivity of contaminants in natural materials and man-made waste forms, and to develop a fundamental understanding of the complex molecular-scale environmental processes, both chemical and biological, that affect the stability, transformations, mobility, and toxicity of contaminant species. These objectives require parallel studies of ''real'' environmental samples, which are complicated multi-phase mixtures with chemical and physical heterogeneities, and of simplified model systems in which variables can be controlled and fundamental processes can be examined. Only by this combination of approaches can a basic understanding of environmental processes at the molecular-scale be achieved.

Molecular Environmental Science and Synchrotron Radiation Facilities An Update of the 1995 DOE-Airlie Report on Molecular Environmental Science

International Nuclear Information System (INIS)

Bargar, John R

1999-01-01

This workshop was requested by Dr. Robert Marianelli, Director of the DOE-BES Chemical Sciences Division, to update the findings of the Workshop on Molecular Environmental Sciences (MES) held at Airlie, VA, in July 1995. The Airlie Workshop Report defined the new interdisciplinary field referred to as Molecular Environmental Science (MES), reviewed the synchrotron radiation methods used in MES research, assessed the adequacy of synchrotron radiation facilities for research in this field, and summarized the beam time requirements of MES users based on a national MES user survey. The objectives of MES research are to provide information on the chemical and physical forms (speciation), spatial distribution, and reactivity of contaminants in natural materials and man-made waste forms, and to develop a fundamental understanding of the complex molecular-scale environmental processes, both chemical and biological, that affect the stability, transformations, mobility, and toxicity of contaminant species. These objectives require parallel studies of ''real'' environmental samples, which are complicated multi-phase mixtures with chemical and physical heterogeneities, and of simplified model systems in which variables can be controlled and fundamental processes can be examined. Only by this combination of approaches can a basic understanding of environmental processes at the molecular-scale be achieved

Machine learning of molecular electronic properties in chemical compound space

International Nuclear Information System (INIS)

Montavon, Grégoire; Müller, Klaus-Robert; Rupp, Matthias; Gobre, Vivekanand; Hansen, Katja; Tkatchenko, Alexandre; Vazquez-Mayagoitia, Alvaro; Anatole von Lilienfeld, O

2013-01-01

The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful, novel and predictive structure–property relationships. Such relationships enable high-throughput screening for relevant properties in an exponentially growing pool of virtual compounds that are synthetically accessible. Here, we present a machine learning model, trained on a database of ab initio calculation results for thousands of organic molecules, that simultaneously predicts multiple electronic ground- and excited-state properties. The properties include atomization energy, polarizability, frontier orbital eigenvalues, ionization potential, electron affinity and excitation energies. The machine learning model is based on a deep multi-task artificial neural network, exploiting the underlying correlations between various molecular properties. The input is identical to ab initio methods, i.e. nuclear charges and Cartesian coordinates of all atoms. For small organic molecules, the accuracy of such a ‘quantum machine’ is similar, and sometimes superior, to modern quantum-chemical methods—at negligible computational cost. (paper)

Machine learning of molecular electronic properties in chemical compound space

Science.gov (United States)

Montavon, Grégoire; Rupp, Matthias; Gobre, Vivekanand; Vazquez-Mayagoitia, Alvaro; Hansen, Katja; Tkatchenko, Alexandre; Müller, Klaus-Robert; Anatole von Lilienfeld, O.

2013-09-01

The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful, novel and predictive structure-property relationships. Such relationships enable high-throughput screening for relevant properties in an exponentially growing pool of virtual compounds that are synthetically accessible. Here, we present a machine learning model, trained on a database of ab initio calculation results for thousands of organic molecules, that simultaneously predicts multiple electronic ground- and excited-state properties. The properties include atomization energy, polarizability, frontier orbital eigenvalues, ionization potential, electron affinity and excitation energies. The machine learning model is based on a deep multi-task artificial neural network, exploiting the underlying correlations between various molecular properties. The input is identical to ab initio methods, i.e. nuclear charges and Cartesian coordinates of all atoms. For small organic molecules, the accuracy of such a ‘quantum machine’ is similar, and sometimes superior, to modern quantum-chemical methods—at negligible computational cost.

Stochastic thermodynamics, fluctuation theorems and molecular machines

International Nuclear Information System (INIS)

Seifert, Udo

2012-01-01

Stochastic thermodynamics as reviewed here systematically provides a framework for extending the notions of classical thermodynamics such as work, heat and entropy production to the level of individual trajectories of well-defined non-equilibrium ensembles. It applies whenever a non-equilibrium process is still coupled to one (or several) heat bath(s) of constant temperature. Paradigmatic systems are single colloidal particles in time-dependent laser traps, polymers in external flow, enzymes and molecular motors in single molecule assays, small biochemical networks and thermoelectric devices involving single electron transport. For such systems, a first-law like energy balance can be identified along fluctuating trajectories. For a basic Markovian dynamics implemented either on the continuum level with Langevin equations or on a discrete set of states as a master equation, thermodynamic consistency imposes a local-detailed balance constraint on noise and rates, respectively. Various integral and detailed fluctuation theorems, which are derived here in a unifying approach from one master theorem, constrain the probability distributions for work, heat and entropy production depending on the nature of the system and the choice of non-equilibrium conditions. For non-equilibrium steady states, particularly strong results hold like a generalized fluctuation–dissipation theorem involving entropy production. Ramifications and applications of these concepts include optimal driving between specified states in finite time, the role of measurement-based feedback processes and the relation between dissipation and irreversibility. Efficiency and, in particular, efficiency at maximum power can be discussed systematically beyond the linear response regime for two classes of molecular machines, isothermal ones such as molecular motors, and heat engines such as thermoelectric devices, using a common framework based on a cycle decomposition of entropy production. (review article)

Evolution of cell cycle control: same molecular machines, different regulation

DEFF Research Database (Denmark)

de Lichtenberg, Ulrik; Jensen, Thomas Skøt; Brunak, Søren

2007-01-01

Decades of research has together with the availability of whole genomes made it clear that many of the core components involved in the cell cycle are conserved across eukaryotes, both functionally and structurally. These proteins are organized in complexes and modules that are activated or deacti......Decades of research has together with the availability of whole genomes made it clear that many of the core components involved in the cell cycle are conserved across eukaryotes, both functionally and structurally. These proteins are organized in complexes and modules that are activated...... for assembling the same molecular machines just in time for action....

Machine Learning

CERN Multimedia

CERN. Geneva

2017-01-01

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

Femtochemistry and femtobiology ultrafast dynamics in molecular science

CERN Document Server

Douhal, Abderrazzak

2002-01-01

This book contains important contributions from top international scientists on the-state-of-the-art of femtochemistry and femtobiology at the beginning of the new millennium. It consists of reviews and papers on ultrafast dynamics in molecular science.The coverage of topics highlights several important features of molecular science from the viewpoint of structure (space domain) and dynamics (time domain). First of all, the book presents the latest developments, such as experimental techniques for understanding ultrafast processes in gas, condensed and complex systems, including biological mol

Coupling Matched Molecular Pairs with Machine Learning for Virtual Compound Optimization.

Science.gov (United States)

Turk, Samo; Merget, Benjamin; Rippmann, Friedrich; Fulle, Simone

2017-12-26

Matched molecular pair (MMP) analyses are widely used in compound optimization projects to gain insights into structure-activity relationships (SAR). The analysis is traditionally done via statistical methods but can also be employed together with machine learning (ML) approaches to extrapolate to novel compounds. The here introduced MMP/ML method combines a fragment-based MMP implementation with different machine learning methods to obtain automated SAR decomposition and prediction. To test the prediction capabilities and model transferability, two different compound optimization scenarios were designed: (1) "new fragments" which occurs when exploring new fragments for a defined compound series and (2) "new static core and transformations" which resembles for instance the identification of a new compound series. Very good results were achieved by all employed machine learning methods especially for the new fragments case, but overall deep neural network models performed best, allowing reliable predictions also for the new static core and transformations scenario, where comprehensive SAR knowledge of the compound series is missing. Furthermore, we show that models trained on all available data have a higher generalizability compared to models trained on focused series and can extend beyond chemical space covered in the training data. Thus, coupling MMP with deep neural networks provides a promising approach to make high quality predictions on various data sets and in different compound optimization scenarios.

On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products.

Science.gov (United States)

Varshney, Kush R; Alemzadeh, Homa

2017-09-01

Machine learning algorithms increasingly influence our decisions and interact with us in all parts of our daily lives. Therefore, just as we consider the safety of power plants, highways, and a variety of other engineered socio-technical systems, we must also take into account the safety of systems involving machine learning. Heretofore, the definition of safety has not been formalized in a machine learning context. In this article, we do so by defining machine learning safety in terms of risk, epistemic uncertainty, and the harm incurred by unwanted outcomes. We then use this definition to examine safety in all sorts of applications in cyber-physical systems, decision sciences, and data products. We find that the foundational principle of modern statistical machine learning, empirical risk minimization, is not always a sufficient objective. We discuss how four different categories of strategies for achieving safety in engineering, including inherently safe design, safety reserves, safe fail, and procedural safeguards can be mapped to a machine learning context. We then discuss example techniques that can be adopted in each category, such as considering interpretability and causality of predictive models, objective functions beyond expected prediction accuracy, human involvement for labeling difficult or rare examples, and user experience design of software and open data.

Marine molecular biology: An emerging field of biological sciences

Digital Repository Service at National Institute of Oceanography (India)

Thakur, N.L.; Jain, R.; Natalio, F.; Hamer, B.; Thakur, A.N.; Muller, W.E.G.

An appreciation of the potential applications of molecular biology is of growing importance in many areas of life sciences, including marine biology. During the past two decades, the development of sophisticated molecular technologies...

Time machine tales the science fiction adventures and philosophical puzzles of time travel

CERN Document Server

Nahin, Paul J

2017-01-01

This book contains a broad overview of time travel in science fiction, along with a detailed examination of the philosophical implications of time travel. The emphasis of this book is now on the philosophical and on science fiction, rather than on physics, as in the author's earlier books on the subject. In that spirit there are, for example, no Tech Notes filled with algebra, integrals, and differential equations, as there are in the first and second editions of TIME MACHINES. Writing about time travel is, today, a respectable business. It hasn’t always been so. After all, time travel, prima facie, appears to violate a fundamental law of nature; every effect has a cause, with the cause occurring before the effect. Time travel to the past, however, seems to allow, indeed to demand, backwards causation, with an effect (the time traveler emerging into the past as he exits from his time machine) occurring before its cause (the time traveler pushing the start button on his machine’s control panel to start his...

New Trends in E-Science: Machine Learning and Knowledge Discovery in Databases

Science.gov (United States)

Brescia, Massimo

2012-11-01

Data mining, or Knowledge Discovery in Databases (KDD), while being the main methodology to extract the scientific information contained in Massive Data Sets (MDS), needs to tackle crucial problems since it has to orchestrate complex challenges posed by transparent access to different computing environments, scalability of algorithms, reusability of resources. To achieve a leap forward for the progress of e-science in the data avalanche era, the community needs to implement an infrastructure capable of performing data access, processing and mining in a distributed but integrated context. The increasing complexity of modern technologies carried out a huge production of data, whose related warehouse management and the need to optimize analysis and mining procedures lead to a change in concept on modern science. Classical data exploration, based on local user own data storage and limited computing infrastructures, is no more efficient in the case of MDS, worldwide spread over inhomogeneous data centres and requiring teraflop processing power. In this context modern experimental and observational science requires a good understanding of computer science, network infrastructures, Data Mining, etc. i.e. of all those techniques which fall into the domain of the so called e-science (recently assessed also by the Fourth Paradigm of Science). Such understanding is almost completely absent in the older generations of scientists and this reflects in the inadequacy of most academic and research programs. A paradigm shift is needed: statistical pattern recognition, object oriented programming, distributed computing, parallel programming need to become an essential part of scientific background. A possible practical solution is to provide the research community with easy-to understand, easy-to-use tools, based on the Web 2.0 technologies and Machine Learning methodology. Tools where almost all the complexity is hidden to the final user, but which are still flexible and able to

Molecular Science Research Center, 1991 annual report

Energy Technology Data Exchange (ETDEWEB)

Knotek, M.L.

1992-03-01

During 1991, the Molecular Science Research Center (MSRC) experienced solid growth and accomplishment and the Environmental, and Molecular Sciences Laboratory (EMSL) construction project moved forward. We began with strong programs in chemical structure and dynamics and theory, modeling, and simulation, and both these programs continued to thrive. We also made significant advances in the development of programs in materials and interfaces and macromolecular structure and dynamics, largely as a result of the key staff recruited to lead these efforts. If there was one pervasive activity for the past year, however, it was to strengthen the role of the EMSL in the overall environmental restoration and waste management (ER/WM) mission at Hanford. These extended activities involved not only MSRC and EMSL staff but all PNL scientific and technical staff engaged in ER/WM programs.

Educational challenges of molecular life science: Characteristics and implications for education and research.

Science.gov (United States)

Tibell, Lena A E; Rundgren, Carl-Johan

2010-01-01

Molecular life science is one of the fastest-growing fields of scientific and technical innovation, and biotechnology has profound effects on many aspects of daily life-often with deep, ethical dimensions. At the same time, the content is inherently complex, highly abstract, and deeply rooted in diverse disciplines ranging from "pure sciences," such as math, chemistry, and physics, through "applied sciences," such as medicine and agriculture, to subjects that are traditionally within the remit of humanities, notably philosophy and ethics. Together, these features pose diverse, important, and exciting challenges for tomorrow's teachers and educational establishments. With backgrounds in molecular life science research and secondary life science teaching, we (Tibell and Rundgren, respectively) bring different experiences, perspectives, concerns, and awareness of these issues. Taking the nature of the discipline as a starting point, we highlight important facets of molecular life science that are both characteristic of the domain and challenging for learning and education. Of these challenges, we focus most detail on content, reasoning difficulties, and communication issues. We also discuss implications for education research and teaching in the molecular life sciences.

New approaches in mathematical biology: Information theory and molecular machines

International Nuclear Information System (INIS)

Schneider, T.

1995-01-01

My research uses classical information theory to study genetic systems. Information theory was founded by Claude Shannon in the 1940's and has had an enormous impact on communications engineering and computer sciences. Shannon found a way to measure information. This measure can be used to precisely characterize the sequence conservation at nucleic-acid binding sites. The resulting methods, by completely replacing the use of ''consensus sequences'', provide better models for molecular biologists. An excess of conservation led us to do experimental work on bacteriophage T7 promoters and the F plasmid IncD repeats. The wonderful fidelity of telephone communications and compact disk (CD) music can be traced directly to Shannon's channel capacity theorem. When rederived for molecular biology, this theorem explains the surprising precision of many molecular events. Through connections with the Second Law of Thermodyanmics and Maxwell's Demon, this approach also has implications for the development of technology at the molecular level. Discussions of these topics are held on the internet news group bionet.info-theo. (author). (Abstract only)

DNA-based machines.

Science.gov (United States)

Wang, Fuan; Willner, Bilha; Willner, Itamar

2014-01-01

The base sequence in nucleic acids encodes substantial structural and functional information into the biopolymer. This encoded information provides the basis for the tailoring and assembly of DNA machines. A DNA machine is defined as a molecular device that exhibits the following fundamental features. (1) It performs a fuel-driven mechanical process that mimics macroscopic machines. (2) The mechanical process requires an energy input, "fuel." (3) The mechanical operation is accompanied by an energy consumption process that leads to "waste products." (4) The cyclic operation of the DNA devices, involves the use of "fuel" and "anti-fuel" ingredients. A variety of DNA-based machines are described, including the construction of "tweezers," "walkers," "robots," "cranes," "transporters," "springs," "gears," and interlocked cyclic DNA structures acting as reconfigurable catenanes, rotaxanes, and rotors. Different "fuels", such as nucleic acid strands, pH (H⁺/OH⁻), metal ions, and light, are used to trigger the mechanical functions of the DNA devices. The operation of the devices in solution and on surfaces is described, and a variety of optical, electrical, and photoelectrochemical methods to follow the operations of the DNA machines are presented. We further address the possible applications of DNA machines and the future perspectives of molecular DNA devices. These include the application of DNA machines as functional structures for the construction of logic gates and computing, for the programmed organization of metallic nanoparticle structures and the control of plasmonic properties, and for controlling chemical transformations by DNA machines. We further discuss the future applications of DNA machines for intracellular sensing, controlling intracellular metabolic pathways, and the use of the functional nanostructures for drug delivery and medical applications.

Molecular environmental science and synchrotron radiation sources

Energy Technology Data Exchange (ETDEWEB)

Brown, G.E. Jr. [Stanford Univ., CA (United States)

1995-12-31

Molecular environmental science is a relatively new field but focuses on the chemical and physical forms of toxic and/or radioactive contaminants in soils, sediments, man-made waste forms, natural waters, and the atmosphere; their possible reactions with inorganic and organic compounds, plants, and organisms in the environment; and the molecular-level factors that control their toxicity, bioavailability, and transport. The chemical speciation of a contaminant is a major factor in determining its behavior in the environment, and synchrotron-based X-ray absorption fine structure (XAFS) spectroscopy is one of the spectroscopies of choice to quantitatively determine speciation of heavy metal contaminants in situ without selective extraction or other sample treatment. The use of high-flux insertion device beam lines at synchrotron sources and multi-element array detectors has permitted XAFS studies of metals such as Se and As in natural soils at concentration levels as low as 50 ppm. The X-ray absorption near edge structure of these metals is particularly useful in determining their oxidation state. Examples of such studies will be presented, and new insertion device beam lines under development at SSRL and the Advanced Photon Source for molecular environmental science applications will be discussed.

Artificial molecular motors

NARCIS (Netherlands)

Kassem, Salma; van Leeuwen, Thomas; Lubbe, Anouk S.; Wilson, Miriam R.; Feringa, Ben L.; Leigh, David A.

2017-01-01

Motor proteins are nature's solution for directing movement at the molecular level. The field of artificial molecular motors takes inspiration from these tiny but powerful machines. Although directional motion on the nanoscale performed by synthetic molecular machines is a relatively new

Machine learning: Trends, perspectives, and prospects.

Science.gov (United States)

Jordan, M I; Mitchell, T M

2015-07-17

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing. Copyright © 2015, American Association for the Advancement of Science.

Molecular Science Research Center 1992 annual report

Energy Technology Data Exchange (ETDEWEB)

Knotek, M.L.

1994-01-01

The Molecular Science Research Center is a designated national user facility, available to scientists from universities, industry, and other national laboratories. After an opening section, which includes conferences hosted, appointments, and projects, this document presents progress in the following fields: chemical structure and dynamics; environmental dynamics and simulation; macromolecular structure and dynamics; materials and interfaces; theory, modeling, and simulation; and computing and information sciences. Appendices are included: MSRC staff and associates, 1992 publications and presentations, activities, and acronyms and abbreviations.

Atomic and molecular science: progress and opportunities

International Nuclear Information System (INIS)

Mathur, D.

2000-01-01

In the contemporary scenario, atomic, molecular and optical (AMO) science focuses on the physical and chemical properties of the common building blocks of matter - atoms, molecules and light. The main characteristic of AMO science is that it is both an intellectually stimulating fundamental science and a powerful enabling science that supports an increasing number of other important areas of science and technology. In brief, the fundamental interests in atoms, molecules and clusters (as well as their ions) include studies of their structure and properties, their optical interactions, collisional properties, including quantum state-resolved studies, and interactions with external fields, solids and surfaces. Fundamental aspects of present-day optical sciences include studies of laser spectroscopy, nonlinear optics, quantum optics, optical interactions with condensed matter, ultrafast optics and coherent light sources. The enabling aspect of AMO science derives from efforts to control atoms, molecules, clusters, charged particles and light more precisely, to accurately to determine, experimentally and theoretically, their properties, and to invent new, methods of generating light with tailor-made properties

Quantum Machine Learning

Science.gov (United States)

Biswas, Rupak

2018-01-01

Quantum computing promises an unprecedented ability to solve intractable problems by harnessing quantum mechanical effects such as tunneling, superposition, and entanglement. The Quantum Artificial Intelligence Laboratory (QuAIL) at NASA Ames Research Center is the space agency's primary facility for conducting research and development in quantum information sciences. QuAIL conducts fundamental research in quantum physics but also explores how best to exploit and apply this disruptive technology to enable NASA missions in aeronautics, Earth and space sciences, and space exploration. At the same time, machine learning has become a major focus in computer science and captured the imagination of the public as a panacea to myriad big data problems. In this talk, we will discuss how classical machine learning can take advantage of quantum computing to significantly improve its effectiveness. Although we illustrate this concept on a quantum annealer, other quantum platforms could be used as well. If explored fully and implemented efficiently, quantum machine learning could greatly accelerate a wide range of tasks leading to new technologies and discoveries that will significantly change the way we solve real-world problems.

Machine medical ethics

CERN Document Server

Pontier, Matthijs

2015-01-01

The essays in this book, written by researchers from both humanities and sciences, describe various theoretical and experimental approaches to adding medical ethics to a machine in medical settings. Medical machines are in close proximity with human beings, and getting closer: with patients who are in vulnerable states of health, who have disabilities of various kinds, with the very young or very old, and with medical professionals. In such contexts, machines are undertaking important medical tasks that require emotional sensitivity, knowledge of medical codes, human dignity, and privacy. As machine technology advances, ethical concerns become more urgent: should medical machines be programmed to follow a code of medical ethics? What theory or theories should constrain medical machine conduct? What design features are required? Should machines share responsibility with humans for the ethical consequences of medical actions? How ought clinical relationships involving machines to be modeled? Is a capacity for e...

MLnet report: training in Europe on machine learning

OpenAIRE

Ellebrecht, Mario; Morik, Katharina

1999-01-01

Machine learning techniques offer opportunities for a variety of applications and the theory of machine learning investigates problems that are of interest for other fields of computer science (e.g., complexity theory, logic programming, pattern recognition). However, the impacts of machine learning can only be recognized by those who know the techniques and are able to apply them. Hence, teaching machine learning is necessary before this field can diversify computer science. In order ...

A nanojet: propulsion of a molecular machine by an asymmetric distribution of reaction--products

Science.gov (United States)

Liverpool, Tanniemola; Golestanian, Ramin; Ajdari, Armand

2006-03-01

A simple model for the reaction-driven propulsion of a small device is proposed as a model for (part of) a molecular machine in aqueous media. Motion of the device is driven by an asymmetric distribution of reaction products. We calculate the propulsive velocity of the device as well as the scale of the velocity fluctuations. We also consider the effects of hydrodynamic flow as well as a number of different scenarios for the kinetics of the reaction.

Application of support vector machine to three-dimensional shape-based virtual screening using comprehensive three-dimensional molecular shape overlay with known inhibitors.

Science.gov (United States)

Sato, Tomohiro; Yuki, Hitomi; Takaya, Daisuke; Sasaki, Shunta; Tanaka, Akiko; Honma, Teruki

2012-04-23

In this study, machine learning using support vector machine was combined with three-dimensional (3D) molecular shape overlay, to improve the screening efficiency. Since the 3D molecular shape overlay does not use fingerprints or descriptors to compare two compounds, unlike 2D similarity methods, the application of machine learning to a 3D shape-based method has not been extensively investigated. The 3D similarity profile of a compound is defined as the array of 3D shape similarities with multiple known active compounds of the target protein and is used as the explanatory variable of support vector machine. As the measures of 3D shape similarity for our new prediction models, the prediction performances of the 3D shape similarity metrics implemented in ROCS, such as ShapeTanimoto and ScaledColor, were validated, using the known inhibitors of 15 target proteins derived from the ChEMBL database. The learning models based on the 3D similarity profiles stably outperformed the original ROCS when more than 10 known inhibitors were available as the queries. The results demonstrated the advantages of combining machine learning with the 3D similarity profile to process the 3D shape information of plural active compounds.

Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity

Science.gov (United States)

Huang, Bing; von Lilienfeld, O. Anatole

2016-10-01

The predictive accuracy of Machine Learning (ML) models of molecular properties depends on the choice of the molecular representation. Inspired by the postulates of quantum mechanics, we introduce a hierarchy of representations which meet uniqueness and target similarity criteria. To systematically control target similarity, we simply rely on interatomic many body expansions, as implemented in universal force-fields, including Bonding, Angular (BA), and higher order terms. Addition of higher order contributions systematically increases similarity to the true potential energy and predictive accuracy of the resulting ML models. We report numerical evidence for the performance of BAML models trained on molecular properties pre-calculated at electron-correlated and density functional theory level of theory for thousands of small organic molecules. Properties studied include enthalpies and free energies of atomization, heat capacity, zero-point vibrational energies, dipole-moment, polarizability, HOMO/LUMO energies and gap, ionization potential, electron affinity, and electronic excitations. After training, BAML predicts energies or electronic properties of out-of-sample molecules with unprecedented accuracy and speed.

Machine Learning Technologies and Their Applications for Science and Engineering Domains Workshop -- Summary Report

Science.gov (United States)

Ambur, Manjula; Schwartz, Katherine G.; Mavris, Dimitri N.

2016-01-01

The fields of machine learning and big data analytics have made significant advances in recent years, which has created an environment where cross-fertilization of methods and collaborations can achieve previously unattainable outcomes. The Comprehensive Digital Transformation (CDT) Machine Learning and Big Data Analytics team planned a workshop at NASA Langley in August 2016 to unite leading experts the field of machine learning and NASA scientists and engineers. The primary goal for this workshop was to assess the state-of-the-art in this field, introduce these leading experts to the aerospace and science subject matter experts, and develop opportunities for collaboration. The workshop was held over a three day-period with lectures from 15 leading experts followed by significant interactive discussions. This report provides an overview of the 15 invited lectures and a summary of the key discussion topics that arose during both formal and informal discussion sections. Four key workshop themes were identified after the closure of the workshop and are also highlighted in the report. Furthermore, several workshop attendees provided their feedback on how they are already utilizing machine learning algorithms to advance their research, new methods they learned about during the workshop, and collaboration opportunities they identified during the workshop.

Potato agriculture, late blight science, and the molecularization of plant pathology.

Science.gov (United States)

Turner, R Steven

2008-01-01

By the mid-1980s nucleic-acid based methods were penetrating the farthest reaches of biological science, triggering rivalries among practitioners, altering relationships among subfields, and transforming the research front. This article delivers a "bottom up" analysis of that transformation at work in one important area of biological science, plant pathology, by tracing the "molecularization" of efforts to understand and control one notorious plant disease -- the late blight of potatoes. It mobilizes the research literature of late blight science as a tool through which to trace the changing typography of the research front from 1983 to 2003. During these years molecularization intensified the traditional fragmentation of the late blight research community, even as it dramatically integrated study of the causal organism into broader areas of biology. In these decades the pathogen responsible for late blight, the oomycete "Phytophthora infestans," was discovered to be undergoing massive, frightening, and still largely unexplained genetic diversification -- a circumstance that lends the episode examined here an urgency that reinforces its historiographical significance as a case-study in the molecularization of the biological sciences.

Application of Machine Learning tools to recognition of molecular patterns in STM images

Science.gov (United States)

Maksov, Artem; Ziatdinov, Maxim; Fujii, Shintaro; Kiguchi, Manabu; Higashibayashi, Shuhei; Sakurai, Hidehiro; Kalinin, Sergei; Sumpter, Bobby

The ability to utilize individual molecules and molecular assemblies as data storage elements has motivated scientist for years, concurrent with the continuous effort to shrink a size of data storage devices in microelectronics industry. One of the critical issues in this effort lies in being able to identify individual molecular assembly units (patterns), on a large scale in an automated fashion of complete information extraction. Here we present a novel method of applying machine learning techniques for extraction of positional and rotational information from scanning tunneling microscopy (STM) images of π-bowl sumanene molecules on gold. We use Markov Random Field (MRF) model to decode the polar rotational states for each molecule in a large scale STM image of molecular film. We further develop an algorithm that uses a convolutional Neural Network combined with MRF and input from density functional theory to classify molecules into different azimuthal rotational classes. Our results demonstrate that a molecular film is partitioned into distinctive azimuthal rotational domains consisting typically of 20-30 molecules. In each domain, the ``bowl-down'' molecules are generally surrounded by six nearest neighbor molecules in ``bowl-up'' configuration, and the resultant overall structure form a periodic lattice of rotational and polar states within each domain. Research was supported by the US Department of Energy.

Fundamental Approaches in Molecular Biology for Communication Sciences and Disorders

Science.gov (United States)

Bartlett, Rebecca S.; Jette, Marie E.; King, Suzanne N.; Schaser, Allison; Thibeault, Susan L.

2012-01-01

Purpose: This contemporary tutorial will introduce general principles of molecular biology, common deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and protein assays and their relevance in the field of communication sciences and disorders. Method: Over the past 2 decades, knowledge of the molecular pathophysiology of human disease has…

BioImg.org: A Catalog of Virtual Machine Images for the Life Sciences.

Science.gov (United States)

Dahlö, Martin; Haziza, Frédéric; Kallio, Aleksi; Korpelainen, Eija; Bongcam-Rudloff, Erik; Spjuth, Ola

2015-01-01

Virtualization is becoming increasingly important in bioscience, enabling assembly and provisioning of complete computer setups, including operating system, data, software, and services packaged as virtual machine images (VMIs). We present an open catalog of VMIs for the life sciences, where scientists can share information about images and optionally upload them to a server equipped with a large file system and fast Internet connection. Other scientists can then search for and download images that can be run on the local computer or in a cloud computing environment, providing easy access to bioinformatics environments. We also describe applications where VMIs aid life science research, including distributing tools and data, supporting reproducible analysis, and facilitating education. BioImg.org is freely available at: https://bioimg.org.

Improved Prediction of Blood-Brain Barrier Permeability Through Machine Learning with Combined Use of Molecular Property-Based Descriptors and Fingerprints.

Science.gov (United States)

Yuan, Yaxia; Zheng, Fang; Zhan, Chang-Guo

2018-03-21

Blood-brain barrier (BBB) permeability of a compound determines whether the compound can effectively enter the brain. It is an essential property which must be accounted for in drug discovery with a target in the brain. Several computational methods have been used to predict the BBB permeability. In particular, support vector machine (SVM), which is a kernel-based machine learning method, has been used popularly in this field. For SVM training and prediction, the compounds are characterized by molecular descriptors. Some SVM models were based on the use of molecular property-based descriptors (including 1D, 2D, and 3D descriptors) or fragment-based descriptors (known as the fingerprints of a molecule). The selection of descriptors is critical for the performance of a SVM model. In this study, we aimed to develop a generally applicable new SVM model by combining all of the features of the molecular property-based descriptors and fingerprints to improve the accuracy for the BBB permeability prediction. The results indicate that our SVM model has improved accuracy compared to the currently available models of the BBB permeability prediction.

Hidden Markov models and other machine learning approaches in computational molecular biology

Energy Technology Data Exchange (ETDEWEB)

Baldi, P. [California Inst. of Tech., Pasadena, CA (United States)

1995-12-31

This tutorial was one of eight tutorials selected to be presented at the Third International Conference on Intelligent Systems for Molecular Biology which was held in the United Kingdom from July 16 to 19, 1995. Computational tools are increasingly needed to process the massive amounts of data, to organize and classify sequences, to detect weak similarities, to separate coding from non-coding regions, and reconstruct the underlying evolutionary history. The fundamental problem in machine learning is the same as in scientific reasoning in general, as well as statistical modeling: to come up with a good model for the data. In this tutorial four classes of models are reviewed. They are: Hidden Markov models; artificial Neural Networks; Belief Networks; and Stochastic Grammars. When dealing with DNA and protein primary sequences, Hidden Markov models are one of the most flexible and powerful alignments and data base searches. In this tutorial, attention is focused on the theory of Hidden Markov Models, and how to apply them to problems in molecular biology.

Environmental Molecular Sciences Laboratory Annual Report: Fiscal Year 2006

Energy Technology Data Exchange (ETDEWEB)

Foster, Nancy S.; Showalter, Mary Ann

2007-03-23

This report describes the activities and research performed at the Environmental Molecular Sciences Laboratory, a Department of Energy national scientific user facility at Pacific Northwest National Laboratory, during Fiscal Year 2006.

Report of the workshop on accelerator-based atomic and molecular science

International Nuclear Information System (INIS)

Meyerhof, W.E.

1981-01-01

This Workshop, held in New London, NH on July 27-30, 1980, had a registration of 43, representing an estimated one-third of all principal investigators in the United States in this research subfield. The workshop was organized into 5 working groups for the purpose of (1) identifying some vital physics problems which experimental and theoretical atomic and molecular science can address with current and projected techniques; (2) establishing facilities and equipment needs required to realize solutions to these problems; (3) formulating suggestions for a coherent national policy concerning this discipline; (4) assessing and projecting the manpower situation; and (5) evaluating the relations of this interdisciplinary science to other fields. Recommedations deal with equipment and operating costs for small accelerator laboratories, especially at universities; instrumentation of ion beam lines dedicated to atomic and molecular science at some large accelerators; development of low-velocity, high charge-state ion sources; synchrotron light sources; improvement or replacement of tandem van de Graaff accelerators; high-energy beam lines for atomic physics; the needs for postdoctoral support in this subfield; new accelerator development; need for representatives from atomic and molecular science on program committees for large national accelerator facilities; and the contributions the field can make to applied physics problems

THE MILKY WAY PROJECT: LEVERAGING CITIZEN SCIENCE AND MACHINE LEARNING TO DETECT INTERSTELLAR BUBBLES

International Nuclear Information System (INIS)

Beaumont, Christopher N.; Williams, Jonathan P.; Goodman, Alyssa A.; Kendrew, Sarah; Simpson, Robert

2014-01-01

We present Brut, an algorithm to identify bubbles in infrared images of the Galactic midplane. Brut is based on the Random Forest algorithm, and uses bubbles identified by >35,000 citizen scientists from the Milky Way Project to discover the identifying characteristics of bubbles in images from the Spitzer Space Telescope. We demonstrate that Brut's ability to identify bubbles is comparable to expert astronomers. We use Brut to re-assess the bubbles in the Milky Way Project catalog, and find that 10%-30% of the objects in this catalog are non-bubble interlopers. Relative to these interlopers, high-reliability bubbles are more confined to the mid-plane, and display a stronger excess of young stellar objects along and within bubble rims. Furthermore, Brut is able to discover bubbles missed by previous searches—particularly bubbles near bright sources which have low contrast relative to their surroundings. Brut demonstrates the synergies that exist between citizen scientists, professional scientists, and machine learning techniques. In cases where ''untrained' citizens can identify patterns that machines cannot detect without training, machine learning algorithms like Brut can use the output of citizen science projects as input training sets, offering tremendous opportunities to speed the pace of scientific discovery. A hybrid model of machine learning combined with crowdsourced training data from citizen scientists can not only classify large quantities of data, but also address the weakness of each approach if deployed alone

THE MILKY WAY PROJECT: LEVERAGING CITIZEN SCIENCE AND MACHINE LEARNING TO DETECT INTERSTELLAR BUBBLES

Energy Technology Data Exchange (ETDEWEB)

Beaumont, Christopher N.; Williams, Jonathan P. [Institute for Astronomy, University of Hawai' i, 2680 Woodlawn Drive, Honolulu, HI 96822 (United States); Goodman, Alyssa A. [Harvard-Smithsonian Center for Astrophysics, 60 Garden Street, Cambridge, MA 02138 (United States); Kendrew, Sarah; Simpson, Robert, E-mail: beaumont@ifa.hawaii.edu [Department of Astrophysics, University of Oxford, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH (United Kingdom)

2014-09-01

We present Brut, an algorithm to identify bubbles in infrared images of the Galactic midplane. Brut is based on the Random Forest algorithm, and uses bubbles identified by >35,000 citizen scientists from the Milky Way Project to discover the identifying characteristics of bubbles in images from the Spitzer Space Telescope. We demonstrate that Brut's ability to identify bubbles is comparable to expert astronomers. We use Brut to re-assess the bubbles in the Milky Way Project catalog, and find that 10%-30% of the objects in this catalog are non-bubble interlopers. Relative to these interlopers, high-reliability bubbles are more confined to the mid-plane, and display a stronger excess of young stellar objects along and within bubble rims. Furthermore, Brut is able to discover bubbles missed by previous searches—particularly bubbles near bright sources which have low contrast relative to their surroundings. Brut demonstrates the synergies that exist between citizen scientists, professional scientists, and machine learning techniques. In cases where ''untrained' citizens can identify patterns that machines cannot detect without training, machine learning algorithms like Brut can use the output of citizen science projects as input training sets, offering tremendous opportunities to speed the pace of scientific discovery. A hybrid model of machine learning combined with crowdsourced training data from citizen scientists can not only classify large quantities of data, but also address the weakness of each approach if deployed alone.

Synthesis of a pH-Sensitive Hetero[4]Rotaxane Molecular Machine that Combines [c2]Daisy and [2]Rotaxane Arrangements.

Science.gov (United States)

Waelès, Philip; Riss-Yaw, Benjamin; Coutrot, Frédéric

2016-05-10

The synthesis of a novel pH-sensitive hetero[4]rotaxane molecular machine through a self-sorting strategy is reported. The original tetra-interlocked molecular architecture combines a [c2]daisy chain scaffold linked to two [2]rotaxane units. Actuation of the system through pH variation is possible thanks to the specific interactions of the dibenzo-24-crown-8 (DB24C8) macrocycles for ammonium, anilinium, and triazolium molecular stations. Selective deprotonation of the anilinium moieties triggers shuttling of the unsubstituted DB24C8 along the [2]rotaxane units. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Machine learning with R

CERN Document Server

Lantz, Brett

2013-01-01

Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or

Advances in Molecular Rotational Spectroscopy for Applied Science

Science.gov (United States)

Harris, Brent; Fields, Shelby S.; Pulliam, Robin; Muckle, Matt; Neill, Justin L.

2017-06-01

Advances in chemical sensitivity and robust, solid-state designs for microwave/millimeter-wave instrumentation compel the expansion of molecular rotational spectroscopy as research tool into applied science. It is familiar to consider molecular rotational spectroscopy for air analysis. Those techniques for molecular rotational spectroscopy are included in our presentation of a more broad application space for materials analysis using Fourier Transform Molecular Rotational Resonance (FT-MRR) spectrometers. There are potentially transformative advantages for direct gas analysis of complex mixtures, determination of unknown evolved gases with parts per trillion detection limits in solid materials, and unambiguous chiral determination. The introduction of FT-MRR as an alternative detection principle for analytical chemistry has created a ripe research space for the development of new analytical methods and sampling equipment to fully enable FT-MRR. We present the current state of purpose-built FT-MRR instrumentation and the latest application measurements that make use of new sampling methods.

Redox control of molecular motion in switchable artificial nanoscale devices.

Science.gov (United States)

Credi, Alberto; Semeraro, Monica; Silvi, Serena; Venturi, Margherita

2011-03-15

The design, synthesis, and operation of molecular-scale systems that exhibit controllable motions of their component parts is a topic of great interest in nanoscience and a fascinating challenge of nanotechnology. The development of this kind of species constitutes the premise to the construction of molecular machines and motors, which in a not-too-distant future could find applications in fields such as materials science, information technology, energy conversion, diagnostics, and medicine. In the past 25 years the development of supramolecular chemistry has enabled the construction of an interesting variety of artificial molecular machines. These devices operate via electronic and molecular rearrangements and, like the macroscopic counterparts, they need energy to work as well as signals to communicate with the operator. Here we outline the design principles at the basis of redox switching of molecular motion in artificial nanodevices. Redox processes, chemically, electrically, or photochemically induced, can indeed supply the energy to bring about molecular motions. Moreover, in the case of electrically and photochemically induced processes, electrochemical and photochemical techniques can be used to read the state of the system, and thus to control and monitor the operation of the device. Some selected examples are also reported to describe the most representative achievements in this research area.

Comparison of combinatorial clustering methods on pharmacological data sets represented by machine learning-selected real molecular descriptors.

Science.gov (United States)

Rivera-Borroto, Oscar Miguel; Marrero-Ponce, Yovani; García-de la Vega, José Manuel; Grau-Ábalo, Ricardo del Corazón

2011-12-27

Cluster algorithms play an important role in diversity related tasks of modern chemoinformatics, with the widest applications being in pharmaceutical industry drug discovery programs. The performance of these grouping strategies depends on various factors such as molecular representation, mathematical method, algorithmical technique, and statistical distribution of data. For this reason, introduction and comparison of new methods are necessary in order to find the model that best fits the problem at hand. Earlier comparative studies report on Ward's algorithm using fingerprints for molecular description as generally superior in this field. However, problems still remain, i.e., other types of numerical descriptions have been little exploited, current descriptors selection strategy is trial and error-driven, and no previous comparative studies considering a broader domain of the combinatorial methods in grouping chemoinformatic data sets have been conducted. In this work, a comparison between combinatorial methods is performed,with five of them being novel in cheminformatics. The experiments are carried out using eight data sets that are well established and validated in the medical chemistry literature. Each drug data set was represented by real molecular descriptors selected by machine learning techniques, which are consistent with the neighborhood principle. Statistical analysis of the results demonstrates that pharmacological activities of the eight data sets can be modeled with a few of families with 2D and 3D molecular descriptors, avoiding classification problems associated with the presence of nonrelevant features. Three out of five of the proposed cluster algorithms show superior performance over most classical algorithms and are similar (or slightly superior in the most optimistic sense) to Ward's algorithm. The usefulness of these algorithms is also assessed in a comparative experiment to potent QSAR and machine learning classifiers, where they perform

Single-molecule imaging and manipulation of biomolecular machines and systems.

Science.gov (United States)

Iino, Ryota; Iida, Tatsuya; Nakamura, Akihiko; Saita, Ei-Ichiro; You, Huijuan; Sako, Yasushi

2018-02-01

Biological molecular machines support various activities and behaviors of cells, such as energy production, signal transduction, growth, differentiation, and migration. We provide an overview of single-molecule imaging methods involving both small and large probes used to monitor the dynamic motions of molecular machines in vitro (purified proteins) and in living cells, and single-molecule manipulation methods used to measure the forces, mechanical properties and responses of biomolecules. We also introduce several examples of single-molecule analysis, focusing primarily on motor proteins and signal transduction systems. Single-molecule analysis is a powerful approach to unveil the operational mechanisms both of individual molecular machines and of systems consisting of many molecular machines. Quantitative, high-resolution single-molecule analyses of biomolecular systems at the various hierarchies of life will help to answer our fundamental question: "What is life?" This article is part of a Special Issue entitled "Biophysical Exploration of Dynamical Ordering of Biomolecular Systems" edited by Dr. Koichi Kato. Copyright © 2017 Elsevier B.V. All rights reserved.

Machine learning properties of materials and molecules with entropy-regularized kernels

Science.gov (United States)

Ceriotti, Michele; Bartók, Albert; CsáNyi, GáBor; de, Sandip

Application of machine-learning methods to physics, chemistry and materials science is gaining traction as a strategy to obtain accurate predictions of the properties of matter at a fraction of the typical cost of quantum mechanical electronic structure calculations. In this endeavor, one can leverage general-purpose frameworks for supervised-learning. It is however very important that the input data - for instance the positions of atoms in a molecule or solid - is processed into a form that reflects all the underlying physical symmetries of the problem, and that possesses the regularity properties that are required by machine-learning algorithms. Here we introduce a general strategy to build a representation of this kind. We will start from existing approaches to compare local environments (basically, groups of atoms), and combine them using techniques borrowed from optimal transport theory, discussing the relation between this idea and additive energy decompositions. We will present a few examples demonstrating the potential of this approach as a tool to predict molecular and materials' properties with an accuracy on par with state-of-the-art electronic structure methods. MARVEL NCCR (Swiss National Science Foundation) and ERC StG HBMAP (European Research Council, G.A. 677013).

Integration of Molecular Pathology, Epidemiology, and Social Science for Global Precision Medicine

Science.gov (United States)

Nishi, Akihiro; Milner, Danny A; Giovannucci, Edward L.; Nishihara, Reiko; Tan, Andy S.; Kawachi, Ichiro; Ogino, Shuji

2015-01-01

Summary The precision medicine concept and the unique disease principle imply that each patient has unique pathogenic processes resulting from heterogeneous cellular genetic and epigenetic alterations, and interactions between cells (including immune cells) and exposures, including dietary, environmental, microbial, and lifestyle factors. As a core method field in population health science and medicine, epidemiology is a growing scientific discipline that can analyze disease risk factors, and develop statistical methodologies to maximize utilization of big data on populations and disease pathology. The evolving transdisciplinary field of molecular pathological epidemiology (MPE) can advance biomedical and health research by linking exposures to molecular pathologic signatures, enhancing causal inference, and identifying potential biomarkers for clinical impact. The MPE approach can be applied to any diseases, although it has been most commonly used in neoplastic diseases (including breast, lung and colorectal cancers) because of availability of various molecular diagnostic tests. However, use of state-of-the-art genomic, epigenomic and other omic technologies and expensive drugs in modern healthcare systems increases racial, ethnic and socioeconomic disparities. To address this, we propose to integrate molecular pathology, epidemiology, and social science. Social epidemiology integrates the latter two fields. The integrative social MPE model can embrace sociology, economics and precision medicine, address global health disparities and inequalities, and elucidate biological effects of social environments, behaviors, and networks. We foresee advancements of molecular medicine, including molecular diagnostics, biomedical imaging, and targeted therapeutics, which should benefit individuals in a global population, by means of an interdisciplinary approach of integrative MPE and social health science. PMID:26636627

Integration of molecular pathology, epidemiology and social science for global precision medicine.

Science.gov (United States)

Nishi, Akihiro; Milner, Danny A; Giovannucci, Edward L; Nishihara, Reiko; Tan, Andy S; Kawachi, Ichiro; Ogino, Shuji

2016-01-01

The precision medicine concept and the unique disease principle imply that each patient has unique pathogenic processes resulting from heterogeneous cellular genetic and epigenetic alterations and interactions between cells (including immune cells) and exposures, including dietary, environmental, microbial and lifestyle factors. As a core method field in population health science and medicine, epidemiology is a growing scientific discipline that can analyze disease risk factors and develop statistical methodologies to maximize utilization of big data on populations and disease pathology. The evolving transdisciplinary field of molecular pathological epidemiology (MPE) can advance biomedical and health research by linking exposures to molecular pathologic signatures, enhancing causal inference and identifying potential biomarkers for clinical impact. The MPE approach can be applied to any diseases, although it has been most commonly used in neoplastic diseases (including breast, lung and colorectal cancers) because of availability of various molecular diagnostic tests. However, use of state-of-the-art genomic, epigenomic and other omic technologies and expensive drugs in modern healthcare systems increases racial, ethnic and socioeconomic disparities. To address this, we propose to integrate molecular pathology, epidemiology and social science. Social epidemiology integrates the latter two fields. The integrative social MPE model can embrace sociology, economics and precision medicine, address global health disparities and inequalities, and elucidate biological effects of social environments, behaviors and networks. We foresee advancements of molecular medicine, including molecular diagnostics, biomedical imaging and targeted therapeutics, which should benefit individuals in a global population, by means of an interdisciplinary approach of integrative MPE and social health science.

Twin support vector machines models, extensions and applications

CERN Document Server

Jayadeva; Chandra, Suresh

2017-01-01

This book provides a systematic and focused study of the various aspects of twin support vector machines (TWSVM) and related developments for classification and regression. In addition to presenting most of the basic models of TWSVM and twin support vector regression (TWSVR) available in the literature, it also discusses the important and challenging applications of this new machine learning methodology. A chapter on “Additional Topics” has been included to discuss kernel optimization and support tensor machine topics, which are comparatively new but have great potential in applications. It is primarily written for graduate students and researchers in the area of machine learning and related topics in computer science, mathematics, electrical engineering, management science and finance.

Can machine learning explain human learning?

NARCIS (Netherlands)

Vahdat, M.; Oneto, L.; Anguita, D.; Funk, M.; Rauterberg, G.W.M.

2016-01-01

Learning Analytics (LA) has a major interest in exploring and understanding the learning process of humans and, for this purpose, benefits from both Cognitive Science, which studies how humans learn, and Machine Learning, which studies how algorithms learn from data. Usually, Machine Learning is

Multi-phase alternative current machine winding design | Khan ...

African Journals Online (AJOL)

... single-phase to 18-phase excitation. Experimental results of a five-phase induction machine supplied from a static five-phase supply are provided to support the proposed design. Keywords: AC machine, Multi-phase machine, Stator winding, Five-phase. International Journal of Engineering, Science and Technology, Vol.

Historical and Epistemological Reflections on the Culture of Machines around the Renaissance: Machines, Machineries and Perpetual Motion

Directory of Open Access Journals (Sweden)

Raffaele Pisano

2015-05-01

Full Text Available This paper is the second part of our recent paper ‘Historical and Epistemological Reflections on the Culture of Machines around the Renaissance: How Science and Technique Work’ (Pisano & Bussotti 2014a. In the first paper—which discussed some aspects of the relations between science and technology from Antiquity to the Renaissance—we highlighted the differences between the Aristotelian/Euclidean tradition and the Archimedean tradition. We also pointed out the way in which the two traditions were perceived around the Renaissance. The Archimedean tradition is connected with machines: its relationship with science and construction of machines should be made clear. It is enough to think that Archimedes mainly dealt with three machines: lever, pulley and screw (and a correlated principle of mechanical advantage. As underlined in the first part, our thesis is that many machines were constructed by people who ignored theory, even though, in other cases, the knowledge of the Archimedean tradition was a precious help in order to build machines. Hence, an a priori idea as to the relations between the Archimedean tradition and construction of machines cannot exist. In this second part we offer some examples of functioning machines constructed by people who ignored any physical theory, whereas, in other cases, the ignorance of some principles—such as the impossibility of a perpetuum mobile—induced the attempt to construct impossible machines. What is very interesting is that these machines did not function, of course, as a perpetuum mobile, but anyway had their functioning and were useful for certain aims, although they were constructed on an idea which is completely wrong from a theoretical point of view. We mainly focus on the Renaissance and early modern period, but we also provide examples of machines built before and after this period. We have followed a chronological order in both parts, starting from the analysis of the situation in

Molecular Gastronomy: A Food Fad or an Interface for Science-based Cooking?

NARCIS (Netherlands)

Linden, van der E.; McClements, D.J.; Ubbink, J.

2008-01-01

A review is given over the field of molecular gastronomy and its relation to science and cooking. We begin with a brief history of the field of molecular gastronomy, the definition of the term itself, and the current controversy surrounding this term. We then highlight the distinction between

Corporate Disruption in the Science of Machine Learning

OpenAIRE

Work, Sam

2016-01-01

This MSc dissertation considers the effects of the current corporate interest on researchers in the field of machine learning. Situated within the field's cyclical history of academic, public and corporate interest, this dissertation investigates how current researchers view recent developments and negotiate their own research practices within an environment of increased commercial interest and funding. The original research consists of in-depth interviews with 12 machine learning researchers...

The Environmental and Molecular Sciences Laboratory project -- Continuous evolution in leadership

International Nuclear Information System (INIS)

Knutson, D.E.; McClusky, J.K.

1994-10-01

The Environmental and Molecular Sciences Laboratory (EMSL) construction project at Pacific Northwest Laboratory (PNL) in Richland, Washington, is a $230M Major Systems Acquisition for the US Department of Energy (DOE). The completed laboratory will be a national user facility that provides unparalleled capabilities for scientists involved in environmental molecular science research. This project, approved for construction by the Secretary of Energy in October 1993, is underway. The United States is embarking on an environmental cleanup effort that dwarfs previous scientific enterprise. Using current best available technology, the projected costs of cleaning up the tens of thousands of toxic waste sites, including DOE sites, is estimated to exceed one trillion dollars. The present state of scientific knowledge regarding the effects of exogenous chemicals on human biology is very limited. Long term environmental research at the molecular level is needed to resolve the concerns, and form the building blocks for a structure of cost effective process improvement and regulatory reform

The Environmental and Molecular Sciences Laboratory project -- Continuous evolution in leadership

Energy Technology Data Exchange (ETDEWEB)

Knutson, D.E.; McClusky, J.K.

1994-10-01

The Environmental and Molecular Sciences Laboratory (EMSL) construction project at Pacific Northwest Laboratory (PNL) in Richland, Washington, is a $230M Major Systems Acquisition for the US Department of Energy (DOE). The completed laboratory will be a national user facility that provides unparalleled capabilities for scientists involved in environmental molecular science research. This project, approved for construction by the Secretary of Energy in October 1993, is underway. The United States is embarking on an environmental cleanup effort that dwarfs previous scientific enterprise. Using current best available technology, the projected costs of cleaning up the tens of thousands of toxic waste sites, including DOE sites, is estimated to exceed one trillion dollars. The present state of scientific knowledge regarding the effects of exogenous chemicals on human biology is very limited. Long term environmental research at the molecular level is needed to resolve the concerns, and form the building blocks for a structure of cost effective process improvement and regulatory reform.

Advances in Machine Learning and Data Mining for Astronomy

Science.gov (United States)

Way, Michael J.; Scargle, Jeffrey D.; Ali, Kamal M.; Srivastava, Ashok N.

2012-03-01

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications. With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.

Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space

International Nuclear Information System (INIS)

Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan; Pronobis, Wiktor; Lilienfeld, O. Anatole von; Müller, Klaus-Robert; Tkatchenko, Alexandre

2015-01-01

Simultaneously accurate and efficient prediction of molecular properties throughout chemical compound space is a critical ingredient toward rational compound design in chemical and pharmaceutical industries. Aiming toward this goal, we develop and apply a systematic hierarchy of efficient empirical methods to estimate atomization and total energies of molecules. These methods range from a simple sum over atoms, to addition of bond energies, to pairwise interatomic force fields, reaching to the more sophisticated machine learning approaches that are capable of describing collective interactions between many atoms or bonds. In the case of equilibrium molecular geometries, even simple pairwise force fields demonstrate prediction accuracy comparable to benchmark energies calculated using density functional theory with hybrid exchange-correlation functionals; however, accounting for the collective many-body interactions proves to be essential for approaching the 'holy grail' of chemical accuracy of 1 kcal/mol for both equilibrium and out-of-equilibrium geometries. This remarkable accuracy is achieved by a vectorized representation of molecules (so-called Bag of Bonds model) that exhibits strong nonlocality in chemical space. The same representation allows us to predict accurate electronic properties of molecules, such as their polarizability and molecular frontier orbital energies

Building a Collaboratory in Environmental and Molecular Science

Energy Technology Data Exchange (ETDEWEB)

Kouzes, R.T.; Myers, J.D.; Devaney, D.M.; Dunning, T.H.; Wise, J.A.

1994-03-01

A Collaboratory is a meta-laboratory that spans multiple geographical areas with collaborators interacting via electronic means. Collaboratories are designed to enable close ties between scientists in a given research area, promote collaborations involving scientists in diverse areas, accelerate the development and dissemination of basic knowledge, and minimize the time-lag between discovery and application. PNL is developing the concept of an Environmental and Molecular Sciences Collaboratory (EMSC) as a natural evolution of the EMSL project. The goal of the EMSC is to increase the efficiency of research and reduce the time required to implement new environmental remediation and preservation technologies. The EMSC will leverage the resources (intellectual and physical) of the EMSL by making them more accessible to remote collaborators as well as by making the resources of remote sites available to local researchers. It will provide a common set of computer hardware and software tools to support remote collaboration, a key step in establishing a collaborative culture for scientists in the theoretical, computational, and experimental molecular sciences across the nation. In short, the EMSC will establish and support an `electronic community of scientists researching and developing innovative environmental preservation and restoration technologies.

Building a Collaboratory in Environmental and Molecular Science

International Nuclear Information System (INIS)

Kouzes, R.T.; Myers, J.D.; Devaney, D.M.; Dunning, T.H.; Wise, J.A.

1994-03-01

A Collaboratory is a meta-laboratory that spans multiple geographical areas with collaborators interacting via electronic means. Collaboratories are designed to enable close ties between scientists in a given research area, promote collaborations involving scientists in diverse areas, accelerate the development and dissemination of basic knowledge, and minimize the time-lag between discovery and application. PNL is developing the concept of an Environmental and Molecular Sciences Collaboratory (EMSC) as a natural evolution of the EMSL project. The goal of the EMSC is to increase the efficiency of research and reduce the time required to implement new environmental remediation and preservation technologies. The EMSC will leverage the resources (intellectual and physical) of the EMSL by making them more accessible to remote collaborators as well as by making the resources of remote sites available to local researchers. It will provide a common set of computer hardware and software tools to support remote collaboration, a key step in establishing a collaborative culture for scientists in the theoretical, computational, and experimental molecular sciences across the nation. In short, the EMSC will establish and support an 'electronic community of scientists researching and developing innovative environmental preservation and restoration technologies

Natural Nano-Machines

Indian Academy of Sciences (India)

Administrator

transport, ion pump, ATP syn- thase. A popularized ..... gas. A lice: I could not understand how A T P m olecules serve as fuels for m olecular m achines. ..... [16] V Balzani, M Venturi and A Credi, Molecular Devices and Machines: a Journey into ...

Amplification macroscopique de mouvements nanométriques induits par des machines moléculaires

OpenAIRE

Goujon , Antoine

2016-01-01

The last twenty years have seen tremendous progresses in the design and synthesis of complex molecular machines, often inspired by the beauty of the machinery found in biological systems. However, amplification of the molecular machines motion over several orders of magnitude above their typical length scale is still an ambitious challenge. This work describes how self-organization of molecular machines or motors allows for the synthesis of materials translating the motions of their component...

An art history of machines?

Directory of Open Access Journals (Sweden)

Daniel Bridgman

2016-12-01

Full Text Available A toast offered in honor of Donald Preziosi on the cusp of his seventy-fifth birthday, this essay considers a range of machine metaphors, their art historical settings, and their implications. Addressing the mythography of Daedalus and his wonder machines in relation to art history’s machinic enterprises, an ancient art-archaeology seminar Preziosi directed at UCLA (in 1988 and the book, Rethinking Art History: Meditations on a Coy Science (1989 form the focus of my thinking about Preziosi’s work. At issue across the essay is the work of recursion, when machines make machines and in so doing create a recessive subjectivity for the maker. The essay ends with the speculation that art history’s disciplinary machinery may owe its generative strength to a perpetual need for replacement parts.

How to build a time machine: the real science of time travel

CERN Document Server

Clegg, Brian

2013-01-01

A pop science look at time travel technology, from Einstein to Ronald Mallett to present day experiments. Forget fiction: time travel is real.In How to Build a Time Machine, Brian Clegg provides an understanding of what time is and how it can be manipulated. He explores the fascinating world of physics and the remarkable possibilities of real time travel that emerge from quantum entanglement, superluminal speeds, neutron star cylinders and wormholes in space. With the fascinating paradoxes of time travel echoing in our minds will we realize that travel into the future might never be possible? Or will we realize there is no limit on what can be achieved, and take on this ultimate challenge? Only time will tell.

Machine learning for adaptive many-core machines a practical approach

CERN Document Server

Lopes, Noel

2015-01-01

The overwhelming data produced everyday and the increasing performance and cost requirements of applications?are transversal to a wide range of activities in society, from science to industry. In particular, the magnitude and complexity of the tasks that Machine Learning (ML) algorithms have to solve are driving the need to devise adaptive many-core machines that scale well with the volume of data, or in other words, can handle Big Data.This book gives a concise view on how to extend the applicability of well-known ML algorithms in Graphics Processing Unit (GPU) with data scalability in mind.

Deep learning: Using machine learning to study biological vision

OpenAIRE

Majaj, Najib; Pelli, Denis

2017-01-01

Today most vision-science presentations mention machine learning. Many neuroscientists use machine learning to decode neural responses. Many perception scientists try to understand recognition by living organisms. To them, machine learning offers a reference of attainable performance based on learned stimuli. This brief overview of the use of machine learning in biological vision touches on its strengths, weaknesses, milestones, controversies, and current directions.

Scientific data management in the environmental molecular sciences laboratory

Energy Technology Data Exchange (ETDEWEB)

Bernard, P.R.; Keller, T.L.

1995-09-01

The Environmental Molecular Sciences Laboratory (EMSL) is currently under construction at Pacific Northwest Laboratory (PNL) for the U.S. Department of Energy (DOE). This laboratory will be used for molecular and environmental sciences research to identify comprehensive solutions to DOE`s environmental problems. Major facilities within the EMSL include the Molecular Sciences Computing Facility (MSCF), a laser-surface dynamics laboratory, a high-field nuclear magnetic resonance (NMR) laboratory, and a mass spectrometry laboratory. The EMSL is scheduled to open early in 1997 and will house about 260 resident and visiting scientists. It is anticipated that at least six (6) terabytes of data will be archived in the first year of operation. An object-oriented database management system (OODBMS) and a mass storage system will be integrated to provide an intelligent, automated mechanism to manage data. The resulting system, called the DataBase Computer System (DBCS), will provide total scientific data management capabilities to EMSL users. A prototype mass storage system based on the National Storage Laboratory`s (NSL) UniTree has been procured and is in limited use. This system consists of two independent hierarchies of storage devices. One hierarchy of lower capacity, slower speed devices provides support for smaller files transferred over the Fiber Distributed Data Interface (FDDI) network. Also part of the system is a second hierarchy of higher capacity, higher speed devices that will be used to support high performance clients (e.g., a large scale parallel processor). The ObjectStore OODBMS will be used to manage metadata for archived datasets, maintain relationships between archived datasets, and -hold small, duplicate subsets of archived datasets (i.e., derivative data). The interim system is called DBCS, Phase 0 (DBCS-0). The production system for the EMSL, DBCS Phase 1 (DBCS-1), will be procured and installed in the summer of 1996.

Analysing and Rationalising Molecular and Materials Databases Using Machine-Learning

Science.gov (United States)

de, Sandip; Ceriotti, Michele

Computational materials design promises to greatly accelerate the process of discovering new or more performant materials. Several collaborative efforts are contributing to this goal by building databases of structures, containing between thousands and millions of distinct hypothetical compounds, whose properties are computed by high-throughput electronic-structure calculations. The complexity and sheer amount of information has made manual exploration, interpretation and maintenance of these databases a formidable challenge, making it necessary to resort to automatic analysis tools. Here we will demonstrate how, starting from a measure of (dis)similarity between database items built from a combination of local environment descriptors, it is possible to apply hierarchical clustering algorithms, as well as dimensionality reduction methods such as sketchmap, to analyse, classify and interpret trends in molecular and materials databases, as well as to detect inconsistencies and errors. Thanks to the agnostic and flexible nature of the underlying metric, we will show how our framework can be applied transparently to different kinds of systems ranging from organic molecules and oligopeptides to inorganic crystal structures as well as molecular crystals. Funded by National Center for Computational Design and Discovery of Novel Materials (MARVEL) and Swiss National Science Foundation.

Molecular tools for bathing water assessment in Europe: Balancing social science research with a rapidly developing environmental science evidence-base.

Science.gov (United States)

Oliver, David M; Hanley, Nick D; van Niekerk, Melanie; Kay, David; Heathwaite, A Louise; Rabinovici, Sharyl J M; Kinzelman, Julie L; Fleming, Lora E; Porter, Jonathan; Shaikh, Sabina; Fish, Rob; Chilton, Sue; Hewitt, Julie; Connolly, Elaine; Cummins, Andy; Glenk, Klaus; McPhail, Calum; McRory, Eric; McVittie, Alistair; Giles, Amanna; Roberts, Suzanne; Simpson, Katherine; Tinch, Dugald; Thairs, Ted; Avery, Lisa M; Vinten, Andy J A; Watts, Bill D; Quilliam, Richard S

2016-02-01

The use of molecular tools, principally qPCR, versus traditional culture-based methods for quantifying microbial parameters (e.g., Fecal Indicator Organisms) in bathing waters generates considerable ongoing debate at the science-policy interface. Advances in science have allowed the development and application of molecular biological methods for rapid (~2 h) quantification of microbial pollution in bathing and recreational waters. In contrast, culture-based methods can take between 18 and 96 h for sample processing. Thus, molecular tools offer an opportunity to provide a more meaningful statement of microbial risk to water-users by providing near-real-time information enabling potentially more informed decision-making with regard to water-based activities. However, complementary studies concerning the potential costs and benefits of adopting rapid methods as a regulatory tool are in short supply. We report on findings from an international Working Group that examined the breadth of social impacts, challenges, and research opportunities associated with the application of molecular tools to bathing water regulations.

Advances in machine learning and data mining for astronomy

CERN Document Server

Way, Michael J

2012-01-01

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health

Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error.

Science.gov (United States)

Faber, Felix A; Hutchison, Luke; Huang, Bing; Gilmer, Justin; Schoenholz, Samuel S; Dahl, George E; Vinyals, Oriol; Kearnes, Steven; Riley, Patrick F; von Lilienfeld, O Anatole

2017-11-14

We investigate the impact of choosing regressors and molecular representations for the construction of fast machine learning (ML) models of 13 electronic ground-state properties of organic molecules. The performance of each regressor/representation/property combination is assessed using learning curves which report out-of-sample errors as a function of training set size with up to ∼118k distinct molecules. Molecular structures and properties at the hybrid density functional theory (DFT) level of theory come from the QM9 database [ Ramakrishnan et al. Sci. Data 2014 , 1 , 140022 ] and include enthalpies and free energies of atomization, HOMO/LUMO energies and gap, dipole moment, polarizability, zero point vibrational energy, heat capacity, and the highest fundamental vibrational frequency. Various molecular representations have been studied (Coulomb matrix, bag of bonds, BAML and ECFP4, molecular graphs (MG)), as well as newly developed distribution based variants including histograms of distances (HD), angles (HDA/MARAD), and dihedrals (HDAD). Regressors include linear models (Bayesian ridge regression (BR) and linear regression with elastic net regularization (EN)), random forest (RF), kernel ridge regression (KRR), and two types of neural networks, graph convolutions (GC) and gated graph networks (GG). Out-of sample errors are strongly dependent on the choice of representation and regressor and molecular property. Electronic properties are typically best accounted for by MG and GC, while energetic properties are better described by HDAD and KRR. The specific combinations with the lowest out-of-sample errors in the ∼118k training set size limit are (free) energies and enthalpies of atomization (HDAD/KRR), HOMO/LUMO eigenvalue and gap (MG/GC), dipole moment (MG/GC), static polarizability (MG/GG), zero point vibrational energy (HDAD/KRR), heat capacity at room temperature (HDAD/KRR), and highest fundamental vibrational frequency (BAML/RF). We present numerical

Advanced molecular devices based on light-driven molecular motors

NARCIS (Netherlands)

Chen, Jiawen

2015-01-01

Nature has provided a large collection of molecular machines and devices that are among the most amazing nanostructures on this planet. These machines are able to operate complex biological processes which are of great importance in our organisms. Inspired by these natural devices, artificial

Environmental Molecular Sciences Laboratory Operations System: Version 4.0 - system requirements specification

Energy Technology Data Exchange (ETDEWEB)

Kashporenko, D.

1996-07-01

This document is intended to provide an operations standard for the Environmental Molecular Sciences Laboratory OPerations System (EMSL OPS). It is directed toward three primary audiences: (1) Environmental Molecular Sciences Laboratory (EMSL) facility and operations personnel; (2) laboratory line managers and staff; and (3) researchers, equipment operators, and laboratory users. It is also a statement of system requirements for software developers of EMSL OPS. The need for a finely tuned, superior research environment as provided by the US Department of Energy`s (DOE) Environmental Molecular Sciences Laboratory has never been greater. The abrupt end of the Cold War and the realignment of national priorities caused major US and competing overseas laboratories to reposition themselves in a highly competitive research marketplace. For a new laboratory such as the EMSL, this means coming into existence in a rapidly changing external environment. For any major laboratory, these changes create funding uncertainties and increasing global competition along with concomitant demands for higher standards of research product quality and innovation. While more laboratories are chasing fewer funding dollars, research ideas and proposals, especially for molecular-level research in the materials and biological sciences, are burgeoning. In such an economically constrained atmosphere, reduced costs, improved productivity, and strategic research project portfolio building become essential to establish and maintain any distinct competitive advantage. For EMSL, this environment and these demands require clear operational objectives, specific goals, and a well-crafted strategy. Specific goals will evolve and change with the evolution of the nature and definition of DOE`s environmental research needs. Hence, EMSL OPS is designed to facilitate migration of these changes with ease into every pertinent job function, creating a facile {open_quotes}learning organization.{close_quotes}

Environmental Molecular Sciences Laboratory 2004 Annual Report

Energy Technology Data Exchange (ETDEWEB)

White, Julia C.

2005-04-17

This 2004 Annual Report describes the research and accomplishments of staff and users of the W.R. Wiley Environmental Molecular Sciences Laboratory (EMSL), located in Richland, Washington. EMSL is a multidisciplinary, national scientific user facility and research organization, operated by Pacific Northwest National Laboratory (PNNL) for the U.S. Department of Energy's Office of Biological and Environmental Research. The resources and opportunities within the facility are an outgrowth of the U.S. Department of Energy's (DOE) commitment to fundamental research for understanding and resolving environmental and other critical scientific issues.

Design of full body workout machine

OpenAIRE

Pathak, Suman

2017-01-01

The purpose of this thesis was to design a full body workout machine. The main goal was to make a workout machine that was inexpensive, covered a small area and light in weight. This thesis was commissioned by HAMK University of Applied Sciences. This topic was proposed by the author himself after realizing the need for an ideal workout machine that would fulfil all one´s requirements. He had been going to gym regularly for two years. Based on his experience, gyms were overcrowded with eq...

Constant size descriptors for accurate machine learning models of molecular properties

Science.gov (United States)

Collins, Christopher R.; Gordon, Geoffrey J.; von Lilienfeld, O. Anatole; Yaron, David J.

2018-06-01

Two different classes of molecular representations for use in machine learning of thermodynamic and electronic properties are studied. The representations are evaluated by monitoring the performance of linear and kernel ridge regression models on well-studied data sets of small organic molecules. One class of representations studied here counts the occurrence of bonding patterns in the molecule. These require only the connectivity of atoms in the molecule as may be obtained from a line diagram or a SMILES string. The second class utilizes the three-dimensional structure of the molecule. These include the Coulomb matrix and Bag of Bonds, which list the inter-atomic distances present in the molecule, and Encoded Bonds, which encode such lists into a feature vector whose length is independent of molecular size. Encoded Bonds' features introduced here have the advantage of leading to models that may be trained on smaller molecules and then used successfully on larger molecules. A wide range of feature sets are constructed by selecting, at each rank, either a graph or geometry-based feature. Here, rank refers to the number of atoms involved in the feature, e.g., atom counts are rank 1, while Encoded Bonds are rank 2. For atomization energies in the QM7 data set, the best graph-based feature set gives a mean absolute error of 3.4 kcal/mol. Inclusion of 3D geometry substantially enhances the performance, with Encoded Bonds giving 2.4 kcal/mol, when used alone, and 1.19 kcal/mol, when combined with graph features.

Elements in nucleotide sensing and hydrolysis of the AAA+ disaggregation machine ClpB: a structure-based mechanistic dissection of a molecular motor

Energy Technology Data Exchange (ETDEWEB)

Zeymer, Cathleen, E-mail: cathleen.zeymer@mpimf-heidelberg.mpg.de; Barends, Thomas R. M.; Werbeck, Nicolas D.; Schlichting, Ilme; Reinstein, Jochen, E-mail: cathleen.zeymer@mpimf-heidelberg.mpg.de [Max Planck Institute for Medical Research, Jahnstrasse 29, 69120 Heidelberg (Germany)

2014-02-01

High-resolution crystal structures together with mutational analysis and transient kinetics experiments were utilized to understand nucleotide sensing and the regulation of the ATPase cycle in an AAA+ molecular motor. ATPases of the AAA+ superfamily are large oligomeric molecular machines that remodel their substrates by converting the energy from ATP hydrolysis into mechanical force. This study focuses on the molecular chaperone ClpB, the bacterial homologue of Hsp104, which reactivates aggregated proteins under cellular stress conditions. Based on high-resolution crystal structures in different nucleotide states, mutational analysis and nucleotide-binding kinetics experiments, the ATPase cycle of the C-terminal nucleotide-binding domain (NBD2), one of the motor subunits of this AAA+ disaggregation machine, is dissected mechanistically. The results provide insights into nucleotide sensing, explaining how the conserved sensor 2 motif contributes to the discrimination between ADP and ATP binding. Furthermore, the role of a conserved active-site arginine (Arg621), which controls binding of the essential Mg{sup 2+} ion, is described. Finally, a hypothesis is presented as to how the ATPase activity is regulated by a conformational switch that involves the essential Walker A lysine. In the proposed model, an unusual side-chain conformation of this highly conserved residue stabilizes a catalytically inactive state, thereby avoiding unnecessary ATP hydrolysis.

Elements in nucleotide sensing and hydrolysis of the AAA+ disaggregation machine ClpB: a structure-based mechanistic dissection of a molecular motor

International Nuclear Information System (INIS)

Zeymer, Cathleen; Barends, Thomas R. M.; Werbeck, Nicolas D.; Schlichting, Ilme; Reinstein, Jochen

2014-01-01

High-resolution crystal structures together with mutational analysis and transient kinetics experiments were utilized to understand nucleotide sensing and the regulation of the ATPase cycle in an AAA+ molecular motor. ATPases of the AAA+ superfamily are large oligomeric molecular machines that remodel their substrates by converting the energy from ATP hydrolysis into mechanical force. This study focuses on the molecular chaperone ClpB, the bacterial homologue of Hsp104, which reactivates aggregated proteins under cellular stress conditions. Based on high-resolution crystal structures in different nucleotide states, mutational analysis and nucleotide-binding kinetics experiments, the ATPase cycle of the C-terminal nucleotide-binding domain (NBD2), one of the motor subunits of this AAA+ disaggregation machine, is dissected mechanistically. The results provide insights into nucleotide sensing, explaining how the conserved sensor 2 motif contributes to the discrimination between ADP and ATP binding. Furthermore, the role of a conserved active-site arginine (Arg621), which controls binding of the essential Mg 2+ ion, is described. Finally, a hypothesis is presented as to how the ATPase activity is regulated by a conformational switch that involves the essential Walker A lysine. In the proposed model, an unusual side-chain conformation of this highly conserved residue stabilizes a catalytically inactive state, thereby avoiding unnecessary ATP hydrolysis

A preliminary exploration of the advanced molecular bio-sciences research center

International Nuclear Information System (INIS)

Yanai, Takanori; Yamada, Yutaka; Tanaka, Kimio; Yamagami, Mutsumi; Sota, Masahiro; Takemura, Tatsuo; Koyama, Kenji; Sato, Fumiaki

2001-01-01

Low dose and low dose rate radiation effects on lifespan, pathological changes, hemopoiesis and cytokine production in mice have been investigated in our laboratory. In the intermediate period of the investigation, an expert committee on radiation biology was organized. The purposes of the committee were to assess previous studies and advise on a future research plan for the Advanced Molecular Bio-Sciences Research Center (AMBIC). The committee emphasized the necessity of molecular research in radiation biology, and proposed the following five subjects: 1) molecular carcinogenesis by low dose radiation; 2) radiation effects on the immune and hemopoietic systems; 3) molecular mechanisms of hereditary effect; 4) noncancer diseases of low dose radiation, and 5) cellular mechanisms by low dose radiation. (author)

New trends in educational activity in the field of mechanism and machine theory

CERN Document Server

Castejon, Cristina

2014-01-01

The First International Symposium on the Education in Mechanism and Machine Science (ISEMMS 2013) aimed to create a stable platform for the interchange of experience among researches of mechanism and machine science. Topics treated include contributions on subjects such as new trends and experiences in mechanical engineering education; mechanism and machine science in mechanical engineering curricula; MMS in engineering programs, such as, for example, methodology, virtual labs and new laws. All papers have been rigorously reviewed and represent the state of the art in their field.

Micro-machined calorimetric biosensors

Science.gov (United States)

Doktycz, Mitchel J.; Britton, Jr., Charles L.; Smith, Stephen F.; Oden, Patrick I.; Bryan, William L.; Moore, James A.; Thundat, Thomas G.; Warmack, Robert J.

2002-01-01

A method and apparatus are provided for detecting and monitoring micro-volumetric enthalpic changes caused by molecular reactions. Micro-machining techniques are used to create very small thermally isolated masses incorporating temperature-sensitive circuitry. The thermally isolated masses are provided with a molecular layer or coating, and the temperature-sensitive circuitry provides an indication when the molecules of the coating are involved in an enthalpic reaction. The thermally isolated masses may be provided singly or in arrays and, in the latter case, the molecular coatings may differ to provide qualitative and/or quantitative assays of a substance.

Mitochondrial AAA proteases--towards a molecular understanding of membrane-bound proteolytic machines.

Science.gov (United States)

Gerdes, Florian; Tatsuta, Takashi; Langer, Thomas

2012-01-01

Mitochondrial AAA proteases play an important role in the maintenance of mitochondrial proteostasis. They regulate and promote biogenesis of mitochondrial proteins by acting as processing enzymes and ensuring the selective turnover of misfolded proteins. Impairment of AAA proteases causes pleiotropic defects in various organisms including neurodegeneration in humans. AAA proteases comprise ring-like hexameric complexes in the mitochondrial inner membrane and are functionally conserved from yeast to man, but variations are evident in the subunit composition of orthologous enzymes. Recent structural and biochemical studies revealed how AAA proteases degrade their substrates in an ATP dependent manner. Intersubunit coordination of the ATP hydrolysis leads to an ordered ATP hydrolysis within the AAA ring, which ensures efficient substrate dislocation from the membrane and translocation to the proteolytic chamber. In this review, we summarize recent findings on the molecular mechanisms underlying the versatile functions of mitochondrial AAA proteases and their relevance to those of the other AAA+ machines. Copyright © 2011 Elsevier B.V. All rights reserved.

Announcing the International Journal of Molecular Sciences Junior Scientists Travel Awards 2016

Directory of Open Access Journals (Sweden)

International Journal of Molecular Sciences Editorial Office

2016-03-01

Full Text Available With the goal of recognizing outstanding contributions to the field of molecular sciences by early-career investigators, including assistant professors, postdoctoral students and PhD students, [...

Nonlinear machine learning in soft materials engineering and design

Science.gov (United States)

Ferguson, Andrew

The inherently many-body nature of molecular folding and colloidal self-assembly makes it challenging to identify the underlying collective mechanisms and pathways governing system behavior, and has hindered rational design of soft materials with desired structure and function. Fundamentally, there exists a predictive gulf between the architecture and chemistry of individual molecules or colloids and the collective many-body thermodynamics and kinetics. Integrating machine learning techniques with statistical thermodynamics provides a means to bridge this divide and identify emergent folding pathways and self-assembly mechanisms from computer simulations or experimental particle tracking data. We will survey a few of our applications of this framework that illustrate the value of nonlinear machine learning in understanding and engineering soft materials: the non-equilibrium self-assembly of Janus colloids into pinwheels, clusters, and archipelagos; engineering reconfigurable ''digital colloids'' as a novel high-density information storage substrate; probing hierarchically self-assembling onjugated asphaltenes in crude oil; and determining macromolecular folding funnels from measurements of single experimental observables. We close with an outlook on the future of machine learning in soft materials engineering, and share some personal perspectives on working at this disciplinary intersection. We acknowledge support for this work from a National Science Foundation CAREER Award (Grant No. DMR-1350008) and the Donors of the American Chemical Society Petroleum Research Fund (ACS PRF #54240-DNI6).

Molecular biology in marine science: Scientific questions, technological approaches, and practical implications

Energy Technology Data Exchange (ETDEWEB)

NONE

1994-12-31

This report describes molecular techniques that could be invaluable in addressing process-oriented problems in the ocean sciences that have perplexed oceanographers for decades, such as understanding the basis for biogeochemical processes, recruitment processes, upper-ocean dynamics, biological impacts of global warming, and ecological impacts of human activities. The coupling of highly sophisticated methods, such as satellite remote sensing, which permits synoptic monitoring of chemical, physical, and biological parameters over large areas, with the power of modern molecular tools for ``ground truthing`` at small scales could allow scientists to address questions about marine organisms and the ocean in which they live that could not be answered previously. Clearly, the marine sciences are on the threshold of an exciting new frontier of scientific discovery and economic opportunity.

Molecular surface science of heterogeneous catalysis. History and perspective

International Nuclear Information System (INIS)

Somorjai, G.A.

1983-08-01

A personal account is given of how the author became involved with modern surface science and how it was employed for studies of the chemistry of surfaces and heterogeneous catalysis. New techniques were developed for studying the properties of the surface monolayers: Auger electron spectroscopy, LEED, XPS, molecular beam surface scattering, etc. An apparatus was developed and used to study hydrocarbon conversion reactions on Pt, CO hydrogenation on Rh and Fe, and NH 3 synthesis on Fe. A model has been developed for the working Pt reforming catalyst. The three molecular ingredients that control catalytic properties are atomic surface structure, an active carbonaceous deposit, and the proper oxidation state of surface atoms. 40 references, 21 figures

Molecular surface science of heterogeneous catalysis. History and perspective

Energy Technology Data Exchange (ETDEWEB)

Somorjai, G.A.

1983-08-01

A personal account is given of how the author became involved with modern surface science and how it was employed for studies of the chemistry of surfaces and heterogeneous catalysis. New techniques were developed for studying the properties of the surface monolayers: Auger electron spectroscopy, LEED, XPS, molecular beam surface scattering, etc. An apparatus was developed and used to study hydrocarbon conversion reactions on Pt, CO hydrogenation on Rh and Fe, and NH/sub 3/ synthesis on Fe. A model has been developed for the working Pt reforming catalyst. The three molecular ingredients that control catalytic properties are atomic surface structure, an active carbonaceous deposit, and the proper oxidation state of surface atoms. 40 references, 21 figures. (DLC)

Bypassing the Kohn-Sham equations with machine learning.

Science.gov (United States)

Brockherde, Felix; Vogt, Leslie; Li, Li; Tuckerman, Mark E; Burke, Kieron; Müller, Klaus-Robert

2017-10-11

Last year, at least 30,000 scientific papers used the Kohn-Sham scheme of density functional theory to solve electronic structure problems in a wide variety of scientific fields. Machine learning holds the promise of learning the energy functional via examples, bypassing the need to solve the Kohn-Sham equations. This should yield substantial savings in computer time, allowing larger systems and/or longer time-scales to be tackled, but attempts to machine-learn this functional have been limited by the need to find its derivative. The present work overcomes this difficulty by directly learning the density-potential and energy-density maps for test systems and various molecules. We perform the first molecular dynamics simulation with a machine-learned density functional on malonaldehyde and are able to capture the intramolecular proton transfer process. Learning density models now allows the construction of accurate density functionals for realistic molecular systems.Machine learning allows electronic structure calculations to access larger system sizes and, in dynamical simulations, longer time scales. Here, the authors perform such a simulation using a machine-learned density functional that avoids direct solution of the Kohn-Sham equations.

Aliens and time in the machine age

Science.gov (United States)

Brake, Mark; Hook, Neil

2006-12-01

The 19th century saw sweeping changes for the development of astrobiology, both in the constituency of empirical science encroaching upon all aspects of life and in the evolution of ideas, with Lyell's Principles of Geology radically raising expectation of the true age of the Earth and the drama of Darwinism questioning biblically literalist accounts of natural history. This paper considers the popular culture spun on the crackling loom of the emergent aspects of astrobiology of the day: Edward Bulwer-Lytton's The Coming Race (1871), which foretold the race of the future, and satirist Samuel Butler's anticipation of machine intelligence, `Darwin Among the Machines', in his Erewhon (1872). Finally, we look at the way Darwin, Huxley and natural selection travelled into space with French astronomer Camille Flammarion's immensely popular Récits de l'infini (Stories of Infinity, 1872), and the social Darwinism of H.G. Wells' The Time Machine (1895) and The War of the Worlds (1898). These works of popular culture presented an effective and inspiring communication of science; their crucial discourse was the reducible gap between the new worlds uncovered by science and exploration and the fantastic strange worlds of the imagination. As such they exemplify a way in which the culture and science of popular astrobiology can be fused.

Machine learning for Big Data analytics in plants.

Science.gov (United States)

Ma, Chuang; Zhang, Hao Helen; Wang, Xiangfeng

2014-12-01

Rapid advances in high-throughput genomic technology have enabled biology to enter the era of 'Big Data' (large datasets). The plant science community not only needs to build its own Big-Data-compatible parallel computing and data management infrastructures, but also to seek novel analytical paradigms to extract information from the overwhelming amounts of data. Machine learning offers promising computational and analytical solutions for the integrative analysis of large, heterogeneous and unstructured datasets on the Big-Data scale, and is gradually gaining popularity in biology. This review introduces the basic concepts and procedures of machine-learning applications and envisages how machine learning could interface with Big Data technology to facilitate basic research and biotechnology in the plant sciences. Copyright © 2014 Elsevier Ltd. All rights reserved.

Cheminformatics Research at the Unilever Centre for Molecular Science Informatics Cambridge.

Science.gov (United States)

Fuchs, Julian E; Bender, Andreas; Glen, Robert C

2015-09-01

The Centre for Molecular Informatics, formerly Unilever Centre for Molecular Science Informatics (UCMSI), at the University of Cambridge is a world-leading driving force in the field of cheminformatics. Since its opening in 2000 more than 300 scientific articles have fundamentally changed the field of molecular informatics. The Centre has been a key player in promoting open chemical data and semantic access. Though mainly focussing on basic research, close collaborations with industrial partners ensured real world feedback and access to high quality molecular data. A variety of tools and standard protocols have been developed and are ubiquitous in the daily practice of cheminformatics. Here, we present a retrospective of cheminformatics research performed at the UCMSI, thereby highlighting historical and recent trends in the field as well as indicating future directions.

A preliminary exploration of Advanced Molecular Bio-Sciences Research Center

International Nuclear Information System (INIS)

Yamada, Yutaka; Yanai, Takanori; Onodera, Jun'ichi; Yamagami, Mutsumi; Sakata, Hiroshi; Sota, Masahiro; Takemura, Tatsuo; Koyama, Kenji; Sato, Fumiaki

2000-01-01

Low-dose and low-dose-rate radiation effects on life-span, pathological changes, hemopoiesis and cytokine production in experimental animals have been investigated in our laboratory. In the intermediate period of the investigation, an expert committee on radiation biology, which was composed of two task groups, was organized. The purposes of the committee were to assess of previous studies and plan future research for Advanced Molecular Bio-Sciences Research Center (AMBIC). In its report, the committee emphasized the necessity of molecular research in radiation biology and ecology, and proposed six subjects for the research: 1) Molecular carcinogenesis of low-dose radiation; 2) Radiation effects on the immune system and hemopoietic system; 3) Molecular mechanisms of hereditary effect; 4) Non cancer effect of low-dose radiation; 5) Gene targeting for ion transport system in plants; 6) Bioremediation with transgenic plant and bacteria. Exploration of the AMBIC project will continue under the committee's direction. (author)

On the use of Cloud Computing and Machine Learning for Large-Scale SAR Science Data Processing and Quality Assessment Analysi

Science.gov (United States)

Hua, H.

2016-12-01

Geodetic imaging is revolutionizing geophysics, but the scope of discovery has been limited by labor-intensive technological implementation of the analyses. The Advanced Rapid Imaging and Analysis (ARIA) project has proven capability to automate SAR data processing and analysis. Existing and upcoming SAR missions such as Sentinel-1A/B and NISAR are also expected to generate massive amounts of SAR data. This has brought to the forefront the need for analytical tools for SAR quality assessment (QA) on the large volumes of SAR data-a critical step before higher-level time series and velocity products can be reliably generated. Initially leveraging an advanced hybrid-cloud computing science data system for performing large-scale processing, machine learning approaches were augmented for automated analysis of various quality metrics. Machine learning-based user-training of features, cross-validation, prediction models were integrated into our cloud-based science data processing flow to enable large-scale and high-throughput QA analytics for enabling improvements to the production quality of geodetic data products.

Man-machine interactions 3

CERN Document Server

Czachórski, Tadeusz; Kozielski, Stanisław

2014-01-01

Man-Machine Interaction is an interdisciplinary field of research that covers many aspects of science focused on a human and machine in conjunction.  Basic goal of the study is to improve and invent new ways of communication between users and computers, and many different subjects are involved to reach the long-term research objective of an intuitive, natural and multimodal way of interaction with machines.  The rapid evolution of the methods by which humans interact with computers is observed nowadays and new approaches allow using computing technologies to support people on the daily basis, making computers more usable and receptive to the user's needs.   This monograph is the third edition in the series and presents important ideas, current trends and innovations in  the man-machine interactions area.  The aim of this book is to introduce not only hardware and software interfacing concepts, but also to give insights into the related theoretical background. Reader is provided with a compilation of high...

Molecular Dynamics Simulations of a Linear Nanomotor Driven by Thermophoretic Forces

DEFF Research Database (Denmark)

Zambrano, Harvey A; Walther, Jens Honore; Jaffe, Richard L.

Molecular Dynamics of a Linear Nanomotor Driven by Thermophoresis Harvey A. Zambrano1, Jens H. Walther1,2 and Richard L. Jaffe3 1Department of Mechanical Engineering, Fluid Mechanics, Technical University of Denmark, DK-2800 Lyngby, Denmark; 2Computational Science and Engineering Laboratory, ETH...... future molecular machines a complete understanding of the friction forces involved on the transport process at the molecular level have to be addressed.18 In this work we perform Molecular Dynamics (MD) simulations using the MD package FASTTUBE19 to study a molecular linear motor consisting of coaxial...... the valence forces within the CNT using Morse, harmonic angle and torsion potentials.19We include a nonbonded carbon-carbon Lennard-Jones potential to describe the vdW interaction between the carbon atoms within the double wall portion of the system. We equilibrate the system at 300K for 0.1 ns, by coupling...

Understanding and modelling man-machine interaction

International Nuclear Information System (INIS)

Cacciabue, P.C.

1996-01-01

This paper gives an overview of the current state of the art in man-machine system interaction studies, focusing on the problems derived from highly automated working environments and the role of humans in the control loop. In particular, it is argued that there is a need for sound approaches to the design and analysis of man-machine interaction (MMI), which stem from the contribution of three expertises in interfacing domains, namely engineering, computer science and psychology: engineering for understanding and modelling plants and their material and energy conservation principles; psychology for understanding and modelling humans an their cognitive behaviours; computer science for converting models in sound simulations running in appropriate computer architectures. (orig.)

Understanding and modelling Man-Machine Interaction

International Nuclear Information System (INIS)

Cacciabue, P.C.

1991-01-01

This paper gives an overview of the current state of the art in man machine systems interaction studies, focusing on the problems derived from highly automated working environments and the role of humans in the control loop. In particular, it is argued that there is a need for sound approaches to design and analysis of Man-Machine Interaction (MMI), which stem from the contribution of three expertises in interfacing domains, namely engineering, computer science and psychology: engineering for understanding and modelling plants and their material and energy conservation principles; psychology for understanding and modelling humans and their cognitive behaviours; computer science for converting models in sound simulations running in appropriate computer architectures. (author)

The East, the West and the universal machine.

Science.gov (United States)

Marchal, Bruno

2017-12-01

After reviewing the basic of theology of Universal Numbers/Machines, as detailed in Marchal (2007), I illustrate how that body of thought might be used to shed some light upon the apparent dichotomy in Eastern/Western spirituality. This paper relies entirely on my previous interdisciplinary work in mathematical logic, computer science and machine's theology, where "theology" is used here in the sense of Plato: it is the truth, or the "truth-theory" (in the sense of logicians) about a machine that the machine can either deduce from some of its primitive beliefs, or can be intuited in some sense that eventually is made clear through the modal logic of machine self-reference. Such a theology appears to be testable, because it has been shown that physics has to be necessarily retrieved from it when we assume the mechanist hypothesis in the cognitive sciences, and this in a unique precise (introspective) way, so that we only need to compare the physics of the introspective machine with the physics inferred from the human observation; and up to now, it is the only theory known to fit both the existence of personal "consciousness" (undoubtable yet unjustifiable truth) and quanta and quantum relationships (Marchal, 1998; Marchal, 2004; Marchal, 2013; Marchal, 2015). Copyright © 2017 Elsevier Ltd. All rights reserved.

Molecular science solving global problems

International Nuclear Information System (INIS)

Dunning, T.H. Jr.; Stults, B.R.

1995-01-01

From the late 1940s to the late 1980s, the Department of Energy (DOE) had a critical role in the Cold War. Many sites were built to contribute to the nation's nuclear weapons effort. However, not enough attention was paid to how the waste generated at these facilities should be handled. As a result, a number of sites fouled the soil around them or dumped low-level radioactive waste into nearby rivers. A DOE laboratory is under construction with a charter to help. Called the Environmental Molecular Sciences Laboratory (EMSL), this national user facility will be located at DOE's Pacific Northwest Laboratory (PNL) in Richland, WA. This laboratory has been funded by DOE and Congress to play a major role as the nation confronts the enormous challenge of reducing environmental and human risks from hundreds of government and industrial waste sites in an economically viable manner. The original proposal for the EMSL took a number of twists and turns on its way to its present form, but one thing remained constant: the belief that safe, permanent, cost-effective solutions to many of the country's environmental problems could be achieved only by multidisciplinary teams working to understand and control molecular processes. The processes of most concern are those that govern the transport and transformation of contaminants, the treatment and storage of high-level mixed wastes, and the risks those contaminants ultimately pose to workers and the public

Frames of scientific evidence: How journalists represent the (un)certainty of molecular medicine in science television programs.

Science.gov (United States)

Ruhrmann, Georg; Guenther, Lars; Kessler, Sabrina Heike; Milde, Jutta

2015-08-01

For laypeople, media coverage of science on television is a gateway to scientific issues. Defining scientific evidence is central to the field of science, but there are still questions if news coverage of science represents scientific research findings as certain or uncertain. The framing approach is a suitable framework to classify different media representations; it is applied here to investigate the frames of scientific evidence in film clips (n=207) taken from science television programs. Molecular medicine is the domain of interest for this analysis, due to its high proportion of uncertain and conflicting research findings and risks. The results indicate that television clips vary in their coverage of scientific evidence of molecular medicine. Four frames were found: Scientific Uncertainty and Controversy, Scientifically Certain Data, Everyday Medical Risks, and Conflicting Scientific Evidence. They differ in their way of framing scientific evidence and risks of molecular medicine. © The Author(s) 2013.

Probabilistic machine learning and artificial intelligence.

Science.gov (United States)

Ghahramani, Zoubin

2015-05-28

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

Probabilistic machine learning and artificial intelligence

Science.gov (United States)

Ghahramani, Zoubin

2015-05-01

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.

COMPUTATIONAL SCIENCE CENTER

International Nuclear Information System (INIS)

DAVENPORT, J.

2006-01-01

Computational Science is an integral component of Brookhaven's multi science mission, and is a reflection of the increased role of computation across all of science. Brookhaven currently has major efforts in data storage and analysis for the Relativistic Heavy Ion Collider (RHIC) and the ATLAS detector at CERN, and in quantum chromodynamics. The Laboratory is host for the QCDOC machines (quantum chromodynamics on a chip), 10 teraflop/s computers which boast 12,288 processors each. There are two here, one for the Riken/BNL Research Center and the other supported by DOE for the US Lattice Gauge Community and other scientific users. A 100 teraflop/s supercomputer will be installed at Brookhaven in the coming year, managed jointly by Brookhaven and Stony Brook, and funded by a grant from New York State. This machine will be used for computational science across Brookhaven's entire research program, and also by researchers at Stony Brook and across New York State. With Stony Brook, Brookhaven has formed the New York Center for Computational Science (NYCCS) as a focal point for interdisciplinary computational science, which is closely linked to Brookhaven's Computational Science Center (CSC). The CSC has established a strong program in computational science, with an emphasis on nanoscale electronic structure and molecular dynamics, accelerator design, computational fluid dynamics, medical imaging, parallel computing and numerical algorithms. We have been an active participant in DOES SciDAC program (Scientific Discovery through Advanced Computing). We are also planning a major expansion in computational biology in keeping with Laboratory initiatives. Additional laboratory initiatives with a dependence on a high level of computation include the development of hydrodynamics models for the interpretation of RHIC data, computational models for the atmospheric transport of aerosols, and models for combustion and for energy utilization. The CSC was formed to bring together

Traditional machining processes research advances

CERN Document Server

2015-01-01

This book collects several examples of research in machining processes. Chapter 1 provides information on polycrystalline diamond tool material and its emerging applications. Chapter 2 is dedicated to the analysis of orthogonal cutting experiments using diamond-coated tools with force and temperature measurements. Chapter 3 describes the estimation of cutting forces and tool wear using modified mechanistic models in high performance turning. Chapter 4 contains information on cutting under gas shields for industrial applications. Chapter 5 is dedicated to the machinability of magnesium and its alloys. Chapter 6 provides information on grinding science. Finally, chapter 7 is dedicated to flexible integration of shape and functional modelling of machine tool spindles in a design framework.    

Build your own time machine

CERN Document Server

Clegg, Brian

2012-01-01

There is no physical law to prevent time travel nothing in physics to say it is impossible. So who is to say it can't be done? In Build Your Own Time Machine, acclaimed science writer Brian Clegg takes inspiration from his childhood heroes, Doctor Who and H. G. Wells, to explain the nature of time. How do we understand it and why measure it the way we do? How did the theories of one man change the way time was perceived by the world? Why wouldn't H. G. Wells's time machine have worked? And what would we need to do to make a real one? Build Your Own Time Machine explores the amazing possib

Science 101: Q--What Is the Physics behind Simple Machines?

Science.gov (United States)

Robertson, Bill

2013-01-01

Bill Robertson thinks that questioning the physics behind simple machines is a great idea because when he encounters the subject of simple machines in textbooks, activities, and classrooms, he seldom encounters, a scientific explanation of how they work. Instead, what one often sees is a discussion of load, effort, fulcrum, actual mechanical…

Molecular Science Research Center annual report

Energy Technology Data Exchange (ETDEWEB)

Knotek, M.L.

1991-01-01

The Chemical Structure and Dynamics group is studying chemical kinetics and reactions dynamics of terrestrial and atmospheric processes as well as the chemistry of complex waste forms and waste storage media. Staff are using new laser systems and surface-mapping techniques in combination with molecular clusters that mimic adsorbate/surface interactions. The Macromolecular Structure and Dynamics group is determining biomolecular structure/function relationships for processes the control the biological transformation of contaminants and the health effects of toxic substances. The Materials and Interfaces program is generating information needed to design and synthesize advanced materials for the analysis and separation of mixed chemical waste, the long-term storage of concentrated hazardous materials, and the development of chemical sensors for environmental monitoring of various organic and inorganic species. The Theory, Modeling, and Simulation group is developing detailed molecular-level descriptions of the chemical, physical, and biological processes in natural and contaminated systems. Researchers are using the full spectrum of computational techniques. The Computer and Information Sciences group is developing new approaches to handle vast amounts of data and to perform calculations for complex natural systems. The EMSL will contain a high-performance computing facility, ancillary computing laboratories, and high-speed data acquisition systems for all major research instruments.

Interdisciplinary research center devoted to molecular environmental science opens

Science.gov (United States)

Vaughan, David J.

In October, a new research center opened at the University of Manchester in the United Kingdom. The center is the product of over a decade of ground-breaking interdisciplinary research in the Earth and related biological and chemical sciences at the university The center also responds to the British governments policy of investing in research infrastructure at key universities.The Williamson Research Centre, the first of its kind in Britain and among the first worldwide, is devoted to the emerging field of molecular environmental science. This field also aims to bring about a revolution in understanding of our environment. Though it may be a less violent revolution than some, perhaps, its potential is high for developments that could affect us all.

Quantum cloning machines and the applications

Energy Technology Data Exchange (ETDEWEB)

Fan, Heng, E-mail: hfan@iphy.ac.cn [Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190 (China); Collaborative Innovation Center of Quantum Matter, Beijing 100190 (China); Wang, Yi-Nan; Jing, Li [School of Physics, Peking University, Beijing 100871 (China); Yue, Jie-Dong [Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190 (China); Shi, Han-Duo; Zhang, Yong-Liang; Mu, Liang-Zhu [School of Physics, Peking University, Beijing 100871 (China)

2014-11-20

No-cloning theorem is fundamental for quantum mechanics and for quantum information science that states an unknown quantum state cannot be cloned perfectly. However, we can try to clone a quantum state approximately with the optimal fidelity, or instead, we can try to clone it perfectly with the largest probability. Thus various quantum cloning machines have been designed for different quantum information protocols. Specifically, quantum cloning machines can be designed to analyze the security of quantum key distribution protocols such as BB84 protocol, six-state protocol, B92 protocol and their generalizations. Some well-known quantum cloning machines include universal quantum cloning machine, phase-covariant cloning machine, the asymmetric quantum cloning machine and the probabilistic quantum cloning machine. In the past years, much progress has been made in studying quantum cloning machines and their applications and implementations, both theoretically and experimentally. In this review, we will give a complete description of those important developments about quantum cloning and some related topics. On the other hand, this review is self-consistent, and in particular, we try to present some detailed formulations so that further study can be taken based on those results.

Quantum cloning machines and the applications

International Nuclear Information System (INIS)

Fan, Heng; Wang, Yi-Nan; Jing, Li; Yue, Jie-Dong; Shi, Han-Duo; Zhang, Yong-Liang; Mu, Liang-Zhu

2014-01-01

No-cloning theorem is fundamental for quantum mechanics and for quantum information science that states an unknown quantum state cannot be cloned perfectly. However, we can try to clone a quantum state approximately with the optimal fidelity, or instead, we can try to clone it perfectly with the largest probability. Thus various quantum cloning machines have been designed for different quantum information protocols. Specifically, quantum cloning machines can be designed to analyze the security of quantum key distribution protocols such as BB84 protocol, six-state protocol, B92 protocol and their generalizations. Some well-known quantum cloning machines include universal quantum cloning machine, phase-covariant cloning machine, the asymmetric quantum cloning machine and the probabilistic quantum cloning machine. In the past years, much progress has been made in studying quantum cloning machines and their applications and implementations, both theoretically and experimentally. In this review, we will give a complete description of those important developments about quantum cloning and some related topics. On the other hand, this review is self-consistent, and in particular, we try to present some detailed formulations so that further study can be taken based on those results

13th International Conference on Man-Machine-Environment System Engineering

CERN Document Server

Dhillon, Balbir

2014-01-01

The integrated and advanced science research topic Man-Machine-Environment System Engineering (MMESE) was first established in China by Professor Shengzhao Long in 1981, with direct support from one of the greatest modern Chinese scientists, Xuesen Qian. In a letter to Shengzhao Long from October 22nd, 1993, Xuesen Qian wrote: “You have created a very important modern science and technology in China!”   MMESE primarily focuses on the relationship between man, machines and the environment, studying the optimum combination of man-machine-environment systems. In this system, “man” refers to people in the workplace (e.g. operators, decision-makers); “ machine” is the general name for any object controlled by man (including tools, machinery, computers, systems and technologies), and “environment” describes the specific working conditions under which man and machine interact (e.g. temperature, noise, vibration, hazardous gases etc.). The three goals of optimization of Man-Machine-Environment system...

14th International Conference on Man-Machine-Environment System Engineering

CERN Document Server

Dhillon, Balbir

2015-01-01

The integrated and advanced science research topic man-machine-environment system engineering (MMESE) was first established in China by Professor Shengzhao Long in 1981, with direct support from one of the greatest modern Chinese scientists, Xuesen Qian. In a letter to Shengzhao Long from October 22nd, 1993, Xuesen Qian wrote: “You have created a very important modern science and technology in China!”   MMESE primarily focuses on the relationship between man, machines and the environment, studying the optimum combination of man-machine-environment systems. In this system, “man” refers to people in the workplace (e.g. operators, decision-makers); “ machine” is the general name for any object controlled by man (including tools, machinery, computers, systems and technologies), and “environment” describes the specific working conditions under which man and machine interact (e.g. temperature, noise, vibration, hazardous gases etc.). The three goals of optimization of man-machine-environment system...

Molecular forensic science of nuclear materials

International Nuclear Information System (INIS)

Wilkerson, Marianne Perry

2010-01-01

We are interested in applying our understanding of actinide chemical structure and bonding to broaden the suite of analytical tools available for nuclear forensic analyses. Uranium- and plutonium-oxide systems form under a variety of conditions, and these chemical species exhibit some of the most complex behavior of metal oxide systems known. No less intriguing is the ability of AnO 2 (An: U, Pu) to form non-stoichiometric species described as AnO 2+x . Environmental studies have shown the value of utilizing the chemical signatures of these actinide oxides materials to understand transport following release into the environment. Chemical speciation of actinide-oxide samples may also provide clues as to the age, source, process history, or transport of the material. The scientific challenge is to identify, measure and understand those aspects of speciation of actinide analytes that carry information about material origin and history most relevant to forensics. Here, we will describe our efforts in material synthesis and analytical methods development that we will use to provide the fundamental science required to characterize actinide oxide molecular structures for forensics science. Structural properties and initial results to measure structural variability of uranium oxide samples using synchrotron-based X-ray Absorption Fine Structure will be discussed.

Hybrid Light-Matter States in a Molecular and Material Science Perspective.

Science.gov (United States)

Ebbesen, Thomas W

2016-11-15

The notion that light and matter states can be hybridized the way s and p orbitals are mixed is a concept that is not familiar to most chemists and material scientists. Yet it has much potential for molecular and material sciences that is just beginning to be explored. For instance, it has already been demonstrated that the rate and yield of chemical reactions can be modified and that the conductivity of organic semiconductors and nonradiative energy transfer can be enhanced through the hybridization of electronic transitions. The hybridization is not limited to electronic transitions; it can be applied for instance to vibrational transitions to selectively perturb a given bond, opening new possibilities to change the chemical reactivity landscape and to use it as a tool in (bio)molecular science and spectroscopy. Such results are not only the consequence of the new eigenstates and energies generated by the hybridization. The hybrid light-matter states also have unusual properties: they can be delocalized over a very large number of molecules (up to ca. 10 5 ), and they become dispersive or momentum-sensitive. Importantly, the hybridization occurs even in the absence of light because it is the zero-point energies of the molecular and optical transitions that generate the new light-matter states. The present work is not a review but rather an Account from the author's point of view that first introduces the reader to the underlying concepts and details of the features of hybrid light-matter states. It is shown that light-matter hybridization is quite easy to achieve: all that is needed is to place molecules or a material in a resonant optical cavity (e.g., between two parallel mirrors) under the right conditions. For vibrational strong coupling, microfluidic IR cells can be used to study the consequences for chemistry in the liquid phase. Examples of modified properties are given to demonstrate the full potential for the molecular and material sciences. Finally an

Turing Machines with One-sided Advice and Acceptance of the co-RE Languages

Czech Academy of Sciences Publication Activity Database

van Leeuwen, J.; Wiedermann, Jiří

2017-01-01

Roč. 153, č. 4 (2017), s. 347-366 ISSN 0169-2968 Grant - others:GA ČR(CZ) GA15-04960S Institutional support: RVO:67985807 Keywords : advice functions * co-RE language s * machine models * Turing machines Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 0.687, year: 2016

Using Machine Learning to Advance Personality Assessment and Theory.

Science.gov (United States)

Bleidorn, Wiebke; Hopwood, Christopher James

2018-05-01

Machine learning has led to important advances in society. One of the most exciting applications of machine learning in psychological science has been the development of assessment tools that can powerfully predict human behavior and personality traits. Thus far, machine learning approaches to personality assessment have focused on the associations between social media and other digital records with established personality measures. The goal of this article is to expand the potential of machine learning approaches to personality assessment by embedding it in a more comprehensive construct validation framework. We review recent applications of machine learning to personality assessment, place machine learning research in the broader context of fundamental principles of construct validation, and provide recommendations for how to use machine learning to advance our understanding of personality.

From Molecule to Molecular Machines

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 23; Issue 3 ... In this review article, the concept of supramolecularchemistry, cooperativity responsible for interactions, techniquesfor determination of thermodynamic parameters ofcooperativity, and the contribution of supramolecular chemistryto ...

Systems biology for molecular life sciences and its impact in biomedicine.

Science.gov (United States)

Medina, Miguel Ángel

2013-03-01

Modern systems biology is already contributing to a radical transformation of molecular life sciences and biomedicine, and it is expected to have a real impact in the clinical setting in the next years. In this review, the emergence of systems biology is contextualized with a historic overview, and its present state is depicted. The present and expected future contribution of systems biology to the development of molecular medicine is underscored. Concerning the present situation, this review includes a reflection on the "inflation" of biological data and the urgent need for tools and procedures to make hidden information emerge. Descriptions of the impact of networks and models and the available resources and tools for applying them in systems biology approaches to molecular medicine are provided as well. The actual current impact of systems biology in molecular medicine is illustrated, reviewing two cases, namely, those of systems pharmacology and cancer systems biology. Finally, some of the expected contributions of systems biology to the immediate future of molecular medicine are commented.

A Turing Machine Simulator.

Science.gov (United States)

Navarro, Aaron B.

1981-01-01

Presents a program in Level II BASIC for a TRS-80 computer that simulates a Turing machine and discusses the nature of the device. The program is run interactively and is designed to be used as an educational tool by computer science or mathematics students studying computational or automata theory. (MP)

Biomimetic molecular design tools that learn, evolve, and adapt

Science.gov (United States)

2017-01-01

A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex bioactive natural products. Recent work is attempting to understand how molecular machines operate so their principles can be copied, and learning how to employ biomimetic evolution and learning methods to solve complex problems in science, medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis robots to carry out unit operations, and to devise closed loop flow self-optimizing chemical synthesis systems. Most scientific innovations and technologies pass through the well-known “S curve”, with slow beginning, an almost exponential growth in capability, and a stable applications period. Adaptive, evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design methods and their potential impacts in chemistry, engineering, and medicine. PMID:28694872

Biomimetic molecular design tools that learn, evolve, and adapt

Directory of Open Access Journals (Sweden)

David A Winkler

2017-06-01

Full Text Available A dominant hallmark of living systems is their ability to adapt to changes in the environment by learning and evolving. Nature does this so superbly that intensive research efforts are now attempting to mimic biological processes. Initially this biomimicry involved developing synthetic methods to generate complex bioactive natural products. Recent work is attempting to understand how molecular machines operate so their principles can be copied, and learning how to employ biomimetic evolution and learning methods to solve complex problems in science, medicine and engineering. Automation, robotics, artificial intelligence, and evolutionary algorithms are now converging to generate what might broadly be called in silico-based adaptive evolution of materials. These methods are being applied to organic chemistry to systematize reactions, create synthesis robots to carry out unit operations, and to devise closed loop flow self-optimizing chemical synthesis systems. Most scientific innovations and technologies pass through the well-known “S curve”, with slow beginning, an almost exponential growth in capability, and a stable applications period. Adaptive, evolving, machine learning-based molecular design and optimization methods are approaching the period of very rapid growth and their impact is already being described as potentially disruptive. This paper describes new developments in biomimetic adaptive, evolving, learning computational molecular design methods and their potential impacts in chemistry, engineering, and medicine.

Integration of molecular machines into supramolecular materials: actuation between equilibrium polymers and crystal-like gels.

Science.gov (United States)

Mariani, Giacomo; Goujon, Antoine; Moulin, Emilie; Rawiso, Michel; Giuseppone, Nicolas; Buhler, Eric

2017-11-30

In this article, the dynamic structure of complex supramolecular polymers composed of bistable [c2]daisy chain rotaxanes as molecular machines that are linked by ureidopyrimidinones (Upy) as recognition moieties was studied. pH actuation of the integrated mechanically active rotaxanes controls the contraction/extension of the polymer chains as well as their physical reticulation. Small-angle neutron and X-ray scattering were used to study in-depth the nanostructure of the contracted and extended polymer aggregates in toluene solution. The supramolecular polymers comprising contracted nanomachines were found to be equilibrium polymers with a mass that is concentration dependent in dilute and semidilute regimes. Surprisingly, the extended polymers form a gel network with a crystal-like internal structure that is independent of concentration and reminiscent of a pearl-necklace network.

The Atomic, Molecular and Optical Science instrument at the Linac Coherent Light Source

Energy Technology Data Exchange (ETDEWEB)

Ferguson, Ken R. [Linac Coherent Light Source, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025 (United States); Department of Applied Physics, Stanford University, 348 Via Pueblo, Stanford, CA 94305 (United States); Bucher, Maximilian; Bozek, John D.; Carron, Sebastian; Castagna, Jean-Charles [Linac Coherent Light Source, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025 (United States); Coffee, Ryan [Linac Coherent Light Source, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025 (United States); Pulse Institute, Stanford University and SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025 (United States); Curiel, G. Ivan; Holmes, Michael; Krzywinski, Jacek; Messerschmidt, Marc; Minitti, Michael; Mitra, Ankush; Moeller, Stefan; Noonan, Peter; Osipov, Timur; Schorb, Sebastian; Swiggers, Michele; Wallace, Alexander; Yin, Jing [Linac Coherent Light Source, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025 (United States); Bostedt, Christoph, E-mail: bostedt@slac.stanford.edu [Linac Coherent Light Source, SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025 (United States); Pulse Institute, Stanford University and SLAC National Accelerator Laboratory, 2575 Sand Hill Road, Menlo Park, CA 94025 (United States)

2015-04-17

A description of the Atomic, Molecular and Optical Sciences (AMO) instrument at the Linac Coherent Light Source is presented. Recent scientific highlights illustrate the imaging, time-resolved spectroscopy and high-power density capabilities of the AMO instrument. The Atomic, Molecular and Optical Science (AMO) instrument at the Linac Coherent Light Source (LCLS) provides a tight soft X-ray focus into one of three experimental endstations. The flexible instrument design is optimized for studying a wide variety of phenomena requiring peak intensity. There is a suite of spectrometers and two photon area detectors available. An optional mirror-based split-and-delay unit can be used for X-ray pump–probe experiments. Recent scientific highlights illustrate the imaging, time-resolved spectroscopy and high-power density capabilities of the AMO instrument.

Nanotechnology: A molecular assembler

Science.gov (United States)

Kelly, T. Ross; Snapper, Marc L.

2017-09-01

The idea of nanometre-scale machines that can assemble molecules has long been thought of as the stuff of science fiction. Such a machine has now been built -- and might herald a new model for organic synthesis. See Letter p.374

Microbiome Tools for Forensic Science.

Science.gov (United States)

Metcalf, Jessica L; Xu, Zhenjiang Z; Bouslimani, Amina; Dorrestein, Pieter; Carter, David O; Knight, Rob

2017-09-01

Microbes are present at every crime scene and have been used as physical evidence for over a century. Advances in DNA sequencing and computational approaches have led to recent breakthroughs in the use of microbiome approaches for forensic science, particularly in the areas of estimating postmortem intervals (PMIs), locating clandestine graves, and obtaining soil and skin trace evidence. Low-cost, high-throughput technologies allow us to accumulate molecular data quickly and to apply sophisticated machine-learning algorithms, building generalizable predictive models that will be useful in the criminal justice system. In particular, integrating microbiome and metabolomic data has excellent potential to advance microbial forensics. Copyright © 2017. Published by Elsevier Ltd.

Molecular automata assembly: principles and simulation of bacterial membrane construction.

Science.gov (United States)

Lahoz-Beltra, R

1997-01-01

The motivation to understand the basic rules and principles governing molecular self-assembly may be relevant to explain in the context of molecular biology the self-organization and biological functions exhibited within cells. This paper presents a molecular automata model to simulate molecular self-assembly introducing the concept of molecular programming to simulate the biological function or operation performed by an assembled molecular state machine. The method is illustrated modelling Escherichia coli membrane construction including the assembly and operation of ATP synthase as well as the assembly of the bacterial flagellar motor. Flagellar motor operation was simulated using a different approach based on state machine definition used in virtual reality systems. The proposed methodology provides a modelling framework for simulation of biological functions performed by cellular components and other biological systems suitable to be modelled as molecular state machines.

Using Phun to Study ``Perpetual Motion'' Machines

Science.gov (United States)

Koreš, Jaroslav

2012-05-01

The concept of "perpetual motion" has a long history. The Indian astronomer and mathematician Bhaskara II (12th century) was the first person to describe a perpetual motion (PM) machine. An example of a 13th- century PM machine is shown in Fig. 1. Although the law of conservation of energy clearly implies the impossibility of PM construction, over the centuries numerous proposals for PM have been made, involving ever more elements of modern science in their construction. It is possible to test a variety of PM machines in the classroom using a program called Phun2 or its commercial version Algodoo.3 The programs are designed to simulate physical processes and we can easily simulate mechanical machines using them. They provide an intuitive graphical environment controlled with a mouse; a programming language is not needed. This paper describes simulations of four different (supposed) PM machines.4

Tanweer Hussain | Speakers | Indian Academy of Sciences

Indian Academy of Sciences (India)

Understanding the principles of design of molecular machines: A structural biology perspective View Presentation / View Video. The determination of the three-dimensional structures of molecular machines, using various approaches of structural biology, has played a major role in understanding the design of individual ...

COMPUTATIONAL SCIENCE CENTER

Energy Technology Data Exchange (ETDEWEB)

DAVENPORT, J.

2006-11-01

Computational Science is an integral component of Brookhaven's multi science mission, and is a reflection of the increased role of computation across all of science. Brookhaven currently has major efforts in data storage and analysis for the Relativistic Heavy Ion Collider (RHIC) and the ATLAS detector at CERN, and in quantum chromodynamics. The Laboratory is host for the QCDOC machines (quantum chromodynamics on a chip), 10 teraflop/s computers which boast 12,288 processors each. There are two here, one for the Riken/BNL Research Center and the other supported by DOE for the US Lattice Gauge Community and other scientific users. A 100 teraflop/s supercomputer will be installed at Brookhaven in the coming year, managed jointly by Brookhaven and Stony Brook, and funded by a grant from New York State. This machine will be used for computational science across Brookhaven's entire research program, and also by researchers at Stony Brook and across New York State. With Stony Brook, Brookhaven has formed the New York Center for Computational Science (NYCCS) as a focal point for interdisciplinary computational science, which is closely linked to Brookhaven's Computational Science Center (CSC). The CSC has established a strong program in computational science, with an emphasis on nanoscale electronic structure and molecular dynamics, accelerator design, computational fluid dynamics, medical imaging, parallel computing and numerical algorithms. We have been an active participant in DOES SciDAC program (Scientific Discovery through Advanced Computing). We are also planning a major expansion in computational biology in keeping with Laboratory initiatives. Additional laboratory initiatives with a dependence on a high level of computation include the development of hydrodynamics models for the interpretation of RHIC data, computational models for the atmospheric transport of aerosols, and models for combustion and for energy utilization. The CSC was formed to

Quantitative structure–activity relationship model for amino acids as corrosion inhibitors based on the support vector machine and molecular design

International Nuclear Information System (INIS)

Zhao, Hongxia; Zhang, Xiuhui; Ji, Lin; Hu, Haixiang; Li, Qianshu

2014-01-01

Highlights: • Nonlinear quantitative structure–activity relationship (QSAR) model was built by the support vector machine. • Descriptors for QSAR model were selected by principal component analysis. • Binding energy was taken as one of the descriptors for QSAR model. • Acidic solution and protonation of the inhibitor were considered. - Abstract: The inhibition performance of nineteen amino acids was studied by theoretical methods. The affection of acidic solution and protonation of inhibitor were considered in molecular dynamics simulation and the results indicated that the protonated amino-group was not adsorbed on Fe (1 1 0) surface. Additionally, a nonlinear quantitative structure–activity relationship (QSAR) model was built by the support vector machine. The correlation coefficient was 0.97 and the root mean square error, the differences between predicted and experimental inhibition efficiencies (%), was 1.48. Furthermore, five new amino acids were theoretically designed and their inhibition efficiencies were predicted by the built QSAR model

Molecular thermodynamics for food science and engineering.

Science.gov (United States)

Nguyen, Phuong-Mai; Guiga, Wafa; Vitrac, Olivier

2016-10-01

We argue that thanks to molecular modeling approaches, many thermodynamic properties required in Food Science and Food Engineering will be calculable within a few hours from first principles in a near future. These new possibilities will enable to bridge via multiscale modeling composition, process and storage effects to reach global optimization, innovative concepts for food or its packaging. An outlook of techniques and a series of examples are given in this perspective. We emphasize solute chemical potentials in polymers, liquids and their mixtures as they cannot be understood and estimated without theory. The presented atomistic and coarse-grained methods offer a natural framework to their conceptualization in polynary systems, entangled or crosslinked homo- or heteropolymers. Copyright © 2016 Elsevier Ltd. All rights reserved.

AAA+ Machines of Protein Destruction in Mycobacteria.

Science.gov (United States)

Alhuwaider, Adnan Ali H; Dougan, David A

2017-01-01

The bacterial cytosol is a complex mixture of macromolecules (proteins, DNA, and RNA), which collectively are responsible for an enormous array of cellular tasks. Proteins are central to most, if not all, of these tasks and as such their maintenance (commonly referred to as protein homeostasis or proteostasis) is vital for cell survival during normal and stressful conditions. The two key aspects of protein homeostasis are, (i) the correct folding and assembly of proteins (coupled with their delivery to the correct cellular location) and (ii) the timely removal of unwanted or damaged proteins from the cell, which are performed by molecular chaperones and proteases, respectively. A major class of proteins that contribute to both of these tasks are the AAA+ (ATPases associated with a variety of cellular activities) protein superfamily. Although much is known about the structure of these machines and how they function in the model Gram-negative bacterium Escherichia coli , we are only just beginning to discover the molecular details of these machines and how they function in mycobacteria. Here we review the different AAA+ machines, that contribute to proteostasis in mycobacteria. Primarily we will focus on the recent advances in the structure and function of AAA+ proteases, the substrates they recognize and the cellular pathways they control. Finally, we will discuss the recent developments related to these machines as novel drug targets.

Machine Learning Applications to Resting-State Functional MR Imaging Analysis.

Science.gov (United States)

Billings, John M; Eder, Maxwell; Flood, William C; Dhami, Devendra Singh; Natarajan, Sriraam; Whitlow, Christopher T

2017-11-01

Machine learning is one of the most exciting and rapidly expanding fields within computer science. Academic and commercial research entities are investing in machine learning methods, especially in personalized medicine via patient-level classification. There is great promise that machine learning methods combined with resting state functional MR imaging will aid in diagnosis of disease and guide potential treatment for conditions thought to be impossible to identify based on imaging alone, such as psychiatric disorders. We discuss machine learning methods and explore recent advances. Copyright © 2017 Elsevier Inc. All rights reserved.

Concept Representation Analysis in the Context of Human-Machine Interactions

DEFF Research Database (Denmark)

Badie, Farshad

2016-01-01

an inductive machine learning paradigm). The results will support figuring out the most significant key points for constructing a conceptual linkage between a human learning theory and a machine learning paradigm. Accordingly, I will construct a conceptual ground for expressing and analysing concepts......This article attempts to make a conceptual and epistemological junction between human learning and machine learning. I will be concerned with specifying and analysing the structure of concepts in the common ground between a concept-based human learning theory and a concept-based machine learning...... in the common ground of human and informatics sciences and in the context of human-machine interplays....

Machine learning landscapes and predictions for patient outcomes

Science.gov (United States)

Das, Ritankar; Wales, David J.

2017-07-01

The theory and computational tools developed to interpret and explore energy landscapes in molecular science are applied to the landscapes defined by local minima for neural networks. These machine learning landscapes correspond to fits of training data, where the inputs are vital signs and laboratory measurements for a database of patients, and the objective is to predict a clinical outcome. In this contribution, we test the predictions obtained by fitting to single measurements, and then to combinations of between 2 and 10 different patient medical data items. The effect of including measurements over different time intervals from the 48 h period in question is analysed, and the most recent values are found to be the most important. We also compare results obtained for neural networks as a function of the number of hidden nodes, and for different values of a regularization parameter. The predictions are compared with an alternative convex fitting function, and a strong correlation is observed. The dependence of these results on the patients randomly selected for training and testing decreases systematically with the size of the database available. The machine learning landscapes defined by neural network fits in this investigation have single-funnel character, which probably explains why it is relatively straightforward to obtain the global minimum solution, or a fit that behaves similarly to this optimal parameterization.

Advanced Digitization Techniques in Retrieval of Mechanism and Machine Science Resources

Science.gov (United States)

Lovasz, E.-Ch.; Gruescu, C. M.; Ciupe, V.; Carabas, I.; Margineanu, D.; Maniu, I.; Dehelean, N.

The European project thinkMOTION works on the purpose of retrieving all-times content regarding mechanisms and machine science by means of creating a digital library, accessible to a broad public through the portal Europeana. DMG-Lib is intended to display the development in the field, from its very beginning up to now days. There is a large range of significant objects available, physically very heterogeneous and needing all to be digitized. The paper presents the workflow, the equipments and specific techniques used in digitization of documents featuring very different characteristics (size, texture, color, degree of preservation, resolution and so on). Once the workflow established on very detailed steps, the development of the workstation is treated. Special equipments designed and assembled at Universitatea "Politehnica" Timisoara are presented. A large series of software applications, including original programs, work for digitization itself, processing of images, management of files, automatic optoelectronic control of capture, storage of information in different stages of processing. An illustrating example is explained, showing the steps followed in order to obtain a clear, high-resolution image from an old original document (very valuable as a historical proof but very poor in quality regarding clarity, contrast and resolution).

Machine Phase Fullerene Nanotechnology: 1996

Science.gov (United States)

Globus, Al; Chancellor, Marisa K. (Technical Monitor)

1997-01-01

NASA has used exotic materials for spacecraft and experimental aircraft to good effect for many decades. In spite of many advances, transportation to space still costs about $10,000 per pound. Drexler has proposed a hypothetical nanotechnology based on diamond and investigated the properties of such molecular systems. These studies and others suggest enormous potential for aerospace systems. Unfortunately, methods to realize diamonoid nanotechnology are at best highly speculative. Recent computational efforts at NASA Ames Research Center and computation and experiment elsewhere suggest that a nanotechnology of machine phase functionalized fullerenes may be synthetically relatively accessible and of great aerospace interest. Machine phase materials are (hypothetical) materials consisting entirely or in large part of microscopic machines. In a sense, most living matter fits this definition. To begin investigation of fullerene nanotechnology, we used molecular dynamics to study the properties of carbon nanotube based gears and gear/shaft configurations. Experiments on C60 and quantum calculations suggest that benzyne may react with carbon nanotubes to form gear teeth. Han has computationally demonstrated that molecular gears fashioned from (14,0) single-walled carbon nanotubes and benzyne teeth should operate well at 50-100 gigahertz. Results suggest that rotation can be converted to rotating or linear motion, and linear motion may be converted into rotation. Preliminary results suggest that these mechanical systems can be cooled by a helium atmosphere. Furthermore, Deepak has successfully simulated using helical electric fields generated by a laser to power fullerene gears once a positive and negative charge have been added to form a dipole. Even with mechanical motion, cooling, and power; creating a viable nanotechnology requires support structures, computer control, a system architecture, a variety of components, and some approach to manufacture. Additional

Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises

Science.gov (United States)

Bone, Daniel; Goodwin, Matthew S.; Black, Matthew P.; Lee, Chi-Chun; Audhkhasi, Kartik; Narayanan, Shrikanth

2015-01-01

Machine learning has immense potential to enhance diagnostic and intervention research in the behavioral sciences, and may be especially useful in investigations involving the highly prevalent and heterogeneous syndrome of autism spectrum disorder. However, use of machine learning in the absence of clinical domain expertise can be tenuous and lead…

clearScience: Infrastructure for Communicating Data-Intensive Science.

Science.gov (United States)

Bot, Brian M; Burdick, David; Kellen, Michael; Huang, Erich S

2013-01-01

Progress in biomedical research requires effective scientific communication to one's peers and to the public. Current research routinely encompasses large datasets and complex analytic processes, and the constraints of traditional journal formats limit useful transmission of these elements. We are constructing a framework through which authors can not only provide the narrative of what was done, but the primary and derivative data, the source code, the compute environment, and web-accessible virtual machines. This infrastructure allows authors to "hand their machine"- prepopulated with libraries, data, and code-to those interested in reviewing or building off of their work. This project, "clearScience," seeks to provide an integrated system that accommodates the ad hoc nature of discovery in the data-intensive sciences and seamless transitions from working to reporting. We demonstrate that rather than merely describing the science being reported, one can deliver the science itself.

The ATLAS Higgs Machine Learning Challenge

CERN Document Server

Cowan, Glen; The ATLAS collaboration; Bourdarios, Claire

2015-01-01

High Energy Physics has been using Machine Learning techniques (commonly known as Multivariate Analysis) since the 1990s with Artificial Neural Net and more recently with Boosted Decision Trees, Random Forest etc. Meanwhile, Machine Learning has become a full blown field of computer science. With the emergence of Big Data, data scientists are developing new Machine Learning algorithms to extract meaning from large heterogeneous data. HEP has exciting and difficult problems like the extraction of the Higgs boson signal, and at the same time data scientists have advanced algorithms: the goal of the HiggsML project was to bring the two together by a “challenge”: participants from all over the world and any scientific background could compete online to obtain the best Higgs to tau tau signal significance on a set of ATLAS fully simulated Monte Carlo signal and background. Instead of HEP physicists browsing through machine learning papers and trying to infer which new algorithms might be useful for HEP, then c...

A survey of machine readable data bases

Science.gov (United States)

Matlock, P.

1981-01-01

Forty-two of the machine readable data bases available to the technologist and researcher in the natural sciences and engineering are described and compared with the data bases and date base services offered by NASA.

The art and science of rotating field machines design a practical approach

CERN Document Server

Ostović, Vlado

2017-01-01

This book highlights procedures utilized by the design departments of leading global manufacturers, offering readers essential insights into the electromagnetic and thermal design of rotating field (induction and synchronous) electric machines. Further, it details the physics of the key phenomena involved in the machines’ operation, conducts a thorough analysis and synthesis of polyphase windings, and presents the tools and methods used in the evaluation of winding performance. The book develops and solves the machines’ magnetic circuits, and determines their electromagnetic forces and torques. Special attention is paid to thermal problems in electrical machines, along with fluid flow computations. With a clear emphasis on the practical aspects of electric machine design and synthesis, the author applies his nearly 40 years of professional experience with electric machine manufacturers – both as an employee and consultant – to provide readers with the tools they need to determine fluid flow parameters...

Machine Translation - A Gentle Introduction

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 3; Issue 7. Machine Translation - A Gentle Introduction. Durgesh D Rao. General Article Volume 3 Issue 7 July 1998 pp 61-70. Fulltext. Click here to view fulltext PDF. Permanent link: https://www.ias.ac.in/article/fulltext/reso/003/07/0061-0070 ...

New trends in atomic and molecular physics. Advanced technological applications

International Nuclear Information System (INIS)

Mohan, Man

2013-01-01

Represents an up-to-date scientific status report on new trends in atomic and molecular physics. Multi-disciplinary approach. Also of interest to researchers in astrophysics and fusion plasma physics. Contains material important for nano- and laser technology. The field of Atomic and Molecular Physics (AMP) has reached significant advances in high-precision experimental measurement techniques. The area covers a wide spectrum ranging from conventional to new emerging multi-disciplinary areas like physics of highly charged ions (HCI), molecular physics, optical science, ultrafast laser technology etc. This book includes the important topics of atomic structure, physics of atomic collision, photoexcitation, photoionization processes, Laser cooling and trapping, Bose Einstein condensation and advanced technology applications of AMP in the fields of astronomy, astrophysics, fusion, biology and nanotechnology. This book is useful for researchers, professors, graduate, post graduate and PhD students dealing with atomic and molecular physics. The book has a wide scope with applications in neighbouring fields like plasma physics, astrophysics, cold collisions, nanotechnology and future fusion energy sources like ITER (international Thermonuclear Experimental Reactor) Tokomak plasma machine which need accurate AMP data.

Art Advancing Science: Filmmaking Leads to Molecular Insights at the Nanoscale.

Science.gov (United States)

Reilly, Charles; Ingber, Donald E

2017-12-26

Many have recognized the potential value of facilitating activities that span the art-science interface for the benefit of society; however, there are few examples that demonstrate how pursuit of an artistic agenda can lead to scientific insights. Here, we describe how we set out to produce an entertaining short film depicting the fertilization of the egg by sperm as a parody of a preview for another Star Wars movie to excite the public about science, but ended up developing a simulation tool for multiscale modeling. To produce an aesthetic that communicates mechanical continuity across spatial scales, we developed custom strategies that integrate physics-based animation software from the entertainment industry with molecular dynamics simulation tools, using experimental data from research publications. Using this approach, we were able to depict biological physicality across multiple spatial scales, from how sperm tails move to collective molecular behavior within the axoneme to how the molecular motor, dynein, produces force at the nanometer scale. The dynein simulations, which were validated by replicating results of past simulations and cryo-electron microscopic studies, also predicted a potential mechanism for how ATP hydrolysis drives dynein motion along the microtubule as well as how dynein changes its conformation when it goes through the power stroke. Thus, pursuit of an artistic work led to insights into biology at the nanoscale as well as the development of a highly generalizable modeling and simulation technology that has utility for nanoscience and any other area of scientific investigation that involves analysis of complex multiscale systems.

Brains--Computers--Machines: Neural Engineering in Science Classrooms

Science.gov (United States)

Chudler, Eric H.; Bergsman, Kristen Clapper

2016-01-01

Neural engineering is an emerging field of high relevance to students, teachers, and the general public. This feature presents online resources that educators and scientists can use to introduce students to neural engineering and to integrate core ideas from the life sciences, physical sciences, social sciences, computer science, and engineering…

Trial of accelerator cells machining with high precision and high efficiency at Okayama region

International Nuclear Information System (INIS)

Yoshikawa, Mitsuo; Yoden, Hiroyuki; Yokomizo, Seiichi; Sumida, Tsuneto; Kunishida, Jun; Oshita, Isao

2005-01-01

In the framework of the project 'Promotion of Science and Technology in Regional Areas' by the Ministry of Education, Culture, Sports, Science and Technology, we have prepared a special apparatus for machining accelerator cells with a high precision and a high efficiency for the future linear collider. A machining with as small an error as 2 micrometers has been realized. Necessary time to finish one accelerator cell is reduced from 128 minutes to 34 minutes due to the suppression of the heating of the object at the machining. If newly developed one chuck method was employed, the precision and efficiency would be further improved. By cutting at both sides of the spindle, the necessary time for machining would be reduced by half. (author)

RNA Polymerase II–The Transcription Machine

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 12; Issue 3. RNA Polymerase II – The Transcription Machine - Nobel Prize in Chemistry 2006. Jiyoti Verma Aruna Naorem Anand Kumar Manimala Sen Parag Sadhale. General Article Volume 12 Issue 3 March 2007 pp 47-53 ...

Brain-machine and brain-computer interfaces.

Science.gov (United States)

Friehs, Gerhard M; Zerris, Vasilios A; Ojakangas, Catherine L; Fellows, Mathew R; Donoghue, John P

2004-11-01

The idea of connecting the human brain to a computer or machine directly is not novel and its potential has been explored in science fiction. With the rapid advances in the areas of information technology, miniaturization and neurosciences there has been a surge of interest in turning fiction into reality. In this paper the authors review the current state-of-the-art of brain-computer and brain-machine interfaces including neuroprostheses. The general principles and requirements to produce a successful connection between human and artificial intelligence are outlined and the authors' preliminary experience with a prototype brain-computer interface is reported.

Light-driven molecular machine at ITIES

DEFF Research Database (Denmark)

Kornyshev, A.A.; Kuimova, M.; Kuznetsov, A.M.

2007-01-01

We suggest a principle of operation of a new molecular device that transforms the energy of light into repetitive mechanical motions. Such a device can also serve as a model system for the study of the effect of electric field on intramolecular electron transfer. We discuss the design of suitable...

An ab initio molecular

Indian Academy of Sciences (India)

mechanisms of two molecular crystals: An ab initio molecular dynamics ... for Computation in Molecular and Materials Science and Department of Chemistry, School of ..... NSAF Foundation of National Natural Science Foun- ... Matter 14 2717.

Educational Challenges of Molecular Life Science: Characteristics and Implications for Education and Research

Science.gov (United States)

Tibell, Lena A. E.; Rundgren, Carl-Johan

2010-01-01

Molecular life science is one of the fastest-growing fields of scientific and technical innovation, and biotechnology has profound effects on many aspects of daily life--often with deep, ethical dimensions. At the same time, the content is inherently complex, highly abstract, and deeply rooted in diverse disciplines ranging from "pure…

Machine learning application in the life time of materials

OpenAIRE

Yu, Xiaojiao

2017-01-01

Materials design and development typically takes several decades from the initial discovery to commercialization with the traditional trial and error development approach. With the accumulation of data from both experimental and computational results, data based machine learning becomes an emerging field in materials discovery, design and property prediction. This manuscript reviews the history of materials science as a disciplinary the most common machine learning method used in materials sc...

An overview of rotating machine systems with high-temperature bulk superconductors

Science.gov (United States)

Zhou, Difan; Izumi, Mitsuru; Miki, Motohiro; Felder, Brice; Ida, Tetsuya; Kitano, Masahiro

2012-10-01

The paper contains a review of recent advancements in rotating machines with bulk high-temperature superconductors (HTS). The high critical current density of bulk HTS enables us to design rotating machines with a compact configuration in a practical scheme. The development of an axial-gap-type trapped flux synchronous rotating machine together with the systematic research works at the Tokyo University of Marine Science and Technology since 2001 are briefly introduced. Developments in bulk HTS rotating machines in other research groups are also summarized. The key issues of bulk HTS machines, including material progress of bulk HTS, in situ magnetization, and cooling together with AC loss at low-temperature operation are discussed.

Kernel Methods for Machine Learning with Life Science Applications

DEFF Research Database (Denmark)

Abrahamsen, Trine Julie

Kernel methods refer to a family of widely used nonlinear algorithms for machine learning tasks like classification, regression, and feature extraction. By exploiting the so-called kernel trick straightforward extensions of classical linear algorithms are enabled as long as the data only appear a...

Journal of Earth System Science | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Earth System Science. Abbas Goli Jirandeh. Articles written in Journal of Earth System Science. Volume 122 Issue 2 April 2013 pp 349-369. Landslide susceptibility mapping using support vector machine and GIS at the Golestan Province, Iran · Hamid Reza Pourghasemi Abbas Goli Jirandeh ...

Tribology and energy efficiency: from molecules to lubricated contacts to complete machines.

Science.gov (United States)

Taylor, Robert Ian

2012-01-01

The impact of lubricants on energy efficiency is considered. Molecular details of base oils used in lubricants can have a great impact on the lubricant's physical properties which will affect the energy efficiency performance of a lubricant. In addition, molecular details of lubricant additives can result in significant differences in measured friction coefficients for machine elements operating in the mixed/boundary lubrication regime. In single machine elements, these differences will result in lower friction losses, and for complete systems (such as cars, trucks, hydraulic circuits, industrial gearboxes etc.) lower fuel consumption or lower electricity consumption can result.

Classifying Structures in the ISM with Machine Learning Techniques

Science.gov (United States)

Beaumont, Christopher; Goodman, A. A.; Williams, J. P.

2011-01-01

The processes which govern molecular cloud evolution and star formation often sculpt structures in the ISM: filaments, pillars, shells, outflows, etc. Because of their morphological complexity, these objects are often identified manually. Manual classification has several disadvantages; the process is subjective, not easily reproducible, and does not scale well to handle increasingly large datasets. We have explored to what extent machine learning algorithms can be trained to autonomously identify specific morphological features in molecular cloud datasets. We show that the Support Vector Machine algorithm can successfully locate filaments and outflows blended with other emission structures. When the objects of interest are morphologically distinct from the surrounding emission, this autonomous classification achieves >90% accuracy. We have developed a set of IDL-based tools to apply this technique to other datasets.

Belowground Carbon Cycling Processes at the Molecular Scale: An EMSL Science Theme Advisory Panel Workshop

Energy Technology Data Exchange (ETDEWEB)

Hess, Nancy J.; Brown, Gordon E.; Plata, Charity

2014-02-21

As part of the Belowground Carbon Cycling Processes at the Molecular Scale workshop, an EMSL Science Theme Advisory Panel meeting held in February 2013, attendees discussed critical biogeochemical processes that regulate carbon cycling in soil. The meeting attendees determined that as a national scientific user facility, EMSL can provide the tools and expertise needed to elucidate the molecular foundation that underlies mechanistic descriptions of biogeochemical processes that control carbon allocation and fluxes at the terrestrial/atmospheric interface in landscape and regional climate models. Consequently, the workshop's goal was to identify the science gaps that hinder either development of mechanistic description of critical processes or their accurate representation in climate models. In part, this report offers recommendations for future EMSL activities in this research area. The workshop was co-chaired by Dr. Nancy Hess (EMSL) and Dr. Gordon Brown (Stanford University).

Using human brain activity to guide machine learning.

Science.gov (United States)

Fong, Ruth C; Scheirer, Walter J; Cox, David D

2018-03-29

Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human brain has long served as a source of inspiration for machine learning, little effort has been made to directly use data collected from working brains as a guide for machine learning algorithms. Here we demonstrate a new paradigm of "neurally-weighted" machine learning, which takes fMRI measurements of human brain activity from subjects viewing images, and infuses these data into the training process of an object recognition learning algorithm to make it more consistent with the human brain. After training, these neurally-weighted classifiers are able to classify images without requiring any additional neural data. We show that our neural-weighting approach can lead to large performance gains when used with traditional machine vision features, as well as to significant improvements with already high-performing convolutional neural network features. The effectiveness of this approach points to a path forward for a new class of hybrid machine learning algorithms which take both inspiration and direct constraints from neuronal data.

Visualization and characterization of individual type III protein secretion machines in live bacteria.

Science.gov (United States)

Zhang, Yongdeng; Lara-Tejero, María; Bewersdorf, Jörg; Galán, Jorge E

2017-06-06

Type III protein secretion machines have evolved to deliver bacterially encoded effector proteins into eukaryotic cells. Although electron microscopy has provided a detailed view of these machines in isolation or fixed samples, little is known about their organization in live bacteria. Here we report the visualization and characterization of the Salmonella type III secretion machine in live bacteria by 2D and 3D single-molecule switching superresolution microscopy. This approach provided access to transient components of this machine, which previously could not be analyzed. We determined the subcellular distribution of individual machines, the stoichiometry of the different components of this machine in situ, and the spatial distribution of the substrates of this machine before secretion. Furthermore, by visualizing this machine in Salmonella mutants we obtained major insights into the machine's assembly. This study bridges a major resolution gap in the visualization of this nanomachine and may serve as a paradigm for the examination of other bacterially encoded molecular machines.

55th International Conference of Machine Design Departments 2014

CERN Document Server

Berka, Ondrej; Petr, Karel; Lopot, František; Dub, Martin

2016-01-01

This book is based on the 55th International Conference of Machine Design Departments 2014 (ICMD 2014) which was hosted by the Czech Technical University in September 2014. It features scientific articles which solve progressive themes from the field of machine design. The book addresses a broad range of themes including tribology, hydraulics, materials science, product innovation and experimental methods. It presents the latest interdisciplinary high-tech work. People with an interest in the latest research results in the field of machine design and manufacturing engineering will value this book with contributions of leading academic scientists and experts from all around the world.

Quo vadis, Intelligent Machine?

Directory of Open Access Journals (Sweden)

Rosemarie Velik

2010-09-01

Full Text Available Artificial Intelligence (AI is a branch of computer science concerned with making computers behave like humans. At least this was the original idea. However, it turned out that this is no task easy to be solved. This article aims to give a comprehensible review on the last 60 years of artificial intelligence taking a philosophical viewpoint. It is outlined what happened so far in AI, what is currently going on in this research area, and what can be expected in future. The goal is to mediate an understanding for the developments and changes in thinking in course of time about how to achieve machine intelligence. The clear message is that AI has to join forces with neuroscience and other brain disciplines in order to make a step towards the development of truly intelligent machines.

Traceability of On-Machine Tool Measurement: A Review

Science.gov (United States)

Gomez-Acedo, Eneko; Kortaberria, Gorka; Olarra, Aitor

2017-01-01

Nowadays, errors during the manufacturing process of high value components are not acceptable in driving industries such as energy and transportation. Sectors such as aerospace, automotive, shipbuilding, nuclear power, large science facilities or wind power need complex and accurate components that demand close measurements and fast feedback into their manufacturing processes. New measuring technologies are already available in machine tools, including integrated touch probes and fast interface capabilities. They provide the possibility to measure the workpiece in-machine during or after its manufacture, maintaining the original setup of the workpiece and avoiding the manufacturing process from being interrupted to transport the workpiece to a measuring position. However, the traceability of the measurement process on a machine tool is not ensured yet and measurement data is still not fully reliable enough for process control or product validation. The scientific objective is to determine the uncertainty on a machine tool measurement and, therefore, convert it into a machine integrated traceable measuring process. For that purpose, an error budget should consider error sources such as the machine tools, components under measurement and the interactions between both of them. This paper reviews all those uncertainty sources, being mainly focused on those related to the machine tool, either on the process of geometric error assessment of the machine or on the technology employed to probe the measurand. PMID:28696358

Traceability of On-Machine Tool Measurement: A Review.

Science.gov (United States)

Mutilba, Unai; Gomez-Acedo, Eneko; Kortaberria, Gorka; Olarra, Aitor; Yagüe-Fabra, Jose A

2017-07-11

Nowadays, errors during the manufacturing process of high value components are not acceptable in driving industries such as energy and transportation. Sectors such as aerospace, automotive, shipbuilding, nuclear power, large science facilities or wind power need complex and accurate components that demand close measurements and fast feedback into their manufacturing processes. New measuring technologies are already available in machine tools, including integrated touch probes and fast interface capabilities. They provide the possibility to measure the workpiece in-machine during or after its manufacture, maintaining the original setup of the workpiece and avoiding the manufacturing process from being interrupted to transport the workpiece to a measuring position. However, the traceability of the measurement process on a machine tool is not ensured yet and measurement data is still not fully reliable enough for process control or product validation. The scientific objective is to determine the uncertainty on a machine tool measurement and, therefore, convert it into a machine integrated traceable measuring process. For that purpose, an error budget should consider error sources such as the machine tools, components under measurement and the interactions between both of them. This paper reviews all those uncertainty sources, being mainly focused on those related to the machine tool, either on the process of geometric error assessment of the machine or on the technology employed to probe the measurand.

A physical implementation of the Turing machine accessed through Web

Directory of Open Access Journals (Sweden)

Marijo Maracic

2008-11-01

Full Text Available A Turing machine has an important role in education in the field of computer science, as it is a milestone in courses related to automata theory, theory of computation and computer architecture. Its value is also recognized in the Computing Curricula proposed by the Association for Computing Machinery (ACM and IEEE Computer Society. In this paper we present a physical implementation of the Turing machine accessed through Web. To enable remote access to the Turing machine, an implementation of the client-server architecture is built. The web interface is described in detail and illustrations of remote programming, initialization and the computation of the Turing machine are given. Advantages of such approach and expected benefits obtained by using remotely accessible physical implementation of the Turing machine as an educational tool in the teaching process are discussed.

Rosalind Franklin and the DNA molecular structure: A case of history of science to learn about the nature of science

Directory of Open Access Journals (Sweden)

José Antonio Acevedo-Díaz

2016-08-01

Full Text Available The Rosalind Franklin’s case regarding the elucidation of the molecular structure of DNA is presented as an interesting story of the history of science to address a set of questions related to the nature of science (NOS from an explicit and reflective approach. The teaching proposal is aimed to the pre-service teachers training in NOS issues and its didactics. Attention is given to both epistemic and non-epistemic aspects in the narration and the NOS questions asked for reflecting about them. Also, some methodological recommendations for implementing the didactic proposal in science classroom are offered. This involves the follows: (i in small groups, the students read the controversy and respond to some questions on NOS; (ii they present their responses to the whole-class; and (iii they revise their initial responses in light of the whole-class discussion.

Development of a machine learning potential for graphene

Science.gov (United States)

Rowe, Patrick; Csányi, Gábor; Alfè, Dario; Michaelides, Angelos

2018-02-01

We present an accurate interatomic potential for graphene, constructed using the Gaussian approximation potential (GAP) machine learning methodology. This GAP model obtains a faithful representation of a density functional theory (DFT) potential energy surface, facilitating highly accurate (approaching the accuracy of ab initio methods) molecular dynamics simulations. This is achieved at a computational cost which is orders of magnitude lower than that of comparable calculations which directly invoke electronic structure methods. We evaluate the accuracy of our machine learning model alongside that of a number of popular empirical and bond-order potentials, using both experimental and ab initio data as references. We find that whilst significant discrepancies exist between the empirical interatomic potentials and the reference data—and amongst the empirical potentials themselves—the machine learning model introduced here provides exemplary performance in all of the tested areas. The calculated properties include: graphene phonon dispersion curves at 0 K (which we predict with sub-meV accuracy), phonon spectra at finite temperature, in-plane thermal expansion up to 2500 K as compared to NPT ab initio molecular dynamics simulations and a comparison of the thermally induced dispersion of graphene Raman bands to experimental observations. We have made our potential freely available online at [http://www.libatoms.org].

Machine speech and speaking about machines

Energy Technology Data Exchange (ETDEWEB)

Nye, A. [Univ. of Wisconsin, Whitewater, WI (United States)

1996-12-31

Current philosophy of language prides itself on scientific status. It boasts of being no longer contaminated with queer mental entities or idealist essences. It theorizes language as programmable variants of formal semantic systems, reimaginable either as the properly epiphenomenal machine functions of computer science or the properly material neural networks of physiology. Whether or not such models properly capture the physical workings of a living human brain is a question that scientists will have to answer. I, as a philosopher, come at the problem from another direction. Does contemporary philosophical semantics, in its dominant truth-theoretic and related versions, capture actual living human thought as it is experienced, or does it instead reflect, regardless of (perhaps dubious) scientific credentials, pathology of thought, a pathology with a disturbing social history.

The ATLAS Higgs machine learning challenge

CERN Document Server

Davey, W; The ATLAS collaboration; Rousseau, D; Cowan, G; Kegl, B; Germain-Renaud, C; Guyon, I

2014-01-01

High Energy Physics has been using Machine Learning techniques (commonly known as Multivariate Analysis) since the 90's with Artificial Neural Net for example, more recently with Boosted Decision Trees, Random Forest etc... Meanwhile, Machine Learning has become a full blown field of computer science. With the emergence of Big Data, Data Scientists are developing new Machine Learning algorithms to extract sense from large heterogeneous data. HEP has exciting and difficult problems like the extraction of the Higgs boson signal, data scientists have advanced algorithms: the goal of the HiggsML project is to bring the two together by a “challenge”: participants from all over the world and any scientific background can compete online ( https://www.kaggle.com/c/higgs-boson ) to obtain the best Higgs to tau tau signal significance on a set of ATLAS full simulated Monte Carlo signal and background. Winners with the best scores will receive money prizes ; authors of the best method (most usable) will be invited t...

European analytical column No. 36 from the Division of Analytical Chemistry (DAC) of the European Association for Chemical and Molecular Sciences (EuCheMS)

DEFF Research Database (Denmark)

Karlberg, Bo; Emons, Hendrik; Andersen, Jens Enevold Thaulov

2008-01-01

European analytical column no. 36 from the division of analytical chemistry (DAC) of the European association for chemical and molecular sciences (EuCheMS)......European analytical column no. 36 from the division of analytical chemistry (DAC) of the European association for chemical and molecular sciences (EuCheMS)...

Material Choice for spindle of machine tools

Science.gov (United States)

Gouasmi, S.; Merzoug, B.; Abba, G.; Kherredine, L.

2012-02-01

The requirements of contemporary industry and the flashing development of modern sciences impose restrictions on the majority of the elements of machines; the resulting financial constraints can be satisfied by a better output of the production equipment. As for those concerning the design, the resistance and the correct operation of the product, these require the development of increasingly precise parts, therefore the use of increasingly powerful tools [5]. The precision of machining and the output of the machine tools are generally determined by the precision of rotation of the spindle, indeed, more this one is large more the dimensions to obtain are in the zone of tolerance and the defects of shape are minimized. During the development of the machine tool, the spindle which by definition is a rotating shaft receiving and transmitting to the work piece or the cutting tool the rotational movement, must be designed according to certain optimal parameters to be able to ensure the precision required. This study will be devoted to the choice of the material of the spindle fulfilling the imposed requirements of precision.

Material Choice for spindle of machine tools

International Nuclear Information System (INIS)

Gouasmi, S; Merzoug, B; Kherredine, L; Abba, G

2012-01-01

The requirements of contemporary industry and the flashing development of modern sciences impose restrictions on the majority of the elements of machines; the resulting financial constraints can be satisfied by a better output of the production equipment. As for those concerning the design, the resistance and the correct operation of the product, these require the development of increasingly precise parts, therefore the use of increasingly powerful tools [5]. The precision of machining and the output of the machine tools are generally determined by the precision of rotation of the spindle, indeed, more this one is large more the dimensions to obtain are in the zone of tolerance and the defects of shape are minimized. During the development of the machine tool, the spindle which by definition is a rotating shaft receiving and transmitting to the work piece or the cutting tool the rotational movement, must be designed according to certain optimal parameters to be able to ensure the precision required. This study will be devoted to the choice of the material of the spindle fulfilling the imposed requirements of precision.

Machine rates for selected forest harvesting machines

Science.gov (United States)

R.W. Brinker; J. Kinard; Robert Rummer; B. Lanford

2002-01-01

Very little new literature has been published on the subject of machine rates and machine cost analysis since 1989 when the Alabama Agricultural Experiment Station Circular 296, Machine Rates for Selected Forest Harvesting Machines, was originally published. Many machines discussed in the original publication have undergone substantial changes in various aspects, not...

Challenging the Science Curriculum Paradigm: Teaching Primary Children Atomic-Molecular Theory

Science.gov (United States)

Haeusler, Carole; Donovan, Jennifer

2017-11-01

Solutions to global issues demand the involvement of scientists, yet concern exists about retention rates in science as students pass through school into University. Young children are curious about science, yet are considered incapable of grappling with abstract and microscopic concepts such as atoms, sub-atomic particles, molecules and DNA. School curricula for primary (elementary) aged children reflect this by their limitation to examining only what phenomena are without providing any explanatory frameworks for how or why they occur. This research challenges the assumption that atomic-molecular theory is too difficult for young children, examining new ways of introducing atomic theory to 9 year olds and seeks to verify their efficacy in producing genuine learning in the participants. Early results in three cases in different schools indicate these novel methods fostered further interest in science, allowed diverse children to engage and learn aspects of atomic theory, and satisfied the children's desire for intellectual challenge. Learning exceeded expectations as demonstrated in the post-interview findings. Learning was also remarkably robust, as demonstrated in two schools 8 weeks after the intervention and, in one school, 1 year after their first exposure to ideas about atoms, elements and molecules.

Separating the Classes of Recursively Enumerable Languages Based on Machine Size

Czech Academy of Sciences Publication Activity Database

van Leeuwen, J.; Wiedermann, Jiří

2015-01-01

Roč. 26, č. 6 (2015), s. 677-695 ISSN 0129-0541 R&D Projects: GA ČR GAP202/10/1333 Grant - others:GA ČR(CZ) GA15-04960S Institutional support: RVO:67985807 Keywords : recursively enumerable languages * RE hierarchy * finite languages * machine size * descriptional complexity * Turing machines with advice Subject RIV: IN - Informatics, Computer Science Impact factor: 0.467, year: 2015

Computational Nanotechnology Molecular Electronics, Materials and Machines

Science.gov (United States)

Srivastava, Deepak; Biegel, Bryan A. (Technical Monitor)

2002-01-01

This presentation covers research being performed on computational nanotechnology, carbon nanotubes and fullerenes at the NASA Ames Research Center. Topics cover include: nanomechanics of nanomaterials, nanotubes and composite materials, molecular electronics with nanotube junctions, kinky chemistry, and nanotechnology for solid-state quantum computers using fullerenes.

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Chemical Sciences. AYOUB KANAANI. Articles written in Journal of Chemical Sciences. Volume 128 Issue 8 August 2016 pp 1211-1221 Regular Article. Synthesis, molecular structure, spectroscopic investigations and computational study of a potential molecular switch of 2-([1 ...

Committee on Atomic, Molecular, and Optical Sciences (CAMOS). Technical progress report ampersand continuation proposal, February 1, 1993--January 31, 1994

International Nuclear Information System (INIS)

Taylor, R.D.

1997-01-01

The Committee on Atomic, Molecular and Optical Sciences (CAMOS) of the National Research Council (NRC) is charged with monitoring the health of the field of atomic, molecular, and optical (AMO) science in the United States. Accordingly, the Committee identifies and examines both broad and specific issues affecting the field. Regular meetings, teleconferences, briefings from agencies and the scientific community, the formation of study panels to prepare reports, and special symposia are among the mechanisms used by the CAMOS to meet its charge. This progress report presents a review of CAMOS activities from February 1, 1993 to January 31, 1994. The details of prior activities are discussed in earlier progress reports. This report also includes the status of activities associated with the CAMOS study on the field that is being conducted by the Panel on the Future of Atomic, Molecular, and Optical Sciences (FAMOS). During the above period, CAMOS has continued to track and participate in, when requested, discussions on the health of the field. Much of the perspective of CAMOS has been presented in the recently-published report Research Briefing on Selected Opportunities in Atomic, Molecular, and Optical Sciences. That report has served as the basis for briefings to representatives of the federal government as well as the community-at-large. In keeping with its charge to monitor the health of the field, CAMOS launched a study designed to highlight future directions of the field

Interactive Multimodal Molecular Set – Designing Ludic Engaging Science Learning Content

DEFF Research Database (Denmark)

Thorsen, Tine Pinholt; Christiansen, Kasper Holm Bonde; Jakobsen Sillesen, Kristian

2014-01-01

This paper reports on an exploratory study investigating 10 primary school students’ interaction with an interactive multimodal molecular set fostering ludic engaging science learning content in primary schools (8th and 9th grade). The concept of the prototype design was to bridge the physical...... and virtual worlds with electronic tags and, through this, blend the familiarity of the computer and toys, to create a tool that provided a ludic approach to learning about atoms and molecules. The study was inspired by the participatory design and informant design methodologies and included design...

Machine learning a Bayesian and optimization perspective

CERN Document Server

Theodoridis, Sergios

2015-01-01

This tutorial text gives a unifying perspective on machine learning by covering both probabilistic and deterministic approaches, which rely on optimization techniques, as well as Bayesian inference, which is based on a hierarchy of probabilistic models. The book presents the major machine learning methods as they have been developed in different disciplines, such as statistics, statistical and adaptive signal processing and computer science. Focusing on the physical reasoning behind the mathematics, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. The book builds carefully from the basic classical methods to the most recent trends, with chapters written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as shor...

The Design, Synthesis, and Study of Solid-State Molecular Rotors: Structure/Function Relationships for Condensed-Phase Anisotropic Dynamics

Science.gov (United States)

Vogelsberg, Cortnie Sue

Amphidynamic crystals are an extremely promising platform for the development of artificial molecular machines and stimuli-responsive materials. In analogy to skeletal muscle, their function will rely upon the collective operation of many densely packed molecular machines (i.e. actin-bound myosin) that are self-assembled in a highly organized anisotropic medium. By choosing lattice-forming elements and moving "parts" with specific functionalities, individual molecular machines may be synthesized and self-assembled in order to carry out desirable functions. In recent years, efforts in the design of amphidynamic materials based on molecular gyroscopes and compasses have shown that a certain amount of free volume is essential to facilitate internal rotation and reorientation within a crystal. In order to further establish structure/function relationships to advance the development of increasingly complex molecular machinery, molecular rotors and a molecular "spinning" top were synthesized and incorporated into a variety of solid-state architectures with different degrees of periodicity, dimensionality, and free volume. Specifically, lamellar molecular crystals, hierarchically ordered periodic mesoporous organosilicas, and metal-organic frameworks were targeted for the development of solid-state molecular machines. Using an array of solid-state nuclear magnetic resonance spectroscopy techniques, the dynamic properties of these novel molecular machine assemblies were determined and correlated with their corresponding structural features. It was found that architecture type has a profound influence on functional dynamics. The study of layered molecular crystals, composed of either molecular rotors or "spinning" tops, probed functional dynamics within dense, highly organized environments. From their study, it was discovered that: 1) crystallographically distinct sites may be utilized to differentiate machine function, 2) halogen bonding interactions are sufficiently

A proposal to establish an international network in molecular microbiology and genetic engineering for scientific cooperation and prevention of misuse of biological sciences in the framework of science for peace

International Nuclear Information System (INIS)

Becker, Y.

1998-01-01

The conference on 'Science and Technology for Construction of Peace' which was organized by the Landau Network Coordination Center and A. Volta Center for Scientific Culture dealt with conversion of military and technological capacities into sustainable civilian application. The ideas regarding the conversion of nuclear warheads into nuclear energy for civilian-use led to the idea that the extension of this trend of thought to molecular biology and genetic engineering, will be a useful contribution to Science for Peace. This idea of developing a Cooperation Network in Molecular Biology and Genetic Engineering that will function parallel to and with the Landau Network Coordination in the 'A. Volta' Center was discussed in the Second International Symposium on Science for Peace, Jerusalem, January 1997. It is the reason for the inclusion of the biological aspects in the deliberations of our Forum. It is hoped that the establishment of an international network in molecular biology and genetic engineering, similar to the Landau Network in physics, will support and achieve the decommissioning of biological weapons. Such a network in microbiology and genetic engineering will contribute to the elimination of biological weapons and to contributions to Science for Peace and to Culture of Peace activities of UNESCO. (author)

Molecular machines regulating the release probability of synaptic vesicles at the active zone.

Directory of Open Access Journals (Sweden)

Christoph eKoerber

2016-03-01

Full Text Available The fusion of synaptic vesicles (SVs with the plasma membrane of the active zone (AZ upon arrival of an action potential (AP at the presynaptic compartment is a tightly regulated probabil-istic process crucial for information transfer. The probability of a SV to release its transmitter content in response to an AP, termed release probability (Pr, is highly diverse both at the level of entire synapses and individual SVs at a given synapse. Differences in Pr exist between different types of synapses, between synapses of the same type, synapses originating from the same axon and even between different SV subpopulations within the same presynaptic terminal. The Pr of SVs at the AZ is set by a complex interplay of different presynaptic properties including the availability of release-ready SVs, the location of the SVs relative to the voltage-gated calcium channels (VGCCs at the AZ, the magnitude of calcium influx upon arrival of the AP, the buffer-ing of calcium ions as well as the identity and sensitivity of the calcium sensor. These properties are not only interconnected, but can also be regulated dynamically to match the requirements of activity patterns mediated by the synapse. Here, we review recent advances in identifying mole-cules and molecular machines taking part in the determination of vesicular Pr at the AZ.

A Review of Related Work on Machine Learning in Semiconductor Manufacturing and Assembly Lines

OpenAIRE

Stanisavljevic, Darko; Spitzer, Michael

2017-01-01

This paper deals with applications of machine learning algorithms in manufacturing. Machine learning can be defined as a field of computer science that gives computers the ability to learn without explicitly developing the needed algorithms. Manufacturing is the production of merchandise by manual labour, machines and tools. The focus of this paper is on automatic production lines. The areas of interest of this paper are semiconductor manufacturing and production on assembly lines. The purpos...

[A new machinability test machine and the machinability of composite resins for core built-up].

Science.gov (United States)

Iwasaki, N

2001-06-01

A new machinability test machine especially for dental materials was contrived. The purpose of this study was to evaluate the effects of grinding conditions on machinability of core built-up resins using this machine, and to confirm the relationship between machinability and other properties of composite resins. The experimental machinability test machine consisted of a dental air-turbine handpiece, a control weight unit, a driving unit of the stage fixing the test specimen, and so on. The machinability was evaluated as the change in volume after grinding using a diamond point. Five kinds of core built-up resins and human teeth were used in this study. The machinabilities of these composite resins increased with an increasing load during grinding, and decreased with repeated grinding. There was no obvious correlation between the machinability and Vickers' hardness; however, a negative correlation was observed between machinability and scratch width.

Machine Learning Approaches in Cardiovascular Imaging.

Science.gov (United States)

Henglin, Mir; Stein, Gillian; Hushcha, Pavel V; Snoek, Jasper; Wiltschko, Alexander B; Cheng, Susan

2017-10-01

Cardiovascular imaging technologies continue to increase in their capacity to capture and store large quantities of data. Modern computational methods, developed in the field of machine learning, offer new approaches to leveraging the growing volume of imaging data available for analyses. Machine learning methods can now address data-related problems ranging from simple analytic queries of existing measurement data to the more complex challenges involved in analyzing raw images. To date, machine learning has been used in 2 broad and highly interconnected areas: automation of tasks that might otherwise be performed by a human and generation of clinically important new knowledge. Most cardiovascular imaging studies have focused on task-oriented problems, but more studies involving algorithms aimed at generating new clinical insights are emerging. Continued expansion in the size and dimensionality of cardiovascular imaging databases is driving strong interest in applying powerful deep learning methods, in particular, to analyze these data. Overall, the most effective approaches will require an investment in the resources needed to appropriately prepare such large data sets for analyses. Notwithstanding current technical and logistical challenges, machine learning and especially deep learning methods have much to offer and will substantially impact the future practice and science of cardiovascular imaging. © 2017 American Heart Association, Inc.

Of Slot Machines and Broken Test Tubes

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 19; Issue 5. Of Slot Machines and Broken Test Tubes. S Mahadevan. General Article Volume 19 Issue 5 May 2014 pp 395-405. Fulltext. Click here to view fulltext PDF. Permanent link: https://www.ias.ac.in/article/fulltext/reso/019/05/0395-0405. Keywords.

Debashish Chowdhury | Speakers | Indian Academy of Sciences

Indian Academy of Sciences (India)

Convener: Debashish Chowdhury, Indian Institute of Technology, Kanpur. Noise and nonequilibrium in ... Reverse-engineering these natural nano-machines and chemical synthesis of artificial molecular machines are opening up the industrial revolution of the 21st century. This multidisciplinary enterprise has benefitted ...

Virtual Machine Language Controls Remote Devices

Science.gov (United States)

2014-01-01

Kennedy Space Center worked with Blue Sun Enterprises, based in Boulder, Colorado, to enhance the company's virtual machine language (VML) to control the instruments on the Regolith and Environment Science and Oxygen and Lunar Volatiles Extraction mission. Now the NASA-improved VML is available for crewed and uncrewed spacecraft, and has potential applications on remote systems such as weather balloons, unmanned aerial vehicles, and submarines.

Explorations in the History of Machines and Mechanisms : Proceedings of HMM2012

CERN Document Server

Ceccarelli, Marco

2012-01-01

This book contains the proceedings of HMM2012, the 4th International Symposium on Historical Developments in the field of Mechanism and Machine Science (MMS). These proceedings cover recent research concerning all aspects of the development of MMS from antiquity until the present and its historiography: machines, mechanisms, kinematics, dynamics, concepts and theories, design methods, collections of methods, collections of models, institutions and biographies.

Molecular Contamination on Anodized Aluminum Components of the Genesis Science Canister

Science.gov (United States)

Burnett, D. S.; McNamara, K. M.; Jurewicz, A.; Woolum, D.

2005-01-01

Inspection of the interior of the Genesis science canister after recovery in Utah, and subsequently at JSC, revealed a darkening on the aluminum canister shield and other canister components. There has been no such observation of film contamination on the collector surfaces, and preliminary spectroscopic ellipsometry measurements support the theory that the films observed on the anodized aluminum components do not appear on the collectors to any significant extent. The Genesis Science Team has made an effort to characterize the thickness and composition of the brown stain and to determine if it is associated with molecular outgassing.Detailed examination of the surfaces within the Genesis science canister reveals that the brown contamination is observed to varying degrees, but only on surfaces exposed in space to the Sun and solar wind hydrogen. In addition, the materials affected are primarily composed of anodized aluminum. A sharp line separating the sun and shaded portion of the thermal closeout panel is shown. This piece was removed from a location near the gold foil collector within the canister. Future plans include a reassembly of the canister components to look for large-scale patterns of contamination within the canister to aid in revealing the root cause.

A transformational product to improve self-control strength: The Chocolate Machine

OpenAIRE

Kehr, Flavius; Hassenzahl, Marc; Laschke, Matthias; Diefenbach, Sarah

2012-01-01

Lack of self-control is at the heart of many undesirable behaviors, such as overeating, overspending, and evenoverworking. While the field of persuasive technologies explicitly searches for ways to change attitudes and behaviors, it more or less neglects the science of self-control. We present the Chocolate Machine, an interactive device to train self-control strength based upon Ego Depletion theory. A longitudinal, control-group, field study showed the machine to increase self-control str...

A review of machine learning in obesity.

Science.gov (United States)

DeGregory, K W; Kuiper, P; DeSilvio, T; Pleuss, J D; Miller, R; Roginski, J W; Fisher, C B; Harness, D; Viswanath, S; Heymsfield, S B; Dungan, I; Thomas, D M

2018-05-01

Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine learning provides sophisticated and elegant tools to describe, classify and predict obesity-related risks and outcomes. Here, we review machine learning methods that predict and/or classify such as linear and logistic regression, artificial neural networks, deep learning and decision tree analysis. We also review methods that describe and characterize data such as cluster analysis, principal component analysis, network science and topological data analysis. We introduce each method with a high-level overview followed by examples of successful applications. The algorithms were then applied to National Health and Nutrition Examination Survey to demonstrate methodology, utility and outcomes. The strengths and limitations of each method were also evaluated. This summary of machine learning algorithms provides a unique overview of the state of data analysis applied specifically to obesity. © 2018 World Obesity Federation.

One of My Favorite Assignments: Automated Teller Machine Simulation.

Science.gov (United States)

Oberman, Paul S.

2001-01-01

Describes an assignment for an introductory computer science class that requires the student to write a software program that simulates an automated teller machine. Highlights include an algorithm for the assignment; sample file contents; language features used; assignment variations; and discussion points. (LRW)

Predicting Solar Activity Using Machine-Learning Methods

Science.gov (United States)

Bobra, M.

2017-12-01

Of all the activity observed on the Sun, two of the most energetic events are flares and coronal mass ejections. However, we do not, as of yet, fully understand the physical mechanism that triggers solar eruptions. A machine-learning algorithm, which is favorable in cases where the amount of data is large, is one way to [1] empirically determine the signatures of this mechanism in solar image data and [2] use them to predict solar activity. In this talk, we discuss the application of various machine learning algorithms - specifically, a Support Vector Machine, a sparse linear regression (Lasso), and Convolutional Neural Network - to image data from the photosphere, chromosphere, transition region, and corona taken by instruments aboard the Solar Dynamics Observatory in order to predict solar activity on a variety of time scales. Such an approach may be useful since, at the present time, there are no physical models of flares available for real-time prediction. We discuss our results (Bobra and Couvidat, 2015; Bobra and Ilonidis, 2016; Jonas et al., 2017) as well as other attempts to predict flares using machine-learning (e.g. Ahmed et al., 2013; Nishizuka et al. 2017) and compare these results with the more traditional techniques used by the NOAA Space Weather Prediction Center (Crown, 2012). We also discuss some of the challenges in using machine-learning algorithms for space science applications.

The laboratory technology of discrete molecular separation: the historical development of gel electrophoresis and the material epistemology of biomolecular science, 1945-1970.

Science.gov (United States)

Chiang, Howard Hsueh-hao

2009-01-01

Preparative and analytical methods developed by separation scientists have played an important role in the history of molecular biology. One such early method is gel electrophoresis, a technique that uses various types of gel as its supporting medium to separate charged molecules based on size and other properties. Historians of science, however, have only recently begun to pay closer attention to this material epistemological dimension of biomolecular science. This paper substantiates the historiographical thread that explores the relationship between modern laboratory practice and the production of scientific knowledge. It traces the historical development of gel electrophoresis from the mid-1940s to the mid-1960s, with careful attention to the interplay between technical developments and disciplinary shifts, especially the rise of molecular biology in this time-frame. Claiming that the early 1950s marked a decisive shift in the evolution of electrophoretic methods from moving boundary to zone electrophoresis, I reconstruct various trajectories in which scientists such as Oliver Smithies sought out the most desirable solid supporting medium for electrophoretic instrumentation. Biomolecular knowledge, I argue, emerged in part from this process of seeking the most appropriate supporting medium that allowed for discrete molecular separation and visualization. The early 1950s, therefore, marked not only an important turning point in the history of separation science, but also a transformative moment in the history of the life sciences as the growth of molecular biology depended in part on the epistemological access to the molecular realm available through these evolving technologies.

Machine Learning Methods to Predict Density Functional Theory B3LYP Energies of HOMO and LUMO Orbitals.

Science.gov (United States)

Pereira, Florbela; Xiao, Kaixia; Latino, Diogo A R S; Wu, Chengcheng; Zhang, Qingyou; Aires-de-Sousa, Joao

2017-01-23

Machine learning algorithms were explored for the fast estimation of HOMO and LUMO orbital energies calculated by DFT B3LYP, on the basis of molecular descriptors exclusively based on connectivity. The whole project involved the retrieval and generation of molecular structures, quantum chemical calculations for a database with >111 000 structures, development of new molecular descriptors, and training/validation of machine learning models. Several machine learning algorithms were screened, and an applicability domain was defined based on Euclidean distances to the training set. Random forest models predicted an external test set of 9989 compounds achieving mean absolute error (MAE) up to 0.15 and 0.16 eV for the HOMO and LUMO orbitals, respectively. The impact of the quantum chemical calculation protocol was assessed with a subset of compounds. Inclusion of the orbital energy calculated by PM7 as an additional descriptor significantly improved the quality of estimations (reducing the MAE in >30%).

The Machine within the Machine

CERN Multimedia

Katarina Anthony

2014-01-01

Although Virtual Machines are widespread across CERN, you probably won't have heard of them unless you work for an experiment. Virtual machines - known as VMs - allow you to create a separate machine within your own, allowing you to run Linux on your Mac, or Windows on your Linux - whatever combination you need.   Using a CERN Virtual Machine, a Linux analysis software runs on a Macbook. When it comes to LHC data, one of the primary issues collaborations face is the diversity of computing environments among collaborators spread across the world. What if an institute cannot run the analysis software because they use different operating systems? "That's where the CernVM project comes in," says Gerardo Ganis, PH-SFT staff member and leader of the CernVM project. "We were able to respond to experimentalists' concerns by providing a virtual machine package that could be used to run experiment software. This way, no matter what hardware they have ...

Automatic Creation of Machine Learning Workflows with Strongly Typed Genetic Programming

Czech Academy of Sciences Publication Activity Database

Křen, T.; Pilát, M.; Neruda, Roman

2017-01-01

Roč. 26, č. 5 (2017), č. článku 1760020. ISSN 0218-2130 R&D Projects: GA ČR GA15-19877S Grant - others:GA MŠk(CZ) LM2015042 Institutional support: RVO:67985807 Keywords : genetic programming * machine learning workflows * asynchronous evolutionary algorithm Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 0.778, year: 2016

Bulletin of Materials Science | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Bulletin of Materials Science. B P Singh. Articles written in Bulletin of Materials Science. Volume 23 Issue 1 February 2000 pp 11-16 Molecular Magnets. Synthesis and magnetic properties of one-dimensional metal oxalate networks as molecular-based magnets · B P Singh B Singh · More Details Abstract ...

Performance of machine learning methods for ligand-based virtual screening.

Science.gov (United States)

Plewczynski, Dariusz; Spieser, Stéphane A H; Koch, Uwe

2009-05-01

Computational screening of compound databases has become increasingly popular in pharmaceutical research. This review focuses on the evaluation of ligand-based virtual screening using active compounds as templates in the context of drug discovery. Ligand-based screening techniques are based on comparative molecular similarity analysis of compounds with known and unknown activity. We provide an overview of publications that have evaluated different machine learning methods, such as support vector machines, decision trees, ensemble methods such as boosting, bagging and random forests, clustering methods, neuronal networks, naïve Bayesian, data fusion methods and others.

Introduction to the theory of machines and languages

Energy Technology Data Exchange (ETDEWEB)

Weidhaas, P. P.

1976-04-01

This text is intended to be an elementary ''guided tour'' through some basic concepts of modern computer science. Various models of computing machines and formal languages are studied in detail. Discussions center around questions such as, ''What is the scope of problems that can or cannot be solved by computers.''

Molecular, metabolic, and genetic control: An introduction

Science.gov (United States)

Tyson, John J.; Mackey, Michael C.

2001-03-01

The living cell is a miniature, self-reproducing, biochemical machine. Like all machines, it has a power supply, a set of working components that carry out its necessary tasks, and control systems that ensure the proper coordination of these tasks. In this Special Issue, we focus on the molecular regulatory systems that control cell metabolism, gene expression, environmental responses, development, and reproduction. As for the control systems in human-engineered machines, these regulatory networks can be described by nonlinear dynamical equations, for example, ordinary differential equations, reaction-diffusion equations, stochastic differential equations, or cellular automata. The articles collected here illustrate (i) a range of theoretical problems presented by modern concepts of cellular regulation, (ii) some strategies for converting molecular mechanisms into dynamical systems, (iii) some useful mathematical tools for analyzing and simulating these systems, and (iv) the sort of results that derive from serious interplay between theory and experiment.

What Makes You Tick? An Empirical Study of Space Science Related Social Media Communications Using Machine Learning

Science.gov (United States)

Hwong, Y. L.; Oliver, C.; Van Kranendonk, M. J.

2016-12-01

The rise of social media has transformed the way the public engages with scientists and science organisations. `Retweet', `Like', `Share' and `Comment' are a few ways users engage with messages on Twitter and Facebook, two of the most popular social media platforms. Despite the availability of big data from these digital footprints, research into social media science communication is scant. This paper presents the results of an empirical study into the processes and outcomes of space science related social media communications using machine learning. The study is divided into two main parts. The first part is dedicated to the use of supervised learning methods to investigate the features of highly engaging messages., e.g. highly retweeted tweets and shared Facebook posts. It is hypothesised that these messages contain certain psycholinguistic features that are unique to the field of space science. We built a predictive model to forecast the engagement levels of social media posts. By using four feature sets (n-grams, psycholinguistics, grammar and social media), we were able to achieve prediction accuracies in the vicinity of 90% using three supervised learning algorithms (Naive Bayes, linear classifier and decision tree). We conducted the same experiments on social media messages from three other fields (politics, business and non-profit) and discovered several features that are exclusive to space science communications: anger, authenticity, hashtags, visual descriptions and a tentative tone. The second part of the study focuses on the extraction of topics from a corpus of texts using topic modelling. This part of the study is exploratory in nature and uses an unsupervised method called Latent Dirichlet Allocation (LDA) to uncover previously unknown topics within a large body of documents. Preliminary results indicate a strong potential of topic model algorithms to automatically uncover themes hidden within social media chatters on space related issues, with

Crossed molecular beams

International Nuclear Information System (INIS)

Lee, Y.T.

1976-01-01

Research activities with crossed molecular beams at Lawrence Berkeley Laboratory during 1976 are described. Topics covered include: scattering of Ar*, Kr*, with Xe; metastable rare gas interactions, He* + H 2 ; an atomic and molecular halogen beam source; a crossed molecular beam study of the Cl + Br 2 → BrCl + Br reaction; O( 3 P) reaction dynamics, development of the high pressure plasma beam source; energy randomization in the Cl + C 2 H 3 Br → Br + C 2 H 3 Cl reaction; high resolution photoionization studies of NO and ICl; photoionization of (H 2 O)/sub n/ and (NH 3 ) 2 ; photoionization mass spectroscopy of NH 3 + and O 3 + ; photo fragmentation of bromine; and construction of chemiluminescence-laser fluorescence crossed molecular beam machine

Evaluation of Machine Learning Methods for LHC Optics Measurements and Corrections Software

CERN Document Server

AUTHOR|(CDS)2206853; Henning, Peter

The field of artificial intelligence is driven by the goal to provide machines with human-like intelligence. However modern science is currently facing problems with high complexity that cannot be solved by humans in the same timescale as by machines. Therefore there is a demand on automation of complex tasks. To identify the category of tasks which can be performed by machines in the domain of optics measurements and correction on the Large Hadron Collider (LHC) is one of the central research subjects of this thesis. The application of machine learning methods and concepts of artificial intelligence can be found in various industry and scientific branches. In High Energy Physics these concepts are mostly used in offline analysis of experiments data and to perform regression tasks. In Accelerator Physics the machine learning approach has not found a wide application yet. Therefore potential tasks for machine learning solutions can be specified in this domain. The appropriate methods and their suitability for...

Inverse Problems in Geodynamics Using Machine Learning Algorithms

Science.gov (United States)

Shahnas, M. H.; Yuen, D. A.; Pysklywec, R. N.

2018-01-01

During the past few decades numerical studies have been widely employed to explore the style of circulation and mixing in the mantle of Earth and other planets. However, in geodynamical studies there are many properties from mineral physics, geochemistry, and petrology in these numerical models. Machine learning, as a computational statistic-related technique and a subfield of artificial intelligence, has rapidly emerged recently in many fields of sciences and engineering. We focus here on the application of supervised machine learning (SML) algorithms in predictions of mantle flow processes. Specifically, we emphasize on estimating mantle properties by employing machine learning techniques in solving an inverse problem. Using snapshots of numerical convection models as training samples, we enable machine learning models to determine the magnitude of the spin transition-induced density anomalies that can cause flow stagnation at midmantle depths. Employing support vector machine algorithms, we show that SML techniques can successfully predict the magnitude of mantle density anomalies and can also be used in characterizing mantle flow patterns. The technique can be extended to more complex geodynamic problems in mantle dynamics by employing deep learning algorithms for putting constraints on properties such as viscosity, elastic parameters, and the nature of thermal and chemical anomalies.

Assessing the Performance of a Machine Learning Algorithm in Identifying Bubbles in Dust Emission

Science.gov (United States)

Xu, Duo; Offner, Stella S. R.

2017-12-01

Stellar feedback created by radiation and winds from massive stars plays a significant role in both physical and chemical evolution of molecular clouds. This energy and momentum leaves an identifiable signature (“bubbles”) that affects the dynamics and structure of the cloud. Most bubble searches are performed “by eye,” which is usually time-consuming, subjective, and difficult to calibrate. Automatic classifications based on machine learning make it possible to perform systematic, quantifiable, and repeatable searches for bubbles. We employ a previously developed machine learning algorithm, Brut, and quantitatively evaluate its performance in identifying bubbles using synthetic dust observations. We adopt magnetohydrodynamics simulations, which model stellar winds launching within turbulent molecular clouds, as an input to generate synthetic images. We use a publicly available three-dimensional dust continuum Monte Carlo radiative transfer code, HYPERION, to generate synthetic images of bubbles in three Spitzer bands (4.5, 8, and 24 μm). We designate half of our synthetic bubbles as a training set, which we use to train Brut along with citizen-science data from the Milky Way Project (MWP). We then assess Brut’s accuracy using the remaining synthetic observations. We find that Brut’s performance after retraining increases significantly, and it is able to identify yellow bubbles, which are likely associated with B-type stars. Brut continues to perform well on previously identified high-score bubbles, and over 10% of the MWP bubbles are reclassified as high-confidence bubbles, which were previously marginal or ambiguous detections in the MWP data. We also investigate the influence of the size of the training set, dust model, evolutionary stage, and background noise on bubble identification.

Switchable reconfiguration of nucleic acid nanostructures by stimuli-responsive DNA machines.

Science.gov (United States)

Liu, Xiaoqing; Lu, Chun-Hua; Willner, Itamar

2014-06-17

CONSPECTUS: The base sequence in DNA dictates structural and reactivity features of the biopolymer. These properties are implemented to use DNA as a unique material for developing the area of DNA nanotechnology. The design of DNA machines represents a rapidly developing research field in the area of DNA nanotechnology. The present Account discusses the switchable reconfiguration of nucleic acid nanostructures by stimuli-responsive DNA machines, and it highlights potential applications and future perspectives of the area. Programmed switchable DNA machines driven by various fuels and antifuels, such as pH, Hg(2+) ions/cysteine, or nucleic acid strands/antistrands, are described. These include the assembly of DNA tweezers, walkers, a rotor, a pendulum, and more. Using a pH-oscillatory system, the oscillatory mechanical operation of a DNA pendulum is presented. Specifically, the synthesis and "mechanical" properties of interlocked DNA rings are described. This is exemplified with the preparation of interlocked DNA catenanes and a DNA rotaxane. The dynamic fuel-driven reconfiguration of the catenane/rotaxane structures is followed by fluorescence spectroscopy. The use of DNA machines as functional scaffolds to reconfigurate Au nanoparticle assemblies and to switch the fluorescence features within fluorophore/Au nanoparticle conjugates between quenching and surface-enhanced fluorescence states are addressed. Specifically, the fluorescence features of the different DNA machines are characterized as a function of the spatial separation between the fluorophore and Au nanoparticles. The experimental results are supported by theoretical calculations. The future development of reconfigurable stimuli-responsive DNA machines involves fundamental challenges, such as the synthesis of molecular devices exhibiting enhanced complexities, the introduction of new fuels and antifuels, and the integration of new payloads being reconfigured by the molecular devices, such as enzymes or

Super-Resolution Molecular and Functional Imaging of Nanoscale Architectures in Life and Materials Science

KAUST Repository

Habuchi, Satoshi

2014-06-12

Super-resolution (SR) fluorescence microscopy has been revolutionizing the way in which we investigate the structures, dynamics, and functions of a wide range of nanoscale systems. In this review, I describe the current state of various SR fluorescence microscopy techniques along with the latest developments of fluorophores and labeling for the SR microscopy. I discuss the applications of SR microscopy in the fields of life science and materials science with a special emphasis on quantitative molecular imaging and nanoscale functional imaging. These studies open new opportunities for unraveling the physical, chemical, and optical properties of a wide range of nanoscale architectures together with their nanostructures and will enable the development of new (bio-)nanotechnology.

Supervised machine learning algorithms to diagnose stress for vehicle drivers based on physiological sensor signals.

Science.gov (United States)

Barua, Shaibal; Begum, Shahina; Ahmed, Mobyen Uddin

2015-01-01

Machine learning algorithms play an important role in computer science research. Recent advancement in sensor data collection in clinical sciences lead to a complex, heterogeneous data processing, and analysis for patient diagnosis and prognosis. Diagnosis and treatment of patients based on manual analysis of these sensor data are difficult and time consuming. Therefore, development of Knowledge-based systems to support clinicians in decision-making is important. However, it is necessary to perform experimental work to compare performances of different machine learning methods to help to select appropriate method for a specific characteristic of data sets. This paper compares classification performance of three popular machine learning methods i.e., case-based reasoning, neutral networks and support vector machine to diagnose stress of vehicle drivers using finger temperature and heart rate variability. The experimental results show that case-based reasoning outperforms other two methods in terms of classification accuracy. Case-based reasoning has achieved 80% and 86% accuracy to classify stress using finger temperature and heart rate variability. On contrary, both neural network and support vector machine have achieved less than 80% accuracy by using both physiological signals.

Effect of Machining Velocity in Nanoscale Machining Operations

International Nuclear Information System (INIS)

Islam, Sumaiya; Khondoker, Noman; Ibrahim, Raafat

2015-01-01

The aim of this study is to investigate the generated forces and deformations of single crystal Cu with (100), (110) and (111) crystallographic orientations at nanoscale machining operation. A nanoindenter equipped with nanoscratching attachment was used for machining operations and in-situ observation of a nano scale groove. As a machining parameter, the machining velocity was varied to measure the normal and cutting forces. At a fixed machining velocity, different levels of normal and cutting forces were generated due to different crystallographic orientations of the specimens. Moreover, after machining operation percentage of elastic recovery was measured and it was found that both the elastic and plastic deformations were responsible for producing a nano scale groove within the range of machining velocities from 250-1000 nm/s. (paper)

On Tight Separation for Blum Measures Applied to Turing Machine Buffer Complexity

Czech Academy of Sciences Publication Activity Database

Šíma, Jiří; Žák, Stanislav

2017-01-01

Roč. 152, č. 4 (2017), s. 397-409 ISSN 0169-2968 R&D Projects: GA ČR GBP202/12/G061; GA ČR GAP202/10/1333 Institutional support: RVO:67985807 Keywords : Turing machine * hierarchy * buffer complexity * diagonalization Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 0.687, year: 2016

Data Science for Institutional and Organizational Economics

NARCIS (Netherlands)

Prüfer, Jens; Prüfer, Patricia

2018-01-01

To which extent can data science methods – such as machine learning, text analysis, or sentiment analysis – push the research frontier in the social sciences? This essay briefly describes the most prominent data science techniques that lend themselves to analyses of institutional and organizational

5th European Conference on Mechanisms Science (EUCOMES)

CERN Document Server

Viadero, Fernando; New Trends in Mechanism and Machine Science : from Fundamentals to Industrial Applications

2015-01-01

This work presents the most recent research in the mechanism and machine science field and its applications. The topics covered include: theoretical kinematics, computational kinematics, mechanism design, experimental mechanics, mechanics of robots, dynamics of machinery, dynamics of multi-body systems, control issues of mechanical systems, mechanisms for biomechanics, novel designs, mechanical transmissions, linkages and manipulators, micro-mechanisms, teaching methods, history of mechanism science and industrial and non-industrial applications. This volume consists of the Proceedings of the 5th European Conference on Mechanisms Science (EUCOMES), that was held in Guimarães, Portugal, from September 16 – 20, 2014. The EUCOMES is the main forum for the European community working in Mechanisms and Machine Science.

How the machine ‘thinks’: Understanding opacity in machine learning algorithms

Directory of Open Access Journals (Sweden)

Jenna Burrell

2016-01-01

Full Text Available This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: (1 opacity as intentional corporate or state secrecy, (2 opacity as technical illiteracy, and (3 an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices (as they are publicly presented, and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is a key to determining which of a variety of technical and non-technical solutions could help to prevent harm.

Prediction of Machine Tool Condition Using Support Vector Machine

International Nuclear Information System (INIS)

Wang Peigong; Meng Qingfeng; Zhao Jian; Li Junjie; Wang Xiufeng

2011-01-01

Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.

Molecular beam studies with a time-of-flight machine

International Nuclear Information System (INIS)

Beijerinck, H.C.W.

1975-01-01

The study concerns the development of the time-of-flight method for the velocity analysis of molecular beams and its application to the measurement of the velocity dependence of the total cross-section of the noble gases. It reviews the elastic scattering theory, both in the framework of classical mechanics and in the quantum mechanical description. Attention is paid to the semiclassical correspondence of classical particle trajectories with the partial waves of the quantum mechanical solution. The total cross-section and the small angle differential cross-section are discussed with special emphasis on their relation. The results of this chapter are used later to derive the correction on the measured total cross-section due to the finite angular resolution of the apparatus. Reviewed also is the available information on the intermolecular potential of the Ar-Ar system. Then a discussion of the measurement of total cross-sections with the molecular beam method and the time-of-flight method is compared to other methods used. It is shown that the single burst time-of-flight method can be developed into a reliable and well-calibrated method for the analysis of the velocity distribution of molecular beams. A comparison of the single burst time-of-flight method with the cross-correlation time-of-flight method shows that the two methods are complementary and that the specific experimental circumstances determine which method is to be preferred. Molecular beam sources are discussed. The peaking factor formalism is introduced and helps to compare the performance of different types of sources. The effusive and the supersonic source are treated and recent experimental results are given. The multichannel source is treated in more detail. For the opaque mode, an experimental investigation of the velocity distribution and the angular distribution of the flow pattern is presented. Comparison of these results with Monte Carlo calculations for free molecular flow in a cylindrical

Environmentally Friendly Machining

CERN Document Server

Dixit, U S; Davim, J Paulo

2012-01-01

Environment-Friendly Machining provides an in-depth overview of environmentally-friendly machining processes, covering numerous different types of machining in order to identify which practice is the most environmentally sustainable. The book discusses three systems at length: machining with minimal cutting fluid, air-cooled machining and dry machining. Also covered is a way to conserve energy during machining processes, along with useful data and detailed descriptions for developing and utilizing the most efficient modern machining tools. Researchers and engineers looking for sustainable machining solutions will find Environment-Friendly Machining to be a useful volume.

Cognitive computing and eScience in health and life science research: artificial intelligence and obesity intervention programs.

Science.gov (United States)

Marshall, Thomas; Champagne-Langabeer, Tiffiany; Castelli, Darla; Hoelscher, Deanna

2017-12-01

To present research models based on artificial intelligence and discuss the concept of cognitive computing and eScience as disruptive factors in health and life science research methodologies. The paper identifies big data as a catalyst to innovation and the development of artificial intelligence, presents a framework for computer-supported human problem solving and describes a transformation of research support models. This framework includes traditional computer support; federated cognition using machine learning and cognitive agents to augment human intelligence; and a semi-autonomous/autonomous cognitive model, based on deep machine learning, which supports eScience. The paper provides a forward view of the impact of artificial intelligence on our human-computer support and research methods in health and life science research. By augmenting or amplifying human task performance with artificial intelligence, cognitive computing and eScience research models are discussed as novel and innovative systems for developing more effective adaptive obesity intervention programs.

4-H Textile Science Beginner Projects.

Science.gov (United States)

Scholl, Jan

This packet contains three 4-H projects for students beginning the sewing sequence of the textile sciences area. The projects cover basics of sewing using sewing machines, more difficult sewing machine techniques, and hand sewing. Each project provides an overview of what the student will learn, what materials are needed, and suggested projects…

Application of Machine Learning Approaches for Protein-protein Interactions Prediction.

Science.gov (United States)

Zhang, Mengying; Su, Qiang; Lu, Yi; Zhao, Manman; Niu, Bing

2017-01-01

Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

16th International Conference on Man-Machine-Environment System Engineering

CERN Document Server

Dhillon, Balbir

2016-01-01

This research topic was first established in China by Professor Shengzhao Long in 1981, with direct support from one of the greatest modern Chinese scientists, Xuesen Qian. In a letter to Shengzhao Long from October 22nd, 1993, Xuesen Qian wrote: “You have created a very important modern science subject and technology in China!” MMESE primarily focuses on the relationship between Man, Machine and Environment, studying the optimum combination of man-machine-environment systems. In this system, “Man” refers to working people as the subject in the workplace (e.g. operators, decision-makers); “Machine” is the general name for any object controlled by Man (including tools, machinery, computers, systems and technologies), and “Environment” describes the specific working conditions under which Man and Machine interact (e.g. temperature, noise, vibration, hazardous gases etc.). The three goals of optimization are to ensure "Safety, High efficiency and Economy" of man-machine-environment systems. These...

Accelerator Mass Spectrometry with 15 UD pelletron at the Nuclear Science Centre, New Delhi

International Nuclear Information System (INIS)

Datta, S.K.

1997-01-01

The 15 UD Pelletron machine is widely used to carry on investigations in a variety of disciplines like nuclear physics, materials science, radiobiology etc. Accelerator Mass Spectrometry studies with 15 UD pelletron machine at Nuclear Science Centre are elaborated

Precision mechatronics based on high-precision measuring and positioning systems and machines

Science.gov (United States)

Jäger, Gerd; Manske, Eberhard; Hausotte, Tino; Mastylo, Rostyslav; Dorozhovets, Natalja; Hofmann, Norbert

2007-06-01

Precision mechatronics is defined in the paper as the science and engineering of a new generation of high precision systems and machines. Nanomeasuring and nanopositioning engineering represents important fields of precision mechatronics. The nanometrology is described as the today's limit of the precision engineering. The problem, how to design nanopositioning machines with uncertainties as small as possible will be discussed. The integration of several optical and tactile nanoprobes makes the 3D-nanopositioning machine suitable for various tasks, such as long range scanning probe microscopy, mask and wafer inspection, nanotribology, nanoindentation, free form surface measurement as well as measurement of microoptics, precision molds, microgears, ring gauges and small holes.

Molecular Nutrition Research—The Modern Way Of Performing Nutritional Science

Directory of Open Access Journals (Sweden)

Arild C. Rustan

2012-12-01

Full Text Available In spite of amazing progress in food supply and nutritional science, and a striking increase in life expectancy of approximately 2.5 months per year in many countries during the previous 150 years, modern nutritional research has a great potential of still contributing to improved health for future generations, granted that the revolutions in molecular and systems technologies are applied to nutritional questions. Descriptive and mechanistic studies using state of the art epidemiology, food intake registration, genomics with single nucleotide polymorphisms (SNPs and epigenomics, transcriptomics, proteomics, metabolomics, advanced biostatistics, imaging, calorimetry, cell biology, challenge tests (meals, exercise, etc., and integration of all data by systems biology, will provide insight on a much higher level than today in a field we may name molecular nutrition research. To take advantage of all the new technologies scientists should develop international collaboration and gather data in large open access databases like the suggested Nutritional Phenotype database (dbNP. This collaboration will promote standardization of procedures (SOP, and provide a possibility to use collected data in future research projects. The ultimate goals of future nutritional research are to understand the detailed mechanisms of action for how nutrients/foods interact with the body and thereby enhance health and treat diet-related diseases.

Molecular Nutrition Research—The Modern Way Of Performing Nutritional Science

Science.gov (United States)

Norheim, Frode; Gjelstad, Ingrid M. F.; Hjorth, Marit; Vinknes, Kathrine J.; Langleite, Torgrim M.; Holen, Torgeir; Jensen, Jørgen; Dalen, Knut Tomas; Karlsen, Anette S.; Kielland, Anders; Rustan, Arild C.; Drevon, Christian A.

2012-01-01

In spite of amazing progress in food supply and nutritional science, and a striking increase in life expectancy of approximately 2.5 months per year in many countries during the previous 150 years, modern nutritional research has a great potential of still contributing to improved health for future generations, granted that the revolutions in molecular and systems technologies are applied to nutritional questions. Descriptive and mechanistic studies using state of the art epidemiology, food intake registration, genomics with single nucleotide polymorphisms (SNPs) and epigenomics, transcriptomics, proteomics, metabolomics, advanced biostatistics, imaging, calorimetry, cell biology, challenge tests (meals, exercise, etc.), and integration of all data by systems biology, will provide insight on a much higher level than today in a field we may name molecular nutrition research. To take advantage of all the new technologies scientists should develop international collaboration and gather data in large open access databases like the suggested Nutritional Phenotype database (dbNP). This collaboration will promote standardization of procedures (SOP), and provide a possibility to use collected data in future research projects. The ultimate goals of future nutritional research are to understand the detailed mechanisms of action for how nutrients/foods interact with the body and thereby enhance health and treat diet-related diseases. PMID:23208524

Molecular nutrition research: the modern way of performing nutritional science.

Science.gov (United States)

Norheim, Frode; Gjelstad, Ingrid Merethe Fange; Hjorth, Marit; Vinknes, Kathrine J; Langleite, Torgrim M; Holen, Torgeir; Jensen, Jørgen; Dalen, Knut Tomas; Karlsen, Anette S; Kielland, Anders; Rustan, Arild C; Drevon, Christian A

2012-12-03

In spite of amazing progress in food supply and nutritional science, and a striking increase in life expectancy of approximately 2.5 months per year in many countries during the previous 150 years, modern nutritional research has a great potential of still contributing to improved health for future generations, granted that the revolutions in molecular and systems technologies are applied to nutritional questions. Descriptive and mechanistic studies using state of the art epidemiology, food intake registration, genomics with single nucleotide polymorphisms (SNPs) and epigenomics, transcriptomics, proteomics, metabolomics, advanced biostatistics, imaging, calorimetry, cell biology, challenge tests (meals, exercise, etc.), and integration of all data by systems biology, will provide insight on a much higher level than today in a field we may name molecular nutrition research. To take advantage of all the new technologies scientists should develop international collaboration and gather data in large open access databases like the suggested Nutritional Phenotype database (dbNP). This collaboration will promote standardization of procedures (SOP), and provide a possibility to use collected data in future research projects. The ultimate goals of future nutritional research are to understand the detailed mechanisms of action for how nutrients/foods interact with the body and thereby enhance health and treat diet-related diseases.

Machine learning in geosciences and remote sensing

Directory of Open Access Journals (Sweden)

David J. Lary

2016-01-01

Full Text Available Learning incorporates a broad range of complex procedures. Machine learning (ML is a subdivision of artificial intelligence based on the biological learning process. The ML approach deals with the design of algorithms to learn from machine readable data. ML covers main domains such as data mining, difficult-to-program applications, and software applications. It is a collection of a variety of algorithms (e.g. neural networks, support vector machines, self-organizing map, decision trees, random forests, case-based reasoning, genetic programming, etc. that can provide multivariate, nonlinear, nonparametric regression or classification. The modeling capabilities of the ML-based methods have resulted in their extensive applications in science and engineering. Herein, the role of ML as an effective approach for solving problems in geosciences and remote sensing will be highlighted. The unique features of some of the ML techniques will be outlined with a specific attention to genetic programming paradigm. Furthermore, nonparametric regression and classification illustrative examples are presented to demonstrate the efficiency of ML for tackling the geosciences and remote sensing problems.

Humans and Machines in the Evolution of AI in Korea

OpenAIRE

Zhang, Byoung-Tak

2016-01-01

Artificial intelligence in Korea is currently prospering. The media is regularly reporting AI-enabled products such as smart advisors, personal robots, autonomous cars, and human-level intelligence machines. The IT industry is investing in deep learning and AI to maintain the global competitive edge in their services and products. The Ministry of Science, ICT, and Future Planning (MSIP) has launched new funding programs in AI and cognitive science to implement the government’s newly adopted e...

ISIS muons for materials and molecular science studies

International Nuclear Information System (INIS)

King, Philip J C; Cottrell, Stephen P; Hillier, Adrian D; Cox, Stephen F J; De Renzi, Roberto

2013-01-01

This paper marks the first 25 years of muon production at ISIS and the creation in that time of a facility dedicated to the use of these elementary particles as unique microscopic probes in condensed matter and molecular science. It introduces the basic techniques of muon spin rotation, relaxation and resonance, collectively known as μSR, that were already in use by specialist groups at other accelerator labs by the mid-1980s. It describes how these techniques have been implemented and made available at ISIS, beginning in 1987, and how they have evolved and improved since then. Ever widening applications embrace magnetism, superconductivity, interstitial diffusion and charge transport, semiconductors and dielectrics, chemical physics and radical chemistry. Over these first 25 years, a fully supported user facility has been established, open to all academic and industrial users. It presently comprises four scheduled instruments, optimized for different types of measurement, together with auxiliary equipment for radiofrequency or microwave spin manipulation and future plans for pump–probe laser excitation. (comment)

Hybrid machining processes perspectives on machining and finishing

CERN Document Server

Gupta, Kapil; Laubscher, R F

2016-01-01

This book describes various hybrid machining and finishing processes. It gives a critical review of the past work based on them as well as the current trends and research directions. For each hybrid machining process presented, the authors list the method of material removal, machining system, process variables and applications. This book provides a deep understanding of the need, application and mechanism of hybrid machining processes.

Molecular pathological epidemiology of epigenetics: emerging integrative science to analyze environment, host, and disease.

Science.gov (United States)

Ogino, Shuji; Lochhead, Paul; Chan, Andrew T; Nishihara, Reiko; Cho, Eunyoung; Wolpin, Brian M; Meyerhardt, Jeffrey A; Meissner, Alexander; Schernhammer, Eva S; Fuchs, Charles S; Giovannucci, Edward

2013-04-01

Epigenetics acts as an interface between environmental/exogenous factors, cellular responses, and pathological processes. Aberrant epigenetic signatures are a hallmark of complex multifactorial diseases (including neoplasms and malignancies such as leukemias, lymphomas, sarcomas, and breast, lung, prostate, liver, and colorectal cancers). Epigenetic signatures (DNA methylation, mRNA and microRNA expression, etc) may serve as biomarkers for risk stratification, early detection, and disease classification, as well as targets for therapy and chemoprevention. In particular, DNA methylation assays are widely applied to formalin-fixed, paraffin-embedded archival tissue specimens as clinical pathology tests. To better understand the interplay between etiological factors, cellular molecular characteristics, and disease evolution, the field of 'molecular pathological epidemiology (MPE)' has emerged as an interdisciplinary integration of 'molecular pathology' and 'epidemiology'. In contrast to traditional epidemiological research including genome-wide association studies (GWAS), MPE is founded on the unique disease principle, that is, each disease process results from unique profiles of exposomes, epigenomes, transcriptomes, proteomes, metabolomes, microbiomes, and interactomes in relation to the macroenvironment and tissue microenvironment. MPE may represent a logical evolution of GWAS, termed 'GWAS-MPE approach'. Although epigenome-wide association study attracts increasing attention, currently, it has a fundamental problem in that each cell within one individual has a unique, time-varying epigenome. Having a similar conceptual framework to systems biology, the holistic MPE approach enables us to link potential etiological factors to specific molecular pathology, and gain novel pathogenic insights on causality. The widespread application of epigenome (eg, methylome) analyses will enhance our understanding of disease heterogeneity, epigenotypes (CpG island methylator

Machines géantes pour sonder l'univers de l'atome

CERN Multimedia

Wilde, M, S

1966-01-01

To always more deeply explore the infinitely small world of the atom, Science is paradoxically brought to build buildings and machines increasingly larger - Giant accelerators producing high energy particle beams that can dissociate the structures of the atomic nucleus

Molecular metal catalysts on supports: organometallic chemistry meets surface science.

Science.gov (United States)

Serna, Pedro; Gates, Bruce C

2014-08-19

-support bonding and structure, which identify the supports as ligands with electron-donor properties that influence reactivity and catalysis. Each of the catalyst design variables has been varied independently, illustrated by mononuclear and tetranuclear iridium on zeolite HY and on MgO and by isostructural rhodium and iridium (diethylene or dicarbonyl) complexes on these supports. The data provide examples resolving the roles of the catalyst design variables and place the catalysis science on a firm foundation of organometallic chemistry linked with surface science. Supported molecular catalysts offer the advantages of characterization in the absence of solvents and with surface-science methods that do not require ultrahigh vacuum. Families of supported metal complexes have been made by replacement of ligands with others from the gas phase. Spectroscopically identified catalytic reaction intermediates help to elucidate catalyst performance and guide design. The methods are illustrated for supported complexes and clusters of rhodium, iridium, osmium, and gold used to catalyze reactions of small molecules that facilitate identification of the ligands present during catalysis: alkene dimerization and hydrogenation, H-D exchange in the reaction of H2 with D2, and CO oxidation. The approach is illustrated with the discovery of a highly active and selective MgO-supported rhodium carbonyl dimer catalyst for hydrogenation of 1,3-butadiene to give butenes.

A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification

Directory of Open Access Journals (Sweden)

Wang Lily

2008-07-01

Full Text Available Abstract Background Cancer diagnosis and clinical outcome prediction are among the most important emerging applications of gene expression microarray technology with several molecular signatures on their way toward clinical deployment. Use of the most accurate classification algorithms available for microarray gene expression data is a critical ingredient in order to develop the best possible molecular signatures for patient care. As suggested by a large body of literature to date, support vector machines can be considered "best of class" algorithms for classification of such data. Recent work, however, suggests that random forest classifiers may outperform support vector machines in this domain. Results In the present paper we identify methodological biases of prior work comparing random forests and support vector machines and conduct a new rigorous evaluation of the two algorithms that corrects these limitations. Our experiments use 22 diagnostic and prognostic datasets and show that support vector machines outperform random forests, often by a large margin. Our data also underlines the importance of sound research design in benchmarking and comparison of bioinformatics algorithms. Conclusion We found that both on average and in the majority of microarray datasets, random forests are outperformed by support vector machines both in the settings when no gene selection is performed and when several popular gene selection methods are used.

Sensor Data Air Pollution Prediction by Machine Learning Methods

Czech Academy of Sciences Publication Activity Database

Vidnerová, Petra; Neruda, Roman

submitted 25. 1. (2018) ISSN 1530-437X R&D Projects: GA ČR GA15-18108S Grant - others:GA MŠk(CZ) LM2015042 Institutional support: RVO:67985807 Keywords : machine learning * sensors * air pollution * deep neural networks * regularization networks Subject RIV: IN - Informatics, Computer Science Impact factor: 2.512, year: 2016

Science, art, and mistery in the statues and in the anatomical machines of the prince of sansevero: the masterpieces of the "Sansevero Chapel".

Science.gov (United States)

Della Monica, Matteo; Galzerano, Domenico; Di Michele, Sara; Acquaviva, Fabio; Gregorio, Giovanni; Lonardo, Fortunato; Sguazzo, Francesca; Scarano, Francesca; Lama, Diana; Scarano, Gioacchino

2013-11-01

During the 18th century in Naples, Raimondo di Sangro, Prince of Sansevero, completed works on the family chapel, the so-called "Cappella Sansevero." The chapel houses statues of extraordinary beauty and spectacularly detailed but also, in the basement, two human skeletons known as the "Anatomical Machines" ("Macchine Anatomiche"). These two skeletons, a man and a pregnant woman, are entirely surrounded by their circulatory systems, just as if these were suddenly fixed. Legend, believed as truth until few years ago, says that Prince Raimondo had prepared and injected an unknown embalming substance in the blood vessels of two of his servants convicting them to eternal fixity. Recent investigations have demonstrated that, while the bones are authentic, the blood vessels are actually extraordinary artifacts that also reproduce some congenital malformations. The dreadful aspect of these two skeletons appears to be in strident contrast with the classic beauty of the statues which glorify and celebrate the ideal of morphology. Conversely, the two Anatomical Machines, protagonists of legends and superstitions since centuries, represent a marvelous example of science mixed with art. © 2013 Wiley Periodicals, Inc.

Computational methods for molecular imaging

CERN Document Server

Shi, Kuangyu; Li, Shuo

2015-01-01

This volume contains original submissions on the development and application of molecular imaging computing. The editors invited authors to submit high-quality contributions on a wide range of topics including, but not limited to: • Image Synthesis & Reconstruction of Emission Tomography (PET, SPECT) and other Molecular Imaging Modalities • Molecular Imaging Enhancement • Data Analysis of Clinical & Pre-clinical Molecular Imaging • Multi-Modal Image Processing (PET/CT, PET/MR, SPECT/CT, etc.) • Machine Learning and Data Mining in Molecular Imaging. Molecular imaging is an evolving clinical and research discipline enabling the visualization, characterization and quantification of biological processes taking place at the cellular and subcellular levels within intact living subjects. Computational methods play an important role in the development of molecular imaging, from image synthesis to data analysis and from clinical diagnosis to therapy individualization. This work will bring readers fro...

International Conference on Systems Science 2016

CERN Document Server

Tomczak, Jakub

2017-01-01

This book gathers the carefully reviewed proceedings of the 19th International Conference on Systems Science, presenting recent research findings in the areas of Artificial Intelligence, Machine Learning, Communication/Networking and Information Technology, Control Theory, Decision Support, Image Processing and Computer Vision, Optimization Techniques, Pattern Recognition, Robotics, Service Science, Web-based Services, Uncertain Systems and Transportation Systems. The International Conference on Systems Science was held in Wroclaw, Poland from September 7 to 9, 2016, and addressed a range of topics, including systems theory, control theory, machine learning, artificial intelligence, signal processing, communication and information technologies, transportation systems, multi-robotic systems and uncertain systems, as well as their applications. The aim of the conference is to provide a platform for communication between young and established researchers and practitioners, fostering future joint research in syst...

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

... Physics · Proceedings – Mathematical Sciences · Resonance – Journal of Science ... Home; Journals; Journal of Chemical Sciences; Special Issues ... 2nd International Symposium on Materials Chemistry (ISMC-2008) ... New Directions of Research in Molecules and Materials ... Theoretical Models for Molecular Structure.

EDITORIAL: Molecular Imaging Technology

Science.gov (United States)

Asai, Keisuke; Okamoto, Koji

2006-06-01

'Molecular Imaging Technology' focuses on image-based techniques using nanoscale molecules as sensor probes to measure spatial variations of various species (molecular oxygen, singlet oxygen, carbon dioxide, nitric monoxide, etc) and physical properties (pressure, temperature, skin friction, velocity, mechanical stress, etc). This special feature, starting on page 1237, contains selected papers from The International Workshop on Molecular Imaging for Interdisciplinary Research, sponsored by the Ministry of Education, Culture, Sports, Science and Technology (MEXT) in Japan, which was held at the Sendai Mediatheque, Sendai, Japan, on 8 9 November 2004. The workshop was held as a sequel to the MOSAIC International Workshop that was held in Tokyo in 2003, to summarize the outcome of the 'MOSAIC Project', a five-year interdisciplinary project supported by Techno-Infrastructure Program, the Special Coordination Fund for Promotion of Science Technology to develop molecular sensor technology for aero-thermodynamic research. The workshop focused on molecular imaging technology and its applications to interdisciplinary research areas. More than 110 people attended this workshop from various research fields such as aerospace engineering, automotive engineering, radiotechnology, fluid dynamics, bio-science/engineering and medical engineering. The purpose of this workshop is to stimulate intermixing of these interdisciplinary fields for further development of molecular sensor and imaging technology. It is our pleasure to publish the seven papers selected from our workshop as a special feature in Measurement and Science Technology. We will be happy if this issue inspires people to explore the future direction of molecular imaging technology for interdisciplinary research.

Machine Protection: Availability for Particle Accelerators

CERN Document Server

Apollonio, Andrea; Schmidt, Ruediger

2015-03-16

Machine availability is a key indicator for the performance of the next generation of particle accelerators. Availability requirements need to be carefully considered during the design phase to achieve challenging objectives in different fields, as e.g. particle physics and material science. For existing and future High-Power facilities, such as ESS (European Spallation Source) and HL-LHC (High-Luminosity LHC), operation with unprecedented beam power requires highly dependable Machine Protection Systems (MPS) to avoid any damage-induced downtime. Due to the high complexity of accelerator systems, finding the optimal balance between equipment safety and accelerator availability is challenging. The MPS architecture, as well as the choice of electronic components, have a large influence on the achievable level of availability. In this thesis novel methods to address the availability of accelerators and their protection systems are presented. Examples of studies related to dependable MPS architectures are given i...

Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas

Directory of Open Access Journals (Sweden)

Gregory P. Way

2018-04-01

Full Text Available Summary: Precision oncology uses genomic evidence to match patients with treatment but often fails to identify all patients who may respond. The transcriptome of these “hidden responders” may reveal responsive molecular states. We describe and evaluate a machine-learning approach to classify aberrant pathway activity in tumors, which may aid in hidden responder identification. The algorithm integrates RNA-seq, copy number, and mutations from 33 different cancer types across The Cancer Genome Atlas (TCGA PanCanAtlas project to predict aberrant molecular states in tumors. Applied to the Ras pathway, the method detects Ras activation across cancer types and identifies phenocopying variants. The model, trained on human tumors, can predict response to MEK inhibitors in wild-type Ras cell lines. We also present data that suggest that multiple hits in the Ras pathway confer increased Ras activity. The transcriptome is underused in precision oncology and, combined with machine learning, can aid in the identification of hidden responders. : Way et al. develop a machine-learning approach using PanCanAtlas data to detect Ras activation in cancer. Integrating mutation, copy number, and expression data, the authors show that their method detects Ras-activating variants in tumors and sensitivity to MEK inhibitors in cell lines. Keywords: Gene expression, machine learning, Ras, NF1, KRAS, NRAS, HRAS, pan-cancer, TCGA, drug sensitivity

Accelerating the discovery of hidden two-dimensional magnets using machine learning and first principle calculations

Science.gov (United States)

Miyazato, Itsuki; Tanaka, Yuzuru; Takahashi, Keisuke

2018-02-01

Two-dimensional (2D) magnets are explored in terms of data science and first principle calculations. Machine learning determines four descriptors for predicting the magnetic moments of 2D materials within reported 216 2D materials data. With the trained machine, 254 2D materials are predicted to have high magnetic moments. First principle calculations are performed to evaluate the predicted 254 2D materials where eight undiscovered stable 2D materials with high magnetic moments are revealed. The approach taken in this work indicates that undiscovered materials can be surfaced by utilizing data science and materials data, leading to an innovative way of discovering hidden materials.

EAST machine assembly and its measurement system

International Nuclear Information System (INIS)

Wu, S.T.

2005-01-01

The EAST (HT-7U) superconducting tokamak consists of a superconducting poloidal field magnet system, a toroidal field magnet system, a vacuum vessel and in-vessel components, thermal shields and a cryostat vessel. The main parts of the machine have been delivered to ASIPP (Institute of Plasma Physics, Chinese Academy of Sciences) successionally from 2003. For its complicated constitution and precise requirement, a reasonable assembly procedure and measurement technique should be defined carefully. Before the assembly procedure, a reference frame has been set up with reference fiducial targets on the wall of the test hall by an industrial measurement system. After the torus of TF coils is formed, a new reference frame will be set up from the position of the TF torus. The vacuum vessel with all inner parts will be installed with reference of the new reference frame. The big size and mass of components, special configuration of the superconducting machine with tight installation tolerances of the HT-7U (EAST) machine result in complicated assembly procedure. The procedure had begun with the installation of the support frame and the base of cryostat vessel last year. In this paper, the requirements of the assembly precise for some key components of the machine are described. The reference frame for the assembly and maintenance is explained. The assembly procedure is introduced

The Basics of Stellites in Machining Perspective

Directory of Open Access Journals (Sweden)

Md Shahanur Hasan

2016-12-01

Full Text Available Stellites are cobalt (Co-based superalloys available in two main combinations: (a a Tungsten (W group with composition of Co-Cr-W-C, and (b a Molybdenum (Mo group containing Co-Cr-Mo-C. Stellites possess outstanding corrosion resistance, oxidation resistance, wear resistance, heat resistance, and low magnetic permeability. Components made of stellites work well in highly corrosive environments and maintain these advantageous properties at elevated temperatures. Components made of stellites are widely used in the oil and gas, automotive, nuclear power, paper and pulp, chemical and petrochemical, refineries, automobile, aerospace and aircraft industries. By virtue of their nonmagnetic, anticorrosive and non-reactivity to human body-fluid properties, stellites are used in medical surgery and in surgical tools, tooth and bone implants and replacements, heart valves, and in heart pacemakers. The hardness range of stellites is from 32 to 55 HRC, which makes stellites brittle materials but they have a low Young’s modulus. Due to their high hardness, dense but non-homogeneous molecular structure and lower thermal conductivity, machining operations for parts made of stellites are extremely difficult, categorising stellites as difficult-to-machine materials like Ti-alloys, inconels, composites and stainless steels. Usually, machine components made of stellites are produced by a deposition method onto steel substrates instead of expensive solid stellite bars. The rough surfaces of deposited stellites are then finished by grinding, rather than some other economic machining process, which is costly and time-consuming, making stellite products very expensive. This paper provides a basic overview of stellites applicable in engineering, their significances and specific applications, advantages and disadvantages in respect of machining processes. A brief review on experimental research on economically rational cutting parameters for turning operations of

Protein-polymer nano-machines. Towards synthetic control of biological processes

Directory of Open Access Journals (Sweden)

Alexander Cameron

2004-09-01

Full Text Available Abstract The exploitation of nature's machinery at length scales below the dimensions of a cell is an exciting challenge for biologists, chemists and physicists, while advances in our understanding of these biological motifs are now providing an opportunity to develop real single molecule devices for technological applications. Single molecule studies are already well advanced and biological molecular motors are being used to guide the design of nano-scale machines. However, controlling the specific functions of these devices in biological systems under changing conditions is difficult. In this review we describe the principles underlying the development of a molecular motor with numerous potential applications in nanotechnology and the use of specific synthetic polymers as prototypic molecular switches for control of the motor function. The molecular motor is a derivative of a TypeI Restriction-Modification (R-M enzyme and the synthetic polymer is drawn from the class of materials that exhibit a temperature-dependent phase transition. The potential exploitation of single molecules as functional devices has been heralded as the dawn of new era in biotechnology and medicine. It is not surprising, therefore, that the efforts of numerous multidisciplinary teams 12. have been focused in attempts to develop these systems. as machines capable of functioning at the low sub-micron and nanometre length-scales 3. However, one of the obstacles for the practical application of single molecule devices is the lack of functional control methods in biological media, under changing conditions. In this review we describe the conceptual basis for a molecular motor (a derivative of a TypeI Restriction-Modification enzyme with numerous potential applications in nanotechnology and the use of specific synthetic polymers as prototypic molecular switches for controlling the motor function 4.

Artificial Intelligence and Data Science in the Automotive Industry

OpenAIRE

Hofmann, Martin; Neukart, Florian; Bäck, Thomas

2017-01-01

Data science and machine learning are the key technologies when it comes to the processes and products with automatic learning and optimization to be used in the automotive industry of the future. This article defines the terms "data science" (also referred to as "data analytics") and "machine learning" and how they are related. In addition, it defines the term "optimizing analytics" and illustrates the role of automatic optimization as a key technology in combination with data analytics. It ...

Bergsonian Comedy and the Human Machines in "Star Wars."

Science.gov (United States)

Roth, Lane

While analyzing humor is difficult, Henri Bergson's concept of comedy (a person acting like a machine) outlined in the classic essay, "Le Rire," in 1900, is probably too narrow a definition. Science fiction film, a genre which has evolved since the publication of Bergson's essay, has also speculated about man and society, often to…

Scheduling Agreeable Jobs On A Single Machine To Minimize ...

African Journals Online (AJOL)

Journal of Applied Science and Technology ... (NP) scheduling of number of jobs on a single machine to minimize weighted number of early and tardy jobs where the earliest start times and latest due date are agreeable (i.e. earliest start time must increase in the same sequence as latest due dates) has been considered.

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

... XIONG1 WEIHUA ZHU1 HEMING XIAO1. Institute for Computation in Molecular and Materials Science and Department of Chemistry, School of Chemical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China; School of Materials Science and Engineering, Nanjing Institute of Technology, ...

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach.

Science.gov (United States)

Ramakrishnan, Raghunathan; Dral, Pavlo O; Rupp, Matthias; von Lilienfeld, O Anatole

2015-05-12

Chemically accurate and comprehensive studies of the virtual space of all possible molecules are severely limited by the computational cost of quantum chemistry. We introduce a composite strategy that adds machine learning corrections to computationally inexpensive approximate legacy quantum methods. After training, highly accurate predictions of enthalpies, free energies, entropies, and electron correlation energies are possible, for significantly larger molecular sets than used for training. For thermochemical properties of up to 16k isomers of C7H10O2 we present numerical evidence that chemical accuracy can be reached. We also predict electron correlation energy in post Hartree-Fock methods, at the computational cost of Hartree-Fock, and we establish a qualitative relationship between molecular entropy and electron correlation. The transferability of our approach is demonstrated, using semiempirical quantum chemistry and machine learning models trained on 1 and 10% of 134k organic molecules, to reproduce enthalpies of all remaining molecules at density functional theory level of accuracy.

Machine terms dictionary

Energy Technology Data Exchange (ETDEWEB)

NONE

1979-04-15

This book gives descriptions of machine terms which includes machine design, drawing, the method of machine, machine tools, machine materials, automobile, measuring and controlling, electricity, basic of electron, information technology, quality assurance, Auto CAD and FA terms and important formula of mechanical engineering.

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

Author Affiliations. Santanu Bhattacharya1 Raghavan Varadarajan2. Department of Organic Chemistry, Indian Institute of Science, Bangalore 560 012; Molecular Biophysics Unit, Indian Institute of Science, Bangalore 560 012 ...

Opportunities and limitations related to the application of plant-derived lipid molecular proxies in soil science

Directory of Open Access Journals (Sweden)

B. Jansen

2017-11-01

Full Text Available The application of lipids in soils as molecular proxies, also often referred to as biomarkers, has dramatically increased in the last decades. Applications range from inferring changes in past vegetation composition, climate, and/or human presence to unraveling the input and turnover of soil organic matter (SOM. The molecules used are extractable and non-extractable lipids, including ester-bound lipids. In addition, the carbon or hydrogen isotopic composition of such molecules is used. While holding great promise, the application of soil lipids as molecular proxies comes with several constraining factors, the most important of which are (i variability in the molecular composition of plant-derived organic matter both internally and between individual plants, (ii variability in (the relative contribution of input pathways into the soil, and (iii the transformation and/or (selective degradation of (some of the molecules once present in the soil. Unfortunately, the information about such constraining factors and their impact on the applicability of molecular proxies is fragmented and scattered. The purpose of this study is to provide a critical review of the current state of knowledge with respect to the applicability of molecular proxies in soil science, specifically focusing on the factors constraining such applicability. Variability in genetic, ontogenetic, and environmental factors influences plant n-alkane patterns in such a way that no unique compounds or specific molecular proxies pointing to, for example, plant community differences or environmental influences, exist. Other components, such as n-alcohols, n-fatty acids, and cutin- and suberin-derived monomers, have received far less attention in this respect. Furthermore, there is a high diversity of input pathways offering both opportunities and limitations for the use of molecular proxies at the same time. New modeling approaches might offer a possibility to unravel such mixed input

Computational Nanotechnology of Molecular Materials, Electronics and Machines

Science.gov (United States)

Srivastava, D.; Biegel, Bryan A. (Technical Monitor)

2002-01-01

This viewgraph presentation covers carbon nanotubes, their characteristics, and their potential future applications. The presentation include predictions on the development of nanostructures and their applications, the thermal characteristics of carbon nanotubes, mechano-chemical effects upon carbon nanotubes, molecular electronics, and models for possible future nanostructure devices. The presentation also proposes a neural model for signal processing.

Molecular Modeling

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education; Volume 9; Issue 5. Molecular Modeling: A Powerful Tool for Drug Design and Molecular Docking. Rama Rao Nadendla. General Article Volume 9 Issue 5 May 2004 pp 51-60. Fulltext. Click here to view fulltext PDF. Permanent link:

Some relations between quantum Turing machines and Turing machines

OpenAIRE

Sicard, Andrés; Vélez, Mario

1999-01-01

For quantum Turing machines we present three elements: Its components, its time evolution operator and its local transition function. The components are related with the components of deterministic Turing machines, the time evolution operator is related with the evolution of reversible Turing machines and the local transition function is related with the transition function of probabilistic and reversible Turing machines.

Choosing between different AI approaches? The scientific benefits of the confrontation, and the new collaborative era between humans and machines

Directory of Open Access Journals (Sweden)

Jordi Vallverdú

2008-07-01

Full Text Available AI is a multidisciplinary activity that involves specialists from several fields, and we can say that the aim of science, and AI science, is solving problems. AI and computer sciences are been creating a new kind of making science, that we can call in silico science. Both models top-eown and bottomup are useful for e-scientific research. There is no a real controversy between them. Besides, the extended mind model of human cognition, involves human-machine interactions. Huge amount of data requires new ways to make and organize scientific practices: supercomputers, grids, distributed computing, specific software and middleware and, basically, more efficient and visual ways to interact with information. This is one of the key points to understand contemporary relationships between humans and machines: usability of scientific data.

Fourth European Conference on Mechanism Science (EUCOMES 2012 Conference)

CERN Document Server

Ceccarelli, Marco; New Trends in Mechanism and Machine Science : Theory and Applications in Engineering

2013-01-01

This book contains the papers of the European Conference on Mechanisms Science (EUCOMES 2012 Conference). The book presents the most recent research developments in the mechanism and machine science field and their applications. Topics addressed are theoretical kinematics, computational kinematics, mechanism design, experimental mechanics, mechanics of robots, dynamics of machinery, dynamics of multi-body systems, control issues of mechanical systems, mechanisms for biomechanics, novel designs, mechanical transmissions, linkages and manipulators, micro-mechanisms, teaching methods, history of mechanism science and industrial and non-industrial applications. This volume will also serve as an interesting reference for European activity in the fields of Mechanism and Machine Science as well as a source of inspiration for future works and developments.

A review of supervised machine learning applied to ageing research.

Science.gov (United States)

Fabris, Fabio; Magalhães, João Pedro de; Freitas, Alex A

2017-04-01

Broadly speaking, supervised machine learning is the computational task of learning correlations between variables in annotated data (the training set), and using this information to create a predictive model capable of inferring annotations for new data, whose annotations are not known. Ageing is a complex process that affects nearly all animal species. This process can be studied at several levels of abstraction, in different organisms and with different objectives in mind. Not surprisingly, the diversity of the supervised machine learning algorithms applied to answer biological questions reflects the complexities of the underlying ageing processes being studied. Many works using supervised machine learning to study the ageing process have been recently published, so it is timely to review these works, to discuss their main findings and weaknesses. In summary, the main findings of the reviewed papers are: the link between specific types of DNA repair and ageing; ageing-related proteins tend to be highly connected and seem to play a central role in molecular pathways; ageing/longevity is linked with autophagy and apoptosis, nutrient receptor genes, and copper and iron ion transport. Additionally, several biomarkers of ageing were found by machine learning. Despite some interesting machine learning results, we also identified a weakness of current works on this topic: only one of the reviewed papers has corroborated the computational results of machine learning algorithms through wet-lab experiments. In conclusion, supervised machine learning has contributed to advance our knowledge and has provided novel insights on ageing, yet future work should have a greater emphasis in validating the predictions.

The application of machine learning techniques in the clinical drug therapy.

Science.gov (United States)

Meng, Huan-Yu; Jin, Wan-Lin; Yan, Cheng-Kai; Yang, Huan

2018-05-25

The development of a novel drug is an extremely complicated process that includes the target identification, design and manufacture, and proper therapy of the novel drug, as well as drug dose selection, drug efficacy evaluation, and adverse drug reaction control. Due to the limited resources, high costs, long duration, and low hit-to-lead ratio in the development of pharmacogenetics and computer technology, machine learning techniques have assisted novel drug development and have gradually received more attention by researchers. According to current research, machine learning techniques are widely applied in the process of the discovery of new drugs and novel drug targets, the decision surrounding proper therapy and drug dose, and the prediction of drug efficacy and adverse drug reactions. In this article, we discussed the history, workflow, and advantages and disadvantages of machine learning techniques in the processes mentioned above. Although the advantages of machine learning techniques are fairly obvious, the application of machine learning techniques is currently limited. With further research, the application of machine techniques in drug development could be much more widespread and could potentially be one of the major methods used in drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Learning Algorithm of Boltzmann Machine Based on Spatial Monte Carlo Integration Method

Directory of Open Access Journals (Sweden)

Muneki Yasuda

2018-04-01

Full Text Available The machine learning techniques for Markov random fields are fundamental in various fields involving pattern recognition, image processing, sparse modeling, and earth science, and a Boltzmann machine is one of the most important models in Markov random fields. However, the inference and learning problems in the Boltzmann machine are NP-hard. The investigation of an effective learning algorithm for the Boltzmann machine is one of the most important challenges in the field of statistical machine learning. In this paper, we study Boltzmann machine learning based on the (first-order spatial Monte Carlo integration method, referred to as the 1-SMCI learning method, which was proposed in the author’s previous paper. In the first part of this paper, we compare the method with the maximum pseudo-likelihood estimation (MPLE method using a theoretical and a numerical approaches, and show the 1-SMCI learning method is more effective than the MPLE. In the latter part, we compare the 1-SMCI learning method with other effective methods, ratio matching and minimum probability flow, using a numerical experiment, and show the 1-SMCI learning method outperforms them.

Support vector machine in machine condition monitoring and fault diagnosis

Science.gov (United States)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. This paper presents a survey of machine condition monitoring and fault diagnosis using support vector machine (SVM). It attempts to summarize and review the recent research and developments of SVM in machine condition monitoring and diagnosis. Numerous methods have been developed based on intelligent systems such as artificial neural network, fuzzy expert system, condition-based reasoning, random forest, etc. However, the use of SVM for machine condition monitoring and fault diagnosis is still rare. SVM has excellent performance in generalization so it can produce high accuracy in classification for machine condition monitoring and diagnosis. Until 2006, the use of SVM in machine condition monitoring and fault diagnosis is tending to develop towards expertise orientation and problem-oriented domain. Finally, the ability to continually change and obtain a novel idea for machine condition monitoring and fault diagnosis using SVM will be future works.

A survey on Barrett's esophagus analysis using machine learning.

Science.gov (United States)

de Souza, Luis A; Palm, Christoph; Mendel, Robert; Hook, Christian; Ebigbo, Alanna; Probst, Andreas; Messmann, Helmut; Weber, Silke; Papa, João P

2018-05-01

This work presents a systematic review concerning recent studies and technologies of machine learning for Barrett's esophagus (BE) diagnosis and treatment. The use of artificial intelligence is a brand new and promising way to evaluate such disease. We compile some works published at some well-established databases, such as Science Direct, IEEEXplore, PubMed, Plos One, Multidisciplinary Digital Publishing Institute (MDPI), Association for Computing Machinery (ACM), Springer, and Hindawi Publishing Corporation. Each selected work has been analyzed to present its objective, methodology, and results. The BE progression to dysplasia or adenocarcinoma shows a complex pattern to be detected during endoscopic surveillance. Therefore, it is valuable to assist its diagnosis and automatic identification using computer analysis. The evaluation of the BE dysplasia can be performed through manual or automated segmentation through machine learning techniques. Finally, in this survey, we reviewed recent studies focused on the automatic detection of the neoplastic region for classification purposes using machine learning methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

2nd Machine Learning School for High Energy Physics

CERN Document Server

2016-01-01

The Second Machine Learning summer school organized by Yandex School of Data Analysis and Laboratory of Methods for Big Data Analysis of National Research University Higher School of Economics will be held in Lund, Sweden from 20 to 26 June 2016. It is hosted by Lund University. The school is intended to cover the relatively young area of data analysis and computational research that has started to emerge in High Energy Physics (HEP). It is known by several names including “Multivariate Analysis”, “Neural Networks”, “Classification/Clusterization techniques”. In more generic terms, these techniques belong to the field of “Machine Learning”, which is an area that is based on research performed in Statistics and has received a lot of attention from the Data Science community. There are plenty of essential problems in High energy Physics that can be solved using Machine Learning methods. These vary from online data filtering and reconstruction to offline data analysis. Students of the school w...

Data Mining and Machine Learning in Astronomy

Science.gov (United States)

Ball, Nicholas M.; Brunner, Robert J.

We review the current state of data mining and machine learning in astronomy. Data Mining can have a somewhat mixed connotation from the point of view of a researcher in this field. If used correctly, it can be a powerful approach, holding the potential to fully exploit the exponentially increasing amount of available data, promising great scientific advance. However, if misused, it can be little more than the black box application of complex computing algorithms that may give little physical insight, and provide questionable results. Here, we give an overview of the entire data mining process, from data collection through to the interpretation of results. We cover common machine learning algorithms, such as artificial neural networks and support vector machines, applications from a broad range of astronomy, emphasizing those in which data mining techniques directly contributed to improving science, and important current and future directions, including probability density functions, parallel algorithms, Peta-Scale computing, and the time domain. We conclude that, so long as one carefully selects an appropriate algorithm and is guided by the astronomical problem at hand, data mining can be very much the powerful tool, and not the questionable black box.

Geospatial and machine learning techniques for wicked social science problems: analysis of crash severity on a regional highway corridor

Science.gov (United States)

Effati, Meysam; Thill, Jean-Claude; Shabani, Shahin

2015-04-01

The contention of this paper is that many social science research problems are too "wicked" to be suitably studied using conventional statistical and regression-based methods of data analysis. This paper argues that an integrated geospatial approach based on methods of machine learning is well suited to this purpose. Recognizing the intrinsic wickedness of traffic safety issues, such approach is used to unravel the complexity of traffic crash severity on highway corridors as an example of such problems. The support vector machine (SVM) and coactive neuro-fuzzy inference system (CANFIS) algorithms are tested as inferential engines to predict crash severity and uncover spatial and non-spatial factors that systematically relate to crash severity, while a sensitivity analysis is conducted to determine the relative influence of crash severity factors. Different specifications of the two methods are implemented, trained, and evaluated against crash events recorded over a 4-year period on a regional highway corridor in Northern Iran. Overall, the SVM model outperforms CANFIS by a notable margin. The combined use of spatial analysis and artificial intelligence is effective at identifying leading factors of crash severity, while explicitly accounting for spatial dependence and spatial heterogeneity effects. Thanks to the demonstrated effectiveness of a sensitivity analysis, this approach produces comprehensive results that are consistent with existing traffic safety theories and supports the prioritization of effective safety measures that are geographically targeted and behaviorally sound on regional highway corridors.

Digitotalar dysmorphism: Molecular elucidation

African Journals Online (AJOL)

obtained for molecular studies. Since the distal arthrogryposes (DAs) are genetically heterogeneous, an unbiased approach to mutation ... Diseases and Molecular Medicine, Department of Pathology, Faculty of Health Sciences, University of Cape Town, South Africa, with an interest in molecular genetics of connective ...

Demystifying computer science for molecular ecologists.

Science.gov (United States)

Belcaid, Mahdi; Toonen, Robert J

2015-06-01

In this age of data-driven science and high-throughput biology, computational thinking is becoming an increasingly important skill for tackling both new and long-standing biological questions. However, despite its obvious importance and conspicuous integration into many areas of biology, computer science is still viewed as an obscure field that has, thus far, permeated into only a few of the biology curricula across the nation. A national survey has shown that lack of computational literacy in environmental sciences is the norm rather than the exception [Valle & Berdanier (2012) Bulletin of the Ecological Society of America, 93, 373-389]. In this article, we seek to introduce a few important concepts in computer science with the aim of providing a context-specific introduction aimed at research biologists. Our goal was to help biologists understand some of the most important mainstream computational concepts to better appreciate bioinformatics methods and trade-offs that are not obvious to the uninitiated. © 2015 John Wiley & Sons Ltd.

Simulations of Quantum Turing Machines by Quantum Multi-Stack Machines

OpenAIRE

Qiu, Daowen

2005-01-01

As was well known, in classical computation, Turing machines, circuits, multi-stack machines, and multi-counter machines are equivalent, that is, they can simulate each other in polynomial time. In quantum computation, Yao [11] first proved that for any quantum Turing machines $M$, there exists quantum Boolean circuit $(n,t)$-simulating $M$, where $n$ denotes the length of input strings, and $t$ is the number of move steps before machine stopping. However, the simulations of quantum Turing ma...

Study of Environmental Data Complexity using Extreme Learning Machine

Science.gov (United States)

Leuenberger, Michael; Kanevski, Mikhail

2017-04-01

The main goals of environmental data science using machine learning algorithm deal, in a broad sense, around the calibration, the prediction and the visualization of hidden relationship between input and output variables. In order to optimize the models and to understand the phenomenon under study, the characterization of the complexity (at different levels) should be taken into account. Therefore, the identification of the linear or non-linear behavior between input and output variables adds valuable information for the knowledge of the phenomenon complexity. The present research highlights and investigates the different issues that can occur when identifying the complexity (linear/non-linear) of environmental data using machine learning algorithm. In particular, the main attention is paid to the description of a self-consistent methodology for the use of Extreme Learning Machines (ELM, Huang et al., 2006), which recently gained a great popularity. By applying two ELM models (with linear and non-linear activation functions) and by comparing their efficiency, quantification of the linearity can be evaluated. The considered approach is accompanied by simulated and real high dimensional and multivariate data case studies. In conclusion, the current challenges and future development in complexity quantification using environmental data mining are discussed. References - Huang, G.-B., Zhu, Q.-Y., Siew, C.-K., 2006. Extreme learning machine: theory and applications. Neurocomputing 70 (1-3), 489-501. - Kanevski, M., Pozdnoukhov, A., Timonin, V., 2009. Machine Learning for Spatial Environmental Data. EPFL Press; Lausanne, Switzerland, p.392. - Leuenberger, M., Kanevski, M., 2015. Extreme Learning Machines for spatial environmental data. Computers and Geosciences 85, 64-73.

Journal of Earth System Science | Indian Academy of Sciences

Indian Academy of Sciences (India)

Articles written in Journal of Earth System Science. Volume 119 Issue 2 April 2010 pp 137-145. Effect of co-operative fuzzy .... The efficacy of support vector machines (SVM) in robust determination of earthquake early warning magnitudes in central Japan · Ramakrushna Reddy Rajesh R Nair · More Details Abstract Fulltext ...

Review of Statistical Learning Methods in Integrated Omics Studies (An Integrated Information Science).

Science.gov (United States)

Zeng, Irene Sui Lan; Lumley, Thomas

2018-01-01

Integrated omics is becoming a new channel for investigating the complex molecular system in modern biological science and sets a foundation for systematic learning for precision medicine. The statistical/machine learning methods that have emerged in the past decade for integrated omics are not only innovative but also multidisciplinary with integrated knowledge in biology, medicine, statistics, machine learning, and artificial intelligence. Here, we review the nontrivial classes of learning methods from the statistical aspects and streamline these learning methods within the statistical learning framework. The intriguing findings from the review are that the methods used are generalizable to other disciplines with complex systematic structure, and the integrated omics is part of an integrated information science which has collated and integrated different types of information for inferences and decision making. We review the statistical learning methods of exploratory and supervised learning from 42 publications. We also discuss the strengths and limitations of the extended principal component analysis, cluster analysis, network analysis, and regression methods. Statistical techniques such as penalization for sparsity induction when there are fewer observations than the number of features and using Bayesian approach when there are prior knowledge to be integrated are also included in the commentary. For the completeness of the review, a table of currently available software and packages from 23 publications for omics are summarized in the appendix.

Machine tool structures

CERN Document Server

Koenigsberger, F

1970-01-01

Machine Tool Structures, Volume 1 deals with fundamental theories and calculation methods for machine tool structures. Experimental investigations into stiffness are discussed, along with the application of the results to the design of machine tool structures. Topics covered range from static and dynamic stiffness to chatter in metal cutting, stability in machine tools, and deformations of machine tool structures. This volume is divided into three sections and opens with a discussion on stiffness specifications and the effect of stiffness on the behavior of the machine under forced vibration c

Molecular Formula and Molecular Weight - NBDC NikkajiRDF | LSDB Archive [Life Science Database Archive metadata

Lifescience Database Archive (English)

Full Text Available List Contact us NBDC NikkajiRDF Molecular Formula and Molecular Weight Data detail Data name Molecular Formula and Molecul...- Description of data contents This RDF data includes molecular formula and molecular weight of chemical sub...ikkajiRDF_MFMW.tar.gz File size: 404 MB Simple search URL - Data acquisition method The data was converted from data of molecul...ar formulas and molecular weights in Basic Information ( http://dbarchive.biosciencedbc.j... Policy | Contact Us Molecular Formula and Molecular Weight - NBDC NikkajiRDF | LSDB Archive ...

Optimal and online preemptive scheduling on uniformly related machines

Czech Academy of Sciences Publication Activity Database

Ebenlendr, Tomáš; Sgall, J.

2009-01-01

Roč. 12, č. 5 (2009), s. 517-527 ISSN 1094-6136 R&D Projects: GA MŠk(CZ) 1M0545; GA AV ČR IAA100190902; GA AV ČR IAA1019401 Institutional research plan: CEZ:AV0Z10190503 Keywords : online scheduling * preemption * uniformly related machines Subject RIV: IN - Informatics, Computer Science Impact factor: 1.265, year: 2009

Electricity of machine tool

International Nuclear Information System (INIS)

Gijeon media editorial department

1977-10-01

This book is divided into three parts. The first part deals with electricity machine, which can taints from generator to motor, motor a power source of machine tool, electricity machine for machine tool such as switch in main circuit, automatic machine, a knife switch and pushing button, snap switch, protection device, timer, solenoid, and rectifier. The second part handles wiring diagram. This concludes basic electricity circuit of machine tool, electricity wiring diagram in your machine like milling machine, planer and grinding machine. The third part introduces fault diagnosis of machine, which gives the practical solution according to fault diagnosis and the diagnostic method with voltage and resistance measurement by tester.

Humanizing machines: Anthropomorphization of slot machines increases gambling.

Science.gov (United States)

Riva, Paolo; Sacchi, Simona; Brambilla, Marco

2015-12-01

Do people gamble more on slot machines if they think that they are playing against humanlike minds rather than mathematical algorithms? Research has shown that people have a strong cognitive tendency to imbue humanlike mental states to nonhuman entities (i.e., anthropomorphism). The present research tested whether anthropomorphizing slot machines would increase gambling. Four studies manipulated slot machine anthropomorphization and found that exposing people to an anthropomorphized description of a slot machine increased gambling behavior and reduced gambling outcomes. Such findings emerged using tasks that focused on gambling behavior (Studies 1 to 3) as well as in experimental paradigms that included gambling outcomes (Studies 2 to 4). We found that gambling outcomes decrease because participants primed with the anthropomorphic slot machine gambled more (Study 4). Furthermore, we found that high-arousal positive emotions (e.g., feeling excited) played a role in the effect of anthropomorphism on gambling behavior (Studies 3 and 4). Our research indicates that the psychological process of gambling-machine anthropomorphism can be advantageous for the gaming industry; however, this may come at great expense for gamblers' (and their families') economic resources and psychological well-being. (c) 2015 APA, all rights reserved).

Impact of an engineering design-based curriculum compared to an inquiry-based curriculum on fifth graders' content learning of simple machines

Science.gov (United States)

Marulcu, Ismail; Barnett, Michael

2016-01-01

Background: Elementary Science Education is struggling with multiple challenges. National and State test results confirm the need for deeper understanding in elementary science education. Moreover, national policy statements and researchers call for increased exposure to engineering and technology in elementary science education. The basic motivation of this study is to suggest a solution to both improving elementary science education and increasing exposure to engineering and technology in it. Purpose/Hypothesis: This mixed-method study examined the impact of an engineering design-based curriculum compared to an inquiry-based curriculum on fifth graders' content learning of simple machines. We hypothesize that the LEGO-engineering design unit is as successful as the inquiry-based unit in terms of students' science content learning of simple machines. Design/Method: We used a mixed-methods approach to investigate our research questions; we compared the control and the experimental groups' scores from the tests and interviews by using Analysis of Covariance (ANCOVA) and compared each group's pre- and post-scores by using paired t-tests. Results: Our findings from the paired t-tests show that both the experimental and comparison groups significantly improved their scores from the pre-test to post-test on the multiple-choice, open-ended, and interview items. Moreover, ANCOVA results show that students in the experimental group, who learned simple machines with the design-based unit, performed significantly better on the interview questions. Conclusions: Our analyses revealed that the design-based Design a people mover: Simple machines unit was, if not better, as successful as the inquiry-based FOSS Levers and pulleys unit in terms of students' science content learning.

From Curve Fitting to Machine Learning

CERN Document Server

Zielesny, Achim

2011-01-01

The analysis of experimental data is at heart of science from its beginnings. But it was the advent of digital computers that allowed the execution of highly non-linear and increasingly complex data analysis procedures - methods that were completely unfeasible before. Non-linear curve fitting, clustering and machine learning belong to these modern techniques which are a further step towards computational intelligence. The goal of this book is to provide an interactive and illustrative guide to these topics. It concentrates on the road from two dimensional curve fitting to multidimensional clus

Black holes, wormholes and time machines

CERN Document Server

Al-Khalili, Jim

2011-01-01

Bringing the material up to date, Black Holes, Wormholes and Time Machines, Second Edition captures the new ideas and discoveries made in physics since the publication of the best-selling first edition. While retaining the popular format and style of its predecessor, this edition explores the latest developments in high-energy astroparticle physics and Big Bang cosmology.The book continues to make the ideas and theories of modern physics easily understood by anyone, from researchers to students to general science enthusiasts. Taking you on a journey through space and time, author Jim Al-Khalil

Code-expanded radio access protocol for machine-to-machine communications

DEFF Research Database (Denmark)

Thomsen, Henning; Kiilerich Pratas, Nuno; Stefanovic, Cedomir

2013-01-01

The random access methods used for support of machine-to-machine, also referred to as Machine-Type Communications, in current cellular standards are derivatives of traditional framed slotted ALOHA and therefore do not support high user loads efficiently. We propose an approach that is motivated b...... subframes and orthogonal preambles, the amount of available contention resources is drastically increased, enabling the massive support of Machine-Type Communication users that is beyond the reach of current systems.......The random access methods used for support of machine-to-machine, also referred to as Machine-Type Communications, in current cellular standards are derivatives of traditional framed slotted ALOHA and therefore do not support high user loads efficiently. We propose an approach that is motivated...... by the random access method employed in LTE, which significantly increases the amount of contention resources without increasing the system resources, such as contention subframes and preambles. This is accomplished by a logical, rather than physical, extension of the access method in which the available system...

Earle K. Plyler Prize Lecture: The Three Pillars of Ultrafast Molecular Science - Time, Phase, Intensity

Science.gov (United States)

Stolow, Albert

polyatomic molecules, including high harmonic generation (HHG). We discuss an experimental method, Channel-Resolved Above Threshold Ionization (CRATI), which directly unveils the electronic channels participating in the attosecond molecular strong field ionization response [10]. This work was supported by the National Research Council of Canada and the Natural Sciences & Engineering Research Council.

Designing anticancer peptides by constructive machine learning.

Science.gov (United States)

Grisoni, Francesca; Neuhaus, Claudia; Gabernet, Gisela; Müller, Alex; Hiss, Jan; Schneider, Gisbert

2018-04-21

Constructive machine learning enables the automated generation of novel chemical structures without the need for explicit molecular design rules. This study presents the experimental application of such a generative model to design membranolytic anticancer peptides (ACPs) de novo. A recurrent neural network with long short-term memory cells was trained on alpha-helical cationic amphipathic peptide sequences and then fine-tuned with 26 known ACPs. This optimized model was used to generate unique and novel amino acid sequences. Twelve of the peptides were synthesized and tested for their activity on MCF7 human breast adenocarcinoma cells and selectivity against human erythrocytes. Ten of these peptides were active against cancer cells. Six of the active peptides killed MCF7 cancer cells without affecting human erythrocytes with at least threefold selectivity. These results advocate constructive machine learning for the automated design of peptides with desired biological activities. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

How Secondary and Tertiary Amide Moieties are Molecular Stations for Dibenzo-24-crown-8 in [2]Rotaxane Molecular Shuttles?

Science.gov (United States)

Riss-Yaw, Benjamin; Morin, Justine; Clavel, Caroline; Coutrot, Frédéric

2017-11-21

Interlocked molecular machines like [2]rotaxanes are intriguing aesthetic molecules. The control of the localization of the macrocycle, which surrounds a molecular axle, along the thread leads to translational isomers of very different properties. Although many moieties have been used as sites of interactions for crown ethers, the very straightforwardly obtained amide motif has more rarely been envisaged as molecular station. In this article, we report the use of secondary and tertiary amide moieties as efficient secondary molecular station in pH-sensitive molecular shuttles. Depending on the N -substitution of the amide station, and on deprotonation or deprotonation-carbamoylation, the actuation of the molecular machinery differs accordingly to very distinct interactions between the axle and the DB24C8.

Life sciences and environmental sciences

Energy Technology Data Exchange (ETDEWEB)

1992-02-01

The DOE laboratories play a unique role in bringing multidisciplinary talents -- in biology, physics, chemistry, computer sciences, and engineering -- to bear on major problems in the life and environmental sciences. Specifically, the laboratories utilize these talents to fulfill OHER's mission of exploring and mitigating the health and environmental effects of energy use, and of developing health and medical applications of nuclear energy-related phenomena. At Lawrence Berkeley Laboratory (LBL) support of this mission is evident across the spectrum of OHER-sponsored research, especially in the broad areas of genomics, structural biology, basic cell and molecular biology, carcinogenesis, energy and environment, applications to biotechnology, and molecular, nuclear and radiation medicine. These research areas are briefly described.

Life sciences and environmental sciences

Energy Technology Data Exchange (ETDEWEB)

1992-02-01

The DOE laboratories play a unique role in bringing multidisciplinary talents -- in biology, physics, chemistry, computer sciences, and engineering -- to bear on major problems in the life and environmental sciences. Specifically, the laboratories utilize these talents to fulfill OHER`s mission of exploring and mitigating the health and environmental effects of energy use, and of developing health and medical applications of nuclear energy-related phenomena. At Lawrence Berkeley Laboratory (LBL) support of this mission is evident across the spectrum of OHER-sponsored research, especially in the broad areas of genomics, structural biology, basic cell and molecular biology, carcinogenesis, energy and environment, applications to biotechnology, and molecular, nuclear and radiation medicine. These research areas are briefly described.

H.G. Wells's Science Fiction: The Cyborg Visual Dromological Discourse

Directory of Open Access Journals (Sweden)

Ehsaneh Eshaghi

2015-01-01

Full Text Available H. G. Wells, as the forefather of science fiction, has used the relative notion of time in his stories such as Time Machine (1895. Speed is the initiator of a discourse in which humans are floating and moving ahead and has become one of the main “discourses” of the human being. Paul Virilio's theory of “dromology”, "vision machine" and “virtual reality”, along with "the aesthetics of disappearance" are applied in criticizing the novel scientific discourse by Wells who engenders a machinic, in the Deleuzian sense, and a cyborg discourse, through which he connotes the imperial narratives and the dromocratic powers. The usage of the Cyborg discourse by Wells in his science fiction stories has been to emphasize how the dromological and vision discourses are the prerequisite to the panoptical discourse through the microscopic and telescopic visions. It is concluded that the splintering frame is the created visual frame in the Wellsian science fiction.

U-Science (Invited)

Science.gov (United States)

Borne, K. D.

2009-12-01

The emergence of e-Science over the past decade as a paradigm for Internet-based science was an inevitable evolution of science that built upon the web protocols and access patterns that were prevalent at that time, including Web Services, XML-based information exchange, machine-to-machine communication, service registries, the Grid, and distributed data. We now see a major shift in web behavior patterns to social networks, user-provided content (e.g., tags and annotations), ubiquitous devices, user-centric experiences, and user-led activities. The inevitable accrual of these social networking patterns and protocols by scientists and science projects leads to U-Science as a new paradigm for online scientific research (i.e., ubiquitous, user-led, untethered, You-centered science). U-Science applications include components from semantic e-science (ontologies, taxonomies, folksonomies, tagging, annotations, and classification systems), which is much more than Web 2.0-based science (Wikis, blogs, and online environments like Second Life). Among the best examples of U-Science are Citizen Science projects, including Galaxy Zoo, Stardust@Home, Project Budburst, Volksdata, CoCoRaHS (the Community Collaborative Rain, Hail and Snow network), and projects utilizing Volunteer Geographic Information (VGI). There are also scientist-led projects for scientists that engage a wider community in building knowledge through user-provided content. Among the semantic-based U-Science projects for scientists are those that specifically enable user-based annotation of scientific results in databases. These include the Heliophysics Knowledgebase, BioDAS, WikiProteins, The Entity Describer, and eventually AstroDAS. Such collaborative tagging of scientific data addresses several petascale data challenges for scientists: how to find the most relevant data, how to reuse those data, how to integrate data from multiple sources, how to mine and discover new knowledge in large databases, how to

Simple machines

CERN Document Server

Graybill, George

2007-01-01

Just how simple are simple machines? With our ready-to-use resource, they are simple to teach and easy to learn! Chocked full of information and activities, we begin with a look at force, motion and work, and examples of simple machines in daily life are given. With this background, we move on to different kinds of simple machines including: Levers, Inclined Planes, Wedges, Screws, Pulleys, and Wheels and Axles. An exploration of some compound machines follows, such as the can opener. Our resource is a real time-saver as all the reading passages, student activities are provided. Presented in s

Molecular Theory of the Living Cell Concepts, Molecular Mechanisms, and Biomedical Applications

CERN Document Server

Ji, Sungchul

2012-01-01

This book presents a comprehensive molecular theory of the living cell based on over thirty concepts, principles and laws imported from thermodynamics, statistical mechanics, quantum mechanics, chemical kinetics, informatics, computer science, linguistics, semiotics, and philosophy. The author formulates physically, chemically and enzymologically realistic molecular mechanisms to account for the basic living processes such as ligand-receptor interactions, protein folding, single-molecule enzymic catalysis, force-generating mechanisms in molecular motors, signal transduction, regulation of the genome-wide RNA metabolism, morphogenesis, the micro-macro coupling in coordination dynamics, the origin of life, and the mechanisms of biological evolution itself. Possible solutions to basic and practical problems facing contemporary biology and biomedical sciences have been suggested, including pharmacotheragnostics and personalized medicine.

Machine performance assessment and enhancement for a hexapod machine

Energy Technology Data Exchange (ETDEWEB)

Mou, J.I. [Arizona State Univ., Tempe, AZ (United States); King, C. [Sandia National Labs., Livermore, CA (United States). Integrated Manufacturing Systems Center

1998-03-19

The focus of this study is to develop a sensor fused process modeling and control methodology to model, assess, and then enhance the performance of a hexapod machine for precision product realization. Deterministic modeling technique was used to derive models for machine performance assessment and enhancement. Sensor fusion methodology was adopted to identify the parameters of the derived models. Empirical models and computational algorithms were also derived and implemented to model, assess, and then enhance the machine performance. The developed sensor fusion algorithms can be implemented on a PC-based open architecture controller to receive information from various sensors, assess the status of the process, determine the proper action, and deliver the command to actuators for task execution. This will enhance a hexapod machine`s capability to produce workpieces within the imposed dimensional tolerances.

Superconducting rotating machines

International Nuclear Information System (INIS)

Smith, J.L. Jr.; Kirtley, J.L. Jr.; Thullen, P.

1975-01-01

The opportunities and limitations of the applications of superconductors in rotating electric machines are given. The relevant properties of superconductors and the fundamental requirements for rotating electric machines are discussed. The current state-of-the-art of superconducting machines is reviewed. Key problems, future developments and the long range potential of superconducting machines are assessed

Magic in the machine: a computational magician's assistant

OpenAIRE

Williams, Howard; McOwan, Peter W.

2014-01-01

A human magician blends science, psychology, and performance to create a magical effect. In this paper we explore what can be achieved when that human intelligence is replaced or assisted by machine intelligence. Magical effects are all in some form based on hidden mathematical, scientific, or psychological principles; often the parameters controlling these underpinning techniques are hard for a magician to blend to maximize the magical effect required. The complexity is often caused by inter...

Magic in the machine: a computational magician's assistant

OpenAIRE

Howard eWilliams; Peter eMcOwan

2014-01-01

A human magician blends science, psychology and performance to create a magical effect. In this paper we explore what can be achieved when that human intelligence is replaced or assisted by machine intelligence. Magical effects are all in some form based on hidden mathematical, scientific or psychological principles; often the parameters controlling these underpinning techniques are hard for a magician to blend to maximise the magical effect required. The complexity is often caused by interac...

Fellowship | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Fellowship. Fellow Profile. Elected: 2007 Section: Plant Sciences. Grover, Prof. Anil Ph.D. (IARI), FNASc, FNAAS, FNA. Date of birth: 15 August 1958. Specialization: Plant Abiotic Stress Responses, Plant Biotechnology, Molecular Biology and Crop Sciences Address: Professor, Department of Plant Molecular Biology, ...

Sustainable machining

CERN Document Server

2017-01-01

This book provides an overview on current sustainable machining. Its chapters cover the concept in economic, social and environmental dimensions. It provides the reader with proper ways to handle several pollutants produced during the machining process. The book is useful on both undergraduate and postgraduate levels and it is of interest to all those working with manufacturing and machining technology.

Chemically intuited, large-scale screening of MOFs by machine learning techniques

Science.gov (United States)

Borboudakis, Giorgos; Stergiannakos, Taxiarchis; Frysali, Maria; Klontzas, Emmanuel; Tsamardinos, Ioannis; Froudakis, George E.

2017-10-01

A novel computational methodology for large-scale screening of MOFs is applied to gas storage with the use of machine learning technologies. This approach is a promising trade-off between the accuracy of ab initio methods and the speed of classical approaches, strategically combined with chemical intuition. The results demonstrate that the chemical properties of MOFs are indeed predictable (stochastically, not deterministically) using machine learning methods and automated analysis protocols, with the accuracy of predictions increasing with sample size. Our initial results indicate that this methodology is promising to apply not only to gas storage in MOFs but in many other material science projects.

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

National Engineering Research Center for Integrated Utilization of Salt Lake Resource, East China University of Science and Technology, Meilong Road 130, Shanghai 200237, China; Van 't Hoff Institute for Molecular Sciences & Amsterdam Center for Multiscale Modeling, University of Amsterdam, 1098 XH Amsterdam, ...

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Chemical Sciences. M PRASANTHI. Articles written in Journal of Chemical Sciences. Volume 129 Issue 5 May 2017 pp 515-531 Regular Article. Assessing therapeutic potential of molecules: molecular property diagnostic suite for tuberculosis (MPDS) · ANAMIKA SINGH GAUR ANSHU ...

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Chemical Sciences. SRIDHARA JANARDHAN. Articles written in Journal of Chemical Sciences. Volume 129 Issue 5 May 2017 pp 515-531 Regular Article. Assessing therapeutic potential of molecules: molecular property diagnostic suite for tuberculosis (MPDS) · ANAMIKA SINGH GAUR ...

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Chemical Sciences. Kishalay Bhar. Articles written in Journal of Chemical Sciences. Volume 125 Issue 4 July 2013 pp 715-721. Syntheses, molecular and crystalline architectures, and luminescence behaviour of terephthalate bridged heptacoordinated dinuclear lead(II) complexes containing a ...

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Chemical Sciences. DIPU SUTRADHAR. Articles written in Journal of Chemical Sciences. Volume 128 Issue 9 September 2016 pp 1377-1384 Regular Article. Two new hexacoordinated coordination polymers of cadmium(II) containing bridging units only: Syntheses, structures and molecular ...

Advanced Electrical Machines and Machine-Based Systems for Electric and Hybrid Vehicles

Directory of Open Access Journals (Sweden)

Ming Cheng

2015-09-01

Full Text Available The paper presents a number of advanced solutions on electric machines and machine-based systems for the powertrain of electric vehicles (EVs. Two types of systems are considered, namely the drive systems designated to the EV propulsion and the power split devices utilized in the popular series-parallel hybrid electric vehicle architecture. After reviewing the main requirements for the electric drive systems, the paper illustrates advanced electric machine topologies, including a stator permanent magnet (stator-PM motor, a hybrid-excitation motor, a flux memory motor and a redundant motor structure. Then, it illustrates advanced electric drive systems, such as the magnetic-geared in-wheel drive and the integrated starter generator (ISG. Finally, three machine-based implementations of the power split devices are expounded, built up around the dual-rotor PM machine, the dual-stator PM brushless machine and the magnetic-geared dual-rotor machine. As a conclusion, the development trends in the field of electric machines and machine-based systems for EVs are summarized.

Asynchronized synchronous machines

CERN Document Server

Botvinnik, M M

1964-01-01

Asynchronized Synchronous Machines focuses on the theoretical research on asynchronized synchronous (AS) machines, which are "hybrids” of synchronous and induction machines that can operate with slip. Topics covered in this book include the initial equations; vector diagram of an AS machine; regulation in cases of deviation from the law of full compensation; parameters of the excitation system; and schematic diagram of an excitation regulator. The possible applications of AS machines and its calculations in certain cases are also discussed. This publication is beneficial for students and indiv

The Molecular Industrial Revolution: Automated Synthesis of Small Molecules.

Science.gov (United States)

Trobe, Melanie; Burke, Martin D

2018-04-09

Today we are poised for a transition from the highly customized crafting of specific molecular targets by hand to the increasingly general and automated assembly of different types of molecules with the push of a button. Creating machines that are capable of making many different types of small molecules on demand, akin to that which has been achieved on the macroscale with 3D printers, is challenging. Yet important progress is being made toward this objective with two complementary approaches: 1) Automation of customized synthesis routes to different targets by machines that enable the use of many reactions and starting materials, and 2) automation of generalized platforms that make many different targets using common coupling chemistry and building blocks. Continued progress in these directions has the potential to shift the bottleneck in molecular innovation from synthesis to imagination, and thereby help drive a new industrial revolution on the molecular scale. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Dostoevsky Machine in Georgetown: scientific translation in the Cold War.

Science.gov (United States)

Gordin, Michael D

2016-04-01

Machine Translation (MT) is now ubiquitous in discussions of translation. The roots of this phenomenon - first publicly unveiled in the so-called 'Georgetown-IBM Experiment' on 9 January 1954 - displayed not only the technological utopianism still associated with dreams of a universal computer translator, but was deeply enmeshed in the political pressures of the Cold War and a dominating conception of scientific writing as both the goal of machine translation as well as its method. Machine translation was created, in part, as a solution to a perceived crisis sparked by the massive expansion of Soviet science. Scientific prose was also perceived as linguistically simpler, and so served as the model for how to turn a language into a series of algorithms. This paper follows the rise of the Georgetown program - the largest single program in the world - from 1954 to the (as it turns out, temporary) collapse of MT in 1964.

Synthetic Ion Channels and DNA Logic Gates as Components of Molecular Robots.

Science.gov (United States)

Kawano, Ryuji

2018-02-19

A molecular robot is a next-generation biochemical machine that imitates the actions of microorganisms. It is made of biomaterials such as DNA, proteins, and lipids. Three prerequisites have been proposed for the construction of such a robot: sensors, intelligence, and actuators. This Minireview focuses on recent research on synthetic ion channels and DNA computing technologies, which are viewed as potential candidate components of molecular robots. Synthetic ion channels, which are embedded in artificial cell membranes (lipid bilayers), sense ambient ions or chemicals and import them. These artificial sensors are useful components for molecular robots with bodies consisting of a lipid bilayer because they enable the interface between the inside and outside of the molecular robot to function as gates. After the signal molecules arrive inside the molecular robot, they can operate DNA logic gates, which perform computations. These functions will be integrated into the intelligence and sensor sections of molecular robots. Soon, these molecular machines will be able to be assembled to operate as a mass microrobot and play an active role in environmental monitoring and in vivo diagnosis or therapy. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Machine Shop Lathes.

Science.gov (United States)

Dunn, James

This guide, the second in a series of five machine shop curriculum manuals, was designed for use in machine shop courses in Oklahoma. The purpose of the manual is to equip students with basic knowledge and skills that will enable them to enter the machine trade at the machine-operator level. The curriculum is designed so that it can be used in…

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Chemical Sciences. M RAM VIVEK. Articles written in Journal of Chemical Sciences. Volume 129 Issue 5 May 2017 pp 515-531 Regular Article. Assessing therapeutic potential of molecules: molecular property diagnostic suite for tuberculosis (MPDS) · ANAMIKA SINGH GAUR ANSHU ...

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Chemical Sciences. Asim Bhaumik. Articles written in Journal of Chemical Sciences. Volume 114 Issue 4 August 2002 pp 451-460. Mesoporous titanium phosphates and related molecular sieves: Synthesis, characterization and applications · Asim Bhaumik · More Details Abstract Fulltext PDF.

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Chemical Sciences. Charusita Chakravarty. Articles written in Journal of Chemical Sciences. Volume 121 Issue 5 September 2009 pp 913-919. Evaluation of collective transport properties of ionic melts from molecular dynamics simulations · Manish Agarwal Charusita Chakravarty · More Details ...

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Chemical Sciences. T SHANMUGANATHAN. Articles written in Journal of Chemical Sciences. Volume 129 Issue 1 January 2017 pp 117-130 Regular Article. Synthesis, in vitro anti-inflammatory activity and molecular docking studies of novel 4,5-diarylthiophene-2-carboxamide derivatives.

Computational methods to study the structure and dynamics of biomolecules and biomolecular processes from bioinformatics to molecular quantum mechanics

CERN Document Server

2014-01-01

Since the second half of the 20th century machine computations have played a critical role in science and engineering. Computer-based techniques have become especially important in molecular biology, since they often represent the only viable way to gain insights into the behavior of a biological system as a whole. The complexity of biological systems, which usually needs to be analyzed on different time- and size-scales and with different levels of accuracy, requires the application of different approaches, ranging from comparative analysis of sequences and structural databases, to the analysis of networks of interdependence between cell components and processes, through coarse-grained modeling to atomically detailed simulations, and finally to molecular quantum mechanics. This book provides a comprehensive overview of modern computer-based techniques for computing the structure, properties and dynamics of biomolecules and biomolecular processes. The twenty-two chapters, written by scientists from all over t...

Machining of Machine Elements Made of Polymer Composite Materials

Science.gov (United States)

Baurova, N. I.; Makarov, K. A.

2017-12-01

The machining of the machine elements that are made of polymer composite materials (PCMs) or are repaired using them is considered. Turning, milling, and drilling are shown to be most widely used among all methods of cutting PCMs. Cutting conditions for the machining of PCMs are presented. The factors that most strongly affect the roughness parameters and the accuracy of cutting PCMs are considered.

Large-Scale Sentinel-1 Processing for Solid Earth Science and Urgent Response using Cloud Computing and Machine Learning

Science.gov (United States)

Hua, H.; Owen, S. E.; Yun, S. H.; Agram, P. S.; Manipon, G.; Starch, M.; Sacco, G. F.; Bue, B. D.; Dang, L. B.; Linick, J. P.; Malarout, N.; Rosen, P. A.; Fielding, E. J.; Lundgren, P.; Moore, A. W.; Liu, Z.; Farr, T.; Webb, F.; Simons, M.; Gurrola, E. M.

2017-12-01

present some of our findings from applying machine learning and data analytics on the processed SAR data streams. We will also present lessons learned on how to ease the SAR community onto interfacing with these cloud-based SAR science data systems.

The latest science and human

International Nuclear Information System (INIS)

Kim, Sang Il; Lee, Hae Du; Lee, Geun Hui

1985-04-01

The book is collective reports on the science and human. The contents of this book are life ethics and technology ethics, conception of human and human science, biotechnology. The tower of Babel in computer age, human brain and robot, new media and communication innovation, status of computer engineering, current condition of development of new media, mass media and violence, crime and scientification of terror, condition of the life and peace, period of machine and literature, religious prophecy and scientific prophecy and hi-tech age and education of science.

The latest science and human

Energy Technology Data Exchange (ETDEWEB)

Kim, Sang Il; Lee, Hae Du; Lee, Geun Hui

1985-04-15

The book is collective reports on the science and human. The contents of this book are life ethics and technology ethics, conception of human and human science, biotechnology. The tower of Babel in computer age, human brain and robot, new media and communication innovation, status of computer engineering, current condition of development of new media, mass media and violence, crime and scientification of terror, condition of the life and peace, period of machine and literature, religious prophecy and scientific prophecy and hi-tech age and education of science.

Study of the Effect of Material Machinability on Quality of Surface Created by Abrasive Water Jet

Czech Academy of Sciences Publication Activity Database

Klichová, Dagmar; Klich, Jiří

2016-01-01

Roč. 149, č. 149 (2016), s. 177-182 E-ISSN 1877-7058. [International Conference on Manufacturing Engineering and Materials, ICMEM 2016. Nový Smokovec, 06.06.2016-10.06.2016] R&D Projects: GA MŠk(CZ) LO1406 Institutional support: RVO:68145535 Keywords : machinability * surface roughness * abrasive water jet * study of quality * aluminium alloy * optical profilometer Subject RIV: JQ - Machines ; Tools http://www.sciencedirect.com/science/article/pii/S1877705816311614

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Chemical Sciences. SABIHA FATIMA. Articles written in Journal of Chemical Sciences. Volume 128 Issue 7 July 2016 pp 1163-1173 Regular Article. Molecular docking, MM/GBSA and 3D-QSAR studies on EGFR inhibitors · RAJU BATHINI SREE KANTH SIVAN SABIHA FATIMA VIJJULATHA ...

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Chemical Sciences. Tapta Kanchan Roy. Articles written in Journal of Chemical Sciences. Volume 121 Issue 5 September 2009 pp 805-810. Effective harmonic oscillator description of anharmonic molecular vibrations · Tapta Kanchan Roy M Durga Prasad · More Details Abstract Fulltext PDF.

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Chemical Sciences. RAJU BATHINI. Articles written in Journal of Chemical Sciences. Volume 128 Issue 7 July 2016 pp 1163-1173 Regular Article. Molecular docking, MM/GBSA and 3D-QSAR studies on EGFR inhibitors · RAJU BATHINI SREE KANTH SIVAN SABIHA FATIMA VIJJULATHA ...

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Chemical Sciences. Navneet Kaur. Articles written in Journal of Chemical Sciences. Volume 126 Issue 1 January 2014 pp 49-54. Anthraquinone-based demultiplexer and other multiple operations at the molecular level · Navneet Kaur Subodh Kumar · More Details Abstract Fulltext PDF.

Learning molecular energies using localized graph kernels

Science.gov (United States)

Ferré, Grégoire; Haut, Terry; Barros, Kipton

2017-03-01

Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although simple to state, these symmetries are complicated to encode into machine learning algorithms. In this paper, we present a machine learning approach based on graph theory that naturally incorporates translation, rotation, and permutation symmetries. Specifically, we use a random walk graph kernel to measure the similarity of two adjacency matrices, each of which represents a local atomic environment. This Graph Approximated Energy (GRAPE) approach is flexible and admits many possible extensions. We benchmark a simple version of GRAPE by predicting atomization energies on a standard dataset of organic molecules.

5. International conference on materials science and condensed matter physics and symposium 'Electrical methods of materials treatment'. Abstracts

International Nuclear Information System (INIS)

2010-09-01

This book includes abstracts of the communications presented at the 5th International Conference on Materials Science and Condensed-Matter Physics and at the Symposium dedicated to the 100th anniversary of academician Boris Lazarenko, the prominent scientist and inventor, the first director of the Institute of Applied Physics of the Academy of Sciences of Moldova. The abstracts presented in the book cover a vast range of subjects, such as: advanced materials and fabrication processes; methods of crystal growth, post-growth technological processes, doping and implantation, fabrication of solid state structures; defect engineering, engineering of molecular assembly; methods of nanostructures and nano materials fabrication and characterization; quantum wells and superlattices; nano composite, nanowires and nano dots; fullerenes and nano tubes, molecular materials, meso- and nano electronics; methods of material and structure characterization; structure and mechanical characterization; optical, electrical, magnetic and superconductor properties, transport processes, nonlinear phenomena, size and interface effects; advances in condensed matter theory; theory of low dimensional systems; modelling of materials and structure properties; development of theoretical methods of solid-state characterization; phase transition; advanced quantum physics for nano systems; device modelling and simulation, device structures and elements; micro- and optoelectronics; photonics; microsensors and micro electro-mechanical systems; microsystems; degradation and reliability, solid-state device design; theory and advanced technologies of electro-physico-chemical and combined methods of materials machining and treatment, including modification of surfaces; theory and advanced technologies of using electric fields, currents and discharges so as to intensify heat mass-transfer, to raise the efficiency of treatment of materials, of biological preparations and foodstuff; modern equipment for

Improving Machining Accuracy of CNC Machines with Innovative Design Methods

Science.gov (United States)

Yemelyanov, N. V.; Yemelyanova, I. V.; Zubenko, V. L.

2018-03-01

The article considers achieving the machining accuracy of CNC machines by applying innovative methods in modelling and design of machining systems, drives and machine processes. The topological method of analysis involves visualizing the system as matrices of block graphs with a varying degree of detail between the upper and lower hierarchy levels. This approach combines the advantages of graph theory and the efficiency of decomposition methods, it also has visual clarity, which is inherent in both topological models and structural matrices, as well as the resiliency of linear algebra as part of the matrix-based research. The focus of the study is on the design of automated machine workstations, systems, machines and units, which can be broken into interrelated parts and presented as algebraic, topological and set-theoretical models. Every model can be transformed into a model of another type, and, as a result, can be interpreted as a system of linear and non-linear equations which solutions determine the system parameters. This paper analyses the dynamic parameters of the 1716PF4 machine at the stages of design and exploitation. Having researched the impact of the system dynamics on the component quality, the authors have developed a range of practical recommendations which have enabled one to reduce considerably the amplitude of relative motion, exclude some resonance zones within the spindle speed range of 0...6000 min-1 and improve machining accuracy.

Machinability of nickel based alloys using electrical discharge machining process

Science.gov (United States)

Khan, M. Adam; Gokul, A. K.; Bharani Dharan, M. P.; Jeevakarthikeyan, R. V. S.; Uthayakumar, M.; Thirumalai Kumaran, S.; Duraiselvam, M.

2018-04-01

The high temperature materials such as nickel based alloys and austenitic steel are frequently used for manufacturing critical aero engine turbine components. Literature on conventional and unconventional machining of steel materials is abundant over the past three decades. However the machining studies on superalloy is still a challenging task due to its inherent property and quality. Thus this material is difficult to be cut in conventional processes. Study on unconventional machining process for nickel alloys is focused in this proposed research. Inconel718 and Monel 400 are the two different candidate materials used for electrical discharge machining (EDM) process. Investigation is to prepare a blind hole using copper electrode of 6mm diameter. Electrical parameters are varied to produce plasma spark for diffusion process and machining time is made constant to calculate the experimental results of both the material. Influence of process parameters on tool wear mechanism and material removal are considered from the proposed experimental design. While machining the tool has prone to discharge more materials due to production of high energy plasma spark and eddy current effect. The surface morphology of the machined surface were observed with high resolution FE SEM. Fused electrode found to be a spherical structure over the machined surface as clumps. Surface roughness were also measured with surface profile using profilometer. It is confirmed that there is no deviation and precise roundness of drilling is maintained.

The achievements of the Z-machine; Les exploits de la Z-machine

Energy Technology Data Exchange (ETDEWEB)

Larousserie, D

2008-03-15

The ZR-machine that represents the latest generation of Z-pinch machines has recently begun preliminary testing before its full commissioning in Albuquerque (Usa). During its test the machine has well operated with electrical currents whose intensities of 26 million Ampere are already 2 times as high as the intensity of the operating current of the previous Z-machine. In 2006 the Z-machine reached temperatures of 2 billions Kelvin while 100 million Kelvin would be sufficient to ignite thermonuclear fusion. In fact the concept of Z-pinch machines was imagined in the fifties but the technological breakthrough that has allowed this recent success and the reborn of Z-machine, was the replacement of gas by an array of metal wires through which the electrical current flows and vaporizes it creating an imploding plasma. It is not well understood why Z-pinch machines generate far more radiation than theoretically expected. (A.C.)

Quantum machine learning.

Science.gov (United States)

Biamonte, Jacob; Wittek, Peter; Pancotti, Nicola; Rebentrost, Patrick; Wiebe, Nathan; Lloyd, Seth

2017-09-13

Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning that is faster than that of classical computers. Recent work has produced quantum algorithms that could act as the building blocks of machine learning programs, but the hardware and software challenges are still considerable.

Molecular Properties of Drugs Interacting with SLC22 Transporters OAT1, OAT3, OCT1, and OCT2: A Machine-Learning Approach.

Science.gov (United States)

Liu, Henry C; Goldenberg, Anne; Chen, Yuchen; Lun, Christina; Wu, Wei; Bush, Kevin T; Balac, Natasha; Rodriguez, Paul; Abagyan, Ruben; Nigam, Sanjay K

2016-10-01

Statistical analysis was performed on physicochemical descriptors of ∼250 drugs known to interact with one or more SLC22 "drug" transporters (i.e., SLC22A6 or OAT1, SLC22A8 or OAT3, SLC22A1 or OCT1, and SLC22A2 or OCT2), followed by application of machine-learning methods and wet laboratory testing of novel predictions. In addition to molecular charge, organic anion transporters (OATs) were found to prefer interacting with planar structures, whereas organic cation transporters (OCTs) interact with more three-dimensional structures (i.e., greater SP3 character). Moreover, compared with OAT1 ligands, OAT3 ligands possess more acyclic tetravalent bonds and have a more zwitterionic/cationic character. In contrast, OCT1 and OCT2 ligands were not clearly distinquishable form one another by the methods employed. Multiple pharmacophore models were generated on the basis of the drugs and, consistent with the machine-learning analyses, one unique pharmacophore created from ligands of OAT3 possessed cationic properties similar to OCT ligands; this was confirmed by quantitative atomic property field analysis. Virtual screening with this pharmacophore, followed by transport assays, identified several cationic drugs that selectively interact with OAT3 but not OAT1. Although the present analysis may be somewhat limited by the need to rely largely on inhibition data for modeling, wet laboratory/in vitro transport studies, as well as analysis of drug/metabolite handling in Oat and Oct knockout animals, support the general validity of the approach-which can also be applied to other SLC and ATP binding cassette drug transporters. This may make it possible to predict the molecular properties of a drug or metabolite necessary for interaction with the transporter(s), thereby enabling better prediction of drug-drug interactions and drug-metabolite interactions. Furthermore, understanding the overlapping specificities of OATs and OCTs in the context of dynamic transporter tissue

Machine protection systems

CERN Document Server

Macpherson, A L

2010-01-01

A summary of the Machine Protection System of the LHC is given, with particular attention given to the outstanding issues to be addressed, rather than the successes of the machine protection system from the 2009 run. In particular, the issues of Safe Machine Parameter system, collimation and beam cleaning, the beam dump system and abort gap cleaning, injection and dump protection, and the overall machine protection program for the upcoming run are summarised.

The influence of the negative-positive ratio and screening database size on the performance of machine learning-based virtual screening.

Science.gov (United States)

Kurczab, Rafał; Bojarski, Andrzej J

2017-01-01

The machine learning-based virtual screening of molecular databases is a commonly used approach to identify hits. However, many aspects associated with training predictive models can influence the final performance and, consequently, the number of hits found. Thus, we performed a systematic study of the simultaneous influence of the proportion of negatives to positives in the testing set, the size of screening databases and the type of molecular representations on the effectiveness of classification. The results obtained for eight protein targets, five machine learning algorithms (SMO, Naïve Bayes, Ibk, J48 and Random Forest), two types of molecular fingerprints (MACCS and CDK FP) and eight screening databases with different numbers of molecules confirmed our previous findings that increases in the ratio of negative to positive training instances greatly influenced most of the investigated parameters of the ML methods in simulated virtual screening experiments. However, the performance of screening was shown to also be highly dependent on the molecular library dimension. Generally, with the increasing size of the screened database, the optimal training ratio also increased, and this ratio can be rationalized using the proposed cost-effectiveness threshold approach. To increase the performance of machine learning-based virtual screening, the training set should be constructed in a way that considers the size of the screening database.

Clustering the Orion B giant molecular cloud based on its molecular emission.

Science.gov (United States)

Bron, Emeric; Daudon, Chloé; Pety, Jérôme; Levrier, François; Gerin, Maryvonne; Gratier, Pierre; Orkisz, Jan H; Guzman, Viviana; Bardeau, Sébastien; Goicoechea, Javier R; Liszt, Harvey; Öberg, Karin; Peretto, Nicolas; Sievers, Albrecht; Tremblin, Pascal

2018-02-01

Previous attempts at segmenting molecular line maps of molecular clouds have focused on using position-position-velocity data cubes of a single molecular line to separate the spatial components of the cloud. In contrast, wide field spectral imaging over a large spectral bandwidth in the (sub)mm domain now allows one to combine multiple molecular tracers to understand the different physical and chemical phases that constitute giant molecular clouds (GMCs). We aim at using multiple tracers (sensitive to different physical processes and conditions) to segment a molecular cloud into physically/chemically similar regions (rather than spatially connected components), thus disentangling the different physical/chemical phases present in the cloud. We use a machine learning clustering method, namely the Meanshift algorithm, to cluster pixels with similar molecular emission, ignoring spatial information. Clusters are defined around each maximum of the multidimensional Probability Density Function (PDF) of the line integrated intensities. Simple radiative transfer models were used to interpret the astrophysical information uncovered by the clustering analysis. A clustering analysis based only on the J = 1 - 0 lines of three isotopologues of CO proves suffcient to reveal distinct density/column density regimes ( n H ~ 100 cm -3 , ~ 500 cm -3 , and > 1000 cm -3 ), closely related to the usual definitions of diffuse, translucent and high-column-density regions. Adding two UV-sensitive tracers, the J = 1 - 0 line of HCO + and the N = 1 - 0 line of CN, allows us to distinguish two clearly distinct chemical regimes, characteristic of UV-illuminated and UV-shielded gas. The UV-illuminated regime shows overbright HCO + and CN emission, which we relate to a photochemical enrichment effect. We also find a tail of high CN/HCO + intensity ratio in UV-illuminated regions. Finer distinctions in density classes ( n H ~ 7 × 10 3 cm -3 ~ 4 × 10 4 cm -3 ) for the densest regions are also

Preliminary Test of Upgraded Conventional Milling Machine into PC Based CNC Milling Machine

International Nuclear Information System (INIS)

Abdul Hafid

2008-01-01

CNC (Computerized Numerical Control) milling machine yields a challenge to make an innovation in the field of machining. With an action job is machining quality equivalent to CNC milling machine, the conventional milling machine ability was improved to be based on PC CNC milling machine. Mechanically and instrumentally change. As a control replacing was conducted by servo drive and proximity were used. Computer programme was constructed to give instruction into milling machine. The program structure of consists GUI model and ladder diagram. Program was put on programming systems called RTX software. The result of up-grade is computer programming and CNC instruction job. The result was beginning step and it will be continued in next time. With upgrading ability milling machine becomes user can be done safe and optimal from accident risk. By improving performance of milling machine, the user will be more working optimal and safely against accident risk. (author)

Resonance – Journal of Science Education | Indian Academy of ...

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education. Durgesh D Rao. Articles written in Resonance – Journal of Science Education. Volume 3 Issue 7 July 1998 pp 61-70 General Article. Machine Translation - A Gentle Introduction · Durgesh D Rao · More Details Fulltext PDF ...

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Chemical Sciences. V Umamaheswari. Articles written in Journal of Chemical Sciences. Volume 112 Issue 4 August 2000 pp 439-448 Inorganic and Analytical. Isomorphous substitution of Mn(II), Ni(II) and Zn(II) in AlPO-31 molecular sieves and study of their catalytic performance.

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Chemical Sciences. YU-HUI HOU. Articles written in Journal of Chemical Sciences. Volume 130 Issue 1 January 2018 pp 6. Efficient click reaction towards novel sulfonamide hybrids by molecular hybridization strategy as antiproliferative agents · DONG-JUN FU YU-HUI HOU SAI-YANG ZHANG ...

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Chemical Sciences. Rajarshi Ghosh. Articles written in Journal of Chemical Sciences. Volume 125 Issue 4 July 2013 pp 723-730. Synthesis, molecular and crystalline architectures, and properties of a mononuclear complex [Co (benzidine)2(NCS)2(OH2)2] · Subhasish Kundu Subhasis Roy ...

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Chemical Sciences. Harinath Yapati. Articles written in Journal of Chemical Sciences. Volume 128 Issue 1 January 2016 pp 43-51 Regular Articles. Synthesis, characterization and studies on antioxidant and molecular docking of metal complexes of 1-(benzo[d]thiazol-2-yl)thiourea · Harinath ...

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Chemical Sciences. Shridhar R Gadre. Articles written in Journal of Chemical Sciences. Volume 121 Issue 5 September 2009 pp 815-821. Signatures of molecular recognition from the topography of electrostatic potential · Dhimoy K Roy P Balanarayan Shridhar R Gadre · More Details Abstract ...

Dynamics of polymers in a good solvent - a molecular dynamics study using the Connection Machine

International Nuclear Information System (INIS)

Shannon, S.R.; Choy, T.C.

1996-01-01

In recent times the use of molecular dynamics simulations has become an important tool in modelling and understanding the dynamics of interacting many-body systems. With recent advances in computing power it is now feasible to perform modelling of systems which contain a large number of interacting particles, and thus to simulate the behaviour of real systems reasonably. Our earlier discoveries of anomalous corrections to scaling behaviour of the Edward's polymer were applied to study the dynamical behaviour of two dimensional polymer systems - either a single chain immersed in a fluid, a pure polymer melt, or with any concentration of polymers in the fluid. By choosing a suitable interaction potential between the fluid particles and the monomers, we are able to study the experimentally observable time dependent structure factor of polymers in a good solvent. Simulations were performed using the Connection Machine CM5 supercomputer at the Australian National University which due to its fast multi- processor nearest neighbour communications facility, enables us to easily model large systems of at least 3000 fluid plus monomer particles. Our study is based on a finite difference solution of Newton's equations of motion i.e. the Verlet algorithm, and the results are used to test current theories of polymer dynamics, which were based primarily on the earlier models proposed by Rouse (1953) and Zimm (1956). In particular dynamical scaling predictions is scrutinised to examine the effects due to the anomalous corrections-to-scaling behaviour found in an earlier work using finite-size scaling analysis of Monte-Carlo data and now understood via a new perturbation concept

Study of on-machine error identification and compensation methods for micro machine tools

International Nuclear Information System (INIS)

Wang, Shih-Ming; Yu, Han-Jen; Lee, Chun-Yi; Chiu, Hung-Sheng

2016-01-01

Micro machining plays an important role in the manufacturing of miniature products which are made of various materials with complex 3D shapes and tight machining tolerance. To further improve the accuracy of a micro machining process without increasing the manufacturing cost of a micro machine tool, an effective machining error measurement method and a software-based compensation method are essential. To avoid introducing additional errors caused by the re-installment of the workpiece, the measurement and compensation method should be on-machine conducted. In addition, because the contour of a miniature workpiece machined with a micro machining process is very tiny, the measurement method should be non-contact. By integrating the image re-constructive method, camera pixel correction, coordinate transformation, the error identification algorithm, and trajectory auto-correction method, a vision-based error measurement and compensation method that can on-machine inspect the micro machining errors and automatically generate an error-corrected numerical control (NC) program for error compensation was developed in this study. With the use of the Canny edge detection algorithm and camera pixel calibration, the edges of the contour of a machined workpiece were identified and used to re-construct the actual contour of the work piece. The actual contour was then mapped to the theoretical contour to identify the actual cutting points and compute the machining errors. With the use of a moving matching window and calculation of the similarity between the actual and theoretical contour, the errors between the actual cutting points and theoretical cutting points were calculated and used to correct the NC program. With the use of the error-corrected NC program, the accuracy of a micro machining process can be effectively improved. To prove the feasibility and effectiveness of the proposed methods, micro-milling experiments on a micro machine tool were conducted, and the results

Prediction of overall in vitro microsomal stability of drug candidates based on molecular modeling and support vector machines. Case study of novel arylpiperazines derivatives.

Directory of Open Access Journals (Sweden)

Szymon Ulenberg

Full Text Available Other than efficacy of interaction with the molecular target, metabolic stability is the primary factor responsible for the failure or success of a compound in the drug development pipeline. The ideal drug candidate should be stable enough to reach its therapeutic site of action. Despite many recent excellent achievements in the field of computational methods supporting drug metabolism studies, a well-recognized procedure to model and predict metabolic stability quantitatively is still lacking. This study proposes a workflow for developing quantitative metabolic stability-structure relationships, taking a set of 30 arylpiperazine derivatives as an example. The metabolic stability of the compounds was assessed in in vitro incubations in the presence of human liver microsomes and NADPH and subsequently quantified by liquid chromatography-mass spectrometry (LC-MS. Density functional theory (DFT calculations were used to obtain 30 models of the molecules, and Dragon software served as a source of structure-based molecular descriptors. For modeling structure-metabolic stability relationships, Support Vector Machines (SVM, a non-linear machine learning technique, were found to be more effective than a regression technique, based on the validation parameters obtained. Moreover, for the first time, general sites of metabolism for arylpiperazines bearing the 4-aryl-2H-pyrido[1,2-c]pyrimidine-1,3-dione system were defined by analysis of Q-TOF-MS/MS spectra. The results indicated that the application of one of the most advanced chemometric techniques combined with a simple and quick in vitro procedure and LC-MS analysis provides a novel and valuable tool for predicting metabolic half-life values. Given the reduced time and simplicity of analysis, together with the accuracy of the predictions obtained, this is a valid approach for predicting metabolic stability using structural data. The approach presented provides a novel, comprehensive and reliable tool

Silicon Micromachines for Science and Technology

International Nuclear Information System (INIS)

Bishop, David J.

2002-01-01

The era of silicon micromechanics is upon us. In areas as diverse as telecommunications, automotive, aerospace, chemistry, entertainment and basic science, the ability to build microscopic machines from silicon is having a revolutionary impact. In my talk, I will discuss what micromachines are, how they are built and show examples of how they will have a revolutionary impact in many areas of science as well as technology.

National Machine Guarding Program: Part 1. Machine safeguarding practices in small metal fabrication businesses.

Science.gov (United States)

Parker, David L; Yamin, Samuel C; Brosseau, Lisa M; Xi, Min; Gordon, Robert; Most, Ivan G; Stanley, Rodney

2015-11-01

Metal fabrication workers experience high rates of traumatic occupational injuries. Machine operators in particular face high risks, often stemming from the absence or improper use of machine safeguarding or the failure to implement lockout procedures. The National Machine Guarding Program (NMGP) was a translational research initiative implemented in conjunction with two workers' compensation insures. Insurance safety consultants trained in machine guarding used standardized checklists to conduct a baseline inspection of machine-related hazards in 221 business. Safeguards at the point of operation were missing or inadequate on 33% of machines. Safeguards for other mechanical hazards were missing on 28% of machines. Older machines were both widely used and less likely than newer machines to be properly guarded. Lockout/tagout procedures were posted at only 9% of machine workstations. The NMGP demonstrates a need for improvement in many aspects of machine safety and lockout in small metal fabrication businesses. © 2015 The Authors. American Journal of Industrial Medicine published by Wiley Periodicals, Inc.

Democratizing data science through data science training.

Science.gov (United States)

Van Horn, John Darrell; Fierro, Lily; Kamdar, Jeana; Gordon, Jonathan; Stewart, Crystal; Bhattrai, Avnish; Abe, Sumiko; Lei, Xiaoxiao; O'Driscoll, Caroline; Sinha, Aakanchha; Jain, Priyambada; Burns, Gully; Lerman, Kristina; Ambite, José Luis

2018-01-01

The biomedical sciences have experienced an explosion of data which promises to overwhelm many current practitioners. Without easy access to data science training resources, biomedical researchers may find themselves unable to wrangle their own datasets. In 2014, to address the challenges posed such a data onslaught, the National Institutes of Health (NIH) launched the Big Data to Knowledge (BD2K) initiative. To this end, the BD2K Training Coordinating Center (TCC; bigdatau.org) was funded to facilitate both in-person and online learning, and open up the concepts of data science to the widest possible audience. Here, we describe the activities of the BD2K TCC and its focus on the construction of the Educational Resource Discovery Index (ERuDIte), which identifies, collects, describes, and organizes online data science materials from BD2K awardees, open online courses, and videos from scientific lectures and tutorials. ERuDIte now indexes over 9,500 resources. Given the richness of online training materials and the constant evolution of biomedical data science, computational methods applying information retrieval, natural language processing, and machine learning techniques are required - in effect, using data science to inform training in data science. In so doing, the TCC seeks to democratize novel insights and discoveries brought forth via large-scale data science training.

Non-conventional electrical machines

CERN Document Server

Rezzoug, Abderrezak

2013-01-01

The developments of electrical machines are due to the convergence of material progress, improved calculation tools, and new feeding sources. Among the many recent machines, the authors have chosen, in this first book, to relate the progress in slow speed machines, high speed machines, and superconducting machines. The first part of the book is dedicated to materials and an overview of magnetism, mechanic, and heat transfer.

Machine Learning and Inverse Problem in Geodynamics

Science.gov (United States)

Shahnas, M. H.; Yuen, D. A.; Pysklywec, R.

2017-12-01

During the past few decades numerical modeling and traditional HPC have been widely deployed in many diverse fields for problem solutions. However, in recent years the rapid emergence of machine learning (ML), a subfield of the artificial intelligence (AI), in many fields of sciences, engineering, and finance seems to mark a turning point in the replacement of traditional modeling procedures with artificial intelligence-based techniques. The study of the circulation in the interior of Earth relies on the study of high pressure mineral physics, geochemistry, and petrology where the number of the mantle parameters is large and the thermoelastic parameters are highly pressure- and temperature-dependent. More complexity arises from the fact that many of these parameters that are incorporated in the numerical models as input parameters are not yet well established. In such complex systems the application of machine learning algorithms can play a valuable role. Our focus in this study is the application of supervised machine learning (SML) algorithms in predicting mantle properties with the emphasis on SML techniques in solving the inverse problem. As a sample problem we focus on the spin transition in ferropericlase and perovskite that may cause slab and plume stagnation at mid-mantle depths. The degree of the stagnation depends on the degree of negative density anomaly at the spin transition zone. The training and testing samples for the machine learning models are produced by the numerical convection models with known magnitudes of density anomaly (as the class labels of the samples). The volume fractions of the stagnated slabs and plumes which can be considered as measures for the degree of stagnation are assigned as sample features. The machine learning models can determine the magnitude of the spin transition-induced density anomalies that can cause flow stagnation at mid-mantle depths. Employing support vector machine (SVM) algorithms we show that SML techniques

A fully programmable 100-spin coherent Ising machine with all-to-all connections

Science.gov (United States)

McMahon, Peter; Marandi, Alireza; Haribara, Yoshitaka; Hamerly, Ryan; Langrock, Carsten; Tamate, Shuhei; Inagaki, Takahiro; Takesue, Hiroki; Utsunomiya, Shoko; Aihara, Kazuyuki; Byer, Robert; Fejer, Martin; Mabuchi, Hideo; Yamamoto, Yoshihisa

We present a scalable optical processor with electronic feedback, based on networks of optical parametric oscillators. The design of our machine is inspired by adiabatic quantum computers, although it is not an AQC itself. Our prototype machine is able to find exact solutions of, or sample good approximate solutions to, a variety of hard instances of Ising problems with up to 100 spins and 10,000 spin-spin connections. This research was funded by the Impulsing Paradigm Change through Disruptive Technologies (ImPACT) Program of the Council of Science, Technology and Innovation (Cabinet Office, Government of Japan).

DNA topology influences molecular machine lifetime in human serum

Science.gov (United States)

Goltry, Sara; Hallstrom, Natalya; Clark, Tyler; Kuang, Wan; Lee, Jeunghoon; Jorcyk, Cheryl; Knowlton, William B.; Yurke, Bernard; Hughes, William L.; Graugnard, Elton

2015-06-01

DNA nanotechnology holds the potential for enabling new tools for biomedical engineering, including diagnosis, prognosis, and therapeutics. However, applications for DNA devices are thought to be limited by rapid enzymatic degradation in serum and blood. Here, we demonstrate that a key aspect of DNA nanotechnology--programmable molecular shape--plays a substantial role in device lifetimes. These results establish the ability to operate synthetic DNA devices in the presence of endogenous enzymes and challenge the textbook view of near instantaneous degradation.DNA nanotechnology holds the potential for enabling new tools for biomedical engineering, including diagnosis, prognosis, and therapeutics. However, applications for DNA devices are thought to be limited by rapid enzymatic degradation in serum and blood. Here, we demonstrate that a key aspect of DNA nanotechnology--programmable molecular shape--plays a substantial role in device lifetimes. These results establish the ability to operate synthetic DNA devices in the presence of endogenous enzymes and challenge the textbook view of near instantaneous degradation. Electronic supplementary information (ESI) available: DNA sequences, fluorophore and quencher properties, equipment design, and degradation studies. See DOI: 10.1039/c5nr02283e

Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

Energy Technology Data Exchange (ETDEWEB)

Kanemoto, Shigeru; Watanabe, Masaya [The University of Aizu, Aizuwakamatsu (Japan); Yusa, Noritaka [Tohoku University, Sendai (Japan)

2014-08-15

The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology.

Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

International Nuclear Information System (INIS)

Kanemoto, Shigeru; Watanabe, Masaya; Yusa, Noritaka

2014-01-01

The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology

Desired machines: cinema and the world in its own image.

Science.gov (United States)

Canales, Jimena

2011-09-01

In 1895 when the Lumière brothers unveiled their cinematographic camera, many scientists were elated. Scientists hoped that the machine would fulfill a desire that had driven research for nearly half a century: that of capturing the world in its own image. But their elation was surprisingly short-lived, and many researchers quickly distanced themselves from the new medium. The cinematographic camera was soon split into two machines, one for recording and one for projecting, enabling it to further escape from the laboratory. The philosopher Henri Bergson joined scientists, such as Etienne-Jules Marey, who found problems with the new cinematographic order. Those who had worked to make the dream come true found that their efforts had been subverted. This essay focuses on the desire to build a cinematographic camera, with the purpose of elucidating how dreams and reality mix in the development of science and technology. It is about desired machines and their often unexpected results. The interplay between what "is" (the technical), what "ought" (the ethical), and what "could" be (the fantastical) drives scientific research.

Journal of Chemical Sciences | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Journal of Chemical Sciences. Swapan K Pati. Articles written in Journal of Chemical Sciences. Volume 115 Issue 5-6 October-December 2003 pp 533-542. Electrostatic potential profile and nonlinear current in an interacting one-dimensional molecular wire · S Lakshmi Swapan K Pati · More Details ...

Bulletin of Materials Science | Indian Academy of Sciences

Indian Academy of Sciences (India)

Home; Journals; Bulletin of Materials Science. YONG J IANG. Articles written in Bulletin of Materials Science. Volume 40 Issue 6 October 2017 pp 1255-1261. Molecular dynamics study on the relaxation properties of bilayered graphene with defects · WEI ZHANG JIU-REN YIN PING ZHANG YAN-HUAI DING YONG J IANG.

Machine Learning Approaches to Increasing Value of Spaceflight Omics Databases

Science.gov (United States)

Gentry, Diana

2017-01-01

The number of spaceflight bioscience mission opportunities is too small to allow all relevant biological and environmental parameters to be experimentally identified. Simulated spaceflight experiments in ground-based facilities (GBFs), such as clinostats, are each suitable only for particular investigations -- a rotating-wall vessel may be 'simulated microgravity' for cell differentiation (hours), but not DNA repair (seconds) -- and introduce confounding stimuli, such as motor vibration and fluid shear effects. This uncertainty over which biological mechanisms respond to a given form of simulated space radiation or gravity, as well as its side effects, limits our ability to baseline spaceflight data and validate mission science. Machine learning techniques autonomously identify relevant and interdependent factors in a data set given the set of desired metrics to be evaluated: to automatically identify related studies, compare data from related studies, or determine linkages between types of data in the same study. System-of-systems (SoS) machine learning models have the ability to deal with both sparse and heterogeneous data, such as that provided by the small and diverse number of space biosciences flight missions; however, they require appropriate user-defined metrics for any given data set. Although machine learning in bioinformatics is rapidly expanding, the need to combine spaceflight/GBF mission parameters with omics data is unique. This work characterizes the basic requirements for implementing the SoS approach through the System Map (SM) technique, a composite of a dynamic Bayesian network and Gaussian mixture model, in real-world repositories such as the GeneLab Data System and Life Sciences Data Archive. The three primary steps are metadata management for experimental description using open-source ontologies, defining similarity and consistency metrics, and generating testing and validation data sets. Such approaches to spaceflight and GBF omics data may

Electrical machines & drives

CERN Document Server

Hammond, P

1985-01-01

Containing approximately 200 problems (100 worked), the text covers a wide range of topics concerning electrical machines, placing particular emphasis upon electrical-machine drive applications. The theory is concisely reviewed and focuses on features common to all machine types. The problems are arranged in order of increasing levels of complexity and discussions of the solutions are included where appropriate to illustrate the engineering implications. This second edition includes an important new chapter on mathematical and computer simulation of machine systems and revised discussions o

Trashing the millenium: Subjectivity and technology in cyberpunk science fiction

Directory of Open Access Journals (Sweden)

J. A. Sey

1992-05-01

Full Text Available 'Cyberpunk’ science fiction is a self-proclaimed movement within the genre which began in the 1980s. As the name suggests, it is an extrapolative form of science fiction which combines an almost obsessional interest in machines (particularly information machines with an anarchic, amoral, streetwise sensibility This paper sketches the development of the movement and seeks to make qualified claims for the radical. potential of its fiction. Of crucial importance are the ways in which human subjectivity (viewed in psychoanalytic terms interacts with 'technological subjectivity' in cyberpunk, particularly with regard to implications of these interactions for oedipalization.

Machine translation

Energy Technology Data Exchange (ETDEWEB)

Nagao, M

1982-04-01

Each language has its own structure. In translating one language into another one, language attributes and grammatical interpretation must be defined in an unambiguous form. In order to parse a sentence, it is necessary to recognize its structure. A so-called context-free grammar can help in this respect for machine translation and machine-aided translation. Problems to be solved in studying machine translation are taken up in the paper, which discusses subjects for semantics and for syntactic analysis and translation software. 14 references.

Engineering of the 'PCAST machine'

International Nuclear Information System (INIS)

Sinnis, J.; Brooks, A.; Brown, T.

1996-01-01

The President's Committee of Advisors on Science and Technology (PCAST) has suggested that a device with a mission of ignition and moderate burn time could address the physics of burning plasmas at a lesser cost than ITER with its more comprehensive physics and technology mission. The Department of Energy commissioned a study to explore this PCAST suggestion. This paper describes the results of the engineering portion of the study of this 'PCAST Machine;' physics is covered in a companion paper authored by G.H. Neilson, et al; and the costs are covered in a companion paper by R.T. Simmons, et al. Both are published in the proceedings of this conference. The study was undertaken by a team under the direction of Bruce Montgomery that included representatives from MIT, PPPL, ORNL, LLNL, GA, Northrup-Grumman, and Stone and Webster. The performance requirements for the PCAST machine are to form and sustain a burning plasma for three helium accumulation times. The philosophy adopted for this design was to achieve the required performance at lower cost by decreasing the major radius to five meters, increasing the toroidal field to 7 tesla, and using stronger shaping. The major device parameters are given. 4 refs., 4 figs., 1 tab

Correlation between use time of machine and decline curve for emerging enterprise information systems

Science.gov (United States)

Chang, Yao-Chung; Lai, Chin-Feng; Chuang, Chi-Cheng; Hou, Cheng-Yu

2018-04-01

With the progress of science and technology, more and more machines are adpot to help human life better and more convenient. When the machines have been used for a longer period of time so that the machine components are getting old, the amount of power comsumption will increase and easily cause the machine to overheat. This also causes a waste of invisible resources. If the Internet of Everything (IoE) technologies are able to be applied into the enterprise information systems for monitoring the machines use time, it can not only make energy can be effectively used, but aslo create a safer living environment. To solve the above problem, the correlation predict model is established to collect the data of power consumption converted into power eigenvalues. This study takes the power eigenvalue as the independent variable and use time as the dependent variable in order to establish the decline curve. Ultimately, the scoring and estimation modules are employed to seek the best power eigenvalue as the independent variable. To predict use time, the correlation is discussed between the use time and the decline curve to improve the entire behavioural analysis of the facilitate recognition of the use time of machines.

Induction machine handbook

CERN Document Server

Boldea, Ion

2002-01-01

Often called the workhorse of industry, the advent of power electronics and advances in digital control are transforming the induction motor into the racehorse of industrial motion control. Now, the classic texts on induction machines are nearly three decades old, while more recent books on electric motors lack the necessary depth and detail on induction machines.The Induction Machine Handbook fills industry's long-standing need for a comprehensive treatise embracing the many intricate facets of induction machine analysis and design. Moving gradually from simple to complex and from standard to

Chaotic Boltzmann machines

Science.gov (United States)

Suzuki, Hideyuki; Imura, Jun-ichi; Horio, Yoshihiko; Aihara, Kazuyuki

2013-01-01

The chaotic Boltzmann machine proposed in this paper is a chaotic pseudo-billiard system that works as a Boltzmann machine. Chaotic Boltzmann machines are shown numerically to have computing abilities comparable to conventional (stochastic) Boltzmann machines. Since no randomness is required, efficient hardware implementation is expected. Moreover, the ferromagnetic phase transition of the Ising model is shown to be characterised by the largest Lyapunov exponent of the proposed system. In general, a method to relate probabilistic models to nonlinear dynamics by derandomising Gibbs sampling is presented. PMID:23558425

Rotating electrical machines

CERN Document Server

Le Doeuff, René

2013-01-01

In this book a general matrix-based approach to modeling electrical machines is promulgated. The model uses instantaneous quantities for key variables and enables the user to easily take into account associations between rotating machines and static converters (such as in variable speed drives).   General equations of electromechanical energy conversion are established early in the treatment of the topic and then applied to synchronous, induction and DC machines. The primary characteristics of these machines are established for steady state behavior as well as for variable speed scenarios. I

Science Academies' Refresher Course on Advances in Molecular ...

Indian Academy of Sciences (India)

microRNAs, Ribozyme; molecular oncology; Genes in development and differentiation; Epigenetics and gene regulation; molecular biology of viruses; Restriction enzymes and modifications; Ge- netic engineering; Neurobiology; Bioinformatics- structural, functional and comparative genomics;. Metagenomics; Genome ...

Resonance – Journal of Science Education | Indian Academy of ...

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education. Ramandeep S Johal. Articles written in Resonance – Journal of Science Education. Volume 22 Issue 12 December 2017 pp 1193-1203 General Article. Pythagorean Means and Carnot Machines: When Music Meets Heat · Ramandeep S Johal · More Details ...

Resonance – Journal of Science Education | Indian Academy of ...

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education. A M Jayannavar. Articles written in Resonance – Journal of Science Education. Volume 22 Issue 7 July 2017 pp 659-676 General Article. Second Law, Landauer's Principle and Autonomous Information Machine · Shubashis Rana A M Jayannavar · More Details ...

Resonance – Journal of Science Education | Indian Academy of ...

Indian Academy of Sciences (India)

Home; Journals; Resonance – Journal of Science Education. Shubashis Rana. Articles written in Resonance – Journal of Science Education. Volume 22 Issue 7 July 2017 pp 659-676 General Article. Second Law, Landauer's Principle and Autonomous Information Machine · Shubashis Rana A M Jayannavar · More Details ...

Your Sewing Machine.

Science.gov (United States)

Peacock, Marion E.

The programed instruction manual is designed to aid the student in learning the parts, uses, and operation of the sewing machine. Drawings of sewing machine parts are presented, and space is provided for the student's written responses. Following an introductory section identifying sewing machine parts, the manual deals with each part and its…

Computational Nanotechnology of Molecular Materials, Electronics, and Actuators with Carbon Nanotubes and Fullerenes

Science.gov (United States)

Srivastava, Deepak; Menon, Madhu; Cho, Kyeongjae; Biegel, Bryan (Technical Monitor)

2001-01-01

The role of computational nanotechnology in developing next generation of multifunctional materials, molecular scale electronic and computing devices, sensors, actuators, and machines is described through a brief review of enabling computational techniques and few recent examples derived from computer simulations of carbon nanotube based molecular nanotechnology.