Search results for: approximate computing
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 1332

Search results for: approximate computing

1002 Global Healthcare Village Based on Mobile Cloud Computing

Authors: Laleh Boroumand, Muhammad Shiraz, Abdullah Gani, Rashid Hafeez Khokhar

Abstract:

Cloud computing being the use of hardware and software that are delivered as a service over a network has its application in the area of health care. Due to the emergency cases reported in most of the medical centers, prompt for an efficient scheme to make health data available with less response time. To this end, we propose a mobile global healthcare village (MGHV) model that combines the components of three deployment model which include country, continent and global health cloud to help in solving the problem mentioned above. In the creation of continent model, two (2) data centers are created of which one is local and the other is global. The local replay the request of residence within the continent, whereas the global replay the requirements of others. With the methods adopted, there is an assurance of the availability of relevant medical data to patients, specialists, and emergency staffs regardless of locations and time. From our intensive experiment using the simulation approach, it was observed that, broker policy scheme with respect to optimized response time, yields a very good performance in terms of reduction in response time. Though, our results are comparable to others when there is an increase in the number of virtual machines (80-640 virtual machines). The proportionality in increase of response time is within 9%. The results gotten from our simulation experiments shows that utilizing MGHV leads to the reduction of health care expenditures and helps in solving the problems of unqualified medical staffs faced by both developed and developing countries.

Keywords: cloud computing (MCC), e-healthcare, availability, response time, service broker policy

Procedia PDF Downloads 377
1001 Applications of AI, Machine Learning, and Deep Learning in Cyber Security

Authors: Hailyie Tekleselase

Abstract:

Deep learning is increasingly used as a building block of security systems. However, neural networks are hard to interpret and typically solid to the practitioner. This paper presents a detail survey of computing methods in cyber security, and analyzes the prospects of enhancing the cyber security capabilities by suggests that of accelerating the intelligence of the security systems. There are many AI-based applications used in industrial scenarios such as Internet of Things (IoT), smart grids, and edge computing. Machine learning technologies require a training process which introduces the protection problems in the training data and algorithms. We present machine learning techniques currently applied to the detection of intrusion, malware, and spam. Our conclusions are based on an extensive review of the literature as well as on experiments performed on real enterprise systems and network traffic. We conclude that problems can be solved successfully only when methods of artificial intelligence are being used besides human experts or operators.

Keywords: artificial intelligence, machine learning, deep learning, cyber security, big data

Procedia PDF Downloads 126
1000 Simulation of the Large Hadrons Collisions Using Monte Carlo Tools

Authors: E. Al Daoud

Abstract:

In many cases, theoretical treatments are available for models for which there is no perfect physical realization. In this situation, the only possible test for an approximate theoretical solution is to compare with data generated from a computer simulation. In this paper, Monte Carlo tools are used to study and compare the elementary particles models. All the experiments are implemented using 10000 events, and the simulated energy is 13 TeV. The mean and the curves of several variables are calculated for each model using MadAnalysis 5. Anomalies in the results can be seen in the muons masses of the minimal supersymmetric standard model and the two Higgs doublet model.

Keywords: Feynman rules, hadrons, Lagrangian, Monte Carlo, simulation

Procedia PDF Downloads 318
999 Decision-Making Strategies on Smart Dairy Farms: A Review

Authors: L. Krpalkova, N. O' Mahony, A. Carvalho, S. Campbell, G. Corkery, E. Broderick, J. Walsh

Abstract:

Farm management and operations will drastically change due to access to real-time data, real-time forecasting, and tracking of physical items in combination with Internet of Things developments to further automate farm operations. Dairy farms have embraced technological innovations and procured vast amounts of permanent data streams during the past decade; however, the integration of this information to improve the whole farm-based management and decision-making does not exist. It is now imperative to develop a system that can collect, integrate, manage, and analyse on-farm and off-farm data in real-time for practical and relevant environmental and economic actions. The developed systems, based on machine learning and artificial intelligence, need to be connected for useful output, a better understanding of the whole farming issue, and environmental impact. Evolutionary computing can be very effective in finding the optimal combination of sets of some objects and, finally, in strategy determination. The system of the future should be able to manage the dairy farm as well as an experienced dairy farm manager with a team of the best agricultural advisors. All these changes should bring resilience and sustainability to dairy farming as well as improving and maintaining good animal welfare and the quality of dairy products. This review aims to provide an insight into the state-of-the-art of big data applications and evolutionary computing in relation to smart dairy farming and identify the most important research and development challenges to be addressed in the future. Smart dairy farming influences every area of management, and its uptake has become a continuing trend.

Keywords: big data, evolutionary computing, cloud, precision technologies

Procedia PDF Downloads 189
998 Digital Homeostasis: Tangible Computing as a Multi-Sensory Installation

Authors: Andrea Macruz

Abstract:

This paper explores computation as a process for design by examining how computers can become more than an operative strategy in a designer's toolkit. It documents this, building upon concepts of neuroscience and Antonio Damasio's Homeostasis Theory, which is the control of bodily states through feedback intended to keep conditions favorable for life. To do this, it follows a methodology through algorithmic drawing and discusses the outcomes of three multi-sensory design installations, which culminated from a course in an academic setting. It explains both the studio process that took place to create the installations and the computational process that was developed, related to the fields of algorithmic design and tangible computing. It discusses how designers can use computational range to achieve homeostasis related to sensory data in a multi-sensory installation. The outcomes show clearly how people and computers interact with different sensory modalities and affordances. They propose using computers as meta-physical stabilizers rather than tools.

Keywords: algorithmic drawing, Antonio Damasio, emotion, homeostasis, multi-sensory installation, neuroscience

Procedia PDF Downloads 107
997 A Simplified Distribution for Nonlinear Seas

Authors: M. A. Tayfun, M. A. Alkhalidi

Abstract:

The exact theoretical expression describing the probability distribution of nonlinear sea-surface elevations derived from the second-order narrowband model has a cumbersome form that requires numerical computations, not well-disposed to theoretical or practical applications. Here, the same narrowband model is re-examined to develop a simpler closed-form approximation suitable for theoretical and practical applications. The salient features of the approximate form are explored, and its relative validity is verified with comparisons to other readily available approximations, and oceanic data.

Keywords: ocean waves, probability distributions, second-order nonlinearities, skewness coefficient, wave steepness

Procedia PDF Downloads 432
996 Design Off-Campus Interactive Cloud-Based Learning Model

Authors: Osamah Al Qadoori

Abstract:

Using cloud computing in educational sectors grow rapidly in UAE. Initially, within Cloud-Learning Environment Students whenever and wherever can remotely join the online-classroom, on the other hand, Cloud-Based Learning is greatly decreasing the infrastructure and the maintenance cost. Nowadays in many schools (K-12), institutes, colleges as well as universities in UAE Cloud-Based Teaching and Learning environments gain a higher demand and concern. Many students don’t use the available online-educational resources effectively. The challenging question is to which extend these educational resources which are installed in the cloud environment are valuable and constructive? In this paper the researcher is seeking to design an expert agent prototype where the huge information being accommodated inside the cloud environment will go through expert filtration before going to be utilized by other clients (students). To achieve this goal, the focus of the present research would be on two different directions the educational human expertise and the automated-educational expert systems.

Keywords: cloud computing, cloud-learning environment, online-classroom, the educational human expertise, the automated-educational expert systems

Procedia PDF Downloads 540
995 Big Data Analytics and Data Security in the Cloud via Fully Homomorphic Encyption Scheme

Authors: Victor Onomza Waziri, John K. Alhassan, Idris Ismaila, Noel Dogonyara

Abstract:

This paper describes the problem of building secure computational services for encrypted information in the Cloud. Computing without decrypting the encrypted data; therefore, it meets the yearning of computational encryption algorithmic aspiration model that could enhance the security of big data for privacy or confidentiality, availability and integrity of the data and user’s security. The cryptographic model applied for the computational process of the encrypted data is the Fully Homomorphic Encryption Scheme. We contribute a theoretical presentations in a high-level computational processes that are based on number theory that is derivable from abstract algebra which can easily be integrated and leveraged in the Cloud computing interface with detail theoretic mathematical concepts to the fully homomorphic encryption models. This contribution enhances the full implementation of big data analytics based on cryptographic security algorithm.

Keywords: big data analytics, security, privacy, bootstrapping, Fully Homomorphic Encryption Scheme

Procedia PDF Downloads 480
994 Effect of Lactone Glycoside on Feeding Deterrence and Nutritive Physiology of Tobacco Caterpillar Spodoptera litura Fabricius (Noctuidae: Lepidoptera)

Authors: Selvamuthukumaran Thirunavukkarasu, Arivudainambi Sundararajan

Abstract:

The plant active molecules with their known mode of action are important leads to the development of newer insecticides. Lactone glycoside was identified earlier as the active principle in Cleistanthus collinus (Roxb.) Benth. (Fam: Euphorbiaceae). It possessed feeding deterrent, insecticidal and insect growth regulatory actions at varying concentrations. Deducing its mode of action opens a possibility of its further development. A no-choice leaf disc bioassay was carried out with lactone glycoside at different doses for different instars and Deterrence Indices were worked out. Using regression analysis concentrations imparting 10, 30 and 50 per cent deterrence (DI10, DI30 & DI50) were worked out. At these doses, effect on nutritional indices like Relative Consumption and Growth Rates (RCR & RGR), Efficiencies of Conversion of Ingested and Digested food (ECI & ECD) and Approximate Digestibility (AD) were worked out. The Relative Consumption and Growth Rate of control and lactone glycoside larva were compared by regression analysis. Regression analysis of deterrence indices revealed that the concentrations needed for imparting 50 per cent deterrence was 60.66, 68.47 and 71.10 ppm for third, fourth and fifth instars respectively. Relative consumption rate (RCR) and relative growth rate (RGR) were reduced. This confirmed the antifeedant action of the fraction. Approximate digestibility (AD) was found greater in treatments indicating reduced faeces because of poor digestibility and retention of food in the gut. Efficiency of conversion of both ingested and digested (ECI and ECD) food was also found to be greatly reduced. This indicated presence of toxic action. This was proved by comparing growth efficiencies of control and lactone glycoside treated larvae. Lactone glycoside was found to possess both feeding deterrent and toxic modes of action. Studies on molecular targets based on this preliminary site of action lead to new insecticide development.

Keywords: Spodoptera litura Fabricius, Cleistanthus collinus (Roxb.) Benth, feeding deterrence, mode of action

Procedia PDF Downloads 155
993 Variants of Mathematical Induction as Strong Proof Techniques in Theory of Computing

Authors: Ahmed Tarek, Ahmed Alveed

Abstract:

In the theory of computing, there are a wide variety of direct and indirect proof techniques. However, mathematical induction (MI) stands out to be one of the most powerful proof techniques for proving hypotheses, theorems, and new results. There are variations of mathematical induction-based proof techniques, which are broadly classified into three categories, such as structural induction (SI), weak induction (WI), and strong induction (SI). In this expository paper, several different variants of the mathematical induction techniques are explored, and the specific scenarios are discussed where a specific induction technique stands out to be more advantageous as compared to other induction strategies. Also, the essential difference among the variants of mathematical induction are explored. The points of separation among mathematical induction, recursion, and logical deduction are precisely analyzed, and the relationship among variations of recurrence relations, and mathematical induction are being explored. In this context, the application of recurrence relations, and mathematical inductions are considered together in a single framework for codewords over a given alphabet.

Keywords: alphabet, codeword, deduction, mathematical, induction, recurrence relation, strong induction, structural induction, weak induction

Procedia PDF Downloads 164
992 Spherical Harmonic Based Monostatic Anisotropic Point Scatterer Model for RADAR Applications

Authors: Eric Huang, Coleman DeLude, Justin Romberg, Saibal Mukhopadhyay, Madhavan Swaminathan

Abstract:

High performance computing (HPC) based emulators can be used to model the scattering from multiple stationary and moving targets for RADAR applications. These emulators rely on the RADAR Cross Section (RCS) of the targets being available in complex scenarios. Representing the RCS using tables generated from electromagnetic (EM) simulations is often times cumbersome leading to large storage requirement. This paper proposed a spherical harmonic based anisotropic scatterer model to represent the RCS of complex targets. The problem of finding the locations and reflection profiles of all scatterers can be formulated as a linear least square problem with a special sparsity constraint. This paper solves this problem using a modified Orthogonal Matching Pursuit algorithm. The results show that the spherical harmonic based scatterer model can effectively represent the RCS data of complex targets.

Keywords: RADAR, RCS, high performance computing, point scatterer model

Procedia PDF Downloads 191
991 Approximately Similarity Measurement of Web Sites Using Genetic Algorithms and Binary Trees

Authors: Doru Anastasiu Popescu, Dan Rădulescu

Abstract:

In this paper, we determine the similarity of two HTML web applications. We are going to use a genetic algorithm in order to determine the most significant web pages of each application (we are not going to use every web page of a site). Using these significant web pages, we will find the similarity value between the two applications. The algorithm is going to be efficient because we are going to use a reduced number of web pages for comparisons but it will return an approximate value of the similarity. The binary trees are used to keep the tags from the significant pages. The algorithm was implemented in Java language.

Keywords: Tag, HTML, web page, genetic algorithm, similarity value, binary tree

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990 Using the M-Learning to Support Learning of the Concept of the Derivative

Authors: Elena F. Ruiz, Marina Vicario, Chadwick Carreto, Rubén Peredo

Abstract:

One of the main obstacles in Mexico’s engineering programs is math comprehension, especially in the Derivative concept. Due to this, we present a study case that relates Mobile Computing and Classroom Learning in the “Escuela Superior de Cómputo”, based on the Educational model of the Instituto Politécnico Nacional (competence based work and problem solutions) in which we propose apps and activities to teach the concept of the Derivative. M- Learning is emphasized as one of its lines, as the objective is the use of mobile devices running an app that uses its components such as sensors, screen, camera and processing power in classroom work. In this paper, we employed Augmented Reality (ARRoC), based on the good results this technology has had in the field of learning. This proposal was developed using a qualitative research methodology supported by quantitative research. The methodological instruments used on this proposal are: observation, questionnaires, interviews and evaluations. We obtained positive results with a 40% increase using M-Learning, from the 20% increase using traditional means.

Keywords: augmented reality, classroom learning, educational research, mobile computing

Procedia PDF Downloads 360
989 Some Results on the Generalized Higher Rank Numerical Ranges

Authors: Mohsen Zahraei

Abstract:

‎In this paper, ‎the notion of ‎rank-k numerical range of rectangular complex matrix polynomials‎ ‎are introduced. ‎Some algebraic and geometrical properties are investigated. ‎Moreover, ‎for ε>0 the notion of Birkhoff-James approximate orthogonality sets for ε-higher ‎rank numerical ranges of rectangular matrix polynomials is also introduced and studied. ‎The proposed definitions yield a natural generalization of the standard higher rank numerical ranges.

Keywords: ‎‎Rank-k numerical range‎, ‎isometry‎, ‎numerical range‎, ‎rectangular matrix polynomials

Procedia PDF Downloads 459
988 Enhancement Dynamic Cars Detection Based on Optimized HOG Descriptor

Authors: Mansouri Nabila, Ben Jemaa Yousra, Motamed Cina, Watelain Eric

Abstract:

Research and development efforts in intelligent Advanced Driver Assistance Systems (ADAS) seek to save lives and reduce the number of on-road fatalities. For traffic and emergency monitoring, the essential but challenging task is vehicle detection and tracking in reasonably short time. This purpose needs first of all a powerful dynamic car detector model. In fact, this paper presents an optimized HOG process based on shape and motion parameters fusion. Our proposed approach mains to compute HOG by bloc feature from foreground blobs using configurable research window and pathway in order to overcome the shortcoming in term of computing time of HOG descriptor and improve their dynamic application performance. Indeed we prove in this paper that HOG by bloc descriptor combined with motion parameters is a very suitable car detector which reaches in record time a satisfactory recognition rate in dynamic outside area and bypasses several popular works without using sophisticated and expensive architectures such as GPU and FPGA.

Keywords: car-detector, HOG, motion, computing time

Procedia PDF Downloads 323
987 Improvement of the Numerical Integration's Quality in Meshless Methods

Authors: Ahlem Mougaida, Hedi Bel Hadj Salah

Abstract:

Several methods are suggested to improve the numerical integration in Galerkin weak form for Meshless methods. In fact, integrating without taking into account the characteristics of the shape functions reproduced by Meshless methods (rational functions, with compact support etc.), causes a large integration error that influences the PDE’s approximate solution. Comparisons between different methods of numerical integration for rational functions are discussed and compared. The algorithms are implemented in Matlab. Finally, numerical results were presented to prove the efficiency of our algorithms in improving results.

Keywords: adaptive methods, meshless, numerical integration, rational quadrature

Procedia PDF Downloads 364
986 Automata-Based String Analysis for Detecting Malware in Android Programs

Authors: Assad Maalouf, Lunjin Lu, James Lynott

Abstract:

We design and implement a precise model of string operations using finite state machine transformers and state transformers to approximate the values string variables can take throughout the execution of the program.We use our model to analyze Android program string variables. Our experimental results show that our string analysis is very efficient at detecting the contextual effect of string operations on the string variables. Our model proved to be very useful when it came to verifying statements about the string variables of the program.

Keywords: abstract interpretation, android, static analysis, string analysis

Procedia PDF Downloads 179
985 Some Conjectures and Programs about Computing the Detour Index of Molecular Graphs of Nanotubes

Authors: Shokofeh Ebrtahimi

Abstract:

Let G be the chemical graph of a molecule. The matrix D = [dij ] is called the detour matrix of G, if dij is the length of longest path between atoms i and j. The sum of all entries above the main diagonal of D is called the detour index of G.Chemical graph theory is the topology branch of mathematical chemistry which applies graph theory to mathematical modelling of chemical phenomena.[1] The pioneers of the chemical graph theory are Alexandru Balaban, Ante Graovac, Ivan Gutman, Haruo Hosoya, Milan Randić and Nenad TrinajstićLet G be the chemical graph of a molecule. The matrix D = [dij ] is called the detour matrix of G, if dij is the length of longest path between atoms i and j. The sum of all entries above the main diagonal of D is called the detour index of G. In this paper, a new program for computing the detour index of molecular graphs of nanotubes by heptagons is determineded. Some Conjectures about detour index of Molecular graphs of nanotubes is included.

Keywords: chemical graph, detour matrix, Detour index, carbon nanotube

Procedia PDF Downloads 292
984 Teaching Computer Programming to Diverse Students: A Comparative, Mixed-Methods, Classroom Research Study

Authors: Almudena Konrad, Tomás Galguera

Abstract:

Lack of motivation and interest is a serious obstacle to students’ learning computing skills. A need exists for a knowledge base on effective pedagogy and curricula to teach computer programming. This paper presents results from research evaluating a six-year project designed to teach complex concepts in computer programming collaboratively, while supporting students to continue developing their computer thinking and related coding skills individually. Utilizing a quasi-experimental, mixed methods design, the pedagogical approaches and methods were assessed in two contrasting groups of students with different socioeconomic status, gender, and age composition. Analyses of quantitative data from Likert-scale surveys and an evaluation rubric, combined with qualitative data from reflective writing exercises and semi-structured interviews yielded convincing evidence of the project’s success at both teaching and inspiring students.

Keywords: computational thinking, computing education, computer programming curriculum, logic, teaching methods

Procedia PDF Downloads 316
983 Forming for Confirmation of Predicted Epoxy Forming Composition Range in Cr-Zn System

Authors: Foad Saadi

Abstract:

Aim of this work was to determine the approximate Epoxy forming composition range of Cr-Zn system for the composites produced by forming compositing. It was predicted by MI edema semi-empirical model that the composition had to be in the range of 30-60 wt. % tin, while Cr-32Zn had the most susceptibility to produce amorphous composite. In the next stage, some different compositions of Cr-Zn were foamingly composited, where one of them had the proper predicted composition. Products were characterized by SDM analysis. There was a good agreement between calculation and experiments, in which Cr-32Zn composite had the most amorphization degree.

Keywords: Cr-Zn system, forming compositing, amorphous composite, MI edema model

Procedia PDF Downloads 296
982 The Effect of Initial Sample Size and Increment in Simulation Samples on a Sequential Selection Approach

Authors: Mohammad H. Almomani

Abstract:

In this paper, we argue the effect of the initial sample size, and the increment in simulation samples on the performance of a sequential approach that used in selecting the top m designs when the number of alternative designs is very large. The sequential approach consists of two stages. In the first stage the ordinal optimization is used to select a subset that overlaps with the set of actual best k% designs with high probability. Then in the second stage the optimal computing budget is used to select the top m designs from the selected subset. We apply the selection approach on a generic example under some parameter settings, with a different choice of initial sample size and the increment in simulation samples, to explore the impacts on the performance of this approach. The results show that the choice of initial sample size and the increment in simulation samples does affect the performance of a selection approach.

Keywords: Large Scale Problems, Optimal Computing Budget Allocation, ordinal optimization, simulation optimization

Procedia PDF Downloads 355
981 Continuous Functions Modeling with Artificial Neural Network: An Improvement Technique to Feed the Input-Output Mapping

Authors: A. Belayadi, A. Mougari, L. Ait-Gougam, F. Mekideche-Chafa

Abstract:

The artificial neural network is one of the interesting techniques that have been advantageously used to deal with modeling problems. In this study, the computing with artificial neural network (CANN) is proposed. The model is applied to modulate the information processing of one-dimensional task. We aim to integrate a new method which is based on a new coding approach of generating the input-output mapping. The latter is based on increasing the neuron unit in the last layer. Accordingly, to show the efficiency of the approach under study, a comparison is made between the proposed method of generating the input-output set and the conventional method. The results illustrated that the increasing of the neuron units, in the last layer, allows to find the optimal network’s parameters that fit with the mapping data. Moreover, it permits to decrease the training time, during the computation process, which avoids the use of computers with high memory usage.

Keywords: neural network computing, continuous functions generating the input-output mapping, decreasing the training time, machines with big memories

Procedia PDF Downloads 283
980 Leveraging Deep Q Networks in Portfolio Optimization

Authors: Peng Liu

Abstract:

Deep Q networks (DQNs) represent a significant advancement in reinforcement learning, utilizing neural networks to approximate the optimal Q-value for guiding sequential decision processes. This paper presents a comprehensive introduction to reinforcement learning principles, delves into the mechanics of DQNs, and explores its application in portfolio optimization. By evaluating the performance of DQNs against traditional benchmark portfolios, we demonstrate its potential to enhance investment strategies. Our results underscore the advantages of DQNs in dynamically adjusting asset allocations, offering a robust portfolio management framework.

Keywords: deep reinforcement learning, deep Q networks, portfolio optimization, multi-period optimization

Procedia PDF Downloads 32
979 Using Mechanical Alloying for Verification of Predicted Glass Forming Composition Range

Authors: F. Saadi, M. Fatahi, M. Heidari

Abstract:

Aim of this work was to determine the approximate glass forming composition range of Ni-Sn system for the alloys produced by mechanical alloying. It was predicted by Miedema semi-empirical model that the composition had to be in the range of 30-60 wt. % tin, while Ni-40Sn had the most susceptibility to produce amorphous alloy. In the next stage, some different compositions of Ni-Sn were mechanically alloyed, where one of them had the proper predicted composition. Products were characterized by XRD analysis. There was a good agreement between calculation and experiments, in which Ni-40Sn alloy had the most amorphization degree.

Keywords: Ni-Sn system, mechanical alloying, Amorphous alloy, Miedema model

Procedia PDF Downloads 437
978 Bioinformatics High Performance Computation and Big Data

Authors: Javed Mohammed

Abstract:

Right now, bio-medical infrastructure lags well behind the curve. Our healthcare system is dispersed and disjointed; medical records are a bit of a mess; and we do not yet have the capacity to store and process the crazy amounts of data coming our way from widespread whole-genome sequencing. And then there are privacy issues. Despite these infrastructure challenges, some researchers are plunging into bio medical Big Data now, in hopes of extracting new and actionable knowledge. They are doing delving into molecular-level data to discover bio markers that help classify patients based on their response to existing treatments; and pushing their results out to physicians in novel and creative ways. Computer scientists and bio medical researchers are able to transform data into models and simulations that will enable scientists for the first time to gain a profound under-standing of the deepest biological functions. Solving biological problems may require High-Performance Computing HPC due either to the massive parallel computation required to solve a particular problem or to algorithmic complexity that may range from difficult to intractable. Many problems involve seemingly well-behaved polynomial time algorithms (such as all-to-all comparisons) but have massive computational requirements due to the large data sets that must be analyzed. High-throughput techniques for DNA sequencing and analysis of gene expression have led to exponential growth in the amount of publicly available genomic data. With the increased availability of genomic data traditional database approaches are no longer sufficient for rapidly performing life science queries involving the fusion of data types. Computing systems are now so powerful it is possible for researchers to consider modeling the folding of a protein or even the simulation of an entire human body. This research paper emphasizes the computational biology's growing need for high-performance computing and Big Data. It illustrates this article’s indispensability in meeting the scientific and engineering challenges of the twenty-first century, and how Protein Folding (the structure and function of proteins) and Phylogeny Reconstruction (evolutionary history of a group of genes) can use HPC that provides sufficient capability for evaluating or solving more limited but meaningful instances. This article also indicates solutions to optimization problems, and benefits Big Data and Computational Biology. The article illustrates the Current State-of-the-Art and Future-Generation Biology of HPC Computing with Big Data.

Keywords: high performance, big data, parallel computation, molecular data, computational biology

Procedia PDF Downloads 363
977 Proteomic Analysis of Cytoplasmic Antigen from Brucella canis to Characterize Immunogenic Proteins Responded with Naturally Infected Dogs

Authors: J. J. Lee, S. R. Sung, E. J. Yum, S. C. Kim, B. H. Hyun, M. Her, H. S. Lee

Abstract:

Canine brucellosis is a critical problem in dogs leading to reproductive diseases which are mainly caused by Brucella canis. There are, nonetheless, not clear symptoms so that it may go unnoticed in most of the cases. Serodiagnosis for canine brucellosis has not been confirmed. Moreover, it has substantial difficulties due to broad cross-reactivity between the rough cell wall antigens of B. canis and heterospecific antibodies present in normal, uninfected dogs. Thus, this study was conducted to characterize the immunogenic proteins in cytoplasmic antigen (CPAg) of B. canis, which defined the antigenic sensitivity of the humoral antibody responses to B. canis-infected dogs. In analysis of B. canis CPAg, first, we extracted and purified the cytoplasmic proteins from cultured B. canis by hot-saline inactivation, ultrafiltration, sonication, and ultracentrifugation step by step according to the sonicated antigen extract method. For characterization of this antigen, we checked the sort and range of each protein on SDS-PAGE and verified the immunogenic proteins leading to reaction with antisera of B. canis-infected dogs. Selected immunodominant proteins were identified using MALDI-MS/MS. As a result, in an immunoproteomic assay, several polypeptides in CPAg on one or two-dimensional electrophoresis (DE) were specifically reacted to antisera from B. canis-infected dogs but not from non-infected dogs. The polypeptides with approximate 150, 80, 60, 52, 33, 26, 17, 15, 13, 11 kDa on 1-DE were dominantly recognized by antisera from B. canis-infected dogs. In the immunoblot profiles on 2-DE, ten immunodominant proteins in CPAg were detected with antisera of infected dogs between pI 3.5-6.5 at approximate 35 to 10 KDa, without any nonspecific reaction with sera in non-infected dogs. Ten immunodominant proteins identified by MALDI-MS/MS were identified as superoxide dismutase, bacteroferritin, amino acid ABC transporter substrate-binding protein, extracellular solute-binding protein family3, transaldolase, 26kDa periplasmic immunogenic protein, Rhizopine-binding protein, enoyl-CoA hydratase, arginase and type1 glyceraldehyde-3-phosphate dehydrogenase. Most of these proteins were determined by their cytoplasmic or periplasmic localization with metabolism and transporter functions. Consequently, this study discovered and identified the prominent immunogenic proteins in B. canis CPAg, highlighting that those antigenic proteins may accomplish a specific serodiagnosis for canine brucellosis. Furthermore, we will evaluate those immunodominant proteins for applying to the advanced diagnostic methods with high specificity and accuracy.

Keywords: Brucella canis, Canine brucellosis, cytoplasmic antigen, immunogenic proteins

Procedia PDF Downloads 147
976 Pod and Wavelets Application for Aerodynamic Design Optimization

Authors: Bonchan Koo, Junhee Han, Dohyung Lee

Abstract:

The research attempts to evaluate the accuracy and efficiency of a design optimization procedure which combines wavelets-based solution algorithm and proper orthogonal decomposition (POD) database management technique. Aerodynamic design procedure calls for high fidelity computational fluid dynamic (CFD) simulations and the consideration of large number of flow conditions and design constraints. Even with significant computing power advancement, current level of integrated design process requires substantial computing time and resources. POD reduces the degree of freedom of full system through conducting singular value decomposition for various field simulations. For additional efficiency improvement of the procedure, adaptive wavelet technique is also being employed during POD training period. The proposed design procedure was applied to the optimization of wing aerodynamic performance. Throughout the research, it was confirmed that the POD/wavelets design procedure could significantly reduce the total design turnaround time and is also able to capture all detailed complex flow features as in full order analysis.

Keywords: POD (Proper Orthogonal Decomposition), wavelets, CFD, design optimization, ROM (Reduced Order Model)

Procedia PDF Downloads 467
975 R Data Science for Technology Management

Authors: Sunghae Jun

Abstract:

Technology management (TM) is important issue in a company improving the competitiveness. Among many activities of TM, technology analysis (TA) is important factor, because most decisions for management of technology are decided by the results of TA. TA is to analyze the developed results of target technology using statistics or Delphi. TA based on Delphi is depended on the experts’ domain knowledge, in comparison, TA by statistics and machine learning algorithms use objective data such as patent or paper instead of the experts’ knowledge. Many quantitative TA methods based on statistics and machine learning have been studied, and these have been used for technology forecasting, technological innovation, and management of technology. They applied diverse computing tools and many analytical methods case by case. It is not easy to select the suitable software and statistical method for given TA work. So, in this paper, we propose a methodology for quantitative TA using statistical computing software called R and data science to construct a general framework of TA. From the result of case study, we also show how our methodology is applied to real field. This research contributes to R&D planning and technology valuation in TM areas.

Keywords: technology management, R system, R data science, statistics, machine learning

Procedia PDF Downloads 458
974 Optimization of Cloud Classification Using Particle Swarm Algorithm

Authors: Riffi Mohammed Amine

Abstract:

A cloud is made up of small particles of liquid water or ice suspended in the atmosphere, which generally do not reach the ground. Various methods are used to classify clouds. This article focuses specifically on a technique known as particle swarm optimization (PSO), an AI approach inspired by the collective behaviors of animals living in groups, such as schools of fish and flocks of birds, and a method used to solve complex classification and optimization problems with approximate solutions. The proposed technique was evaluated using a series of second-generation METOSAT images taken by the MSG satellite. The acquired results indicate that the proposed method gave acceptable results.

Keywords: remote sensing, particle swarm optimization, clouds, meteorological image

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973 An Approximation Method for Exact Boundary Controllability of Euler-Bernoulli

Authors: A. Khernane, N. Khelil, L. Djerou

Abstract:

The aim of this work is to study the numerical implementation of the Hilbert uniqueness method for the exact boundary controllability of Euler-Bernoulli beam equation. This study may be difficult. This will depend on the problem under consideration (geometry, control, and dimension) and the numerical method used. Knowledge of the asymptotic behaviour of the control governing the system at time T may be useful for its calculation. This idea will be developed in this study. We have characterized as a first step the solution by a minimization principle and proposed secondly a method for its resolution to approximate the control steering the considered system to rest at time T.

Keywords: boundary control, exact controllability, finite difference methods, functional optimization

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