Search results for: complex network

Authors: P. R. Dushantha Chaminda, Peng Kai

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In this review paper, we enclose the thought of wireless ad hoc networks and particularly mobile ad hoc network (MANET), their field of study, intention, concern, benefit and disadvantages, modifications, with relation of AODV routing protocol. Mobile computing is developing speedily with progression in wireless communications and wireless networking protocols. Making communication easy, we function most wireless network devices and sensor networks, movable, battery-powered, thus control on a highly constrained energy budget. However, progress in battery technology presents that only little improvements in battery volume can be expected in the near future. Moreover, recharging or substitution batteries is costly or unworkable, it is preferable to support energy waste level of devices low.

Keywords: wireless ad hoc network, energy efficient routing protocols, AODV, EOAODV, AODVEA, AODVM, AOMDV, FF-AOMDV, AOMR-LM

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Authors: John Pomerat, Aviv Segev

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Recently, deep learning has had many theoretical breakthroughs. For deep learning to be successful in the industry, however, there need to be practical algorithms capable of handling many real-world hiccups preventing the immediate application of a learning algorithm. Although AI promises to revolutionize the healthcare industry, getting access to patient data in order to train learning algorithms has not been easy. One proposed solution to this is data- sharing. In this paper, we propose an alternative protocol, based on multi-party computation, to train deep learning models while maintaining both the privacy and security of training data. We examine three methods of training neural networks in this way: Transfer learning, average ensemble learning, and series network learning. We compare these methods to the equivalent model obtained through data-sharing across two different experiments. Additionally, we address the security concerns of this protocol. While the motivating example is healthcare, our findings regarding multi-party computation of neural network training are purely theoretical and have use-cases outside the domain of healthcare.

Keywords: neural network aggregation, multi-party computation, transfer learning, average ensemble learning

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Authors: Yossiri Ariyakul, Piyakiat Insom, Poonyawat Sangiamkulthavorn, Takamichi Nakamoto

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In this paper, the method of optimization algorithm for sensor network comprised of movable sensor nodes which can be used for odor source localization was proposed. A sensor node is composed of an odor sensor, an anemometer, and a wireless communication module. The odor intensity measured from the sensor nodes are sent to the processor to perform the localization based on optimization algorithm by which the odor source localization map is obtained as a result. The map can represent the exact position of the odor source or show the direction toward it remotely. The proposed method was experimentally validated by creating the odor source localization map using three, four, and five sensor nodes in which the accuracy to predict the position of the odor source can be observed.

Keywords: odor sensor, odor source localization, optimization, sensor network

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Authors: Leyla Noroozbabaee, David Nickerson

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We introduce a modular, consistent, reusable bond graph model of the renal nephron’s proximal convoluted tubule (PCT), which can reproduce biological behaviour. In this work, we focus on ion and volume transport in the proximal convoluted tubule of the renal nephron. Modelling complex systems requires complex modelling problems to be broken down into manageable pieces. This can be enabled by developing models of subsystems that are subsequently coupled hierarchically. Because they are based on a graph structure. In the current work, we define two modular subsystems: the resistive module representing the membrane and the capacitive module representing solution compartments. Each module is analyzed based on thermodynamic processes, and all the subsystems are reintegrated into circuit theory in network thermodynamics. The epithelial transport system we introduce in the current study consists of five transport membranes and four solution compartments. Coupled dissipations in the system occur in the membrane subsystems and coupled free-energy increasing, or decreasing processes appear in solution compartment subsystems. These structural subsystems also consist of elementary thermodynamic processes: dissipations, free-energy change, and power conversions. We provide free and open access to the Python implementation to ensure our model is accessible, enabling the reader to explore the model through setting their simulations and reproducibility tests.

Keywords: Bond Graph, Epithelial Transport, Water Transport, Mathematical Modeling

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Authors: JaeHwan Yang, Da-Woon Jeong, Seung-Young Kho, Dong-Kyu Kim

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In terms of ITS, information on link characteristic is an essential factor for plan or operation. But in practical cases, not every link has installed sensors on it. The link that does not have data on it is called “Missing Link”. The purpose of this study is to impute data of these missing links. To get these data, this study applies the machine learning method. With the machine learning process, especially for the deep learning process, missing link data can be estimated from present link data. For deep learning process, this study uses “Recurrent Neural Network” to take time-series data of road. As input data, Dedicated Short-range Communications (DSRC) data of Dalgubul-daero of Daegu Metropolitan Area had been fed into the learning process. Neural Network structure has 17 links with present data as input, 2 hidden layers, for 1 missing link data. As a result, forecasted data of target link show about 94% of accuracy compared with actual data.

Keywords: data estimation, link data, machine learning, road network

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Authors: Hycham Aboutaleb, Bruno Monsuez

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Current systems complexity has reached a degree that requires addressing conception and design issues while taking into account all the necessary aspects. Therefore, one of the main challenges is the way complex systems are specified and designed. The exponential growing effort, cost and time investment of complex systems in modeling phase emphasize the need for a paradigm, a framework and a environment to handle the system model complexity. For that, it is necessary to understand the expectations of the human user of the model and his limits. This paper presents a generic framework for designing complex systems, highlights the requirements a system model needs to fulfill to meet human user expectations, and defines the refined functional as well as non functional requirements modeling tools needs to meet to be useful in model-based system engineering.

Keywords: system modeling, modeling language, modeling requirements, framework

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Authors: Pablo J. Valverde, Jaime E. Fernandez

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This paper shows under which conditions public corruption can emerge. The theoretical model includes variables such as the public employee wage (w), a control corruption parameter (c), and the group size of interactions (GS) between clusters of public officers and contractors. The system behavior is analyzed using phase diagrams based on combinations of such parameters (c, w, GS). Numerical simulations are implemented in order to contrast analytic results based on Nash equilibria of the theoretical model. Major findings include the functional relationship between wages and network topology, which attempts to reduce the emergence of corrupt behavior.

Keywords: public corruption, game theory, complex systems, Nash equilibrium.

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Authors: Reza Alikhani

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This research focuses on the establishment of horizontal cooperation among companies to enhance their operational efficiency and competitiveness. The study proposes an approach to horizontal collaboration, called coalition configuration, which involves partnering companies sharing distribution centers in a network design problem. The paper investigates which coalition should be formed in each distribution center to minimize the total cost of the network. Moreover, potential uncertainties, such as operational and disruption risks, are considered during the collaborative design phase. To address this problem, a two-stage robust optimization model for collaborative distribution network design under surging demand and facility disruptions is presented, along with a column-and-constraint generation algorithm to obtain exact solutions tailored to the proposed formulation. Extensive numerical experiments are conducted to analyze solutions obtained by the model in various scenarios, including decisions ranging from fully centralized to fully decentralized settings, collaborative versus non-collaborative approaches, and different amounts of uncertainty budgets. The results show that the coalition formation mechanism proposes some solutions that are competitive with the savings of the grand coalition. The research also highlights that collaboration increases network flexibility and resilience while reducing costs associated with demand and capacity uncertainties.

Keywords: logistics, warehouse sharing, robust facility location, collaboration for resilience

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Authors: Venugopal Kummamuru

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Corporate Governance (CG) is of utmost importance for running a company ethically. It is essential for the growth and success of the corporation. It is intended to increase the accountability of an organization to the larger context of the business environment. The general principles of CG include and are related to Shareholder recognition, Stakeholder interests, and focus on Corporate Social Responsibility (CSR), Clear Board responsibilities, Ethical behavior, and Business transparency. Network Marketing Organizations (NMOs) focus on marketing through direct-sales using people who are associated with the organization but are not their employees. This paper tries to study the importance of Ethics and CSR in an NMO and suggest a basic guideline for CG in NMO(s). This paper could be used as a basis or starting point for conducting an in-depth research to understand the difference in CG practices between NMO(s) and other organizations and define a standard set of guidelines for CG practice.

Keywords: corporate governance, corporate responsibility, direct selling, network marketing

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Authors: Sun Lim, Kyun Jung

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In this paper, we describe the control strategy of high speed parallel robot system with EtherCAT network. This work deals the parallel robot system with centralized control on the real-time operating system such as window TwinCAT3. Most control scheme and algorithm is implemented master platform on the PC, the input and output interface is ported on the slave side. The data is transferred by maximum 20usecond with 1000byte. EtherCAT is very high speed and stable industrial network. The control strategy with EtherCAT is very useful and robust on Ethernet network environment. The developed parallel robot is controlled pre-design nonlinear controller for 6G/0.43 cycle time of pick and place motion tracking. The experiment shows the good design and validation of the controller.

Keywords: parallel robot control, etherCAT, nonlinear control, parallel robot inverse kinematic

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Authors: Abolfazl Bezaatpour, Sahar Khatami

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Fe3O4 nanoparticles were prepared by the co-precipitation method and silica was then coated on the magnetic nanoparticles followed by modification with (3-aminopropyl) trimethoxysilane. Then, dioxomolybdenum(VI) Schiff base complex of N,N′-bis(5-chloromethyl-salicylidine)-1,2-phenylenediamine) was immobilized on the surface of magnetic nanoparticles as a heterogeneous catalyst. The catalyst was identified by scanning electron microscopy (SEM) and vibrating sample magnetometer (VSM), X-ray diffraction, IR spectroscopy, diffuse reflectance spectra and atomic absorption spectroscopy techniques. The catalyst shows excellent catalytic activity in epoxidation of olefins using tert-butylhydroperoxide in 1,2-dichloroethane. In this report, the supported complex exhibited 100% selectivity for epoxidation with 100% conversion for cyclooctene. Nanocatalyst can be easily recovered by a magnetic field and reused for subsequent reactions for at least 5 times with less deterioration in catalytic activity.

Keywords: dioxomolybdenum (VI), epoxidation, nanocatalysis, nanoparticles, Schiff base

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Authors: O. I. Nkwonta, B. Dzwairo, J. Adeyemo, A. Jaiyola, N. Sawyerr, F. Otieno

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The effective management of water resources is of great importance to ensure the supply of water resources to support changing water requirements over a selected planning horizon and in a sustainable and cost-effective way. Essentially, the purpose of the water resources planning process is to balance the available water resources in a system with the water requirements and losses to which the system is subjected. In such situations, Water resources yield and planning model can be used to solve those difficulties. It has an advantage over other models by managing model runs, developing a representative system network, modelling incremental sub-catchments, creating a variety of standard system features, special modelling features, and run result output options.

Keywords: complex, water resources, planning, cost effective and management

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Authors: Jungyeol Hong, Dongjoo Park

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The primary purpose of this study is to develop a methodological framework to predict daily subway ridership at a station-level and to examine the association between subway ridership and bus demand incorporating commercial business facility in the vicinity of each subway station. The socio-economic characteristics, land-use, and built environment as factors may have an impact on subway ridership. However, it should be considered not only the endogenous relationship between bus and subway demand but also the characteristics of commercial business within a subway station’s sphere of influence, and integrated transit network topology. Regarding a statistical approach to estimate subway ridership at a station level, therefore it should be considered endogeneity and heteroscedastic issues which might have in the subway ridership prediction model. This study focused on both discovering the impacts of bus demand, commercial business characteristics, and network topology on subway ridership and developing more precise subway ridership estimation accounting for its statistical bias. The spatial scope of the study covers entire Seoul city in South Korea and includes 243 stations with the temporal scope set at twenty-four hours with one-hour interval time panels each. The data for subway and bus ridership was collected Seoul Smart Card data from 2015 and 2016. Three-Stage Least Square(3SLS) approach was applied to develop daily subway ridership model as capturing the endogeneity and heteroscedasticity between bus and subway demand. Independent variables incorporating in the modeling process were commercial business characteristics, social-economic characteristics, safety index, transit facility attributes, and dummies for seasons and time zone. As a result, it was found that bus ridership and subway ridership were endogenous each other and they had a significantly positive sign of coefficients which means one transit mode could increase another transportation mode’s ridership. In other words, two transit modes of subway and bus have a mutual relationship instead of the competitive relationship. The commercial business characteristics are the most critical dimension among the independent variables. The variables of commercial business facility rate in the paper containing six types; medical, educational, recreational, financial, food service, and shopping. From the model result, a higher rate in medical, financial buildings, shopping, and food service facility lead to increment of subway ridership at a station, while recreational and educational facility shows lower subway ridership. The complex network theory was applied for estimating integrated network topology measures that cover the entire Seoul transit network system, and a framework for seeking an impact on subway ridership. The centrality measures were found to be significant and showed a positive sign indicating higher centrality led to more subway ridership at a station level. The results of model accuracy tests by out of samples provided that 3SLS model has less mean square error rather than OLS and showed the methodological approach for the 3SLS model was plausible to estimate more accurate subway ridership. Acknowledgement: This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science and ICT (2017R1C1B2010175).

Keywords: subway ridership, bus ridership, commercial business characteristic, endogeneity, network topology

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Authors: Sergo Tsiramua, Sulkhan Sulkhanishvili, Elisabed Asabashvili, Lazare Kvirtia

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The paper presents logical-probabilistic methods, models, and algorithms for reliability assessment of complex systems, based on which a web application for structural analysis and reliability assessment of systems was created. It is important to design systems based on structural analysis, research, and evaluation of efficiency indicators. One of the important efficiency criteria is the reliability of the system, which depends on the components of the structure. Quantifying the reliability of large-scale systems is a computationally complex process, and it is advisable to perform it with the help of a computer. Logical-probabilistic modeling is one of the effective means of describing the structure of a complex system and quantitatively evaluating its reliability, which was the basis of our application. The reliability assessment process included the following stages, which were reflected in the application: 1) Construction of a graphical scheme of the structural reliability of the system; 2) Transformation of the graphic scheme into a logical representation and modeling of the shortest ways of successful functioning of the system; 3) Description of system operability condition with logical function in the form of disjunctive normal form (DNF); 4) Transformation of DNF into orthogonal disjunction normal form (ODNF) using the orthogonalization algorithm; 5) Replacing logical elements with probabilistic elements in ODNF, obtaining a reliability estimation polynomial and quantifying reliability; 6) Calculation of “weights” of elements of system. Using the logical-probabilistic methods, models and algorithms discussed in the paper, a special software was created, by means of which a quantitative assessment of the reliability of systems of a complex structure is produced. As a result, structural analysis of systems, research, and designing of optimal structure systems are carried out.

Keywords: complex systems, logical-probabilistic methods, orthogonalization algorithm, reliability of systems, “weights” of elements

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Authors: Mst Shapna Akter, Hossain Shahriar

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Neural network approaches are machine learning methods used in many domains, such as healthcare and cyber security. Neural networks are mostly known for dealing with image datasets. While training with the images, several fundamental mathematical operations are carried out in the Neural Network. The operation includes a number of algebraic and mathematical functions, including derivative, convolution, and matrix inversion and transposition. Such operations require higher processing power than is typically needed for computer usage. Central Processing Unit (CPU) is not appropriate for a large image size of the dataset as it is built with serial processing. While Graphics Processing Unit (GPU) has parallel processing capabilities and, therefore, has higher speed. This paper uses advanced Neural Network techniques such as VGG16, Resnet50, Densenet, Inceptionv3, Xception, Mobilenet, XGBOOST-VGG16, and our proposed models to compare CPU and GPU resources. A system for classifying autism disease using face images of an autistic and non-autistic child was used to compare performance during testing. We used evaluation matrices such as Accuracy, F1 score, Precision, Recall, and Execution time. It has been observed that GPU runs faster than the CPU in all tests performed. Moreover, the performance of the Neural Network models in terms of accuracy increases on GPU compared to CPU.

Keywords: autism disease, neural network, CPU, GPU, transfer learning

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Authors: C. Mayr, J. Periya, A. Kariminezhad

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In this paper, we developed a learning framework for the classification of vulnerable road users (VRU) by their range-Doppler signatures. The frequency-modulated continuous-wave (FMCW) radar raw data is first pre-processed to obtain robust object range-Doppler maps per coherent time interval. The complex-valued range-Doppler maps captured from our outdoor measurements are further fed into a convolutional neural network (CNN) to learn the classification. This CNN has gone through a hyperparameter optimization process for improved learning. By learning VRU range-Doppler signatures, the three classes 'pedestrian', 'dog', and 'noise' are classified with an average accuracy of almost 95%. Interestingly, this classification accuracy holds for a combined longitudinal and lateral object trajectories.

Keywords: machine learning, radar, signal processing, autonomous driving

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Authors: A. Fayad, Q. Alqhazaly, T. Cinkler

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In light of the substantial increase in end-user requirements and the incessant need of network operators to upgrade the capabilities of access networks, in this paper, the performance of the different modulation formats on eight-channels Time and Wavelength Division Multiplexing Passive Optical Network (TWDM-PON) transmission system has been examined and compared. Limitations and features of modulation formats have been determined to outline the most suitable design to enhance the data rate and transmission reach to obtain the best performance of the network. The considered modulation formats are On-Off Keying Non-Return-to-Zero (NRZ-OOK), Carrier Suppressed Return to Zero (CSRZ), Duo Binary (DB), Modified Duo Binary (MODB), Quadrature Phase Shift Keying (QPSK), and Differential Quadrature Phase Shift Keying (DQPSK). The performance has been analyzed by varying transmission distances and bit rates under different channel spacing. Furthermore, the system is evaluated in terms of minimum Bit Error Rate (BER) and Quality factor (Qf) without applying any dispersion compensation technique, or any optical amplifier. Optisystem software was used for simulation purposes.

Keywords: BER, DuoBinary, NRZ-OOK, TWDM-PON

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Authors: Kyu Jin Kim, Byung Kwan Oh, Hyo Seon Park

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The seismic responses-based structural health monitoring system has been performed to reduce seismic damage. The inter-story drift ratio which is the major index of the seismic capacity assessment is employed for estimating the seismic damage of buildings. Meanwhile, seismic response analysis to estimate the structural responses of building demands significantly high computational cost due to increasing number of high-rise and large buildings. To estimate the inter-story drift ratio of buildings from the earthquake efficiently, this paper suggests the estimation method of inter-story drift for buildings using an artificial neural network (ANN). In the method, the radial basis function neural network (RBFNN) is integrated with optimization algorithm to optimize the variable through evolutionary learning that refers to evolutionary radial basis function neural network (ERBFNN). The estimation method estimates the inter-story drift without seismic response analysis when the new earthquakes are subjected to buildings. The effectiveness of the estimation method is verified through a simulation using multi-degree of freedom system.

Keywords: structural health monitoring, inter-story drift ratio, artificial neural network, radial basis function neural network, genetic algorithm

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Authors: Chung-Ming Lo, Kevin Li-Chun Hsieh

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The central nervous system in the World Health Organization defines grade 2, 3, 4 gliomas according to the aggressiveness. For brain tumors, using image examination would have a lower risk than biopsy. Besides, it is a challenge to extract relevant tissues from biopsy operation. Observing the whole tumor structure and composition can provide a more objective assessment. This study further proposed a computer-aided diagnosis (CAD) system based on a convolutional neural network to quantitatively evaluate a tumor's malignancy from brain magnetic resonance imaging. A total of 30 grade 2, 43 grade 3, and 57 grade 4 gliomas were collected in the experiment. Transferred parameters from AlexNet were fine-tuned to classify the target brain tumors and achieved an accuracy of 98% and an area under the receiver operating characteristics curve (Az) of 0.99. Without pre-trained features, only 61% of accuracy was obtained. The proposed convolutional neural network can accurately and efficiently classify grade 2, 3, and 4 gliomas. The promising accuracy can provide diagnostic suggestions to radiologists in the clinic.

Keywords: convolutional neural network, computer-aided diagnosis, glioblastoma, magnetic resonance imaging

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Authors: Sven Tackenberg, Sönke Duckwitz, Andreas Petz, Christopher M. Schlick

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This paper introduces an extension to the well-established Resource-Constrained Project Scheduling Problem (RCPSP) to apply it to complex maintenance problems. The problem is to assign technicians to a team which has to process several tasks with multi-level skill requirements during a work shift. Here, several alternative activities for a task allow both, the temporal shift of activities or the reallocation of technicians and tools. As a result, switches from one valid work process variant to another can be considered and may be selected by the developed evolutionary algorithm based on the present skill level of technicians or the available tools. An additional complication of the observed scheduling problem is that the locations of the construction sites are only temporarily accessible during a day. Due to intensive rail traffic, the available time slots for maintenance and repair works are extremely short and are often distributed throughout the day. To identify efficient working periods, a first concept of a Bayesian network is introduced and is integrated into the extended RCPSP with pre-emptive and non-pre-emptive tasks. Thereby, the Bayesian network is used to calculate the probability of a maintenance task to be processed during a specific period of the shift. Focusing on the domain of maintenance of the railway infrastructure in metropolitan areas as the most unproductive implementation process at construction site, the paper illustrates how the extended RCPSP can be applied for maintenance planning support. A multi-criteria evolutionary algorithm with a problem representation is introduced which is capable of revising technician-task allocations, whereas the duration of the task may be stochastic. The approach uses a novel activity list representation to ensure easily describable and modifiable elements which can be converted into detailed shift schedules. Thereby, the main objective is to develop a shift plan which maximizes the utilization of each technician due to a minimization of the waiting times caused by rail traffic. The results of the already implemented core algorithm illustrate a fast convergence towards an optimal team composition for a shift, an efficient sequence of tasks and a high probability of the subsequent implementation due to the stochastic durations of the tasks. In the paper, the algorithm for the extended RCPSP is analyzed in experimental evaluation using real-world example problems with various size, resource complexity, tightness and so forth.

Keywords: maintenance management, scheduling, resource constrained project scheduling problem, genetic algorithms

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Authors: Cedric Leong, Parth Desai, Parth Patel

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The purpose of this project was to develop a neural network based on qualitative team data to predict alliance scores to determine winners of matches in the FIRST Robotics Competition (FRC). The game for the competition changes every year with different objectives and game objects, however the idea was to create a prediction system which can be reused year by year using some of the statistics that are constant through different games, making our system adaptable to future games as well. Aerial Assist is the FRC game for 2014, and is played in alliances of 3 teams going against one another, namely the Red and Blue alliances. This application takes any 6 teams paired into 2 alliances of 3 teams and generates the prediction for the final score between them.

Keywords: artifical neural network, prediction system, qualitative team data, FIRST Robotics Competition (FRC)

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Authors: William Ho, Agus Wicaksana

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Anecdotal evidence in the last decade shows that the occurrence of disruptive events and uncertainties in the supply chain is increasing. The coupling of these events with the nature of an increasingly complex and interdependent business environment leads to devastating impacts that quickly propagate within and across organizations. For example, the recent COVID-19 pandemic increased the global supply chain disruption frequency by at least 20% in 2020 and is projected to have an accumulative cost of $13.8 trillion by 2024. This crisis raises attention to organizational resilience to weather business uncertainty. However, the concept has been criticized for being vague and lacking a consistent definition, thus reducing the significance of the concept for practice and research. This study is intended to solve that issue by providing a comprehensive review of the conceptualization, measurement, and antecedents of operational resilience that have been discussed in the supply chain risk management literature (SCRM). We performed a Scholarly Network Analysis, combining citation-based and text-based approaches, on 252 articles published from 2000 to 2021 in top-tier journals based on three parameters: AJG ranking and ABS ranking, UT Dallas and FT50 list, and editorial board review. We utilized a hybrid scholarly network analysis by combining citation-based and text-based approaches to understand the conceptualization, measurement, and antecedents of operational resilience in the SCRM literature. Specifically, we employed a Bibliographic Coupling Analysis in the research cluster formation stage and a Co-words Analysis in the research cluster interpretation and analysis stage. Our analysis reveals three major research clusters of resilience research in the SCRM literature, namely (1) supply chain network design and optimization, (2) organizational capabilities, and (3) digital technologies. We portray the research process in the last two decades in terms of the exemplar studies, problems studied, commonly used approaches and theories, and solutions provided in each cluster. We then provide a conceptual framework on the conceptualization and antecedents of resilience based on studies in these clusters and highlight potential areas that need to be studied further. Finally, we leverage the concept of abnormal operating performance to propose a new measurement strategy for resilience. This measurement overcomes the limitation of most current measurements that are event-dependent and focus on the resistance or recovery stage - without capturing the growth stage. In conclusion, this study provides a robust literature review through a scholarly network analysis that increases the completeness and accuracy of research cluster identification and analysis to understand conceptualization, antecedents, and measurement of resilience. It also enables us to perform a comprehensive review of resilience research in SCRM literature by including research articles published during the pandemic and connects this development with a plethora of articles published in the last two decades. From the managerial perspective, this study provides practitioners with clarity on the conceptualization and critical success factors of firm resilience from the SCRM perspective.

Keywords: supply chain risk management, organizational resilience, scholarly network analysis, systematic literature review

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Authors: Prikhil Agrawal, N. Priyanka

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With the increase of millions of users on Internet day by day, it is very essential to maintain highly reliable and secured data communication between various corporations. Although there are various traditional security imparting techniques such as antivirus software, password protection, data encryption, biometrics and firewall etc. But still network security has become the main issue in various leading companies. So IDSs have become an essential component in terms of security, as it can detect various network attacks and respond quickly to such occurrences. IDSs are used to detect unauthorized access to a computer system. This paper describes various intrusion detection techniques using GA approach. The intrusion detection problem has become a challenging task due to the conception of miscellaneous computer networks under various vulnerabilities. Thus the damage caused to various organizations by malicious intrusions can be mitigated and even be deterred by using this powerful tool.

Keywords: genetic algorithm (GA), intrusion detection system (IDS), dataset, network security

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Authors: Hamed K. Esfahani, Bithin Datta

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Transport of reactive chemical contaminant species in groundwater aquifers is a complex and highly non-linear physical and geochemical process especially for real life scenarios. Simulating this transport process involves solving complex nonlinear equations and generally requires huge computational time for a given aquifer study area. Development of optimal remediation strategies in aquifers may require repeated solution of such complex numerical simulation models. To overcome this computational limitation and improve the computational feasibility of large number of repeated simulations, Genetic Programming based trained surrogate models are developed to approximately simulate such complex transport processes. Transport process of acid mine drainage, a hazardous pollutant is first simulated using a numerical simulated model: HYDROGEOCHEM 5.0 for a contaminated aquifer in a historic mine site. Simulation model solution results for an illustrative contaminated aquifer site is then approximated by training and testing a Genetic Programming (GP) based surrogate model. Performance evaluation of the ensemble GP models as surrogate models for the reactive species transport in groundwater demonstrates the feasibility of its use and the associated computational advantages. The results show the efficiency and feasibility of using ensemble GP surrogate models as approximate simulators of complex hydrogeologic and geochemical processes in a contaminated groundwater aquifer incorporating uncertainties in historic mine site.

Keywords: geochemical transport simulation, acid mine drainage, surrogate models, ensemble genetic programming, contaminated aquifers, mine sites

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Authors: William Whiteley, Jens Gregor

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In recent developments of direct neural network based positron emission tomography (PET) reconstruction, two prominent architectures have emerged for converting measurement data into images: 1) networks that contain fully-connected layers; and 2) networks that primarily use a convolutional encoder-decoder architecture. In this paper, we present a multi-modal direct PET reconstruction method called MDPET, which is a hybrid approach that combines the advantages of both types of networks. MDPET processes raw data in the form of sinograms and histo-images in concert with attenuation maps to produce high quality multi-slice PET images (e.g., 8x440x440). MDPET is trained on a large whole-body patient data set and evaluated both quantitatively and qualitatively against target images reconstructed with the standard PET reconstruction benchmark of iterative ordered subsets expectation maximization. The results show that MDPET outperforms the best previously published direct neural network methods in measures of bias, signal-to-noise ratio, mean absolute error, and structural similarity.

Keywords: deep learning, image reconstruction, machine learning, neural network, positron emission tomography

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Authors: N. Tadayon, H. Yousefnia, S. Zolghadri, A. Ramazani, A. R. Jalilian

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In this study, 68Ga-PDTMP was prepared as a new agent for bone imaging. 68Ga was obtained from SnO2 based generator. A certain volume of the PDTMP solution was added to the vial containing 68GaCl3 and the pH of the mixture was adjusted to 4 using HEPES. Radiochemical purity of the radiolabelled complex was checked by thin layer chromatography. Biodistribution of this new agent was assessed in rats after intravenously injection of the complex. For this purpose, the rats were killed at specified times after injection and the weight and activity of each organ was measured. Injected dose per gram was calculated by dividing the activity of each organ to the total injected activity and the mass of each organ. As expected the most of the activity was accumulated in the bone tissue. The radiolabelled compound was extracted from blood very fast. This new bone-seeking complex can be considered as a good candidate of PET-based radiopharmaceutical for imaging of bone metastases.

Keywords: biodistribution, Ga-68, imaging, PDTMP

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Authors: Oya Bulut, Oguzhan Avci, Zafer Bulut, Atilla Simsek

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Livestock breeders have focused on the improvement of production traits with little or no attention for improvement of disease resistance traits. In order to determine the association between the genetic structure of the individual gene loci with possibility of the occurrence and the development of diseases, MHC (major histocompatibility complex) are frequently used. Because of their importance in the immune system, MHC locus is considered as candidate genes for resistance/susceptibility against to different diseases. Major histocompatibility complex (MHC) molecules play a critical role in both innate and adaptive immunity and have been considered candidate molecular markers of an association between polymorphisms and resistance/susceptibility to diseases. The purpose of this study is to give some information about MHC genes become an important area of study in recent years in terms of animal husbandry and determine the relation between MHC genes and resistance/susceptibility to disease.

Keywords: MHC, polymorphism, disease, resistance

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Authors: Priyanka Agrawal, Janou Koskamp, Amir Raoof, Mariette Wolthers

Abstract:

Calcite is the main mineral found in carbonate rocks, which form significant hydrocarbon reservoirs and subsurface repositories for CO2 sequestration. The injected CO2 mixes with the reservoir fluid and disturbs the geochemical equilibrium, triggering calcite dissolution. Different combinations of fluid chemistry and injection rate may therefore result in different evolution of porosity, permeability and dissolution patterns. To model the changes in porosity and permeability Kozeny-Carman equation K∝〖(∅)〗^n is used, where K is permeability and ∅ is porosity. The value of n is mostly based on experimental data or pore network models. In pore network models, this derivation is based on accuracy of relation used for conductivity and pore volume change. In fact, at a single pore scale, this relationship is the result of the pore shape development due to dissolution. We have prepared a new reactive transport model for a single pore which simulates the complex chemical reaction of carbonic-acid induced calcite dissolution and subsequent pore-geometry evolution at a single pore scale. We use COMSOL Multiphysics package 5.3 for the simulation. COMSOL utilizes the arbitary-Lagrangian Eulerian (ALE) method for the free-moving domain boundary. We examined the effect of flow rate on the evolution of single pore shape profiles due to calcite dissolution. We used three flow rates to cover diffusion dominated and advection-dominated transport regimes. The fluid in diffusion dominated flow (Pe number 0.037 and 0.37) becomes less reactive along the pore length and thus produced non-uniform pore shapes. However, for the advection-dominated flow (Pe number 3.75), the fast velocity of the fluid keeps the fluid relatively more reactive towards the end of the pore length, thus yielding uniform pore shape. Different pore shapes in terms of inlet opening vs overall pore opening will have an impact on the relation between changing volumes and conductivity. We have related the shape of pore with the Pe number which controls the transport regimes. For every Pe number, we have derived the relation between conductivity and porosity. These relations will be used in the pore network model to get the porosity and permeability variation.

Keywords: single pore, reactive transport, calcite system, moving boundary

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Authors: Syed Asif Hassan, Ritu Barthwal

Abstract:

Topoisomerase I and II α plays a crucial role in the DNA-maintenance in all living cells, and for this reason, inhibitors of this enzyme have been much studied. In this paper, we have described the inhibitory effect of the aqueous extract of Picrorrhiza kurroa on human topoisomerases by measuring the relaxation of superhelical plasmid pBR322 DNA. The aqueous extract inhibited topoisomerase I and II α in a concentration-dependent manner (Inhibitory concentration (IC) ≈ 25 and 50 µg, respectively). By stabilization studies of topoisomerase I-DNA complex and preincubation studies of topoisomerase I and II α with the extract; we conclude that the possible mechanism of inhibition is both; 1) stabilization of covalent complex of topo I-DNA complex and 2) direct inhibition of the enzyme topoisomerases. These findings might explain the antineoplastic activity of Picrorrhiza kurroa and encourage new studies to elucidate the usefulness of the extract as a potent antineoplastic agent.

Keywords: Picrorrhiza kurroa, topoisomerase I and II α, inhibition, antineoplastic agent

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Authors: J. K. Alhassan, B. Attah, S. Misra

Abstract:

Human beings have the ability to make logical decisions. Although human decision - making is often optimal, it is insufficient when huge amount of data is to be classified. medical dataset is a vital ingredient used in predicting patients health condition. In other to have the best prediction, there calls for most suitable machine learning algorithms. This work compared the performance of Artificial Neural Network (ANN) and Decision Tree Algorithms (DTA) as regards to some performance metrics using diabetes data. The evaluations was done using weka software and found out that DTA performed better than ANN. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) were the two algorithms used for ANN, while RegTree and LADTree algorithms were the DTA models used. The Root Mean Squared Error (RMSE) of MLP is 0.3913,that of RBF is 0.3625, that of RepTree is 0.3174 and that of LADTree is 0.3206 respectively.

Keywords: artificial neural network, classification, decision tree algorithms, diabetes mellitus

Procedia PDF Downloads 404