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Sample records for networks ann logistic

  1. On The Comparison of Artificial Neural Network (ANN) and ...

    African Journals Online (AJOL)

    West African Journal of Industrial and Academic Research ... This work presented the results of an experimental comparison of two models: Multinomial Logistic Regression (MLR) and Artificial Neural Network (ANN) for ... Keywords: Multinomial Logistic Regression, Artificial Neural Network, Correct classification rate.

  2. Data Logistics in Network Computing

    CERN Multimedia

    CERN. Geneva; Marquina, Miguel Angel

    2005-01-01

    In distributed computing environments, performance is often dominated by the time that it takes to move data over a network. In the case of data-centric applications, or Data Grids, this problem of data movement becomes one of the overriding concerns. This talk describes techniques for improving data movement in Grid environments that we refer to as 'logistics.' We demonstrate that by using storage and cooperative forwarding 'in' the network, we can improve end to end throughput in many cases. Our approach offers clear performance benefits for high-bandwidth, high-latency networks. This talk will introduce the Logistical Session Layer (LSL) and provide experimental results from that system.

  3. Application of artificial neural networks (ANNs) in wine technology.

    Science.gov (United States)

    Baykal, Halil; Yildirim, Hatice Kalkan

    2013-01-01

    In recent years, neural networks have turned out as a powerful method for numerous practical applications in a wide variety of disciplines. In more practical terms neural networks are one of nonlinear statistical data modeling tools. They can be used to model complex relationships between inputs and outputs or to find patterns in data. In food technology artificial neural networks (ANNs) are useful for food safety and quality analyses, predicting chemical, functional and sensory properties of various food products during processing and distribution. In wine technology, ANNs have been used for classification and for predicting wine process conditions. This review discusses the basic ANNs technology and its possible applications in wine technology.

  4. Research on 6R Military Logistics Network

    Science.gov (United States)

    Jie, Wan; Wen, Wang

    The building of military logistics network is an important issue for the construction of new forces. This paper has thrown out a concept model of 6R military logistics network model based on JIT. Then we conceive of axis spoke y logistics centers network, flexible 6R organizational network, lean 6R military information network based grid. And then the strategy and proposal for the construction of the three sub networks of 6Rmilitary logistics network are given.

  5. Reverse Logistics Network Structures and Design

    NARCIS (Netherlands)

    M. Fleischmann (Moritz)

    2001-01-01

    textabstractLogistics network design is commonly recognized as a strategic supply chain issue of prime importance. The location of production facilities, storage concepts, and transportation strategies are major determinants of supply chain performance. This chapter considers logistics network

  6. Logistical networking: a global storage network

    Science.gov (United States)

    Beck, Micah; Moore, Terry

    2005-01-01

    The absence of an adequate distributed storage infrastructure for data buffering has become a significant impediment to the flow of work in the wide area, data intensive collaborations that are increasingly characteristic of leading edge research in several fields. One solution to this problem, pioneered under DOE's SciDAC program, is Logistical Networking, which provides a framework for a globally scalable, maximally interoperable storage network based on the Internet Backplane Protocol (IBP). This paper provides a brief overview of the Logistical Networking (LN) architecture, the middleware developed to exploit its value, and a few of the applications that some of research communities have made of it. This work is supported by the Department of Energy SciDAC Program (esp. Terascale Supernova Initiative) under grant #DE-FC02-01ER25465 and by the Center for Gyokinetic Particle Simulation of Turbulent Transport in Burning Plasmas, under award #DE-FG02-04ER25651. Additional support was provided by the Center for Information Technology Research (CITR) of the University of Tennessee. The infrastructure used in this work was supported by the NSF CISE Research Infrastructure program, EIA-9972889 and Research Resources program EIA-022444.

  7. Logistic control in automated transportation networks

    NARCIS (Netherlands)

    Ebben, Mark

    2001-01-01

    Increasing congestion problems lead to a search for alternative transportation systems. Automated transportation networks, possibly underground, are an option. Logistic control systems are essential for future implementations of such automated transportation networks. This book contributes to the

  8. Designing and Evaluating Sustainable Logistics Networks

    NARCIS (Netherlands)

    J. Quariguasi Frota Neto (João); J.M. Bloemhof-Ruwaard (Jacqueline); J.A.E.E. van Nunen (Jo); H.W.G.M. van Heck (Eric)

    2006-01-01

    textabstractThe objective in this paper is to shed light into the design of logistic networks balancing profit and the environment. More specifically we intend to i) determine the main factors influencing environmental performance and costs in logistic networks ii) present a comprehensive framework

  9. Reverse Logistics Network Structures and Design

    OpenAIRE

    Fleischmann, Moritz

    2001-01-01

    textabstractLogistics network design is commonly recognized as a strategic supply chain issue of prime importance. The location of production facilities, storage concepts, and transportation strategies are major determinants of supply chain performance. This chapter considers logistics network design for the particular case of closed-loop supply chains. We highlight key issues that companies are facing when deciding upon the logistics implementation of a product recovery initiative. In partic...

  10. Innovation and network development of logistics firms

    OpenAIRE

    Cui, Lianguang

    2011-01-01

    This compilation thesis consists of a cover and five appended articles. The research purpose of this thesis is to investigate the third party logistics phenomena from the logistics firm’s perspective with a focus on logistics innovation and network development. The thesis applies a qualitative research method and employs multiple case studies. Resource-based view, industrial network approach and strategy-as-practice perspective have been applied and combined to analyze the empirical findings....

  11. Flexible Sensor Network Reprogramming for Logistics

    NARCIS (Netherlands)

    Evers, L.; Havinga, Paul J.M.; Kuper, Jan

    2007-01-01

    Besides the currently realized applications, Wireless Sensor Networks can be put to use in logistics processes. However, doing so requires a level of flexibility and safety not provided by the current WSN software platforms. This paper discusses a logistics scenario, and presents SensorScheme, a

  12. IDI diesel engine performance and exhaust emission analysis using biodiesel with an artificial neural network (ANN)

    National Research Council Canada - National Science Library

    Prasada Rao, K; Victor Babu, T; Anuradha, G; Appa Rao, B.V

    ...) engine fueled with Rice Bran Methyl Ester (RBME) with Isopropanol additive. The investigation is done through a combination of experimental data analysis and artificial neural network (ANN) modeling...

  13. On The Comparison of Artificial Neural Network (ANN) and ...

    African Journals Online (AJOL)

    PROF. OLIVER OSUAGWA

    real life problems ranging from management sciences, business schools, and others [10], [12],. [14], [17]. Moreover, this study aims at comparisons of the model performance of neural network and statistical technique (Multinomial Logistic. Regression) in view of other objectives, using secondary data from the department of.

  14. Visual NNet: An Educational ANN's Simulation Environment Reusing Matlab Neural Networks Toolbox

    Science.gov (United States)

    Garcia-Roselló, Emilio; González-Dacosta, Jacinto; Lado, Maria J.; Méndez, Arturo J.; Garcia Pérez-Schofield, Baltasar; Ferrer, Fátima

    2011-01-01

    Artificial Neural Networks (ANN's) are nowadays a common subject in different curricula of graduate and postgraduate studies. Due to the complex algorithms involved and the dynamic nature of ANN's, simulation software has been commonly used to teach this subject. This software has usually been developed specifically for learning purposes, because…

  15. Planning logistics network for recyclables collection

    Directory of Open Access Journals (Sweden)

    Ratković Branislava

    2014-01-01

    Full Text Available Rapid urbanization, intensified industrialization, rise of income, and a more sophisticated form of consumerism are leading to an increase in the amount and toxicity of waste all over the world. Whether reused, recycled, incinerated or put into landfill sites, the management of household and industrial waste yield financial and environmental costs. This paper presents a modeling approach that can be used for designing one part of recycling logistics network through defining optimal locations of collection points, and possible optimal scheduling of vehicles for collecting recyclables. [Projekat Ministarstva nauke Republike Srbije, br. TR36005

  16. Artificial neural networks versus bivariate logistic regression in prediction diagnosis of patients with hypertension and diabetes.

    Science.gov (United States)

    Adavi, Mehdi; Salehi, Masoud; Roudbari, Masoud

    2016-01-01

    Diabetes and hypertension are important non-communicable diseases and their prevalence is important for health authorities. The aim of this study was to determine the predictive precision of the bivariate Logistic Regression (LR) and Artificial Neutral Network (ANN) in concurrent diagnosis of diabetes and hypertension. This cross-sectional study was performed with 12000 Iranian people in 2013 using stratified- cluster sampling. The research questionnaire included information on hypertension and diabetes and their risk factors. A perceptron ANN with two hidden layers was applied to data. To build a joint LR model and ANN, SAS 9.2 and Matlab software were used. The AUC was used to find the higher accurate model for predicting diabetes and hypertension. The variables of gender, type of cooking oil, physical activity, family history, age, passive smokers and obesity entered to the LR model and ANN. The odds ratios of affliction to both diabetes and hypertension is high in females, users of solid oil, with no physical activity, with positive family history, age of equal or higher than 55, passive smokers and those with obesity. The AUC for LR model and ANN were 0.78 (p=0.039) and 0.86 (p=0.046), respectively. The best model for concurrent affliction to hypertension and diabetes is ANN which has higher accuracy than the bivariate LR model.

  17. Comparison of the performance of log-logistic regression and artificial neural networks for predicting breast cancer relapse.

    Science.gov (United States)

    Faradmal, Javad; Soltanian, Ali Reza; Roshanaei, Ghodratollah; Khodabakhshi, Reza; Kasaeian, Amir

    2014-01-01

    Breast cancer is the most common cancers in female populations. The exact cause is not known, but is most likely to be a combination of genetic and environmental factors. Log-logistic model (LLM) is applied as a statistical method for predicting survival and it influencing factors. In recent decades, artificial neural network (ANN) models have been increasingly applied to predict survival data. The present research was conducted to compare log-logistic regression and artificial neural network models in prediction of breast cancer (BC) survival. A historical cohort study was established with 104 patients suffering from BC from 1997 to 2005. To compare the ANN and LLM in our setting, we used the estimated areas under the receiver-operating characteristic (ROC) curve (AUC) and integrated AUC (iAUC). The data were analyzed using R statistical software. The AUC for the first, second and third years after diagnosis are 0.918, 0.780 and 0.800 in ANN, and 0.834, 0.733 and 0.616 in LLM, respectively. The mean AUC for ANN was statistically higher than that of the LLM (0.845 vs. 0.744). Hence, this study showed a significant difference between the performance in terms of prediction by ANN and LLM. This study demonstrated that the ability of prediction with ANN was higher than with the LLM model. Thus, the use of ANN method for prediction of survival in field of breast cancer is suggested.

  18. Creating the networking enterprises - logistics determinants

    Directory of Open Access Journals (Sweden)

    Ewa Kulińska

    2014-06-01

    Full Text Available Background: The article describes the determinants of creating network enterprises with peculiar consideration of logistic factors which are conditioning the organization of processes, exchange of resources and competences. On the basis of literature analysis, there is proposed a model of creating network enterprises. A model is verified in the application part of the thesis. Methods: Within the publication a literature review of submitted scope of the interest was presented, as well as the empirical research. A research substance attaches the enterprises created on the basis of the reactivation of organizations which has collapsed due to bankruptcy proceeding. The research was based upon direct interviews with employees of the net-forming entities. Results and conclusions: Results of the research shows that taking up the cooperation and net-cooperation was the only possibility for new entities to come into existence, that were  based upon old assets and human resources liquidated during bankruptcy proceeding. There was indentified many determinants of enterprises network cooperation, however due to the research a conclusion draws, that basic factors of creating network cooperation are those which are profit-achieving oriented.

  19. Logistic regression against a divergent Bayesian network

    Directory of Open Access Journals (Sweden)

    Noel Antonio Sánchez Trujillo

    2015-01-01

    Full Text Available This article is a discussion about two statistical tools used for prediction and causality assessment: logistic regression and Bayesian networks. Using data of a simulated example from a study assessing factors that might predict pulmonary emphysema (where fingertip pigmentation and smoking are considered; we posed the following questions. Is pigmentation a confounding, causal or predictive factor? Is there perhaps another factor, like smoking, that confounds? Is there a synergy between pigmentation and smoking? The results, in terms of prediction, are similar with the two techniques; regarding causation, differences arise. We conclude that, in decision-making, the sum of both: a statistical tool, used with common sense, and previous evidence, taking years or even centuries to develop; is better than the automatic and exclusive use of statistical resources.

  20. Sodium Adsorption Ratio (SAR) Prediction of the Chalghazi River Using Artificial Neural Network (ANN) Iran

    OpenAIRE

    Gholamreza Asadollahfardi; Azadeh Hemati; Saber Moradinejad; Rashin Asadollahfardi

    2013-01-01

    Considering the significance of the Sodium Adsorption Ratio (SAR) for growing plants, its prediction is essential for water quality management for irrigation. The SAR prediction in Chelghazy River in Kurdistan, northwest of Iran, using an Artificial Neural Network (ANN) was studied. The study applied the Multilayer Perceptron (MLP) of the ANN to average monthly data, which was collected by the water authority of the Kurdistan province for the period of 1998-2009. The input parameters of the M...

  1. Artificial Neural Networks (ANNs for flood forecasting at Dongola Station in the River Nile, Sudan

    Directory of Open Access Journals (Sweden)

    Sulafa Hag Elsafi

    2014-09-01

    Full Text Available Heavy seasonal rains cause the River Nile in Sudan to overflow and flood the surroundings areas. The floods destroy houses, crops, roads, and basic infrastructure, resulting in the displacement of people. This study aimed to forecast the River Nile flow at Dongola Station in Sudan using an Artificial Neural Network (ANN as a modeling tool and validated the accuracy of the model against actual flow. The ANN model was formulated to simulate flows at a certain location in the river reach, based on flow at upstream locations. Different procedures were applied to predict flooding by the ANN. Readings from stations along the Blue Nile, White Nile, Main Nile, and River Atbara between 1965 and 2003 were used to predict the likelihood of flooding at Dongola Station. The analysis indicated that the ANN provides a reliable means of detecting the flood hazard in the River Nile.

  2. Application of wireless sensor network technology in logistics information system

    Science.gov (United States)

    Xu, Tao; Gong, Lina; Zhang, Wei; Li, Xuhong; Wang, Xia; Pan, Wenwen

    2017-04-01

    This paper introduces the basic concepts of active RFID (WSN-ARFID) based on wireless sensor networks and analyzes the shortcomings of the existing RFID-based logistics monitoring system. Integrated wireless sensor network technology and the scrambling point of RFID technology. A new real-time logistics detection system based on WSN and RFID, a model of logistics system based on WSN-ARFID is proposed, and the feasibility of this technology applied to logistics field is analyzed.

  3. Strategies on the Implementation of China's Logistics Information Network

    Science.gov (United States)

    Dong, Yahui; Li, Wei; Guo, Xuwen

    The economic globalization and trend of e-commerce network have determined that the logistics industry will be rapidly developed in the 21st century. In order to achieve the optimal allocation of resources, a worldwide rapid and sound customer service system should be established. The establishment of a corresponding modern logistics system is the inevitable choice of this requirement. It is also the inevitable choice for the development of modern logistics industry in China. The perfect combination of modern logistics and information network can better promote the development of the logistics industry. Through the analysis of Status of Logistics Industry in China, this paper summed up the domestic logistics enterprise logistics information system in the building of some common problems. According to logistics information systems planning methods and principles set out logistics information system to optimize the management model.

  4. Basic concepts of artificial neural network (ANN) modeling and its application in pharmaceutical research.

    Science.gov (United States)

    Agatonovic-Kustrin, S; Beresford, R

    2000-06-01

    Artificial neural networks (ANNs) are biologically inspired computer programs designed to simulate the way in which the human brain processes information. ANNs gather their knowledge by detecting the patterns and relationships in data and learn (or are trained) through experience, not from programming. An ANN is formed from hundreds of single units, artificial neurons or processing elements (PE), connected with coefficients (weights), which constitute the neural structure and are organised in layers. The power of neural computations comes from connecting neurons in a network. Each PE has weighted inputs, transfer function and one output. The behavior of a neural network is determined by the transfer functions of its neurons, by the learning rule, and by the architecture itself. The weights are the adjustable parameters and, in that sense, a neural network is a parameterized system. The weighed sum of the inputs constitutes the activation of the neuron. The activation signal is passed through transfer function to produce a single output of the neuron. Transfer function introduces non-linearity to the network. During training, the inter-unit connections are optimized until the error in predictions is minimized and the network reaches the specified level of accuracy. Once the network is trained and tested it can be given new input information to predict the output. Many types of neural networks have been designed already and new ones are invented every week but all can be described by the transfer functions of their neurons, by the learning rule, and by the connection formula. ANN represents a promising modeling technique, especially for data sets having non-linear relationships which are frequently encountered in pharmaceutical processes. In terms of model specification, artificial neural networks require no knowledge of the data source but, since they often contain many weights that must be estimated, they require large training sets. In addition, ANNs can combine

  5. A Mathematical Model to Improve the Performance of Logistics Network

    Directory of Open Access Journals (Sweden)

    Muhammad Izman Herdiansyah

    2012-01-01

    Full Text Available The role of logistics nowadays is expanding from just providing transportation and warehousing to offering total integrated logistics. To remain competitive in the global market environment, business enterprises need to improve their logistics operations performance. The improvement will be achieved when we can provide a comprehensive analysis and optimize its network performances. In this paper, a mixed integer linier model for optimizing logistics network performance is developed. It provides a single-product multi-period multi-facilities model, as well as the multi-product concept. The problem is modeled in form of a network flow problem with the main objective to minimize total logistics cost. The problem can be solved using commercial linear programming package like CPLEX or LINDO. Even in small case, the solver in Excel may also be used to solve such model.Keywords: logistics network, integrated model, mathematical programming, network optimization

  6. A robust optimization model for green regional logistics network design with uncertainty in future logistics demand

    Directory of Open Access Journals (Sweden)

    Dezhi Zhang

    2015-12-01

    Full Text Available This article proposes a new model to address the design problem of a sustainable regional logistics network with uncertainty in future logistics demand. In the proposed model, the future logistics demand is assumed to be a random variable with a given probability distribution. A set of chance constraints with regard to logistics service capacity and environmental impacts is incorporated to consider the sustainability of logistics network design. The proposed model is formulated as a two-stage robust optimization problem. The first-stage problem before the realization of future logistics demand aims to minimize a risk-averse objective by determining the optimal location and size of logistics parks with CO2 emission taxes consideration. The second stage after the uncertain logistics demand has been determined is a scenario-based stochastic logistics service route choices equilibrium problem. A heuristic solution algorithm, which is a combination of penalty function method, genetic algorithm, and Gauss–Seidel decomposition approach, is developed to solve the proposed model. An illustrative example is given to show the application of the proposed model and solution algorithm. The findings show that total social welfare of the logistics system depends very much on the level of uncertainty in future logistics demand, capital budget for logistics parks, and confidence levels of the chance constraints.

  7. artificial neural network (ann) approach to electrical load

    African Journals Online (AJOL)

    2004-08-18

    Aug 18, 2004 ... UNIVERSITY POWER HOUSE. A.A.AKINTOLA", G.A. ADEROUNMU and O.E. ... The model was tested using two of the seven feeders of the Obafemi. Awolowo University electric network. The results of .... The architecture of a neural network is the specific arrangement and connections of the neurons that.

  8. Risk assessment of logistics outsourcing based on BP neural network

    Science.gov (United States)

    Liu, Xiaofeng; Tian, Zi-you

    The purpose of this article is to evaluate the risk of the enterprises logistics outsourcing. To get this goal, the paper first analysed he main risks existing in the logistics outsourcing, and then set up a risk evaluation index system of the logistics outsourcing; second applied BP neural network into the logistics outsourcing risk evaluation and used MATLAB to the simulation. It proved that the network error is small and has strong practicability. And this method can be used by enterprises to evaluate the risks of logistics outsourcing.

  9. Reverse logistics network design: a holistic life cycle approach

    National Research Council Canada - National Science Library

    Daaboul, Joanna; Le Duigou, Julien; Penciuc, Diana; Eynard, Benoît

    2014-01-01

    .... This article addresses the issue of designing the reverse logistics network assuring the needed volume of recycled aluminum for the production of L-shaped front lower control arms for personal cars. It details the developed method and demonstrator for designing a reverse logistics network based on a life cycle assessment.

  10. AML (Logistics Center) Local Area Network -

    Data.gov (United States)

    Department of Transportation — The AML LAN is designed to facilitate the services and resources needed to support the operations of the FAA Logistics Center users. The AML LAN provides support for...

  11. ESTIMASI HUBUNGAN KUANTITATIF STRUKTUR-AKTIVITAS (HKSA MENGGUNAKAN ARTIFICIAL NEURAL NETWORKS (ANN

    Directory of Open Access Journals (Sweden)

    Supriyanto

    2009-05-01

    Full Text Available The Quantitative structure-Activity Relationship (QSAR study has been performed on pattern of structure-molecule relationship. Artificial Neural Network (ANN model used to estimate pattern of enzyme activity structure-molecule and atomic location in three-dimension for compound of flavonoid as the predictors. Value of determination coefficient used to compare between actual value and value of estimating by ANN models based on enzyme’s wavelength, so resulting cross validating is obtained. We use Quasy-Newton algorithm with Broyden-Fletcher-Goldfarb-Shanno (BFGS procedure.

  12. Artificial neural networks (ANN): prediction of sensory measurements from instrumental data

    OpenAIRE

    Carvalho,Naiara Barbosa; Minim,Valéria Paula Rodrigues; Silva,Rita de Cássia dos Santos Navarro; Della Lucia,Suzana Maria; Minim,Luis Aantonio

    2013-01-01

    The objective of this study was to predict by means of Artificial Neural Network (ANN), multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters). Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combination...

  13. Artificial neural network (ANN) approach for modeling Zn(II) adsorption in batch process

    Energy Technology Data Exchange (ETDEWEB)

    Yildiz, Sayiter [Engineering Faculty, Cumhuriyet University, Sivas (Turkmenistan)

    2017-09-15

    Artificial neural networks (ANN) were applied to predict adsorption efficiency of peanut shells for the removal of Zn(II) ions from aqueous solutions. Effects of initial pH, Zn(II) concentrations, temperature, contact duration and adsorbent dosage were determined in batch experiments. The sorption capacities of the sorbents were predicted with the aid of equilibrium and kinetic models. The Zn(II) ions adsorption onto peanut shell was better defined by the pseudo-second-order kinetic model, for both initial pH, and temperature. The highest R{sup 2} value in isotherm studies was obtained from Freundlich isotherm for the inlet concentration and from Temkin isotherm for the sorbent amount. The high R{sup 2} values prove that modeling the adsorption process with ANN is a satisfactory approach. The experimental results and the predicted results by the model with the ANN were found to be highly compatible with each other.

  14. Robust Optimization of Uncertain Logistics Networks

    NARCIS (Netherlands)

    Hosseini, Sara; Dullaert, Wout

    2011-01-01

    Decision makers should concentrate on managing any probable risk of the logistics system, starting from the design phase. As a result, unforeseen conditions during implementation will be less likely to invalidate the basic design plan or disturb the performance targets. One of the main difficulties

  15. Artificial neural network (ANN)-based prediction of depth filter loading capacity for filter sizing.

    Science.gov (United States)

    Agarwal, Harshit; Rathore, Anurag S; Hadpe, Sandeep Ramesh; Alva, Solomon J

    2016-11-01

    This article presents an application of artificial neural network (ANN) modelling towards prediction of depth filter loading capacity for clarification of a monoclonal antibody (mAb) product during commercial manufacturing. The effect of operating parameters on filter loading capacity was evaluated based on the analysis of change in the differential pressure (DP) as a function of time. The proposed ANN model uses inlet stream properties (feed turbidity, feed cell count, feed cell viability), flux, and time to predict the corresponding DP. The ANN contained a single output layer with ten neurons in hidden layer and employed a sigmoidal activation function. This network was trained with 174 training points, 37 validation points, and 37 test points. Further, a pressure cut-off of 1.1 bar was used for sizing the filter area required under each operating condition. The modelling results showed that there was excellent agreement between the predicted and experimental data with a regression coefficient (R2 ) of 0.98. The developed ANN model was used for performing variable depth filter sizing for different clarification lots. Monte-Carlo simulation was performed to estimate the cost savings by using different filter areas for different clarification lots rather than using the same filter area. A 10% saving in cost of goods was obtained for this operation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1436-1443, 2016. © 2016 American Institute of Chemical Engineers.

  16. IDI diesel engine performance and exhaust emission analysis using biodiesel with an artificial neural network (ANN)

    OpenAIRE

    K. Prasada Rao; T. Victor Babu; Anuradha, G.; B.V. Appa Rao

    2016-01-01

    Biodiesel is receiving increasing attention each passing day because of its fuel properties and compatibility. This study investigates the performance and emission characteristics of single cylinder four stroke indirect diesel injection (IDI) engine fueled with Rice Bran Methyl Ester (RBME) with Isopropanol additive. The investigation is done through a combination of experimental data analysis and artificial neural network (ANN) modeling. The study used IDI engine experimental data to evaluat...

  17. Artificial neural networks (ANN: prediction of sensory measurements from instrumental data

    Directory of Open Access Journals (Sweden)

    Naiara Barbosa Carvalho

    2013-12-01

    Full Text Available The objective of this study was to predict by means of Artificial Neural Network (ANN, multilayer perceptrons, the texture attributes of light cheesecurds perceived by trained judges based on instrumental texture measurements. Inputs to the network were the instrumental texture measurements of light cheesecurd (imitative and fundamental parameters. Output variables were the sensory attributes consistency and spreadability. Nine light cheesecurd formulations composed of different combinations of fat and water were evaluated. The measurements obtained by the instrumental and sensory analyses of these formulations constituted the data set used for training and validation of the network. Network training was performed using a back-propagation algorithm. The network architecture selected was composed of 8-3-9-2 neurons in its layers, which quickly and accurately predicted the sensory texture attributes studied, showing a high correlation between the predicted and experimental values for the validation data set and excellent generalization ability, with a validation RMSE of 0.0506.

  18. Dispersion compensation of fiber optic communication system with direct detection using artificial neural networks (ANNs)

    Science.gov (United States)

    Maghrabi, Mahmoud M. T.; Kumar, Shiva; Bakr, Mohamed H.

    2018-02-01

    This work introduces a powerful digital nonlinear feed-forward equalizer (NFFE), exploiting multilayer artificial neural network (ANN). It mitigates impairments of optical communication systems arising due to the nonlinearity introduced by direct photo-detection. In a direct detection system, the detection process is nonlinear due to the fact that the photo-current is proportional to the absolute square of the electric field intensity. The proposed equalizer provides the most efficient computational cost with high equalization performance. Its performance is comparable to the benchmark compensation performance achieved by maximum-likelihood sequence estimator. The equalizer trains an ANN to act as a nonlinear filter whose impulse response removes the intersymbol interference (ISI) distortions of the optical channel. Owing to the proposed extensive training of the equalizer, it achieves the ultimate performance limit of any feed-forward equalizer (FFE). The performance and efficiency of the equalizer is investigated by applying it to various practical short-reach fiber optic communication system scenarios. These scenarios are extracted from practical metro/media access networks and data center applications. The obtained results show that the ANN-NFFE compensates for the received BER degradation and significantly increases the tolerance to the chromatic dispersion distortion.

  19. Predicting the Deflections of Micromachined Electrostatic Actuators Using Artificial Neural Network (ANN

    Directory of Open Access Journals (Sweden)

    Hing Wah LEE

    2009-03-01

    Full Text Available In this study, a general purpose Artificial Neural Network (ANN model based on the feed-forward back-propagation (FFBP algorithm has been used to predict the deflections of a micromachined structures actuated electrostatically under different loadings and geometrical parameters. A limited range of simulation results obtained via CoventorWare™ numerical software will be used initially to train the neural network via back-propagation algorithm. The micromachined structures considered in the analyses are diaphragm, fixed-fixed beams and cantilevers. ANN simulation results are compared with results obtained via CoventorWare™ simulations and existing analytical work for validation purpose. The proposed ANN model accurately predicts the deflections of the micromachined structures with great reduction of simulation efforts, establishing the method superiority. This method can be extended for applications in other sensors particularly for modeling sensors applying electrostatic actuation which are difficult in nature due to the inherent non-linearity of the electro-mechanical coupling response.

  20. IDI diesel engine performance and exhaust emission analysis using biodiesel with an artificial neural network (ANN

    Directory of Open Access Journals (Sweden)

    K. Prasada Rao

    2017-09-01

    Full Text Available Biodiesel is receiving increasing attention each passing day because of its fuel properties and compatibility. This study investigates the performance and emission characteristics of single cylinder four stroke indirect diesel injection (IDI engine fueled with Rice Bran Methyl Ester (RBME with Isopropanol additive. The investigation is done through a combination of experimental data analysis and artificial neural network (ANN modeling. The study used IDI engine experimental data to evaluate nine engine performance and emission parameters including Exhaust Gas Temperature (E.G.T, Brake Specific Fuel Consumption (BSFC, Brake Thermal Efficiency (B.The and various emissions like Hydrocarbons (HC, Carbon monoxide (CO, Carbon dioxide (CO2, Oxygen (O2, Nitrogen oxides (NOX and smoke. For the ANN modeling standard back propagation algorithm was found to be the optimum choice for training the model. A multi-layer perception (MLP network was used for non-linear mapping between the input and output parameters. It was found that ANN was able to predict the engine performance and exhaust emissions with a correlation coefficient of 0.995, 0.980, 0.999, 0.985, 0.999, 0.999, 0.980, 0.999, and 0.999 for E.G.T, BSFC, B.The, HC, O2, CO2, CO, NOX, smoke respectively.

  1. Multifraktalitas dan Studi Komparatif Prediksi Indeks dengan Metode Arima dan Artificial Neural Network (ANN

    Directory of Open Access Journals (Sweden)

    Harjum Muharam

    2008-09-01

    Full Text Available This paper discusses technical analysis widely used by investors. There are many methods that exist and used by investor to predict the future value of a stock. In this paper we start from finding the value of Hurst (H exponent of LQ 45 Index to know the form of the Index. From H value, we could determinate that the time series data is purely random, or ergodic and ant persistent, or persistent to a certain trend. Two prediction tools were chosen, ARIMA (Auto Regressive Integrated Moving Average which is the de facto standard for univariate prediction model in econometrics and Artificial Neural Network (ANN Back Propagation. Data left from ARIMA is used as an input for both methods. We compared prediction error from each method to determine which method is better. The result shows that LQ45 Index is persistent to a certain trend therefore predictable and for outputted sample data ARIMA outperforms ANN.

  2. Impact of Different Carbon Policies on City Logistics Network

    Directory of Open Access Journals (Sweden)

    Yang Jianhua

    2015-01-01

    Full Text Available A programming model for a four-layer urban logistics distribution network is constructed and revised based on three types of carbon emissions policies such as Carbon tax, carbon emissions Cap, Carbon Trade. Effects of different policies on logistics costs and carbon emissions are analyzed based on a spatial Logistics Infrastructure layout of Beijing. Research findings are as follows: First, based on low-carbon policies, the logistics costs and carbon emissions can be changed by different modes of transport in a certain extent; second, only when carbon taxes and carbon trading prices are higher, carbon taxes and carbon trading policies can reduce carbon emissions while not significantly increase logistics costs at the same time, and more effectively achieve carbon reduction targets than use carbon cap policy.

  3. Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN).

    Science.gov (United States)

    Park, Sechan; Kim, Minjeong; Kim, Minhae; Namgung, Hyeong-Gyu; Kim, Ki-Tae; Cho, Kyung Hwa; Kwon, Soon-Bark

    2018-01-05

    The indoor air quality of subway systems can significantly affect the health of passengers since these systems are widely used for short-distance transit in metropolitan urban areas in many countries. The particles generated by abrasion during subway operations and the vehicle-emitted pollutants flowing in from the street in particular affect the air quality in underground subway stations. Thus the continuous monitoring of particulate matter (PM) in underground station is important to evaluate the exposure level of PM to passengers. However, it is difficult to obtain indoor PM data because the measurement systems are expensive and difficult to install and operate for significant periods of time in spaces crowded with people. In this study, we predicted the indoor PM concentration using the information of outdoor PM, the number of subway trains running, and information on ventilation operation by the artificial neural network (ANN) model. As well, we investigated the relationship between ANN's performance and the depth of underground subway station. ANN model showed a high correlation between the predicted and actual measured values and it was able to predict 67∼80% of PM at 6 subway station. In addition, we found that platform shape and depth influenced the model performance. Copyright © 2017 Elsevier B.V. All rights reserved.

  4. Prediction of Tourism Demand in Iran by Using Artificial Neural Network (ANN and Supporting Vector Machine (SVR

    Directory of Open Access Journals (Sweden)

    Seyedehelham Sadatiseyedmahalleh

    2016-02-01

    Full Text Available This research examines and proves this effectiveness connected with artificial neural networks (ANNs as an alternative approach to the use of Support Vector Machine (SVR in the tourism research. This method can be used for the tourism industry to define the turism’s demands in Iran. The outcome reveals the use of ANNs in tourism research might result in better quotations when it comes to prediction bias and accuracy. Even more applications of ANNs in the context of tourism demand evaluation is needed to establish and validate the effects.

  5. A Methodology for Assessing Eco-efficiency in Logistics Networks

    NARCIS (Netherlands)

    J. Quariguasi Frota Neto (João); G. Walther; J.M. Bloemhof-Ruwaard (Jacqueline); J.A.E.E. van Nunen (Jo); T. Spengler

    2006-01-01

    textabstractRecent literature on sustainable logistics networks points to two important questions: (i) How to spot the preferred solution(s) balancing environmental and business concerns? (ii) How to improve the understanding of the trade-offs between these two dimensions? We posit that a complete

  6. A Methodology for Assessing Eco-Efficiency in Logistics Networks

    NARCIS (Netherlands)

    J. Quariguasi Frota Neto (João); G. Walther; J.M. Bloemhof-Ruwaard (Jacqueline); J.A.E.E. van Nunen (Jo); T. Spengler

    2007-01-01

    textabstractRecent literature on sustainable logistics networks points to two important questions: (i) How to spot the preferred solution(s) balancing environmental and business concerns? (ii) How to improve the understanding of the trade-offs between these two dimensions? We posit that a complete

  7. A Methodology for Assessing Eco-Efficiency in Logistics Networks

    NARCIS (Netherlands)

    Quariguasi Frota Neto, J.; Walther, G.; Bloemhof, J.M.; Nunen, van J.A.E.E.; Spengler, T.

    2009-01-01

    Recent literature on sustainable logistics networks points to two important questions: (i) How to spot the preferred solution(s) balancing environmental and business concerns? (ii) How to improve the understanding of the trade-offs between these two dimensions? We posit that a visual exploration of

  8. Network Design in Reverse Logistics: A Quantitative Model

    NARCIS (Netherlands)

    Krikke, H.R.; Kooij, E.J.; Schuur, Peter; Speranza, M. Grazia; Stähly, Paul

    1999-01-01

    The introduction of (extended) producer responsibility forces Original Equipment Manufacturers to solve entirely new managerial problems. One of the issues concerns the physical design of the reverse logistic network, which is a problem that fits into the class of facility-location problems. Since

  9. Predicting company growth using logistic regression and neural networks

    Directory of Open Access Journals (Sweden)

    Marijana Zekić-Sušac

    2016-12-01

    Full Text Available The paper aims to establish an efficient model for predicting company growth by leveraging the strengths of logistic regression and neural networks. A real dataset of Croatian companies was used which described the relevant industry sector, financial ratios, income, and assets in the input space, with a dependent binomial variable indicating whether a company had high-growth if it had annualized growth in assets by more than 20% a year over a three-year period. Due to a large number of input variables, factor analysis was performed in the pre -processing stage in order to extract the most important input components. Building an efficient model with a high classification rate and explanatory ability required application of two data mining methods: logistic regression as a parametric and neural networks as a non -parametric method. The methods were tested on the models with and without variable reduction. The classification accuracy of the models was compared using statistical tests and ROC curves. The results showed that neural networks produce a significantly higher classification accuracy in the model when incorporating all available variables. The paper further discusses the advantages and disadvantages of both approaches, i.e. logistic regression and neural networks in modelling company growth. The suggested model is potentially of benefit to investors and economic policy makers as it provides support for recognizing companies with growth potential, especially during times of economic downturn.

  10. Hybrid intelligence systems and artificial neural network (ANN approach for modeling of surface roughness in drilling

    Directory of Open Access Journals (Sweden)

    Ch. Sanjay

    2014-12-01

    Full Text Available In machining processes, drilling operation is material removal process that has been widely used in manufacturing since industrial revolution. The useful life of cutting tool and its operating conditions largely controls the economics of machining operations. Drilling is most frequently performed material removing process and is used as a preliminary step for many operations, such as reaming, tapping, and boring. Drill wear has a bad effect on the surface finish and dimensional accuracy of the work piece. The surface finish of a machined part is one of the most important quality characteristics in manufacturing industries. The primary objective of this research is the prediction of suitable parameters for surface roughness in drilling. Cutting speed, cutting force, and machining time were given as inputs to the adaptive fuzzy neural network and neuro-fuzzy analysis for estimating the values of surface roughness by using 2, 3, 4, and 5 membership functions. The best structures were selected based on minimum of summation of square with the actual values with the estimated values by artificial neural fuzzy inference system (ANFIS and neuro-fuzzy systems. For artificial neural network (ANN analysis, the number of neurons was selected from 1, 2, 3, … , 20. The learning rate was selected as .5 and .5 smoothing factor was used. The inputs were selected as cutting speed, feed, machining time, and thrust force. The best structures of neural networks were selected based on the criteria as the minimum of summation of square with the actual value of surface roughness. Drilling experiments with 10 mm size were performed at two cutting speeds and feeds. Comparative analysis has been done between the actual values and the estimated values obtained by ANFIS, neuro-fuzzy, and ANN analysis.

  11. Robust Optimization of Fourth Party Logistics Network Design under Disruptions

    Directory of Open Access Journals (Sweden)

    Jia Li

    2015-01-01

    Full Text Available The Fourth Party Logistics (4PL network faces disruptions of various sorts under the dynamic and complex environment. In order to explore the robustness of the network, the 4PL network design with consideration of random disruptions is studied. The purpose of the research is to construct a 4PL network that can provide satisfactory service to customers at a lower cost when disruptions strike. Based on the definition of β-robustness, a robust optimization model of 4PL network design under disruptions is established. Based on the NP-hard characteristic of the problem, the artificial fish swarm algorithm (AFSA and the genetic algorithm (GA are developed. The effectiveness of the algorithms is tested and compared by simulation examples. By comparing the optimal solutions of the 4PL network for different robustness level, it is indicated that the robust optimization model can evade the market risks effectively and save the cost in the maximum limit when it is applied to 4PL network design.

  12. Logistic regression and artificial neural network models for mapping of regional-scale landslide susceptibility in volcanic mountains of West Java (Indonesia)

    Science.gov (United States)

    Ngadisih, Bhandary, Netra P.; Yatabe, Ryuichi; Dahal, Ranjan K.

    2016-05-01

    West Java Province is the most landslide risky area in Indonesia owing to extreme geo-morphological conditions, climatic conditions and densely populated settlements with immense completed and ongoing development activities. So, a landslide susceptibility map at regional scale in this province is a fundamental tool for risk management and land-use planning. Logistic regression and Artificial Neural Network (ANN) models are the most frequently used tools for landslide susceptibility assessment, mainly because they are capable of handling the nature of landslide data. The main objective of this study is to apply logistic regression and ANN models and compare their performance for landslide susceptibility mapping in volcanic mountains of West Java Province. In addition, the model application is proposed to identify the most contributing factors to landslide events in the study area. The spatial database built in GIS platform consists of landslide inventory, four topographical parameters (slope, aspect, relief, distance to river), three geological parameters (distance to volcano crater, distance to thrust and fault, geological formation), and two anthropogenic parameters (distance to road, land use). The logistic regression model in this study revealed that slope, geological formations, distance to road and distance to volcano are the most influential factors of landslide events while, the ANN model revealed that distance to volcano crater, geological formation, distance to road, and land-use are the most important causal factors of landslides in the study area. Moreover, an evaluation of the model showed that the ANN model has a higher accuracy than the logistic regression model.

  13. SU-E-T-206: Improving Radiotherapy Toxicity Based On Artificial Neural Network (ANN) for Head and Neck Cancer Patients

    Energy Technology Data Exchange (ETDEWEB)

    Cho, Daniel D; Wernicke, A Gabriella; Nori, Dattatreyudu; Chao, KSC; Parashar, Bhupesh; Chang, Jenghwa [Weill Cornell Medical College, NY, NY (United States)

    2014-06-01

    Purpose/Objective(s): The aim of this study is to build the estimator of toxicity using artificial neural network (ANN) for head and neck cancer patients Materials/Methods: An ANN can combine variables into a predictive model during training and considered all possible correlations of variables. We constructed an ANN based on the data from 73 patients with advanced H and N cancer treated with external beam radiotherapy and/or chemotherapy at our institution. For the toxicity estimator we defined input data including age, sex, site, stage, pathology, status of chemo, technique of external beam radiation therapy (EBRT), length of treatment, dose of EBRT, status of post operation, length of follow-up, the status of local recurrences and distant metastasis. These data were digitized based on the significance and fed to the ANN as input nodes. We used 20 hidden nodes (for the 13 input nodes) to take care of the correlations of input nodes. For training ANN, we divided data into three subsets such as training set, validation set and test set. Finally, we built the estimator for the toxicity from ANN output. Results: We used 13 input variables including the status of local recurrences and distant metastasis and 20 hidden nodes for correlations. 59 patients for training set, 7 patients for validation set and 7 patients for test set and fed the inputs to Matlab neural network fitting tool. We trained the data within 15% of errors of outcome. In the end we have the toxicity estimation with 74% of accuracy. Conclusion: We proved in principle that ANN can be a very useful tool for predicting the RT outcomes for high risk H and N patients. Currently we are improving the results using cross validation.

  14. Indonesian Maritime Logistics Network Optimization Using Mixed Integer Programming

    Directory of Open Access Journals (Sweden)

    Arry Rahmawan Komarudin

    2017-01-01

    Full Text Available Some causes of the high logistic costs and significant disparity in Indonesia’s commodity price is the imbalanced distribution of the flow of goods between west and east regional of Indonesia and the slow development infrastructure. This issue can be happened because maritime transport infrastructure in Indonesia have not served effectively yet,. Therefore, the provider of shipping services should be able to design the Indonesian maritime logistics network that best suits with the current maritime conditions and provide the greatest benefits. This study aims to obtain a model of decision-making at all levels (strategic, tactical, operational to determine the type of vessel, the shipping routes, and cargo allocation that gives the maximum profit. Main purpose from this study is to develop decision-making model for maritime logistics network design at all levels (strategic, tactical, operational by using Mixed Integer Programming which can solve the combination of fleet-design, ship-scheduling, and cargo routing problems simultaneously.

  15. Anne K. Bang: Islamic Sufi Networks in the Western Indian Ocean (c. 1880-1940. Ripples of Reform.

    Directory of Open Access Journals (Sweden)

    Angelika Brodersen

    2015-03-01

    Full Text Available This contribution offers a review of Anne K. Bang's book: Islamic Sufi Networks in the Western Indian Ocean (c. 1880-1940. Ripples of Reform. Islam in Africa, Volume 16. Leiden: Brill 2014. xiv + 227 pages, € 104.00, ISBN 978-900-425-1342.

  16. Application of Artificial Neural Networks (ANNs for Weight Predictions of Blue Crabs (Callinectes sapidus RATHBUN, 1896 Using Predictor Variables

    Directory of Open Access Journals (Sweden)

    C. TURELI BILEN

    2011-10-01

    Full Text Available An evaluation of the performance of artificial networks (ANNs to estimate the weights of blue crab (Callinectes sapidus catches in Yumurtalık Cove (Iskenderun Bay that uses measured predictor variables is presented, including carapace width (CW, sex (male, female and female with eggs, and sampling month. Blue crabs (n=410 were collected each month between 15 September 1996 and 15 May 1998. Sex, CW, and sampling month were used and specified in the input layer of the network. The weights of the blue crabs were utilized in the output layer of the network. A multi-layer perception architecture model was used and was calibrated with the Levenberg Marguardt (LM algorithm. Finally, the values were determined by the ANN model using the actual data. The mean square error (MSE was measured as 3.3, and the best results had a correlation coefficient (R of 0.93. We compared the predictive capacity of the general linear model (GLM versus the Artificial Neural Network model (ANN for the estimation of the weights of blue crabs from independent field data. The results indicated the higher performance capacity of the ANN to predict weights compared to the GLM (R=0.97 vs. R=0.95, raw variable when evaluated against independent field data.

  17. Interacting Bose gas, the logistic law, and complex networks

    Science.gov (United States)

    Sowa, A.

    2015-01-01

    We discuss a mathematical link between the Quantum Statistical Mechanics and the logistic growth and decay processes. It is based on an observation that a certain nonlinear operator evolution equation, which we refer to as the Logistic Operator Equation (LOE), provides an extension of the standard model of noninteracting bosons. We discuss formal solutions (asymptotic formulas) for a special calibration of the LOE, which sets it in the number-theoretic framework. This trick, in the tradition of Julia and Bost-Connes, makes it possible for us to tap into the vast resources of classical mathematics and, in particular, to construct explicit solutions of the LOE via the Dirichlet series. The LOE is applicable to a range of modeling and simulation tasks, from characterization of interacting boson systems to simulation of some complex man-made networks. The theoretical results enable numerical simulations, which, in turn, shed light at the unique complexities of the rich and multifaceted models resulting from the LOE.

  18. Wavelet transform-based artificial neural networks (WT-ANN) in PM10 pollution level estimation, based on circular variables.

    Science.gov (United States)

    Shekarrizfard, Maryam; Karimi-Jashni, A; Hadad, K

    2012-01-01

    In this paper, a novel method in the estimation and prediction of PM(10) is introduced using wavelet transform-based artificial neural networks (WT-ANN). First, the application of wavelet transform, selected for its temporal shift properties and multiresolution analysis characteristics enabling it to reduce disturbing perturbations in input training set data, is presented. Afterward, the circular statistical indices which are used in this method are formally introduced in order to investigate the relation between PM(10) levels and circular meteorological variables. Then, the results of the simulation of PM(10) based on WT-ANN by use of MATLAB software are discussed. The results of the above-mentioned simulation show an enhanced accuracy and speed in PM(10) estimation/prediction and a high degree of robustness compared with traditional ANN models.

  19. Sustainable Logistics Network Modeling for Enterprise Supply Chain

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    Lan Zhu

    2017-01-01

    Full Text Available With the expansion of the study about green logistics, sustainable supply chain management (SSCM has appeared as a new concept in current economic circumstance. This paper studies the sustainability optimization of enterprise logistics network from a strategic perspective and proposes a multiobjective sustainable logistics optimization model considering three dimensions of sustainability: economy, environment, and society. In this model, the environment factor was measured with a Life Cycle Assessment (LCA method based on Chinese Life Cycle Database (CLCD, while for social factors, Sustainability Reporting Guidelines (GRI are utilized to quantify the social performance. Moreover, the model was solved with an adapted version of the ε-constraint method named augment constraint algorithm (AUGMENCON through GAMS software. The numerical experiment results of a computer manufacturer supply chain show that the proposed model is able to integrate all dimensions of sustainability and simultaneously prove the capability of AUGMENCON in providing a set of trade-off solutions for the decision makers to make different decisions under different environment and social requirements.

  20. Artificial Neural Network (ANN) Model to Predict Depression among Geriatric Population at a Slum in Kolkata, India.

    Science.gov (United States)

    Sau, Arkaprabha; Bhakta, Ishita

    2017-05-01

    Depression is one of the most important causes of mortality and morbidity among the geriatric population. Although, the aging brain is more vulnerable to depression, it cannot be considered as physiological and an inevitable part of ageing. Various sociodemographic and morbidity factors are responsible for the depression among them. Using Artificial Neural Network (ANN) model depression can be predicted from various sociodemographic variables and co morbid conditions even at community level by the grass root level health care workers. To predict depression among geriatric population from sociodemographic and morbidity attributes using ANN. An observational descriptive study with cross-sectional design was carried out at a slum under the service area of Bagbazar Urban Health and Training Centre (UHTC) in Kolkata. Among 126 elderlies under Bagbazar UHTC, 105 were interviewed using predesigned and pretested schedule. Depression status was assessed using 30 item Geriatric Depression Scale. WEKA 3.8.0 was used to develop the ANN model and test its performance. Prevalence of depression among the study population was 45.7%. Various sociodemographic variables like age, gender, literacy, living spouse, working status, personal income, family type, substance abuse and co morbid conditions like visual problem, mobility problem, hearing problem and sleeping problem were taken into consideration to develop the model. Prediction accuracy of this ANN model was 97.2%. Depression among geriatric population can be predicted accurately using ANN model from sociodemographic and morbidity attributes.

  1. Artificial neural network (ANN velocity better identifies benign prostatic hyperplasia but not prostate cancer compared with PSA velocity

    Directory of Open Access Journals (Sweden)

    Lein Michael

    2008-09-01

    Full Text Available Abstract Background To validate an artificial neural network (ANN based on the combination of PSA velocity (PSAV with a %free PSA-based ANN to enhance the discrimination between prostate cancer (PCa and benign prostate hyperplasia (BPH. Methods The study comprised 199 patients with PCa (n = 49 or BPH (n = 150 with at least three PSA estimations and a minimum of three months intervals between the measurements. Patients were classified into three categories according to PSAV and ANN velocity (ANNV calculated with the %free based ANN "ProstataClass". Group 1 includes the increasing PSA and ANN values, Group 2 the stable values, and Group 3 the decreasing values. Results 71% of PCa patients typically have an increasing PSAV. In comparison, the ANNV only shows this in 45% of all PCa patients. However, BPH patients benefit from ANNV since the stable values are significantly more (83% vs. 65% and increasing values are less frequently (11% vs. 21% if the ANNV is used instead of the PSAV. Conclusion PSAV has only limited usefulness for the detection of PCa with only 71% increasing PSA values, while 29% of all PCa do not have the typical PSAV. The ANNV cannot improve the PCa detection rate but may save 11–17% of unnecessary prostate biopsies in known BPH patients.

  2. Logistics Mode and Network Planning for Recycle of Crop Straw Resources

    OpenAIRE

    Zhou, Lingyun; Gu, Weidong; Zhang, Qing

    2013-01-01

    To realize the straw biomass industrialized development, it should speed up building crop straw resource recycle logistics network, increasing straw recycle efficiency, and reducing straw utilization cost. On the basis of studying straw recycle process, this paper presents innovative concept and property of straw recycle logistics network, analyses design thinking of straw recycle logistics network, and works out straw recycle logistics mode and network topological structure. Finally, it come...

  3. The Segmentation of Point Clouds with K-Means and ANN (artifical Neural Network)

    Science.gov (United States)

    Kuçak, R. A.; Özdemir, E.; Erol, S.

    2017-05-01

    Segmentation of point clouds is recently used in many Geomatics Engineering applications such as the building extraction in urban areas, Digital Terrain Model (DTM) generation and the road or urban furniture extraction. Segmentation is a process of dividing point clouds according to their special characteristic layers. The present paper discusses K-means and self-organizing map (SOM) which is a type of ANN (Artificial Neural Network) segmentation algorithm which treats the segmentation of point cloud. The point clouds which generate with photogrammetric method and Terrestrial Lidar System (TLS) were segmented according to surface normal, intensity and curvature. Thus, the results were evaluated. LIDAR (Light Detection and Ranging) and Photogrammetry are commonly used to obtain point clouds in many remote sensing and geodesy applications. By photogrammetric method or LIDAR method, it is possible to obtain point cloud from terrestrial or airborne systems. In this study, the measurements were made with a Leica C10 laser scanner in LIDAR method. In photogrammetric method, the point cloud was obtained from photographs taken from the ground with a 13 MP non-metric camera.

  4. THE SEGMENTATION OF POINT CLOUDS WITH K-MEANS AND ANN (ARTIFICAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    R. A. Kuçak

    2017-05-01

    Full Text Available Segmentation of point clouds is recently used in many Geomatics Engineering applications such as the building extraction in urban areas, Digital Terrain Model (DTM generation and the road or urban furniture extraction. Segmentation is a process of dividing point clouds according to their special characteristic layers. The present paper discusses K-means and self-organizing map (SOM which is a type of ANN (Artificial Neural Network segmentation algorithm which treats the segmentation of point cloud. The point clouds which generate with photogrammetric method and Terrestrial Lidar System (TLS were segmented according to surface normal, intensity and curvature. Thus, the results were evaluated. LIDAR (Light Detection and Ranging and Photogrammetry are commonly used to obtain point clouds in many remote sensing and geodesy applications. By photogrammetric method or LIDAR method, it is possible to obtain point cloud from terrestrial or airborne systems. In this study, the measurements were made with a Leica C10 laser scanner in LIDAR method. In photogrammetric method, the point cloud was obtained from photographs taken from the ground with a 13 MP non-metric camera.

  5. Resource Allocation Optimization Model of Collaborative Logistics Network Based on Bilevel Programming

    Directory of Open Access Journals (Sweden)

    Xiao-feng Xu

    2017-01-01

    Full Text Available Collaborative logistics network resource allocation can effectively meet the needs of customers. It can realize the overall benefit maximization of the logistics network and ensure that collaborative logistics network runs orderly at the time of creating value. Therefore, this article is based on the relationship of collaborative logistics network supplier, the transit warehouse, and sellers, and we consider the uncertainty of time to establish a bilevel programming model with random constraints and propose a genetic simulated annealing hybrid intelligent algorithm to solve it. Numerical example shows that the method has stronger robustness and convergence; it can achieve collaborative logistics network resource allocation rationalization and optimization.

  6. Prediction by Artificial Neural Networks (ANN of the diffusivity, mass, moisture, volume and solids on osmotically dehydrated yacon (Smallantus sonchifolius

    Directory of Open Access Journals (Sweden)

    Julio Rojas Naccha

    2012-09-01

    Full Text Available The predictive ability of Artificial Neural Network (ANN on the effect of the concentration (30, 40, 50 y 60 % w/w and temperature (30, 40 y 50°C of fructooligosaccharides solution, in the mass, moisture, volume and solids of osmodehydrated yacon cubes, and in the coefficients of the water means effective diffusivity with and without shrinkage was evaluated. The Feedforward type ANN with the Backpropagation training algorithms and the Levenberg-Marquardt weight adjustment was applied, using the following topology: 10-5 goal error, 0.01 learning rate, 0.5 moment coefficient, 2 input neurons, 6 output neurons, one hidden layer with 18 neurons, 15 training stages and logsig-pureline transfer functions. The overall average error achieved by the ANN was 3.44% and correlation coefficients were bigger than 0.9. No significant differences were found between the experimental values and the predicted values achieved by the ANN and with the predicted values achieved by a statistical model of second-order polynomial regression (p > 0.95.

  7. A memetic algorithm for bi-objective integrated forward/reverse logistics network design

    NARCIS (Netherlands)

    Pishvaee, Mir Saman; Farahani, Reza Zanjirani; Dullaert, Wout

    Logistics network design is a major strategic issue due to its impact on the efficiency and responsiveness of the supply chain. This paper proposes a model for integrated logistics network design to avoid the sub-optimality caused by a separate, sequential design of forward and reverse logistics

  8. The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

    Science.gov (United States)

    Saro, Lee; Woo, Jeon Seong; Kwan-Young, Oh; Moung-Jin, Lee

    2016-02-01

    The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs) followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS). These factors were analysed using artificial neural network (ANN) and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50%) and a test set (50%). A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10%) was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%). Of the weights used in the artificial neural network model, `slope' yielded the highest weight value (1.330), and `aspect' yielded the lowest value (1.000). This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

  9. The spatial prediction of landslide susceptibility applying artificial neural network and logistic regression models: A case study of Inje, Korea

    Directory of Open Access Journals (Sweden)

    Saro Lee

    2016-02-01

    Full Text Available The aim of this study is to predict landslide susceptibility caused using the spatial analysis by the application of a statistical methodology based on the GIS. Logistic regression models along with artificial neutral network were applied and validated to analyze landslide susceptibility in Inje, Korea. Landslide occurrence area in the study were identified based on interpretations of optical remote sensing data (Aerial photographs followed by field surveys. A spatial database considering forest, geophysical, soil and topographic data, was built on the study area using the Geographical Information System (GIS. These factors were analysed using artificial neural network (ANN and logistic regression models to generate a landslide susceptibility map. The study validates the landslide susceptibility map by comparing them with landslide occurrence areas. The locations of landslide occurrence were divided randomly into a training set (50% and a test set (50%. A training set analyse the landslide susceptibility map using the artificial network along with logistic regression models, and a test set was retained to validate the prediction map. The validation results revealed that the artificial neural network model (with an accuracy of 80.10% was better at predicting landslides than the logistic regression model (with an accuracy of 77.05%. Of the weights used in the artificial neural network model, ‘slope’ yielded the highest weight value (1.330, and ‘aspect’ yielded the lowest value (1.000. This research applied two statistical analysis methods in a GIS and compared their results. Based on the findings, we were able to derive a more effective method for analyzing landslide susceptibility.

  10. Transmission Risks of Schistosomiasis Japonica: Extraction from Back-propagation Artificial Neural Network and Logistic Regression Model

    Science.gov (United States)

    Xu, Jun-Fang; Xu, Jing; Li, Shi-Zhu; Jia, Tia-Wu; Huang, Xi-Bao; Zhang, Hua-Ming; Chen, Mei; Yang, Guo-Jing; Gao, Shu-Jing; Wang, Qing-Yun; Zhou, Xiao-Nong

    2013-01-01

    Background The transmission of schistosomiasis japonica in a local setting is still poorly understood in the lake regions of the People's Republic of China (P. R. China), and its transmission patterns are closely related to human, social and economic factors. Methodology/Principal Findings We aimed to apply the integrated approach of artificial neural network (ANN) and logistic regression model in assessment of transmission risks of Schistosoma japonicum with epidemiological data collected from 2339 villagers from 1247 households in six villages of Jiangling County, P.R. China. By using the back-propagation (BP) of the ANN model, 16 factors out of 27 factors were screened, and the top five factors ranked by the absolute value of mean impact value (MIV) were mainly related to human behavior, i.e. integration of water contact history and infection history, family with past infection, history of water contact, infection history, and infection times. The top five factors screened by the logistic regression model were mainly related to the social economics, i.e. village level, economic conditions of family, age group, education level, and infection times. The risk of human infection with S. japonicum is higher in the population who are at age 15 or younger, or with lower education, or with the higher infection rate of the village, or with poor family, and in the population with more than one time to be infected. Conclusion/Significance Both BP artificial neural network and logistic regression model established in a small scale suggested that individual behavior and socioeconomic status are the most important risk factors in the transmission of schistosomiasis japonica. It was reviewed that the young population (≤15) in higher-risk areas was the main target to be intervened for the disease transmission control. PMID:23556015

  11. Global Logistic Network of Courier Services for the 21" Century

    Directory of Open Access Journals (Sweden)

    Ratko Zelenika

    2006-09-01

    Full Text Available The development of logistics has contributed a lot to all thebranches of economy. A successful economic subject cannotexist without a well-developed logistic branch. In economicsthe organization of logistics is becoming a strategic element regardingthe policy making of enterprises. Logistics belongs to anarea that will play an important role in our lives; therefore, thedevelopment of a logistic system is of an exceptional significancefor the economy and also for the non-economic sphere ofactivities. So, modem logistics enables us to bring into line differentinterests in management of material current. Global organizersof logistic services are developing and specializing insome essential services, such as combining of cargo into assemblyconsignments and also their distribution. The biggest globaltenderers of logistic services are considered, DHL, TNT,FEDEX, UPS ...

  12. THE INFORMATIONAL SYSTEM FOR THE COLLABORATIVE LOGISTICS NETWORKS

    Directory of Open Access Journals (Sweden)

    NAIANA ŢARCĂ

    2011-01-01

    Full Text Available This paper presents an informatic system designed for collaborative logistic networks. The informational system is composed of structured informational modules that can easily be modified in order to facilitate the testing of the different algorithms that are being used. The informational system has two components, in the form of web application modules, which are connected to the user-specific modules (THE CLIENT WEB APPLICATION and to the server-specific modules (THE SERVER WEB APPLICATION, respectively. These two modules operate the transmission of information, the demands of the client and the offers generated by the server. The designed informational system has been tested in actual operating conditions, by co-optating ten EMSs from the Bihor county area. Some of the elements considered positive by the users, in the testing period, were: usability, the automatic assignment of a motor vehicle according to the characteristics of the product, the automatic route generation, the selection of goods according to the cluster “route” of the system.

  13. An Optimal Hierarchical Decision Model for a Regional Logistics Network with Environmental Impact Consideration

    Science.gov (United States)

    Zhang, Dezhi; Li, Shuangyan

    2014-01-01

    This paper proposes a new model of simultaneous optimization of three-level logistics decisions, for logistics authorities, logistics operators, and logistics users, for regional logistics network with environmental impact consideration. The proposed model addresses the interaction among the three logistics players in a complete competitive logistics service market with CO2 emission charges. We also explicitly incorporate the impacts of the scale economics of the logistics park and the logistics users' demand elasticity into the model. The logistics authorities aim to maximize the total social welfare of the system, considering the demand of green logistics development by two different methods: optimal location of logistics nodes and charging a CO2 emission tax. Logistics operators are assumed to compete with logistics service fare and frequency, while logistics users minimize their own perceived logistics disutility given logistics operators' service fare and frequency. A heuristic algorithm based on the multinomial logit model is presented for the three-level decision model, and a numerical example is given to illustrate the above optimal model and its algorithm. The proposed model provides a useful tool for modeling competitive logistics services and evaluating logistics policies at the strategic level. PMID:24977209

  14. Application of Artificial Neural Network (ANN for the prediction of EL-AGAMY wastewater treatment plant performance-EGYPT

    Directory of Open Access Journals (Sweden)

    Mahmoud S. Nasr

    2012-03-01

    Full Text Available A reliable model for any Wastewater Treatment Plant WWTP is essential in order to provide a tool for predicting its performance and to form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. This paper focuses on applying an Artificial Neural Network (ANN approach with a Feed-Forward Back-Propagation to predict the performance of EL-AGAMY WWTP-Alexandria in terms of Chemical Oxygen Demand (COD, Biochemical Oxygen Demand (BOD and Total Suspended Solids (TSSs data gathered during a research over a 1-year period. The study signifies that the ANN can predict the plant performance with correlation coefficient (R between the observed and predicted output variables reached up to 0.90. Moreover, ANN provides an effective analyzing and diagnosing tool to understand and simulate the non-linear behavior of the plant, and is used as a valuable performance assessment tool for plant operators and decision makers.

  15. PREDIKSI MASA KEDALUWARSA WAFER DENGAN ARTIFICIAL NEURAL NETWORK (ANN BERDASARKAN PARAMETER NILAI KAPASITANSI (Prediction of Wafer Shelf Life Using Artificial Neural Network Based on Capacitance Parameter

    Directory of Open Access Journals (Sweden)

    Erna Rusliana Muhamad Saleh

    2014-02-01

    Full Text Available Wafer is type of biscuit frequently found on expired condition in market, therefore prediction method should be implemented to avoid this condition. apart from the prediction of shelf-life of wafer done by laboratory test, which were time-consuming, expensive, required trained panelists, complex equipment and suitable ambience, artificial neural network (ANN based dielectric parameters was proposed in nthis study. The aim of study was to develop model to predict shelf-life employing aNN based capacitance parameter. Back propagation algorithm with trial and error was applied in variations of nodes per hidden layer, number of hidden layers, activation functions, the function of learnings and epochs. The result of study was the model was able to predict wafer shelf-life. The accuracy level was shown by low MSE value (0.01 and high coefficient correlation value (89.25%. Keywords: artificial Neural Network, shelf-life, waffer, dielectric, capacitance   ABSTRAK Wafer adalah jenis makanan kering yang sering ditemukan kedaluwarsa. Penentuan masa kedaluwarsa dengan observasi laboratorium memiliki beberapa kelemahan, diantaranya memakan waktu, panelis terlatih, suasana yang tepat, biaya dan alat uji yang kompleks. alternatif solusinya adalah penggunaan artificial Neural Network (ANN berbasiskan parameter kapasitansi. Tujuan kerja ilmiah ini adalah untuk memprediksi masa kedaluwarsa wafer menggunakan aNN berbasiskan parameter kapasitansi. algoritma pembelajaran yang digunakan adalah Backpropagation dengan trial and error variasi jumlah node per hidden layer, jumlah hidden layer, fungsi aktivasi, fungsi pembelajaran dan epoch. Hasil prediksi menunjukkan bahwa aNN hasil pelatihan yang dikombinasikan dengan parameter kapasitansi mampu memprediksi masa kedaluwarsa wafer dengan MSE terendah 0,01 dan R tertinggi 89,25%. Kata kunci: Jaringan Syaraf Tiruan, masa kedaluwarsa, wafer, dielektrik, kapasitansi

  16. Determination of oil well production performance using artificial neural network (ANN linked to the particle swarm optimization (PSO tool

    Directory of Open Access Journals (Sweden)

    Mohammad Ali Ahmadi

    2015-06-01

    In this work, novel and rigorous methods based on two different types of intelligent approaches including the artificial neural network (ANN linked to the particle swarm optimization (PSO tool are developed to precisely forecast the productivity of horizontal wells under pseudo-steady-state conditions. It was found that there is very good match between the modeling output and the real data taken from the literature, so that a very low average absolute error percentage is attained (e.g., <0.82%. The developed techniques can be also incorporated in the numerical reservoir simulation packages for the purpose of accuracy improvement as well as better parametric sensitivity analysis.

  17. Application of back-propagation artificial neural network (ANN) to predict crystallite size and band gap energy of ZnO quantum dots

    Science.gov (United States)

    Pelicano, Christian Mark; Rapadas, Nick; Cagatan, Gerard; Magdaluyo, Eduardo

    2017-12-01

    Herein, the crystallite size and band gap energy of zinc oxide (ZnO) quantum dots were predicted using artificial neural network (ANN). Three input factors including reagent ratio, growth time, and growth temperature were examined with respect to crystallite size and band gap energy as response factors. The generated results from neural network model were then compared with the experimental results. Experimental crystallite size and band gap energy of ZnO quantum dots were measured from TEM images and absorbance spectra, respectively. The Levenberg-Marquardt (LM) algorithm was used as the learning algorithm for the ANN model. The performance of the ANN model was then assessed through mean square error (MSE) and regression values. Based on the results, the ANN modelling results are in good agreement with the experimental data.

  18. Integration of logistics network in local food supply chains

    OpenAIRE

    Bosona, Techane

    2013-01-01

    The demand for locally produced food is increasing as global food supply system has considerably affected the confidence of consumers by increasing tonne-kilometres, food safety risk, environmental impact, and disconnecting local food producers and consumers. However, local food suppliers are not in the position to compete with large scale food supply systems due to high logistics costs. The overall objective of this study was to improve the logistics management of local food supply chains. I...

  19. NETWORK-CENTRIC WARFARE AND SOME PARTICULAR ASPECTS OF LOGISTICS BASED ON NETWORKING

    Directory of Open Access Journals (Sweden)

    Petrişor JALBĂ

    2015-04-01

    Full Text Available Within the framework of the current revolution in military affairs, at the End of the Cold War a new concept was born: the concept of War Based on Computer Networking or NCW Network Centric-Warfare which was established as a central element of modern military operations. Determined by theprogress recorded in the field of communication systems of all types, technology of information (HI-Tech, IT, war based on computer networking brings a change in the war paradigm and its inherent components In this respect, logistics based on computer networking represents one of the ways in which the reality of the battlefield is preserved which enhances the joint perspective upon the military forces.

  20. A New Hybrid Method Logistic Regression and Feedforward Neural Network for Lung Cancer Data

    OpenAIRE

    Taner Tunç

    2012-01-01

    Logistic regression (LR) is a conventional statistical technique used for data classification problem. Logistic regression is a model-based method, and it uses nonlinear model structure. Another technique used for classification is feedforward artificial neural networks. Feedforward artificial neural network is a data-based method which can model nonlinear models through its activation function. In this study, a hybrid approach of model-based logistic regression technique and data-based artif...

  1. On the Simulation-Based Reliability of Complex Emergency Logistics Networks in Post-Accident Rescues.

    Science.gov (United States)

    Wang, Wei; Huang, Li; Liang, Xuedong

    2018-01-06

    This paper investigates the reliability of complex emergency logistics networks, as reliability is crucial to reducing environmental and public health losses in post-accident emergency rescues. Such networks' statistical characteristics are analyzed first. After the connected reliability and evaluation indices for complex emergency logistics networks are effectively defined, simulation analyses of network reliability are conducted under two different attack modes using a particular emergency logistics network as an example. The simulation analyses obtain the varying trends in emergency supply times and the ratio of effective nodes and validates the effects of network characteristics and different types of attacks on network reliability. The results demonstrate that this emergency logistics network is both a small-world and a scale-free network. When facing random attacks, the emergency logistics network steadily changes, whereas it is very fragile when facing selective attacks. Therefore, special attention should be paid to the protection of supply nodes and nodes with high connectivity. The simulation method provides a new tool for studying emergency logistics networks and a reference for similar studies.

  2. Transmission Risks of Schistosomiasis Japonica: Extraction from Back-propagation Artificial Neural Network and Logistic Regression Model: e2123

    National Research Council Canada - National Science Library

    Jun-Fang Xu; Jing Xu; Shi-Zhu Li; Tia-Wu Jia; Xi-Bao Huang; Hua-Ming Zhang; Mei Chen; Guo-Jing Yang; Shu-Jing Gao; Qing-Yun Wang; Xiao-Nong Zhou

    2013-01-01

    ...), and its transmission patterns are closely related to human, social and economic factors. Methodology/Principal Findings We aimed to apply the integrated approach of artificial neural network (ANN...

  3. Application of experimental design approach and artificial neural network (ANN) for the determination of potential micellar-enhanced ultrafiltration process.

    Science.gov (United States)

    Rahmanian, Bashir; Pakizeh, Majid; Mansoori, Seyed Ali Akbar; Abedini, Reza

    2011-03-15

    In this study, micellar-enhanced ultrafiltration (MEUF) was applied to remove zinc ions from wastewater efficiently. Frequently, experimental design and artificial neural networks (ANNs) have been successfully used in membrane filtration process in recent years. In the present work, prediction of the permeate flux and rejection of metal ions by MEUF was tested, using design of experiment (DOE) and ANN models. In order to reach the goal of determining all the influential factors and their mutual effect on the overall performance the fractional factorial design has been used. The results show that due to the complexity in generalization of the MEUF process by any mathematical model, the neural network proves to be a very promising method in compared with fractional factorial design for the purpose of process simulation. These mathematical models are found to be reliable and predictive tools with an excellent accuracy, because their AARE was ±0.229%, ±0.017%, in comparison with experimental values for permeate flux and rejection, respectively. Copyright © 2010 Elsevier B.V. All rights reserved.

  4. A mathematical model for optimization of an integrated network logistic design

    Directory of Open Access Journals (Sweden)

    Lida Tafaghodi

    2011-10-01

    Full Text Available In this study, the integrated forward/reverse logistics network is investigated, and a capacitated multi-stage, multi-product logistics network design is proposed by formulating a generalized logistics network problem into a mixed-integer nonlinear programming model (MINLP for minimizing the total cost of the closed-loop supply chain network. Moreover, the proposed model is solved by using optimization solver, which provides the decisions related to the facility location problem, optimum quantity of shipped product, and facility capacity. Numerical results show the power of the proposed MINLP model to avoid th sub-optimality caused by separate design of forward and reverse logistics networks and to handle various transportation modes and periodic demand.

  5. Tactical/Operational Decision Making for Designing Green Logistics Networks

    OpenAIRE

    Mallidis, Ioannis; Dekker, Rommert; Vlachos, Dimitrios

    2013-01-01

    textabstractCap and trade regulations along with an increasing consumer and company demand for green products and services constitute two major drivers for motivating corporations to adopt green practices. However, the adoption of such practices usually increases their operational costs. Therefore, the trade-off between “green” and cost-optimal policies is a common challenge for most organizations, at least in developed countries. The purpose of this paper is to assess alternative logistic ne...

  6. The use of artificial neural network (ANN) for the prediction and simulation of oil degradation in wastewater by AOP.

    Science.gov (United States)

    Mustafa, Yasmen A; Jaid, Ghydaa M; Alwared, Abeer I; Ebrahim, Mothana

    2014-06-01

    The application of advanced oxidation process (AOP) in the treatment of wastewater contaminated with oil was investigated in this study. The AOP investigated is the homogeneous photo-Fenton (UV/H2O2/Fe(+2)) process. The reaction is influenced by the input concentration of hydrogen peroxide H2O2, amount of the iron catalyst Fe(+2), pH, temperature, irradiation time, and concentration of oil in the wastewater. The removal efficiency for the used system at the optimal operational parameters (H2O2 = 400 mg/L, Fe(+2) = 40 mg/L, pH = 3, irradiation time = 150 min, and temperature = 30 °C) for 1,000 mg/L oil load was found to be 72%. The study examined the implementation of artificial neural network (ANN) for the prediction and simulation of oil degradation in aqueous solution by photo-Fenton process. The multilayered feed-forward networks were trained by using a backpropagation algorithm; a three-layer network with 22 neurons in the hidden layer gave optimal results. The results show that the ANN model can predict the experimental results with high correlation coefficient (R (2) = 0.9949). The sensitivity analysis showed that all studied variables (H2O2, Fe(+2), pH, irradiation time, temperature, and oil concentration) have strong effect on the oil degradation. The pH was found to be the most influential parameter with relative importance of 20.6%.

  7. Bifurcation behaviors of synchronized regions in logistic map networks with coupling delay

    Science.gov (United States)

    Tang, Longkun; Wu, Xiaoqun; Lü, Jinhu; Lu, Jun-an

    2015-03-01

    Network synchronized regions play an extremely important role in network synchronization according to the master stability function framework. This paper focuses on network synchronous state stability via studying the effects of nodal dynamics, coupling delay, and coupling way on synchronized regions in Logistic map networks. Theoretical and numerical investigations show that (1) network synchronization is closely associated with its nodal dynamics. Particularly, the synchronized region bifurcation points through which the synchronized region switches from one type to another are in good agreement with those of the uncoupled node system, and chaotic nodal dynamics can greatly impede network synchronization. (2) The coupling delay generally impairs the synchronizability of Logistic map networks, which is also dominated by the parity of delay for some nodal parameters. (3) A simple nonlinear coupling facilitates network synchronization more than the linear one does. The results found in this paper will help to intensify our understanding for the synchronous state stability in discrete-time networks with coupling delay.

  8. Detection of Static Air-Gap Eccentricity in Three Phase induction Motor by Using Artificial Neural Network (ANN

    Directory of Open Access Journals (Sweden)

    Hayder O. Alwan

    2017-05-01

    Full Text Available This paper presents the effect of the static air-gap eccentricity on the performance of a three phase induction motor .The Artificial Neural Network (ANN approach has been used to detect this fault .This technique depends upon the amplitude of the positive and negative harmonics of the frequency. Two motors of (2.2 Kw have been used to achieve the actual fault and desirable data at no-load, half-load and full-load conditions. Motor Current Signature analysis (MCSA based on stator current has been used to detect eccentricity fault. Feed forward neural network and error back propagation training algorithms are used to perform the motor fault detection. The inputs of artificial neural network are the amplitudes of the positive and negative harmonics and the speed, and the output is the type of fault. The training of neural network is achieved by data through the experiments test on healthy and faulty motor and the diagnostic system can discriminate between “healthy” and “faulty” machine.

  9. A New Hybrid Method Logistic Regression and Feedforward Neural Network for Lung Cancer Data

    Directory of Open Access Journals (Sweden)

    Taner Tunç

    2012-01-01

    Full Text Available Logistic regression (LR is a conventional statistical technique used for data classification problem. Logistic regression is a model-based method, and it uses nonlinear model structure. Another technique used for classification is feedforward artificial neural networks. Feedforward artificial neural network is a data-based method which can model nonlinear models through its activation function. In this study, a hybrid approach of model-based logistic regression technique and data-based artificial neural network was proposed for classification purposes. The proposed approach was applied to lung cancer data, and obtained results were compared. It was seen that the proposed hybrid approach was superior to logistic regression and feedforward artificial neural networks with respect to many criteria.

  10. The Concept of a Logistics Centre Model as a Nodal Point of a Transport and Logistics Network

    Science.gov (United States)

    Krzyżaniak, Stanisław; Hajdul, Marcin; Fechner, Ireneusz

    2012-06-01

    The paper presents a concept of a logistics centre model. The model is based on defined output/input flows of goods. Therefore it is possible to determine and estimate the load of elements of the centre infrastructure being at the disposal of individual service providers operating within the centre. Input and output flows are defined on the basis of the balance of flows directed to the centre from the whole logistics and transport network and are considered split into: product groups, types of transport, various transport forms of cargo. The seasonal nature of these flows has been taken into consideration. The calculations of infrastructure load assume that every service provider operating within the centre can also use elements of infrastructure owned by other centre users. The proposed model can serve various purposes. An emphasis is put herein on the possibility of estimating the degree of the transport and logistics infrastructure load of the centre on the basis of historical data as well as on the basis of forecasts. As a result, the needs related to the access to the infrastructure can be specified and used to determine and verify investment plans.

  11. A multimodal logistics service network design with time windows and environmental concerns

    OpenAIRE

    Zhang, Dezhi; He, Runzhong; Li, Shuangyan; Wang, Zhongwei

    2017-01-01

    The design of a multimodal logistics service network with customer service time windows and environmental costs is an important and challenging issue. Accordingly, this work established a model to minimize the total cost of multimodal logistics service network design with time windows and environmental concerns. The proposed model incorporates CO2 emission costs to determine the optimal transportation mode combinations and investment selections for transfer nodes, which consider transport cos...

  12. Sustainable reverse logistics network design for household plastic waste

    NARCIS (Netherlands)

    Bing, X.; Bloemhof, J.M.; Vorst, van der J.G.A.J.

    2014-01-01

    Plastic recycling is a legal requirement and can yield environmental benefits. In the Netherlands, there is a complex network of various collection methods, separation centers, sorting centers and reprocessors. The first step of the recycling system, separating plastics from other waste, can occur

  13. Methodologies for the assessment of earthquake-triggered landslides hazard. A comparison of Logistic Regression and Artificial Neural Network models.

    Science.gov (United States)

    García-Rodríguez, M. J.; Malpica, J. A.; Benito, B.

    2009-04-01

    In recent years, interest in landslide hazard assessment studies has increased substantially. They are appropriate for evaluation and mitigation plan development in landslide-prone areas. There are several techniques available for landslide hazard research at a regional scale. Generally, they can be classified in two groups: qualitative and quantitative methods. Most of qualitative methods tend to be subjective, since they depend on expert opinions and represent hazard levels in descriptive terms. On the other hand, quantitative methods are objective and they are commonly used due to the correlation between the instability factors and the location of the landslides. Within this group, statistical approaches and new heuristic techniques based on artificial intelligence (artificial neural network (ANN), fuzzy logic, etc.) provide rigorous analysis to assess landslide hazard over large regions. However, they depend on qualitative and quantitative data, scale, types of movements and characteristic factors used. We analysed and compared an approach for assessing earthquake-triggered landslides hazard using logistic regression (LR) and artificial neural networks (ANN) with a back-propagation learning algorithm. One application has been developed in El Salvador, a country of Central America where the earthquake-triggered landslides are usual phenomena. In a first phase, we analysed the susceptibility and hazard associated to the seismic scenario of the 2001 January 13th earthquake. We calibrated the models using data from the landslide inventory for this scenario. These analyses require input variables representing physical parameters to contribute to the initiation of slope instability, for example, slope gradient, elevation, aspect, mean annual precipitation, lithology, land use, and terrain roughness, while the occurrence or non-occurrence of landslides is considered as dependent variable. The results of the landslide susceptibility analysis are checked using landslide

  14. MODELS AND METHODS FOR LOGISTICS HUB LOCATION: A REVIEW TOWARDS TRANSPORTATION NETWORKS DESIGN

    Directory of Open Access Journals (Sweden)

    Carolina Luisa dos Santos Vieira

    Full Text Available ABSTRACT Logistics hubs affect the distribution patterns in transportation networks since they are flow-concentrating structures. Indeed, the efficient moving of goods throughout supply chains depends on the design of such networks. This paper presents a literature review on the logistics hub location problem, providing an outline of modeling approaches, solving techniques, and their applicability to such context. Two categories of models were identified. While multi-criteria models may seem best suited to find optimal locations, they do not allow an assessment of the impact of new hubs on goods flow and on the transportation network. On the other hand, single-criterion models, which provide location and flow allocation information, adopt network simplifications that hinder an accurate representation of the relationshipbetween origins, destinations, and hubs. In view of these limitations we propose future research directions for addressing real challenges of logistics hubs location regarding transportation networks design.

  15. Prediction ofWater Quality Parameters (NO3, CL in Karaj Riverby Usinga Combinationof Wavelet Neural Network, ANN and MLRModels

    Directory of Open Access Journals (Sweden)

    T. Rajaee

    2016-10-01

    Full Text Available IntroductionThe water quality is an issue of ongoing concern. Evaluation of the quantity and quality of running waters is considerable in hydro-environmental management.The prediction and control of the quality of Karaj river water, as one of the important needed water supply sources of Tehran, possesses great importance. In this study, Performance of Artificial Neural Network (ANN, Wavelet Neural Network combination (WANN and multi linear regression (MLR models, to predict next month the Nitrate (NO3 and Chloride (CL ions of "gate ofBylaqan sluice" station located in Karaj River has been evaluated. Materials and MethodsIn this research two separate ANN models for prediction of NO3 and CL has been expanded. Each one of the parameters for prediction (NO3 / CL has been put related to the past amounts of the same time series (NO3 / CL and its amounts of Q in past months.From astatisticalperiod of10yearswas usedforthe input of the models. Hence 80% of entire data from (96 initial months of data as training set, next 10% of data (12 months and 10% of the end of time series (terminal 12 months were considered as for validation and test of the models, respectively. In WANNcombination model, the real monthly observed time series of river discharge (Q and mentioned qualityparameters(NO3 / CL were decomposed to some sub-time series at different levels by wavelet analysis.Then the decomposed quality parameters to predict and Q time series were used at different levels as inputs to the ANN technique for predicting one-step-ahead Nitrate and Chloride. These time series play various roles in the original time series and the behavior of each is distinct, so the contribution to the original time series varies from each other. In addition, prediction of high NO3 and CL values greater than mean of data that have great importancewere investigated by the models. The capability of the models was evaluated by Coefficient of Efficiency (E and the Root Mean Square

  16. Risks Analysis of Logistics Financial Business Based on Evidential Bayesian Network

    Directory of Open Access Journals (Sweden)

    Ying Yan

    2013-01-01

    Full Text Available Risks in logistics financial business are identified and classified. Making the failure of the business as the root node, a Bayesian network is constructed to measure the risk levels in the business. Three importance indexes are calculated to find the most important risks in the business. And more, considering the epistemic uncertainties in the risks, evidence theory associate with Bayesian network is used as an evidential network in the risk analysis of logistics finance. To find how much uncertainty in root node is produced by each risk, a new index, epistemic importance, is defined. Numerical examples show that the proposed methods could provide a lot of useful information. With the information, effective approaches could be found to control and avoid these sensitive risks, thus keep logistics financial business working more reliable. The proposed method also gives a quantitative measure of risk levels in logistics financial business, which provides guidance for the selection of financing solutions.

  17. Comprehensive Logistics

    CERN Document Server

    Gudehus, Timm

    2012-01-01

    Modern logistics comprises operative logistics, analytical logistics and management of logistic networks. Central task of operative logistics is the efficient supply of required goods at the right place within the right time. Tasks of analytical logistics are designing optimal networks and systems, developing strategies for planning, scheduling and operation, and organizing efficient order and performance processes. Logistic management plans, implements and operates logistic networks and schedules orders, stocks and resources. This reference-book offers a unique survey of modern logistics. It contains proven strategies, rules and tools for the solution of a multitude of logistic problems. The analytically derived algorithms and formulas can be used for the computer-based planning of logistic systems and for the dynamic scheduling of orders and resources in supply networks. They enable significant improvements of performance, quality and costs. Their application is demonstrated by several examples from industr...

  18. (ann) based dynamic voltage restorer

    African Journals Online (AJOL)

    HOD

    artificial intelligence to provide smart triggering pulses for the DVR to mitigate and to provide compensation against ... the starting of large induction motor [6]. ... ANN-based DVR under voltage sags and swells phenomena. In this case, the ANN is trained off-line, and the trained network is employed for on-line control.

  19. Integrated forward/reverse logistics network design under uncertainty with pricing for collection of used products

    DEFF Research Database (Denmark)

    Fattahi, Mohammad; Govindan, Kannan

    2017-01-01

    This paper addresses design and planning of an integrated forward/reverse logistics network over a planning horizon with multiple tactical periods. In the network, demand for new products and potential return of used products are stochastic. Furthermore, collection amounts of used products...

  20. A Lyapunov-Razumikhin approach for stability analysis of logistics networks with time-delays

    Science.gov (United States)

    Dashkovskiy, Sergey; Karimi, Hamid Reza; Kosmykov, Michael

    2012-05-01

    Logistics network represents a complex system where different elements that are logistic locations interact with each other. This interaction contains delays caused by time needed for delivery of the material. Complexity of the system, time-delays and perturbations in a customer demand may cause unstable behaviour of the network. This leads to the loss of the customers and high inventory costs. Thus the investigation of the network on stability is desired during its design. In this article we consider local input-to-state stability of such logistics networks. Their behaviour is described by a functional differential equation with a constant time-delay. We are looking for verifiable conditions that guarantee stability of the network under consideration. Lyapunov-Razumikhin functions and the local small gain condition are utilised to obtain such conditions. Our stability conditions for the logistics network are based on the information about the interconnection properties between logistic locations and their production rates. Finally, numerical results are provided to demonstrate the proposed approach.

  1. Intensification of Development of Mixed Transportation of Freight in Ukraine through Formation of the Network of Transportation and Logistic Centres and Transportation and Logistic Clusters

    Directory of Open Access Journals (Sweden)

    Karpenko Oksana O.

    2013-11-01

    Full Text Available Development of mixed transportation is a prospective direction of development of the transportation system of Ukraine. The article analyses the modern state of development of mixed transportation of freight in Ukraine. The most popular types of combined transportation (refers to multi-modal are container and contrailer trains, which are formed both in Ukraine (Viking and Yaroslav and in other countries, first of all, Belarus (Zubr. One of the reasons of underdevelopment of mixed transportation of freight in Ukraine is absence of a developed network of transportation and logistic centres. The article offers to form a network of transportation and logistic centres in Ukraine as a way of intensification of development of mixed transportations of freight, since they facilitate co-ordination of use of various types of transport and support integrated management of material flows. Transportation and logistic centres should become a start-up complex, around which transportation and logistic clusters would be gradually formed. Transportation and logistic clusters is a new efficient form of network organisation and management of transportation and logistic services and they also ensure growth of efficiency of use of the regional transportation and logistic potential of Ukraine. The article shows prospective supporting transportation and logistic centres and centres of formation of transportation and logistic clusters in the territory of Ukraine. Formation of efficient transportation and logistic system of Ukraine on the basis of a network of transportation and logistic clusters would facilitate entering of Ukraine into the world transportation environment and would allow acceleration of introduction of efficient logistic schemes of freight delivery, in particular, mixed transportation of freight.

  2. A multimodal logistics service network design with time windows and environmental concerns.

    Directory of Open Access Journals (Sweden)

    Dezhi Zhang

    Full Text Available The design of a multimodal logistics service network with customer service time windows and environmental costs is an important and challenging issue. Accordingly, this work established a model to minimize the total cost of multimodal logistics service network design with time windows and environmental concerns. The proposed model incorporates CO2 emission costs to determine the optimal transportation mode combinations and investment selections for transfer nodes, which consider transport cost, transport time, carbon emission, and logistics service time window constraints. Furthermore, genetic and heuristic algorithms are proposed to set up the abovementioned optimal model. A numerical example is provided to validate the model and the abovementioned two algorithms. Then, comparisons of the performance of the two algorithms are provided. Finally, this work investigates the effects of the logistics service time windows and CO2 emission taxes on the optimal solution. Several important management insights are obtained.

  3. A multimodal logistics service network design with time windows and environmental concerns.

    Science.gov (United States)

    Zhang, Dezhi; He, Runzhong; Li, Shuangyan; Wang, Zhongwei

    2017-01-01

    The design of a multimodal logistics service network with customer service time windows and environmental costs is an important and challenging issue. Accordingly, this work established a model to minimize the total cost of multimodal logistics service network design with time windows and environmental concerns. The proposed model incorporates CO2 emission costs to determine the optimal transportation mode combinations and investment selections for transfer nodes, which consider transport cost, transport time, carbon emission, and logistics service time window constraints. Furthermore, genetic and heuristic algorithms are proposed to set up the abovementioned optimal model. A numerical example is provided to validate the model and the abovementioned two algorithms. Then, comparisons of the performance of the two algorithms are provided. Finally, this work investigates the effects of the logistics service time windows and CO2 emission taxes on the optimal solution. Several important management insights are obtained.

  4. Developing weighted criteria to evaluate lean reverse logistics through analytical network process

    Science.gov (United States)

    Zagloel, Teuku Yuri M.; Hakim, Inaki Maulida; Krisnawardhani, Rike Adyartie

    2017-11-01

    Reverse logistics is a part of supply chain that bring materials from consumers back to manufacturer in order to gain added value or do a proper disposal. Nowadays, most companies are still facing several problems on reverse logistics implementation which leads to high waste along reverse logistics processes. In order to overcome this problem, Madsen [Framework for Reverse Lean Logistics to Enable Green Manufacturing, Eco Design 2009: 6th International Symposium on Environmentally Conscious Design and Inverse Manufacturing, Sapporo, 2009] has developed a lean reverse logistics framework as a step to eliminate waste by implementing lean on reverse logistics. However, the resulted framework sets aside criteria used to evaluate its performance. This research aims to determine weighted criteria that can be used as a base on reverse logistics evaluation by considering lean principles. The resulted criteria will ensure reverse logistics are kept off from waste, thus implemented efficiently. Analytical Network Process (ANP) is used in this research to determine the weighted criteria. The result shows that criteria used for evaluation lean reverse logistics are Innovation and Learning (35%), Economic (30%), Process Flow Management (14%), Customer Relationship Management (13%), Environment (6%), and Social (2%).

  5. Gene expression profiling of breast cancer survivability by pooled cDNA microarray analysis using logistic regression, artificial neural networks and decision trees.

    Science.gov (United States)

    Chou, Hsiu-Ling; Yao, Chung-Tay; Su, Sui-Lun; Lee, Chia-Yi; Hu, Kuang-Yu; Terng, Harn-Jing; Shih, Yun-Wen; Chang, Yu-Tien; Lu, Yu-Fen; Chang, Chi-Wen; Wahlqvist, Mark L; Wetter, Thomas; Chu, Chi-Ming

    2013-03-19

    Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR. The collection of microarray datasets from the Gene Expression Omnibus, four breast cancer datasets were pooled for predicting five-year breast cancer relapse. After data compilation, 757 subjects, 5 clinical variables and 13,452 genetic variables were aggregated. The bootstrap method, Mann-Whitney U test and 20-fold cross-validation were performed to investigate candidate genes with 100 most-significant p-values. The predictive powers of DT, LR and ANN models were assessed using accuracy and the area under ROC curve. The associated genes were evaluated using Cox regression. The DT models exhibited the lowest predictive power and the poorest extrapolation when applied to the test samples. The ANN models displayed the best predictive power and showed the best extrapolation. The 21 most-associated genes, as determined by integration of each model, were analyzed using Cox regression with a 3.53-fold (95% CI: 2.24-5.58) increased risk of breast cancer five-year recurrence. The 21 selected genes can predict breast cancer recurrence. Among these genes, CCNB1, PLK1 and TOP2A are in the cell cycle G2/M DNA damage checkpoint pathway. Oncologists can offer the genetic information for patients when understanding the gene expression profiles on breast cancer recurrence.

  6. Railway optimal network simulation for the development of regional transport-logistics system

    Directory of Open Access Journals (Sweden)

    Mikhail Borisovich Petrov

    2013-12-01

    Full Text Available The dependence of logistics on mineral fuel is a stable tendency of regions development, though when making strategic plans of logistics in the regions, it is necessary to provide the alternative possibilities of power-supply sources change together with population density, transport infrastructure peculiarities, and demographic changes forecast. On the example of timber processing complex of the Sverdlovsk region, the authors suggest the algorithm of decision of the optimal logistics infrastructure allocation. The problem of regional railway network organization at the stage of slow transition from the prolonged stagnation to the new development is carried out. The transport networks’ configurations of countries on the Pacific Rim, which successfully developed nowadays, are analyzed. The authors offer some results of regional transport network simulation on the basis of artificial intelligence method. These methods let to solve the task with incomplete data. The ways of the transport network improvement in the Sverdlovsk region are offered.

  7. Comparison between an artificial neural network and logistic regression in predicting acute graft-vs-host disease after unrelated donor hematopoietic stem cell transplantation in thalassemia patients.

    Science.gov (United States)

    Caocci, Giovanni; Baccoli, Roberto; Vacca, Adriana; Mastronuzzi, Angela; Bertaina, Alice; Piras, Eugenia; Littera, Roberto; Locatelli, Franco; Carcassi, Carlo; La Nasa, Giorgio

    2010-05-01

    There is growing interest in the development of prognostic models for predicting the occurrence of acute graft-vs-host disease (aGVHD) after unrelated donor hematopoietic stem cell transplantation. A high number of variables have been shown to play a role in aGVHD, but the search for a predictive algorithm is still ongoing. Artificial neural networks (ANNs) represent an attractive alternative to multivariate analysis for clinical prognosis. So far, no reports have investigated the ability of ANNs in predicting HSCT outcome. We compared the prognostic performance of ANNs with that of logistic regression (LR) in 78 beta-thalassemia major patients given unrelated donor hematopoietic stem cell transplantation. Twenty-four independent variables were analyzed for their potential impact on outcomes. Twenty-six patients (33.3%) developed grade II to IV aGVHD. In multivariate analysis, homozygosity for donor KIR haplotype A (p = 0.03), donor age (p = 0.05), and donor homozygosity for the deletion of the human leukocyte antigen-G 14-bp polymorphism (p = 0.05) were independently significantly correlated to aGVHD. The mean sensitivity of LR and ANNs (capability of predicting aGVHD in patients who developed aGVHD) in test datasets was 21.7% and 83.3%, respectively (p < 0.001); the mean specificity (capability of predicting absence of aGVHD in patients who did not develop aGVHD) was 80.5% and 90.1%, respectively (p = NS). Although ANNs are unable to calculate the weight of single variables on outcomes, they were found to have a better performance than LR. A combination of these two methods could be more efficient in predicting outcomes and help tailor GVHD prophylaxis regimens according to the predicted risk of each patient. Whether ANN technology will provide better predictive performance when applied to other datasets remains to be confirmed. 2010 ISEH-Society for Hematology and Stem Cells. Published by Elsevier Inc. All rights reserved.

  8. Classifying Sources Influencing Indoor Air Quality (IAQ Using Artificial Neural Network (ANN

    Directory of Open Access Journals (Sweden)

    Shaharil Mad Saad

    2015-05-01

    Full Text Available Monitoring indoor air quality (IAQ is deemed important nowadays. A sophisticated IAQ monitoring system which could classify the source influencing the IAQ is definitely going to be very helpful to the users. Therefore, in this paper, an IAQ monitoring system has been proposed with a newly added feature which enables the system to identify the sources influencing the level of IAQ. In order to achieve this, the data collected has been trained with artificial neural network or ANN—a proven method for pattern recognition. Basically, the proposed system consists of sensor module cloud (SMC, base station and service-oriented client. The SMC contain collections of sensor modules that measure the air quality data and transmit the captured data to base station through wireless network. The IAQ monitoring system is also equipped with IAQ Index and thermal comfort index which could tell the users about the room’s conditions. The results showed that the system is able to measure the level of air quality and successfully classify the sources influencing IAQ in various environments like ambient air, chemical presence, fragrance presence, foods and beverages and human activity.

  9. A fuzzy multi-objective optimization model for sustainable reverse logistics network design

    DEFF Research Database (Denmark)

    Govindan, Kannan; Paam, Parichehr; Abtahi, Amir Reza

    2016-01-01

    Decreasing the environmental impact, increasing the degree of social responsibility, and considering the economic motivations of organizations are three significant features in designing a reverse logistics network under sustainability respects. Developing a model, which can simultaneously consider...... a multi-echelon multi-period multi-objective model for a sustainable reverse logistics network. To reflect all aspects of sustainability, we try to minimize the present value of costs, as well as environmental impacts, and optimize the social responsibility as objective functions of the model. In order...

  10. Local area network terminal management in support of stock point logistics integrated communications environment (SPLICE)

    OpenAIRE

    Barnes, Jerry D.

    1982-01-01

    Approved for public release; distribution is unlimited This thesis examines the questions of user requirements, design considerations, and network environment for a local area network Terminal Management function in support of the Naval Supply Systems Command's Stock Point Logistics Integrated Communications Environment (SPLICE). Criteria are developed from this examination. They include process-process communication, virtual terminal, and user defined screen capabilities as well as a ne...

  11. Determine the optimal carrier selection for a logistics network based on multi-commodity reliability criterion

    Science.gov (United States)

    Lin, Yi-Kuei; Yeh, Cheng-Ta

    2013-05-01

    From the perspective of supply chain management, the selected carrier plays an important role in freight delivery. This article proposes a new criterion of multi-commodity reliability and optimises the carrier selection based on such a criterion for logistics networks with routes and nodes, over which multiple commodities are delivered. Carrier selection concerns the selection of exactly one carrier to deliver freight on each route. The capacity of each carrier has several available values associated with a probability distribution, since some of a carrier's capacity may be reserved for various orders. Therefore, the logistics network, given any carrier selection, is a multi-commodity multi-state logistics network. Multi-commodity reliability is defined as a probability that the logistics network can satisfy a customer's demand for various commodities, and is a performance indicator for freight delivery. To solve this problem, this study proposes an optimisation algorithm that integrates genetic algorithm, minimal paths and Recursive Sum of Disjoint Products. A practical example in which multi-sized LCD monitors are delivered from China to Germany is considered to illustrate the solution procedure.

  12. Using ROC curves to compare neural networks and logistic regression for modeling individual noncatastrophic tree mortality

    Science.gov (United States)

    Susan L. King

    2003-01-01

    The performance of two classifiers, logistic regression and neural networks, are compared for modeling noncatastrophic individual tree mortality for 21 species of trees in West Virginia. The output of the classifier is usually a continuous number between 0 and 1. A threshold is selected between 0 and 1 and all of the trees below the threshold are classified as...

  13. Modelling the Cost Performance of a Given Logistics Network Operating Under Regular and Irregular Conditions

    NARCIS (Netherlands)

    Janic, M.

    2009-01-01

    This paper develops an analytical model for the assessment of the cost performance of a given logistics network operating under regular and irregular (disruptive) conditions. In addition, the paper aims to carry out a sensitivity analysis of this cost with respect to changes of the most influencing

  14. Hybrid optimization and simulation to design a logistics network for distributing perishable products

    NARCIS (Netherlands)

    Keizer, de M.; Haijema, R.; Bloemhof-Ruwaard, J.M.; Vorst, van der J.G.A.J.

    2015-01-01

    Dynamics in product quality complicate the design of logistics networks for perishable products, like flowers and other agricultural products. Complications especially arise when multiple products from different origins have to come together for processes like bundling. This paper presents a new

  15. Business case Oce: Reverse logistic network re-design for copiers

    NARCIS (Netherlands)

    Krikke, H.R.; van Harten, Aart; Schuur, Peter

    1999-01-01

    The introduction of extended producer responsibility forces Original Equipment Manufacturers to set up a logistic network for take back, processing and recovery of discarded products. In this paper, we discuss a business case study carried out at Océ, a copier firm in Venlo (NL). It concerns the

  16. Autonomous Control and the Internet of Things : Increasing Robustness, Scalability and Agilityin Logistic Networks

    Science.gov (United States)

    Uckelmann, Dieter; Isenberg, M.-A.; Teucke, M.; Halfar, H.; Scholz-Reiter, B.

    The Internet of Things and Autonomous Control are considered key concepts to enhance logistic processes in supply networks. Here the question that often comes up is about how both concepts relate to each other. The principal aim of this article is to evaluate, whether the Internet of Things and the paradigm of Autonomous Control in logistics can complement each other's capabilities. There are numerous different architectural approaches towards an Internet of Things. In this article, the EPCglobal Network is chosen, as it provides a standardised and well accepted approach based on open interfaces. A state of the art analysis is performed, concerning the existing technologies and research on Autonomous Control and the EPCglobal Network. In an integrative approach both concepts are merged. In this context, the EPCglobal Network is used as an information broker for Autonomous Logistic Objects . One possible application of this integrative approach is described based on an intelligent truck to illustrate the potential of the extended EPCglobal framework through means of Autonomous Control.

  17. Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM and Artificial Neural Network (ANN

    Directory of Open Access Journals (Sweden)

    Maria Grazia De Giorgi

    2014-08-01

    Full Text Available A high penetration of wind energy into the electricity market requires a parallel development of efficient wind power forecasting models. Different hybrid forecasting methods were applied to wind power prediction, using historical data and numerical weather predictions (NWP. A comparative study was carried out for the prediction of the power production of a wind farm located in complex terrain. The performances of Least-Squares Support Vector Machine (LS-SVM with Wavelet Decomposition (WD were evaluated at different time horizons and compared to hybrid Artificial Neural Network (ANN-based methods. It is acknowledged that hybrid methods based on LS-SVM with WD mostly outperform other methods. A decomposition of the commonly known root mean square error was beneficial for a better understanding of the origin of the differences between prediction and measurement and to compare the accuracy of the different models. A sensitivity analysis was also carried out in order to underline the impact that each input had in the network training process for ANN. In the case of ANN with the WD technique, the sensitivity analysis was repeated on each component obtained by the decomposition.

  18. An inquiry into the characteristics, applicability and prerequisites of Radio-Frequency Identification (RFID solutions in transport networks and logistics

    Directory of Open Access Journals (Sweden)

    Antoniu Ovidiu Balint

    2013-09-01

    Full Text Available The use of intelligent solution represents the key factor for developing both the logistics sector and the economic environment. This paper analyses the Radio-Frequency Identification (RFID technology and its role in the Supply Chain Management (SCM especially in logistics and transport networks. The main objective is to demonstrate that RFID represents a solution for improving the transport networks and logistics sector by implementing various complex and intelligent solutions that can improve the actual economic environment

  19. A Multi-Stage Reverse Logistics Network Problem by Using Hybrid Priority-Based Genetic Algorithm

    Science.gov (United States)

    Lee, Jeong-Eun; Gen, Mitsuo; Rhee, Kyong-Gu

    Today remanufacturing problem is one of the most important problems regarding to the environmental aspects of the recovery of used products and materials. Therefore, the reverse logistics is gaining become power and great potential for winning consumers in a more competitive context in the future. This paper considers the multi-stage reverse Logistics Network Problem (m-rLNP) while minimizing the total cost, which involves reverse logistics shipping cost and fixed cost of opening the disassembly centers and processing centers. In this study, we first formulate the m-rLNP model as a three-stage logistics network model. Following for solving this problem, we propose a Genetic Algorithm pri (GA) with priority-based encoding method consisting of two stages, and introduce a new crossover operator called Weight Mapping Crossover (WMX). Additionally also a heuristic approach is applied in the 3rd stage to ship of materials from processing center to manufacturer. Finally numerical experiments with various scales of the m-rLNP models demonstrate the effectiveness and efficiency of our approach by comparing with the recent researches.

  20. Design of a Multiobjective Reverse Logistics Network Considering the Cost and Service Level

    Directory of Open Access Journals (Sweden)

    Shuang Li

    2012-01-01

    Full Text Available Reverse logistics, which is induced by various forms of used products and materials, has received growing attention throughout this decade. In a highly competitive environment, the service level is an important criterion for reverse logistics network design. However, most previous studies about product returns only focused on the total cost of the reverse logistics and neglected the service level. To help a manufacturer of electronic products provide quality postsale repair service for their consumer, this paper proposes a multiobjective reverse logistics network optimisation model that considers the objectives of the cost, the total tardiness of the cycle time, and the coverage of customer zones. The Nondominated Sorting Genetic Algorithm II (NSGA-II is employed for solving this multiobjective optimisation model. To evaluate the performance of NSGA-II, a genetic algorithm based on weighted sum approach and Multiobjective Simulated Annealing (MOSA are also applied. The performance of these three heuristic algorithms is compared using numerical examples. The computational results show that NSGA-II outperforms MOSA and the genetic algorithm based on weighted sum approach. Furthermore, the key parameters of the model are tested, and some conclusions are drawn.

  1. Logistic Growth for the Nuzi Cuneiform Tablets: Analyzing Family Networks in Ancient Mesopotamia

    OpenAIRE

    Ueda, Sumie; Makino, Kumi; Itoh, Yoshiaki; Tsuchiya, Takashi

    2013-01-01

    We reconstruct the year of publication of each cuneiform tablet of the Nuzi society in ancient Mesopotamia. The tablets, are on land transaction, marriage, loan, slavery contracts etc. The number of tablets seem to increase by logistic growth until saturation. It may show the dynamics of concentration of lands or other properties into few powerful families in a period of about twenty years. We reconstruct family trees and social networks of Nuzi and estimate the publication years of cuneiform...

  2. Logistic growth for the Nuzi cuneiform tablets: Analyzing family networks in ancient Mesopotamia

    Science.gov (United States)

    Ueda, Sumie; Makino, Kumi; Itoh, Yoshiaki; Tsuchiya, Takashi

    2015-03-01

    We reconstruct the published year of each cuneiform tablet of the Nuzi society in ancient Mesopotamia. The tablets are on land transaction, marriage, loan, slavery contracts, etc. The number of tablets seems to increase by logistic growth. It may show the dynamics of concentration of lands or other properties into few powerful families in a period of about sixty years and most of them are in about thirty years. We reconstruct family trees and social networks of Nuzi and estimate the published years of cuneiform tablets consistently with the trees and networks, formulating least squares problems with linear inequality constraints.

  3. Simulation of Snowmelt Runoff Using SRM Model and Comparison With Neural Networks ANN and ANFIS (Case Study: Kardeh dam basin

    Directory of Open Access Journals (Sweden)

    morteza akbari

    2017-03-01

    of the basin with 2962 meters above sea level. Kardeh dam was primarily constructed on the Kardehriver for providing drinking and agriculture water demand with an annual volume rate of 21.23 million cubic meters. Satellite image: To estimate the level of snow cover, the satellite Landsat ETM+ data at path 35-159, rows 34-159 over the period 2001-2002 were used. Surfaces covered with snow were separated bysnow distinction normalized index (NDSI, But due to the lack of training data for image classification (areas with snow and no snow, the k-means unsupervised classification algorithm was used. Extracting the data from the meteorological and hydrological Since only a gauging station exists at the Kardeh dam site, the daily discharge data recorded at these stations was used. To extract meteorological parameters such as precipitation and temperature data, the records of the three stations Golmakan, Mashhad and Ghouchan, as the stations closest to the dam basin Kardeh were used. The purpose of this study was to simulate snowmelt runoff using SRM hydrological models and to compare the results with the outputs of the neural network models such as the ANN and the ANFIS model. Flow simulation was carried out using SRM, ANN model with the Multilayer Perceptron with back-propagation algorithm, and Sugeno type ANFIS. To evaluate the performance of the models in addition to the standard statistics such as mean square error or mean absolute percentage error, the regression coefficient measures and the difference in volume were used. The results showed that all three models are almost similar in terms of statistical parameters MSE and R and the differences were negligible. SRM model: SRM model is a daily hydrological model. This equation is composed of different components including 14 parameters. The input values were calculated based on the equations of degree-day factor. The evaluation of the model was performed with flow subside factor, coefficient and subtracting volume

  4. Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA).

    Science.gov (United States)

    Arab, Mohammad M; Yadollahi, Abbas; Ahmadi, Hamed; Eftekhari, Maliheh; Maleki, Masoud

    2017-01-01

    The efficiency of a hybrid systems method which combined artificial neural networks (ANNs) as a modeling tool and genetic algorithms (GAs) as an optimizing method for input variables used in ANN modeling was assessed. Hence, as a new technique, it was applied for the prediction and optimization of the plant hormones concentrations and combinations for in vitro proliferation of Garnem (G × N15) rootstock as a case study. Optimizing hormones combination was surveyed by modeling the effects of various concentrations of cytokinin-auxin, i.e., BAP, KIN, TDZ, IBA, and NAA combinations (inputs) on four growth parameters (outputs), i.e., micro-shoots number per explant, length of micro-shoots, developed callus weight (CW) and the quality index (QI) of plantlets. Calculation of statistical values such as R2 (coefficient of determination) related to the accuracy of ANN-GA models showed a considerably higher prediction accuracy for ANN models, i.e., micro-shoots number: R2 = 0.81, length of micro-shoots: R2 = 0.87, CW: R2 = 0.88, QI: R2 = 0.87. According to the results, among the input variables, BAP (19.3), KIN (9.64), and IBA (2.63) showed the highest values of variable sensitivity ratio for proliferation rate. The GA showed that media containing 1.02 mg/l BAP in combination with 0.098 mg/l IBA could lead to the optimal proliferation rate (10.53) for G × N15 rootstock. Another objective of the present study was to compare the performance of predicted and optimized cytokinin-auxin combination with the best optimized obtained concentrations of our other experiments. Considering three growth parameters (length of micro-shoots, micro-shoots number, and proliferation rate), the last treatment was found to be superior to the rest of treatments for G × N15 rootstock in vitro multiplication. Very little difference between the ANN predicted and experimental data confirmed high capability of ANN-GA method in predicting new optimized protocols for plant in vitro propagation.

  5. Mathematical Modeling and Optimizing of in Vitro Hormonal Combination for G × N15 Vegetative Rootstock Proliferation Using Artificial Neural Network-Genetic Algorithm (ANN-GA

    Directory of Open Access Journals (Sweden)

    Mohammad M. Arab

    2017-11-01

    Full Text Available The efficiency of a hybrid systems method which combined artificial neural networks (ANNs as a modeling tool and genetic algorithms (GAs as an optimizing method for input variables used in ANN modeling was assessed. Hence, as a new technique, it was applied for the prediction and optimization of the plant hormones concentrations and combinations for in vitro proliferation of Garnem (G × N15 rootstock as a case study. Optimizing hormones combination was surveyed by modeling the effects of various concentrations of cytokinin–auxin, i.e., BAP, KIN, TDZ, IBA, and NAA combinations (inputs on four growth parameters (outputs, i.e., micro-shoots number per explant, length of micro-shoots, developed callus weight (CW and the quality index (QI of plantlets. Calculation of statistical values such as R2 (coefficient of determination related to the accuracy of ANN-GA models showed a considerably higher prediction accuracy for ANN models, i.e., micro-shoots number: R2 = 0.81, length of micro-shoots: R2 = 0.87, CW: R2 = 0.88, QI: R2 = 0.87. According to the results, among the input variables, BAP (19.3, KIN (9.64, and IBA (2.63 showed the highest values of variable sensitivity ratio for proliferation rate. The GA showed that media containing 1.02 mg/l BAP in combination with 0.098 mg/l IBA could lead to the optimal proliferation rate (10.53 for G × N15 rootstock. Another objective of the present study was to compare the performance of predicted and optimized cytokinin–auxin combination with the best optimized obtained concentrations of our other experiments. Considering three growth parameters (length of micro-shoots, micro-shoots number, and proliferation rate, the last treatment was found to be superior to the rest of treatments for G × N15 rootstock in vitro multiplication. Very little difference between the ANN predicted and experimental data confirmed high capability of ANN-GA method in predicting new optimized protocols for plant in vitro

  6. Use of artificial neural network (ANN) for the development of bioprocess using Pinus roxburghii fallen foliages for the release of polyphenols and reducing sugars.

    Science.gov (United States)

    Vats, Siddharth; Negi, Sangeeta

    2013-07-01

    In present study, different parameters, i.e., percentage of NaOH, loading volume, microwave power (watt) and volume of water during pretreatment were optimized by ANN for release of polyphenols and sugars from pine fallen foliage. ANN used was feed forward back propagation type with 72 input, 72 output and 10 hidden layers coupled with Lvenberg-Marquardt (LM) training algorithms. The predicted optimal values by generated neural network for alkali pretreatment were 6 ml (0.5% NaOH)/g of substrate, soaking time of 10 min followed by 1 min of 100 W microwave. Pretreated sample on enzymatic hydrolysis at 50°C for 20 h with cocktail of cellulase, xylanase and laccase produced by locally isolated consortia released 668.9 mg/g of total sugar and 265.06 mg/g of total polyphenols. Optimization by ANN showed good yield, therefore, indicating its suitability for bioprocess modeling and control for release of reducing sugars and polyphenols from pine foliage. Copyright © 2013 Elsevier Ltd. All rights reserved.

  7. Modeling of biosorption of Cu(II) by alkali-modified spent tea leaves using response surface methodology (RSM) and artificial neural network (ANN)

    Science.gov (United States)

    Ghosh, Arpita; Das, Papita; Sinha, Keka

    2015-06-01

    In the present work, spent tea leaves were modified with Ca(OH)2 and used as a new, non-conventional and low-cost biosorbent for the removal of Cu(II) from aqueous solution. Response surface methodology (RSM) and artificial neural network (ANN) were used to develop predictive models for simulation and optimization of the biosorption process. The influence of process parameters (pH, biosorbent dose and reaction time) on the biosorption efficiency was investigated through a two-level three-factor (23) full factorial central composite design with the help of Design Expert. The same design was also used to obtain a training set for ANN. Finally, both modeling methodologies were statistically compared by the root mean square error and absolute average deviation based on the validation data set. Results suggest that RSM has better prediction performance as compared to ANN. The biosorption followed Langmuir adsorption isotherm and it followed pseudo-second-order kinetic. The optimum removal efficiency of the adsorbent was found as 96.12 %.

  8. Logistics orchestration in the ornamental plant supply chain network: towards responsive and differentiated demand driven networks

    NARCIS (Netherlands)

    Vorst, van der J.G.A.J.; Duineveld, M.P.J.; Scheer, F.P.

    2007-01-01

    At the moment the Dutch ornamental plant sector has a dominant international position fulfilling about 44% of the European market. However, emerging markets are positioned at a great distance requiring new logistics concepts to operate efficiently and effectively, new marketing channels become

  9. Logistics Distribution Center Location Evaluation Based on Genetic Algorithm and Fuzzy Neural Network

    Science.gov (United States)

    Shao, Yuxiang; Chen, Qing; Wei, Zhenhua

    Logistics distribution center location evaluation is a dynamic, fuzzy, open and complicated nonlinear system, which makes it difficult to evaluate the distribution center location by the traditional analysis method. The paper proposes a distribution center location evaluation system which uses the fuzzy neural network combined with the genetic algorithm. In this model, the neural network is adopted to construct the fuzzy system. By using the genetic algorithm, the parameters of the neural network are optimized and trained so as to improve the fuzzy system’s abilities of self-study and self-adaptation. At last, the sampled data are trained and tested by Matlab software. The simulation results indicate that the proposed identification model has very small errors.

  10. Selected aspects of the logistics network of public hospitals in the competitive market of health services

    Directory of Open Access Journals (Sweden)

    Justyna Majchrzak-Lepczyk

    2016-12-01

    Full Text Available Background: The below considerations provide an overview of the issues of sustainable development, logistics, to financial engineering instruments and the role of intellectual capital in the process of transformation of public hospitals. The aim of this research was to assess the competitiveness of the network of public hospitals in the market of health services based on literature studies, as well as empirical research. Methods: Empirical study using a questionnaire survey was conducted in the period from January 2007 to December 2011, in the area of Warmia and Mazury, Pomerania and Wielkopolska. The goal of this questionnaire survey was to know the medical staff reviews issues related to adaptation to the nature of the network of public hospitals methods and logistics tools, sustainable development, corporate social responsibility - CSR. The study was carried out in 104 public hospitals, on a sample of 8975 respondents. Results and conclusions: Analysis of the completed study showed that the logistic processes and their improvement in the health sector play a significant role. The surveyed entities explicitly draw attention to the need for information systems,  pro-environment activities, access to information, or the use of GS1 global standards. These tools allow you to increase the efficiency of supply chains, ensuring not only tracking and tracing of products from the manufacturer to the patient, but also enabling better protection against making a mistake or counterfeit products.

  11. Comprehensive Transportation Logistics Network Level Layout Based on Principal Component Factor and Cluster Analysis

    Directory of Open Access Journals (Sweden)

    Zhang Jingrong

    2017-01-01

    Full Text Available Comprehensive Transportation Logistics Network (CTLN acts as a crucial prop and fundamental carrier for regional economic and social development. Firstly, an index system for evaluating the development of regional Comprehensive Transportation Logistics (CTL nodes is established; then regional CTLN nodes are ranked according to their importance by the method of Principal Component Analysis(PCA, and main factors affecting the development of regional CTL nodes are analyzed by applying factor analysis, and regional CTL nodes are classified according to their feature similarities by applying cluster analysis; and then level structure of constructing regional CTLN is proposed. Finally, combined with geographic locations of different nodes, level layout model of CTLN of the whole region is obtained. Taking Henan province this region as an instance, level layout model of hub-and-spoke CTLN taking Zhengzhou at its core is proposed after analysis, providing a reference basis for constructing CTLN in whole province scientifically and reasonably.

  12. Assessing the Reliability of a Multistate Logistics Network under the Transportation Cost Constraint

    Directory of Open Access Journals (Sweden)

    Qi Zeng

    2016-01-01

    Full Text Available Reliability and transportation cost are two important indicators to measure the performance of logistics network. As a combination of reliability and transportation cost, the performance index R(d,c of logistics network is defined as the probability that at least d units of flow demand can be successfully transmitted from the source to the destination with the total transportation cost less than or equal to c. In this paper, an algorithm is developed to calculate R(d,c in terms of (d,c-minimal paths ((d,c-MPs for short. The proposed algorithm employs a decomposition technique to divide the search space of (d,c-MPs such that the search space of (d,c-MPs can be dramatically reduced, and thereby (d,c-MPs can be efficiently obtained. An example is provided to illustrate the proposed algorithm. Finally, computational experiments conducted on one benchmark network indicate that the proposed method has an advantage over the existing methods.

  13. Designing of Competitive Logistics Network for Liquefied Petroleum Gas in Conditions of Imperfect Competition

    Directory of Open Access Journals (Sweden)

    Boris Aržek

    2007-07-01

    Full Text Available The existing network for liquefied gas in the Republic ofCroatia is characterized by numerous weaknesses, led by lackof investment into LPG branch that has been going on foryears, i. e. outdated equipment and apparatuses, insufficientstorage space, outdated vehicles. The existing LPG logisticsnetwork has the following drawbacks: LPG market withoutcompetition, outdated legislative that imposes restrictions onimporters, lack of marketing know-how, insufficient investmentsfunding ( .. . Since foreign investors have been showingincreased interest in entering the Croatian LPG market, itseemed appropriate to investigate and elaborate decisive factorsand effects of constructing a new competitive logistics networkin conditions of imperfect competition.

  14. Multimodal Logistics Network Design over Planning Horizon through a Hybrid Meta-Heuristic Approach

    Science.gov (United States)

    Shimizu, Yoshiaki; Yamazaki, Yoshihiro; Wada, Takeshi

    Logistics has been acknowledged increasingly as a key issue of supply chain management to improve business efficiency under global competition and diversified customer demands. This study aims at improving a quality of strategic decision making associated with dynamic natures in logistics network optimization. Especially, noticing an importance to concern with a multimodal logistics under multiterms, we have extended a previous approach termed hybrid tabu search (HybTS). The attempt intends to deploy a strategic planning more concretely so that the strategic plan can link to an operational decision making. The idea refers to a smart extension of the HybTS to solve a dynamic mixed integer programming problem. It is a two-level iterative method composed of a sophisticated tabu search for the location problem at the upper level and a graph algorithm for the route selection at the lower level. To keep efficiency while coping with the resulting extremely large-scale problem, we invented a systematic procedure to transform the original linear program at the lower-level into a minimum cost flow problem solvable by the graph algorithm. Through numerical experiments, we verified the proposed method outperformed the commercial software. The results indicate the proposed approach can make the conventional strategic decision much more practical and is promising for real world applications.

  15. A hybrid solution approach for a multi-objective closed-loop logistics network under uncertainty

    Science.gov (United States)

    Mehrbod, Mehrdad; Tu, Nan; Miao, Lixin

    2014-09-01

    The design of closed-loop logistics (forward and reverse logistics) has attracted growing attention with the stringent pressures of customer expectations, environmental concerns and economic factors. This paper considers a multi-product, multi-period and multi-objective closed-loop logistics network model with regard to facility expansion as a facility location-allocation problem, which more closely approximates real-world conditions. A multi-objective mixed integer nonlinear programming formulation is linearized by defining new variables and adding new constraints to the model. By considering the aforementioned model under uncertainty, this paper develops a hybrid solution approach by combining an interactive fuzzy goal programming approach and robust counterpart optimization based on three well-known robust counterpart optimization formulations. Finally, this paper compares the results of the three formulations using different test scenarios and parameter-sensitive analysis in terms of the quality of the final solution, CPU time, the level of conservatism, the degree of closeness to the ideal solution, the degree of balance involved in developing a compromise solution, and satisfaction degree.

  16. Maintenance and Logistics Support for the International Monitoring System Network of the CTBTO

    Science.gov (United States)

    Haslinger, F.; Brely, N.; Akrawy, M.

    2007-05-01

    The global network of the International Monitoring System (IMS) of the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO), once completed, will consist of 321 monitoring facilities of four different technologies: hydroacoustic, seismic, infrasonic, and radionuclide. As of today, about 65% of the installations are completed and contribute data to the products issued by the International Data Centre (IDC) of the CTBTO. In order to accomplish the task to reliably collect evidence for any potential nuclear test explosion anywhere on the planet, all stations are required to perform to very high data availability requirements (at least 98% data availability over a 12-month period). To enable reaching this requirement, a three-layer concept has been developed to allow efficient support of the IMS stations: Operations, Maintenance and Logistics, and Engineering. Within this concept Maintenance and Logistics provide second level support of the stations, whereby problems arising at the station are assigned through the IMS ticket system to Maintenance if they cannot be resolved on the Operations level. Maintenance will then activate the required resources to appropriately address and ultimately resolve the problem. These resources may be equipment support contracts, other third party contracts, or the dispatch of a maintenance team. Engineering Support will be activated if the problem requires redesign of the station or after catastrophic failures when a total rebuild of a station may be necessary. In this model, Logistics Support is responsible for parts replenishment and support contract management. Logistics Support also collects and analyzes relevant failure mode and effect information, develops supportability models, and has the responsibility for document management, obsolescence, risk & quality, and configuration management, which are key elements for efficient station support. Maintenance Support in addition is responsible for maintenance strategies, for

  17. A Methodology to Exploit Profit Allocation in Logistics Joint Distribution Network Optimization

    Directory of Open Access Journals (Sweden)

    Yong Wang

    2015-01-01

    Full Text Available Logistics joint distribution network (LJDN optimization involves vehicle routes scheduling and profit allocation for multiple distribution centers. This is essentially a combinational and cooperative game optimization problem seeking to serve a number of customers with a fleet of vehicles and allocate profit among multiple centers. LJDN routing optimization based on customer clustering units can alleviate the computational complexity and improve the calculation accuracy. In addition, the profit allocation mechanism can be realized based on cooperative game theory through a negotiation procedure by the Logistics Service Provider (LSP. This paper establishes a model to minimize the total cost of the multiple centers joint distribution network when each distribution center is assigned to serve a series of distribution units. An improved particle swarm optimization (PSO algorithm is presented to tackle the model formulation by assigning distribution centers (DCs to distribution units. Improved PSO algorithm combines merits of PSO algorithm and genetic algorithm (GA with global and local search capabilities. Finally, a Shapley value model based on cooperative game theory is proposed to obtain the optimal profit allocation strategy among distribution centers from nonempty coalitions. The computational results from a case study in Guiyang city, China, suggest the optimal sequential coalition of distribution centers can be achieved according to Strictly Monotonic Path (SMP.

  18. (ann) based dynamic voltage restorer

    African Journals Online (AJOL)

    HOD

    artificial intelligence to provide smart triggering pulses for the DVR to mitigate and to provide compensation against voltage sags and swells. The Artificial Neural Network (ANN) was trained ... 90% of the nominal rms value and lasting for 0.5cycles. (10msec for 50Hz power system) up to 1 minute. It is considered as the most ...

  19. Comparison between Possibilistic c-Means (PCM and Artificial Neural Network (ANN Classification Algorithms in Land use/ Land cover Classification

    Directory of Open Access Journals (Sweden)

    Ganchimeg Ganbold

    2017-03-01

    Full Text Available There are several statistical classification algorithms available for landuse/land cover classification. However, each has a certain bias orcompromise. Some methods like the parallel piped approach in supervisedclassification, cannot classify continuous regions within a feature. Onthe other hand, while unsupervised classification method takes maximumadvantage of spectral variability in an image, the maximally separableclusters in spectral space may not do much for our perception of importantclasses in a given study area. In this research, the output of an ANNalgorithm was compared with the Possibilistic c-Means an improvementof the fuzzy c-Means on both moderate resolutions Landsat8 and a highresolution Formosat 2 images. The Formosat 2 image comes with an8m spectral resolution on the multispectral data. This multispectral imagedata was resampled to 10m in order to maintain a uniform ratio of1:3 against Landsat 8 image. Six classes were chosen for analysis including:Dense forest, eucalyptus, water, grassland, wheat and riverine sand. Using a standard false color composite (FCC, the six features reflecteddifferently in the infrared region with wheat producing the brightestpixel values. Signature collection per class was therefore easily obtainedfor all classifications. The output of both ANN and FCM, were analyzedseparately for accuracy and an error matrix generated to assess the qualityand accuracy of the classification algorithms. When you compare theresults of the two methods on a per-class-basis, ANN had a crisperoutput compared to PCM which yielded clusters with pixels especiallyon the moderate resolution Landsat 8 imagery.

  20. Support vector machine regression (SVR/LS-SVM)--an alternative to neural networks (ANN) for analytical chemistry? Comparison of nonlinear methods on near infrared (NIR) spectroscopy data.

    Science.gov (United States)

    Balabin, Roman M; Lomakina, Ekaterina I

    2011-04-21

    In this study, we make a general comparison of the accuracy and robustness of five multivariate calibration models: partial least squares (PLS) regression or projection to latent structures, polynomial partial least squares (Poly-PLS) regression, artificial neural networks (ANNs), and two novel techniques based on support vector machines (SVMs) for multivariate data analysis: support vector regression (SVR) and least-squares support vector machines (LS-SVMs). The comparison is based on fourteen (14) different datasets: seven sets of gasoline data (density, benzene content, and fractional composition/boiling points), two sets of ethanol gasoline fuel data (density and ethanol content), one set of diesel fuel data (total sulfur content), three sets of petroleum (crude oil) macromolecules data (weight percentages of asphaltenes, resins, and paraffins), and one set of petroleum resins data (resins content). Vibrational (near-infrared, NIR) spectroscopic data are used to predict the properties and quality coefficients of gasoline, biofuel/biodiesel, diesel fuel, and other samples of interest. The four systems presented here range greatly in composition, properties, strength of intermolecular interactions (e.g., van der Waals forces, H-bonds), colloid structure, and phase behavior. Due to the high diversity of chemical systems studied, general conclusions about SVM regression methods can be made. We try to answer the following question: to what extent can SVM-based techniques replace ANN-based approaches in real-world (industrial/scientific) applications? The results show that both SVR and LS-SVM methods are comparable to ANNs in accuracy. Due to the much higher robustness of the former, the SVM-based approaches are recommended for practical (industrial) application. This has been shown to be especially true for complicated, highly nonlinear objects.

  1. Predicting fuelwood prices in Greece with the use of ARIMA models, artificial neural networks and a hybrid ARIMA-ANN model

    Energy Technology Data Exchange (ETDEWEB)

    Koutroumanidis, Theodoros [Department of Agricultural Development, Democritus University of Thrace, Pantazidou 193, 68200 Orestiada (Greece); Ioannou, Konstantinos [Laboratory of Forest Informatics, School of Forestry and Natural Environment, Aristotle University of Thessaloniki, Box 247, 54 124 Thessaloniki (Greece); Arabatzis, Garyfallos [Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, Pantazidou 193, 68200 Orestiada (Greece)

    2009-09-15

    Throughout history, energy resources have acquired a strategic significance for the economic growth and social welfare of any country. The large-scale oil crisis of 1973 coupled with various environmental protection issues, have led many countries to look for new, alternative energy sources. Biomass and fuelwood in particular, constitutes a major renewable energy source (RES) that can make a significant contribution, as a substitute for oil. This paper initially provides a description of the contribution of renewable energy sources to the production of electricity, and also examines the role of forests in the production of fuelwood in Greece. Following this, autoregressive integrated moving average (ARIMA) models, artificial neural networks (ANN) and a hybrid model are used to predict the future selling prices of the fuelwood (from broadleaved and coniferous species) produced by Greek state forest farms. The use of the ARIMA-ANN hybrid model provided the optimum prediction results, thus enabling decision-makers to proceed with a more rational planning for the production and fuelwood market. (author)

  2. Dynamic Network Logistic Regression: A Logistic Choice Analysis of Inter- and Intra-Group Blog Citation Dynamics in the 2004 US Presidential Election.

    Science.gov (United States)

    Almquist, Zack W; Butts, Carter T

    2013-10-01

    Methods for analysis of network dynamics have seen great progress in the past decade. This article shows how Dynamic Network Logistic Regression techniques (a special case of the Temporal Exponential Random Graph Models) can be used to implement decision theoretic models for network dynamics in a panel data context. We also provide practical heuristics for model building and assessment. We illustrate the power of these techniques by applying them to a dynamic blog network sampled during the 2004 US presidential election cycle. This is a particularly interesting case because it marks the debut of Internet-based media such as blogs and social networking web sites as institutionally recognized features of the American political landscape. Using a longitudinal sample of all Democratic National Convention/Republican National Convention-designated blog citation networks, we are able to test the influence of various strategic, institutional, and balance-theoretic mechanisms as well as exogenous factors such as seasonality and political events on the propensity of blogs to cite one another over time. Using a combination of deviance-based model selection criteria and simulation-based model adequacy tests, we identify the combination of processes that best characterizes the choice behavior of the contending blogs.

  3. System evaluation of logistics performance: Proposal for a supply network in a Public Higher Education Institution

    Directory of Open Access Journals (Sweden)

    Alberto de Oliveira Cardoso Neto

    2017-06-01

    Full Text Available The recent quest for efficiency in public companies in Brazil was one of the motives to elaborate this paper, which had a public Institution of Higher Education (IHE as its subject of study. The IHE profiled possesses a multi-campus structure and the distribution of its consumer items is performed by the institution’s own warehouse. Through field research, it became apparent that the supply of these items had some problems, such as items out of stock, orders with delayed delivery, items past their shelf life etc. Therefore, this paper article aimed to propose an evaluation system of the logistical services at the IHE studied, based on performance indicators developed from mangers’ perceptions about the problems occurring in the distribution of consumer items. In addition, an index, calculated from diverse indicators, was proposed which would be able to express the performance of the logistics service of the IHE studied, and reflect the perceptions of the main users of this service. It is understood that the solution proposed here can be applied to any two-echelon supply network.

  4. Manufacturing enterprise’s logistics operational cost simulation and optimization from the perspective of inter-firm network

    Directory of Open Access Journals (Sweden)

    Chun Fu

    2015-05-01

    Full Text Available Purpose: By studying the case of a Changsha engineering machinery manufacturing firm, this paper aims to find out the optimization tactics to reduce enterprise’s logistics operational cost. Design/methodology/approach: This paper builds the structure model of manufacturing enterprise’s logistics operational costs from the perspective of inter-firm network and simulates the model based on system dynamics. Findings: It concludes that applying system dynamics in the research of manufacturing enterprise’s logistics cost control can better reflect the relationship of factors in the system. And the case firm can optimize the logistics costs by implement joint distribution. Research limitations/implications: This study still lacks comprehensive consideration about the variables quantities and quantitative of the control factors. In the future, we should strengthen the collection of data and information about the engineering manufacturing firms and improve the logistics operational cost model. Practical implications: This study puts forward some optimization tactics to reduce enterprise’s logistics operational cost. And it is of great significance for enterprise’s supply chain management optimization and logistics cost control. Originality/value: Differing from the existing literatures, this paper builds the structure model of manufacturing enterprise’s logistics operational costs from the perspective of inter-firm network and simulates the model based on system dynamics.

  5. Location and logistics

    OpenAIRE

    Alumura, Sibel A.; Karab, Bahar Y.; Melo, M. Teresa

    2013-01-01

    Facility location decisions play a critical role in designing logistics networks. This article provides some guidelines on how location decisions and logistics functions can be integrated into a single mathematical model to optimize the configuration of a logistics network. This will be illustrated by two generic models, one supporting the design of a forward logistics network and the other addressing the specific requirements of a reverse logistics network. Several special cases and extensio...

  6. Network Intrusion Detection through Discriminative Feature Selection by Using Sparse Logistic Regression

    Directory of Open Access Journals (Sweden)

    Reehan Ali Shah

    2017-11-01

    Full Text Available Intrusion detection system (IDS is a well-known and effective component of network security that provides transactions upon the network systems with security and safety. Most of earlier research has addressed difficulties such as overfitting, feature redundancy, high-dimensional features and a limited number of training samples but feature selection. We approach the problem of feature selection via sparse logistic regression (SPLR. In this paper, we propose a discriminative feature selection and intrusion classification based on SPLR for IDS. The SPLR is a recently developed technique for data analysis and processing via sparse regularized optimization that selects a small subset from the original feature variables to model the data for the purpose of classification. A linear SPLR model aims to select the discriminative features from the repository of datasets and learns the coefficients of the linear classifier. Compared with the feature selection approaches, like filter (ranking and wrapper methods that separate the feature selection and classification problems, SPLR can combine feature selection and classification into a unified framework. The experiments in this correspondence demonstrate that the proposed method has better performance than most of the well-known techniques used for intrusion detection.

  7. Designing a Reverse Logistics Network for End-of-Life Vehicles Recovery

    Directory of Open Access Journals (Sweden)

    Masoud Zarei

    2010-01-01

    Full Text Available The environmental factors are receiving increasing attention in different life cycle stages of products. When a product reaches its End-Of-Life (EOL stage, the management of its recovery process is affected by the environmental and also economical factors. Selecting efficient methods for the collection and recovery of EOL products has become an important issue. The European Union Directive 2000/53/EC extends the responsibility of the vehicle manufacturers to the postconsumer stage of the vehicle. In order to fulfill the requirements of this Directive and also efficient management of the whole recovery process, the conceptual framework of a reverse logistics network is presented. The distribution of new vehicles in an area and also collecting the End-of-Life Vehicles (ELVs and their recovery are considered jointly. It is assumed that the new vehicles distributors are also responsible for collecting the ELVs. Then a mathematical model is developed which minimizes the costs of setting up the network and also the relevant transportation costs. Because of the complexity of the model, a solution methodology based on the genetic algorithm is designed which enables achieving good quality solutions in a reasonable algorithm run time.

  8. Manufacturing enterprise’s logistics operational cost simulation and optimization from the perspective of inter-firm network

    OpenAIRE

    Chun Fu; Zhenzhen Shuai

    2015-01-01

    Purpose: By studying the case of a Changsha engineering machinery manufacturing firm, this paper aims to find out the optimization tactics to reduce enterprise’s logistics operational cost. Design/methodology/approach: This paper builds the structure model of manufacturing enterprise’s logistics operational costs from the perspective of inter-firm network and simulates the model based on system dynamics. Findings: It concludes that applying system dynamics in the research of manufacturing ent...

  9. Biotreatment of zinc-containing wastewater in a sulfidogenic CSTR: Performance and artificial neural network (ANN) modelling studies.

    Science.gov (United States)

    Sahinkaya, Erkan

    2009-05-15

    Sulfidogenic treatment of sulfate (2-10g/L) and zinc (65-677mg/L) containing simulated wastewater was studied in a mesophilic (35 degrees C) CSTR. Ethanol was supplemented (COD/sulfate=0.67) as carbon and energy source for sulfate-reducing bacteria (SRB). The robustness of the system was studied by increasing Zn, COD and sulfate loadings. Sulfate removal efficiency, which was 70% at 2g/L feed sulfate concentration, steadily decreased with increasing feed sulfate concentration and reached 40% at 10g/L. Over 99% Zn removal was attained due to the formation of zinc-sulfide precipitate. COD removal efficiency at 2g/L feed sulfate concentration was over 94%, whereas, it steadily decreased due to the accumulation of acetate at higher loadings. Alkalinity produced from acetate oxidation increased wastewater pH remarkably when feed sulfate concentration was 5g/L or lower. Electron flow from carbon oxidation to sulfate reduction averaged 83+/-13%. The rest of the electrons were most likely coupled with fermentative reactions as the amount of methane production was insignificant. The developed ANN model was very successful as an excellent to reasonable match was obtained between the measured and the predicted concentrations of sulfate (R=0.998), COD (R=0.993), acetate (R=0.976) and zinc (R=0.827) in the CSTR effluent.

  10. Comparison of logistic regression and artificial neural network in low back pain prediction: second national health survey.

    Science.gov (United States)

    Parsaeian, M; Mohammad, K; Mahmoudi, M; Zeraati, H

    2012-01-01

    The purpose of this investigation was to compare empirically predictive ability of an artificial neural network with a logistic regression in prediction of low back pain. Data from the second national health survey were considered in this investigation. This data includes the information of low back pain and its associated risk factors among Iranian people aged 15 years and older. Artificial neural network and logistic regression models were developed using a set of 17294 data and they were validated in a test set of 17295 data. Hosmer and Lemeshow recommendation for model selection was used in fitting the logistic regression. A three-layer perceptron with 9 inputs, 3 hidden and 1 output neurons was employed. The efficiency of two models was compared by receiver operating characteristic analysis, root mean square and -2 Loglikelihood criteria. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the logistic regression was 0.752 (0.004), 0.3832 and 14769.2, respectively. The area under the ROC curve (SE), root mean square and -2Loglikelihood of the artificial neural network was 0.754 (0.004), 0.3770 and 14757.6, respectively. Based on these three criteria, artificial neural network would give better performance than logistic regression. Although, the difference is statistically significant, it does not seem to be clinically significant.

  11. A multi-echelon reverse logistics network design for product recovery-a case of truck tire remanufacturing

    DEFF Research Database (Denmark)

    Sasikumar, P.; Kannan, Govindan; Haq, A. Noorul

    2010-01-01

    called retreading) from the used tire is proposed in this work. The implementation of such remanufacturing system usually requires an appropriate reverse logistics network for choosing the physical locations, facilities, and transportation links to convey the used products from customers...... to the remanufacturing facility and from there to secondary markets. The main objective of this work is to develop a mixed integer nonlinear programming model for maximizing the profit of a multi-echelon reverse logistics network and also to present a real-life case study of truck tire remanufacturing for the secondary...

  12. Logistics of community smallpox control through contact tracing and ring vaccination: a stochastic network model

    Directory of Open Access Journals (Sweden)

    Portnoy Diane L

    2004-08-01

    Full Text Available Abstract Background Previous smallpox ring vaccination models based on contact tracing over a network suggest that ring vaccination would be effective, but have not explicitly included response logistics and limited numbers of vaccinators. Methods We developed a continuous-time stochastic simulation of smallpox transmission, including network structure, post-exposure vaccination, vaccination of contacts of contacts, limited response capacity, heterogeneity in symptoms and infectiousness, vaccination prior to the discontinuation of routine vaccination, more rapid diagnosis due to public awareness, surveillance of asymptomatic contacts, and isolation of cases. Results We found that even in cases of very rapidly spreading smallpox, ring vaccination (when coupled with surveillance is sufficient in most cases to eliminate smallpox quickly, assuming that 95% of household contacts are traced, 80% of workplace or social contacts are traced, and no casual contacts are traced, and that in most cases the ability to trace 1–5 individuals per day per index case is sufficient. If smallpox is assumed to be transmitted very quickly to contacts, it may at times escape containment by ring vaccination, but could be controlled in these circumstances by mass vaccination. Conclusions Small introductions of smallpox are likely to be easily contained by ring vaccination, provided contact tracing is feasible. Uncertainties in the nature of bioterrorist smallpox (infectiousness, vaccine efficacy support continued planning for ring vaccination as well as mass vaccination. If initiated, ring vaccination should be conducted without delays in vaccination, should include contacts of contacts (whenever there is sufficient capacity and should be accompanied by increased public awareness and surveillance.

  13. Comparison of two data mining techniques in labeling diagnosis to Iranian pharmacy claim dataset: artificial neural network (ANN) versus decision tree model.

    Science.gov (United States)

    Rezaei-Darzi, Ehsan; Farzadfar, Farshad; Hashemi-Meshkini, Amir; Navidi, Iman; Mahmoudi, Mahmoud; Varmaghani, Mehdi; Mehdipour, Parinaz; Soudi Alamdari, Mahsa; Tayefi, Batool; Naderimagham, Shohreh; Soleymani, Fatemeh; Mesdaghinia, Alireza; Delavari, Alireza; Mohammad, Kazem

    2014-12-01

    This study aimed to evaluate and compare the prediction accuracy of two data mining techniques, including decision tree and neural network models in labeling diagnosis to gastrointestinal prescriptions in Iran. This study was conducted in three phases: data preparation, training phase, and testing phase. A sample from a database consisting of 23 million pharmacy insurance claim records, from 2004 to 2011 was used, in which a total of 330 prescriptions were assessed and used to train and test the models simultaneously. In the training phase, the selected prescriptions were assessed by both a physician and a pharmacist separately and assigned a diagnosis. To test the performance of each model, a k-fold stratified cross validation was conducted in addition to measuring their sensitivity and specificity. Generally, two methods had very similar accuracies. Considering the weighted average of true positive rate (sensitivity) and true negative rate (specificity), the decision tree had slightly higher accuracy in its ability for correct classification (83.3% and 96% versus 80.3% and 95.1%, respectively). However, when the weighted average of ROC area (AUC between each class and all other classes) was measured, the ANN displayed higher accuracies in predicting the diagnosis (93.8% compared with 90.6%). According to the result of this study, artificial neural network and decision tree model represent similar accuracy in labeling diagnosis to GI prescription.

  14. Predicting drug-target interactions by dual-network integrated logistic matrix factorization

    Science.gov (United States)

    Hao, Ming; Bryant, Stephen H.; Wang, Yanli

    2017-01-01

    In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.

  15. Reverse logistics network for municipal solid waste management: The inclusion of waste pickers as a Brazilian legal requirement.

    Science.gov (United States)

    Ferri, Giovane Lopes; Chaves, Gisele de Lorena Diniz; Ribeiro, Glaydston Mattos

    2015-06-01

    This study proposes a reverse logistics network involved in the management of municipal solid waste (MSW) to solve the challenge of economically managing these wastes considering the recent legal requirements of the Brazilian Waste Management Policy. The feasibility of the allocation of MSW material recovery facilities (MRF) as intermediate points between the generators of these wastes and the options for reuse and disposal was evaluated, as well as the participation of associations and cooperatives of waste pickers. This network was mathematically modelled and validated through a scenario analysis of the municipality of São Mateus, which makes the location model more complete and applicable in practice. The mathematical model allows the determination of the number of facilities required for the reverse logistics network, their location, capacities, and product flows between these facilities. The fixed costs of installation and operation of the proposed MRF were balanced with the reduction of transport costs, allowing the inclusion of waste pickers to the reverse logistics network. The main contribution of this study lies in the proposition of a reverse logistics network for MSW simultaneously involving legal, environmental, economic and social criteria, which is a very complex goal. This study can guide practices in other countries that have realities similar to those in Brazil of accelerated urbanisation without adequate planning for solid waste management, added to the strong presence of waste pickers that, through the characteristic of social vulnerability, must be included in the system. In addition to the theoretical contribution to the reverse logistics network problem, this study aids in decision-making for public managers who have limited technical and administrative capacities for the management of solid wastes. Copyright © 2015 Elsevier Ltd. All rights reserved.

  16. iAnn

    DEFF Research Database (Denmark)

    Jimenez, Rafael C; Albar, Juan P; Bhak, Jong

    2013-01-01

    We present iAnn, an open source community-driven platform for dissemination of life science events, such as courses, conferences and workshops. iAnn allows automatic visualisation and integration of customised event reports. A central repository lies at the core of the platform: curators add subm...... disseminated to all portals that query the system. To facilitate the visualization of announcements, iAnn provides powerful filtering options and views, integrated in Google Maps and Google Calendar. All iAnn widgets are freely available....

  17. Ann tuleb Rakverest Võrru

    Index Scriptorium Estoniae

    2009-01-01

    Võru kultuurimajas Kannel etendub 17. aprillil Rakvere teatri noortelavastus "Kuidas elad? ...Ann?!" Aidi Valliku jutustuse põhjal. Lavastaja Sven Heiberg. Mängivad ka Viljandi Kultuuriakadeemia teatritudengid

  18. Performance Parameters Analysis of an XD3P Peugeot Engine Using Artificial Neural Networks (ANN) Concept in MATLAB

    Science.gov (United States)

    Rangaswamy, T.; Vidhyashankar, S.; Madhusudan, M.; Bharath Shekar, H. R.

    2015-04-01

    The current trends of engineering follow the basic rule of innovation in mechanical engineering aspects. For the engineers to be efficient, problem solving aspects need to be viewed in a multidimensional perspective. One such methodology implemented is the fusion of technologies from other disciplines in order to solve the problems. This paper mainly deals with the application of Neural Networks in order to analyze the performance parameters of an XD3P Peugeot engine (used in Ministry of Defence). The basic propaganda of the work is divided into two main working stages. In the former stage, experimentation of an IC engine is carried out in order to obtain the primary data. In the latter stage the primary database formed is used to design and implement a predictive neural network in order to analyze the output parameters variation with respect to each other. A mathematical governing equation for the neural network is obtained. The obtained polynomial equation describes the characteristic behavior of the built neural network system. Finally, a comparative study of the results is carried out.

  19. Corporate and supply chain network governance of third party logistics service providers: Effects on buyers’ intention to continue the relationship

    Directory of Open Access Journals (Sweden)

    Salih Börteçine Avci

    2017-06-01

    Full Text Available This study focuses on the impact of corporate governance, supply chain network governance and competencies such as sales and logistics competence on buyers’ intention to relationship continuity. A total number of 258 questionnaires were distributed to Turkish manufacturing firms, selected using cross-sectional sampling method from the Istanbul and Edirne Chamber of Commerce and Industry in Turkey. The data of survey was analysed using PLS-SEM model with WARP PLS 5.0 software. Our findings indicate that corporate governance and supply chain network governance seem to have a positive effect on sales competence and logistics competence, and together, they influence buyers’ intention to relationship continuity. In this respect, the outcomes of this study may provide valuable insights for the third-party logistics (3PL literature in terms of buyers’ intention to relationship continuity.

  20. An Optimization Model for Expired Drug Recycling Logistics Networks and Government Subsidy Policy Design Based on Tri-level Programming

    Directory of Open Access Journals (Sweden)

    Hui Huang

    2015-07-01

    Full Text Available In order to recycle and dispose of all people’s expired drugs, the government should design a subsidy policy to stimulate users to return their expired drugs, and drug-stores should take the responsibility of recycling expired drugs, in other words, to be recycling stations. For this purpose it is necessary for the government to select the right recycling stations and treatment stations to optimize the expired drug recycling logistics network and minimize the total costs of recycling and disposal. This paper establishes a tri-level programming model to study how the government can optimize an expired drug recycling logistics network and the appropriate subsidy policies. Furthermore, a Hybrid Genetic Simulated Annealing Algorithm (HGSAA is proposed to search for the optimal solution of the model. An experiment is discussed to illustrate the good quality of the recycling logistics network and government subsides obtained by the HGSAA. The HGSAA is proven to have the ability to converge on the global optimal solution, and to act as an effective algorithm for solving the optimization problem of expired drug recycling logistics network and government subsidies.

  1. Towards a Diagnostic Instrument to Identify Improvement Opportunities for Quality Controlled Logistics in Agrifood Supply Chain Networks

    NARCIS (Netherlands)

    Vorst, van der J.G.A.J.; Kooten, van O.; Luning, P.A.

    2011-01-01

    Western-European consumers have become not only more demanding on product availability in retail outlets but also on other food attributes such as quality, integrity, and safety. When (re)designing food supply-chain networks, from a logistics point of view, one has to consider these demands next to

  2. An Optimization Model for Expired Drug Recycling Logistics Networks and Government Subsidy Policy Design Based on Tri-level Programming

    Science.gov (United States)

    Huang, Hui; Li, Yuyu; Huang, Bo; Pi, Xing

    2015-01-01

    In order to recycle and dispose of all people’s expired drugs, the government should design a subsidy policy to stimulate users to return their expired drugs, and drug-stores should take the responsibility of recycling expired drugs, in other words, to be recycling stations. For this purpose it is necessary for the government to select the right recycling stations and treatment stations to optimize the expired drug recycling logistics network and minimize the total costs of recycling and disposal. This paper establishes a tri-level programming model to study how the government can optimize an expired drug recycling logistics network and the appropriate subsidy policies. Furthermore, a Hybrid Genetic Simulated Annealing Algorithm (HGSAA) is proposed to search for the optimal solution of the model. An experiment is discussed to illustrate the good quality of the recycling logistics network and government subsides obtained by the HGSAA. The HGSAA is proven to have the ability to converge on the global optimal solution, and to act as an effective algorithm for solving the optimization problem of expired drug recycling logistics network and government subsidies. PMID:26184252

  3. Selected aspects of the logistics network of public hospitals in the competitive market of health services

    National Research Council Canada - National Science Library

    Justyna Majchrzak-Lepczyk; Benedykt Bober

    2016-01-01

    Background: The below considerations provide an overview of the issues of sustainable development, logistics, to financial engineering instruments and the role of intellectual capital in the process...

  4. Manufacturing plant location selection in logistics network using Analytic Hierarchy Process

    Directory of Open Access Journals (Sweden)

    Ping-Yu Chang

    2015-11-01

    Full Text Available Purpose: In recent years, numerous companies have moved their manufacturing plants to China to capitalize on lower cost and tax. Plant location has such an impact on cost, stocks, and logistics network but location selection in the company is usually based on subjective preference of high ranking managers. Such a decision-making process might result in selecting a location with a lower fixed cost but a higher operational cost. Therefore, this research adapts real data from an electronics company to develop a framework that incorporates both quantitative and qualitative factors for selecting new plant locations. Design/methodology/approach: In-depth interviews were conducted with 12 high rank managers (7 of them are department manager, 2 of them are vice-president, 1 of them is senior engineer, and 2 of them are plant manager in the departments of construction, finance, planning, production, and warehouse to determine the important factors. A questionnaire survey is then conducted for comparing factors which are analyzed using the Analytic Hierarchy Process (AHP. Findings: Results show that the best location chosen by the developed framework coincides well with the company’s primal production base. The results have been presented to the company’s high ranking managers for realizing the accuracy of the framework. Positive responses of the managers indicate usefulness of implementing the proposed model into reality, which adds to the value of this research. Practical implications: The proposed framework can save numerous time-consuming meetings called to compromise opinions and conflictions from different departments in location selection. Originality/value: This paper adapts the Analytic Hierarchy Process (AHP to incorporate quantitative and qualitative factors which are obtained through in-depth interviews with high rank managers in a company into the location decision.

  5. Logistical Support for the Installation of the Plate Boundary Observatory GPS and Borehole Strainmeter Networks

    Science.gov (United States)

    Kurnik, C.; Austin, K.; Coyle, B.; Dittmann, T.; Feaux, K.; Friesen, B.; Johnson, W.; Mencin, D.; Pauk, B.; Walls, C.

    2007-12-01

    The Plate Boundary Observatory (PBO), part of the NSF-funded EarthScope project, is designed to study the three- dimensional strain field resulting from deformation across the active boundary zone between the Pacific and North American plates in the western United States. To meet these goals, UNAVCO will install 880 continuous GPS stations, 103 borehole strainmeter stations, 28 tiltmeters, and five laser strainmeters by October 2008. Such a broad network presents significant logisitical challenges, including moving supplies, equipment, and personnel around 6 million square kilometers, and this requires accurate tracking and careful planning. The PBO logistics chain includes the PBO headquarters at UNAVCO in Boulder, Colorado and five regional offices in the continental United States and Alaska, served by dozens of suppliers spread across the globe. These offices are responsible for building and maintaining sites in their region. Most equipment and supplies first arrive in Boulder, where they are tagged and entered into a UNAVCO-wide equipment database, assembled and quality checked as necessary, and sent on to the appropriate regional office. Larger items which are costly to store and ship from Boulder, such as batteries or long sections of stainless steel pipe and bar required for monuments, are shipped directly from the supplier to each region as needed. These supplies and equipment are also tracked through the ordering, delivery, installation, and maintenance cycle via Earned Value Management techniques which allow us to meet NSF and other Federal procurement rules. Early prototypes and assembly configurations aid the development of material and supply budgets. A thorough understanding of Federal procurement rules at project start up is critical as the project moves forward.

  6. Reverse logistics network for municipal solid waste management: The inclusion of waste pickers as a Brazilian legal requirement

    Energy Technology Data Exchange (ETDEWEB)

    Ferri, Giovane Lopes, E-mail: giovane.ferri@aluno.ufes.br [Department of Engineering and Technology, Federal University of Espírito Santo – UFES, Rodovia BR 101 Norte, Km 60, Bairro Litorâneo, São Mateus, ES, 29.932-540 (Brazil); Diniz Chaves, Gisele de Lorena, E-mail: gisele.chaves@ufes.br [Department of Engineering and Technology, Federal University of Espírito Santo – UFES, Rodovia BR 101 Norte, Km 60, Bairro Litorâneo, São Mateus, ES, 29.932-540 (Brazil); Ribeiro, Glaydston Mattos, E-mail: glaydston@pet.coppe.ufrj.br [Transportation Engineering Programme, Federal University of Rio de Janeiro – UFRJ, Centro de Tecnologia, Bloco H, Sala 106, Cidade Universitária, Rio de Janeiro, 21949-900 (Brazil)

    2015-06-15

    Highlights: • We propose a reverse logistics network for MSW involving waste pickers. • A generic facility location mathematical model was validated in a Brazilian city. • The results enable to predict the capacity for screening and storage centres (SSC). • We minimise the costs for transporting MSW with screening and storage centres. • The use of SSC can be a potential source of revenue and a better use of MSW. - Abstract: This study proposes a reverse logistics network involved in the management of municipal solid waste (MSW) to solve the challenge of economically managing these wastes considering the recent legal requirements of the Brazilian Waste Management Policy. The feasibility of the allocation of MSW material recovery facilities (MRF) as intermediate points between the generators of these wastes and the options for reuse and disposal was evaluated, as well as the participation of associations and cooperatives of waste pickers. This network was mathematically modelled and validated through a scenario analysis of the municipality of São Mateus, which makes the location model more complete and applicable in practice. The mathematical model allows the determination of the number of facilities required for the reverse logistics network, their location, capacities, and product flows between these facilities. The fixed costs of installation and operation of the proposed MRF were balanced with the reduction of transport costs, allowing the inclusion of waste pickers to the reverse logistics network. The main contribution of this study lies in the proposition of a reverse logistics network for MSW simultaneously involving legal, environmental, economic and social criteria, which is a very complex goal. This study can guide practices in other countries that have realities similar to those in Brazil of accelerated urbanisation without adequate planning for solid waste management, added to the strong presence of waste pickers that, through the

  7. Auditing data reliability in international logistics : An application of bayesian networks

    NARCIS (Netherlands)

    Liu, L.; Daniels, H.A.M.; Triepels, R.J.M.A.; Hammoudi, S.; Maciaszek, L.; Cordeiro, J.

    2014-01-01

    Data reliability closely relates to the risk management in international logistics. Unreliable data negatively affect the business in various ways. Due to the competence specialization and cooperation among the business partners in a logistics chain, the business in a focal company is inevitably

  8. Design of an integrated forward and reverse logistics network optimi-zation model for commercial goods management

    Directory of Open Access Journals (Sweden)

    Eva Ponce-Cueto

    2015-01-01

    Full Text Available In this study, an optimization model is formulated for designing an integrated forward and reverse logistics network in the consumer goods industry. The resultant model is a mixed-integer linear programming model (MILP. Its purpose is to minimize the total costs of the closed-loop supply chain network. It is important to note that the design of the logistics network may involve a trade-off between the total costs and the optimality in commercial goods management. The model comprises a discrete set as potential locations of unlimited capacity warehouses and fixed locations of customers’ zones. It provides decisions related to the facility location and customers’ requirements satisfaction, all of this related with the inventory and shipment decisions of the supply chain. Finally, an application of this model is illustrated by a real-life case in the food and drinks industry. We can conclude that this model can significantly help companies to make decisions about problems associated with logistics network design.

  9. Preliminary Analysis of the efficacy of Artificial neural Network (ANN) and Cellular Automaton (CA) based Land Use Models in Urban Land-Use Planning

    Science.gov (United States)

    Harun, R.

    2013-05-01

    This research provides an opportunity of collaboration between urban planners and modellers by providing a clear theoretical foundations on the two most widely used urban land use models, and assessing the effectiveness of applying the models in urban planning context. Understanding urban land cover change is an essential element for sustainable urban development as it affects ecological functioning in urban ecosystem. Rapid urbanization due to growing inclination of people to settle in urban areas has increased the complexities in predicting that at what shape and size cities will grow. The dynamic changes in the spatial pattern of urban landscapes has exposed the policy makers and environmental scientists to great challenge. But geographic science has grown in symmetry to the advancements in computer science. Models and tools are developed to support urban planning by analyzing the causes and consequences of land use changes and project the future. Of all the different types of land use models available in recent days, it has been found by researchers that the most frequently used models are Cellular Automaton (CA) and Artificial Neural Networks (ANN) models. But studies have demonstrated that the existing land use models have not been able to meet the needs of planners and policy makers. There are two primary causes identified behind this prologue. First, there is inadequate understanding of the fundamental theories and application of the models in urban planning context i.e., there is a gap in communication between modellers and urban planners. Second, the existing models exclude many key drivers in the process of simplification of the complex urban system that guide urban spatial pattern. Thus the models end up being effective in assessing the impacts of certain land use policies, but cannot contribute in new policy formulation. This paper is an attempt to increase the knowledge base of planners on the most frequently used land use model and also assess the

  10. A Hybrid ANN-GA Model to Prediction of Bivariate Binary Responses: Application to Joint Prediction of Occurrence of Heart Block and Death in Patients with Myocardial Infarction.

    Science.gov (United States)

    Mirian, Negin-Sadat; Sedehi, Morteza; Kheiri, Soleiman; Ahmadi, Ali

    2016-01-01

    In medical studies, when the joint prediction about occurrence of two events should be anticipated, a statistical bivariate model is used. Due to the limitations of usual statistical models, other methods such as Artificial Neural Network (ANN) and hybrid models could be used. In this paper, we propose a hybrid Artificial Neural Network-Genetic Algorithm (ANN-GA) model to prediction the occurrence of heart block and death in myocardial infarction (MI) patients simultaneously. For fitting and comparing the models, 263 new patients with definite diagnosis of MI hospitalized in Cardiology Ward of Hajar Hospital, Shahrekord, Iran, from March, 2014 to March, 2016 were enrolled. Occurrence of heart block and death were employed as bivariate binary outcomes. Bivariate Logistic Regression (BLR), ANN and hybrid ANN-GA models were fitted to data. Prediction accuracy was used to compare the models. The codes were written in Matlab 2013a and Zelig package in R3.2.2. The prediction accuracy of BLR, ANN and hybrid ANN-GA models was obtained 77.7%, 83.69% and 93.85% for the training and 78.48%, 84.81% and 96.2% for the test data, respectively. In both training and test data set, hybrid ANN-GA model had better accuracy. ANN model could be a suitable alternative for modeling and predicting bivariate binary responses when the presuppositions of statistical models are not met in actual data. In addition, using optimization methods, such as hybrid ANN-GA model, could improve precision of ANN model.

  11. Redesigning a three-echelon logistics network over multiple time periods with transportation mode selection and outsourcing opportunities

    OpenAIRE

    Cortinhal, M. J.; Lopes, M. J.; Melo, M. T.

    2014-01-01

    We address the problem of designing/redesigning a multi-echelon logistics network over a multi-period planning horizon. Strategic decisions comprise opening new plants and warehouses at candidate sites and selecting their capacities from a set of available discrete sizes. Capacity expansion may occur more than once over the time horizon both at new locations and at existing facilities. Capacity contraction is a viable option as well that involves closing existing plants and/or warehouses. The...

  12. Anne-Ly Reimaa : "Suhtlemisel on oluline avatus" / Anne-Ly Reimaa ; interv. Tiia Linnard

    Index Scriptorium Estoniae

    Reimaa, Anne-Ly

    2005-01-01

    Ilmunud ka: Severnoje Poberezhje : Subbota 3. september lk. 5. Intervjueeritav oma tööst Brüsselis, kus esindab Eesti linnade liitu ja Eesti maaomavalitsuste liitu. Arvamust avaldavad Anne Jundas ja Kaia Kaldvee. Lisa: CV

  13. Integrated disaster relief logistics: a stepping stone towards viable civil-military networks?

    Science.gov (United States)

    Tatham, Peter; Rietjens, Sebastiaan Bas

    2016-01-01

    The twenty-first century has seen a significant rise in all forms of disasters and this has resulted in military and humanitarian organisations becoming more frequently engaged in the provision of support to those affected. Achieving an efficient and effective logistic preparation and response is one of the key elements in mitigating the impact of such events, but the establishment of mechanisms to deliver an appropriately integrated civil-military approach remains elusive. Not least because of the high percentage of assistance budgets spent on logistics, this area is considered to represent fertile ground for developing improved processes and understanding. In practice, the demands placed on civilian and military logisticians are broadly similar, as is the solution space. Speaking a common language and using common concepts, it is argued, therefore, that the logistic profession should be in the vanguard of the development of an improved civil-military interface. © 2016 The Author(s). Disasters © Overseas Development Institute, 2016.

  14. A Comparison of Logistic Regression, Neural Networks, and Classification Trees Predicting Success of Actuarial Students

    Science.gov (United States)

    Schumacher, Phyllis; Olinsky, Alan; Quinn, John; Smith, Richard

    2010-01-01

    The authors extended previous research by 2 of the authors who conducted a study designed to predict the successful completion of students enrolled in an actuarial program. They used logistic regression to determine the probability of an actuarial student graduating in the major or dropping out. They compared the results of this study with those…

  15. Developing a decision-making framework for levels of logistics outsourcing in food supply chain networks

    NARCIS (Netherlands)

    Hsiao, L.; Vorst, van der J.G.A.J.; Kemp, R.G.M.; Omta, S.W.F.

    2010-01-01

    Purpose – The purpose of this paper is to present a decision-making framework for outsourcing levels of logistics activities. These are: execution level of basic activities (such as transportation, warehousing); value-added activities; planning and control level of activities (such as transportation

  16. Study on Maritime Logistics Warehousing Center Model and Precision Marketing Strategy Optimization Based on Fuzzy Method and Neural Network Model

    Directory of Open Access Journals (Sweden)

    Xiao Kefeng

    2017-08-01

    Full Text Available The bulk commodity, different with the retail goods, has a uniqueness in the location selection, the chosen of transportation program and the decision objectives. How to make optimal decisions in the facility location, requirement distribution, shipping methods and the route selection and establish an effective distribution system to reduce the cost has become a burning issue for the e-commerce logistics, which is worthy to be deeply and systematically solved. In this paper, Logistics warehousing center model and precision marketing strategy optimization based on fuzzy method and neural network model is proposed to solve this problem. In addition, we have designed principles of the fuzzy method and neural network model to solve the proposed model because of its complexity. Finally, we have solved numerous examples to compare the results of lingo and Matlab, we use Matlab and lingo just to check the result and to illustrate the numerical example, we can find from the result, the multi-objective model increases logistics costs and improves the efficiency of distribution time.

  17. Containerisation, box logistics and global supply chains: The integration of ports and liner shipping networks

    OpenAIRE

    Notteboom, T; Rodrigue, J.-P.

    2008-01-01

    In 2006, container shipping celebrated its 50th anniversary as an innovation that had a tremendous impact on the geography of production and distribution. Production became globalised by a better usage of comparative advantages while distribution systems were able to interact more efficiently. This paper analyses the mounting pressures on box logistics in light of global supply chains. It will be demonstrated that the basic principle of containerisation remained the same notwithstanding scale...

  18. Comparison of estimation capabilities of response surface methodology (RSM with artificial neural network (ANN in lipase-catalyzed synthesis of palm-based wax ester

    Directory of Open Access Journals (Sweden)

    Salleh Abu

    2007-08-01

    Full Text Available Abstract Background Wax esters are important ingredients in cosmetics, pharmaceuticals, lubricants and other chemical industries due to their excellent wetting property. Since the naturally occurring wax esters are expensive and scarce, these esters can be produced by enzymatic alcoholysis of vegetable oils. In an enzymatic reaction, study on modeling and optimization of the reaction system to increase the efficiency of the process is very important. The classical method of optimization involves varying one parameter at a time that ignores the combined interactions between physicochemical parameters. RSM is one of the most popular techniques used for optimization of chemical and biochemical processes and ANNs are powerful and flexible tools that are well suited to modeling biochemical processes. Results The coefficient of determination (R2 and absolute average deviation (AAD values between the actual and estimated responses were determined as 1 and 0.002844 for ANN training set, 0.994122 and 1.289405 for ANN test set, and 0.999619 and 0.0256 for RSM training set respectively. The predicted optimum condition was: reaction time 7.38 h, temperature 53.9°C, amount of enzyme 0.149 g, and substrate molar ratio 1:3.41. The actual experimental percentage yield was 84.6% at optimum condition, which compared well to the maximum predicted value by ANN (83.9% and RSM (85.4%. The order of effective parameters on wax ester percentage yield were; respectively, time with 33.69%, temperature with 30.68%, amount of enzyme with 18.78% and substrate molar ratio with 16.85%, whereas R2 and AAD were determined as 0.99998696 and 1.377 for ANN, and 0.99991515 and 3.131 for RSM respectively. Conclusion Though both models provided good quality predictions in this study, yet the ANN showed a clear superiority over RSM for both data fitting and estimation capabilities.

  19. An ANN That Applies Pragmatic Decision on Texts.

    Science.gov (United States)

    Aretoulaki, Maria; Tsujii, Jun-ichi

    A computer-based artificial neural network (ANN) that learns to classify sentences in a text as important or unimportant is described. The program is designed to select the sentences that are important enough to be included in composition of an abstract of the text. The ANN is embedded in a conventional symbolic environment consisting of…

  20. Optimization of microwave-assisted extraction of total extract, stevioside and rebaudioside-A from Stevia rebaudiana (Bertoni) leaves, using response surface methodology (RSM) and artificial neural network (ANN) modelling.

    Science.gov (United States)

    Ameer, Kashif; Bae, Seong-Woo; Jo, Yunhee; Lee, Hyun-Gyu; Ameer, Asif; Kwon, Joong-Ho

    2017-08-15

    Stevia rebaudiana (Bertoni) consists of stevioside and rebaudioside-A (Reb-A). We compared response surface methodology (RSM) and artificial neural network (ANN) modelling for their estimation and predictive capabilities in building effective models with maximum responses. A 5-level 3-factor central composite design was used to optimize microwave-assisted extraction (MAE) to obtain maximum yield of target responses as a function of extraction time (X 1 : 1-5min), ethanol concentration, (X 2 : 0-100%) and microwave power (X 3 : 40-200W). Maximum values of the three output parameters: 7.67% total extract yield, 19.58mg/g stevioside yield, and 15.3mg/g Reb-A yield, were obtained under optimum extraction conditions of 4min X 1 , 75% X 2 , and 160W X 3 . The ANN model demonstrated higher efficiency than did the RSM model. Hence, RSM can demonstrate interaction effects of inherent MAE parameters on target responses, whereas ANN can reliably model the MAE process with better predictive and estimation capabilities. Copyright © 2017. Published by Elsevier Ltd.

  1. Classifying machinery condition using oil samples and binary logistic regression

    Science.gov (United States)

    Phillips, J.; Cripps, E.; Lau, John W.; Hodkiewicz, M. R.

    2015-08-01

    The era of big data has resulted in an explosion of condition monitoring information. The result is an increasing motivation to automate the costly and time consuming human elements involved in the classification of machine health. When working with industry it is important to build an understanding and hence some trust in the classification scheme for those who use the analysis to initiate maintenance tasks. Typically "black box" approaches such as artificial neural networks (ANN) and support vector machines (SVM) can be difficult to provide ease of interpretability. In contrast, this paper argues that logistic regression offers easy interpretability to industry experts, providing insight to the drivers of the human classification process and to the ramifications of potential misclassification. Of course, accuracy is of foremost importance in any automated classification scheme, so we also provide a comparative study based on predictive performance of logistic regression, ANN and SVM. A real world oil analysis data set from engines on mining trucks is presented and using cross-validation we demonstrate that logistic regression out-performs the ANN and SVM approaches in terms of prediction for healthy/not healthy engines.

  2. Planning the City Logistics Terminal Location by Applying the Green p-Median Model and Type-2 Neurofuzzy Network.

    Science.gov (United States)

    Pamučar, Dragan; Vasin, Ljubislav; Atanasković, Predrag; Miličić, Milica

    2016-01-01

    The paper herein presents green p-median problem (GMP) which uses the adaptive type-2 neural network for the processing of environmental and sociological parameters including costs of logistics operators and demonstrates the influence of these parameters on planning the location for the city logistics terminal (CLT) within the discrete network. CLT shows direct effects on increment of traffic volume especially in urban areas, which further results in negative environmental effects such as air pollution and noise as well as increased number of urban populations suffering from bronchitis, asthma, and similar respiratory infections. By applying the green p-median model (GMM), negative effects on environment and health in urban areas caused by delivery vehicles may be reduced to minimum. This model creates real possibilities for making the proper investment decisions so as profitable investments may be realized in the field of transport infrastructure. The paper herein also includes testing of GMM in real conditions on four CLT locations in Belgrade City zone.

  3. Planning the City Logistics Terminal Location by Applying the Green p-Median Model and Type-2 Neurofuzzy Network

    Directory of Open Access Journals (Sweden)

    Dragan Pamučar

    2016-01-01

    Full Text Available The paper herein presents green p-median problem (GMP which uses the adaptive type-2 neural network for the processing of environmental and sociological parameters including costs of logistics operators and demonstrates the influence of these parameters on planning the location for the city logistics terminal (CLT within the discrete network. CLT shows direct effects on increment of traffic volume especially in urban areas, which further results in negative environmental effects such as air pollution and noise as well as increased number of urban populations suffering from bronchitis, asthma, and similar respiratory infections. By applying the green p-median model (GMM, negative effects on environment and health in urban areas caused by delivery vehicles may be reduced to minimum. This model creates real possibilities for making the proper investment decisions so as profitable investments may be realized in the field of transport infrastructure. The paper herein also includes testing of GMM in real conditions on four CLT locations in Belgrade City zone.

  4. Planning the City Logistics Terminal Location by Applying the Green p-Median Model and Type-2 Neurofuzzy Network

    Science.gov (United States)

    Pamučar, Dragan; Vasin, Ljubislav; Atanasković, Predrag; Miličić, Milica

    2016-01-01

    The paper herein presents green p-median problem (GMP) which uses the adaptive type-2 neural network for the processing of environmental and sociological parameters including costs of logistics operators and demonstrates the influence of these parameters on planning the location for the city logistics terminal (CLT) within the discrete network. CLT shows direct effects on increment of traffic volume especially in urban areas, which further results in negative environmental effects such as air pollution and noise as well as increased number of urban populations suffering from bronchitis, asthma, and similar respiratory infections. By applying the green p-median model (GMM), negative effects on environment and health in urban areas caused by delivery vehicles may be reduced to minimum. This model creates real possibilities for making the proper investment decisions so as profitable investments may be realized in the field of transport infrastructure. The paper herein also includes testing of GMM in real conditions on four CLT locations in Belgrade City zone. PMID:27195005

  5. CREATING A RISK-BASED NETWORK FOR HAZMAT LOGISTICS BY ROUTE PRIORITIZATION WITH AHP

    Directory of Open Access Journals (Sweden)

    Watchara SATTAYAPRASERT

    2008-01-01

    A mathematical model, formulated by the criteria priority of each factor and its risk level was used for the prioritization. The results show that participants rank the risk of having a road accident as the top priority, followed by the risk of carriage unit explosion and the consequences of an incident (47.8%, 27.4% and 24.8% respectively. To examine this model, data for logistics of gasoline and diesel fuel in Rayong Province (Thailand were used for the model application. From the calculation, it was possible to reduce the risk of road accidents and the consequences by approximately 6.8% and 38.5% respectively by choosing the best alternative route instead of the current one.

  6. Campaign-level dynamic network modelling for spaceflight logistics for the flexible path concept

    Science.gov (United States)

    Ho, Koki; de Weck, Olivier L.; Hoffman, Jeffrey A.; Shishko, Robert

    2016-06-01

    This paper develops a network optimization formulation for dynamic campaign-level space mission planning. Although many past space missions have been designed mainly from a mission-level perspective, a campaign-level perspective will be important for future space exploration. In order to find the optimal campaign-level space transportation architecture, a mixed-integer linear programming (MILP) formulation with a generalized multi-commodity flow and a time-expanded network is developed. Particularly, a new heuristics-based method, a partially static time-expanded network, is developed to provide a solution quickly. The developed method is applied to a case study containing human exploration of a near-Earth object (NEO) and Mars, related to the concept of the Flexible Path. The numerical results show that using the specific combinations of propulsion technologies, in-situ resource utilization (ISRU), and other space infrastructure elements can reduce the initial mass in low-Earth orbit (IMLEO) significantly. In addition, the case study results also show that we can achieve large IMLEO reduction by designing NEO and Mars missions together as a campaign compared with designing them separately owing to their common space infrastructure pre-deployment. This research will be an important step toward efficient and flexible campaign-level space mission planning.

  7. Ann Tenno salapaigad / Margit Tõnson

    Index Scriptorium Estoniae

    Tõnson, Margit, 1978-

    2011-01-01

    Fotograaf Ann Tenno aiandushuvist, pildistamisest maailma erinevates paikades. Uutest suundadest (fototöötlus, fractal art, soojuskaameraga pildistamine) tema loomingus. Katkendeid Ann Tenno 2010. aastal ilmunud proosaraamatust "Üle unepiiri"

  8. Feature Selection and ANN Solar Power Prediction

    Directory of Open Access Journals (Sweden)

    Daniel O’Leary

    2017-01-01

    Full Text Available A novel method of solar power forecasting for individuals and small businesses is developed in this paper based on machine learning, image processing, and acoustic classification techniques. Increases in the production of solar power at the consumer level require automated forecasting systems to minimize loss, cost, and environmental impact for homes and businesses that produce and consume power (prosumers. These new participants in the energy market, prosumers, require new artificial neural network (ANN performance tuning techniques to create accurate ANN forecasts. Input masking, an ANN tuning technique developed for acoustic signal classification and image edge detection, is applied to prosumer solar data to improve prosumer forecast accuracy over traditional macrogrid ANN performance tuning techniques. ANN inputs tailor time-of-day masking based on error clustering in the time domain. Results show an improvement in prediction to target correlation, the R2 value, lowering inaccuracy of sample predictions by 14.4%, with corresponding drops in mean average error of 5.37% and root mean squared error of 6.83%.

  9. Modeling Small Unmanned Aerial System Mishaps Using Logistic Regression and Artificial Neural Networks

    Science.gov (United States)

    2012-03-22

    Flights on Air Frame 854 1 251 59.2 NFAPT Number of Flights on Autopilot Type 772 1 447 146 NFLOC Number of Flights at Location 805 1 564 206 NFP ...DSTNLF 5 TIME 6 DSLF 7 DSLM 8 TEMP 9 MAXWIND 10 MAN 11 NFTN 12 NFP 13 NFAF 14 NFTOT 15 NFLOC 16 PROT 17 NOISE The test set...The architecture for the 7-feature model (containing features MAN, NFTN, NFP , NFAF, NFTOT, NFLOC, and PROT) was constructed and 100 networks using

  10. Application of Ordinal Logistic Regression and Artifical Neural Networks in a Study of Student Satistaction

    Directory of Open Access Journals (Sweden)

    Meral Yay

    2009-06-01

    Full Text Available Measuring student satisfaction is an important issue especially for university administration, in order to improvestudent services and opportunities. The major objective of this study is to provide a solution for this issue.Consequently, student satisfaction has been measured with an ordered five-point Likert scale. A student satisfactionquestionnaire was applied to a total of 314 university students, consisting of 208 female and 106 male students, andsatisfaction was measured by asking students to respond to 19 questionnaire items. Ordinal regression and artificalneural network methods were applied to the collected data which emphasized the differences between the twomethods in terms of the correct classification percentages

  11. Synchronous slowing down in coupled logistic maps via random network topology

    Science.gov (United States)

    Wang, Sheng-Jun; Du, Ru-Hai; Jin, Tao; Wu, Xing-Sen; Qu, Shi-Xian

    2016-03-01

    The speed and paths of synchronization play a key role in the function of a system, which has not received enough attention up to now. In this work, we study the synchronization process of coupled logistic maps that reveals the common features of low-dimensional dissipative systems. A slowing down of synchronization process is observed, which is a novel phenomenon. The result shows that there are two typical kinds of transient process before the system reaches complete synchronization, which is demonstrated by both the coupled multiple-period maps and the coupled multiple-band chaotic maps. When the coupling is weak, the evolution of the system is governed mainly by the local dynamic, i.e., the node states are attracted by the stable orbits or chaotic attractors of the single map and evolve toward the synchronized orbit in a less coherent way. When the coupling is strong, the node states evolve in a high coherent way toward the stable orbit on the synchronized manifold, where the collective dynamics dominates the evolution. In a mediate coupling strength, the interplay between the two paths is responsible for the slowing down. The existence of different synchronization paths is also proven by the finite-time Lyapunov exponent and its distribution.

  12. Model for Building a Distribution Network Based on the Multivariate Analysis of the Industrial and Logistical Potential of Regions

    Directory of Open Access Journals (Sweden)

    Alexander Vladimirovich Kirillov

    2015-12-01

    Full Text Available The international integration of the Russian economy is connected to the need of the realization of the competitive advantages of the geopolitical position of Russia, the industrial potential of regions, the logistic infrastructure of transport corridors. This article discusses the design model of the supply chain (distribution network based on the multivariate analysis and the methodology of the substantiation of its configuration based on the cost factors and the level of the logistics infrastructure development. For solving the problem of placing one or more logistics centers in the service area, a two-stage algorithm is used. At the first stage, the decisions on the reasonability of the choice of one or another version of the development are made with А. В. Кириллов, В. Е. Целин 345 ЭКОНОМИКА РЕГИОНА №4 (2015 the use of the “Make or Buy” standard model. The criterion of decision making is the guaranteed overcoming of the threshold of “indifference” taking into account the statistical characteristics of costs for options of “buy” and “make” depending on the volume of consumption of goods or services. At the second stage, the Ardalan’s heuristic method is used for the evaluation of the choice of placing one or more logistics centers in the service area. The model parameters are based on the assessment of the development prospects of the region and its investment potential (existence and composition of employment, production, natural resources, financial and consumer opportunities, institutional, innovation, infrastructure capacity. Furthermore, such criteria as a regional financial appeal, professionally trained specialists, the competitive advantages of the promoted company and others are analyzed. An additional criterion is the development of the priority matrix, which considers such factors as difficulties of customs registration and certification, a level of regional transport

  13. Kõnelused Tartus / Anne Untera

    Index Scriptorium Estoniae

    Untera, Anne, 1951-

    2007-01-01

    8.-10. V Tartus toimunud eesti, läti ja saksa kunstiteadlaste ühisseminarist. Alexander Knorre rääkis Karl August Senffi, Ilona Audere Friedrich Ludwig von Maydelli, Mai Levin Karl Alexander von Winkleri, Kristiana Abele Johann Walter-Kurau (1869-1932), Anne Untera Konstantin ja Sally von Kügelgeni, Epp Preem Julie Hagen-Schwartzi, Friedrich Gross Eduard von Gebhardti ja Katharina Hadding Ida Kerkoviuse (1879-1970) loomingust

  14. The Logistic of Empty Containers′ Return in the Liner-Shipping Network

    Directory of Open Access Journals (Sweden)

    Belayachi Naima

    2017-09-01

    Full Text Available The Maritime Transport is a favoured tool regarding the universal exchanges because it has gone through several evolutions. Indeed, the containerization is considered as one of the most remarkable improvements in the shipping. The containers are rented by shipping companies. However, these companies meet an empty container availability problem at some ports of Maritime Transport Network (MTN to satisfy the demands of clients. The objective of this work is to solve the problem of the imbalance of the distribution of containers and look for empty containers at less cost to meet the demands of clients. As a result, the authors propose an application to represent the MTN, and provide a balanced distribution of containers. The work presented in this article is based on a heuristic method by neighbourhood. It allows the process of the clients' demands and transfers of full containers as well as the research of empty containers by optimizing the cost of theirs return.

  15. Optimising reverse logistics network to support policy-making in the case of Electrical and Electronic Equipment.

    Science.gov (United States)

    Achillas, Ch; Vlachokostas, Ch; Aidonis, D; Moussiopoulos, N; Iakovou, E; Banias, G

    2010-12-01

    Due to the rapid growth of Waste Electrical and Electronic Equipment (WEEE) volumes, as well as the hazardousness of obsolete electr(on)ic goods, this type of waste is now recognised as a priority stream in the developed countries. Policy-making related to the development of the necessary infrastructure and the coordination of all relevant stakeholders is crucial for the efficient management and viability of individually collected waste. This paper presents a decision support tool for policy-makers and regulators to optimise electr(on)ic products' reverse logistics network. To that effect, a Mixed Integer Linear Programming mathematical model is formulated taking into account existing infrastructure of collection points and recycling facilities. The applicability of the developed model is demonstrated employing a real-world case study for the Region of Central Macedonia, Greece. The paper concludes with presenting relevant obtained managerial insights. Copyright © 2010 Elsevier Ltd. All rights reserved.

  16. Placing Safety Stock in Logistic Networks under Guaranteed-Service Time Inventory Models: An Application to the Automotive Industry

    Directory of Open Access Journals (Sweden)

    L.A. Moncayo-Martínez

    2014-06-01

    Full Text Available The aim of this paper is to solve the problem of placing safety stock over a Logistic Network (LN that is represented by a Generic Bill of Materials (GBOM. Thus the LN encompasses supplying, assembling, and delivering stages. We describe, in detail, the recursive algorithm based on Dynamic Programming (DP to solve the placing safety stock problem under guaranteed-service time models. We also develop a java-based application (JbA that both models the LN and runs the recursive DP algorithm. We solved a real case of a company that manufactures fixed brake and clutch pedal modules of cars’ brake system. After running JbA, the levels of inventory decreased by zero in 55 out of 65 stages.

  17. A critical appraisal of logistic regression-based nomograms, artificial neural networks, classification and regression-tree models, look-up tables and risk-group stratification models for prostate cancer.

    Science.gov (United States)

    Chun, Felix K-H; Karakiewicz, Pierre I; Briganti, Alberto; Walz, Jochen; Kattan, Michael W; Huland, Hartwig; Graefen, Markus

    2007-04-01

    To evaluate several methods of predicting prostate cancer-related outcomes, i.e. nomograms, look-up tables, artificial neural networks (ANN), classification and regression tree (CART) analyses and risk-group stratification (RGS) models, all of which represent valid alternatives. We present four direct comparisons, where a nomogram was compared to either an ANN, a look-up table, a CART model or a RGS model. In all comparisons we assessed the predictive accuracy and performance characteristics of both models. Nomograms have several advantages over ANN, look-up tables, CART and RGS models, the most fundamental being a higher predictive accuracy and better performance characteristics. These results suggest that nomograms are more accurate and have better performance characteristics than their alternatives. However, ANN, look-up tables, CART analyses and RGS models all rely on methodologically sound and valid alternatives, which should not be abandoned.

  18. Space partitioning strategies for indoor WLAN positioning with cascade-connected ANN structures.

    Science.gov (United States)

    Borenović, Miloš; Nešković, Aleksandar; Budimir, Djuradj

    2011-02-01

    Position information in indoor environments can be procured using diverse approaches. Due to the ubiquitous presence of WLAN networks, positioning techniques in these environments are the scope of intense research. This paper explores two strategies for space partitioning when utilizing cascade-connected Artificial Neural Networks (ANNs) structures for indoor WLAN positioning. A set of cascade-connected ANN structures with different space partitioning strategies are compared mutually and to the single ANN structure. The benefits of using cascade-connected ANNs structures are shown and discussed in terms of the size of the environment, number of subspaces and partitioning strategy. The optimal cascade-connected ANN structures with space partitioning show up to 50% decrease in median error and up to 12% decrease in the average error with respect to the single ANN model. Finally, the single ANN and the optimal cascade-connected ANN model are compared against other well-known positioning techniques.

  19. Inverse estimation of multiple muscle activations based on linear logistic regression.

    Science.gov (United States)

    Sekiya, Masashi; Tsuji, Toshiaki

    2017-07-01

    This study deals with a technology to estimate the muscle activity from the movement data using a statistical model. A linear regression (LR) model and artificial neural networks (ANN) have been known as statistical models for such use. Although ANN has a high estimation capability, it is often in the clinical application that the lack of data amount leads to performance deterioration. On the other hand, the LR model has a limitation in generalization performance. We therefore propose a muscle activity estimation method to improve the generalization performance through the use of linear logistic regression model. The proposed method was compared with the LR model and ANN in the verification experiment with 7 participants. As a result, the proposed method showed better generalization performance than the conventional methods in various tasks.

  20. Optimizing the Removal of Rhodamine B in Aqueous Solutions by Reduced Graphene Oxide-Supported Nanoscale Zerovalent Iron (nZVI/rGO Using an Artificial Neural Network-Genetic Algorithm (ANN-GA

    Directory of Open Access Journals (Sweden)

    Xuedan Shi

    2017-06-01

    Full Text Available Rhodamine B (Rh B is a toxic dye that is harmful to the environment, humans, and animals, and thus the discharge of Rh B wastewater has become a critical concern. In the present study, reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO was used to treat Rh B aqueous solutions. The nZVI/rGO composites were synthesized with the chemical deposition method and were characterized using scanning electron microscopy (SEM, X-ray diffraction (XRD, Raman spectroscopy, N2-sorption, and X-ray photoelectron spectroscopy (XPS analysis. The effects of several important parameters (initial pH, initial concentration, temperature, and contact time on the removal of Rh B by nZVI/rGO were optimized by response surface methodology (RSM and artificial neural network hybridized with genetic algorithm (ANN-GA. The results suggest that the ANN-GA model was more accurate than the RSM model. The predicted optimum value of Rh B removal efficiency (90.0% was determined using the ANN-GA model, which was compatible with the experimental value (86.4%. Moreover, the Langmuir, Freundlich, and Temkin isotherm equations were applied to fit the adsorption equilibrium data, and the Freundlich isotherm was the most suitable model for describing the process for sorption of Rh B onto the nZVI/rGO composites. The maximum adsorption capacity based on the Langmuir isotherm was 87.72 mg/g. The removal process of Rh B could be completed within 20 min, which was well described by the pseudo-second order kinetic model.

  1. Optimizing the Removal of Rhodamine B in Aqueous Solutions by Reduced Graphene Oxide-Supported Nanoscale Zerovalent Iron (nZVI/rGO) Using an Artificial Neural Network-Genetic Algorithm (ANN-GA)

    Science.gov (United States)

    Shi, Xuedan; Ruan, Wenqian; Hu, Jiwei; Fan, Mingyi; Cao, Rensheng; Wei, Xionghui

    2017-01-01

    Rhodamine B (Rh B) is a toxic dye that is harmful to the environment, humans, and animals, and thus the discharge of Rh B wastewater has become a critical concern. In the present study, reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) was used to treat Rh B aqueous solutions. The nZVI/rGO composites were synthesized with the chemical deposition method and were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), Raman spectroscopy, N2-sorption, and X-ray photoelectron spectroscopy (XPS) analysis. The effects of several important parameters (initial pH, initial concentration, temperature, and contact time) on the removal of Rh B by nZVI/rGO were optimized by response surface methodology (RSM) and artificial neural network hybridized with genetic algorithm (ANN-GA). The results suggest that the ANN-GA model was more accurate than the RSM model. The predicted optimum value of Rh B removal efficiency (90.0%) was determined using the ANN-GA model, which was compatible with the experimental value (86.4%). Moreover, the Langmuir, Freundlich, and Temkin isotherm equations were applied to fit the adsorption equilibrium data, and the Freundlich isotherm was the most suitable model for describing the process for sorption of Rh B onto the nZVI/rGO composites. The maximum adsorption capacity based on the Langmuir isotherm was 87.72 mg/g. The removal process of Rh B could be completed within 20 min, which was well described by the pseudo-second order kinetic model. PMID:28587196

  2. Optimizing the Removal of Rhodamine B in Aqueous Solutions by Reduced Graphene Oxide-Supported Nanoscale Zerovalent Iron (nZVI/rGO) Using an Artificial Neural Network-Genetic Algorithm (ANN-GA).

    Science.gov (United States)

    Shi, Xuedan; Ruan, Wenqian; Hu, Jiwei; Fan, Mingyi; Cao, Rensheng; Wei, Xionghui

    2017-06-03

    Rhodamine B (Rh B) is a toxic dye that is harmful to the environment, humans, and animals, and thus the discharge of Rh B wastewater has become a critical concern. In the present study, reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) was used to treat Rh B aqueous solutions. The nZVI/rGO composites were synthesized with the chemical deposition method and were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), Raman spectroscopy, N₂-sorption, and X-ray photoelectron spectroscopy (XPS) analysis. The effects of several important parameters (initial pH, initial concentration, temperature, and contact time) on the removal of Rh B by nZVI/rGO were optimized by response surface methodology (RSM) and artificial neural network hybridized with genetic algorithm (ANN-GA). The results suggest that the ANN-GA model was more accurate than the RSM model. The predicted optimum value of Rh B removal efficiency (90.0%) was determined using the ANN-GA model, which was compatible with the experimental value (86.4%). Moreover, the Langmuir, Freundlich, and Temkin isotherm equations were applied to fit the adsorption equilibrium data, and the Freundlich isotherm was the most suitable model for describing the process for sorption of Rh B onto the nZVI/rGO composites. The maximum adsorption capacity based on the Langmuir isotherm was 87.72 mg/g. The removal process of Rh B could be completed within 20 min, which was well described by the pseudo-second order kinetic model.

  3. Identification of Relevant Phytochemical Constituents for Characterization and Authentication of Tomatoes by General Linear Model Linked to Automatic Interaction Detection (GLM-AID and Artificial Neural Network Models (ANNs.

    Directory of Open Access Journals (Sweden)

    Marcos Hernández Suárez

    Full Text Available There are a large number of tomato cultivars with a wide range of morphological, chemical, nutritional and sensorial characteristics. Many factors are known to affect the nutrient content of tomato cultivars. A complete understanding of the effect of these factors would require an exhaustive experimental design, multidisciplinary scientific approach and a suitable statistical method. Some multivariate analytical techniques such as Principal Component Analysis (PCA or Factor Analysis (FA have been widely applied in order to search for patterns in the behaviour and reduce the dimensionality of a data set by a new set of uncorrelated latent variables. However, in some cases it is not useful to replace the original variables with these latent variables. In this study, Automatic Interaction Detection (AID algorithm and Artificial Neural Network (ANN models were applied as alternative to the PCA, AF and other multivariate analytical techniques in order to identify the relevant phytochemical constituents for characterization and authentication of tomatoes. To prove the feasibility of AID algorithm and ANN models to achieve the purpose of this study, both methods were applied on a data set with twenty five chemical parameters analysed on 167 tomato samples from Tenerife (Spain. Each tomato sample was defined by three factors: cultivar, agricultural practice and harvest date. General Linear Model linked to AID (GLM-AID tree-structured was organized into 3 levels according to the number of factors. p-Coumaric acid was the compound the allowed to distinguish the tomato samples according to the day of harvest. More than one chemical parameter was necessary to distinguish among different agricultural practices and among the tomato cultivars. Several ANN models, with 25 and 10 input variables, for the prediction of cultivar, agricultural practice and harvest date, were developed. Finally, the models with 10 input variables were chosen with fit's goodness

  4. Identification of Relevant Phytochemical Constituents for Characterization and Authentication of Tomatoes by General Linear Model Linked to Automatic Interaction Detection (GLM-AID) and Artificial Neural Network Models (ANNs).

    Science.gov (United States)

    Hernández Suárez, Marcos; Astray Dopazo, Gonzalo; Larios López, Dina; Espinosa, Francisco

    2015-01-01

    There are a large number of tomato cultivars with a wide range of morphological, chemical, nutritional and sensorial characteristics. Many factors are known to affect the nutrient content of tomato cultivars. A complete understanding of the effect of these factors would require an exhaustive experimental design, multidisciplinary scientific approach and a suitable statistical method. Some multivariate analytical techniques such as Principal Component Analysis (PCA) or Factor Analysis (FA) have been widely applied in order to search for patterns in the behaviour and reduce the dimensionality of a data set by a new set of uncorrelated latent variables. However, in some cases it is not useful to replace the original variables with these latent variables. In this study, Automatic Interaction Detection (AID) algorithm and Artificial Neural Network (ANN) models were applied as alternative to the PCA, AF and other multivariate analytical techniques in order to identify the relevant phytochemical constituents for characterization and authentication of tomatoes. To prove the feasibility of AID algorithm and ANN models to achieve the purpose of this study, both methods were applied on a data set with twenty five chemical parameters analysed on 167 tomato samples from Tenerife (Spain). Each tomato sample was defined by three factors: cultivar, agricultural practice and harvest date. General Linear Model linked to AID (GLM-AID) tree-structured was organized into 3 levels according to the number of factors. p-Coumaric acid was the compound the allowed to distinguish the tomato samples according to the day of harvest. More than one chemical parameter was necessary to distinguish among different agricultural practices and among the tomato cultivars. Several ANN models, with 25 and 10 input variables, for the prediction of cultivar, agricultural practice and harvest date, were developed. Finally, the models with 10 input variables were chosen with fit's goodness between 44 and 100

  5. Ultrasound assisted biodiesel production from sesame (Sesamum indicum L.) oil using barium hydroxide as a heterogeneous catalyst: Comparative assessment of prediction abilities between response surface methodology (RSM) and artificial neural network (ANN).

    Science.gov (United States)

    Sarve, Antaram; Sonawane, Shriram S; Varma, Mahesh N

    2015-09-01

    The present study estimates the prediction capability of response surface methodology (RSM) and artificial neural network (ANN) models for biodiesel synthesis from sesame (Sesamum indicum L.) oil under ultrasonication (20 kHz and 1.2 kW) using barium hydroxide as a basic heterogeneous catalyst. RSM based on a five level, four factor central composite design, was employed to obtain the best possible combination of catalyst concentration, methanol to oil molar ratio, temperature and reaction time for maximum FAME content. Experimental data were evaluated by applying RSM integrating with desirability function approach. The importance of each independent variable on the response was investigated by using sensitivity analysis. The optimum conditions were found to be catalyst concentration (1.79 wt%), methanol to oil molar ratio (6.69:1), temperature (31.92°C), and reaction time (40.30 min). For these conditions, experimental FAME content of 98.6% was obtained, which was in reasonable agreement with predicted one. The sensitivity analysis confirmed that catalyst concentration was the main factors affecting the FAME content with the relative importance of 36.93%. The lower values of correlation coefficient (R(2)=0.781), root mean square error (RMSE=4.81), standard error of prediction (SEP=6.03) and relative percent deviation (RPD=4.92) for ANN compared to those R(2) (0.596), RMSE (6.79), SEP (8.54) and RPD (6.48) for RSM proved better prediction capability of ANN in predicting the FAME content. Copyright © 2015 Elsevier B.V. All rights reserved.

  6. A simulation-based robust biofuel facility location model for an integrated bio-energy logistics network

    Directory of Open Access Journals (Sweden)

    Jae-Dong Hong

    2014-10-01

    Full Text Available Purpose: The purpose of this paper is to propose a simulation-based robust biofuel facility location model for solving an integrated bio-energy logistics network (IBLN problem, where biomass yield is often uncertain or difficult to determine.Design/methodology/approach: The IBLN considered in this paper consists of four different facilities: farm or harvest site (HS, collection facility (CF, biorefinery (BR, and blending station (BS. Authors propose a mixed integer quadratic modeling approach to simultaneously determine the optimal CF and BR locations and corresponding biomass and bio-energy transportation plans. The authors randomly generate biomass yield of each HS and find the optimal locations of CFs and BRs for each generated biomass yield, and select the robust locations of CFs and BRs to show the effects of biomass yield uncertainty on the optimality of CF and BR locations. Case studies using data from the State of South Carolina in the United State are conducted to demonstrate the developed model’s capability to better handle the impact of uncertainty of biomass yield.Findings: The results illustrate that the robust location model for BRs and CFs works very well in terms of the total logistics costs. The proposed model would help decision-makers find the most robust locations for biorefineries and collection facilities, which usually require huge investments, and would assist potential investors in identifying the least cost or important facilities to invest in the biomass and bio-energy industry.Originality/value: An optimal biofuel facility location model is formulated for the case of deterministic biomass yield. To improve the robustness of the model for cases with probabilistic biomass yield, the model is evaluated by a simulation approach using case studies. The proposed model and robustness concept would be a very useful tool that helps potential biofuel investors minimize their investment risk.

  7. Estimation of the chemical-induced eye injury using a weight-of-evidence (WoE) battery of 21 artificial neural network (ANN) c-QSAR models (QSAR-21): part I: irritation potential.

    Science.gov (United States)

    Verma, Rajeshwar P; Matthews, Edwin J

    2015-03-01

    Evaluation of potential chemical-induced eye injury through irritation and corrosion is required to ensure occupational and consumer safety for industrial, household and cosmetic ingredient chemicals. The historical method for evaluating eye irritant and corrosion potential of chemicals is the rabbit Draize test. However, the Draize test is controversial and its use is diminishing - the EU 7th Amendment to the Cosmetic Directive (76/768/EEC) and recast Regulation now bans marketing of new cosmetics having animal testing of their ingredients and requires non-animal alternative tests for safety assessments. Thus, in silico and/or in vitro tests are advocated. QSAR models for eye irritation have been reported for several small (congeneric) data sets; however, large global models have not been described. This report describes FDA/CFSAN's development of 21 ANN c-QSAR models (QSAR-21) to predict eye irritation using the ADMET Predictor program and a diverse training data set of 2928 chemicals. The 21 models had external (20% test set) and internal validation and average training/verification/test set statistics were: 88/88/85(%) sensitivity and 82/82/82(%) specificity, respectively. The new method utilized multiple artificial neural network (ANN) molecular descriptor selection functionalities to maximize the applicability domain of the battery. The eye irritation models will be used to provide information to fill the critical data gaps for the safety assessment of cosmetic ingredient chemicals. Copyright © 2014 Elsevier Inc. All rights reserved.

  8. Eco-efficient based logistics network design in hybrid manufacturing/ remanufacturing system in low-carbon economy

    Directory of Open Access Journals (Sweden)

    Yacan Wang

    2013-03-01

    Full Text Available Purpose: Low-carbon economy requires the pursuit of eco-efficiency, which is a win-win situation between economic and environmental efficiency. In this paper the question of trading off the economic and environmental effects embodied in eco-efficiency in the hybrid manufacturing/remanufacturing logistics network design in the context of low-carbon economy is examined.Design/methodology/approach: A multi-objective mixed integer linear programming model to find the optimal facility locations and materials flow allocation is established. In the objective function, three minimum targets are set: economic cost, CO2 emission and waste generation. Through an iterative algorithm, the Pareto Boundary of the problem is obtained.Findings: The results of numeric study show that in order to achieve a Pareto improvement over an original system, three of the critical rates (i.e. return rate, recovery rate, and cost substitute rate should be increased.Practical implications: To meet the need of low-carbon dioxide, an iso- CO2 emission curve in which decision makers have a series of optimal choices with the same CO2 emission but different cost and waste generation is plotted. Each choice may have different network design but all of these are Pareto optimal solutions, which provide a comprehensive evaluation of both economics and ecology for the decision making.Originality/value: This research chooses carbon emission as one of the three objective functions and uses Pareto sets to analyze how to balance profitability and environmental impacts in designing remanufacturing closed-loop supply chain in the context of low-carbon economy.

  9. Integrated network analysis and logistic regression modeling identify stage-specific genes in Oral Squamous Cell Carcinoma.

    Science.gov (United States)

    Randhawa, Vinay; Acharya, Vishal

    2015-07-16

    Oral squamous cell carcinoma (OSCC) is associated with substantial mortality and morbidity but, OSCC can be difficult to detect at its earliest stage due to its molecular complexity and clinical behavior. Therefore, identification of key gene signatures at an early stage will be highly helpful. The aim of this study was to identify key genes associated with progression of OSCC stages. Gene expression profiles were classified into cancer stage-related modules, i.e., groups of genes that are significantly related to a clinical stage. For prioritizing the candidate genes, analysis was further restricted to genes with high connectivity and a significant association with a stage. To assess predictive power of these genes, a classification model was also developed and tested by 5-fold cross validation and on an independent dataset. The identified genes were enriched for significant processes and functional pathways, and various genes were found to be directly implicated in OSCC. Forward and stepwise, multivariate logistic regression analyses identified 13 key genes whose expression discriminated early- and late-stage OSCC with predictive accuracy (area under curve; AUC) of ~0.81 in a 5-fold cross-validation strategy. The proposed network-driven integrative analytical approach can identify multiple genes significantly related to an OSCC stage; the classification model that is developed with these genes may help to distinguish cancer stages. The proposed genes and model hold promise for monitoring of OSCC stage progression, and our findings may facilitate cancer detection at an earlier stage, resulting in improved treatment outcomes.

  10. Reverse Logistics

    OpenAIRE

    Kulikova, Olga

    2016-01-01

    This thesis was focused on the analysis of the concept of reverse logistics and actual reverse processes which are implemented in mining industry and finding solutions for the optimization of reverse logistics in this sphere. The objective of this paper was the assessment of the development of reverse logistics in mining industry on the example of potash production. The theoretical part was based on reverse logistics and mining waste related literature and provided foundations for further...

  11. LOGISTIC MANAGEMENT

    OpenAIRE

    Florin Tudor Ph. D Student

    2011-01-01

    Logistics is the support function of an organization and it means having the right object, at the right place, in the right time. Logistic management involves planing, organizing, leading, coordinating and controlling the logistic activities of an organization. In military science, maintaining one's supply lines while disrupting those of the enemy is a crucial element of military strategy, since an armed force without resources and transportation is defenseless. Logistics is the management of...

  12. Classification of Urban Aerial Data Based on Pixel Labelling with Deep Convolutional Neural Networks and Logistic Regression

    Science.gov (United States)

    Yao, W.; Poleswki, P.; Krzystek, P.

    2016-06-01

    The recent success of deep convolutional neural networks (CNN) on a large number of applications can be attributed to large amounts of available training data and increasing computing power. In this paper, a semantic pixel labelling scheme for urban areas using multi-resolution CNN and hand-crafted spatial-spectral features of airborne remotely sensed data is presented. Both CNN and hand-crafted features are applied to image/DSM patches to produce per-pixel class probabilities with a L1-norm regularized logistical regression classifier. The evidence theory infers a degree of belief for pixel labelling from different sources to smooth regions by handling the conflicts present in the both classifiers while reducing the uncertainty. The aerial data used in this study were provided by ISPRS as benchmark datasets for 2D semantic labelling tasks in urban areas, which consists of two data sources from LiDAR and color infrared camera. The test sites are parts of a city in Germany which is assumed to consist of typical object classes including impervious surfaces, trees, buildings, low vegetation, vehicles and clutter. The evaluation is based on the computation of pixel-based confusion matrices by random sampling. The performance of the strategy with respect to scene characteristics and method combination strategies is analyzed and discussed. The competitive classification accuracy could be not only explained by the nature of input data sources: e.g. the above-ground height of nDSM highlight the vertical dimension of houses, trees even cars and the nearinfrared spectrum indicates vegetation, but also attributed to decision-level fusion of CNN's texture-based approach with multichannel spatial-spectral hand-crafted features based on the evidence combination theory.

  13. Role of logistics centres in national logistics system

    Directory of Open Access Journals (Sweden)

    2010-06-01

    Full Text Available This article presents a division of logistics centres according to various criteria and specifies their role in a national logistics system. It provides a classification of the main logistics network nodes. It also describes those features of logistics centres that have an impact on zoning development of towns and regions. The current situation in the national logistics system has been presented here against theoretical analyses and also a concept for the development of a logistics centre network in Poland has been formulated.

  14. Prediction of 305 d milk yield in Jersey Cattle Using ANN Modelling

    African Journals Online (AJOL)

    ozcan_eren

    Abstract. Artificial neural networks (ANNs) have been shown to be a powerful tool for system modelling in a wide range of .... neural networks have been applied to predict milk yield in dairy sheep (Salehi et al., 1988). Kominakis et al. ... It consists of the choice of ANN algorithm, the structure (number of layers and number of ...

  15. Spectrophotometric determination of synthetic colorants using PSO-GA-ANN.

    Science.gov (United States)

    Benvidi, Ali; Abbasi, Saleheh; Gharaghani, Sajjad; Dehghan Tezerjani, Marzieh; Masoum, Saeed

    2017-04-01

    Four common food colorants, containing tartrazine, sunset yellow, ponceau 4R and methyl orange, are simultaneously quantified without prior chemical separation. In this study, an effective artificial neural network (ANN) method is designed for modeling multicomponent absorbance data with the presence of shifts or changes of peak shapes in spectroscopic analysis. Gradient descent methods such as Levenberg-Marquardt function are usually used to determine the parameters of ANN. However, these methods may provide inappropriate parameters. In this paper, we propose combination of genetic algorithms (GA) and partial swarm optimization (PSO) to optimize parameters of ANN, and then the algorithm is used to process the relationship between the absorbance data and the concentration of analytes. The hybrid algorithm has the benefits of both PSO and GA techniques. The performance of this algorithm is compared to the performance of PSO-ANN, PC-ANN and ANN based Levenberg-Marquardt function. The obtained results revealed that the designed model can accurately determine colorant concentrations in real and synthetic samples. According to the observations, it is clear that the proposed hybrid method is a powerful tool to estimate the concentration of food colorants with a high degree of overlap using nonlinear artificial neural network. Copyright © 2016 Elsevier Ltd. All rights reserved.

  16. Reducing Risks in Logistics Outsourcing

    OpenAIRE

    Regan, A C; Wang, Chuanxu

    2003-01-01

    Many firms have turned to logistics outsourcing as a way to restructure their distribution networks and gain competitive advantages. Logistics outsourcing in which a third party logistics (3PL) provider is contracted for all or part of an organization's logistics operations has seen consistently increasing use. Although there are clearly pros and cons of using logistics outsourcing, the full extent of both of these has not been adequately examined. This paper begins to examine some of these r...

  17. Application of the method for analysis and solution of problems – MASP in the logistics of a big network of the retail

    Directory of Open Access Journals (Sweden)

    Milton Crispim dos Santos

    2016-11-01

    Full Text Available The objective of this study is to apply MASP in the logistics area of a supermarket belonging to a Large Retail Network, initially identifying existing problems in the company’s logistics and then solving the most severe problems according to the above mentioned tool. The research was a qualitative and exploratory case study. The data collection tool used was a questionnaire with 10 open-ended and closed-ended questions, which was distributed to 31 research subjects, composed of employees who occupy positions of management and leadership in 16 sectors of the supermarket. The results of the research identified 11 problems in logistics , with the most relevant being excess stock, which was addressed by the MASP tool through the implementation of 8 steps, aided by other quality tools already established in the literature.

  18. Computational Logistics

    DEFF Research Database (Denmark)

    Pacino, Dario; Voss, Stefan; Jensen, Rune Møller

    2013-01-01

    This book constitutes the refereed proceedings of the 4th International Conference on Computational Logistics, ICCL 2013, held in Copenhagen, Denmark, in September 2013. The 19 papers presented in this volume were carefully reviewed and selected for inclusion in the book. They are organized...... in topical sections named: maritime shipping, road transport, vehicle routing problems, aviation applications, and logistics and supply chain management....

  19. Computational Logistics

    DEFF Research Database (Denmark)

    This book constitutes the refereed proceedings of the 4th International Conference on Computational Logistics, ICCL 2013, held in Copenhagen, Denmark, in September 2013. The 19 papers presented in this volume were carefully reviewed and selected for inclusion in the book. They are organized...... in topical sections named: maritime shipping, road transport, vehicle routing problems, aviation applications, and logistics and supply chain management....

  20. Reverse logistics

    NARCIS (Netherlands)

    M.P. de Brito (Marisa); S.D.P. Flapper; R. Dekker (Rommert)

    2002-01-01

    textabstractThis paper gives an overview of scientific literature that describes and discusses cases of reverse logistics activities in practice. Over sixty case studies are considered. Based on these studies we are able to indicate critical factors for the practice of reverse logistics. In

  1. Transmission Risks of Schistosomiasis Japonica: Extraction from Back-propagation Artificial Neural Network and Logistic Regression Model: e2123

    National Research Council Canada - National Science Library

    Jun-Fang Xu; Jing Xu; Shi-Zhu Li; Tia-Wu Jia; Xi-Bao Huang; Hua-Ming Zhang; Mei Chen; Guo-Jing Yang; Shu-Jing Gao; Qing-Yun Wang; Xiao-Nong Zhou

    2013-01-01

    ...) and logistic regression model in assessment of transmission risks of Schistosoma japonicum with epidemiological data collected from 2339 villagers from 1247 households in six villages of Jiangling County, P.R. China...

  2. Transmission risks of schistosomiasis japonica: extraction from back-propagation artificial neural network and logistic regression model

    National Research Council Canada - National Science Library

    Xu, Jun-Fang; Xu, Jing; Li, Shi-Zhu; Jia, Tia-Wu; Huang, Xi-Bao; Zhang, Hua-Ming; Chen, Mei; Yang, Guo-Jing; Gao, Shu-Jing; Wang, Qing-Yun; Zhou, Xiao-Nong

    2013-01-01

    ...) and logistic regression model in assessment of transmission risks of Schistosoma japonicum with epidemiological data collected from 2339 villagers from 1247 households in six villages of Jiangling County, P.R. China...

  3. Comparison of Artificial Neural Network (ANN Model Development Methods for Prediction of Macroinvertebrate Communities in the Zwalm River Basin in Flanders, Belgium

    Directory of Open Access Journals (Sweden)

    Andy P. Dedecker

    2002-01-01

    Full Text Available Modelling has become an interesting tool to support decision making in water management. River ecosystem modelling methods have improved substantially during recent years. New concepts, such as artificial neural networks, fuzzy logic, evolutionary algorithms, chaos and fractals, cellular automata, etc., are being more commonly used to analyse ecosystem databases and to make predictions for river management purposes. In this context, artificial neural networks were applied to predict macroinvertebrate communities in the Zwalm River basin (Flanders, Belgium. Structural characteristics (meandering, substrate type, flow velocity and physical and chemical variables (dissolved oxygen, pH were used as predictive variables to predict the presence or absence of macroinvertebrate taxa in the headwaters and brooks of the Zwalm River basin. Special interest was paid to the frequency of occurrence of the taxa as well as the selection of the predictors and variables to be predicted on the prediction reliability of the developed models. Sensitivity analyses allowed us to study the impact of the predictive variables on the prediction of presence or absence of macroinvertebrate taxa and to define which variables are the most influential in determining the neural network outputs.

  4. Conference Logistics Management 2013

    CERN Document Server

    Haasis, Hans-Dietrich; Kopfer, Herbert; Kotzab, Herbert; Schönberger, Jörn

    2015-01-01

    This contributed volume contains the collected research papers presented at the Logistik-Management-Konferenz 2013 organized by the VHB Wissenschaftliche Kommission Logistik, held in Bremen 2013. The papers reflect the current state-of-the-art in logistics and supply chain management, focusing on environmental sustainability in logistics and supply chain network dynamics and control. The target audience primarily comprises research experts in the field as well as practitioners but the book may also be beneficial for graduate students.

  5. Logistical Worlds

    Directory of Open Access Journals (Sweden)

    Ned Rossiter

    2014-03-01

    Full Text Available As the managerial art and science of coordinating the movement of people, finance and things, logistical operations are central to contemporary capital. Despite its materiality in the form of communications and transport infrastructure, logistics remains an abstract machine for many. This is largely due to the compartmental structure of global supply chains and the invisibility of code. In registering the mediating force of logistics, the essay considers parametric politics as an architecture of intervention for both game design and software development. There are implications here not only for gameplay, but also the invention of method and governance of labour. How, for instance, might game design facilitate the production of a political knowledge of logistics? This becomes a matter to address for labour power vis-à-vis collective research on infrastructure, software and global supply chains.

  6. SELECTED PROBLEMS OF REVERSE LOGISTICS IN POLAND

    OpenAIRE

    Agata Mesjasz-Lech

    2009-01-01

    This paper presents the essence of reverse logistics and directions of physical and information flows between logistic network partners. It also analyses effects of implementation of the principles of reverse logistics in Poland in the years 2004-2007

  7. Use of quantitative-structure property relationship (QSPR) and artificial neural network (ANN) based approaches for estimating the octanol-water partition coefficients of the 209 chlorinated trans-azobenzene congeners.

    Science.gov (United States)

    Wilczyńska-Piliszek, Agata J; Piliszek, Sławomir; Falandysz, Jerzy

    2012-01-01

    Polychlorinated azobenzenes (PCABs) can be found as contaminant by products in 3,4-dichloroaniline and its derivatives and in the herbicides Diuron, Linuron, Methazole, Neburon, Propanil and SWEP. Trans congeners of PCABs are physically and chemically more stable and so are environmentally relevant, when compared to unstable cis congeners. In this study, to fulfill gaps on environmentally relevant partitioning properties of PCABs, the values of n-octanol/water partition coefficients (log K(OW)) have been determined for 209 congeners of chloro-trans-azobenzene (Ct-AB) by means of quantitative structure-property relationship (QSPR) approach and artificial neural networks (ANN) predictive ability. The QSPR methods used based on geometry optimalization and quantum-chemical structural descriptors, which were computed on the level of density functional theory (DFT) using B3LYP functional and 6-311++G basis set in Gaussian 03 and of the semi-empirical quantum chemistry method (PM6) of the molecular orbital package (MOPAC). Polychlorinated dibenzo-p-dioxins (PCDDs), -furans (PCDFs) and -biphenyls (PCBs), to which PCABs are related, were reference compounds in this study. An experimentally obtained data on physical and chemical properties of PCDD/Fs and PCBs were reference data for ANN predictions of log K(OW) values of Ct-ABs in this study. Both calculation methods gave similar results in term of absolute log K(OW) values, while the models generated by PM6 are considered highly efficient in time spent, when compared to these by DFT. The estimated log K(OW) values of 209 Ct-ABs varied between 5.22-5.57 and 5.45-5.60 for Mono-, 5.56-6.00 and 5.59-6.07 for Di-, 5.89-6.56 and 5.91-6.46 for Tri-, 6.10-7.05 and 6.13-6.80 for Tetra-, 6.43-7.39 and 6.48-7.14 for Penta-, 6.61-7.78 and 6.98-7.42 for Hexa-, 7.41-7.94 and 7.34-7.86 for Hepta-, 7.99-8.17 and 7.72-8.20 for Octa-, 8.35-8.42 and 8.10-8.62 for NonaCt-ABs, and 8.52-8.60 and 8.81-8.83 for DecaCt-AB. These log K(OW) values

  8. Nox: Anne Carson's Scrapbook Elegy

    OpenAIRE

    Palleau-Papin, Françoise

    2014-01-01

    In the narrative Nox, Anne Carson composes an elegy for her deceased brother, as much as an elegy to the reproduction of the work of art, from the wax tablet to the digital age, by way of the stencil reproduction, to sustain a reflection on our times. She thus invites her readers to a creative reading, that encompasses loss and death.; Dans son récit Nox, Anne Carson compose une élégie à son frère disparu autant qu'une élégie à l'histoire de la reproduction de l'œuvre d'art, depuis la tablett...

  9. Appraisal of ANN and ANFIS for Predicting Vertical Total Electron ...

    African Journals Online (AJOL)

    The propagation of the GPS signals are interfered by free electrons which are the massive particles in the ionosphere region and results in delays in the ... Artificial Neural Network (ANN) and Adaptive Neuro Fuzzy Inference System (ANFIS) algorithms have been developed for the prediction of VTEC in the ionosphere.

  10. Flow forecast by SWAT model and ANN in Pracana basin, Portugal

    NARCIS (Netherlands)

    Demirel, M.C.; Venancio, Anabela; Kahya, Ercan

    2009-01-01

    This study provides a unique opportunity to analyze the issue of flow forecast based on the soil and water assessment tool (SWAT) and artificial neural network (ANN) models. In last two decades, the ANNs have been extensively applied to various water resources system problems. In this study, the

  11. Customer-oriented network trade and logistics of firewood; Metsahakkeen ja pilkkeiden asiakaslahtoeinen verkkokauppa ja logistiikka - PUUT34

    Energy Technology Data Exchange (ETDEWEB)

    Tahvanainen, T.; Sikanen, L. [Finnish Forest Research Institute, Helsinki (Finland)

    2005-07-01

    The small-scale use of firewood is the second largest source of wood based energy after industrial residues in Finland. Objectives of this project, funded by European Regional Development Fund via Tekes and Finnish companies, were to develop logistic systems for small scale use of wood fuels and produce information and material for advisors and consults. The small-scale use of wood fuels increases constantly and e-commerce of chopped firewood is developing especially in Eastern Finland. Currently, the most severe bottlenecks are in the integration of production and delivery logistics, availability of raw material, as well as in the non-professional way of working. In the project, technological alternatives of supply chains, cost structures as well as constraints and preconditions for the economically sustainable operations were clarified. Project ended with following results: 'Typical features of North-Karelian firewood entrepreneur', identifying wood fuel resources in forest planning, new biomass models for estimating availability of energy wood in young stands, simulation studies about delivery logistics, cost structure of firewood supply chains and feasibility of integrating firewood transport to other transport services. Also education and training materials were produced for advisory organizations, like Finnish forestry centers. (orig.)

  12. Customer-oriented network trade and logistics of firewood; Metsaehakkeen ja pilkkeiden asiakaslaehtoeinen verkkokauppa ja logistiikka - PUUT34

    Energy Technology Data Exchange (ETDEWEB)

    Tahvanainen, T. [Joensuun Tiedepuisto Oy, Joensuu (Finland); Sikanen, L. [Finnish Forest Research Institute, Joensuu (Finland)

    2007-07-01

    The small-scale use of firewood is the second largest source of wood based energy after industrial residues in Finland. Objectives of this project, funded by European Regional Development Fund via Tekes and Finnish companies, were to develop logistic systems for small scale use of wood fuels and produce information and material for advisors and consults. The small-scale use of wood fuels increases constantly and e-commerce of chopped firewood is developing especially in Eastern Finland. Currently, the most severe bottlenecks are in the integration of production and delivery logistics, availability of raw material, as well as in the non-professional way of working. In the project, technological alternatives of supply chains, cost structures as well as constraints and preconditions for the economically sustainable operations were clarified. Project ended with following results: 'Typical features of North-Karelian firewood entrepreneur', identifying wood fuel resources in forest planning, new biomass models for estimating availability of energy wood in young stands, simulation studies about delivery logistics, cost structure of firewood supply chains and feasibility of integrating firewood transport to other transport services. Also education and training materials were produced for advisory organizations, like Finnish forestry centers. (orig.)

  13. Estimation of Paddy Equilibrium Moisture Sorption Using ANNs

    Science.gov (United States)

    Amiri Chayjan, R.; Moazez, Y.

    In this research, Artificial Neural Networks (ANNs) used for prediction of Equilibrium Moisture Content (EMC) of three varieties of paddy (Sadri, Tarom and Khazar) as a new method. Feed forward back propagation and cascade forward back propagation networks with Levenberg-Marquardt and Bayesian regularization training algorithms used for training of input patterns. Optimized trained network has the ability of EMC prediction to test patterns at thermal boundary of 20-40°C and relative humidity boundary of 13.5-87% with R2 = 0.9929 and mean absolute error 0.0229. Comparison between optimized ANN result and empirical model of Henderson showed that artificial neural network not only can simultaneously predict the EMC of samples of all varieties but also has better coefficient of determination and less mean absolute error.

  14. Sustainability in logistics practice

    NARCIS (Netherlands)

    Hans-Heinrich Glöckner; Reinder Pieters; Stef Weijers

    2009-01-01

    This conceptual paper wants to emphasis the use of the concept of sustainability within logistics and especially transportation. While working on a new tool to help companies develop sustainable European networks, we discovered that we want to use a specific concept of sustainability: People, planet

  15. Artificial neural network for prediction of distant metastasis in colorectal cancer.

    Science.gov (United States)

    Biglarian, Akbar; Bakhshi, Enayatollah; Gohari, Mahmood Reza; Khodabakhshi, Reza

    2012-01-01

    Artificial neural networks (ANNs) are flexible and nonlinear models which can be used by clinical oncologists in medical research as decision making tools. This study aimed to predict distant metastasis (DM) of colorectal cancer (CRC) patients using an ANN model. The data of this study were gathered from 1219 registered CRC patients at the Research Center for Gastroenterology and Liver Disease of Shahid Beheshti University of Medical Sciences, Tehran, Iran (January 2002 and October 2007). For prediction of DM in CRC patients, neural network (NN) and logistic regression (LR) models were used. Then, the concordance index (C index) and the area under receiver operating characteristic curve (AUROC) were used for comparison of neural network and logistic regression models. Data analysis was performed with R 2.14.1 software. The C indices of ANN and LR models for colon cancer data were calculated to be 0.812 and 0.779, respectively. Based on testing dataset, the AUROC for ANN and LR models were 0.82 and 0.77, respectively. This means that the accuracy of ANN prediction was better than for LR prediction. The ANN model is a suitable method for predicting DM and in that case is suggested as a good classifier that usefulness to treatment goals.

  16. Construction of hazard maps of Hantavirus contagion using Remote Sensing, logistic regression and Artificial Neural Networks: case Araucan\\'ia Region, Chile

    CERN Document Server

    Alvarez, G; Salinas, R

    2016-01-01

    In this research, methods and computational results based on statistical analysis and mathematical modelling, data collection in situ in order to make a hazard map of Hanta Virus infection in the region of Araucania, Chile are presented. The development of this work involves several elements such as Landsat satellite images, biological information regarding seropositivity of Hanta Virus and information concerning positive cases of infection detected in the region. All this information has been processed to find a function that models the danger of contagion in the region, through logistic regression analysis and Artificial Neural Networks

  17. Anne-Ly Võlli: Iga inimene ja asutus vajab omamoodi lähenemist / Anne-Ly Võlli ; intervjueerinud Jaanika Kressa

    Index Scriptorium Estoniae

    Võlli, Anne-Ly, 1976-

    2009-01-01

    MTÜ Jõgevamaa Omavalitsuste Aktiviseerimiskeskus kinnitas avaliku konkursi tulemusel juhatuse liikmeks Anne-Ly Võlli, kelle ülesandeks on keskuse tegevuse juhtimine ja koostöö arendamine partneromavalitsuste ja teiste koostööpartnerite vahel

  18. LFC based adaptive PID controller using ANN and ANFIS techniques

    Directory of Open Access Journals (Sweden)

    Mohamed I. Mosaad

    2014-12-01

    Full Text Available This paper presents an adaptive PID Load Frequency Control (LFC for power systems using Neuro-Fuzzy Inference Systems (ANFIS and Artificial Neural Networks (ANN oriented by Genetic Algorithm (GA. PID controller parameters are tuned off-line by using GA to minimize integral error square over a wide-range of load variations. The values of PID controller parameters obtained from GA are used to train both ANFIS and ANN. Therefore, the two proposed techniques could, online, tune the PID controller parameters for optimal response at any other load point within the operating range. Testing of the developed techniques shows that the adaptive PID-LFC could preserve optimal performance over the whole loading range. Results signify superiority of ANFIS over ANN in terms of performance measures.

  19. Models of logistic regression analysis, support vector machine, and back-propagation neural network based on serum tumor markers in colorectal cancer diagnosis.

    Science.gov (United States)

    Zhang, B; Liang, X L; Gao, H Y; Ye, L S; Wang, Y G

    2016-05-13

    We evaluated the application of three machine learning algorithms, including logistic regression, support vector machine and back-propagation neural network, for diagnosing congenital heart disease and colorectal cancer. By inspecting related serum tumor marker levels in colorectal cancer patients and healthy subjects, early diagnosis models for colorectal cancer were built using three machine learning algorithms to assess their corresponding diagnostic values. Except for serum alpha-fetoprotein, the levels of 11 other serum markers of patients in the colorectal cancer group were higher than those in the benign colorectal cancer group (P model and back-propagation, a neural network diagnosis model was built with diagnostic accuracies of 82 and 75%, sensitivities of 85 and 80%, and specificities of 80 and 70%, respectively. Colorectal cancer diagnosis models based on the three machine learning algorithms showed high diagnostic value and can help obtain evidence for the early diagnosis of colorectal cancer.

  20. Playing tag with ANN: boosted top identification with pattern recognition

    Energy Technology Data Exchange (ETDEWEB)

    Almeida, Leandro G. [Institut de Biologie de l’École Normale Supérieure (IBENS), Inserm 1024- CNRS 8197,46 rue d’Ulm, 75005 Paris (France); Backović, Mihailo [Center for Cosmology, Particle Physics and Phenomenology - CP3,Universite Catholique de Louvain,Louvain-la-neuve (Belgium); Cliche, Mathieu [Laboratory for Elementary Particle Physics, Cornell University,Ithaca, NY 14853 (United States); Lee, Seung J. [Department of Physics, Korea Advanced Institute of Science and Technology,335 Gwahak-ro, Yuseong-gu, Daejeon 305-701 (Korea, Republic of); School of Physics, Korea Institute for Advanced Study,Seoul 130-722 (Korea, Republic of); Perelstein, Maxim [Laboratory for Elementary Particle Physics, Cornell University,Ithaca, NY 14853 (United States)

    2015-07-17

    Many searches for physics beyond the Standard Model at the Large Hadron Collider (LHC) rely on top tagging algorithms, which discriminate between boosted hadronic top quarks and the much more common jets initiated by light quarks and gluons. We note that the hadronic calorimeter (HCAL) effectively takes a “digital image' of each jet, with pixel intensities given by energy deposits in individual HCAL cells. Viewed in this way, top tagging becomes a canonical pattern recognition problem. With this motivation, we present a novel top tagging algorithm based on an Artificial Neural Network (ANN), one of the most popular approaches to pattern recognition. The ANN is trained on a large sample of boosted tops and light quark/gluon jets, and is then applied to independent test samples. The ANN tagger demonstrated excellent performance in a Monte Carlo study: for example, for jets with p{sub T} in the 1100–1200 GeV range, 60% top-tag efficiency can be achieved with a 4% mis-tag rate. We discuss the physical features of the jets identified by the ANN tagger as the most important for classification, as well as correlations between the ANN tagger and some of the familiar top-tagging observables and algorithms.

  1. Immigrant maternal depression and social networks. A multilevel Bayesian spatial logistic regression in South Western Sydney, Australia.

    Science.gov (United States)

    Eastwood, John G; Jalaludin, Bin B; Kemp, Lynn A; Phung, Hai N; Barnett, Bryanne E W

    2013-09-01

    The purpose is to explore the multilevel spatial distribution of depressive symptoms among migrant mothers in South Western Sydney and to identify any group level associations that could inform subsequent theory building and local public health interventions. Migrant mothers (n=7256) delivering in 2002 and 2003 were assessed at 2-3 weeks after delivery for risk factors for depressive symptoms. The binary outcome variables were Edinburgh Postnatal Depression Scale scores (EPDS) of >9 and >12. Individual level variables included were: financial income, self-reported maternal health, social support network, emotional support, practical support, baby trouble sleeping, baby demanding and baby not content. The group level variable reported here is aggregated social support networks. We used Bayesian hierarchical multilevel spatial modelling with conditional autoregression. Migrant mothers were at higher risk of having depressive symptoms if they lived in a community with predominantly Australian-born mothers and strong social capital as measured by aggregated social networks. These findings suggest that migrant mothers are socially isolated and current home visiting services should be strengthened for migrant mothers living in communities where they may have poor social networks. Copyright © 2013 The Authors. Published by Elsevier Ltd.. All rights reserved.

  2. Annely Peebo kutsus presidendi kontserdile / Maria Ulfsak

    Index Scriptorium Estoniae

    Ulfsak, Maria, 1981-

    2003-01-01

    Laulja Anneli Peebo kohtus president Arnold Rüütliga, et anda üle kutse Andrea Bocelli ja Annely Peebo ühiskontserdile. Vt. samas: Andrea Bocelli ja Annely Peebo kontsert Tallinna lauluväljakul 23. augustil; Andrea Bocelli

  3. Ede Kurreli preemia Anneli Tammikule

    Index Scriptorium Estoniae

    2004-01-01

    23. septembrist Eesti Tarbekunsti- ja Disainimuuseumis Maria Puki ja Ivar Lubjaku kujundatud eesti kaasaegse ehtekunsti näitus "Laegas". Avamisel esitleti EKA ehte- ja sepakunsti eriala tutvustavat raamatut "Metall 2" ja anti 2004. a. Ede Kurreli preemia Anneli Tammikule roostevabast terasest fotosöövitatud sarja "2D-3D credit" eest. Parima üliõpilastöö autor Kertu Tuberg. 24. IX toimuva ettekannete päeva kava

  4. Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; SubbaRao; Harish, N.; Lokesha

    Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN), Support Vector Machine (SVM) and Adaptive Neuro Fuzzy Inference system (ANFIS) models...

  5. Logistic Incentive Structures Reflected in Irregular Logistic Procedures

    Science.gov (United States)

    1980-01-31

    34 Washington Head- quarters Services, Directorate for Information Operations and Reports, 14 November. Festinger , Leon . (1950) Social Pressures in...Group Norms and Responsibility for Property A-22 3.3 Communication Networks and Irregular Logistic Procedures A-23 4.0 The Psychological Dimension A-25...Irregular Logistic Procedures A-31 4.2 Motivational Psychology Perspective A-32 4.2.1 Job-Related Incentives and Irregular Logistic Procedures A-33

  6. Green Maritime Logistics

    DEFF Research Database (Denmark)

    Psaraftis, Harilaos N.

    2014-01-01

    chain management. The traditional analysis of these problems has been in terms of cost- benefit and other optimization criteria from the point of view of the logistics provider, carrier, shipper, or other end-user. Such traditional analysis by and large either ignores environmental issues, or considers......Typical problems in maritime logistics include, among others, optimal ship speed, ship routing and scheduling, fleet deployment, fleet size and mix, weather routing, intermodal network design, modal split, transshipment, queuing at ports, terminal management, berth allocation, and total supply...... them of secondary importance. Green maritime logistics tries to bring the environmental dimension into the problem, and specifically the dimension of emissions reduction, by analyzing various trade-offs and exploring ‘win-win’ solutions. This talk takes a look at the trade-offs that are at stake...

  7. A novel artificial neural network method for biomedical prediction based on matrix pseudo-inversion.

    Science.gov (United States)

    Cai, Binghuang; Jiang, Xia

    2014-04-01

    Biomedical prediction based on clinical and genome-wide data has become increasingly important in disease diagnosis and classification. To solve the prediction problem in an effective manner for the improvement of clinical care, we develop a novel Artificial Neural Network (ANN) method based on Matrix Pseudo-Inversion (MPI) for use in biomedical applications. The MPI-ANN is constructed as a three-layer (i.e., input, hidden, and output layers) feed-forward neural network, and the weights connecting the hidden and output layers are directly determined based on MPI without a lengthy learning iteration. The LASSO (Least Absolute Shrinkage and Selection Operator) method is also presented for comparative purposes. Single Nucleotide Polymorphism (SNP) simulated data and real breast cancer data are employed to validate the performance of the MPI-ANN method via 5-fold cross validation. Experimental results demonstrate the efficacy of the developed MPI-ANN for disease classification and prediction, in view of the significantly superior accuracy (i.e., the rate of correct predictions), as compared with LASSO. The results based on the real breast cancer data also show that the MPI-ANN has better performance than other machine learning methods (including support vector machine (SVM), logistic regression (LR), and an iterative ANN). In addition, experiments demonstrate that our MPI-ANN could be used for bio-marker selection as well. Copyright © 2013 Elsevier Inc. All rights reserved.

  8. Comparison of Artificial Neural Network with Logistic Regression as Classification Models for Variable Selection for Prediction of Breast Cancer Patient Outcomes

    Directory of Open Access Journals (Sweden)

    Valérie Bourdès

    2010-01-01

    Full Text Available The aim of this study was to compare multilayer perceptron neural networks (NNs with standard logistic regression (LR to identify key covariates impacting on mortality from cancer causes, disease-free survival (DFS, and disease recurrence using Area Under Receiver-Operating Characteristics (AUROC in breast cancer patients. From 1996 to 2004, 2,535 patients diagnosed with primary breast cancer entered into the study at a single French centre, where they received standard treatment. For specific mortality as well as DFS analysis, the ROC curves were greater with the NN models compared to LR model with better sensitivity and specificity. Four predictive factors were retained by both approaches for mortality: clinical size stage, Scarff Bloom Richardson grade, number of invaded nodes, and progesterone receptor. The results enhanced the relevance of the use of NN models in predictive analysis in oncology, which appeared to be more accurate in prediction in this French breast cancer cohort.

  9. PERIODIC REVIEW SYSTEM FOR INVENTORY REPLENISHMENT CONTROL FOR A TWO-ECHELON LOGISTICS NETWORK UNDER DEMAND UNCERTAINTY: A TWO-STAGE STOCHASTIC PROGRAMING APPROACH

    Directory of Open Access Journals (Sweden)

    P.S.A. Cunha

    Full Text Available ABSTRACT Here, we propose a novel methodology for replenishment and control systems for inventories of two-echelon logistics networks using a two-stage stochastic programming, considering periodic review and uncertain demands. In addition, to achieve better customer services, we introduce a variable rationing rule to address quantities of the item in short. The devised models are reformulated into their deterministic equivalent, resulting in nonlinear mixed-integer programming models, which are then approximately linearized. To deal with the uncertain nature of the item demand levels, we apply a Monte Carlo simulation-based method to generate finite and discrete sets of scenarios. Moreover, the proposed approach does not require restricted assumptions to the behavior of the probabilistic phenomena, as does several existing methods in the literature. Numerical experiments with the proposed approach for randomly generated instances of the problem show results with errors around 1%.

  10. Biomass Logistics

    Energy Technology Data Exchange (ETDEWEB)

    J. Richard Hess; Kevin L. Kenney; William A. Smith; Ian Bonner; David J. Muth

    2015-04-01

    Equipment manufacturers have made rapid improvements in biomass harvesting and handling equipment. These improvements have increased transportation and handling efficiencies due to higher biomass densities and reduced losses. Improvements in grinder efficiencies and capacity have reduced biomass grinding costs. Biomass collection efficiencies (the ratio of biomass collected to the amount available in the field) as high as 75% for crop residues and greater than 90% for perennial energy crops have also been demonstrated. However, as collection rates increase, the fraction of entrained soil in the biomass increases, and high biomass residue removal rates can violate agronomic sustainability limits. Advancements in quantifying multi-factor sustainability limits to increase removal rate as guided by sustainable residue removal plans, and mitigating soil contamination through targeted removal rates based on soil type and residue type/fraction is allowing the use of new high efficiency harvesting equipment and methods. As another consideration, single pass harvesting and other technologies that improve harvesting costs cause biomass storage moisture management challenges, which challenges are further perturbed by annual variability in biomass moisture content. Monitoring, sampling, simulation, and analysis provide basis for moisture, time, and quality relationships in storage, which has allowed the development of moisture tolerant storage systems and best management processes that combine moisture content and time to accommodate baled storage of wet material based upon “shelf-life.” The key to improving biomass supply logistics costs has been developing the associated agronomic sustainability and biomass quality technologies and processes that allow the implementation of equipment engineering solutions.

  11. APPLICATION OF INTERFIRM NETWORKS CONCEPTS IN THE RETAIL SECTION: AN APPLICATION OF THE BROKER CONCEPTS AND OPERATORS LOGISTICS IN DISTRIBUTING COMPANIES OF FOODS

    Directory of Open Access Journals (Sweden)

    Gesinaldo Ataíde Cândido

    2006-06-01

    Full Text Available One of main the practical ones adopted for the organizations in the current environment business-oriented and of management has been the application of the principles of nets, based in the concepts of the partnership, the association and the co-operation. The experiences of this new type of practical of management have been successful in diverse economic sectors, creating better conditions for the attainment of competitive advantages. In this work, a study is made to see the possibility of application in the sector of food distribution, inside of the new perspectives of the supply management and the logistic one. In this direction, the work makes a diagnosis of the retail sector, verifying the possibilities of the application of the principles of nets in a together operating company to the representation sector and food distribution, from a process of strategic change, considering a management model based in the concepts of brokers and logistic operator, which they substitute and/or they incorporate the diverse involved agents with the food distribution, which are: the commercial representative, the deliverer and the wholesaler. Key words: competitiveness, network organizational, chain of supply.

  12. [Anne Arold. Kontrastive Analyse...] / Paul Alvre

    Index Scriptorium Estoniae

    Alvre, Paul, 1921-2008

    2001-01-01

    Arvustus: Arold, Anne. Kontrastive analyse der Wortbildungsmuster im Deutschen und im Estnischen (am Beispiel der Aussehensadjektive). Tartu, 2000. (Dissertationes philologiae germanicae Universitatis Tartuensis)

  13. Mary Anne Chambers | IDRC - International Development Research ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    A former Member of Provincial Parliament, Mary Anne served as Minister of Training, Colleges and Universities, and Minister of Children and Youth Services in the Government of Ontario. She is also a former senior vice-president of Scotiabank. A graduate of the University of Toronto, Mary Anne has received honorary ...

  14. ANN-Based Control of a Wheeled Inverted Pendulum System Using an Extended DBD Learning Algorithm

    Directory of Open Access Journals (Sweden)

    David Cruz

    2016-05-01

    Full Text Available This paper presents a dynamic model for a self-balancing vehicle using the Euler-Lagrange approach. The design and deployment of an artificial neuronal network (ANN in a closed-loop control is described. The ANN is characterized by integration of the extended delta-bar-delta algorithm (DBD, which accelerates the adjustment of synaptic weights. The results of the control strategy in the dynamic model of the robot are also presented.

  15. DESIGN OF A VISUAL INTERFACE FOR ANN BASED SYSTEMS

    Directory of Open Access Journals (Sweden)

    Ramazan BAYINDIR

    2008-01-01

    Full Text Available Artificial intelligence application methods have been used for control of many systems with parallel of technological development besides conventional control techniques. Increasing of artificial intelligence applications have required to education in this area. In this paper, computer based an artificial neural network (ANN software has been presented to learning and understanding of artificial neural networks. By means of the developed software, the training of the artificial neural network according to the inputs provided and a test action can be performed by changing the components such as iteration number, momentum factor, learning ratio, and efficiency function of the artificial neural networks. As a result of the study a visual education set has been obtained that can easily be adapted to the real time application.

  16. Autonomous Co-operation and Control in Complex Adaptive Logistic Systems - Contributions and Limitations for the Innovation Capability of International Supply Networks

    Science.gov (United States)

    Hülsmann, Michael; Cordes, Philip

    This paper aims to analyze the potential contributions of the organization principle autonomous co-operation and control to the innovation capabilities of logistics systems and their sub-systems like single organizations. Therefore, the concept of Complex Adaptive Logistics Systems (CALS) will be introduced and the essentiality of the heterogeneity of the elements within logistics systems for their innovation capabilities will be emphasized. One possible driver for homogeneity is the so-called dominant logic.

  17. Stellar Parameter Determination With J-Plus Using Artificial Neural Networks

    Science.gov (United States)

    Whitten, Devin D.

    2017-10-01

    The J-PLUS narrow-band filter system provides a unique opportunity for the determination of stellar parameters and chemical abundances from photometry alone. Mapping stellar magnitudes to estimates of surface temperature, [Fe/H], and [C/Fe] is an excellent application of machine learning and in particular, artificial neural networks (ANN). The logistics and performance of this ANN methodology is explored with the J-PLUS Early Data Release, as well as the potential impact of stellar parameters from J-PLUS on the field of Galactic chemical evolution.

  18. Prediction of Splitting Tensile Strength of Concrete Containing Zeolite and Diatomite by ANN

    Directory of Open Access Journals (Sweden)

    E. Gülbandılar

    2017-01-01

    Full Text Available This study was designed to investigate with two different artificial neural network (ANN prediction model for the behavior of concrete containing zeolite and diatomite. For purpose of constructing this model, 7 different mixes with 63 specimens of the 28, 56 and 90 days splitting tensile strength experimental results of concrete containing zeolite, diatomite, both zeolite and diatomite used in training and testing for ANN systems was gathered from the tests. The data used in the ANN models are arranged in a format of seven input parameters that cover the age of samples, Portland cement, zeolite, diatomite, aggregate, water and hyper plasticizer and an output parameter which is splitting tensile strength of concrete. In the model, the training and testing results have shown that two different ANN systems have strong potential as a feasible tool for predicting 28, 56 and 90 days the splitting tensile strength of concrete containing zeolite and diatomite.

  19. Business logistics: importance and some research opportunities

    OpenAIRE

    Ballou,Ronald H.

    1997-01-01

    Business logistics is defined and reasons are given as to why it is a vital area of management. Political and economic trends are highlighted to show that it is even increasing in importance. Current research in business logistics is discussed with a focus on the design of the logistics network as it is aided by computer modeling. Research opportunities are identified to both improve modeling for network design and better specify the information inputs to the design process.Logística empresar...

  20. Logistics Sourcing Strategies in Supply Chain Design

    OpenAIRE

    Liu, Liwen

    2007-01-01

    A company's logistics sourcing strategy determines whether it structures and organizeslogistics within the company or company group or integrates logistics upstream and downstreamin the supply chain. First, three different types of logistics sourcing strategies in supply chaindesign are described and the theoretical background for the development of these strategies,including both transaction cost theory and network theory, is analyzed. Two special casesabout logistics sourcing strategy decis...

  1. Ann Back Propagation For Forecasting And Simulation Hydroclimatology Data

    Directory of Open Access Journals (Sweden)

    Syaefudin Suhaedi

    2017-10-01

    Full Text Available Government policies in distributing fertilizers and seeds of food crops such as rice and crops depend on the growing season of the farmers. Therefore before conducting the distribution it is necessary to spread early planting season in each region farmers so that the result of distribution is optimal. One of the alternatives that must be done first is to predict the pattern of hydroclimatological data cycle of the coming year to see the pattern of data of previous years. In this case required a method that can be used to predict the hydroclimatological data. The exact method used to make predictions is Artificial Neural Network ANN Back Propagation. As a follow-up step will be predicted by this ANN will be used to build system planning optimal cropping pattern for agricultural crops to avoid harvest failure puso in order to obtain maximum production results so as to support national food security. Based on the results of the simulation is known that ANN Back Propagation with two hidden layer are able to predict hydroclimatological data with an average accuracy of 95.72 - 96.61. While the prediction validation obtained an average percentage error of 1.12 with the accuracy of 99.76. The data used for training testing validation and prediction are data in Central Lombok NTB Indonesia.

  2. Managing Reverse Logistics or Reversing Logistics Management?

    OpenAIRE

    Brito, Marisa

    2004-01-01

    textabstractIn the past, supply chains were busy fine-tuning the logistics from raw material to the end customer. Today an increasing flow of products is going back in the chain. Thus, companies have to manage reverse logistics as well.This thesis contributes to a better understanding of reverse logistics. The thesis brings insights on reverse logistics decision-making and it lays down theoretical principles for reverse logistics as a research field.In particular it puts together a framework ...

  3. Management of Logistics Planning

    OpenAIRE

    Bjørnar Aas; Wallace, Stein W.

    2010-01-01

    Logistics problems are gradually becoming more complex and a better understanding of logistics management as a subject is a key to deal with the new challenges. A core element of logistics management is logistics planning, which substitutes for low customer service levels, high waste, and the use of buffers and slacks in the execution of logistic activities. Furthermore, the availability of information and problem-solving capabilities are established as the core parts of logistics planning. B...

  4. Outsourcing Reverse Logistics

    OpenAIRE

    Logožar, Klavdij

    2008-01-01

    The external supply of logistics services is part of a trend toward outsourcing non-core logistics activities. The scope of third-party logistics may range from a relatively limited combination of activities (e.g. transportation, packaging and warehousing) to a comprehensive set of logistics services. The author first discusses the characteristics of business logistics in the manufacturing firms, which could be helpful in understanding of outsourcing logistics services. The author points out ...

  5. Complex logistics audit system

    Directory of Open Access Journals (Sweden)

    Zuzana Marková

    2010-02-01

    Full Text Available Complex logistics audit system is a tool for realization of logistical audit in the company. The current methods for logistics auditare based on “ad hok” analysis of logisticsl system. This paper describes system for complex logistics audit. It is a global diagnosticsof logistics processes and functions of enterprise. The goal of logistics audit is to provide comparative documentation for managementabout state of logistics in company and to show the potential of logistics changes in order to achieve more effective companyperformance.

  6. Modelling flow dynamics in water distribution networks using ...

    African Journals Online (AJOL)

    DR OKE

    Keywords: Artificial neural network; Leakage detection technique; Water distribution; Leakages ... techniques, artificial neural networks (ANNs), genetic algorithms (GA), and probabilistic and evidential reasoning. ANNs are mimicry of ..... Implementation of an online artificial intelligence district meter area flow meter data.

  7. Artificial neural network modelling

    CERN Document Server

    Samarasinghe, Sandhya

    2016-01-01

    This book covers theoretical aspects as well as recent innovative applications of Artificial Neural networks (ANNs) in natural, environmental, biological, social, industrial and automated systems. It presents recent results of ANNs in modelling small, large and complex systems under three categories, namely, 1) Networks, Structure Optimisation, Robustness and Stochasticity 2) Advances in Modelling Biological and Environmental Systems and 3) Advances in Modelling Social and Economic Systems. The book aims at serving undergraduates, postgraduates and researchers in ANN computational modelling. .

  8. THE FEMINISM AND FEMININITY OF ANN VERONICA IN H. G. WELLS' ANN VERONICA

    Directory of Open Access Journals (Sweden)

    Liem Satya Limanta

    2002-01-01

    Full Text Available H.G. Well's Ann Veronica structurally seems to be divided into two parts; the first deals with Ann Veronica's struggle to get equality with men and freedom in most aspects of life, such as in politics, economics, education, and sexuality; the second describes much the other side of her individuality which she cannot deny, namely her femininity, such as her crave for love, marriage, maternity, and beauty. H.G. Wells describes vividly the two elements in Ann Veronica, feminism and femininity. As a feminist, Ann Veronica rebelled against her authoritative Victorian father, who regarded women only as men's property to be protected from the harsh world outside. On the other side, Ann could not deny her being a woman after she fell in love with Capes. Her femininity from the second half of the novel then is explored. Although the novel ends with the depiction of the domestic life of Ann Veronica, it does not mean that the feminism is gone altogether. The key point is that the family life Ann chooses as a `submissive' wife and good mother is her choice. It is very different if it is forced on her to do. Thus, this novel depicts both sides of Ann Veronica, her feminism and her femininity.

  9. ANN-based wavelet analysis for predicting electrical signal from photovoltaic power supply system

    Energy Technology Data Exchange (ETDEWEB)

    Mellit, A. [Medea Univ., Medea (Algeria). Inst. of Science Engineering, Dept. of Electronics

    2007-07-01

    This study was conducted to predict different electrical signals from a photovoltaic power supply system (PVPS) using an artificial neural networks (ANN) with wavelet analysis. It involved the creation of a database of electrical signals (PV-generator current, voltage, battery current voltage, regulator current and voltage) obtained from an experimental PVPS system installed in the south of Algeria. The potential applications were for sizing and analyzing the performance of PVPS systems; control of maximum power point tracker (MPPT) in order to deliver the maximum energy from the PV-array; prediction of the optimal configuration (PV-array and battery sizing) of PVPS systems; expert configuration of PV-systems; faults diagnosis; supervision; and, control and monitoring. First, based on the wavelet analysis each electrical signal was mapped in several time frequency domains. The PV-system was then divided into 3-subsystems corresponding to ANN-PV generator model, ANN-battery model, and ANN-regulator model. An example of day-by-day prediction for each electrical signal was presented. The results of the proposed approach were in good agreement with experimental results. In addition, the accuracy of the proposed approach was more satisfactory when only ANN was used. It was concluded that this methodology offers the possibility of developing a new expert configuration of PVPS by implementing the soft computing ANN-wavelet program with a digital signal processing (DSP) circuit. 26 refs., 1 tab., 5 figs.

  10. Classification Models to Predict Survival of Kidney Transplant Recipients Using Two Intelligent Techniques of Data Mining and Logistic Regression.

    Science.gov (United States)

    Nematollahi, M; Akbari, R; Nikeghbalian, S; Salehnasab, C

    2017-01-01

    Kidney transplantation is the treatment of choice for patients with end-stage renal disease (ESRD). Prediction of the transplant survival is of paramount importance. The objective of this study was to develop a model for predicting survival in kidney transplant recipients. In a cross-sectional study, 717 patients with ESRD admitted to Nemazee Hospital during 2008-2012 for renal transplantation were studied and the transplant survival was predicted for 5 years. The multilayer perceptron of artificial neural networks (MLP-ANN), logistic regression (LR), Support Vector Machine (SVM), and evaluation tools were used to verify the determinant models of the predictions and determine the independent predictors. The accuracy, area under curve (AUC), sensitivity, and specificity of SVM, MLP-ANN, and LR models were 90.4%, 86.5%, 98.2%, and 49.6%; 85.9%, 76.9%, 97.3%, and 26.1%; and 84.7%, 77.4%, 97.5%, and 17.4%, respectively. Meanwhile, the independent predictors were discharge time creatinine level, recipient age, donor age, donor blood group, cause of ESRD, recipient hypertension after transplantation, and duration of dialysis before transplantation. SVM and MLP-ANN models could efficiently be used for determining survival prediction in kidney transplant recipients.

  11. Logistic chain modelling

    NARCIS (Netherlands)

    Slats, P.A.; Bhola, B.; Evers, J.J.M.; Dijkhuizen, G.

    1995-01-01

    Logistic chain modelling is very important in improving the overall performance of the total logistic chain. Logistic models provide support for a large range of applications, such as analysing bottlenecks, improving customer service, configuring new logistic chains and adapting existing chains to

  12. Managing Reverse Logistics or Reversing Logistics Management?

    NARCIS (Netherlands)

    M.P. de Brito (Marisa)

    2004-01-01

    textabstractIn the past, supply chains were busy fine-tuning the logistics from raw material to the end customer. Today an increasing flow of products is going back in the chain. Thus, companies have to manage reverse logistics as well.This thesis contributes to a better understanding of reverse

  13. Identification of drought in Dhalai river watershed using MCDM and ANN models

    Science.gov (United States)

    Aher, Sainath; Shinde, Sambhaji; Guha, Shantamoy; Majumder, Mrinmoy

    2017-03-01

    An innovative approach for drought identification is developed using Multi-Criteria Decision Making (MCDM) and Artificial Neural Network (ANN) models from surveyed drought parameter data around the Dhalai river watershed in Tripura hinterlands, India. Total eight drought parameters, i.e., precipitation, soil moisture, evapotranspiration, vegetation canopy, cropping pattern, temperature, cultivated land, and groundwater level were obtained from expert, literature and cultivator survey. Then, the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) were used for weighting of parameters and Drought Index Identification (DII). Field data of weighted parameters in the meso scale Dhalai River watershed were collected and used to train the ANN model. The developed ANN model was used in the same watershed for identification of drought. Results indicate that the Limited-Memory Quasi-Newton algorithm was better than the commonly used training method. Results obtained from the ANN model shows the drought index developed from the study area ranges from 0.32 to 0.72. Overall analysis revealed that, with appropriate training, the ANN model can be used in the areas where the model is calibrated, or other areas where the range of input parameters is similar to the calibrated region for drought identification.

  14. Prediction of IRI in short and long terms for flexible pavements: ANN and GMDH methods

    NARCIS (Netherlands)

    Ziari, H.; Sobhani, J.; Ayoubinejad, J.; Hartmann, Timo

    2015-01-01

    Prediction of pavement condition is one of the most important issues in pavement management systems. In this paper, capabilities of artificial neural networks (ANNs) and group method of data handling (GMDH) methods in predicting flexible pavement conditions were analysed in three levels: in 1 year,

  15. PREDICTION OF DAILY ACTIVE AND REACTIVE ENERGY CONSUMPTION FOR CITY BYY ANN

    OpenAIRE

    ETEM KÖKLÜKAYA

    1997-01-01

    Artıfıcal neural network (ANN), is used in predctıon of energy and load as it used ın many dıfferent areas of electrıc power system. Energy consuptıon usage center has non-linear veriatıon characteristic.

  16. Artificial neural network accurately predicts hepatitis B surface antigen seroclearance.

    Directory of Open Access Journals (Sweden)

    Ming-Hua Zheng

    Full Text Available BACKGROUND & AIMS: Hepatitis B surface antigen (HBsAg seroclearance and seroconversion are regarded as favorable outcomes of chronic hepatitis B (CHB. This study aimed to develop artificial neural networks (ANNs that could accurately predict HBsAg seroclearance or seroconversion on the basis of available serum variables. METHODS: Data from 203 untreated, HBeAg-negative CHB patients with spontaneous HBsAg seroclearance (63 with HBsAg seroconversion, and 203 age- and sex-matched HBeAg-negative controls were analyzed. ANNs and logistic regression models (LRMs were built and tested according to HBsAg seroclearance and seroconversion. Predictive accuracy was assessed with area under the receiver operating characteristic curve (AUROC. RESULTS: Serum quantitative HBsAg (qHBsAg and HBV DNA levels, qHBsAg and HBV DNA reduction were related to HBsAg seroclearance (P<0.001 and were used for ANN/LRM-HBsAg seroclearance building, whereas, qHBsAg reduction was not associated with ANN-HBsAg seroconversion (P = 0.197 and LRM-HBsAg seroconversion was solely based on qHBsAg (P = 0.01. For HBsAg seroclearance, AUROCs of ANN were 0.96, 0.93 and 0.95 for the training, testing and genotype B subgroups respectively. They were significantly higher than those of LRM, qHBsAg and HBV DNA (all P<0.05. Although the performance of ANN-HBsAg seroconversion (AUROC 0.757 was inferior to that for HBsAg seroclearance, it tended to be better than those of LRM, qHBsAg and HBV DNA. CONCLUSIONS: ANN identifies spontaneous HBsAg seroclearance in HBeAg-negative CHB patients with better accuracy, on the basis of easily available serum data. More useful predictors for HBsAg seroconversion are still needed to be explored in the future.

  17. Conceptual Aspects in the Modeling of Logistical Risk of the Networked Information Economy with the Use of Tools of Natural Computing

    Directory of Open Access Journals (Sweden)

    Vitlinskyy Valdemar V.

    2016-11-01

    Full Text Available Information and communication tools and technologies are rapidly changing daily lives of people and business processes in economic activity primarily in the field of logistics. In particular, the innovative nature of these transformations leads to the emergence of new logistical risks, changing the essence of the existing ones, which needs to be taken into account in the management of logistics systems at various levels. Besides, the problem of Big Data has become increasingly urgent, which, on the one hand, can improve the validity of making managerial decisions, on the other hand — they (Big Data require modern tools for their production, processing and analysis. As such tools there can be used methods and models of natural computing. In the paper the basics of ant and bee algorithms, the particle swarm method, artificial immune systems are summarized; the possibilities of their application in the modeling of various types of logistical risk are demonstrated, the formalization of the problem of risk modeling with the use of an artificial immune system being given as a conditional example.

  18. Phosphorus component in AnnAGNPS

    Science.gov (United States)

    Yuan, Y.; Bingner, R.L.; Theurer, F.D.; Rebich, R.A.; Moore, P.A.

    2005-01-01

    The USDA Annualized Agricultural Non-Point Source Pollution model (AnnAGNPS) has been developed to aid in evaluation of watershed response to agricultural management practices. Previous studies have demonstrated the capability of the model to simulate runoff and sediment, but not phosphorus (P). The main purpose of this article is to evaluate the performance of AnnAGNPS on P simulation using comparisons with measurements from the Deep Hollow watershed of the Mississippi Delta Management Systems Evaluation Area (MDMSEA) project. A sensitivity analysis was performed to identify input parameters whose impact is the greatest on P yields. Sensitivity analysis results indicate that the most sensitive variables of those selected are initial soil P contents, P application rate, and plant P uptake. AnnAGNPS simulations of dissolved P yield do not agree well with observed dissolved P yield (Nash-Sutcliffe coefficient of efficiency of 0.34, R2 of 0.51, and slope of 0.24); however, AnnAGNPS simulations of total P yield agree well with observed total P yield (Nash-Sutcliffe coefficient of efficiency of 0.85, R2 of 0.88, and slope of 0.83). The difference in dissolved P yield may be attributed to limitations in model simulation of P processes. Uncertainties in input parameter selections also affect the model's performance.

  19. Ann Arbor, Michigan: Solar in Action (Brochure)

    Energy Technology Data Exchange (ETDEWEB)

    2011-10-01

    This brochure provides an overview of the challenges and successes of Ann Arbor, Michigan, a 2007 Solar America City awardee, on the path toward becoming a solar-powered community. Accomplishments, case studies, key lessons learned, and local resource information are given.

  20. Obituary: Anne Barbara Underhill, 1920-2003

    Science.gov (United States)

    Roman, Nancy Grace

    2003-12-01

    Anne was born in Vancouver, British Columbia on 12 June 1920. Her parents were Frederic Clare Underhill, a civil engineer and Irene Anna (née Creery) Underhill. She had a twin brother and three younger brothers. As a young girl she was active in Girl Guides and graduated from high school winning the Lieutenant Governor's medal as one of the top students in the Province. She also excelled in high school sports. Her mother died when Anne was 18 and, while undertaking her university studies, Anne assisted in raising her younger brothers. Her twin brother was killed in Italy during World War II (1944), a loss that Anne felt deeply. Possibly because of fighting to get ahead in astronomy, a field overwhelming male when she started, she frequently appeared combative. At the University of British Columbia, Anne obtained a BA (honors) in Chemistry (1942), followed by a MA in 1944. After working for the NRC in Montreal for a year, she studied at the University of Toronto prior to entering the University of Chicago in 1946 to obtain her PhD. Her thesis was the first model computed for a multi-layered stellar atmosphere (1948). During this time she worked with Otto Struve, developing a lifetime interest in hot stars and the analysis of their high dispersion spectra. She received two fellowships from the University Women of Canada. She received a U.S. National Research Fellowship to work at the Copenhagen Observatory, and upon its completion, she returned to British Columbia to work at the Dominion Astrophysical Observatory as a research scientist from 1949--1962. During this period she spent a year at Harvard University as a visiting professor and at Princeton where she used their advanced computer to write the first code for modeling stellar atmospheres. Anne was invited to the University of Utrecht (Netherlands) as a full professor in 1962. She was an excellent teacher, well liked by the students in her classes, and by the many individuals that she guided throughout her

  1. Katherine Anne Porter on Her Contemporaries.

    Science.gov (United States)

    Bridges, Phyllis

    Personal experiences with and critical judgments of leading artists and intellectuals of the twentieth century are recorded in Katherine Anne Porter's essays, letters and conversations which provide snapshots of her attitudes and encounters. Porter's commentaries about such contemporaries as Ernest Hemingway, William Faulkner, Saul Bellow,…

  2. Artificial neural network to aid differentiation of malignant and benign breast masses by ultrasound imaging

    Science.gov (United States)

    Song, Jae H.; Venkatesh, Santosh S.; Conant, Emily F.; Cary, Ted W.; Arger, Peter H.; Sehgal, Chandra M.

    2005-04-01

    The goal of this study is to evaluate an Artificial Neural Network (ANN) for differentiating benign and malignant breast masses on ultrasound scans. The ANN was designed with three layers (input, hidden and output layer), where a sigmoidal (hyperbolic tangent) response function is used as an activation function at each unit. Data from 54 patients with biopsy-proven malignant (N=20) and benign (N=34) masses were used to evaluate the diagnostic performance of the ANN. Of the seven quantitative features extracted from ultrasound images, only four showed statistically significant difference between the two categories. These features were margin sharpness, margin echogenicity, angular continuity, and age of patients. The diagnostic performance was evaluated by round-robin substitution to negate bias due to small sample size. All the input features were standardized to zero-mean and unit-variance to prevent non-uniform learning, which can generate unwanted error. The outputs of the network were analyzed by Receiver Operating Characteristics (ROC). The resulting area under the ROC curve Az was 0.856 +/- 0.058 with 95% confidence limit from 0.734 to 0.936, providing 76.5% specificity at 95% sensitivity. The performance of the ANN was comparable to the performance by logistic regression analysis reported by our group earlier. These results suggest that an ANN when combined with sonography can effectively classify malignant and benign breast lesions.

  3. Logistic Fuel Processor Development

    National Research Council Canada - National Science Library

    Salavani, Reza

    2004-01-01

    The Air Base Technologies Division of the Air Force Research Laboratory has developed a logistic fuel processor that removes the sulfur content of the fuel and in the process converts logistic fuel...

  4. SIMULATION AND IMPLEMENTATION OF PID-ANN CONTROLLER FOR CHOPPER FED EMBEDDED PMDC MOTOR

    Directory of Open Access Journals (Sweden)

    M. Madheswaran

    2012-04-01

    Full Text Available The closed loop control of PMDC drive with an inner current controller and an outer PID-ANN (Proportional Integral Derivative – Artificial Neural Network based speed controller is designed and presented in this paper. Motor is fed by DC / DC buck converter (DC Chopper. The controller is used to change the duty cycle of the converter and thereby, the voltage fed to the PMDC motor to regulate the speed. The PID-ANN controller designed was evaluated by computer simulation and it was implemented using an 8051 based embedded system. This system will operate in forward motoring with variable speed.

  5. Effectiveness of ANN for seismic behaviour prediction considering geometric configuration effect in concrete gravity dams

    Directory of Open Access Journals (Sweden)

    Mohd. Saqib

    2016-09-01

    Full Text Available In this study, an Artificial Neural Networks (ANN model is built and verified for quick estimation of the structural parameter obtained for a concrete gravity dam section due to seismic excitation. The database of numerous inputs and outputs obtained through Abaqus which are further converted into dimensionless forms in the statistical software (MATLAB to build the ANN model. The developed model can be used for accurate estimation of this parameter. The results showed an excellent capability of the model to predict the outputs with high accuracy and reduced computational time.

  6. Reverse logistics - a framework

    OpenAIRE

    Brito, Marisa; Dekker, Rommert

    2002-01-01

    textabstractIn this paper we define and compare Reverse Logistics definitions. We start by giving an understanding framework of Reverse Logistics: the why-what-how. By this means, we put in context the driving forces for Reverse Logistics, a typology of return reasons, a classification of products, processes and actors. In addition we provide a decision framework for Reverse Logistics and we present it according to long, medium and short term decisions, i.e. strategic-tactic-operational decis...

  7. PROCESSING REVERSE LOGISTICS INVENTORIES

    OpenAIRE

    Bajor, Ivona; Novačko, Luka; Ogrizović, Dario

    2014-01-01

    Developed logistics systems have organized reverse logistics flows and are continuously analyzing product returns, tending to detect patterns in oscillations of returning products in certain time periods. Inventory management in reverse logistics systems depends on different criteria, regarding goods categories, formed contracts between subjects of supply chains, uncertainty in manufacturer’s quantities of DOA (dead on arrival) products, etc. The developing logistics systems, such as the Croa...

  8. Recognition of morphometric vertebral fractures by artificial neural networks: analysis from GISMO Lombardia Database.

    Directory of Open Access Journals (Sweden)

    Cristina Eller-Vainicher

    Full Text Available BACKGROUND: It is known that bone mineral density (BMD predicts the fracture's risk only partially and the severity and number of vertebral fractures are predictive of subsequent osteoporotic fractures (OF. Spinal deformity index (SDI integrates the severity and number of morphometric vertebral fractures. Nowadays, there is interest in developing algorithms that use traditional statistics for predicting OF. Some studies suggest their poor sensitivity. Artificial Neural Networks (ANNs could represent an alternative. So far, no study investigated ANNs ability in predicting OF and SDI. The aim of the present study is to compare ANNs and Logistic Regression (LR in recognising, on the basis of osteoporotic risk-factors and other clinical information, patients with SDI≥1 and SDI≥5 from those with SDI = 0. METHODOLOGY: We compared ANNs prognostic performance with that of LR in identifying SDI≥1/SDI≥5 in 372 women with postmenopausal-osteoporosis (SDI≥1, n = 176; SDI = 0, n = 196; SDI≥5, n = 51, using 45 variables (44 clinical parameters plus BMD. ANNs were allowed to choose relevant input data automatically (TWIST-system-Semeion. Among 45 variables, 17 and 25 were selected by TWIST-system-Semeion, in SDI≥1 vs SDI = 0 (first and SDI≥5 vs SDI = 0 (second analysis. In the first analysis sensitivity of LR and ANNs was 35.8% and 72.5%, specificity 76.5% and 78.5% and accuracy 56.2% and 75.5%, respectively. In the second analysis, sensitivity of LR and ANNs was 37.3% and 74.8%, specificity 90.3% and 87.8%, and accuracy 63.8% and 81.3%, respectively. CONCLUSIONS: ANNs showed a better performance in identifying both SDI≥1 and SDI≥5, with a higher sensitivity, suggesting its promising role in the development of algorithm for predicting OF.

  9. Recognition of Morphometric Vertebral Fractures by Artificial Neural Networks: Analysis from GISMO Lombardia Database

    Science.gov (United States)

    Eller-Vainicher, Cristina; Chiodini, Iacopo; Santi, Ivana; Massarotti, Marco; Pietrogrande, Luca; Cairoli, Elisa; Beck-Peccoz, Paolo; Longhi, Matteo; Galmarini, Valter; Gandolini, Giorgio; Bevilacqua, Maurizio; Grossi, Enzo

    2011-01-01

    Background It is known that bone mineral density (BMD) predicts the fracture's risk only partially and the severity and number of vertebral fractures are predictive of subsequent osteoporotic fractures (OF). Spinal deformity index (SDI) integrates the severity and number of morphometric vertebral fractures. Nowadays, there is interest in developing algorithms that use traditional statistics for predicting OF. Some studies suggest their poor sensitivity. Artificial Neural Networks (ANNs) could represent an alternative. So far, no study investigated ANNs ability in predicting OF and SDI. The aim of the present study is to compare ANNs and Logistic Regression (LR) in recognising, on the basis of osteoporotic risk-factors and other clinical information, patients with SDI≥1 and SDI≥5 from those with SDI = 0. Methodology We compared ANNs prognostic performance with that of LR in identifying SDI≥1/SDI≥5 in 372 women with postmenopausal-osteoporosis (SDI≥1, n = 176; SDI = 0, n = 196; SDI≥5, n = 51), using 45 variables (44 clinical parameters plus BMD). ANNs were allowed to choose relevant input data automatically (TWIST-system-Semeion). Among 45 variables, 17 and 25 were selected by TWIST-system-Semeion, in SDI≥1 vs SDI = 0 (first) and SDI≥5 vs SDI = 0 (second) analysis. In the first analysis sensitivity of LR and ANNs was 35.8% and 72.5%, specificity 76.5% and 78.5% and accuracy 56.2% and 75.5%, respectively. In the second analysis, sensitivity of LR and ANNs was 37.3% and 74.8%, specificity 90.3% and 87.8%, and accuracy 63.8% and 81.3%, respectively. Conclusions ANNs showed a better performance in identifying both SDI≥1 and SDI≥5, with a higher sensitivity, suggesting its promising role in the development of algorithm for predicting OF. PMID:22076144

  10. Toward sustainable logistics

    NARCIS (Netherlands)

    Soysal, Mehmet; Bloemhof-Ruwaard, Jacqueline M.

    2017-01-01

    The fast evolution of sustainability leads to the development of a new fast-growing concept called sustainable logistics management. This research addresses recent business trends and challenges in logistics and their implications for sustainable logistics management. Additionally, we discuss policy

  11. Reverse logistics - a framework

    NARCIS (Netherlands)

    M.P. de Brito (Marisa); R. Dekker (Rommert)

    2002-01-01

    textabstractIn this paper we define and compare Reverse Logistics definitions. We start by giving an understanding framework of Reverse Logistics: the why-what-how. By this means, we put in context the driving forces for Reverse Logistics, a typology of return reasons, a classification of

  12. Criteria of Categorizing Logistics and Distribution Centres

    Directory of Open Access Journals (Sweden)

    Darko Babić

    2011-07-01

    Full Text Available Logistics and distribution centres represent very significant infrastructure elements of the macro-logistic system. The creation of the logistics and distribution centres and their connection into a wide (global network have resulted in the creation of conditions for an adequate distribution of labour and significant increase in the productivity of all the logistics elements and processes, noting that the logistics and distribution centres in this concept have a superregional significance. This paper represents the summary (results of the research that was carried out on a large number of logistics and distribution centres with the aim of considering the complexity and the issues related to the logistics and distribution centres and the distribution network, their elements and action of the subsystems according to the following criteria: spatial, technical, technological, and organizational, with the aim of defining the categorisation model of the logistics and distribution centres. The analysis of the selected data collected during the research has resulted in defining of the categorisation model of the logistics and distribution centres which foresees six categories. Each of the foreseen categories has been defined according to the set model by the mentioned traffic, technical and technological, and organisational characteristics and the level of service. This is precisely where the application of the categorisation model of the logistics and distribution centres can be found, which will define the relevant categories of the centres applicable in the creation of effective distribution

  13. KSC ISS Logistics Support

    Science.gov (United States)

    Tellado, Joseph

    2014-01-01

    The presentation contains a status of KSC ISS Logistics Operations. It basically presents current top level ISS Logistics tasks being conducted at KSC, current International Partner activities, hardware processing flow focussing on late Stow operations, list of KSC Logistics POC's, and a backup list of Logistics launch site services. This presentation is being given at the annual International Space Station (ISS) Multi-lateral Logistics Maintenance Control Panel meeting to be held in Turin, Italy during the week of May 13-16. The presentatiuon content doesn't contain any potential lessons learned.

  14. Parameters estimation of squirrel-cage induction motors using ANN and ANFIS

    Directory of Open Access Journals (Sweden)

    Mehdi Ahmadi Jirdehi

    2016-03-01

    Full Text Available In the transient behavior analysis of a squirrel-cage induction motor, the parameters of the single-cage and double-cage models are studied. These parameters are usually hard to obtain. This paper presents two new methods to predict the induction motor parameters in the single-cage and double-cage models based on artificial neural network (ANN and adaptive neuro-fuzzy inference system (ANFIS. For this purpose, the experimental data (manufacturer data of 20 induction motors with the different power are used. The experimental data are including of the starting torque and current, maximum torque, full load sleep, efficiency, rated active power and reactive power. The obtained results from the proposed ANN and ANFIS models are compared with each other and with the experimental data, which show a good agreement between the predicted values and the experimental data. But the proposed ANFIS model is more accurate than the proposed ANN model.

  15. Damage level prediction of non-reshaped berm breakwater using ANN, SVM and ANFIS models

    Directory of Open Access Journals (Sweden)

    Sukomal Mandal

    2012-06-01

    Full Text Available The damage analysis of coastal structure is very important as it involves many design parameters to be considered for the better and safe design of structure. In the present study experimental data for non-reshaped berm breakwater are collected from Marine Structures Laboratory, Department of Applied Mechanics and Hydraulics, NITK, Surathkal, India. Soft computing techniques like Artificial Neural Network (ANN, Support Vector Machine (SVM and Adaptive Neuro Fuzzy Inference system (ANFIS models are constructed using experimental data sets to predict the damage level of non-reshaped berm breakwater. The experimental data are used to train ANN, SVM and ANFIS models and results are determined in terms of statistical measures like mean square error, root mean square error, correlation coefficient and scatter index. The result shows that soft computing techniques i.e., ANN, SVM and ANFIS can be efficient tools in predicting damage levels of non reshaped berm breakwater.

  16. RSM and ANN Modeling of Micro Wire Electrical Discharge Machining of AL 2024 T351

    Directory of Open Access Journals (Sweden)

    Sivaprakasam Palani

    2015-01-01

    Full Text Available This paper presents modeling and analysis of machining characteristics of Micro Wire Electro Discharge Machining (Micro-WEDM process on Aluminium alloy (AL 2024 T351 using the Response Surface Methodology (RSM and Artificial Neural Network (ANN. The input variables of Micro-WEDM process were voltage, capacitance and feed rate. The surface roughness and material removal rate are considered as a response variables. Experiments were carried out on Aluminium alloy using Central Composite Design (CCD. The RSM and ANN models have been developed based on experimental designs. Analysis of variance (ANOVA has been employed to test the significance of RSM model. It has been found out that all the three process parameters are significant and their interaction effects are also significant on the surface roughness and material removal rate. Finally predicted values were compared with ANN.

  17. Analysis of RF MEMS Capacitive Switches by Using Switch EM ANN Models

    Directory of Open Access Journals (Sweden)

    Z. Marinković

    2015-11-01

    Full Text Available Artificial neural networks (ANNs have appeared to be an alternative to the conventional models of RF MEMS switches. In this paper, neural models of an RF MEMS capacitive switch are developed and used for the electrical design of the switch. Namely, an ANN model relating the switch resonant frequency and the bridge dimensions is used to analyze efficiently the switch behavior with changes of bridge dimensions. Furthermore, it is illustrated how the developed model can be used for the determination of bridge dimensions in order to achieve the desired switch resonant frequency. In addition, application of a switch inverse ANN model for the determination of bridge dimensions is analyzed as well.

  18. Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms

    Directory of Open Access Journals (Sweden)

    Neelamegam Premalatha

    2016-06-01

    Full Text Available Global solar radiation (GSR is an essential parameter for the design and operation of solar energy systems. Long-standing records of global solar radiation data are not available in many places because of the cost and maintenance of the measuring instruments. The major objective of this work is to develop an ANN model for accurately predicting solar radiation. Two ANN models with four different algorithms are considered in the present study. Meteorological data collected for the last 10 years from five different locations across India have been used to train the models. The best ANN algorithm and model are identified based on minimum mean absolute error (MAE and root mean square error (RMSE and maximum linear correlation coefficient (R. Further, the present study confirms that prediction accuracy of the ANN model depends on the complete set of data being used for training the network for the intended application. The developed ANN model has a low mean absolute percentage error (MAPE which ascertains the accuracy and suitability of the model to predict the monthly average global radiation so as to design or evaluate solar energy installations, where the meteorological data measuring facilities are not in place in India.

  19. Using ANN and EPR models to predict carbon monoxide concentrations in urban area of Tabriz

    Directory of Open Access Journals (Sweden)

    Mohammad Shakerkhatibi

    2015-09-01

    Full Text Available Background: Forecasting of air pollutants has become a popular topic of environmental research today. For this purpose, the artificial neural network (AAN technique is widely used as a reliable method for forecasting air pollutants in urban areas. On the other hand, the evolutionary polynomial regression (EPR model has recently been used as a forecasting tool in some environmental issues. In this research, we compared the ability of these models to forecast carbon monoxide (CO concentrations in the urban area of Tabriz city. Methods: The dataset of CO concentrations measured at the fixed stations operated by the East Azerbaijan Environmental Office along with meteorological data obtained from the East Azerbaijan Meteorological Bureau from March 2007 to March 2013, were used as input for the ANN and EPR models. Results: Based on the results, the performance of ANN is more reliable in comparison with EPR. Using the ANN model, the correlation coefficient values at all monitoring stations were calculated above 0.85. Conversely, the R2 values for these stations were obtained <0.41 using the EPR model. Conclusion: The EPR model could not overcome the nonlinearities of input data. However, the ANN model displayed more accurate results compared to the EPR. Hence, the ANN models are robust tools for predicting air pollutant concentrations.

  20. Comparison of single and modular ANN based fault detector and ...

    African Journals Online (AJOL)

    user

    development of the architecture of modular ANN based fault detector and classifier is same as that of single ANN based fault detector and classifier. The final architecture of modular ..... phases B1, C2 are high and other phases outputs are well below the threshold limit 0.3. However, neutral “N” output of single. ANN based ...

  1. Comprehensive heat transfer correlation for water/ethylene glycol-based graphene (nitrogen-doped graphene) nanofluids derived by artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS)

    Science.gov (United States)

    Savari, Maryam; Moghaddam, Amin Hedayati; Amiri, Ahmad; Shanbedi, Mehdi; Ayub, Mohamad Nizam Bin

    2017-10-01

    Herein, artificial neural network and adaptive neuro-fuzzy inference system are employed for modeling the effects of important parameters on heat transfer and fluid flow characteristics of a car radiator and followed by comparing with those of the experimental results for testing data. To this end, two novel nanofluids (water/ethylene glycol-based graphene and nitrogen-doped graphene nanofluids) were experimentally synthesized. Then, Nusselt number was modeled with respect to the variation of inlet temperature, Reynolds number, Prandtl number and concentration, which were defined as the input (design) variables. To reach reliable results, we divided these data into train and test sections to accomplish modeling. Artificial networks were instructed by a major part of experimental data. The other part of primary data which had been considered for testing the appropriateness of the models was entered into artificial network models. Finally, predictad results were compared to the experimental data to evaluate validity. Confronted with high-level of validity confirmed that the proposed modeling procedure by BPNN with one hidden layer and five neurons is efficient and it can be expanded for all water/ethylene glycol-based carbon nanostructures nanofluids. Finally, we expanded our data collection from model and could present a fundamental correlation for calculating Nusselt number of the water/ethylene glycol-based nanofluids including graphene or nitrogen-doped graphene.

  2. Humanitarian Logistics - A new form of logistics?

    CSIR Research Space (South Africa)

    Ittmann, HW

    2005-08-01

    Full Text Available The science of supply chain management in business has advanced so much in the last ten years, especially through the use of IT. How can knowledge be used to help in the humanitarian context, where no science of disaster logistics yet exists...

  3. ANN Modeling of a Chemical Humidity Sensing Mechanism

    Directory of Open Access Journals (Sweden)

    Souhil KOUDA

    2010-10-01

    Full Text Available This work aims to achieve a modeling of a resistive-type humidity sensing mechanism (RHSM. This model takes into account the parameters of non-linearity, hysteresis, temperature, frequency, substrate type. Furthermore, we investigated the TiO2 and PMAPTAC concentrations effects on the humidity sensing properties in our model. Using neuronal networks and Matlab environment, we have done the training to realize an analytical model ANN and create a component, accurately express the above parameters variations, for our sensing mechanism model in the PSPICE simulator library. Simulation has been used to evaluate the effect of variations of non-linearity, hysteresis, temperature, frequency, substrate type and TiO2 and PMAPTAC concentrations effects, where the output of this model is identical to the output of the chemical humidity sensing mechanism used.

  4. LOGISTICS - EVOLUTION THROUGH INNOVATION

    Directory of Open Access Journals (Sweden)

    Petrache Alexandru Constantin

    2015-07-01

    Full Text Available The current economic conditions, the rapidity with which the exchange of information, resources and products in the market takes place makes the logistics seem appreciably less significant. However, the importance of logistics has been presented in the military field, through strategies that have led to wining of the great wars that mankind has seen, through the supply of troops with food or moving military equipment. The literature in the field of logistics has numerous works on this topic. But while most focuses on efficient ways of carrying out the component activities of logistics or the strategies of organizations with regard to logistics or its functions, research on dynamics of logistics is underdeveloped. To be able to propose new methods or strategies of logistic activities is necessary to understand the development of this concept, determinant factors and economic and social conditions that gave rise to such developments. Thus, after a presentation of the main landmarks of the historical development of logistics we highlight the importance of the innovation within an organization's value chain innovation, in particular, and how to conduct the business in general. Using generations of innovation identified in the literature, we determine the generation of logistics development, taking into account innovation and how to conduct business. In addition for a better highlight of the own vision over the logistics generations identified, we will present the graphical concept for each generation in part. Last but not least, for each generation identified we try to allocate the chronological landmarks featured in order to reinforce the importance played by innovation in the development of the logistics industry and to give future directions of research within this topic. The study took into account the information presented in articles, books and websites of the relevant specialty in logistics and innovation to be able to build and expose a

  5. On-line dynamic monitoring automotive exhausts: using BP-ANN for distinguishing multi-components

    Science.gov (United States)

    Zhao, Yudi; Wei, Ruyi; Liu, Xuebin

    2017-10-01

    Remote sensing-Fourier Transform infrared spectroscopy (RS-FTIR) is one of the most important technologies in atmospheric pollutant monitoring. It is very appropriate for on-line dynamic remote sensing monitoring of air pollutants, especially for the automotive exhausts. However, their absorption spectra are often seriously overlapped in the atmospheric infrared window bands, i.e. MWIR (3 5μm). Artificial Neural Network (ANN) is an algorithm based on the theory of the biological neural network, which simplifies the partial differential equation with complex construction. For its preferable performance in nonlinear mapping and fitting, in this paper we utilize Back Propagation-Artificial Neural Network (BP-ANN) to quantitatively analyze the concentrations of four typical industrial automotive exhausts, including CO, NO, NO2 and SO2. We extracted the original data of these automotive exhausts from the HITRAN database, most of which virtually overlapped, and established a mixed multi-component simulation environment. Based on Beer-Lambert Law, concentrations can be retrieved from the absorbance of spectra. Parameters including learning rate, momentum factor, the number of hidden nodes and iterations were obtained when the BP network was trained with 80 groups of input data. By improving these parameters, the network can be optimized to produce necessarily higher precision for the retrieved concentrations. This BP-ANN method proves to be an effective and promising algorithm on dealing with multi-components analysis of automotive exhausts.

  6. Structure-activity correlations for illicit amphetamines using ANN and constitutional descriptors.

    Science.gov (United States)

    Gosav, S; Praisler, M; Dorohoi, D O; Popa, G

    2006-12-15

    The goal of this study was to develop an expert system capable to identify the potential biological activity of new substances having a molecular structure similar to illicit amphetamines. For this purpose we have designed two types of artificial neural network (ANN) systems, which have been trained to classify amphetamines according to their toxicological activity (stimulant amphetamines or hallucinogenic amphetamines) and distinguish them from nonamphetamines. Such a system is essential for testing new molecular structures for epidemiological, clinical, and forensic purposes. The first type of artificial neural network is a "spectral" neural network, which has as input variables the most important 100 absorption intensities from a total of 260 measured for each normalized infrared spectrum 10cm(-1) apart. The spectral data consists of a database built with the GC-FT-IR spectra of the most popular drugs of abuse (mainly central stimulants, hallucinogens, sympathomimetic amines, narcotics and other potent analgesics), precursors and derivatized counterparts. All samples were also characterized by their constitutional descriptors (CDs). For each sample, a number of 45 CDs were computed and introduced as input variables for a second type of ANN, which uses a structural database. The efficiency of this "structural" artificial neural network (CD-ANN) has been improved by optimizing the training set and increasing the number of input variables (CDs). A comparative analysis of the spectral and the structural networks is presented.

  7. Modelling flow dynamics in water distribution networks using ...

    African Journals Online (AJOL)

    Computational approaches can be used to detect leakages in water distribution networks. One such approach is the Artificial Neural Networks (ANNs) technique. The advantage of ANNs is that they are robust and can be used to model complex linear and non-linear systems without making implicit assumptions. ANNs can ...

  8. Maritime logistics: contemporary issues

    National Research Council Canada - National Science Library

    Song, D.W; Panayides, P.M

    2012-01-01

    Contemporary practice and scientific innovation consider the logistics aspects of shipping and seaport operations as one of the most important areas for future development of competitive advantages...

  9. Wavelets Application in Prediction of Friction Stir Welding Parameters of Alloy Joints from Vibroacoustic ANN-Based Model

    Directory of Open Access Journals (Sweden)

    Emilio Jiménez-Macías

    2014-01-01

    Full Text Available This paper analyses the correlation between the acoustic emission signals and the main parameters of friction stir welding process based on artificial neural networks (ANNs. The acoustic emission signals in Z and Y directions have been acquired by the AE instrument NI USB-9234. Statistical and temporal parameters of discomposed acoustic emission signals using Wavelet Transform have been used as input of the ANN. The outputs of the ANN model include the parameters of tool rotation speed and travel speed, and tool profile, as well as the tensile strength. A multilayer feed-forward neural network has been selected and trained, using Levenberg-Marquardt algorithm for different network architectures. Finally, an analysis of the comparison between the measured and the calculated data is presented. The model obtained can be used to model and develop an automatic control of the parameters of the process and mechanical properties of joint, based on the acoustic emission signals.

  10. Quality controlled logistics in vegetable supply chain networks: how can an individual batch reach an individual consumer in hte optimal state?

    NARCIS (Netherlands)

    Schouten, R.E.; Kooten, van O.; Vorst, van der J.G.A.J.; Marcelis, W.J.; Luning, P.A.

    2012-01-01

    Western-European consumers have become demanding on product availability in retail outlets and vegetable attributes such as quality, integrity, safety. When (re)designing vegetable supply chain networks one has to take these demands into consideration, next to traditional efficiency and

  11. Development and Testing of an ANN Model for Estimation of Runoff from a Snow Covered Catchment

    Science.gov (United States)

    Bhadra, A.; Bandyopadhyay, A.; Chakraborty, S.; Roy, S.; Kumar, T.

    2017-06-01

    In this study, an attempt has been made to develop an ANN model to estimate runoff from a snow covered catchment of eastern Himalaya using feed-forward back-propagation algorithm with Levenberg-Marquardt optimization technique. The ANN model was programmed in C++ whereas a user-friendly GUI was developed in VB. The effects of past days rainfall and present day temperature data was observed on the performance of the selected ANN architecture in modelling snowmelt and monsoon season runoff. For this purpose, 8 years' (2003-2010) daily data (rainfall, temperature, and discharge) were collected from CWC which were again divided into two parts (2003-2008 and 2009-2010) for training and testing of the ANN model, respectively. Initially it was found that the network can produce acceptable results with only rainfall data as input, but it needs at least past 3 days rainfall data to account for the antecedent moisture condition of the catchment. Networks 4-16-16-1 (with past 3 days rainfall) and 6-18-18-18-1 (with past 5 days rainfall) resulted modelling efficiency of 79.38 and 82.06% in training and 55.13 and 61.06% in validation, respectively. However, addition of present day temperature data as another input improved the performance in both training (ME 83.10 and 82.22%) and testing (ME 62.64 and 61.89%) marginally.

  12. Reverse logistics in the Brazilian construction industry.

    Science.gov (United States)

    Nunes, K R A; Mahler, C F; Valle, R A

    2009-09-01

    In Brazil most Construction and Demolition Waste (C&D waste) is not recycled. This situation is expected to change significantly, since new federal regulations oblige municipalities to create and implement sustainable C&D waste management plans which assign an important role to recycling activities. The recycling organizational network and its flows and components are fundamental to C&D waste recycling feasibility. Organizational networks, flows and components involve reverse logistics. The aim of this work is to introduce the concepts of reverse logistics and reverse distribution channel networks and to study the Brazilian C&D waste case.

  13. Prediction of event-based stormwater runoff quantity and quality by ANNs developed using PMI-based input selection

    Science.gov (United States)

    He, Jianxun; Valeo, Caterina; Chu, Angus; Neumann, Norman F.

    2011-03-01

    SummaryEvent-based stormwater runoff quantity and quality modeling remains a challenge since the processes of rainfall induced pollutant discharge are not completely understood. The complexity of physically-based models often limits the practical use of quality models in practice. Artificial neural networks (ANNs) are a data driven modeling approach that can avoid the necessity of fully understanding complex physical processes. In this paper, feed-forward multi-layer perceptron (MLP) network, a popular type of ANN, was applied to predict stormwater runoff quantity and quality including turbidity, specific conductance, water temperature, pH, and dissolved oxygen (DO) in storm events. A recently proposed input selection algorithm based on partial mutual information (PMI), which identifies input variables in a stepwise manner, was employed to select input variable sets for the development of ANNs. The ANNs developed via this approach could produce satisfactory prediction of event-based stormwater runoff quantity and quality. In particular, this approach demonstrated a superior performance over the approach involving ANNs fed by inputs selected using partial correlation and all potential inputs in flow modeling. This result suggests the applicability of PMI in developing ANN models. In addition, the ANN for flow outperformed conventional multiple linear regression (MLR) and multiple nonlinear regression (MNLR) models. For an ANN development of turbidity (multiplied by flow rate) and specific conductance, significant improvement was achieved by including a previous 3-week total rainfall amount into their input variable sets. This antecedent rainfall variable is considered a factor in the availability of land surface pollutants for wash-off. A sensitivity analysis demonstrated the potential role of this rainfall variable on modeling particulate solids and dissolved matters in stormwater runoff.

  14. Use of test-day records to predict first lactation 305-day milk yield using artificial neural network in Kenyan Holstein-Friesian dairy cows.

    Science.gov (United States)

    Njubi, D M; Wakhungu, J W; Badamana, M S

    2010-04-01

    The study is focused on the capability of artificial neural networks (ANNs) to predict next month and first lactation 305-day milk yields (FLMY305) of Kenyan Holstein-Friesian (KHF) dairy cows based on a few available test days (TD) records in early lactation. The developed model was compared with multiple linear regressions (MLR). A total of 39,034 first parity TD records of KHF dairy cows collected over 102 herds were analyzed. Different ANNs were modeled and the best performing number of hidden layers and neurons and training algorithms retained. The best ANN model had one hidden layer of logistic transfer function for all models, but hidden nodes varied from 2 to 7. The R (2) value for ANNs for training, validation, and test data were consistently high showing that the models captured the features accurately. The R (2), r, and root mean square were consistently superior for ANN than MLR but significantly different (p > 0.05). The prediction equation with four variables, i.e., first, second, third, and fourth TD milk yield, gave adequate accuracy (79.0%) in estimating the FLMY305 from TD yield. It emerges from this study that the ANN model can be an alternative for prediction of FLMY305 and monthly TD in KHF.

  15. Intermodal Logistics Centres and Freight Corridors – Concepts and Trends

    National Research Council Canada - National Science Library

    Norbert Wagener

    2017-01-01

    Background: The development of international freight corridors, as the Trans European Network and new rail and inland shipping corridors in Asia and Africa, require efficient logistics centres along...

  16. Behavioral Operations in Logistics

    NARCIS (Netherlands)

    J. de Vries (Jelle)

    2016-01-01

    markdownabstractIn the world of logistics, a considerable share of all work is automated and performed by machines or robots. An examination of the existing logistics research reflects this image, since a substantial share of the studies focus on automated processes, and perfectly predictable

  17. International Logistics Science Conference

    CERN Document Server

    Hompel, Michael; Meier, J

    2014-01-01

    The importance of logistics in all its variations is still increasing. New technologies emerge, new planning methods and algorithms are developed, only to face a market with a growing complexity and the need of weighting monetary costs against ecological impact. Mastering these challenges requires a scientific viewpoint on logistics, but always with applications in mind. This volume presents up-to-date logistics research in all its diversity and interconnectedness. It grew out of the “International Logistics Science Conference” (ILSC) held in Dortmund in September 2013, bringing together leading scientists and young academics from nine different countries. The conference was jointly organized by the “Efficiency Cluster Logistics” and the “Fraunhofer Institute for Material Flow and Logistics”. The Program Committee used a double blind review process to choose the 12 strongest contributions, which were then grouped in four areas: - Sustainability logistics, including electric mobility, smart inform...

  18. SIMULATION OF LOGISTICS PROCESSES

    Directory of Open Access Journals (Sweden)

    Yu. Taranenko

    2016-08-01

    Full Text Available The article deals with the theoretical basis of the simulation. The study shows the simulation of logistic processes in industrial countries is an integral part of many economic projects aimed at the creation or improvement of logistics systems. The paper was used model Beer Game for management of logistics processes in the enterprise. The simulation model implements in AnyLogic package. AnyLogic product allows us to consider the logistics processes as an integrated system, which allows reaching better solutions. Logistics process management involves pooling the sales market, production and distribution to ensure the temporal level of customer service at the lowest cost overall. This made it possible to conduct experiments and to determine the optimal size of the warehouse at the lowest cost.

  19. Mortality Predicted Accuracy for Hepatocellular Carcinoma Patients with Hepatic Resection Using Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Herng-Chia Chiu

    2013-01-01

    Full Text Available The aim of this present study is firstly to compare significant predictors of mortality for hepatocellular carcinoma (HCC patients undergoing resection between artificial neural network (ANN and logistic regression (LR models and secondly to evaluate the predictive accuracy of ANN and LR in different survival year estimation models. We constructed a prognostic model for 434 patients with 21 potential input variables by Cox regression model. Model performance was measured by numbers of significant predictors and predictive accuracy. The results indicated that ANN had double to triple numbers of significant predictors at 1-, 3-, and 5-year survival models as compared with LR models. Scores of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC of 1-, 3-, and 5-year survival estimation models using ANN were superior to those of LR in all the training sets and most of the validation sets. The study demonstrated that ANN not only had a great number of predictors of mortality variables but also provided accurate prediction, as compared with conventional methods. It is suggested that physicians consider using data mining methods as supplemental tools for clinical decision-making and prognostic evaluation.

  20. Logistics and logistics support in crisis management and citizen protection

    OpenAIRE

    HOLEJŠOVSKÝ, Jan

    2010-01-01

    ABSTRAKT LOGISTICS AND LOGISTICS SUPPORT IN CRISIS MANAGEMENT AND CITIZEN PROTECTION The graduation thesis on topic ``Logistics and logistics support in crisis management and citizen protection" is divided into several chapters, which in summary are a material presenting information about logistics and logistics support in crisis management and citizen protection. This was one of the aims at this work. Chapters I., II., III., IV. describe logistics and logistics support, crisis management, ci...

  1. Tertiary Logistics in the Focus of All Logistics

    OpenAIRE

    Ratko Zelenika; Mirjana Grèiæ; Helga Pavliæ Skender

    2008-01-01

    Trade logistics, traffic logistics, transport logistics and warehouse logistics are just some of the tertiary logistics which enables production processes of all economic sector products and services. Tertiary logistics representing the tertiary economic sector is the most sofisticated and the most important logistics due to the characteristics of the tertiary sector as a service sector that promotes business conditions in all economic sectors. Accordingly, tertiary logistics has a crucial ro...

  2. Optimization of the Production of Extracellular Polysaccharide from the Shiitake Medicinal Mushroom Lentinus edodes (Agaricomycetes) Using Mutation and a Genetic Algorithm-Coupled Artificial Neural Network (GA-ANN).

    Science.gov (United States)

    Adeeyo, Adeyemi Ojutalayo; Lateef, Agbaje; Gueguim-Kana, Evariste Bosco

    2016-01-01

    Exopolysaccharide (EPS) production by a strain of Lentinus edodes was studied via the effects of treatments with ultraviolet (UV) irradiation and acridine orange. Furthermore, optimization of EPS production was studied using a genetic algorithm coupled with an artificial neural network in submerged fermentation. Exposure to irradiation and acridine orange resulted in improved EPS production (2.783 and 5.548 g/L, respectively) when compared with the wild strain (1.044 g/L), whereas optimization led to improved productivity (23.21 g/L). The EPS produced by various strains also demonstrated good DPPH scavenging activities of 45.40-88.90%, and also inhibited the growth of Escherichia coli and Klebsiella pneumoniae. This study shows that multistep optimization schemes involving physical-chemical mutation and media optimization can be an attractive strategy for improving the yield of bioactives from medicinal mushrooms. To the best of our knowledge, this report presents the first reference of a multistep approach to optimizing EPS production in L. edodes.

  3. A constructive algorithm for unsupervised learning with incremental neural network

    OpenAIRE

    Wang, Jenq-Haur; Wang, Hsin-Yang; Chen, Yen-Lin; Liu, Chuan-Ming

    2015-01-01

    Artificial neural network (ANN) has wide applications such as data processing and classification. However, comparing with other classification methods, ANN needs enormous memory space and training time to build the model. This makes ANN infeasible in practical applications. In this paper, we try to integrate the ideas of human learning mechanism with the existing models of ANN. We propose an incremental neural network construction framework for unsupervised learning. In this framework, a neur...

  4. Applied logistic regression

    CERN Document Server

    Hosmer, David W; Sturdivant, Rodney X

    2013-01-01

     A new edition of the definitive guide to logistic regression modeling for health science and other applications This thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-

  5. Security in Logistics

    Science.gov (United States)

    Cempírek, Václav; Nachtigall, Petr; Široký, Jaromír

    2016-12-01

    This paper deals with security of logistic chains according to incorrect declaration of transported goods, fraudulent transport and forwarding companies and possible threats caused by political influences. The main goal of this paper is to highlight possible logistic costs increase due to these fraudulent threats. An analysis of technological processes will beis provided, and an increase of these transport times considering the possible threatswhich will beis evaluated economic costs-wise. In the conclusion, possible threat of companies'` efficiency in logistics due to the costs`, means of transport and increase in human resources` increase will beare pointed out.

  6. Green Maritime Logistics

    DEFF Research Database (Denmark)

    Psaraftis, Harilaos N.

    2016-01-01

    By green maritime logistics we mean achieving an acceptable environmental performance of the maritime transport logistical supply chain while at the same time respecting traditional economic criteria. In this paper the environmental focus is on maritime emissions. Achieving such goal may involve...... several trade-offs, and win-win solutions are typically sought. However, finding these solutions may be more difficult than may appear at first glance. The purpose of this paper is to provide a concise overview of the challenges of green maritime logistics and present some examples, both for greenhouse...

  7. FEEDBACK AND LOGISTICS CONTROLLING

    Directory of Open Access Journals (Sweden)

    Mehesne Berek Szilvia

    2015-07-01

    Full Text Available The following things led to that the feedback, the supervision and improvement of the processes have become more pronounced: continuous rise in the importance of logistics; increase in complexity of its content; its activity becoming more complex. These activities are necessary for the optimum information supply. The intensification of market competition requires the corporations to possess exact and up-to-date information about their activities. Complexity of the logistics system presumes a parallel application of an effective feedback, supervision and management system simultaneously with the given logistics system. The indispensability of logistics is also proved by the fact that it can be found sporadically (in the form of logistics departments or in a complex way in case of each organization. The logistical approach means a huge support in the management since it contains the complexity, the handling as a unit in order to ensure a harmony of the different corporate departments and part activities. In addition to the professional application of a logistics system, there is an opportunity to coordinate the relations inside an organization as well as between the organizations and to handle them as a unit. The sine qua non of the success of logistical processes is a harmony of the devices applied. The controlling system is a device for feeding back the processes of a corporate system. By means of the checkpoints intercalated into the processes, the logistics controlling provides information for the leadership which contributes even more to the complex approach of logistics system. By dint of the logistics controlling, the monitoring and coordination of every logistical part activity become possible with the help of information supply ensured by the logistics controlling. The logistics controlling reviews, assesses and coordinates; these activities have an effect on the cost and income management. Its reason is to be searched in the built

  8. Predicting prostate biopsy outcome: artificial neural networks and polychotomous regression are equivalent models.

    Science.gov (United States)

    Lawrentschuk, Nathan; Lockwood, Gina; Davies, Peter; Evans, Andy; Sweet, Joan; Toi, Ants; Fleshner, Neil E

    2011-03-01

    Complex statistical models utilizing multiple inputs to derive a risk assessment may benefit prostate cancer (PC) detection where focus has been on prostate-specific antigen (PSA). This study develops a polychotomous logistic regression (PR) model and an artificial neural network (ANN) for predicting biopsy results, particularly for clinically significant PC. There were 3,025 men undergoing TRUS-guided biopsy (BX) with PSA <10 ng/ml selected. BX outcome classified as benign, atypical small acinar proliferation or high-grade prostatic intraepithelial neoplasia (ASAP/PIN), non-significant (NSPC) or clinically significant PC (CSPC). PR and ANN models were developed to distinguish between BX categories. Predictors were age, PSA, abnormal digital rectal examination (DRE), positive transrectal ultrasound (TRUS) and prostate volume. Among the BXs, 44% were benign, 14% ASAP/PIN, 16% NSPC and 25% CSPC. Median age, PSA and volume were 64 years, 5.7 ng/ml and 50 cc. TRUS lesion was present in 47%, and DRE was abnormal in 39%. PR and ANN models did not differ on percentage BX outcomes correctly predicted (55, 57%, respectively) and were equally poor for both ASAP/PIN (0%) and NSPC (2%). For PR and ANN, 74-78% ASAP/PIN predicted benign, 2% NSPC and 20-24% CSPC. For NSPC, 69-71% predicted benign, 27-29% CSPC. Benign outcomes were well identified (86-88%), although 12-13% classified CSPC. CSPC was correctly identified in 65-66% with misclassifications largely benign (33% for PR and ANN). Neither PR nor ANN was able to distinguish between the four biopsy outcomes: ASAP/PIN and NSPC were not distinguished from benign or CSPC. ANN did not perform better than PR. Inclusion of additional predictors may increase the performance of statistical models in predicting BX outcome.

  9. Prediction of persistent hemodynamic depression after carotid angioplasty and stenting using artificial neural network model.

    Science.gov (United States)

    Jeon, Jin Pyeong; Kim, Chulho; Oh, Byoung-Doo; Kim, Sun Jeong; Kim, Yu-Seop

    2017-12-05

    To assess and compare predictive factors for persistent hemodynamic depression (PHD) after carotid artery angioplasty and stenting (CAS) using artificial neural network (ANN) and multiple logistic regression (MLR) or support vector machines (SVM) models. A retrospective data set of patients (n=76) who underwent CAS from 2007 to 2014 was used as input (training cohort) to a back-propagation ANN using TensorFlow platform. PHD was defined when systolic blood pressure was less than 90mmHg or heart rate was less 50 beats/min that lasted for more than one hour. The resulting ANN was prospectively tested in 33 patients (test cohort) and compared with MLR or SVM models according to accuracy and receiver operating characteristics (ROC) curve analysis. No significant difference in baseline characteristics between the training cohort and the test cohort was observed. PHD was observed in 21 (27.6%) patients in the training cohort and 10 (30.3%) patients in the test cohort. In the training cohort, the accuracy of ANN for the prediction of PHD was 98.7% and the area under the ROC curve (AUROC) was 0.961. In the test cohort, the number of correctly classified instances was 32 (97.0%) using the ANN model. In contrast, the accuracy rate of MLR or SVM model was both 75.8%. ANN (AUROC: 0.950; 95% CI [confidence interval]: 0.813-0.996) showed superior predictive performance compared to MLR model (AUROC: 0.796; 95% CI: 0.620-0.915, p<0.001) or SVM model (AUROC: 0.885; 95% CI: 0.725-0.969, p<0.001). The ANN model seems to have more powerful prediction capabilities than MLR or SVM model for persistent hemodynamic depression after CAS. External validation with a large cohort is needed to confirm our results. Copyright © 2017. Published by Elsevier B.V.

  10. Comparative Analysis of ANN and SVM Models Combined with Wavelet Preprocess for Groundwater Depth Prediction

    Directory of Open Access Journals (Sweden)

    Ting Zhou

    2017-10-01

    Full Text Available Reliable prediction of groundwater depth fluctuations has been an important component in sustainable water resources management. In this study, a data-driven prediction model combining discrete wavelet transform (DWT preprocess and support vector machine (SVM was proposed for groundwater depth forecasting. Regular artificial neural networks (ANN, regular SVM, and wavelet preprocessed artificial neural networks (WANN models were also developed for comparison. These methods were applied to the monthly groundwater depth records over a period of 37 years from ten wells in the Mengcheng County, China. Relative absolute error (RAE, Pearson correlation coefficient (r, root mean square error (RMSE, and Nash-Sutcliffe efficiency (NSE were adopted for model evaluation. The results indicate that wavelet preprocess extremely improved the training and test performance of ANN and SVM models. The WSVM model provided the most precise and reliable groundwater depth prediction compared with ANN, SVM, and WSVM models. The criterion of RAE, r, RMSE, and NSE values for proposed WSVM model are 0.20, 0.97, 0.18 and 0.94, respectively. Comprehensive comparisons and discussion revealed that wavelet preprocess extremely improves the prediction precision and reliability for both SVM and ANN models. The prediction result of SVM model is superior to ANN model in generalization ability and precision. Nevertheless, the performance of WANN is superior to SVM model, which further validates the power of data preprocess in data-driven prediction models. Finally, the optimal model, WSVM, is discussed by comparing its subseries performances as well as model performance stability, revealing the efficiency and universality of WSVM model in data driven prediction field.

  11. Modeling of local scour depth downstream hydraulic structures in trapezoidal channel using GEP and ANNs

    Directory of Open Access Journals (Sweden)

    Yasser Abdallah Mohamed Moussa

    2013-12-01

    Full Text Available Local scour downstream stilling basins is so complex that it makes it difficult to establish a general empirical model to provide accurate estimation for scour depth. Lack estimation of local scour can endanger to stability of hydraulic structure and can cause risk of failure. This paper presents Gene expression program (GEP and artificial neural network (ANNs, to simulate local scour depth downstream hydraulic structures. The experimental data is collected from the literature for the scour depth downstream the stilling basin through a trapezoidal channel. Using GEP approach gives satisfactory results compared with artificial neural network (ANN and multiple linear regression (MLR modeling in predicting the scour depth downstream of hydraulic structures.

  12. Anneli Randla kaitses doktorikraadi Cambridge'is / Anneli Randla ; interv. Reet Varblane

    Index Scriptorium Estoniae

    Randla, Anneli, 1970-

    1999-01-01

    5. mail kaitses Cambridge'is esimese eesti kunstiteadlasena doktorikraadi Anneli Randla. Töö teema: kerjusmungaordukloostrite arhitektuur Põhja-Euroopas. Juhendaja dr. Deborah Howard. Doktorikraadile esitatavatest nõudmistest, doktoritöö kaitsmisest, magistrikraadi kaitsnu õppimisvõimalustest Cambridge's.

  13. Logistics innovation development

    DEFF Research Database (Denmark)

    Pedrosa, Alex; Blazevic, Vera; Jasmand, Claudia

    2014-01-01

    Purpose This paper aims to investigate the microfoundations—boundary-spanning employees activities and behaviors—of deepening and broadening customer knowledge during logistics innovation development. Design/methodology/approach A multiple case study approach (six case studies) was adopted...... to investigate the role of boundary-spanning employees in deepening and broadening customer knowledge while developing logistics service innovations. Findings The results show that boundary-spanning employees’ engage sequentially in deepening and broadening customer knowledge throughout the logistics innovation......, this paper contributes to the understanding of the individuals’ behaviors underlying organization-level phenomena, such as logistics service providers’ customer knowledge acquisition. Practical implications (if applicable) Our findings illustrate that boundary-spanning employees might be well advised...

  14. Logistics Innovation Process Revisited

    DEFF Research Database (Denmark)

    Gammelgaard, Britta; Su, Shong-Iee Ivan; Yang, Su-Lan

    2011-01-01

    Purpose – The purpose of this paper is to learn more about logistics innovation processes and their implications for the focal organization as well as the supply chain, especially suppliers. Design/methodology/approach – The empirical basis of the study is a longitudinal action research project...... that was triggered by the practical needs of new ways of handling material flows of a hospital. This approach made it possible to revisit theory on logistics innovation process. Findings – Apart from the tangible benefits reported to the case hospital, five findings can be extracted from this study: the logistics...... on internal stakeholders as on external relationships; and logistics innovation process may start out as a dialectic, conflict ridden process and end up in a well-ordered goal-oriented teleological process. Research limitations/implications – In general, the study contributes to the knowledge base...

  15. Logistics and Inventory System -

    Data.gov (United States)

    Department of Transportation — The Logistics and Inventory System (LIS) is the agencys primary supply/support automation tool. The LIS encompasses everything from order entry by field specialists...

  16. Improving Healthcare Logistics Processes

    DEFF Research Database (Denmark)

    Feibert, Diana Cordes

    provision whilst providing high quality care. Logistics activities in hospitals provide a significant opportunity for cost containment in healthcare through the implementation of best practices. Literature provides little guidance on how to improve healthcare logistics processes. This study investigates...... at hospitals in Denmark and the US investigating three different types of processes: bed logistics, hospital cleaning, and pharmaceutical distribution. Based on an analysis and comparison of the case studies, a set of factors were identified influencing the decision on how to improve healthcare logistics......Healthcare costs are increasing due to an ageing population and more sophisticated technologies and treatments. At the same time, patients expect high quality care at an affordable cost. The healthcare industry has therefore experienced increasing pressures to reduce the cost of healthcare...

  17. Maritime logistics: contemporary issues

    National Research Council Canada - National Science Library

    Song, D.W; Panayides, P.M

    2012-01-01

    .... It brings together leading authorities in the field to consider for the first time maritime logistics, and in doing so it defines the area, registers its boundaries and contributes to its development...

  18. Green Logistics Management

    Science.gov (United States)

    Chang, Yoon S.; Oh, Chang H.

    Nowadays, environmental management becomes a critical business consideration for companies to survive from many regulations and tough business requirements. Most of world-leading companies are now aware that environment friendly technology and management are critical to the sustainable growth of the company. The environment market has seen continuous growth marking 532B in 2000, and 590B in 2004. This growth rate is expected to grow to 700B in 2010. It is not hard to see the environment-friendly efforts in almost all aspects of business operations. Such trends can be easily found in logistics area. Green logistics aims to make environmental friendly decisions throughout a product lifecycle. Therefore for the success of green logistics, it is critical to have real time tracking capability on the product throughout the product lifecycle and smart solution service architecture. In this chapter, we introduce an RFID based green logistics solution and service.

  19. Research on logistics scheduling based on PSO

    Science.gov (United States)

    Bao, Huifang; Zhou, Linli; Liu, Lei

    2017-08-01

    With the rapid development of e-commerce based on the network, the logistics distribution support of e-commerce is becoming more and more obvious. The optimization of vehicle distribution routing can improve the economic benefit and realize the scientific of logistics [1]. Therefore, the study of logistics distribution vehicle routing optimization problem is not only of great theoretical significance, but also of considerable value of value. Particle swarm optimization algorithm is a kind of evolutionary algorithm, which is based on the random solution and the optimal solution by iteration, and the quality of the solution is evaluated through fitness. In order to obtain a more ideal logistics scheduling scheme, this paper proposes a logistics model based on particle swarm optimization algorithm.

  20. Behavioral Operations in Logistics

    OpenAIRE

    Vries, Jelle

    2016-01-01

    markdownabstractIn the world of logistics, a considerable share of all work is automated and performed by machines or robots. An examination of the existing logistics research reflects this image, since a substantial share of the studies focus on automated processes, and perfectly predictable systems. This is however not the whole picture. People play an essential role in almost every node in a supply chain as well, and when human behavior is involved things become less predictable. The roles...

  1. Logistics and Strategy

    Science.gov (United States)

    2014-12-04

    2014 by: , Director, Graduate Degree Programs Robert F. Baumann, PhD The opinions and conclusions expressed herein are those of the...Washington, DC: Center for Military History, 1993), 244. 24 Spencer C. Tucker and Priscilla Mary Roberts , Encyclopedia of World War I (Santa Barbara...General Delos Emmons , and other leaders to create Joint Logistical Plans to synchronize logistical activities, this problem continued to plague Allied

  2. Lunar Commercial Mining Logistics

    Science.gov (United States)

    Kistler, Walter P.; Citron, Bob; Taylor, Thomas C.

    2008-01-01

    Innovative commercial logistics is required for supporting lunar resource recovery operations and assisting larger consortiums in lunar mining, base operations, camp consumables and the future commercial sales of propellant over the next 50 years. To assist in lowering overall development costs, ``reuse'' innovation is suggested in reusing modified LTS in-space hardware for use on the moon's surface, developing product lines for recovered gases, regolith construction materials, surface logistics services, and other services as they evolve, (Kistler, Citron and Taylor, 2005) Surface logistics architecture is designed to have sustainable growth over 50 years, financed by private sector partners and capable of cargo transportation in both directions in support of lunar development and resource recovery development. The author's perspective on the importance of logistics is based on five years experience at remote sites on Earth, where remote base supply chain logistics didn't always work, (Taylor, 1975a). The planning and control of the flow of goods and materials to and from the moon's surface may be the most complicated logistics challenges yet to be attempted. Affordability is tied to the innovation and ingenuity used to keep the transportation and surface operations costs as low as practical. Eleven innovations are proposed and discussed by an entrepreneurial commercial space startup team that has had success in introducing commercial space innovation and reducing the cost of space operations in the past. This logistics architecture offers NASA and other exploring nations a commercial alternative for non-essential cargo. Five transportation technologies and eleven surface innovations create the logistics transportation system discussed.

  3. Passenger Flows Estimation of Light Rail Transit (LRT System in Izmir, Turkey Using Multiple Regression and ANN Methods

    Directory of Open Access Journals (Sweden)

    Mustafa Özuysal

    2012-01-01

    Full Text Available Passenger flow estimation of transit systems is essential for new decisions about additional facilities and feeder lines. For increasing the efficiency of an existing transit line, stations which are insufficient for trip production and attraction should be examined first. Such investigation supports decisions for feeder line projects which may seem necessary or futile according to the findings. In this study, passenger flow of a light rail transit (LRT system in Izmir, Turkey is estimated by using multiple regression and feed-forward back-propagation type of artificial neural networks (ANN. The number of alighting passengers at each station is estimated as a function of boarding passengers from other stations. It is found that ANN approach produced significantly better estimations specifically for the low passenger attractive stations. In addition, ANN is found to be more capable for the determination of trip-attractive parts of LRT lines.   Keywords: light rail transit, multiple regression, artificial neural networks, public transportation

  4. Research on the Logistics Supply Chain in Port Logistics Transportation

    OpenAIRE

    Wang Yan-liang

    2013-01-01

    The aim of this study is to improve and increase the logistics system effectiveness and to solve the problem of optimal movement of different flows. Logistics transport carrying the world on material resources transfer exchange important mission and economic development and our lives are closely linked, logistics chain logistics transport occupies an important position and in the e logistics chain in port logistics has play a decisive role. For many coastal countries port logistics is the eco...

  5. ANN reconstruction of geoelectrical parameters of the Minou fault zone by scalar CSAMT data

    Science.gov (United States)

    Spichak, V.; Fukuoka, K.; Kobayashi, T.; Mogi, T.; Popova, I.; Shima, H.

    2002-01-01

    Scalar controlled source AMT data collected in a northern part of the Minou fault area (Kyushu Island, Japan) are interpreted by means of the ANN Expert System MT-NET in terms of 3-D earth macro-parameters. A number of synthetic responses created in advance by means of forward modeling in typical 3-D geoelectrical models (conductive and resistive local bodies, fault, dyke, etc.) formed sequences for teaching an artificial neural network (ANN). MT-NET, once taught to the correspondence between the data images and the model parameters, is able to recognize unknown parameters given even incomplete and noisy data. The results of ANN reconstruction are compared with the resistivity distribution obtained for the same area using fast 3-D imaging based on synthesis of 1-D Bostick transforms of the apparent resistivities beneath each site as well as on 2-D TM mode inversion along four profiles. The best-fitting model reconstructed by ANN belongs to the guessed model class formed by "dykes buried in the two-layered earth", on the one hand, and to the equivalence class formed by all models giving rms misfit less than the noise level in the data, on the other hand.

  6. Assessment of spatial distribution of soil heavy metals using ANN-GA, MSLR and satellite imagery.

    Science.gov (United States)

    Naderi, Arman; Delavar, Mohammad Amir; Kaboudin, Babak; Askari, Mohammad Sadegh

    2017-05-01

    This study aims to assess and compare heavy metal distribution models developed using stepwise multiple linear regression (MSLR) and neural network-genetic algorithm model (ANN-GA) based on satellite imagery. The source identification of heavy metals was also explored using local Moran index. Soil samples (n = 300) were collected based on a grid and pH, organic matter, clay, iron oxide contents cadmium (Cd), lead (Pb) and zinc (Zn) concentrations were determined for each sample. Visible/near-infrared reflectance (VNIR) within the electromagnetic ranges of satellite imagery was applied to estimate heavy metal concentrations in the soil using MSLR and ANN-GA models. The models were evaluated and ANN-GA model demonstrated higher accuracy, and the autocorrelation results showed higher significant clusters of heavy metals around the industrial zone. The higher concentration of Cd, Pb and Zn was noted under industrial lands and irrigation farming in comparison to barren and dryland farming. Accumulation of industrial wastes in roads and streams was identified as main sources of pollution, and the concentration of soil heavy metals was reduced by increasing the distance from these sources. In comparison to MLSR, ANN-GA provided a more accurate indirect assessment of heavy metal concentrations in highly polluted soils. The clustering analysis provided reliable information about the spatial distribution of soil heavy metals and their sources.

  7. Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA-ANN model

    Energy Technology Data Exchange (ETDEWEB)

    Cadenas, Erasmo [Facultad de Ingenieria Mecanica, Universidad Michoacana de San Nicolas de Hidalgo, Santiago Tapia No. 403, Centro (Mexico); Rivera, Wilfrido [Centro de Ivestigacion en Energia, Universidad Nacional Autonoma de Mexico, Apartado Postal 34, Temixco 62580, Morelos (Mexico)

    2010-12-15

    In this paper the wind speed forecasting in the Isla de Cedros in Baja California, in the Cerro de la Virgen in Zacatecas and in Holbox in Quintana Roo is presented. The time series utilized are average hourly wind speed data obtained directly from the measurements realized in the different sites during about one month. In order to do wind speed forecasting Hybrid models consisting of Autoregressive Integrated Moving Average (ARIMA) models and Artificial Neural Network (ANN) models were developed. The ARIMA models were first used to do the wind speed forecasting of the time series and then with the obtained errors ANN were built taking into account the nonlinear tendencies that the ARIMA technique could not identify, reducing with this the final errors. Once the Hybrid models were developed 48 data out of sample for each one of the sites were used to do the wind speed forecasting and the results were compared with the ARIMA and the ANN models working separately. Statistical error measures such as the mean error (ME), the mean square error (MSE) and the mean absolute error (MAE) were calculated to compare the three methods. The results showed that the Hybrid models predict the wind velocities with a higher accuracy than the ARIMA and ANN models in the three examined sites. (author)

  8. Outcome assessment of patients with metastatic renal cell carcinoma under systemic therapy using artificial neural networks.

    Science.gov (United States)

    Buchner, Alexander; Kendlbacher, Martin; Nuhn, Philipp; Tüllmann, Cordula; Haseke, Nicolas; Stief, Christian G; Staehler, Michael

    2012-03-01

    The outcome of patients with advanced renal cell carcinoma (RCC) under systemic therapy shows remarkable variability, and there is a need to identify prognostic parameters that allow individual prognostic stratification and selection of optimal therapy. Artificial neural networks (ANN) are software systems that can be trained to recognize complex data patterns. In this study, we used ANNs to identify poor prognosis of patients with RCC based on common clinical parameters available at the beginning of systemic therapy. Data from patients with RCC who started systemic therapy were collected prospectively in a single center database; 175 data sets with follow-up data (median, 36 months) were available for analysis. Age, sex, body mass index, performance status, histopathologic parameters, time interval between primary tumor and detection of metastases, type of systemic therapy, number of metastases, and metastatic sites were used as input data for the ANN. The target variable was overall survival after 36 months. Logistic regression models were constructed by using the same variables. Death after 36 months occurred in 26% of the patients in the tyrosine kinase inhibitors group and in 37% of the patients in the immunotherapy group (P = .22). ANN achieved 95% overall accuracy and significantly outperformed logistic regression models (78% accuracy). Pathologic T classification, invasion of vessels, and tumor grade had the highest impact on the network's decision. ANN is a promising approach for individual risk stratification of patients with advanced RCC under systemic therapy, based on clinical parameters, and can help to optimize the therapeutic strategy. Copyright © 2012 Elsevier Inc. All rights reserved.

  9. Development of a new software tool, based on ANN technology, in neutron spectrometry and dosimetry research

    Energy Technology Data Exchange (ETDEWEB)

    Ortiz R, J.M.; Martinez B, M.R.; Vega C, H.R. [Universidad Autonoma de Zacatecas, Av. Ramon Lopez Velarde 801, A.P. 336, 98000 Zacatecas (Mexico)

    2007-07-01

    Artificial Intelligence is a branch of study which enhances the capability of computers by giving them human-like intelligence. The brain architecture has been extensively studied and attempts have been made to emulate it as in the Artificial Neural Network technology. A large variety of neural network architectures have been developed and they have gained wide-spread popularity over the last few decades. Their application is considered as a substitute for many classical techniques that have been used for many years, as in the case of neutron spectrometry and dosimetry research areas. In previous works, a new approach called Robust Design of Artificial Neural network was applied to build an ANN topology capable to solve the neutron spectrometry and dosimetry problems within the Mat lab programming environment. In this work, the knowledge stored at Mat lab ANN's synaptic weights was extracted in order to develop for first time a customized software application based on ANN technology, which is proposed to be used in the neutron spectrometry and simultaneous dosimetry fields. (Author)

  10. Sonu Shamdasani interviewed by Ann Casement.

    Science.gov (United States)

    Shamdasani, Sonu

    2010-02-01

    Sonu Shamdasani interviewed by Ann Casement about Jung's The Red Book: Liber Novus in the course of which they range over issues to do with what drew Shamdasani to Jung; how he came to be involved in editing, translating and publishing Liber Novus; why he is so passionate about it; where it stands in relation to Jung's other work; some of the central figures that appear in the book such as Philemon and Izdubar; what Liber Novus might offer training candidates and succeeding generations of Jungians; how it has changed Shamdasani's own impression of Jung and what he hopes this enormous project will achieve; why Jung did not publish it in his own lifetime and whether he was mistaken in not doing so; and what impact the publication of Liber Novus will have on Jung's reputation worldwide as well as within the Jungian community.

  11. Efficiency and Logistics

    CERN Document Server

    Hompel, Michael; Klumpp, Matthias

    2013-01-01

    The „EffizienzCluster LogistikRuhr“ was a winner in the Leading Edge Science Cluster competition run by the German federal Ministry of Education and Research. The mission and aim of the „EffizienzCluster LogistikRuhr“ is to facilitate tomorrow’s individuality – in the sense of individual goods supply, mobility, and production – using 75 percent of today’s resources. Efficiency – both in economical and ecological terms – is enabled by state-of-the-art and innovative logistical solutions including transportation, production and intralogistics. These proceedings “Efficiency and Logistics” give first answers from 27 research projects as an insight into the current state of research of Europe’s leading research and development cluster in logistics and as a contribution to the discussion on how logistics as a science can help to cope with foreseeable resource shortage and sustainability as global challenges.

  12. Logistics innovation development

    DEFF Research Database (Denmark)

    Pedrosa, Alex; Blazevic, Vera; Jasmand, Claudia

    2015-01-01

    innovation development. Data were analyzed for similarities and differences in BSEs’ knowledge acquisition and their interactions with customers across six innovations. Findings – Results show that BSEs engage sequentially in deepening and broadening customer knowledge throughout the logistics innovation......Purpose – The purpose of this paper is to investigate the microfoundations of customer knowledge acquisition during logistics innovation development. Specifically, the authors explore the activities and behaviors of employees with customer contact (i.e. boundary-spanning employees (BSEs)) to deepen...... limitations/implications – In general, this paper contributes to the understanding of the individuals’ behaviors underlying organization-level phenomena, such as logistics service providers’ customer knowledge acquisition. Practical implications – Findings illustrate that BSEs are well advised to concentrate...

  13. Assessment of earthquake-triggered landslide susceptibility in El Salvador based on an Artificial Neural Network model

    Directory of Open Access Journals (Sweden)

    M. J. García-Rodríguez

    2010-06-01

    Full Text Available This paper presents an approach for assessing earthquake-triggered landslide susceptibility using artificial neural networks (ANNs. The computational method used for the training process is a back-propagation learning algorithm. It is applied to El Salvador, one of the most seismically active regions in Central America, where the last severe destructive earthquakes occurred on 13 January 2001 (Mw 7.7 and 13 February 2001 (Mw 6.6. The first one triggered more than 600 landslides (including the most tragic, Las Colinas landslide and killed at least 844 people.

    The ANN is designed and programmed to develop landslide susceptibility analysis techniques at a regional scale. This approach uses an inventory of landslides and different parameters of slope instability: slope gradient, elevation, aspect, mean annual precipitation, lithology, land use, and terrain roughness. The information obtained from ANN is then used by a Geographic Information System (GIS to map the landslide susceptibility. In a previous work, a Logistic Regression (LR was analysed with the same parameters considered in the ANN as independent variables and the occurrence or non-occurrence of landslides as dependent variables. As a result, the logistic approach determined the importance of terrain roughness and soil type as key factors within the model. The results of the landslide susceptibility analysis with ANN are checked using landslide location data. These results show a high concordance between the landslide inventory and the high susceptibility estimated zone. Finally, a comparative analysis of the ANN and LR models are made. The advantages and disadvantages of both approaches are discussed using Receiver Operating Characteristic (ROC curves.

  14. Prospects of application of artificial neural networks for forecasting of cargo transportation volume in transport systems

    Directory of Open Access Journals (Sweden)

    D. T. Yakupov

    2017-01-01

    Full Text Available The purpose of research – to identify the prospects for the use of neural network approach in relation to the tasks of economic forecasting of logistics performance, in particular of volume freight traffic in the transport system promiscuous regional freight traffic, as well as to substantiate the effectiveness of the use of artificial neural networks (ANN, as compared with the efficiency of traditional extrapolative methods of forecasting. The authors consider the possibility of forecasting to use ANN for these economic indicators not as an alternative to the traditional methods of statistical forecasting, but as one of the available simple means for solving complex problems.Materials and methods. When predicting the ANN, three methods of learning were used: 1 the Levenberg-Marquardt algorithm-network training stops when the generalization ceases to improve, which is shown by the increase in the mean square error of the output value; 2 Bayes regularization method - network training is stopped in accordance with the minimization of adaptive weights; 3 the method of scaled conjugate gradients, which is used to find the local extremum of a function on the basis of information about its values and gradient. The Neural Network Toolbox package is used for forecasting. The neural network model consists of a hidden layer of neurons with a sigmoidal activation function and an output neuron with a linear activation function, the input values of the dynamic time series, and the predicted value is removed from the output. For a more objective assessment of the prospects of the ANN application, the results of the forecast are presented in comparison with the results obtained in predicting the method of exponential smoothing.Results. When predicting the volumes of freight transportation by rail, satisfactory indicators of the verification of forecasting by both the method of exponential smoothing and ANN had been obtained, although the neural network

  15. Logistics Indicators Could Improve Logistics Performance of Hospitals

    Directory of Open Access Journals (Sweden)

    Wajong Andre M. R.

    2017-01-01

    Full Text Available Hospitals are institutions in which numerous transactions take place. A significant amount of those transactions happen in the realm of logistics. Logistifics is one of the concern of the hospital’s managing entities. Hospitals must provide a variety of logistics within a limited amount of time. The implementation of hospital logistics indicators were expected to assist the management in improving performance in the logistics units of hospitals in the Province of Jakarta. It was expected that by attending to a number of variables, namely information technology support, logistics management, logistics business intelligence, and logistics performance, such improvement could be achieved. The result of this study provided a description of major indicators that could improve the logistics performance in hospital industry located within the Province of Jakarta. Index Terms: hospital logistics indicator, information technology support, logistics business intelligence, logistic management, logistics performance.

  16. Beam orientation in stereotactic radiosurgery using an artificial neural network.

    Science.gov (United States)

    Skrobala, Agnieszka; Malicki, Julian

    2014-05-01

    To investigate the feasibility of using an artificial neural network (ANN) to generate beam orientations in stereotactic radiosurgery (SRS). A dataset of 669 intracranial lesions was used to build, train, and validate three ANNs. In ANN1, Cartesian coordinates described the localization of the PTV and OARs. In ANN2, a genetic algorithm was used to optimize the model. In ANN3, vectors were used to define the distance between the PTV and OARs. In all ANNs, inputs consisted of the treatment plan parameters plus the patient's particular geometric parameters; outputs were beam and table angles. The ANN- and human-generated plans were then compared using dose-volume histograms, root-mean-square (RMS) and Gamma index methods. The mean volume of PTV covered by the 95% isodose was 99.2% in the MP's plan vs. 99.3%, 98.5% and 99.2% for ANN1, ANN2, and ANN3, respectively. No significant differences were observed between the plans. ANN1 showed the best agreement (Gamma index) with the human planner. While RMS errors in the three ANN models were comparable, ANN1 showed the lowest (best) values. ANN models were able to determine beam orientation in SRS. ANN-generated treatment plans were comparable to human-designed plans. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  17. On The Comparison of Artificial Neural Network (ANN) and ...

    African Journals Online (AJOL)

    West African Journal of Industrial and Academic Research. Journal Home · ABOUT · Advanced Search · Current Issue · Archives · Journal Home > Vol 13, No 1 (2015) >. Log in or Register to get access to full text downloads.

  18. artificial neural network (ann) approach to electrical load

    African Journals Online (AJOL)

    2004-08-18

    Aug 18, 2004 ... validation, pattern recognition, prediction and multivariable quality applications. Some of its benefits are: - Reduced maintenance costs. Minimized chances of catastrophic failures. : Early error detection and trend analysis. • Significant reduction in data analysis tasks/time. Robust, accurate, and operate in ...

  19. Application of ANN and fuzzy logic algorithms for streamflow ...

    Indian Academy of Sciences (India)

    Tokar and Johnson (1999) developed ANN model to predict daily streamflow from daily rainfall, evaporation, temperature and snowmelt for watershed. ANN can also be applied to streamflow forecasting (Shivakumar et al. 2002;. Sinha Jitendra et al. 2013), reservoir inflow fore- casting (Jain and Srivastava 1999), sediment ...

  20. Logistics Takes Command

    NARCIS (Netherlands)

    Marullo, F.

    2015-01-01

    The term "logistics" derives from the Greek logizomai standing for the art of reckoning, organising, planning. Through time it achieved a strict military connotation, dealing with the composition, lodging and movements of troops, the arrangement of provisions in hostile territories, the

  1. Logistics Services Communication

    CERN Multimedia

    2006-01-01

    Members of the personnel are invited to take note that only parcels corresponding to official orders or contracts will be handled at CERN. Individuals are not authorised to have private merchandise delivered to them at CERN and private deliveries will not be accepted by the Goods Reception services. Thank you for your understanding. Logistics Services - FI Department - 79947

  2. Managing Construction Logistics

    Directory of Open Access Journals (Sweden)

    Denny McGeorge

    2010-12-01

    Full Text Available Book ReviewManaging Construction LogisticsGary Sullivan; Stephen Barthorpe and Stephen Robbins Publishers : Wiley-Blackwell 2010, 304 pages, ISBN 978-1-4051-5124-5 (paperback, GBP 49.99, EUR 60.00,  AUD 99.95, NZD  115.00. 

  3. NATO Logistics Handbook

    National Research Council Canada - National Science Library

    2007-01-01

    ...), is intended as a simple guide to logistics in NATO. It does not attempt to examine current issues or provide answers to the problems that logisticians will face, but it rather aims at introducing them to some of the basic principles, policies...

  4. Outlook City Logistics 2017

    NARCIS (Netherlands)

    Boer, E. den; Kok, R.; Ploos van Amstel, W.; Quak, H.J.; Wagter, H.

    2017-01-01

    When it comes to City Logistics, the need to innovate comes from multiple directions. Greenhouse gas emissions must be drastically reduced while at the same time maintaining a vibrant and healthy economy, demanding robust action from each and every sector, including transport. At the same time,

  5. Logistics Services Communication

    CERN Multimedia

    2006-01-01

    Members of the personnel are invited to take note that only parcels corresponding to official orders or contracts will be handled at CERN. Individuals are not authorisedto have private merchandise delivered to them at CERN and private deliveries will not be accepted by the Goods Reception services. Thank you for your understanding. Logistics Services - FI Department - 79947

  6. Geo-Information Logistical Modeling

    Directory of Open Access Journals (Sweden)

    Nikolaj I. Kovalenko

    2014-11-01

    Full Text Available This paper examines geo-information logistical modeling. The author illustrates the similarities between geo-informatics and logistics in the area of spatial objectives; illustrates that applying geo-data expands the potential of logistics; brings to light geo-information modeling as the basis of logistical modeling; describes the types of geo-information logistical modeling; describes situational geo-information modeling as a variety of geo-information logistical modeling.

  7. Space Station fluid management logistics

    Science.gov (United States)

    Dominick, Sam M.

    1990-01-01

    Viewgraphs and discussion on space station fluid management logistics are presented. Topics covered include: fluid management logistics - issues for Space Station Freedom evolution; current fluid logistics approach; evolution of Space Station Freedom fluid resupply; launch vehicle evolution; ELV logistics system approach; logistics carrier configuration; expendable fluid/propellant carrier description; fluid carrier design concept; logistics carrier orbital operations; carrier operations at space station; summary/status of orbital fluid transfer techniques; Soviet progress tanker system; and Soviet propellant resupply system observations.

  8. Spare Parts Logistics and Installed Base Information

    NARCIS (Netherlands)

    M.N. Jalil (Muhammad); R.A. Zuidwijk (Rob); M. Fleischmann (Moritz); J.A.E.E. van Nunen (Jo)

    2009-01-01

    textabstractMany of the challenges in spare parts logistics emerge due to the combination of large service networks, and sporadic/slow-moving demand. Customer heterogeneity and stringent service deadlines entail further challenges. Meanwhile, high revenues rates in service operations motivate

  9. FPGA implementation of adaptive ANN controller for speed regulation of permanent magnet stepper motor drives

    Energy Technology Data Exchange (ETDEWEB)

    Hasanien, Hany M., E-mail: Hanyhasanien@ieee.or [Dept. of Elec. Power and Machines, Faculty of Eng., Ain Shams Univ., Cairo (Egypt)

    2011-02-15

    This paper presents a novel adaptive artificial neural network (ANN) controller, which applies on permanent magnet stepper motor (PMSM) for regulating its speed. The dynamic response of the PMSM with the proposed controller is studied during the starting process under the full load torque and under load disturbance. The effectiveness of the proposed adaptive ANN controller is then compared with that of the conventional PI controller. The proposed methodology solves the problem of nonlinearities and load changes of PMSM drives. The proposed controller ensures fast and accurate dynamic response with an excellent steady state performance. Matlab/Simulink tool is used for this dynamic simulation study. The main contribution of this work is the implementation of the proposed controller on field programmable gate array (FPGA) hardware to drive the stepper motor. The driver is built on FPGA Spartan-3E Starter from Xilinx. Experimental results are presented to demonstrate the validity and effectiveness of the proposed control scheme.

  10. Discrete wavelet transform coupled with ANN for daily discharge forecasting into Três Marias reservoir

    Science.gov (United States)

    Santos, C. A. G.; Freire, P. K. M. M.; Silva, G. B. L.; Silva, R. M.

    2014-09-01

    This paper proposes the use of discrete wavelet transform (DWT) to remove the high-frequency components (details) of an original signal, because the noises generally present in time series (e.g. streamflow records) may influence the prediction quality. Cleaner signals could then be used as inputs to an artificial neural network (ANN) in order to improve the model performance of daily discharge forecasting. Wavelet analysis provides useful decompositions of original time series in high and low frequency components. The present application uses the Coiflet wavelets to decompose hydrological data, as there have been few reports in the literature. Finally, the proposed technique is tested using the inflow records to the Três Marias reservoir in São Francisco River basin, Brazil. This transformed signal is used as input for an ANN model to forecast inflows seven days ahead, and the error RMSE decreased by more than 50% (i.e. from 454.2828 to 200.0483).

  11. EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN.

    Science.gov (United States)

    Djemal, Ridha; AlSharabi, Khalil; Ibrahim, Sutrisno; Alsuwailem, Abdullah

    2017-01-01

    Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new computer aided diagnosis (CAD) of autism ‎based on electroencephalography (EEG) signal analysis is investigated. The proposed method is based on discrete wavelet transform (DWT), entropy (En), and artificial neural network (ANN). DWT is used to decompose EEG signals into approximation and details coefficients to obtain EEG subbands. The feature vector is constructed by computing Shannon entropy values from each EEG subband. ANN classifies the corresponding EEG signal into normal or autistic based on the extracted features. The experimental results show the effectiveness of the proposed method for assisting autism diagnosis. A receiver operating characteristic (ROC) curve metric is used to quantify the performance of the proposed method. The proposed method obtained promising results tested using real dataset provided by King Abdulaziz Hospital, Jeddah, Saudi Arabia.

  12. ANN based Estimation of Ultra High Energy (UHE) Shower Size using Radio Data

    Science.gov (United States)

    Sinha, Kalpana Roy; Datta, Pranayee; Sarma, Kandarpa Kumar

    2013-02-01

    Size estimation is a challenging area in the field of Ultra High Energy (UHE) showers where actual measurements are always associated with uncertainty of events and imperfections in detection mechanisms. The subtle variations resulting out of such factors incorporate certain random behaviour in the readings provided by shower detectors for subsequent processing. Field strength recorded by radio detectors may also be affected by this statistical nature. Hence there is a necessity of development of a system which can remain immune to such random behaviour and provide resilient readings to subsequent stages. Here, we propose a system based on Artificial Neural Network (ANN) which accepts radio field strength recorded by radio detectors and provides estimates of shower sizes in the UHE region. The ANN in feed-forward form is trained with a range of shower events with which it can effectively handle the randomness observed in the detector reading due to imperfections in the experimental apparatus and related set-up.

  13. 5th International Conference on Dynamics in Logistics

    CERN Document Server

    Kotzab, Herbert; Pannek, Jürgen

    2017-01-01

    These proceedings contain research papers presented at the 5th International Conference on Dynamics in Logistics, held in Bremen, Germany, February 2016. The conference is concerned with dynamic aspects of logistic processes and networks. The spectrum of topics reaches from modeling, planning and control of processes over supply chain management and maritime logistics to innovative technologies and robotic applications for cyber-physical production and logistic systems. The growing dynamic confronts the area of logistics with completely new challenges: it must become possible to describe, identify and analyze the process changes. Moreover, logistic processes and networks must be redevised to be rapidly and flexibly adaptable to continuously changing conditions. The book primarily addresses researchers and practitioners from the field of industrial engineering and logistics, but it may also be beneficial for graduate students.

  14. [Understanding logistic regression].

    Science.gov (United States)

    El Sanharawi, M; Naudet, F

    2013-10-01

    Logistic regression is one of the most common multivariate analysis models utilized in epidemiology. It allows the measurement of the association between the occurrence of an event (qualitative dependent variable) and factors susceptible to influence it (explicative variables). The choice of explicative variables that should be included in the logistic regression model is based on prior knowledge of the disease physiopathology and the statistical association between the variable and the event, as measured by the odds ratio. The main steps for the procedure, the conditions of application, and the essential tools for its interpretation are discussed concisely. We also discuss the importance of the choice of variables that must be included and retained in the regression model in order to avoid the omission of important confounding factors. Finally, by way of illustration, we provide an example from the literature, which should help the reader test his or her knowledge. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

  15. Logistics innovation development

    DEFF Research Database (Denmark)

    Pedrosa, Alex; Blazevic, Vera; Jasmand, Claudia

    2015-01-01

    innovation development. Data was analyzed for similarities and differences in boundary-spanning employees’ knowledge acquisition and their interactions with customers across six innovations. Findings — Results show that boundary-spanning employees engage sequentially in deepening and broadening customer......Purpose — This paper aims to investigate the microfoundations of customer knowledge acquisition during logistics innovation development. Specifically, we explore the activities and behaviors of employees with customer contact (i.e., boundary-spanning employees) to deepen and broaden their knowledge...... and diverse customer firm members. Research limitations/implications — In general, this paper contributes to the understanding of the individuals’ behaviors underlying organization-level phenomena, such as logistics service providers’ customer knowledge acquisition. Practical implications — Findings...

  16. Strategic planning for minimizing CO2 emissions using LP model based on forecasted energy demand by PSO Algorithm and ANN

    Energy Technology Data Exchange (ETDEWEB)

    Yousefi, M.; Omid, M.; Rafiee, Sh. [Department of Agricultural Machinery Engineering, University of Tehran, Karaj (Iran, Islamic Republic of); Ghaderi, S.F. [Department of Industrial Engineering, University of Tehran, Tehran (Iran, Islamic Republic of)

    2013-07-01

    Iran's primary energy consumption (PEC) was modeled as a linear function of five socioeconomic and meteorological explanatory variables using particle swarm optimization (PSO) and artificial neural networks (ANNs) techniques. Results revealed that ANN outperforms PSO model to predict test data. However, PSO technique is simple and provided us with a closed form expression to forecast PEC. Energy demand was forecasted by PSO and ANN using represented scenario. Finally, adapting about 10% renewable energy revealed that based on the developed linear programming (LP) model under minimum CO2 emissions, Iran will emit about 2520 million metric tons CO2 in 2025. The LP model indicated that maximum possible development of hydropower, geothermal and wind energy resources will satisfy the aim of minimization of CO2 emissions. Therefore, the main strategic policy in order to reduce CO2 emissions would be exploitation of these resources.

  17. Humanitarian logistics and sustainability

    CERN Document Server

    Leeuw, Sander; Regattieri, Alberto; Souza, Robert

    2015-01-01

    This contributed volume combines conceptual and strategic research articles dealing with the "why" and "to what end" of sustainable operations in humanitarian logistics, as well as operational research contributions regarding the "how" from the United Nations as well as from researchers and organizations from different countries (Germany, Australia, Singapore, Italy, Denmark, Jordan). The target audience primarily comprises research experts, decision makers  and practitioners in the field, but the book may also be beneficial for graduate students.

  18. Social Manufacturing and Logistics

    OpenAIRE

    Ittermann, Peter; Niehaus, Jonathan; Hirsch-Kreinsen, Hartmut; Dregger, Johannes; ten Hompel, Michael

    2016-01-01

    This paper is dealing with the ongoing debate of the digitization of german industry, the so-called „Industrie 4.0“, and its social consequences. The discussed new technologies like cyber-physical production systems, autonomous logistic systems and smart devices are about to get integrated in work places, that are embedded in existing organizational and social structures, thus making ‘complementary innovations’ and a coordinated design necessary. Our paper presents a human-centered design of ...

  19. Logistic regression models

    CERN Document Server

    Hilbe, Joseph M

    2009-01-01

    This book really does cover everything you ever wanted to know about logistic regression … with updates available on the author's website. Hilbe, a former national athletics champion, philosopher, and expert in astronomy, is a master at explaining statistical concepts and methods. Readers familiar with his other expository work will know what to expect-great clarity.The book provides considerable detail about all facets of logistic regression. No step of an argument is omitted so that the book will meet the needs of the reader who likes to see everything spelt out, while a person familiar with some of the topics has the option to skip "obvious" sections. The material has been thoroughly road-tested through classroom and web-based teaching. … The focus is on helping the reader to learn and understand logistic regression. The audience is not just students meeting the topic for the first time, but also experienced users. I believe the book really does meet the author's goal … .-Annette J. Dobson, Biometric...

  20. Alice-Anne Martin (1926 - 2016)

    CERN Multimedia

    2016-01-01

    Alice-Anne Martin, known as “Schu” from her maiden name Schubert, passed away on 8 January 2016.   (Image: Gérard Bertin) Hired the year CERN was founded, 1954, when the construction of the Laboratory had not even begun, Schu first worked at the Villa de Cointrin (a historic building now within the grounds of Geneva airport) as a secretary. In this role, she typed the convention between CERN and the Swiss Confederation, prepared by Stéphanie Tixier, as well as some of the "Yellow Reports" that have marked key points in the Laboratory’s history. For example, using a special typewriter with two keyboards – Latin and Greek – she typed the Yellow Report on the KAM theorem by Rolf Hagedorn. Schu also worked with Felix Bloch, the first Director-General of CERN, and later became the secretary of Herbert Coblenz, the first CERN librarian. She was head of the team that edited the proceedings of the ...

  1. Artificial Neural Networks and Instructional Technology.

    Science.gov (United States)

    Carlson, Patricia A.

    1991-01-01

    Artificial neural networks (ANN), part of artificial intelligence, are discussed. Such networks are fed sample cases (training sets), learn how to recognize patterns in the sample data, and use this experience in handling new cases. Two cognitive roles for ANNs (intelligent filters and spreading, associative memories) are examined. Prototypes…

  2. Logistic innovations in transport

    Directory of Open Access Journals (Sweden)

    Mirosław Antonowicz

    2014-03-01

    Full Text Available Introduction: The article discusses the issue of logistic innovations in transport. The essentials of logistic innovations in transport together with some examples of specific innovations are presented. The role of the client's needs in transport innovations is indicated. The most vital postulates affecting the innovativeness of shipping companies and derived from the author's experience as well as scholarly publications, are time, safety, reliability as well as comprehensiveness of service offer. Following the analysis of the issue, and on the grounds of Kaizen's and Lean's method, the concept of continuous innovations is suggested as very useful for the development of transport. The potential of clusters as the source of logistic innovations in transport is emphasised. Methods: The discussion of the issue was preceded by the author's analysis of written sources on innovativeness, the evaluation of ratings of innovativeness as well as the analysis of rewarded innovative solutions in transport subsequent to the businesses participation in the programme of innovative solutions in transport. The role of innovation practical business operations is argued following the analysis of some strategic documents such as: 2011 White Paper and the Strategy for the Development of Transport by 2020 adopted by the Polish government in 2013. Aim: The aim of the article is to present the role and significance of the issue of logistic innovations in transport and to cite instances of practical solutions implemented by shipping companies, the solutions which resulted in measurable effects. Following the author's observation of the instances of innovative solutions as well as his analysis of the ratings of innovativeness, the article aims to present the conclusions as for the specific kinds of activities which are indispensable to foster innovativeness in transport. Conclusions: The conclusions derived from the author's analyses and observations show that logistic

  3. An Introduction to Logistic Regression.

    Science.gov (United States)

    Cizek, Gregory J.; Fitzgerald, Shawn M.

    1999-01-01

    Where linearity cannot be assumed, logistic regression may be appropriate. This article describes conditions and tests for using logistic regression; introduces the logistic-regression model, the use of logistic-regression software, and some applications in published literature. Univariate and multiple independent-variable conditions and…

  4. Driver drowsiness detection using ANN image processing

    Science.gov (United States)

    Vesselenyi, T.; Moca, S.; Rus, A.; Mitran, T.; Tătaru, B.

    2017-10-01

    The paper presents a study regarding the possibility to develop a drowsiness detection system for car drivers based on three types of methods: EEG and EOG signal processing and driver image analysis. In previous works the authors have described the researches on the first two methods. In this paper the authors have studied the possibility to detect the drowsy or alert state of the driver based on the images taken during driving and by analyzing the state of the driver’s eyes: opened, half-opened and closed. For this purpose two kinds of artificial neural networks were employed: a 1 hidden layer network and an autoencoder network.

  5. Final Technical Report, Wind Generator Project (Ann Arbor)

    Energy Technology Data Exchange (ETDEWEB)

    Geisler, Nathan [City of Ann Arbor, MI (United States)

    2017-03-20

    A Final Technical Report (57 pages) describing educational exhibits and devices focused on wind energy, and related outreach activities and programs. Project partnership includes the City of Ann Arbor, MI and the Ann Arbor Hands-on Museum, along with additional sub-recipients, and U.S. Department of Energy/Office of Energy Efficiency and Renewable Energy (EERE). Report relays key milestones and sub-tasks as well as numerous graphics and images of five (5) transportable wind energy demonstration devices and five (5) wind energy exhibits designed and constructed between 2014 and 2016 for transport and use by the Ann Arbor Hands-on Museum.

  6. HOW INTEGRATED IS INTEGRATED LOGISTICS?

    Directory of Open Access Journals (Sweden)

    P.J. Pretorius

    2012-01-01

    Full Text Available Industry and academia still have a major problem in grasping the extent and relationships ofall functions involved in integrated logistics. This is because business logistics and logistics engineering developed separately, each with their own following, resulting in the two so-called different -disciplines ignoring each other.

    By analysing the definition of integrated logistic support, it is possible to identify the necessary conditions for an integrated logistics model. From these necessary conditions, a model of the management functions and technical activities is constructed to suggest the purpose and place of all logistics functions to work in an integrated way towards the goal of the organisation. The model suggests that logistics engineering and business logistics are both required in the organisation and that it is of vital importance that these functions are executed in unison . This model is different to previous models in that it approaches logistics from the organisations viewpoint rather than from a logistics viewpoint.

  7. HOW INTEGRATED IS INTEGRATED LOGISTICS?

    OpenAIRE

    P.J. Pretorius

    2012-01-01

    Industry and academia still have a major problem in grasping the extent and relationships ofall functions involved in integrated logistics. This is because business logistics and logistics engineering developed separately, each with their own following, resulting in the two so-called different -disciplines ignoring each other.

    By analysing the definition of integrated logistic support, it is possible to identify the necessary conditions for an integrated logistics model. From these ne...

  8. System for Business Logistics Management

    OpenAIRE

    González Camargo, Carlos Alberto

    2014-01-01

    This paper presents the methodology, design and results of the logistics management information system implementation in order to support the supply chain management decision making. The first step was to review existing indicators such as supply, warehousing, transportation, distribution, customer service, reverse logistics, risk logistics and logistics costs. Additional tools were designed for each of these areas that will improve internal logistics problems of companies. Based on these too...

  9. Stability analysis of rubblemound breakwater using ANN

    Digital Repository Service at National Institute of Oceanography (India)

    Mandal, S.; Rao, S.; Manjunath, Y.R.; Kim, D.H.

    1993, Nielsen 1988). Networks have an ability to recognize the hidden pattern in the data and accordingly estimate the values. The biggest merit is its ability to deal with fuzzy information whose interrelation us ambiguous or whose functional...

  10. Logistic service providers and sustainable physical distribution

    Directory of Open Access Journals (Sweden)

    Stef Weijers

    2012-06-01

    Full Text Available Background: Logistic Service Providers main concern was to ensure reliability for a low price (Christopher, 2005. Dutch Logistic Service Providers still have these two aspects at the top of their list, but also have to take in a new aspect: sustainability. 88% Of the investigated Logistic Service Providers have included sustainability in the company's goals. These Logistic Service Providers have developed different strategies to achieve a higher level of sustainability. This paper presents the results of a study into what Logistic Service Providers say what they are doing, or intend to do, to improve sustainability for their transport services. In this way insight is given in the attitude of Dutch Logistic Service Providers towards sustainability and how they intend to translate this into business practise: internal solutions or new methods incorporating external partners. Methods: Various methods of the investigations were used, among which the analysis of the statements about the sustainabilityon the websites of various companies as well as the questionnaire per Internet. The research covered 50 largest logistics companies operating in the Netherlands and 60 companies that competed for the award "Lean and Green" advertised in the Netherlands. In addition, the Internet survey was answered by 41 companies that belong to the network of our university. Results: The investigation has shown that sustainability is handled by the logistics company as an integral part of the corporate strategy. In contrast, shippers depend in the choice of logistics services primarily on such classical aspects as the reliability or the price and the sustainability play a minor role. Conclusions: Trying to find methods to improve the sustainability, Dutch logistics service providers, in the first place, look for solutions that increase the efficiency and therefore the cost reduction potential. Solutions, which require the involvement of clients, were less often

  11. Data mining methods in the prediction of Dementia: A real-data comparison of the accuracy, sensitivity and specificity of linear discriminant analysis, logistic regression, neural networks, support vector machines, classification trees and random forests.

    Science.gov (United States)

    Maroco, João; Silva, Dina; Rodrigues, Ana; Guerreiro, Manuela; Santana, Isabel; de Mendonça, Alexandre

    2011-08-17

    Dementia and cognitive impairment associated with aging are a major medical and social concern. Neuropsychological testing is a key element in the diagnostic procedures of Mild Cognitive Impairment (MCI), but has presently a limited value in the prediction of progression to dementia. We advance the hypothesis that newer statistical classification methods derived from data mining and machine learning methods like Neural Networks, Support Vector Machines and Random Forests can improve accuracy, sensitivity and specificity of predictions obtained from neuropsychological testing. Seven non parametric classifiers derived from data mining methods (Multilayer Perceptrons Neural Networks, Radial Basis Function Neural Networks, Support Vector Machines, CART, CHAID and QUEST Classification Trees and Random Forests) were compared to three traditional classifiers (Linear Discriminant Analysis, Quadratic Discriminant Analysis and Logistic Regression) in terms of overall classification accuracy, specificity, sensitivity, Area under the ROC curve and Press'Q. Model predictors were 10 neuropsychological tests currently used in the diagnosis of dementia. Statistical distributions of classification parameters obtained from a 5-fold cross-validation were compared using the Friedman's nonparametric test. Press' Q test showed that all classifiers performed better than chance alone (p classification accuracy (Median (Me) = 0.76) an area under the ROC (Me = 0.90). However this method showed high specificity (Me = 1.0) but low sensitivity (Me = 0.3). Random Forest ranked second in overall accuracy (Me = 0.73) with high area under the ROC (Me = 0.73) specificity (Me = 0.73) and sensitivity (Me = 0.64). Linear Discriminant Analysis also showed acceptable overall accuracy (Me = 0.66), with acceptable area under the ROC (Me = 0.72) specificity (Me = 0.66) and sensitivity (Me = 0.64). The remaining classifiers showed overall classification accuracy above a median value of 0.63, but for most

  12. voltage compensation using artificial neural network

    African Journals Online (AJOL)

    Offor Theophilos

    VOLTAGE COMPENSATION USING ARTIFICIAL NEURAL NETWORK: A CASE STUDY OF. RUMUOLA ... using artificial neural network (ANN) controller based dynamic voltage restorer (DVR). ... substation by simulating with sample of average voltage for Omerelu, Waterlines, Rumuola, Shell Industrial and Barracks.

  13. Hubane hambaravi / Ann-Liis Ojaots, Arina Palm-Lillepea

    Index Scriptorium Estoniae

    Ojaots, Ann-Liis

    2014-01-01

    Intervjuu KliinikPluss hambaravikliiniku omaniku Arina Palm-Lillepea ja psühholoogi Ann-Liis Ojaotsaga Sõpruse Ärimajas asuva hambaravi erilisest lähenemisest hambaravile ja teistele toitumisega seotud haigustele nagu nt buliimia

  14. Anne-Marie Sargueil: ilu on kasulik / intervjueerinud Emilie Toomela

    Index Scriptorium Estoniae

    Sargueil, Anne-Marie

    2015-01-01

    Prantsuse Disainiinstituudi juht Anne-Marie Sargueil rääkis prantsuse ja skandinaavia disainist, prantslaste disainieelistustest, uutest suundadest disaini valdkonnas, Eesti Tarbekunsti- ja Disainimuuseumis avatud näitusest "20 prantsuse disainiikooni"

  15. Education in logistics and training of non-logistic personnel

    Directory of Open Access Journals (Sweden)

    Marko D. Andrejić

    2011-01-01

    Full Text Available The significance of education in logistics and education and training of cadets who belong to non-logistic services (non-logistic personnel will be presented. The logistical aspects of education of non-logistic personnel are elaborated as well as the knowledge in the area of logistics which is necessary to be transferred through the educational process to non-logistic personnel for the successful accomplishment of their functional duties. A general approach and the methods of logistics education and improvement of non-logistic personnel are presented as well as the institutional prerequisites necessary for improving the quality of logistics education and training. The quality of the knowledge in this area and its implementation into the methods of thinking and decision making of non-logistic personnel affect the cooperation between the non-logistic and the logistic personnel, directly contributing to the quality of life and working conditions of units and institutions as well as to the quality and synergy in task accomplishments in the framework defined by the missions of the Army of Serbia. The necessary content and means of its transfer to cadets are discussed since they are supposed to be available at all levels and forms of education, depending on the previous cadet education levels. The theoretical bases and experiences shown are of general character and they have a universal application in the process of education. Introduction In our recent defense theory and operational practice, logistics education of non-logistic personnel is not sufficiently analyzed either in organizational or technological aspects, considering the concept and the logic of a systematic and a situational approach. The analysis of the experiences gained from operational practice shows a slight decrease in the quality of task accomplishment due to the lack of necessary logistic knowledge and habits as well as an increased communication gap between logistic and non-logistic

  16. Modeling the proportion of cut slopes rock on forest roads using artificial neural network and ordinal linear regression.

    Science.gov (United States)

    Babapour, R; Naghdi, R; Ghajar, I; Ghodsi, R

    2015-07-01

    Rock proportion of subsoil directly influences the cost of embankment in forest road construction. Therefore, developing a reliable framework for rock ratio estimation prior to the road planning could lead to more light excavation and less cost operations. Prediction of rock proportion was subjected to statistical analyses using the application of Artificial Neural Network (ANN) in MATLAB and five link functions of ordinal logistic regression (OLR) according to the rock type and terrain slope properties. In addition to bed rock and slope maps, more than 100 sample data of rock proportion were collected, observed by geologists, from any available bed rock of every slope class. Four predictive models were developed for rock proportion, employing independent variables and applying both the selected probit link function of OLR and Layer Recurrent and Feed forward back propagation networks of Neural Networks. In ANN, different numbers of neurons are considered for the hidden layer(s). Goodness of the fit measures distinguished that ANN models produced better results than OLR with R (2) = 0.72 and Root Mean Square Error = 0.42. Furthermore, in order to show the applicability of the proposed approach, and to illustrate the variability of rock proportion resulted from the model application, the optimum models were applied to a mountainous forest in where forest road network had been constructed in the past.

  17. Prediction of Frequency for Simulation of Asphalt Mix Fatigue Tests Using MARS and ANN

    Directory of Open Access Journals (Sweden)

    Ali Reza Ghanizadeh

    2014-01-01

    Full Text Available Fatigue life of asphalt mixes in laboratory tests is commonly determined by applying a sinusoidal or haversine waveform with specific frequency. The pavement structure and loading conditions affect the shape and the frequency of tensile response pulses at the bottom of asphalt layer. This paper introduces two methods for predicting the loading frequency in laboratory asphalt fatigue tests for better simulation of field conditions. Five thousand (5000 four-layered pavement sections were analyzed and stress and strain response pulses in both longitudinal and transverse directions was determined. After fitting the haversine function to the response pulses by the concept of equal-energy pulse, the effective length of the response pulses were determined. Two methods including Multivariate Adaptive Regression Splines (MARS and Artificial Neural Network (ANN methods were then employed to predict the effective length (i.e., frequency of tensile stress and strain pulses in longitudinal and transverse directions based on haversine waveform. It is indicated that, under controlled stress and strain modes, both methods (MARS and ANN are capable of predicting the frequency of loading in HMA fatigue tests with very good accuracy. The accuracy of ANN method is, however, more than MARS method. It is furthermore shown that the results of the present study can be generalized to sinusoidal waveform by a simple equation.

  18. ANN-PSO Integrated Optimization Methodology for Intelligent Control of MMC Machining

    Science.gov (United States)

    Chandrasekaran, Muthumari; Tamang, Santosh

    2017-08-01

    Metal Matrix Composites (MMC) show improved properties in comparison with non-reinforced alloys and have found increased application in automotive and aerospace industries. The selection of optimum machining parameters to produce components of desired surface roughness is of great concern considering the quality and economy of manufacturing process. In this study, a surface roughness prediction model for turning Al-SiCp MMC is developed using Artificial Neural Network (ANN). Three turning parameters viz., spindle speed ( N), feed rate ( f) and depth of cut ( d) were considered as input neurons and surface roughness was an output neuron. ANN architecture having 3 -5 -1 is found to be optimum and the model predicts with an average percentage error of 7.72 %. Particle Swarm Optimization (PSO) technique is used for optimizing parameters to minimize machining time. The innovative aspect of this work is the development of an integrated ANN-PSO optimization method for intelligent control of MMC machining process applicable to manufacturing industries. The robustness of the method shows its superiority for obtaining optimum cutting parameters satisfying desired surface roughness. The method has better convergent capability with minimum number of iterations.

  19. Typology of Logistics Curricula – Four Categories of Logistics of Logistics Undergraduate Education in Europe

    OpenAIRE

    Tarvo Niine; Ott Koppel

    2015-01-01

    The field of logistics education is rather colorful. The range of possible topics is huge and so each curriculum has unique approach towards which topic areas to emphasize and which to treat briefly or even omit. There is only little effective standardization in logistics education. This study examines the content of 42 undergraduate logistics curricula in Europe via cluster analysis, with a goal to propose a typology of logistics curricula. The findings define the spectrum of logistics educa...

  20. A data-parallelism approach for PSO-ANN based medical image reconstruction on a multi-core system

    Directory of Open Access Journals (Sweden)

    Subramanian Kartheeswaran

    Full Text Available This paper presents the sequential and parallel data decomposition strategies implemented on a Particle Swarm Optimization (PSO algorithm based Artificial Neural Network (PSO-ANN weights optimization for image reconstruction. The application system is developed for the reconstruction of two-dimensional spatial standard Computed Tomography (CT phantom images. It is running on a multi-core computer by varying the number of cores. The feed forward ANN initializes the weight between the ‘ideal’ images that are reconstructed using filtered back projection (FBP technique and the corresponding projection data of CT phantom. In an earlier work, ANN training time is too long. Hence, we propose that the ANN exemplar datasets are decomposed into subsets. Using these subsets, artificial sub neural nets (subnets are initialized and each subnet initial weights are optimized using PSO. Consequently, it was observed that the sequential approach of the proposed method consumes more training time. Hence the parallel strategy is attempted to reduce the computational training time. The parallel approach is further explored for image reconstruction from ‘noisy’ and ‘limited-angle’ datasets also. Keywords: Image reconstruction, Filtered back projection, Artificial neural networks, Particle swarm optimization, Multi-core processors

  1. In memoriam dr. Anne van Wijngaarden (1925-2004)

    OpenAIRE

    Broekhuizen, S.; Laar, van, V.

    2005-01-01

    Op 4 oktober 2004 overleed Dr. Anne van Wijngaarden op 78-jarige leeftijd in zijn huis bij Millac- Carlux, Frankrijk. Hij was een van de Nederlandse oprichters van de Vereniging voor Zoogdierkunde en Zoogdierbescherming. Nadat zijn eindexamen op de middelbare school wilde Anne aanvankelijk geologie studeren, maar toen de verplichte excursies te duur bleken, werd het biologie. Nog voordat hij doctoraal-examen had gedaan, kreeg hij een baan bij de Plantenziektenkundige Dienst. Zijn eerste opdra...

  2. Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

    Energy Technology Data Exchange (ETDEWEB)

    Ortiz R, J. M. [Escuela Politecnica Superior, Departamento de Electrotecnia y Electronica, Avda. Menendez Pidal s/n, Cordoba (Spain); Martinez B, M. R.; Vega C, H. R. [Universidad Autonoma de Zacatecas, Unidad Academica de Estudios Nucleares, Calle Cipres No. 10, Fracc. La Penuela, 98068 Zacatecas (Mexico); Gallego D, E.; Lorente F, A. [Universidad Politecnica de Madrid, Departamento de Ingenieria Nuclear, ETSI Industriales, C. Jose Gutierrez Abascal 2, 28006 Madrid (Spain); Mendez V, R.; Los Arcos M, J. M.; Guerrero A, J. E., E-mail: morvymm@yahoo.com.m [CIEMAT, Laboratorio de Metrologia de Radiaciones Ionizantes, Avda. Complutense 22, 28040 Madrid (Spain)

    2011-02-15

    With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks (Ann) have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Ann still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning Ann parameters. In recent years the use of hybrid technologies, combining Ann and genetic algorithms, has been utilized to. In this work, several Ann topologies were trained and tested using Ann and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out. (Author)

  3. Logistics Indicators Could Improve Logistics Performance of Hospitals

    OpenAIRE

    Wajong Andre M. R.

    2017-01-01

    Hospitals are institutions in which numerous transactions take place. A significant amount of those transactions happen in the realm of logistics. Logistifics is one of the concern of the hospital’s managing entities. Hospitals must provide a variety of logistics within a limited amount of time. The implementation of hospital logistics indicators were expected to assist the management in improving performance in the logistics units of hospitals in the Province of Jakarta. It was expected that...

  4. Resource allocation using ANN in LTE

    Science.gov (United States)

    Yigit, Tuncay; Ersoy, Mevlut

    2017-07-01

    LTE is the 4th generation wireless network technology, which provides flexible bandwidth, higher data speeds and lower delay. Difficulties may be experienced upon an increase in the number of users in LTE. The objective of this study is to ensure a faster solution to any such resource allocation problems which might arise upon an increase in the number of users. A fast and effective solution has been obtained by making use of Artificial Neural Network. As a result, fast working artificial intelligence methods may be used in resource allocation problems during operation.

  5. Neutron spectrometry and dosimetry with ANNs

    Energy Technology Data Exchange (ETDEWEB)

    Vega C, H. R.; Hernandez D, V. M. [Unidad Academica de Estudios Nucleares, Universidad Autonoma de Zacatecas, Cipres 10, Fracc. La Penuela, 98068 Zacatecas (Mexico); Gallego, E.; Lorente, A. [Departamento de Ingenieria Nuclear, Universidad Politecnica de Madrid, Jose Gutierrez Abascal 2, 28006 Madrid (Spain)], e-mail: fermineutron@yahoo.com

    2009-10-15

    Artificial neural networks technology has been applied to unfold the neutron spectra and to calculate the effective dose, the ambient equivalent dose, and the personal dose equivalent for {sup 252}Cf and {sup 241}AmBe neutron sources. A Bonner sphere spectrometry with a {sup 6}LiI(Eu) scintillator was utilized to measure the count rates of the spheres that were utilized as input in two artificial neural networks, one for spectrometry and another for dosimetry. Spectra and the ambient dose equivalent were also obtained with BUNKIUT code and the UTA4 response matrix. With both procedures spectra and ambient dose equivalent agrees in less than 10%. (author)

  6. A Squeezed Artificial Neural Network for the Symbolic Network Reliability Functions of Binary-State Networks.

    Science.gov (United States)

    Yeh, Wei-Chang

    Network reliability is an important index to the provision of useful information for decision support in the modern world. There is always a need to calculate symbolic network reliability functions (SNRFs) due to dynamic and rapid changes in network parameters. In this brief, the proposed squeezed artificial neural network (SqANN) approach uses the Monte Carlo simulation to estimate the corresponding reliability of a given designed matrix from the Box-Behnken design, and then the Taguchi method is implemented to find the appropriate number of neurons and activation functions of the hidden layer and the output layer in ANN to evaluate SNRFs. According to the experimental results of the benchmark networks, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN-based approach with at least 16.6% improvement in the median absolute deviation in the cost of extra 2 s on average for all experiments.Network reliability is an important index to the provision of useful information for decision support in the modern world. There is always a need to calculate symbolic network reliability functions (SNRFs) due to dynamic and rapid changes in network parameters. In this brief, the proposed squeezed artificial neural network (SqANN) approach uses the Monte Carlo simulation to estimate the corresponding reliability of a given designed matrix from the Box-Behnken design, and then the Taguchi method is implemented to find the appropriate number of neurons and activation functions of the hidden layer and the output layer in ANN to evaluate SNRFs. According to the experimental results of the benchmark networks, the comparison appears to support the superiority of the proposed SqANN method over the traditional ANN-based approach with at least 16.6% improvement in the median absolute deviation in the cost of extra 2 s on average for all experiments.

  7. Empty Container Logistics

    Directory of Open Access Journals (Sweden)

    Jakov Karmelić

    2012-05-01

    Full Text Available Within the whole world container traffic, the largest share of containers is in the status of repositioning. Container repositioning results from the need for harmonization between the point of empty container accumulation and the point of demand, and waiting time for the availability of the first next transport of cargo. This status of containers on the container market is the consequence of imbalances in the worldwide trade distribution on most important shipping routes. The need for fast and effective reallocation of empty containers causes high costs and often represents an obstacle affecting the efficiency of port container terminals and inland carriers.In accordance with the above issue, this paper is mainly focused on the analysis of the data concerning global container capacities and the roots of container equipment imbalances, with the aim of determining the importance of empty container management and the need for empty container micro-logistic planning at the spread port area.

  8. Regional Trade and Logistics Infrastructure: A Spatial Approach

    OpenAIRE

    Sabater, Luisa Alamá; Ramos, Laura Marquez; Burguet, Celestino Suarez

    2011-01-01

    This paper aims to analyze whether the existing logistics platforms network in Spain affects Spanish transport demand by using a spatial framework. In particular, we use demand for transport to export goods to other Spanish provinces as a proxy for logistics infrastructure demand in Spain. Then, we obtain data for trade flows between provinces (NUTS3) in the year 2007. We also obtain data about the number and area of logistics platforms existing in each Spanish province to proxy for the trans...

  9. Solving Complex Logistics Problems with Multi-Artificial Intelligent System

    OpenAIRE

    Tse, Y.K.; R.H. Lie; Chan, T M

    2009-01-01

    The economy, which has become more information intensive, more global and more technologically dependent, is undergoing dramatic changes. The role of logistics is also becoming more and more important. In logistics, the objective of service providers is to fulfill all customers? demands while adapting to the dynamic changes of logistics networks so as to achieve a higher degree of customer satisfaction and therefore a higher return on investment. In order to provide high quali...

  10. Intermodal Logistics Centres and Freight Corridors – Concepts and Trends

    OpenAIRE

    Norbert Wagener

    2017-01-01

    . Background: The development of international freight corridors, as the Trans European Network and new rail and inland shipping corridors in Asia and Africa, require efficient logistics centres along these corridors which serve as intermodal interfaces and provide a variety of different logistics service functions. The definition of the term logistics centre differs between countries and implies different functionalities. Locations are often selected randomly and business models are opportun...

  11. Sustainable Logistics Responses to a Global Challenge

    CERN Document Server

    Bretzke, Wolf-Rüdiger

    2013-01-01

    Currently the notion of "sustainability" is used in an inflationary manner. Therefore the authors start with a definition which is stable to serve as an anchor for further research as well as for discussions among scientists, managers and politicians, ideally across different disciplines. The character of this book is purely conceptual. The argumentation is based on comparison of new and demanding requisites with existing models (process and network architectures in the field of logistics). Formerly neglected impacts on the environment will be included. Main features of a new approach will be developed which are capable to avoid these impacts and to align logistics with the requirements of sustainability. In order to make logistics sustainable large parts will have to be reinvented. The focus needs to be on decoupling transportation activities from economic growth rates.

  12. Application of neural networks for the calculation of technical losses of electric energy in air power lines 6-35 kV

    Directory of Open Access Journals (Sweden)

    Володимир Леонідович Бакулевський

    2015-11-01

    Full Text Available A model for the calculation of technical losses of electricity in the air lines with voltage of 6-35 kV based on neural networks with due regard to meteorological factors has been worked out; the main components of the model have been considered and researched; the best ones being selected, that is: a set of input variables, volume of excerpts (training, control and testing, architecture and network activation function, network learning algorithm was proposed. Simulation was conducted in OS STATISTICA Neural Networks. Input variables are: transmission line (TL active load, transmission line rated voltage, transmission line cross section and length of wire, average air temperature, wind speed, rainfall availability; output variable – that is technical losses in electric transmission line. To select the optimal input vector model the data selection methods were used: variables testing using trial and error method, variables stepped inclusion and exclusion algorithm. It has been proved that the most important variables are TL active load and average air temperature. all input variables under review should be included in the created artificial neural network (ANN. It was determined that the optimal volume for ANN training set given parameters made 250 observations, control and test excerpts volume were respectively 250 and 332 observations. It has been proved that the best type of architecture is multilayer perceptron ANN that being compared to radial basis functions and generalized regression network is characterized by minimal errors and complexity of the network. ANN of the following architecture: multilayer perceptron, 7 neurons in the input layer, 5 neurons in the hidden layer, 1 output neuron, logistics as activation function – has been taken optimal

  13. Forecasting of nonlinear time series using ANN

    Directory of Open Access Journals (Sweden)

    Ahmed Tealab

    2017-06-01

    Full Text Available When forecasting time series, it is important to classify them according linearity behavior that the linear time series remains at the forefront of academic and applied research, it has often been found that simple linear time series models usually leave certain aspects of economic and financial data unexplained. The dynamic behavior of most of the time series in our real life with its autoregressive and inherited moving average terms issue the challenge to forecast nonlinear times series that contain inherited moving average terms using computational intelligence methodologies such as neural networks. It is rare to find studies that concentrate on forecasting nonlinear times series that contain moving average terms. In this study, we demonstrate that the common neural networks are not efficient for recognizing the behavior of nonlinear or dynamic time series which has moving average terms and hence low forecasting capability. This leads to the importance of formulating new models of neural networks such as Deep Learning neural networks with or without hybrid methodologies such as Fuzzy Logic.

  14. Performance measurement of plate fin heat exchanger by exploration: ANN, ANFIS, GA, and SA

    Directory of Open Access Journals (Sweden)

    A.K. Gupta

    2017-01-01

    Full Text Available An experimental work is conducted on counter flow plate fin compact heat exchanger using offset strip fin under different mass flow rates. The training, testing, and validation set of data has been collected by conducting experiments. Next, artificial neural network merged with Genetic Algorithm (GA utilized to measure the performance of plate-fin compact heat exchanger. The main aim of present research is to measure the performance of plate-fin compact heat exchanger and to provide full explanations. An artificial neural network predicted simulated data, which verified with experimental data under 10–20% error. Then, the authors examined two well-known global search techniques, simulated annealing and the genetic algorithm. The proposed genetic algorithm and Simulated Annealing (SA results have been summarized. The parameters are impartially important for good results. With the emergence of a new data-driven modeling technique, Neuro-fuzzy based systems are established in academic and practical applications. The neuro-fuzzy interference system (ANFIS has also been examined to undertake the problem related to plate-fin heat exchanger performance measurement under various parameters. Moreover, Parallel with ANFIS model and Artificial Neural Network (ANN model has been created with emphasizing the accuracy of the different techniques. A wide range of statistical indicators used to assess the performance of the models. Based on the comparison, it was revealed that technical ANFIS improve the accuracy of estimates in the small pool and tropical ANN.

  15. Factor Analysis of the Northeast Economic Zone's Logistics Competition

    Science.gov (United States)

    Shen, Wei; Wang, Jiaxin; Li, Tingting

    Along with the expanding regional logistics network, the area's logistics competition is very important because it enhances the entire area's economy. This paper is based on both the domestic and foreign theories and methods used with related subjects to study the content of the area's logistics competition. According to the level of economic development in Northeast region, the scale of logistics demand, the situation of logistics supply as well as the level of development of information, all establish the urban logistics competitiveness index system using the factor analysis method through the SPSS system to evaluate the competitiveness of the urban logistics which is very representative of the Northeast economic area. Finally, this paper uses the fore mentioned system to calculate the scores of 27 city's separated into 3 categories: The first including 4 cities (Shenyang, Harbin, Dalian, Changchun), the second having 3 cities (Jilin, Daqing, Anshan) and the final category has 20 cities (Fushun, Songyuan, Qiqihar, Baotou etc.).

  16. Control Systems for Logistics Performance

    OpenAIRE

    Virolainen, V.-M.

    1991-01-01

    This text is concerned with identifying and outlining the various aspects of logistics control and performance measurement process. The objective is to identify, based on available literature, methods and techniques which can be used to measure the performance of logistics. Logistics management is essentially a task of balancing between minimizing cost and ensuring availability objectives. Availability can be seen as the output of logistics system. On the input side, management is concer...

  17. LOGISTICS IN A TOURISM MARKET

    OpenAIRE

    Tkachuk, Yuliya

    2017-01-01

    The objective of the thesis was to analyze the role of the logistics concepts applied for the tourism market. The thesis covers methods and algorithms used for formation a tour package and building a route of the trip, means of transport used for transportation of tourists, information systems in a tourism logistics and sustainability aspect of a tourism logistics. For theoretical framework, a desk research method was used which included an analysis of service logistics, transportatio...

  18. An ANN-based approach to predict blast-induced ground vibration of Gol-E-Gohar iron ore mine, Iran

    Directory of Open Access Journals (Sweden)

    Mahdi Saadat

    2014-02-01

    Full Text Available Blast-induced ground vibration is one of the inevitable outcomes of blasting in mining projects and may cause substantial damage to rock mass as well as nearby structures and human beings. In this paper, an attempt has been made to present an application of artificial neural network (ANN to predict the blast-induced ground vibration of the Gol-E-Gohar (GEG iron mine, Iran. A four-layer feed-forward back propagation multi-layer perceptron (MLP was used and trained with Levenberg–Marquardt algorithm. To construct ANN models, the maximum charge per delay, distance from blasting face to monitoring point, stemming and hole depth were taken as inputs, whereas peak particle velocity (PPV was considered as an output parameter. A database consisting of 69 data sets recorded at strategic and vulnerable locations of GEG iron mine was used to train and test the generalization capability of ANN models. Coefficient of determination (R2 and mean square error (MSE were chosen as the indicators of the performance of the networks. A network with architecture 4-11-5-1 and R2 of 0.957 and MSE of 0.000722 was found to be optimum. To demonstrate the supremacy of ANN approach, the same 69 data sets were used for the prediction of PPV with four common empirical models as well as multiple linear regression (MLR analysis. The results revealed that the proposed ANN approach performs better than empirical and MLR models.

  19. Developing Future Strategic Logistics Leaders

    Science.gov (United States)

    2013-03-01

    A partnership between senior logistics leaders , PME developers, and personnel managers is essential to constructing and maintaining strategic...Commander in the area of leader development, while overseeing the implementation and daily management of logistic leader development. The current structure... managers is essential to constructing and maintaining strategic leader development frameworks. The risk of not making adjustments to logistics Professional

  20. Logistics Implications of Composite Wings

    Science.gov (United States)

    1993-12-01

    Stock Funding 59 Summary 60 Notes 60 5 DEPOT SUPPORT FOR COMPOSITE WINGS 63 Definition of Logistics 63 What Is a Depot? 63 Air Force...impacts of composite wings on the depot structure. Definition of Logistics Logistics is the foundation for sustaining all Air Force operations. The

  1. Unveiling the Concept of Packaging Logistics

    OpenAIRE

    Bramklev, Caroline; Olsson, Annika; Orremo, Fredrik; Wallin, Claes

    2001-01-01

    An increasingly competitive environment, technological advancements and globalisation are a few of the factors placing higher demands on today’s company activities, e.g. development, production and distribution processes. Company activities have hereby become a complex system, even more so if one company is seen as part of a network of companies. The aim of this article is to present and elaborate on an approach that deals with these matters in an integrated manner – packaging logistics, wher...

  2. Assessment of neural network, frequency ratio and regression models for landslide susceptibility analysis

    Science.gov (United States)

    Pradhan, B.; Buchroithner, M. F.; Mansor, S.

    2009-04-01

    This paper presents the assessment results of spatially based probabilistic three models using Geoinformation Techniques (GIT) for landslide susceptibility analysis at Penang Island in Malaysia. Landslide locations within the study areas were identified by interpreting aerial photographs, satellite images and supported with field surveys. Maps of the topography, soil type, lineaments and land cover were constructed from the spatial data sets. There are nine landslide related factors were extracted from the spatial database and the neural network, frequency ratio and logistic regression coefficients of each factor was computed. Landslide susceptibility maps were drawn for study area using neural network, frequency ratios and logistic regression models. For verification, the results of the analyses were compared with actual landslide locations in study area. The verification results show that frequency ratio model provides higher prediction accuracy than the ANN and regression models.

  3. Intelligent retail logistics scheduling

    Energy Technology Data Exchange (ETDEWEB)

    Rowe, J.; Jewers, K. [Stamford House, London (United Kingdom); Codd, A.; Alcock, A. [Inference Corp., Slough, Berkshire (United Kingdom)

    1996-12-31

    The Supply Chain Integrated Ordering Network (SCION) Depot Bookings system automates the planning and scheduling of perishable and non-perishable commodities and the vehicles that carry them into J. Sainsbury depots. This is a strategic initiative, enabling the business to make the key move from weekly to daily ordering. The system is mission critical, managing the inwards flow of commodities from suppliers into J. Sainsbury`s depots. The system leverages Al techniques to provide a business solution that meets challenging functional and performance needs. The SCION Depot Bookings system is operational providing schedules for 22 depots across the UK.

  4. A Computationally Intelligent Approach to the Detection of Wormhole Attacks in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Mohammad Nurul Afsar Shaon

    2017-05-01

    Full Text Available A wormhole attack is one of the most critical and challenging security threats for wireless sensor networks because of its nature and ability to perform concealed malicious activities. This paper proposes an innovative wormhole detection scheme to detect wormhole attacks using computational intelligence and an artificial neural network (ANN. Most wormhole detection schemes reported in the literature assume the sensors are uniformly distributed in a network, and, furthermore, they use statistical and topological information and special hardware for their detection. However, these schemes may perform poorly in non-uniformly distributed networks, and, moreover, they may fail to defend against “out of band” and “in band” wormhole attacks. The aim of the proposed research is to develop a detection scheme that is able to detect all kinds of wormhole attacks in both uniformly and non-uniformly distributed sensor networks. Furthermore, the proposed research does not require any special hardware and causes no significant network overhead throughout the network. Most importantly, the probable location of the malicious nodes can be identified by the proposed ANN based detection scheme. We evaluate the efficacy of the proposed detection scheme in terms of detection accuracy, false positive rate, and false negative rate. The performance of the proposed algorithm is also compared with other machine learning techniques (i.e. SVM and regularized nonlinear logistic regression (LR based detection models. The simulation results show that proposed ANN based algorithm outperforms the SVM or LR based detection schemes in terms of detection accuracy, false positive rate, and false negative rates.

  5. Anne Veski : "Ju siis ei ole minu rahvusvaheline kuulsus meie presidendi kõrvu jõudnud" / Anne Veski ; interv. Tiia Linnard

    Index Scriptorium Estoniae

    Veski, Anne, 1956-

    2008-01-01

    Laulja Anne Veski arutlusi kontserttegevusest Venemaal ja elust Eestis. Muuhulgas on juttu ka sellest, et Anne Veskit pole kunagi kutsutud presidendi iseseisvuspäeva vastuvõtule. Ilmunud ka: Severnoje Poberezhje 20. märts 2008, lk. 6

  6. Technology in health care logistics

    DEFF Research Database (Denmark)

    Jørgensen, Pelle; Wallin, Michael

    an area with huge efficiency potential. However, hospital logistics are facing two problems. 1) Hospital logistics have to a large extent been dealt with using a departmental approach resulting in sub-optimised logistics. 2) In industry, the use of different technological solutions to perform and control...... the logistical processes is very common. This has however not to the same extent been the case within hospitals, where technology primarily has been used within the clinical settings. There is therefore a large potential in using technology to improve the logistics. Based on these considerations the research...... presents an analytical model that can analyse the logistical system using a holistic approach, and explore the possibility of using technology to improve the current system. A logistical system is one of the different flows happening at a hospital. Included in the analytical model is a performance...

  7. Representation of Female Poetics in Anne Finch’s Poe

    Directory of Open Access Journals (Sweden)

    Dilek Sarikaya

    2009-11-01

    Full Text Available Anne Kingsmill Finch, the Countess of Winchelsea (1661-1720, holds an undeniably significant position in the history of women's writing. Although she was among the well-known poets during the 18th century, Anne Finch has only recently received literary appreciation which she deserves. Instead of complying with the conventions of masculine tradition, and prefers to follow a different path from her contemporaries. She concentrates mainly on the negative stereotypes of women and questions the assumptions of 18th century masculine poetic conventions. Undermining the socially constructed ideas about female identity, Anne Finch is very outspoken in her critique of male resistance to the poetry of women. In her poetry, Finch courageously demonstrates a challenging attitude to the prejudiced opinions about women's creative poetic capabilities. Hence, the poetry of Anne Finch which seem to be personal in its exploration of the experiences of a woman poet in the 18th century, it is in fact, fully embedded with ideological arguments trying to change strongly established conventions of writing in her own time. Therefore, through a detailed analysis of her poetry, the major concern of this paper will be to demonstrate that Anne Finch's poetry is specifically committed to the creation of a consciousness for women writers, distinguishing herself and her poetry from the general trend of her own period which attempts to eliminate women writers from the domain of male writing tradition.

  8. Quality Assessment in the Logistics of Rail Passenger Transport

    Directory of Open Access Journals (Sweden)

    Dedík Milan

    2017-01-01

    Full Text Available The article is focused on analysis and rating of the quality in the passenger railway transport logistics. At first is described the relationships between logistics and transport, logistical demands for transport and functions of logistics in rail transport. In the third chapter is explained quality assessment including the description of the quality indicators in passenger rail transport and making a proposal of the new methodology for this rating. Consequently this methodology is tested for chosen transport route and is concerned with monitoring quality of the transport routes in the railway network.

  9. Use of Ubiquitous Technologies in Military Logistic System in Iran

    Science.gov (United States)

    Jafari, P.; Sadeghi-Niaraki, A.

    2013-09-01

    This study is about integration and evaluation of RFID and ubiquitous technologies in military logistic system management. Firstly, supply chain management and the necessity of a revolution in logistic systems especially in military area, are explained. Secondly RFID and ubiquitous technologies and the advantages of their use in supply chain management are introduced. Lastly a system based on these technologies for controlling and increasing the speed and accuracy in military logistic system in Iran with its unique properties, is presented. The system is based on full control of military logistics (supplies) from the time of deployment to replenishment using sensor network, ubiquitous and RFID technologies.

  10. USE OF UBIQUITOUS TECHNOLOGIES IN MILITARY LOGISTIC SYSTEM IN IRAN

    Directory of Open Access Journals (Sweden)

    P. Jafari

    2013-09-01

    Full Text Available This study is about integration and evaluation of RFID and ubiquitous technologies in military logistic system management. Firstly, supply chain management and the necessity of a revolution in logistic systems especially in military area, are explained. Secondly RFID and ubiquitous technologies and the advantages of their use in supply chain management are introduced. Lastly a system based on these technologies for controlling and increasing the speed and accuracy in military logistic system in Iran with its unique properties, is presented. The system is based on full control of military logistics (supplies from the time of deployment to replenishment using sensor network, ubiquitous and RFID technologies.

  11. 8th Workshop on Logistics and Supply Chain Management

    CERN Document Server

    Kaminsky, Phil; Müller, Thomas

    2015-01-01

    This contributed volume presents selected research papers from the 8th workshop on Logistics and Supply Chain Management, which was held in October 2013 in Berkeley, California. It focuses on the topical issue of quantitative approaches in logistics and supply chain management, mainly covering facility location and location routing; vehicle routing and scheduling; courier, express and parcel service network design; healthcare logistics as well as logistics risk management. The target audience primarily comprises research experts and practitioners in the field, but the book will also be beneficial to graduate students.

  12. Quantification of phenylpropanoids in commercial Echinacea products using TLC with video densitometry as detection technique and ANN for data modelling.

    Science.gov (United States)

    Agatonovic-Kustrin, S; Loescher, Christine M; Singh, Ragini

    2013-01-01

    Echinacea preparations are among the most popular herbal remedies worldwide. Although it is generally assigned immune enhancement activities, the effectiveness of Echinacea is highly dependent on the Echinacea species, part of the plant used, the age of the plant, its location and the method of extraction. The aim of this study was to investigate the capacity of an artificial neural network (ANN) to analyse thin-layer chromatography (TLC) chromatograms as fingerprint patterns for quantitative estimation of three phenylpropanoid markers (chicoric acid, chlorogenic acid and echinacoside) in commercial Echinacea products. By applying samples with different weight ratios of marker compounds to the system, a database of chromatograms was constructed. One hundred and one signal intensities in each of the TLC chromatograms were correlated to the amounts of applied echinacoside, chlorogenic acid and chicoric acid using an ANN. The developed ANN correlation was used to quantify the amounts of three marker compounds in Echinacea commercial formulations. The minimum quantifiable level of 63, 154 and 98 ng and the limit of detection of 19, 46 and 29 ng were established for echinacoside, chlorogenic acid and chicoric acid respectively. A novel method for quality control of herbal products, based on TLC separation, high-resolution digital plate imaging and ANN data analysis has been developed. The method proposed can be adopted for routine evaluation of the phytochemical variability in Echinacea formulations available in the market. Copyright © 2012 John Wiley & Sons, Ltd.

  13. The Royal Summer Palace, Ferdinand I and Anne

    Directory of Open Access Journals (Sweden)

    Sylva Dobalová

    2015-12-01

    Full Text Available This essay examines the iconography of the best-known relief from the renaissance Royal Summer Palace at the Prague Castle, depicting Ferdinand I of Habsburg and his wife Anne Jagiello. It highlights its marriage symbolism and the question of the dowry. In the relief Anne, heiress to the Czech Lands, gives her husband an olive branch symbolising peace. In the context of the political significance of the palace’s decoration the relief expresses Ferdinand’s view of his claim to the Bohemian throne, based on his marriage to the heiress. Due to opposition from the Bohemian Estates, this finally became his lawful right in 1545, 24 years after the royal wedding. The Italian sculptor Paolo della Stella expressed a search for a peaceful solution to Ferdinand’s succession. The relief was carved between 1540 and 1550. The interpretations do not rule out the possibility that it was made after Anne had died (1547.

  14. Typology of Logistics Curricula – Four Categories of Logistics of Logistics Undergraduate Education in Europe

    Directory of Open Access Journals (Sweden)

    Tarvo Niine

    2015-05-01

    Full Text Available The field of logistics education is rather colorful. The range of possible topics is huge and so each curriculum has unique approach towards which topic areas to emphasize and which to treat briefly or even omit. There is only little effective standardization in logistics education. This study examines the content of 42 undergraduate logistics curricula in Europe via cluster analysis, with a goal to propose a typology of logistics curricula. The findings define the spectrum of logistics education in four clusters, ranging from „business administration“ with little focus on most specific logistics topics, through „interdisciplinary logistics management“ and „modern transport management“ to „logistics engineering“ with a strong quantitative and technology approach. However, a problem remains that curriculum title does not always reflect actual profile. A typical title “logistics management” might in one case contain various engineering elements, but in another none at all. Such findings point out the need for curricula boards to decide, if trying to cover the entire scope by „one-size-fits-all“ program is the best option or if more distinct focus is needed. In both cases this decision needs to be intentional, agreed and clearly communicated to avoid confusing students and society. Furthermore, the study points out the need to refine standards of competences in logistics, especially for logistics engineering.

  15. 4th International Conference on Dynamics in Logistics

    CERN Document Server

    Pannek, Jürgen; Thoben, Klaus-Dieter

    2016-01-01

    This contributed volume brings together research papers presented at the 4th International Conference on Dynamics in Logistics, held in Bremen, Germany in February 2014. The conference focused on the identification, analysis and description of the dynamics of logistics processes and networks. Topics covered range from the modeling and planning of processes, to innovative methods like autonomous control and knowledge management, to the latest technologies provided by radio frequency identification, mobile communication, and networking. The growing dynamic poses wholly new challenges: logistics processes and networks must be(come) able to rapidly and flexibly adapt to constantly changing conditions. The book primarily addresses the needs of researchers and practitioners from the field of logistics, but will also be beneficial for graduate students.

  16. The Emergence of City Logistics

    DEFF Research Database (Denmark)

    Gammelgaard, Britta; Aastrup, Jesper

    2014-01-01

    Purpose: Many city logistics projects in Europe have failed. The purpose of this article is to increase understanding of how city logistics emerge. A better understanding of the complex organizational processes with many actors and stakeholders in city logistics projects may prevent further...... failures. Design/methodology/approach: Theory on organizational change is applied to capture the processes leading to emergence of city logistics. The methodology is process analysis on a single longitudinal case. Findings: The emergence of the Copenhagen city logistics project can be understood....... The study aims at understanding the social processes towards reduced congestion and greenhouse gas emissions from goods transport in inner cities. Originality/value: By better understanding the organization processes leading to implementation of city logistics, other projects in other cities may learn from...

  17. The Emergence of City Logistics

    DEFF Research Database (Denmark)

    Gammelgaard, Britta

    2015-01-01

    Purpose: – Many city logistics projects in Europe have failed. A better understanding of the complex organizational change processes in city logistics projects with many stakeholders may expand city logistics capabilities and thereby help prevent future failures. The purpose of this paper...... is therefore to increase understanding of how city logistics emerge, and secondarily, to investigate whether such processes can be managed at all. Design/methodology/approach: – A paradigm shift in urban planning creates new ways of involving stakeholders in new sustainability measures such as city logistics....... Organizational change theory is applied to capture the social processes leading to emergence of city logistics. The methodology is a qualitative processual analysis of a single longitudinal case. Findings: – The change process took different forms over time. At the time of concluding the analysis, positive...

  18. Modelagem de redes logísticas com custos de inventário calculados a partir da cobertura de estoque Logistic networks modeling with inventory carrying costs calculated using inventory turnover

    Directory of Open Access Journals (Sweden)

    Ricardo Hamad

    2011-01-01

    Full Text Available A consideração dos custos de inventário tem se tornado cada vez mais importante na análise dos trade-offs e na tomada de decisões sobre redes logísticas. O artigo propõe uma metodologia para tratamento do custo de inventário a partir da cobertura de estoque esperada em cada instalação, incorporada na modelagem matemática da localização de fábricas e/ou centros de distribuição em redes logísticas com vários elos, representando uma nova versão do modelo apresentado em Hamad e Gualda (2008. As principais contribuições desta metodologia em relação a outros métodos encontrados na literatura são a facilidade de aplicação do método, a inclusão de restrições ligadas à capacidade de armazenagem e a consideração do custo total de inventário, e não apenas do custo relacionado aos produtos modelados.Inventory carrying cost has become very important in the analysis of the trade-offs and an important component to be considered when taking decisions about a logistic network. This paper proposes a methodology incorporated in a multi-echelon sourcing decision model, to be considered as a new version of the model presented in Hamad, Gualda (2008. It treats the carrying costs using the inventory Days-on-hand estimate in each echelon of the chain (plants and/or Distribution Centers. The main contributions of this methodology compared to other options found in the literature are the simplicity of its application, the consideration of all inventory costs (not only the ones related to the products being modeled and, also, the inclusion of constraints related to warehousing capacity.

  19. Análise do lead time nos processos logísticos de uma rede varejista de flores/Analysis of the lead time in the logistics processes of a retail flower network

    National Research Council Canada - National Science Library

    Luciana Torres Correia de Mello; Hugo Carlos Mansano Dornfeld; Givaldo Guilherme dos Santos; Débora Passos; Rafael Ribeiro; Moacir Godinho Filho

    2016-01-01

    .... The collected data related to the operations processing times showed characteristics of logistics processes involved in the distribution of raw material, from its acquisition to delivery at each branch office...

  20. Case Study on Logistics Performance

    OpenAIRE

    Shahryar Sorooshian; Manimekalai Jambulingam; Javad Dodangeh

    2013-01-01

    The paper presents research carried out at a medium‐size manufacturing organization in east Asia. The study tries to highlight the importance of supply chain management; specifically, our aim for this study is to understand logistics and performance measurement in the logistics and supply chain, and we include a theoretical discussion of online data collected and a case study of the logistic performance of a real organization. The study also examines the performance of the selected company, i...

  1. LOGISTIC MANAGEMENT IN COMMERCE ENTERPRISES

    OpenAIRE

    Tomasz Lis; Marek Lis

    2009-01-01

    The importance of logistic management for the general developmentstrategy of functioning of the enterprise is a stimulus to consider it in terms of newmethodology in enterprise management. Taking into account the up-to-date experiencesof logistics, either as a scientific branch or a practice, logistic management cannot betreated in the same way as the system management, through its aims, or the strategicmanagement understood as new method of management.

  2. Artificial neural networks applied to quantitative elemental analysis of organic material using PIXE

    Energy Technology Data Exchange (ETDEWEB)

    Correa, R. [Universidad Tecnologica Metropolitana, Departamento de Fisica, Av. Jose Pedro Alessandri 1242, Nunoa, Santiago (Chile)]. E-mail: rcorrea@utem.cl; Chesta, M.A. [Universidad Nacional de Cordoba, Facultad de Matematica, Astronomia y Fisica, Medina Allende s/n Ciudad Universitaria, 5000 Cordoba (Argentina)]. E-mail: chesta@famaf.unc.edu.ar; Morales, J.R. [Universidad de Chile, Facultad de Ciencias, Departamento de Fisica, Las Palmeras 3425, Nunoa, Santiago (Chile)]. E-mail: rmorales@uchile.cl; Dinator, M.I. [Universidad de Chile, Facultad de Ciencias, Departamento de Fisica, Las Palmeras 3425, Nunoa, Santiago (Chile)]. E-mail: mdinator@uchile.cl; Requena, I. [Universidad de Granada, Departamento de Ciencias de la Computacion e Inteligencia Artificial, Daniel Saucedo Aranda s/n, 18071 Granada (Spain)]. E-mail: requena@decsai.ugr.es; Vila, I. [Universidad de Chile, Facultad de Ciencias, Departamento de Ecologia, Las Palmeras 3425, Nunoa, Santiago (Chile)]. E-mail: limnolog@uchile.cl

    2006-08-15

    An artificial neural network (ANN) has been trained with real-sample PIXE (particle X-ray induced emission) spectra of organic substances. Following the training stage ANN was applied to a subset of similar samples thus obtaining the elemental concentrations in muscle, liver and gills of Cyprinus carpio. Concentrations obtained with the ANN method are in full agreement with results from one standard analytical procedure, showing the high potentiality of ANN in PIXE quantitative analyses.

  3. Beating the bookmakers - Using artificial neural networks to profit from football betting

    OpenAIRE

    Borøy-Johnsen, Simon

    2017-01-01

    Artificial Neural Networks (ANNs) have throughout the years been used for several different purposes. Problems spanning from image classification to text generation have all been subject to ANNs. In this report, ANNs were used in order to predict the outcomes of football matches. Using data from the football statistics web site www.whoscored.com, ANNs were constructed in order to predict the outcomes of matches from two successive seasons of the English Premier League. The predictions wer...

  4. The harmonics detection method based on neural network applied ...

    African Journals Online (AJOL)

    user

    Consequently, many structures based on artificial neural network (ANN) have been developed in the literature, The most significant ... Keywords: Artificial Neural Networks (ANN), p-q theory, (SAPF), Harmonics, Total Harmonic Distortion. 1. ..... and pure shunt active fitters, IEEE 38th Conf on Industry Applications, Vol. 2, pp.

  5. In-plant logistics optimalization

    OpenAIRE

    Maroušek, Jan

    2008-01-01

    In a theoretical part of this work there is introduced a general view on logistics and its development, than a view on creating value for customer, basics of lean production, lean thinking and following methods as Six Sigma and Value Stream Mapping. At the end of the theoretical part there is mentioned outsourcing of logistics services and view on relationship between logistics provider and a client. In a practical part there is introduced a project of in-plant logistics optimalization in a f...

  6. Situational Awareness and Logistics Division

    Data.gov (United States)

    Federal Laboratory Consortium — Volpe's Situational Awareness and Logistics Division researches, develops, implements, and analyzes advanced systems to protect, enhance, and ensure resilienceof the...

  7. Application of ANN and PCA to two-phase flow evaluation using radioisotopes

    Directory of Open Access Journals (Sweden)

    Hanus Robert

    2017-01-01

    Full Text Available In the two-phase flow measurements a method involving the absorption of gamma radiation can be applied among others. Analysis of the signals from the scintillation probes can be used to determine the number of flow parameters and to recognize flow structure. Three types of flow regimes as plug, bubble, and transitional plug – bubble flows were considered in this work. The article shows how features of the signals in the time and frequency domain can be used to build the artificial neural network (ANN to recognize the structure of the gas-liquid flow in a horizontal pipeline. In order to reduce the number of signal features the principal component analysis (PCA was used. It was found that the reduction of signals features allows for building a network with better performance.

  8. A New Artificial Neural Network Approach in Solving Inverse Kinematics of Robotic Arm (Denso VP6242)

    OpenAIRE

    Ahmed R. J. Almusawi; L. Canan Dülger; Sadettin Kapucu

    2016-01-01

    This paper presents a novel inverse kinematics solution for robotic arm based on artificial neural network (ANN) architecture. The motion of robotic arm is controlled by the kinematics of ANN. A new artificial neural network approach for inverse kinematics is proposed. The novelty of the proposed ANN is the inclusion of the feedback of current joint angles configuration of robotic arm as well as the desired position and orientation in the input pattern of neural network, while the traditional...

  9. New logistics protocols for distributed interactive simulation

    Science.gov (United States)

    Taylor, Darrin; Morrison, John; Katz, Warren; Felton, Erik; Herman, Deborah A.

    1995-06-01

    In today's environment, the transportation and maintenance of military forces is nearly as important as combat operations. Rapid deployment to regions of low-intensity conflict will become a very common training scenario for the U.S. military. Thus it is desirable to apply distributed simulation technology to train logistics personnel in their combat support roles. Currently, distributed interactive simulation (DIS) only contains rudimentary logistics protocols. This paper introduces new protocols designed to handle the logistics problem. The Newtonian protocol takes a physics-based approach to modeling interactions on the simulation network. This protocol consists of a family of protocol data units (PDUs) which are used to communicate forces in different circumstances. The protocol implements a small set of physical relations. This represents a flexible and general mechanism to describe battlefield interactions between network entities. The migratory object protocol (MOP) family addresses the transfer of control. General mechanisms provide the means to simulate resupply, repair, and maintenance of entities at any level of abstraction (individual soldier to division). It can also increase the fidelity of mine laying, enable handover of weapons for terminal guidance, allow for the distribution of aggregate-level simulation entities, provide capabilities for the simulation of personnel, etc.

  10. Artificial neural network for predicting pathological stage of clinically localized prostate cancer in a Taiwanese population.

    Science.gov (United States)

    Tsao, Chih-Wei; Liu, Ching-Yu; Cha, Tai-Lung; Wu, Sheng-Tang; Sun, Guang-Huan; Yu, Dah-Shyong; Chen, Hong-I; Chang, Sun-Yran; Chen, Shih-Chang; Hsu, Chien-Yeh

    2014-10-01

    We developed an artificial neural network (ANN) model to predict prostate cancer pathological staging in patients prior to when they received radical prostatectomy as this is more effective than logistic regression (LR), or combined use of age, prostate-specific antigen (PSA), body mass index (BMI), digital rectal examination (DRE), trans-rectal ultrasound (TRUS), biopsy Gleason sum, and primary biopsy Gleason grade. Our study evaluated 299 patients undergoing retro-pubic radical prostatectomy or robotic-assisted laparoscopic radical prostatectomy surgical procedures with pelvic lymph node dissection. The results were intended to predict the pathological stage of prostate cancer (T2 or T3) after radical surgery. The predictive ability of ANN was compared with LR and validation of the 2007 Partin Tables was estimated by the areas under the receiving operating characteristic curve (AUCs). Of the 299 patients we evaluated, 109 (36.45%) displayed prostate cancer with extra-capsular extension (ECE), and 190 (63.55%) displayed organ-confined disease (OCD). LR analysis showed that only PSA and BMI were statistically significant predictors of prostate cancer with capsule invasion. Overall, ANN outperformed LR significantly (0.795 ± 0.023 versus 0.746 ± 0.025, p = 0.016). Validation using the current Partin Tables for the participants of our study was assessed, and the predictive capacity of AUC for OCD was 0.695. ANN was superior to LR at predicting OCD in prostate cancer. Compared with the validation of current Partin Tables for the Taiwanese population, the ANN model resulted in larger AUCs and more accurate prediction of the pathologic stage of prostate cancer. Copyright © 2014. Published by Elsevier B.V.

  11. Optimum coagulant forecasting by modeling jar test experiments using ANNs

    Directory of Open Access Journals (Sweden)

    S. Haghiri

    2018-01-01

    Full Text Available Currently, the proper utilization of water treatment plants and optimizing their use is of particular importance. Coagulation and flocculation in water treatment are the common ways through which the use of coagulants leads to instability of particles and the formation of larger and heavier particles, resulting in improvement of sedimentation and filtration processes. Determination of the optimum dose of such a coagulant is of particular significance. A high dose, in addition to adding costs, can cause the sediment to remain in the filtrate, a dangerous condition according to the standards, while a sub-adequate dose of coagulants can result in the reducing the required quality and acceptable performance of the coagulation process. Although jar tests are used for testing coagulants, such experiments face many constraints with respect to evaluating the results produced by sudden changes in input water because of their significant costs, long time requirements, and complex relationships among the many factors (turbidity, temperature, pH, alkalinity, etc. that can influence the efficiency of coagulant and test results. Modeling can be used to overcome these limitations; in this research study, an artificial neural network (ANN multi-layer perceptron (MLP with one hidden layer has been used for modeling the jar test to determine the dosage level of used coagulant in water treatment processes. The data contained in this research have been obtained from the drinking water treatment plant located in Ardabil province in Iran. To evaluate the performance of the model, the mean squared error (MSE and correlation coefficient (R2 parameters have been used. The obtained values are within an acceptable range that demonstrates the high accuracy of the models with respect to the estimation of water-quality characteristics and the optimal dosages of coagulants; so using these models will allow operators to not only reduce costs and time taken to perform

  12. Partitioning and interpolation based hybrid ARIMA–ANN model for ...

    Indian Academy of Sciences (India)

    Time series forecasting; ARIMA; ANN; partitioning and interpolation; Box–Jenkins methodology ... Further, on different experimental TSD like sunspots TSD and electricity price TSD, the proposed hybrid model is applied along with four existing state-of-the-art models and it is found that the proposed model outperforms all ...

  13. 2016-2017 Travel Expense Reports for Mary Anne Chambers ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Beata Bialic

    Date(s):. 2016-08-14 to 2016-08-23. Destination(s):. Peru/Colombia. Airfare: $3,484.87. Other. Transportation: $0.00. Accommodation: $1,942.21. Meals and. Incidentals: $395.27. Other: $75.50. Total: $5,897.85. Comments: 2016-2017 Travel Expense Reports for Mary. Anne Chambers, Governor, Chairperson of the.

  14. 2016-2017 Travel Expense Reports for Margaret Ann Biggs ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Beata Bialic

    Purpose: Internal IDRC meetings. Date(s):. 2016-07-04 to 2016-07-06. Destination(s):. Ottawa. Airfare: $0.00. Other. Transportation: $39.00. Accommodation: $0.00. Meals and. Incidentals: $25.43. Other: $0.00. Total: $64.43. Comments: 2016-2017 Travel Expense Reports for. Margaret Ann Biggs, Chairperson.

  15. 2016-2017 Travel Expense Reports for Mary Anne Chambers ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Beata Bialic

    Purpose: Internal IDRC meetings. Date(s):. 2016-07-06. Destination(s):. Ottawa. Airfare: $482.11. Other. Transportation: $64.30. Accommodation: $0.00. Meals and. Incidentals: $25.28. Other: $0.00. Total: $571.69. Comments: 2016-2017 Travel Expense Reports for Mary. Anne Chambers, Governor, Chairperson of the.

  16. 2016-2017 Travel Expense Reports for Mary Anne Chambers ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Beata Bialic

    Purpose: Board meetings. Date(s):. 2016-11-20 to 2016-11-23. Destination(s):. Ottawa. Airfare: $445.14. Other. Transportation: $29.05. Accommodation: $786.80. Meals and. Incidentals: $76.79. Other: $0.00. Total: $1,337.78. Comments: 2016-2017 Travel Expense Reports for Mary. Anne Chambers, Governor, Chairperson ...

  17. 2017-2018 Travel Expense Reports for Mary Anne Chambers ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Chantal Taylor

    Ottawa. Airfare: $368.41. Other. Transportation: $69.95. Accommodation: $542.79. Meals and. Incidentals: $164.42. Other: $0.00. Total: $1,145.57. Comments: From residence in Thornhill, Ontario. 2017-2018 Travel Expense Reports for Mary. Anne Chambers, Governor, Chairperson of the. Human Resources Committee.

  18. 2016-2017 Travel Expense Reports for Mary Anne Chambers ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    chantal taylor

    Purpose: Board meetings. Date(s):. 2017-03-19 to 2017-03-22. Destination(s):. Ottawa. Airfare: $121.05. Other. Transportation: $51.92. Accommodation: $926.48. Meals and. Incidentals: $190.40. Other: $0.00. Total: $1,289.85. Comments: 2016-2017 Travel Expense Reports for Mary. Anne Chambers, Governor ...

  19. Eesti NATO ukselävel / Mari-Ann Kelam

    Index Scriptorium Estoniae

    Kelam, Mari-Ann, 1946-

    2002-01-01

    Seda, et NATO liitumisläbirääkimistele kutsutavate seas on ka Eesti, saab veel tänagi pidada üheks meie iseseisva riikluse suursaavutuseks, kui mitte imeks, kirjutab Riigikogu liige Mari-Ann Kelam. Autor: Isamaaliit. Parlamendisaadik

  20. Klaas ja mõis / Maie-Ann Raun

    Index Scriptorium Estoniae

    Raun, Maie-Ann, 1938-

    2007-01-01

    Klaasikunstinäitus "Ringkäik" Albu mõisas, kuraatorid Virve Kiil, Kati Kerstna, Kairi Orgusaar. Eksponeeritakse Tiina Sarapu, Mare Saare, Eeva Käsperi, Kai Kiudsoo-Värvi, Pilvi Ojamaa, Merle Bukoveci, Kalli Seina, Viivi-Ann Keerdo, Liisi Junolaineni, Kristiina Uslari, Ivo Lille töid

  1. Ants Orasest ja Anne Lange monograafiast / Jüri Talvet

    Index Scriptorium Estoniae

    Talvet, Jüri, 1945-

    2005-01-01

    Arvustus: Oras, Ants. Luulekool. I, Apoloogia / koostajad Hando Runnel ja Jaak Rähesoo. Tartu : Ilmamaa, 2003 ; Oras, Ants. Luulekool II, Meistriklass. Tartu : Ilmamaa, 2004 ; Lange, Anne. Ants Oras : [kirjandusteadlane, -kriitik ja tõlkija (1900-1982)]. Tartu : Ilmamaa, 2004

  2. Application of ANN and fuzzy logic algorithms for streamflow ...

    Indian Academy of Sciences (India)

    The present study focusses on development of models using ANN and fuzzy logic (FL) algorithm for predicting the streamflow for catchment of Savitri River Basin. The input vector to these models were daily rainfall, mean daily evaporation, mean daily temperature and lag streamflow used. In the present study, 20 years ...

  3. Carol Ann Duffy: A Preliminary Bibliography | Bala | Gender and ...

    African Journals Online (AJOL)

    Carol Ann Duffy: A Preliminary Bibliography. I Bala. Abstract. No Abstract. Full Text: EMAIL FULL TEXT EMAIL FULL TEXT · DOWNLOAD FULL TEXT DOWNLOAD FULL TEXT · AJOL African Journals Online. HOW TO USE AJOL... for Researchers · for Librarians · for Authors · FAQ's · More about AJOL · AJOL's Partners ...

  4. Toimus Endel Annuse bibliograafiapäev / Anne Valmas

    Index Scriptorium Estoniae

    Valmas, Anne, 1941-2017

    2012-01-01

    15. veebruaril 2012 peeti TLÜAR-is bibliograafiapäeva esimest korda Endel Annuse nimelisena, anti üle bibliograafiaauhind. Parimaks tunnistati Anneli Sepa koostatud "Uku Masing 100 : bibliograafia 1923-2009" ning "Eesti tehnikaartiklid 1936-1944. Registrid 1918-1944", mille koostas Riina Prööm ning isikunimede koostaja oli Helje Riives

  5. Decolonizing the Choctaws: Teaching LeAnne Howe's "Shell Shaker"

    Science.gov (United States)

    Hollrah, Patrice

    2004-01-01

    "Shell Shaker" (2001) by LeAnne Howe (Choctaw) is a novel that gives students an opportunity to learn that the history and culture of the Choctaw Nation of Oklahoma are alive today. Winner of the Before Columbus Foundation American Book Award in 2002, the novel deals with two parallel stories that converge in the present, one about the eighteenth…

  6. Vene ja prantsuse kunsti imetlemisest Londonis / Ann Alari

    Index Scriptorium Estoniae

    Alari, Ann

    2008-01-01

    Näitus "Venemaalt pärit prantsuse ja vene shedöövrid" kuningliku kunstiakadeemia saalides Londonis. Vaatluse all oli ajavahemik 1870 kuni 1925. Tööd olid pärit Moskvas elanud tekstiilitöösturitest suurärimeeste Sergei Shtshukini ja Ivan Morozovi kogudest, mis 1917. a. natsionaliseeriti. Kuraator Ann Dumas

  7. Kõvõmba olli Võrun kuun / Anneli Saro

    Index Scriptorium Estoniae

    Saro, Anneli, 1968-

    2007-01-01

    9.-11. nov. Võru Kultuurimajas Kannel toimuvast X harrastusteatrite riigifestivalist. Grand prix pälvis Teater Randlane (Jüri Tuuliku "Meretagune asi", lavastaja Maie Matvei), parim meesnäitleja Tiit Tammesson ("Ohvrit mängides"), parim naisnäitleja Anne Pangsepp ("Meretagune asi"), ansamblipreemia Tartu Üliõpilasteater (Fjodor Dostojevski "Sortsid", lavastaja Kalev Kudu)

  8. 2011 : Qu'elle année !

    CERN Multimedia

    Staff Association

    2012-01-01

    « Quelle année ! Et quelle fin d’année ! La star de l’année a été le LHC, avec ses expériences, qui une fois de plus ont été sous les feux de la rampe. Mais on doit aussi citer toute une troupe d’acteurs importants, dans des domaines aussi différents que l’antimatière et l’expérience CLOUD. » Voilà ce que le Directeur général nous a écrit le 20 décembre dans son message avec ses vœux de fin d’année. Sans oublier, bien sûr, les fameux neutrinos hyperrapides vers Gran Sasso qui ont mis le CERN sur le devant de la scène mondiale. Ces succès qui font la fierté et la force de l’Organisation ont été rendus possibles «&...

  9. ANNE TONER. Ellipsis in English Literature: Signs of Omission

    DEFF Research Database (Denmark)

    Lupton, Tina Jane

    2016-01-01

    As a freshman, I once met a PhD Student writing about the use of the comma in Jane Austen. For years the thesis topic kept me entertained as an example of how narrowly focused literary study could become. Reading Anne Toner's Ellipsis in English Literature commits me to a recantation of that joke...

  10. Hiina tervendus / kommenteerivad Anne, Julia, Weihong Song, Fagang Ren

    Index Scriptorium Estoniae

    2013-01-01

    Tallinnas Tulika 19 asuvast Bai Lan Hiina massaažisalongist, kus ravitakse kuputeraapia, gua sha kraapimisplaatide, moksa, nõelravi ja punktmassaaži abil. Tui na massaaži ja hiina loodusteraapia protseduure kommenteerivad spetsialistid ning patsiendid Anne ja Julia

  11. Leipzigi muinsuskaitse mess ja konverents 2006. aastal / Anneli Randla

    Index Scriptorium Estoniae

    Randla, Anneli, 1970-

    2007-01-01

    25.-28. X Leipzigis toimunud Euroopa restaureerimise, muinsuskaitse ja linnauuenduse messist "Denkmal 2006", kus osales 40 riiki 380 väljapanekuga. Konverentsist " Denkmalpflege in Europa. Aktuelle Tendenzen im Umgang mit dem historischen Erbe". Anneli Randla ettekanne puudutas konserveerimise teooriat ja praktikat Eestis

  12. Information Logistics Research report 2

    NARCIS (Netherlands)

    Willems, A.; Hajdasinski, A.K.; Willems, J.

    2009-01-01

    The goal of this research report is to further explore the concept of Information Logistics (IL), which refers to the usage and dispatch of information and methods of logistics able to support those processes. This report is based upon 6 questions that examine IL in organizations, healthcare

  13. Delayed logistic population models revisited

    OpenAIRE

    Liz, Eduardo

    2014-01-01

    We discuss the global dynamics of some logistic models governed by delay-differential equations. We focus on models of exploited populations, and study the changes in the dynamics as the harvesting effort is increased. We get new results and highlight the link among different logistic equations usually employed in population models.

  14. INFORMATION STREAMS OF LOGISTICAL SYSTEMS

    Directory of Open Access Journals (Sweden)

    E. N. Zhivitskaya

    2013-01-01

    Full Text Available The problem of formalisation and practical realisation of information streams of logistical systems, as the basic component of a separate kind of systems the logistical information systems having the features and properties that allows to investigate them by means of methods, applicable to information systems is considered.

  15. Integrated Logistics Guide. Second Edition

    Science.gov (United States)

    1994-06-14

    Identify design opportunities for Improved DEFINITION of logistics and reliability and maintainability supportability (R&M) parameters Technological...cope with rapid 5.4 RISK MANAGEMENT changes. An F-16 system simulation model was used to determine the impact of pro- 5.4.1 Delayed Definition of Logistics posed

  16. Outsourcing Operational Logistics: Buyer Beware

    National Research Council Canada - National Science Library

    Woody, Lamont

    2003-01-01

    One of the key tasks from U.S. Secretary of Defense Donald H. Rumsfeld to his Service Chiefs is to reduce DOD's overall logistic cost and footprint while transforming the warfighter-to-logistics (tooth-to-tail) force structure...

  17. Logistic Regression: Concept and Application

    Science.gov (United States)

    Cokluk, Omay

    2010-01-01

    The main focus of logistic regression analysis is classification of individuals in different groups. The aim of the present study is to explain basic concepts and processes of binary logistic regression analysis intended to determine the combination of independent variables which best explain the membership in certain groups called dichotomous…

  18. Simulation Integrated Design for Logistics

    NARCIS (Netherlands)

    Veeke, H.P.M.

    2003-01-01

    The design of an innovative logistic system is a complex problem in the solution of which many disciplines are involved. Each discipline developed its own way of conceptual modeling for a logistic system based on a mono disciplinary perception. In essence this leads to a communication problem

  19. Classifying hospitals as mortality outliers: logistic versus hierarchical logistic models.

    Science.gov (United States)

    Alexandrescu, Roxana; Bottle, Alex; Jarman, Brian; Aylin, Paul

    2014-05-01

    The use of hierarchical logistic regression for provider profiling has been recommended due to the clustering of patients within hospitals, but has some associated difficulties. We assess changes in hospital outlier status based on standard logistic versus hierarchical logistic modelling of mortality. The study population consisted of all patients admitted to acute, non-specialist hospitals in England between 2007 and 2011 with a primary diagnosis of acute myocardial infarction, acute cerebrovascular disease or fracture of neck of femur or a primary procedure of coronary artery bypass graft or repair of abdominal aortic aneurysm. We compared standardised mortality ratios (SMRs) from non-hierarchical models with SMRs from hierarchical models, without and with shrinkage estimates of the predicted probabilities (Model 1 and Model 2). The SMRs from standard logistic and hierarchical models were highly statistically significantly correlated (r > 0.91, p = 0.01). More outliers were recorded in the standard logistic regression than hierarchical modelling only when using shrinkage estimates (Model 2): 21 hospitals (out of a cumulative number of 565 pairs of hospitals under study) changed from a low outlier and 8 hospitals changed from a high outlier based on the logistic regression to a not-an-outlier based on shrinkage estimates. Both standard logistic and hierarchical modelling have identified nearly the same hospitals as mortality outliers. The choice of methodological approach should, however, also consider whether the modelling aim is judgment or improvement, as shrinkage may be more appropriate for the former than the latter.

  20. Capacitance Estimation Algorithm based on DC-Link Voltage Harmonics Using ANN in Three-Phase Motor Drive Systems

    DEFF Research Database (Denmark)

    Soliman, Hammam Abdelaal Hammam; Davari, Pooya; Wang, Huai

    2017-01-01

    to industry. In this digest, a condition monitoring methodology that estimates the capacitance value of the dc-link capacitor in a three phase Front-End diode bridge motor drive is proposed. The proposed software methodology is based on Artificial Neural Network (ANN) algorithm. The harmonics of the dc......-link voltage are used as training data to the Artificial Neural Network. Fast Fourier Transform (FFT) of the dc-link voltage is analysed in order to study the impact of capacitance variation on the harmonics order. Laboratory experiments are conducted to validate the proposed methodology and the error analysis...

  1. Quality of investments in logistics

    Directory of Open Access Journals (Sweden)

    Borut Jereb

    2014-06-01

    Full Text Available The issue problem of paper is that base on researches connected with analysis of investments in logistics based on the review of 100 pooled manuscripts from different sources (scientific journals and online magazines from 1996 to 2012 it was showed that there don't exist quality as well as leadership has no a good overview of the investment in logistics. It was claimed that the quality point of view should be demanding part of investments in logistics. Val Log was proposed as an answer to the issues of managing quality of investments in logistics at the tactical level in organizations. By Val Log it is possible to overcome the myth that logistics projects cost money while business projects bring money. Val Log also provides detailed instructions for goals and metrics for each process. By doing changes the quality should be the obvious pillar of our affords doing it.

  2. Is Artificial Neural Network Suitable for Damage Level Determination of Rc- Structures?

    OpenAIRE

    Baltacıoğlu, A. K.; Öztürk, B.; Civalek, Ö.; Akgöz, B.

    2010-01-01

    In the present study, an artificial neural network (ANN) application is introduced for estimation of damage level of reinforced concrete structures. Back-propagation learning algorithm is adopted. A typical neural network architecture is proposed and some conclusions are presented. Applicability of artificial neural network (ANN) for the assessment of earthquake related damage is investigated

  3. RISK ANALYSIS AND EVALUATION FOR CRITICAL LOGISTICAL INFRASTRUCTURE

    Directory of Open Access Journals (Sweden)

    Sascha Düerkop

    2016-12-01

    Full Text Available Logistical infrastructure builds the backbone of an economy. Without an effective logistical infrastructure in place, the supply for both enterprises and consumers might not be met. But even a high-quality logistical infrastructure can be threatened by risks. Thus, it is important to identify, analyse, and evaluate risks for logistical infrastructure that might threaten logistical processes. Only if those risks are known and their impact estimated, decision makers can implement counteractive measures to reduce risks. In this article, we develop a network-based approach that allows for the evaluation of risks and their consequences onto the logistical network. We will demonstrate the relevance of this approach by applying it to the logistics network of the central German state of Hesse. Even though transport data is extensively tracked and recorded nowadays, typical daily risks, like accidents on a motorway, and extraordinary risks, like a bridge at risk to collapse, terrorist attacks or climate-related catastrophes, are not systematically anticipated. Several studies unveiled recently that the overall impact for an economy of possible failures of single nodes and/or edges in a network are not calculated, and particularly critical edges are not identified in advance. We address this information gap by a method that helps to identify and quantify risks in a given network. To reach this objective, we define a mathematical optimization model that quantifies the current “risk-related costs” of the overall network and quantify the risk by investigating the change of the overall costs in the case a risk is realized.

  4. Achieving Consistent Near-Optimal Pattern Recognition Accuracy Using Particle Swarm Optimization to Pre-Train Artificial Neural Networks

    Science.gov (United States)

    Nikelshpur, Dmitry O.

    2014-01-01

    Similar to mammalian brains, Artificial Neural Networks (ANN) are universal approximators, capable of yielding near-optimal solutions to a wide assortment of problems. ANNs are used in many fields including medicine, internet security, engineering, retail, robotics, warfare, intelligence control, and finance. "ANNs have a tendency to get…

  5. Sensitive quantitative predictions of peptide-MHC binding by a 'Query by Committee' artificial neural network approach

    DEFF Research Database (Denmark)

    Buus, S; Lauemøller, S L; Worning, P

    2003-01-01

    We have generated Artificial Neural Networks (ANN) capable of performing sensitive, quantitative predictions of peptide binding to the MHC class I molecule, HLA-A*0204. We have shown that such quantitative ANN are superior to conventional classification ANN, that have been trained to predict...

  6. Usage of case-based reasoning, neural network and adaptive neuro-fuzzy inference system classification techniques in breast cancer dataset classification diagnosis.

    Science.gov (United States)

    Huang, Mei-Ling; Hung, Yung-Hsiang; Lee, Wen-Ming; Li, R K; Wang, Tzu-Hao

    2012-04-01

    Breast cancer is a common to females worldwide. Today, technological advancements in cancer treatment innovations have increased the survival rates. Many theoretical and experimental studies have shown that a multiple classifier system is an effective technique for reducing prediction errors. This study compared the particle swarm optimizer (PSO) based artificial neural network (ANN), the adaptive neuro-fuzzy inference system (ANFIS), and a case-based reasoning (CBR) classifier with a logistic regression model and decision tree model. It also applied three classification techniques to the Mammographic Mass Data Set, and measured its improvements in accuracy and classification errors. The experimental results showed that, the best CBR-based classification accuracy is 83.60%, and the classification accuracies of the PSO-based ANN classifier and ANFIS are 91.10% and 92.80%, respectively.

  7. Logistics and logistics processes in a tourism destination

    OpenAIRE

    Mrnjavac, Edna; Ivanovic, Sasa

    2007-01-01

    Logistics is a scientific discipline, a business function and an activity that, in recent times, is increasingly being applied to economic and social activities, because it enables the production of a quality product at a moderate cost. With regard to the trend for globally applying the principles of logistics to all spheres of human activities, the phenomenon of tourism presents a special challenge. Moreover, it is becoming evident that a quality tourism product, the design of which is linke...

  8. An experimental study of interstitial lung tissue classification in HRCT images using ANN and role of cost functions

    Science.gov (United States)

    Dash, Jatindra K.; Kale, Mandar; Mukhopadhyay, Sudipta; Khandelwal, Niranjan; Prabhakar, Nidhi; Garg, Mandeep; Kalra, Naveen

    2017-03-01

    In this paper, we investigate the effect of the error criteria used during a training phase of the artificial neural network (ANN) on the accuracy of the classifier for classification of lung tissues affected with Interstitial Lung Diseases (ILD). Mean square error (MSE) and the cross-entropy (CE) criteria are chosen being most popular choice in state-of-the-art implementations. The classification experiment performed on the six interstitial lung disease (ILD) patterns viz. Consolidation, Emphysema, Ground Glass Opacity, Micronodules, Fibrosis and Healthy from MedGIFT database. The texture features from an arbitrary region of interest (AROI) are extracted using Gabor filter. Two different neural networks are trained with the scaled conjugate gradient back propagation algorithm with MSE and CE error criteria function respectively for weight updation. Performance is evaluated in terms of average accuracy of these classifiers using 4 fold cross-validation. Each network is trained for five times for each fold with randomly initialized weight vectors and accuracies are computed. Significant improvement in classification accuracy is observed when ANN is trained by using CE (67.27%) as error function compared to MSE (63.60%). Moreover, standard deviation of the classification accuracy for the network trained with CE (6.69) error criteria is found less as compared to network trained with MSE (10.32) criteria.

  9. A Framework for Reverse Logistics

    OpenAIRE

    Brito, Marisa; Dekker, Rommert

    2003-01-01

    textabstractReverse Logistics has been stretching out worldwide, involving all the layers of supply chains in various industry sectors. While some actors in the chain have been forced to take products back, others have pro-actively done so, attracted by the value in used products One way or the other, Reverse Logistics has become a key competence in modern supply chains. In this paper, we present a content analysis of reverse logistics issues. To do so, we propose a content framework focusing...

  10. Use of an artificial neural network to predict risk factors of nosocomial infection in lung cancer patients.

    Science.gov (United States)

    Chen, Jie; Pan, Qin-Shi; Hong, Wan-Dong; Pan, Jingye; Zhang, Wen-Hui; Xu, Gang; Wang, Yu-Min

    2014-01-01

    Statistical methods to analyze and predict the related risk factors of nosocomial infection in lung cancer patients are various, but the results are inconsistent. A total of 609 patients with lung cancer were enrolled to allow factor comparison using Student's t-test or the Mann-Whitney test or the Chi-square test. Variables that were significantly related to the presence of nosocomial infection were selected as candidates for input into the final ANN model. The area under the receiver operating characteristic (ROC) curve (AUC) was used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. The prevalence of nosocomial infection from lung cancer in this entire study population was 20.1% (165/609), nosocomial infections occurring in sputum specimens (85.5%), followed by blood (6.73%), urine (6.0%) and pleural effusions (1.82%). It was shown that long term hospitalization (≥ 22 days, P= 0.000), poor clinical stage (IIIb and IV stage, P=0.002), older age (≥ 61 year old, P=0.023), and use the hormones were linked to nosocomial infection and the ANN model consisted of these four factors .The artificial neural network model with variables consisting of age, clinical stage, time of hospitalization, and use of hormones should be useful for predicting nosocomial infection in lung cancer cases.

  11. Development of logistics information systems

    Directory of Open Access Journals (Sweden)

    Milan Ž. Dronjak

    2012-10-01

    Full Text Available An adequate logistics information system provides real time automated data processing, distribution and of information according to Terrain Commander's requirements, which leads to timely fulfillment of logistic demands of units. SAP ERP The SAP ERP application is the integrated (ERP software capable of working with complex organisational structures, personnel, equipment, and finance. It enables planning and combining forces for every type of operations. The system also enables the determination of the readiness status of modelled forces. ISL The Information System for Logistics (ISL is a comprehensive information system of the Ministry of Defence (MoD and the Army of the Czech Republic that covers many areas: acquisition, supply, ammunition and equipment maintenance, logistics, etc. The ISL provides support for military logistics in all important areas of consumer and acquisition logistics, satisfaying all kinds of Defence Forces needs in the country and abroad. LOGFAS The information system LOGFAS comprises Logistics Database (LOGBASE, Movement and Transportation software (M&T, Allied Command Europe Resource Optimisation Software System (ACROSS and Logistic Reporting System (LOGREP. The Logistics Database LOGBASE represents a logistics information source and also a database related to assets, forces, geography, infrastructure, targets, supplies, movements and medical data. The main software tools which rely on the LOGBASE are M&T, ACROSS and LOGREP. GCSS-Army The original impetus to create the SALE came in the mid-90s when the United States Department of Defense (DoD started a logistics modernisation programme. One of tasks was to build The Single Army Logistics Enterprise (SALE for the purpose of covering the whole organisational structure of the DoD. There are three components of the SALE: GCSS-Army, PLM+ and LMP. Each of them uses the commercial Enterprise Resource Planning SAP Software with a Web access. The main component, GCSS

  12. Estimation of Optimum Dilution in the GMAW Process Using Integrated ANN-GA

    Directory of Open Access Journals (Sweden)

    P. Sreeraj

    2013-01-01

    Full Text Available To improve the corrosion resistant properties of carbon steel, usually cladding process is used. It is a process of depositing a thick layer of corrosion resistant material over carbon steel plate. Most of the engineering applications require high strength and corrosion resistant materials for long-term reliability and performance. By cladding these properties can be achieved with minimum cost. The main problem faced on cladding is the selection of optimum combinations of process parameters for achieving quality clad and hence good clad bead geometry. This paper highlights an experimental study to optimize various input process parameters (welding current, welding speed, gun angle, and contact tip to work distance and pinch to get optimum dilution in stainless steel cladding of low carbon structural steel plates using gas metal arc welding (GMAW. Experiments were conducted based on central composite rotatable design with full replication technique, and mathematical models were developed using multiple regression method. The developed models have been checked for adequacy and significance. In this study, artificial neural network (ANN and genetic algorithm (GA techniques were integrated and labeled as integrated ANN-GA to estimate optimal process parameters in GMAW to get optimum dilution.

  13. Experimental Study and ANN Dual-Time Scale Perturbation Model of Electrokinetic Properties of Microbiota.

    Science.gov (United States)

    Liu, Yong; Munteanu, Cristian R; Fernandez-Lozano, Carlos; Pazos, Alejandro; Ran, Tao; Tan, Zhiliang; Yu, Yizun; Zhou, Chuanshe; Tang, Shaoxun; González-Díaz, Humberto

    2017-01-01

    The electrokinetic properties of the rumen microbiota are involved in cell surface adhesion and microbial metabolism. An in vitro study was carried out in batch culture to determine the effects of three levels of special surface area (SSA) of biomaterials and four levels of surface tension (ST) of culture medium on electrokinetic properties (Zeta potential, ξ; electrokinetic mobility, μe), fermentation parameters (volatile fatty acids, VFAs), and ST over fermentation processes (ST-a, γ). The obtained results were combined with previously published data (digestibility, D; pH; concentration of ammonia nitrogen, c(NH3-N)) to establish a predictive artificial neural network (ANN) model. Concepts of dual-time series analysis, perturbation theory (PT), and Box-Jenkins Operators were applied for the first time to develop an ANN model to predict the variations of the electrokinetic properties of microbiota. The best dual-time series Radial Basis Functions (RBR) model for ξ of rumen microbiota predicted ξ for >30,000 cases with a correlation coefficient >0.8. This model provided insight into the correlations between electrokinetic property (zeta potential) of rumen microbiota and the perturbations of physical factors (specific surface area and surface tension) of media, digestibility of substrate, and their metabolites (NH3-N, VFAs) in relation to environmental factors.

  14. Experimental Study and ANN Dual-Time Scale Perturbation Model of Electrokinetic Properties of Microbiota

    Directory of Open Access Journals (Sweden)

    Yong Liu

    2017-06-01

    Full Text Available The electrokinetic properties of the rumen microbiota are involved in cell surface adhesion and microbial metabolism. An in vitro study was carried out in batch culture to determine the effects of three levels of special surface area (SSA of biomaterials and four levels of surface tension (ST of culture medium on electrokinetic properties (Zeta potential, ξ; electrokinetic mobility, μe, fermentation parameters (volatile fatty acids, VFAs, and ST over fermentation processes (ST-a, γ. The obtained results were combined with previously published data (digestibility, D; pH; concentration of ammonia nitrogen, c(NH3-N to establish a predictive artificial neural network (ANN model. Concepts of dual-time series analysis, perturbation theory (PT, and Box-Jenkins Operators were applied for the first time to develop an ANN model to predict the variations of the electrokinetic properties of microbiota. The best dual-time series Radial Basis Functions (RBR model for ξ of rumen microbiota predicted ξ for >30,000 cases with a correlation coefficient >0.8. This model provided insight into the correlations between electrokinetic property (zeta potential of rumen microbiota and the perturbations of physical factors (specific surface area and surface tension of media, digestibility of substrate, and their metabolites (NH3-N, VFAs in relation to environmental factors.

  15. EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN

    Directory of Open Access Journals (Sweden)

    Ridha Djemal

    2017-01-01

    Full Text Available Autism spectrum disorder (ASD is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new computer aided diagnosis (CAD of autism ‎based on electroencephalography (EEG signal analysis is investigated. The proposed method is based on discrete wavelet transform (DWT, entropy (En, and artificial neural network (ANN. DWT is used to decompose EEG signals into approximation and details coefficients to obtain EEG subbands. The feature vector is constructed by computing Shannon entropy values from each EEG subband. ANN classifies the corresponding EEG signal into normal or autistic based on the extracted features. The experimental results show the effectiveness of the proposed method for assisting autism diagnosis. A receiver operating characteristic (ROC curve metric is used to quantify the performance of the proposed method. The proposed method obtained promising results tested using real dataset provided by King Abdulaziz Hospital, Jeddah, Saudi Arabia.

  16. Tribological behaviour predictions of r-GO reinforced Mg composite using ANN coupled Taguchi approach

    Science.gov (United States)

    Kavimani, V.; Prakash, K. Soorya

    2017-11-01

    This paper deals with the fabrication of reduced graphene oxide (r-GO) reinforced Magnesium Metal Matrix Composite (MMC) through a novel solvent based powder metallurgy route. Investigations over basic and functional properties of developed MMC reveals that addition of r-GO improvises the microhardness upto 64 HV but however decrement in specific wear rate is also notified. Visualization of worn out surfaces through SEM images clearly explains for the occurrence of plastic deformation and the presence of wear debris because of ploughing out action. Taguchi coupled Artificial Neural Network (ANN) technique is adopted to arrive at optimal values of the input parameters such as load, reinforcement weight percentage, sliding distance and sliding velocity and thereby achieve minimal target output value viz. specific wear rate. Influence of any of the input parameter over specific wear rate studied through ANOVA reveals that load acting on pin has a major influence with 38.85% followed by r-GO wt. % of 25.82%. ANN model developed to predict specific wear rate value based on the variation of input parameter facilitates better predictability with R-value of 98.4% when compared with the outcomes of regression model.

  17. Elemental Study on Auscultaiting Diagnosis Support System of Hemodialysis Shunt Stenosis by ANN

    Science.gov (United States)

    Suzuki, Yutaka; Fukasawa, Mizuya; Mori, Takahiro; Sakata, Osamu; Hattori, Asobu; Kato, Takaya

    It is desired to detect stenosis at an early stage to use hemodailysis shunt for longer time. Stethoscope auscultation of vascular murmurs is useful noninvasive diagnostic approach, but an experienced expert operator is necessary. Some experts often say that the high-pitch murmurs exist if the shunt becomes stenosed, and some studies report that there are some features detected at high frequency by time-frequency analysis. However, some of the murmurs are difficult to detect, and the final judgment is difficult. This study proposes a new diagnosis support system to screen stenosis by using vascular murmurs. The system is performed using artificial neural networks (ANN) with the analyzed frequency data by maximum entropy method (MEM). The author recorded vascular murmurs both before percutaneous transluminal angioplasty (PTA) and after. Examining the MEM spectral characteristics of the high-pitch stenosis murmurs, three features could be classified, which covered 85 percent of stenosis vascular murmurs. The features were learnt by the ANN, and judged. As a result, a percentage of judging the classified stenosis murmurs was 100%, and that of normal was 86%.

  18. Spent wash decolourization using nano-Al2O3/kaolin photocatalyst: Taguchi and ANN approach

    Directory of Open Access Journals (Sweden)

    Charles David

    2015-09-01

    Full Text Available The intense colour of the spent wash effluent leads to crucial ecological issue when released untreated into the environment. The decolourization of distillery spent wash effluent is known to be a very challenging task. In this study, the degradation of organic pollutants in the form of colour was performed using nano photocatalyst prepared using aluminium oxide (Al2O3 nanoparticle and kaolin clay. As-synthesized nano-Al2O3/kaolin composites were used as photocatalyst for colour degradation of spent wash effluent. The process parameters such as dosage, pH, temperature and agitation were optimized to attain the maximum decolourization efficiency. The structural and the textural characteristics of the photocatalyst were analysed by X-ray diffraction (XRD, Brunauer–Emmett–Teller (BET surface area analysis, High Resolution Scanning Electron Microscope (HRSEM and Energy Dispersive X-ray (EDAX. Optimization of the process parameters using Taguchi Orthogonal Array (OA design resulted in a maximum of 80% spent wash decolourization. Using Artificial Neural Network (ANN, a two layered feedforward backpropagation model resulted as the best performance and predictive model for spent wash decolourization. The experimental data were found to be in excellent agreement with the predicted results from the ANN model.

  19. Forecasting currency circulation data of Bank Indonesia by using hybrid ARIMAX-ANN model

    Science.gov (United States)

    Prayoga, I. Gede Surya Adi; Suhartono, Rahayu, Santi Puteri

    2017-05-01

    The purpose of this study is to forecast currency inflow and outflow data of Bank Indonesia. Currency circulation in Indonesia is highly influenced by the presence of Eid al-Fitr. One way to forecast the data with Eid al-Fitr effect is using autoregressive integrated moving average with exogenous input (ARIMAX) model. However, ARIMAX is a linear model, which cannot handle nonlinear correlation structures of the data. In the field of forecasting, inaccurate predictions can be considered caused by the existence of nonlinear components that are uncaptured by the model. In this paper, we propose a hybrid model of ARIMAX and artificial neural networks (ANN) that can handle both linear and nonlinear correlation. This method was applied for 46 series of currency inflow and 46 series of currency outflow. The results showed that based on out-of-sample root mean squared error (RMSE), the hybrid models are up to10.26 and 10.65 percent better than ARIMAX for inflow and outflow series, respectively. It means that ANN performs well in modeling nonlinear correlation of the data and can increase the accuracy of linear model.

  20. Use of an Artificial Neural Network to Construct a Model of Predicting Deep Fungal Infection in Lung Cancer Patients.

    Science.gov (United States)

    Chen, Jian; Chen, Jie; Ding, Hong-Yan; Pan, Qin-Shi; Hong, Wan-Dong; Xu, Gang; Yu, Fang-You; Wang, Yu-Min

    2015-01-01

    The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model analysis, retrospective analysis, and so on, but the results are inconsistent. A total of 696 patients with lung cancer were enrolled. The factors were compared employing Student's t-test or the Mann-Whitney test or the Chi-square test and variables that were significantly related to the presence of deep fungal infection selected as candidates for input into the final artificial neural network analysis (ANN) model. The receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. The prevalence of deep fungal infection from lung cancer in this entire study population was 32.04%(223/696), deep fungal infections occur in sputum specimens 44.05% (200/454). The ratio of candida albicans was 86.99% (194/223) in the total fungi. It was demonstrated that older (≥65 years), use of antibiotics, low serum albumin concentrations (≤37.18 g /L), radiotherapy, surgery, low hemoglobin hyperlipidemia (≤93.67 g /L), long time of hospitalization (≥14 days) were apt to deep fungal infection and the ANN model consisted of the seven factors. The AUC of ANN model (0.829±0.019) was higher than that of LR model (0.756±0.021). The artificial neural network model with variables consisting of age, use of antibiotics, serum albumin concentrations, received radiotherapy, received surgery, hemoglobin, time of hospitalization should be useful for predicting the deep fungal infection in lung cancer.