Search results for: outer definable subset.

Authors: Sen Yung Lee, Chih Cheng Huang, Te Wen Tu

Abstract:

A solution methodology without using integral transformation is proposed to develop analytical solutions for transient heat conduction in nonuniform hollow cylinders with time-dependent boundary condition at the outer surface. It is shown that if the thermal conductivity and the specific heat of the medium are in arbitrary polynomial function forms, the closed solutions of the system can be developed. The influence of physical properties on the temperature distribution of the system is studied. A numerical example is given to illustrate the efficiency and the accuracy of the solution methodology.

Keywords: Analytical solution, nonuniform hollow cylinder, time-dependent boundary condition, transient heat conduction.

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Authors: Meenakshi Bansal, Iqbal Singh, Parvinder S. Sandhu

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In over deployed sensor networks, one approach to Conserve energy is to keep only a small subset of sensors active at Any instant. For the coverage problems, the monitoring area in a set of points that require sensing, called demand points, and consider that the node coverage area is a circle of range R, where R is the sensing range, If the Distance between a demand point and a sensor node is less than R, the node is able to cover this point. We consider a wireless sensor network consisting of a set of sensors deployed randomly. A point in the monitored area is covered if it is within the sensing range of a sensor. In some applications, when the network is sufficiently dense, area coverage can be approximated by guaranteeing point coverage. In this case, all the points of wireless devices could be used to represent the whole area, and the working sensors are supposed to cover all the sensors. We also introduce Hybrid Algorithm and challenges related to coverage in sensor networks.

Keywords: Wireless sensor networks, network coverage, Energy conservation, Hybrid Algorithms.

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Authors: J. R. Wang, H. C. Chen, C. Shih, S. W. Chen, J. H. Yang, Y. Chiang

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In this study, we focus on the establishment of the analysis model for Maanshan PWR nuclear power plant (NPP) by using RADTRAD and SNAP codes with the FSAR, manuals, and other data. In order to evaluate the cumulative dose at the Exclusion Area Boundary (EAB) and Low Population Zone (LPZ) outer boundary, Maanshan NPP RADTRAD/SNAP model was used to perform the analysis of the DBA LOCA case. The analysis results of RADTRAD were similar to FSAR data. These analysis results were lower than the failure criteria of 10 CFR 100.11 (a total radiation dose to the whole body, 250 mSv; a total radiation dose to the thyroid from iodine exposure, 3000 mSv).

Keywords: RADionuclide, transport, removal, and dose estimation, RADTRAD, symbolic nuclear analysis package, SNAP, dose, PWR.

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Authors: Barzin Gavtash, Khalid Hussain, Mohammad Layeghi, Saeed Sadeghi Lafmejani

Abstract:

This research aims at modeling and simulating the effects of nanofluids on cylindrical heat pipes thermal performance using the ANSYS-FLUENT CFD commercial software. The heat pipe outer wall temperature distribution, thermal resistance, liquid pressure and axial velocity in presence of suspended nano-scaled solid particle (i.e. Cu, Al2O3 and TiO2) within the fluid (water) were investigated. The effect of particle concentration and size were explored and it is concluded that the thermal performance of the heat pipe is improved when using nanofluid as the system working fluid. Additionally, it was observed that the thermal resistance of the heat pipe drops as the particle concentration level increases and particle radius decreases.

Keywords: CFD, Heat Pipe, Nanofluid, Thermal resistance

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Authors: Kamil Aida-zade, C. Ardil

Abstract:

We study the problem of synthesis of lumped sources control for the objects with distributed parameters on the basis of continuous observation of phase state at given points of object. In the proposed approach the phase state space (phase space) is beforehand somehow partitioned at observable points into given subsets (zones). The synthesizing control actions therewith are taken from the class of piecewise constant functions. The current values of control actions are determined by the subset of phase space that contains the aggregate of current states of object at the observable points (in these states control actions take constant values). In the paper such synthesized control actions are called zone control actions. A technique to obtain optimal values of zone control actions with the use of smooth optimization methods is given. With this aim, the formulas of objective functional gradient in the space of zone control actions are obtained.

Keywords: Feedback control, distributed systems, smooth optimization methods, lumped control synthesis.

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Authors: N. W. U. D. Chathurani, Shlomo Geva, Vinod Chandran, Proboda Rajapaksha

Abstract:

Selecting an appropriate image representation is the most important factor in implementing an effective Content-Based Image Retrieval (CBIR) system. This paper presents a multi-feature fusion approach for efficient CBIR, based on the distance distribution of features and relative feature weights at the time of query processing. It is a simple yet effective approach, which is free from the effect of features' dimensions, ranges, internal feature normalization and the distance measure. This approach can easily be adopted in any feature combination to improve retrieval quality. The proposed approach is empirically evaluated using two benchmark datasets for image classification (a subset of the Corel dataset and Oliva and Torralba) and compared with existing approaches. The performance of the proposed approach is confirmed with the significantly improved performance in comparison with the independently evaluated baseline of the previously proposed feature fusion approaches.

Keywords: Feature fusion, image retrieval, membership function, normalization.

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Authors: N. P. Tsephe, S. D. Eyono Obono

Abstract:

Rural tourism has many economical, environmental, and socio-cultural benefits. However, the development of rural tourism compared to urban tourism is also faced with several challenges added to the disadvantages of rural tourism. The aim of this study is to design a model of the factors affecting the motivations of rural tourists, in an attempt to improve the understanding of rural tourism motivation for the development of that form of tourism. The proposed model is based on a sound theoretical framework. It was designed following a literature review of tourism motivation theoretical frameworks and of rural tourism motivation factors. The tourism motivation theoretical framework that fitted to the best all rural tourism motivation factors was then chosen as the basis for the proposed model. This study hence found that the push and pull tourism motivation framework and the inner and outer directed values theory are the most adequate theoretical frameworks for the modeling of rural tourism motivation.

Keywords: Motivation factors, rural tourism, tourism motivation theories.

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Authors: Lu Si, Jie Yu, Shasha Li, Jun Ma, Lei Luo, Qingbo Wu, Yongqi Ma, Zhengji Liu

Abstract:

Instance selection (IS) technique is used to reduce the data size to improve the performance of data mining methods. Recently, to process very large data set, several proposed methods divide the training set into some disjoint subsets and apply IS algorithms independently to each subset. In this paper, we analyze the limitation of these methods and give our viewpoint about how to divide and conquer in IS procedure. Then, based on fast condensed nearest neighbor (FCNN) rule, we propose a large data sets instance selection method with MapReduce framework. Besides ensuring the prediction accuracy and reduction rate, it has two desirable properties: First, it reduces the work load in the aggregation node; Second and most important, it produces the same result with the sequential version, which other parallel methods cannot achieve. We evaluate the performance of FCNN-MR on one small data set and two large data sets. The experimental results show that it is effective and practical.

Keywords: Instance selection, data reduction, MapReduce, kNN.

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Authors: Neil Morgan

Abstract:

The notion of Threshold Concepts has offered a fertile new perspective on the transformative effects of mastery of particular concepts on student understanding of subject matter and their developing identities as inductees into disciplinary discourse communities. Only by successfully traversing essential knowledge thresholds can neophytes achieve the more sophisticated understandings of subject matter possessed by mature members of a discipline. This paper uses thematic analysis of disciplinary guiding principles to identify nine candidate Threshold Concepts that appear to underpin effective TESOL practice. The relationship between these candidate TESOL Threshold Concepts, TESOL principles, and TESOL instructional techniques appears to be amenable to a schematic representation based on superordinate categories of TESOL practitioner concern and, as such, offers an alternative to the view of Threshold Concepts as a privileged subset of disciplinary core concepts. The paper concludes by exploring the potential of a Threshold Concepts framework to productively inform TESOL initial teacher education (ITE) and in-service education and training (INSET).

Keywords: TESOL, threshold concepts, TESOL principles, TESOL ITE/INSET, community of practice.

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Authors: Othmane Fahim, Naoual Belouaggadia. Charifa David, Mohamed Ezzine

Abstract:

To make better use of cooling resources, systems have been derived on the basis of the use of night radiator systems for heat pumping. Using the TRNSYS tool we determined the influence of the climatic characteristics of the two zones in Morocco on the temperature of the outer surface of a Photovoltaic Thermal Panel “PVT” made of aluminum. The proposal to improve the performance of the panel allowed us to have little heat absorption during the day and give the same performance of a panel made of aluminum at night. The variation in the granite-based panel temperature recorded a deviation from the other materials of 0.5 °C, 2.5 °C on the first day respectively in Marrakech and Casablanca, and 0.2 °C and 3.2 °C on the second night. Power varied between 110.16 and 32.01 W/m² marked in Marrakech, to be the most suitable area to practice night cooling by night radiation.

Keywords: Morocco, TRANSYS, radiative cooling.

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Authors: Fl. Loury, P. Sablonière, C. Lamoureux, G. Magnier, Th. Gutierrez

Abstract:

A method is proposed to extract faithful representative patterns from data set of observations when they are suffering from non-negligible fluctuations. Supposing time interval between measurements to be extremely small compared to observation time, it consists in defining first a subset of intermediate time intervals characterizing coherent behavior. Data projection on these intervals gives a set of curves out of which an ideally “perfect” one is constructed by taking the sup limit of them. Then comparison with average real curve in corresponding interval gives an efficiency parameter expressing the degradation consecutive to fluctuation effect. The method is applied to sunlight data collected in a specific place, where ideal sunlight is the one resulting from direct exposure at location latitude over the year, and efficiency is resulting from action of meteorological parameters, mainly cloudiness, at different periods of the year. The extracted information already gives interesting element of decision, before being used for analysis of plant control.

Keywords: Base Input Reconstruction, Data Mining, Efficiency Factor, Information Pattern Operator.

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Authors: Anastasia K. Eleftheriadou, Athanasios Sfetsos, Nikolaos Gounaris

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The current paper presents an extensive bottom-up framework for assessing building sector-specific vulnerability to climate change: energy supply and demand. The research focuses on the application of downscaled seasonal models for estimating energy performance of buildings in Greece. The ARW-WRF model has been set-up and suitably parameterized to produce downscaled climatological fields for Greece, forced by the output of the CFSv2 model. The outer domain, D01/Europe, included 345 x 345 cells of horizontal resolution 20 x 20 km2 and the inner domain, D02/Greece, comprised 180 x 180 cells of 5 x 5 km2 horizontal resolution. The model run has been setup for a period with a forecast horizon of 6 months, storing outputs on a six hourly basis.

Keywords: Urban environment, vulnerability, climate change, energy performance, seasonal forecast models.

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Authors: Zicheng Wang

Abstract:

Offshore wind energy provides a huge potential for the expansion of renewable energies to the coastal countries. High demands are required concerning the shape and type of foundations for offshore wind turbines (OWTs) to find an economically, technically and environmentally-friendly optimal solution. A promising foundation concept is the hybrid foundation system, which consists of a steel plate attached to the outer side of a hollow steel pipe pile. In this study, the bearing behavior of a large diameter foundation is analyzed using a 3-dimensional finite element (FE) model. Non-linear plastic soil behavior is considered. The results of the numerical simulations are compared to highlight the priority of the hybrid foundation to the conventional monopile foundation.

Keywords: Hybrid foundation system, mechanical parameters, plastic soil behaviors, numerical simulations.

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Authors: S. Dorbani, F. Kharchi, F. Salem, K. Arroudj, N. Chioukh

Abstract:

The need for thermal comfort of buildings, with the aim of saving energy, has always generated a big interest during the development of methods, to improve the mode of construction. In the present paper, which is concerned by the valorization of locally abundant materials, mixtures of plaster and dune sand have been studied. To point out the thermal performances of these mixtures, a comparative study has been established between this product and the two materials most commonly used in construction, the concrete and hollow brick. The results showed that optimal mixture is made with 1/3 plaster and 2/3 dune sand. This mortar achieved significant increases in the mechanical strengths, which allow it to be used as a carrier element for buildings, of up to two levels. The element obtained offers an acceptable thermal insulation, with a decrease the outer-wall construction thickness.

Keywords: Local materials, mortar, plaster, dune sand, compaction, mechanical performance, thermal performance.

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Authors: Yusuf Yasa, Erkan Mese

Abstract:

This paper investigates the thermal issue of permanent magnet synchronous generator which is frequently used in direct drive gearless small-scale wind turbine applications. Permanent Magnet Synchronous Generator (PMSG) is designed with 2.5 kW continuous and 6 kW peak power. Then considering generator geometry, mechanical design of wind turbine is performed. Thermal analysis and optimization is carried out considering all wind turbine components to reach realistic results. This issue is extremely important in research and development (R&D) process for wind turbine applications.

Keywords: Direct drive, gearless wind turbine, permanent magnet synchronous generator (PMSG), small-scale wind turbine, thermal management.

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Authors: Romisaa Ali

Abstract:

The Policy Gradient approach is a subset of the Deep Reinforcement Learning (DRL) combines Deep Neural Networks (DNN) with Reinforcement Learning (RL). This approach finds the optimal policy of robot movement, based on the experience it gains from interaction with its environment. Unlike previous policy gradient algorithms, which were unable to handle the two types of error variance and bias introduced by the DNN model due to over- or underestimation, this algorithm is capable of handling both types of error variance and bias. This article will discuss the state-of-the-art SOTA policy gradient technique, trust region policy optimization (TRPO), by applying this method in various environments compared to another policy gradient method, the Proximal Policy Optimization (PPO), to explain their robust optimization, using this SOTA to gather experience data during various training phases after observing the impact of hyper-parameters on neural network performance.

Keywords: Deep neural networks, deep reinforcement learning, Proximal Policy Optimization, state-of-the-art, trust region policy optimization.

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Authors: Stephan Rupp, Matthias Elter, Michael Breitung, Walter Zink, Christian Küblbeck

Abstract:

Camera calibration is an indispensable step for augmented reality or image guided applications where quantitative information should be derived from the images. Usually, a camera calibration is obtained by taking images of a special calibration object and extracting the image coordinates of projected calibration marks enabling the calculation of the projection from the 3d world coordinates to the 2d image coordinates. Thus such a procedure exhibits typical steps, including feature point localization in the acquired images, camera model fitting, correction of distortion introduced by the optics and finally an optimization of the model-s parameters. In this paper we propose to extend this list by further step concerning the identification of the optimal subset of images yielding the smallest overall calibration error. For this, we present a Monte Carlo based algorithm along with a deterministic extension that automatically determines the images yielding an optimal calibration. Finally, we present results proving that the calibration can be significantly improved by automated image selection.

Keywords: Camera Calibration, Discrete Optimization, Monte Carlo Method.

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Authors: Tessy Badriyah, Jim S. Briggs, Dave R. Prytherch

Abstract:

It is well known that Logistic Regression is the gold standard method for predicting clinical outcome, especially predicting risk of mortality. In this paper, the Decision Tree method has been proposed to solve specific problems that commonly use Logistic Regression as a solution. The Biochemistry and Haematology Outcome Model (BHOM) dataset obtained from Portsmouth NHS Hospital from 1 January to 31 December 2001 was divided into four subsets. One subset of training data was used to generate a model, and the model obtained was then applied to three testing datasets. The performance of each model from both methods was then compared using calibration (the χ2 test or chi-test) and discrimination (area under ROC curve or c-index). The experiment presented that both methods have reasonable results in the case of the c-index. However, in some cases the calibration value (χ2) obtained quite a high result. After conducting experiments and investigating the advantages and disadvantages of each method, we can conclude that Decision Trees can be seen as a worthy alternative to Logistic Regression in the area of Data Mining.

Keywords: Decision Trees, Logistic Regression, clinical outcome, risk of mortality.

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Authors: Irina Spaka, Rita Birkenfelde, Svetlana Kozireva, Jevgenija Osmjana, Madara Upmane, ElenaKashuba, Irina Kholodnyuk Holodnuka

Abstract:

Epstein-Barr virus (EBV) is implicated in the pathogenesis of the endemic Burkitt-s lymphoma (BL). The EBVpositive BL-derived cell lines initially maintain the original tumor phenotype of EBV infection (latency I, LatI), but most of them drift toward a lymphoblast phenotype of EBV latency III (LatIII) during in vitro culturing. The aim of the present work was to characterize the B-cell subsets in EBV-positive BL cell lines and to verify whether a particular cell subset correlates with the type of EBV infection. The phenotype analysis of two EBV-negative and eleven EBV-positive (three of LatI and eight of LatIII) BL cell lines was performed by polychromatic flow cytomery, based on expression pattern of CD19, CD10, CD38, CD27, and CD5 markers. Two cell subsets, CD19+CD10+ and CD19+CD10-, were defined in LatIII BL cell lines. In both subsets, the CD27 and CD5 cell surface expression was detected in a proportion of the cells.

Keywords: B-cell subsets, Burkitt's lymphoma cell lines, EBV latency, phenotype profiles.

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Authors: Lambros V. Skarlas, Grigorios N. Beligiannis, Efstratios F. Georgopoulos, Adam V. Adamopoulos

Abstract:

The aim of this contribution is to present a new approach in modeling the electrical activity of the human heart. A recurrent artificial neural network is being used in order to exhibit a subset of the dynamics of the electrical behavior of the human heart. The proposed model can also be used, when integrated, as a diagnostic tool of the human heart system. What makes this approach unique is the fact that every model is being developed from physiological measurements of an individual. This kind of approach is very difficult to apply successfully in many modeling problems, because of the complexity and entropy of the free variables describing the complex system. Differences between the modeled variables and the variables of an individual, measured at specific moments, can be used for diagnostic purposes. The sensor fusion used in order to optimize the utilization of biomedical sensors is another point that this paper focuses on. Sensor fusion has been known for its advantages in applications such as control and diagnostics of mechanical and chemical processes.

Keywords: Artificial Neural Networks, Diagnostic System, Health Condition Modeling Tool, Heart Diagnostics Model, Heart Electricity Model.

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Authors: Hyunchul Ahn, William X. S. Wong

Abstract:

Corporate credit rating prediction is one of the most important topics, which has been studied by researchers in the last decade. Over the last decade, researchers are pushing the limit to enhance the exactness of the corporate credit rating prediction model by applying several data-driven tools including statistical and artificial intelligence methods. Among them, multiclass support vector machine (MSVM) has been widely applied due to its good predictability. However, heuristics, for example, parameters of a kernel function, appropriate feature and instance subset, has become the main reason for the critics on MSVM, as they have dictate the MSVM architectural variables. This study presents a hybrid MSVM model that is intended to optimize all the parameter such as feature selection, instance selection, and kernel parameter. Our model adopts genetic algorithm (GA) to simultaneously optimize multiple heterogeneous design factors of MSVM.

Keywords: Corporate credit rating prediction, feature selection, genetic algorithms, instance selection, multiclass support vector machines.

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Authors: Aimilia P. Doukeli, Athanasios S. Lioumpas, George K. Karagiannidis, Panayiotis V. Frangos, P. Takis Mathiopoulos

Abstract:

In diversity rich environments, such as in Ultra- Wideband (UWB) applications, the a priori determination of the number of strong diversity branches is difficult, because of the considerably large number of diversity paths, which are characterized by a variety of power delay profiles (PDPs). Several Rake implementations have been proposed in the past, in order to reduce the number of the estimated and combined paths. To this aim, we introduce two adaptive Rake receivers, which combine a subset of the resolvable paths considering simultaneously the quality of both the total combining output signal-to-noise ratio (SNR) and the individual SNR of each path. These schemes achieve better adaptation to channel conditions compared to other known receivers, without further increasing the complexity. Their performance is evaluated in different practical UWB channels, whose models are based on extensive propagation measurements. The proposed receivers compromise between the power consumption, complexity and performance gain for the additional paths, resulting in important savings in power and computational resources.

Keywords: Adaptive Rake receivers, diversity techniques, fading channels, UWB channel.

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Authors: Chingtham Tejbanta Singh, Shivashankar B. Nair

Abstract:

This paper explores an application of an adaptive learning mechanism for robots based on the natural immune system. Most of the research carried out so far are based either on the innate or adaptive characteristics of the immune system, we present a combination of these to achieve behavior arbitration wherein a robot learns to detect vulnerable areas of a track and adapts to the required speed over such portions. The test bed comprises of two Lego robots deployed simultaneously on two predefined near concentric tracks with the outer robot capable of helping the inner one when it misaligns. The helper robot works in a damage-control mode by realigning itself to guide the other robot back onto its track. The panic-stricken robot records the conditions under which it was misaligned and learns to detect and adapt under similar conditions thereby making the overall system immune to such failures.

Keywords: Adaptive, AIS, Behavior Arbitration, ClonalSelection, Immune System, Innate, Robot, Self Healing.

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Authors: M. Michael Gromiha, Y. Yabuki, K. Imai, P. Horton, K. Fukui

Abstract:

We have developed a database for membrane protein functions, which has more than 3000 experimental data on functionally important amino acid residues in membrane proteins along with sequence, structure and literature information. Further, we have proposed different methods for identifying membrane proteins based on their functions: (i) discrimination of membrane transport proteins from other globular and membrane proteins and classifying them into channels/pores, electrochemical and active transporters, and (ii) β-signal for the insertion of mitochondrial β-barrel outer membrane proteins and potential targets. Our method showed an accuracy of 82% in discriminating transport proteins and 68% to classify them into three different transporters. In addition, we have identified a motif for targeting β-signal and potential candidates for mitochondrial β-barrel membrane proteins. Our methods can be used as effective tools for genome-wide annotations.

Keywords: Membrane proteins, database, transporters, discrimination, β-signal.

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Authors: Mattheos K. Protopapas, Elias B. Kosmatopoulos

Abstract:

An iterative algorithm is proposed and tested in Cournot Game models, which is based on the convergence of sequential best responses and the utilization of a genetic algorithm for determining each player-s best response to a given strategy profile of its opponents. An extra outer loop is used, to address the problem of finite accuracy, which is inherent in genetic algorithms, since the set of feasible values in such an algorithm is finite. The algorithm is tested in five Cournot models, three of which have convergent best replies sequence, one with divergent sequential best replies and one with “local NE traps"[14], where classical local search algorithms fail to identify the Nash Equilibrium. After a series of simulations, we conclude that the algorithm proposed converges to the Nash Equilibrium, with any level of accuracy needed, in all but the case where the sequential best replies process diverges.

Keywords: Best response, Cournot oligopoly, genetic algorithms, Nash equilibrium.

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Authors: Fatemeh Valizadeh Gamchi, Edward L. Boone

Abstract:

This research focuses on Lyme disease, a widespread infectious condition in the United States caused by the bacterium Borrelia burgdorferi sensu stricto. It is critical to identify environmental and economic elements that are contributing to the spread of the disease. This study examined data from Virginia to identify a subset of explanatory variables significant for Lyme disease case numbers. To identify relevant variables and avoid overfitting, linear poisson, and regularization regression methods such as ridge, lasso, and elastic net penalty were employed. Cross-validation was performed to acquire tuning parameters. The methods proposed can automatically identify relevant disease count covariates. The efficacy of the techniques was assessed using four criteria on three simulated datasets. Finally, using the Virginia Department of Health’s Lyme disease dataset, the study successfully identified key factors, and the results were consistent with previous studies.

Keywords: Lyme disease, Poisson generalized linear model, Ridge regression, Lasso Regression, elastic net regression.

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Authors: Rameswar Debnath, Haruhisa Takahashi

Abstract:

An evolutionary method whose selection and recombination operations are based on generalization error-bounds of support vector machine (SVM) can select a subset of potentially informative genes for SVM classifier very efficiently [7]. In this paper, we will use the derivative of error-bound (first-order criteria) to select and recombine gene features in the evolutionary process, and compare the performance of the derivative of error-bound with the error-bound itself (zero-order) in the evolutionary process. We also investigate several error-bounds and their derivatives to compare the performance, and find the best criteria for gene selection and classification. We use 7 cancer-related human gene expression datasets to evaluate the performance of the zero-order and first-order criteria of error-bounds. Though both criteria have the same strategy in theoretically, experimental results demonstrate the best criterion for microarray gene expression data.

Keywords: support vector machine, generalization error-bound, feature selection, evolutionary algorithm, microarray data

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Authors: H. Sezgin, O. B. Berkalp, R. Mishra, J. Militky

Abstract:

In the last few decades, due to their advanced properties, there has been an increasing interest in hybrid composite materials. In this study, the effect of different stacking sequences of jute and carbon fabric plies on dynamic mechanical properties of composite laminates were investigated. Vacuum bagging system was used to fabricate the composite samples. Each composite laminate was reinforced with two plies of jute fabric and two plies of carbon fabric by varying the position of layers. Dynamic mechanical analyzer (DMA) was used to examine the dynamic mechanical properties of composite laminates with increasing temperature. Results showed that the composite sample, which has carbon fabric at the outer layers, has the highest storage and loss modulus. Besides, it was observed that glass transition temperature (T_g) of samples are close to each other and at about 75 °C.

Keywords: Differential scanning calorimetry dynamic mechanical analysis, textile reinforced composites, thermogravimetric analysis.

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Authors: Matta S. N. S. Kiran, Manikantadhar Maheswaram, Akshat Upadhyay, Rohit Mishra, Bhagat Singh

Abstract:

A bearing is a very common and useful component of mechanical systems in order to transfer power from one end to another. Therefore, to ensure the accountability and robustness of the rotating mechanical systems, the bearing part's health condition must be checked at regular intervals, also known as preventive maintenance. This condition may lead to unnecessary higher maintenance costs and later result in higher production costs. These costs can be minimized by diagnosing the faulty bearing in its incipient stage. This paper describes an approach to detect rolling bearing defects based on Empirical Mode Decomposition. The novelty of the proposed methodology is validated experimentally using Case Western Reserve University bearing's data sets. The selected data sets comprise the two vibration signals, i.e., inner race and outer, for healthy and faulty conditions.

Keywords: Ball bearing, denoising, signal processing, statistical indicators.

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Authors: Maryam Hsaini, Mostafa Nouri-Baygi

Abstract:

A critical problem in wireless sensor networks is limited battery and memory of nodes. Therefore, each node in the network could maintain only a subset of its neighbors to communicate with. This will increase the battery usage in the network because each packet should take more hops to reach its destination. In order to tackle these problems, spanner graphs are defined. Since each node has a small degree in a spanner graph and the distance in the graph is not much greater than its actual geographical distance, spanner graphs are suitable candidates to be used for the topology of a wireless sensor network. In this paper, we study Yao graphs and their behavior for a randomly selected set of points. We generate several random point sets and compare the properties of their Yao graphs with the complete graph. Based on our data sets, we obtain several charts demonstrating how Yao graphs behave for a set of randomly chosen point set. As the results show, the stretch factor of a Yao graph follows a normal distribution. Furthermore, the stretch factor is in average far less than the worst case stretch factor proved for Yao graphs in previous results. Furthermore, we use Yao graph for a realistic point set and study its stretch factor in real world.

Keywords: Wireless sensor network, spanner graph, Yao Graph.

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