Search results for: small cell networks.

Authors: Alaa Al Hawari, Mohammad Khader, Tarek Zayed, Osama Moselhi

Abstract:

Ground Penetrating Radar (GPR) is one of the most effective electromagnetic techniques for non-destructive non-invasive subsurface features investigation. Water leak from pipelines is the most common undesirable reason of potable water losses. Rapid detection of such losses is going to enhance the use of the Water Distribution Networks (WDN) and decrease threatens associated with water mains leaks. In this study, GPR approach was developed to detect leaks by implementing an appropriate imaging analyzing strategy based on image refinement, reflection polarity and reflection amplitude that would ease the process of interpreting the collected raw radargram image.

Keywords: Water Networks, Leakage, Water pipelines, Ground Penetrating Radar.

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Authors: Aaminah Hassan

Abstract:

Advertisements carry a great potential to influence our lives because they are crafted to meet particular ends. Stereotypical representation in advertisements is capable of forming unconscious attitudes among people towards any gender and their abilities. This study focuses on gender representation in Pakistani prime time advertisements. For this purpose, 13 advertisements were selected from three different categories of foods and beverages, cosmetics, cell phones and cellular networks from the prime time slots of one of the leading Pakistani entertainment channel, ‘Urdu 1’. Both quantitative and qualitative analyses are carried out for range of variables like gender, age, roles, activities, setting, appearance and voice overs. The results revealed that gender representation in advertisements is stereotypical. Moreover, in few instances, the portrayal of women is not only culturally inappropriate but is demeaning to the image of women as well. Their bodily charm is used to promote products. Comparing different entertainment channels for their prime time advertisements and broadening the scope of this research will yield greater implications for the researchers who want to carry out the similar research. It is hoped that the current study would help in the promotion of media literacy among the viewers and media authorities in Pakistan.

Keywords: Advertisements, content analysis, gender, prime time.

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Authors: Fayez Mostafa Alhamoui, Adnan Hadi Mahdi Al- Helali

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The main issue in designing a wireless sensor network (WSN) is the finding of a proper routing protocol that complies with the several requirements of high reliability, short latency, scalability, low power consumption, and many others. This paper proposes a novel routing algorithm that complies with these design requirements. The new routing protocol divides the WSN into several subnetworks and each sub-network is divided into several clusters. This division is designed to reduce the number of radio transmission and hence decreases the power consumption. The network division may be changed dynamically to adapt with the network changes and allows the realization of the design requirements.

Keywords: Wireless sensor networks, routing protocols, ad hoc topology, cluster, sub-network, WSN design requirements.

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Authors: Na Li, Samuel Simon Araya, Søren Knudsen Kær

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This study investigates the effects of operating parameters of different current density, temperature and pressure on the performance of a proton exchange membrane (PEM) water electrolysis stack. A 7-cell PEM water electrolysis stack was assembled and tested under different operation modules. The voltage change and polarization curves under different test conditions, namely current density, temperature and pressure, were recorded. Results show that higher temperature has positive effect on overall stack performance, where temperature of 80 ℃ improved the cell performance greatly. However, the cathode pressure and current density has little effect on stack performance.

Keywords: PEM electrolysis stack, current density, temperature, pressure.

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Authors: Xingyuan Qu, Shouming Zhong

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In this paper, the problem of stability criteria of neural networks (NNs) with two-additive time-varying delay compenents is investigated. The relationship between the time-varying delay and its lower and upper bounds is taken into account when estimating the upper bound of the derivative of Lyapunov functional. As a result, some improved delay stability criteria for NNs with two-additive time-varying delay components are proposed. Finally, a numerical example is given to illustrate the effectiveness of the proposed method.

Keywords: Delay-dependent stability, time-varying delays, Lyapunov functional, linear matrix inequality (LMI).

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Authors: J. Junk, M. Eickermann

Abstract:

The Small Hive Beetle, Aethina tumida (Coleoptera: Nitidulidae) is a parasite for honey bee colonies, Apis mellifera, and was recently introduced to the European continent, accidentally. Based on the literature, a model was developed by using regional meteorological variables (daily values of minimum, maximum and mean air temperature as well as mean soil temperature at 50 mm depth) to calculate the time-point of hive invasion by A. tumida in springtime, the development duration of pupae as well as the number of generations of A. tumida per year. Luxembourg was used as a test region for our model for 2005 to 2013. The model output indicates a successful surviving of the Small Hive Beetle in Luxembourg with two up to three generations per year. Additionally, based on our meteorological data sets a first migration of SHB to apiaries can be expected from mid of March up to April. Our approach can be transferred easily to other countries to estimate the risk potential for a successful introduction and spreading of A. tumida in Western Europe.

Keywords: Aethina tumida, air temperature, larval development, soil temperature.

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Authors: Daiming Wang

Abstract:

In this paper, by using Mawhin-s continuation theorem of coincidence degree and a method based on delay differential inequality, some sufficient conditions are obtained for the existence and global exponential stability of periodic solutions of cellular neural networks with distributed delays and impulses on time scales. The results of this paper generalized previously known results.

Keywords: Periodic solutions, global exponential stability, coincidence degree, M-matrix.

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Authors: Abdesselem Dakhli, Wajdi Bellil, Chokri Ben Amar

Abstract:

DNA Barcode provides good sources of needed information to classify living species. The classification problem has to be supported with reliable methods and algorithms. To analyze species regions or entire genomes, it becomes necessary to use the similarity sequence methods. A large set of sequences can be simultaneously compared using Multiple Sequence Alignment which is known to be NP-complete. However, all the used methods are still computationally very expensive and require significant computational infrastructure. Our goal is to build predictive models that are highly accurate and interpretable. In fact, our method permits to avoid the complex problem of form and structure in different classes of organisms. The empirical data and their classification performances are compared with other methods. Evenly, in this study, we present our system which is consisted of three phases. The first one, is called transformation, is composed of three sub steps; Electron-Ion Interaction Pseudopotential (EIIP) for the codification of DNA Barcodes, Fourier Transform and Power Spectrum Signal Processing. Moreover, the second phase step is an approximation; it is empowered by the use of Multi Library Wavelet Neural Networks (MLWNN). Finally, the third one, is called the classification of DNA Barcodes, is realized by applying the algorithm of hierarchical classification.

Keywords: DNA Barcode, Electron-Ion Interaction Pseudopotential, Multi Library Wavelet Neural Networks.

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Authors: Laurent Enel, Franck Guillem

Abstract:

This paper considers the benefits gained by using an efficient quality of service management such as DiffServ technique to improve the performance of military communications. Low delay and no blockage must be achieved especially for real time tactical data. All traffic flows generated by different applications do not need same bandwidth, same latency, same error ratio and this scalable technique of packet management based on priority levels is analysed. End to end architectures supporting various traffic flows and including lowbandwidth and high-delay HF or SHF military links as well as unprotected Internet sub domains are studied. A tuning of Diffserv parameters is proposed in accordance with different loads of various traffic and different operational situations.

Keywords: Military data networks, Quality of service, Tacticalsystems.

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Authors: Ali Dehghani, Mojtaba Hakimzadeh, Amir Habibi, Navid Mehdizadeh Afroozi

Abstract:

In this paper an isolated wind-diesel hybrid power system has been considered for reactive power control study having an induction generator for wind power conversion and synchronous alternator with automatic voltage regulator (AVR) for diesel unit is presented. The dynamic voltage stability evaluation is dependent on small signal analysis considering a Static VAR Compensator (SVC) and IEEE type -I excitation system. It's shown that the variable reactive power source like SVC is crucial to meet the varying demand of reactive power by induction generator and load and to acquire an excellent voltage regulation of the system with minimum fluctuations. Integral square error (ISE) criterion can be used to evaluate the optimum setting of gain parameters. Finally the dynamic responses of the power systems considered with optimum gain setting will also be presented.

Keywords: SVC, Small Signal Stability, Reactive Power, Control, Hybrid System.

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Authors: A. Shishkin, A. Korjakins, V. Mironovs

Abstract:

Present paper describes method of obtaining clay ceramic foam (CCF) and foam concrete (FC), by direct foaming with high speed mixer-disperser (HSMD). Three foaming agents (FA) are compared for the FC and CCF production: SCHÄUMUNGSMITTEL W 53 FLÜSSIG (Zschimmer & Schwarz Gmbh, Germany), SCF- 1245 (Sika, test sample, Latvia) and FAB-12 (Elade, Latvija). CCF were obtained at 950, 1000°C, 1150°C and 1150°C firing temperature and have mechanical compressive strength 1.2, 2.55 and 4.3 MPa and porosity 79.4, 75.1, 71.6%, respectively. Obtained FC has 6-14 MPa compressive strength and porosity 44-55%. The goal of this work was development of a sustainable and durable ceramic cellular structures using HSMD.

Keywords: Ceramic foam, foam concrete, clay foam, open cell, close cell, direct foaming.

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Authors: Yan Yu, Wang Yu, Chen Xintong, Liu Yi, Zhang Yanzhong, Wang Yanji, Chen Xingyu, Zhang Miaocheng, Tong Yi

Abstract:

Neuromorphic computing based on spiking neural networks (SNNs) has emerged as a promising avenue for building the next generation of intelligent computing systems. Owing to their high-density integration, low power, and outstanding nonlinearity, memristors have attracted emerging attention on achieving SNNs. However, fabricating a low-power and robust memristor-based spiking neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a TiO2-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, used to realize single layer fully connected (FC) SNNs. Moreover, our TiO2-based resistive switching (RS) memristors realize spiking-time-dependent-plasticity (STDP), originating from the Ag diffusion-based filamentary mechanism. This work demonstrates that TiO2-based memristors may provide an efficient method to construct hardware neuromorphic computing systems.

Keywords: Leaky integrate-and-fire, memristor, spiking neural networks, spiking-time-dependent-plasticity.

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Authors: A. Abid, F. M. Abbou, H. T. Ewe

Abstract:

Optical Bursts Switching (OBS) is a relatively new optical switching paradigm. Contention and burst loss in OBS networks are major concerns. To resolve contentions, an interesting alternative to discarding the entire data burst is to partially drop the burst. Partial burst dropping is based on burst segmentation concept that its implementation is constrained by some technical challenges, besides the complexity added to the algorithms and protocols on both edge and core nodes. In this paper, the burst segmentation concept is investigated, and an implementation scheme is proposed and evaluated. An appropriate dropping policy that effectively manages the size of the segmented data bursts is presented. The dropping policy is further supported by a new control packet format that provides constant transmission overhead.

Keywords: Burst length, Burst Segmentation, Optical BurstSwitching.

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Authors: M. Kakavand, S. A. Naeini

Abstract:

Dynamic properties of soil in small strains, especially for geotechnical engineers, are important for describing the behavior of soil and estimation of the earth structure deformations and structures, especially significant structures. This paper presents the effect of density on the shear modulus and damping ratio of saturated clean sand at various isotropic confining pressures. For this purpose, the specimens were compared with two different relative densities, loose Dr = 30% and dense Dr = 70%. Dynamic parameters were attained from a series of consolidated undrained fixed – free type torsional resonant column tests in small strain. Sand No. 161 is selected for this paper. The experiments show that by increasing sand density and confining pressure, the shear modulus increases and the damping ratio decreases.

Keywords: Dynamic properties, shear modulus, damping ratio, clean sand, density, confining pressure, resonant column/torsional simple shear.

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Authors: Paul Amoateng, Patrick B. Cobbinah, Kwasi Ofori-Kumah

Abstract:

Although the importance of small and medium-size enterprises (SMEs) to local development is globally recognized, less attention is given to their design, development and promotion particularly in developing countries. The main focus of this paper is to examine the process of designing, developing and promoting SMEs in developing countries. Results of a study conducted in a SMEs’ enclave in Kumasi (Ghana) are presented and discussed. Results show that although SMEs in developing countries remain a major source of livelihood for many individuals, their potential contribution to local development can be enhanced and sustained through the creation of common geographical enclaves for related SMEs. Findings indicated that the concentration of SMEs involved in wood processing in one location in Kumasi has reduced the cost of production (e.g., transportation), and resulted in marginal increase in sales for many SMEs, despite the widespread challenges of lack of access to credit and low promotion of products.

Keywords: Developing countries, Kumasi, local development, small and medium-size enterprises.

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Authors: Ritu Vijay, Rekha Govil

Abstract:

The design of a complete expansion that allows for compact representation of certain relevant classes of signals is a central problem in signal processing applications. Achieving such a representation means knowing the signal features for the purpose of denoising, classification, interpolation and forecasting. Multilayer Neural Networks are relatively a new class of techniques that are mathematically proven to approximate any continuous function arbitrarily well. Radial Basis Function Networks, which make use of Gaussian activation function, are also shown to be a universal approximator. In this age of ever-increasing digitization in the storage, processing, analysis and communication of information, there are numerous examples of applications where one needs to construct a continuously defined function or numerical algorithm to approximate, represent and reconstruct the given discrete data of a signal. Many a times one wishes to manipulate the data in a way that requires information not included explicitly in the data, which is done through interpolation and/or extrapolation. Tidal data are a very perfect example of time series and many statistical techniques have been applied for tidal data analysis and representation. ANN is recent addition to such techniques. In the present paper we describe the time series representation capabilities of a special type of ANN- Radial Basis Function networks and present the results of tidal data representation using RBF. Tidal data analysis & representation is one of the important requirements in marine science for forecasting.

Keywords: ANN, RBF, Tidal Data.

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Authors: M. Chavleishvili, E. Kharaishvili, G. Erkomaishvili

Abstract:

The article presents the development trends of farms, estimates on the optimal scope of farming, as well as the experience of local and foreign countries in this area. As well, the advantages of small and large farms are discussed; herewith, the scales of farms are compared to the local reality. The study analyzes the results of farm operations and the possibilities of diversification of farms. The indicators of an effective use of land resources and land fragmentation are measured; also, a comparative analysis with other countries is presented, in particular, the measurements of agricultural lands for farming, as well as the indicators of population ensuring. The conducted research shows that most of the farms in Georgia are small and their development is at the initial stage, which outlines that the country has a high resource potential to increase the scale of the farming industry and its full integration into market relations. On the basis of the obtained results, according to the research on the scale of farming in Georgia and the identification of hampering factors of farming development, the conclusions are presented and the relevant recommendations are suggested.

Keywords: Farm cooperatives, farms, farm scale, land fragmentation, small and large farms.

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Authors: A. Greco, N. Mammone, F.C. Morabito, M.Versaci

Abstract:

This paper is mainly concerned with the application of a novel technique of data interpretation to the characterization and classification of measurements of plasma columns in Tokamak reactors for nuclear fusion applications. The proposed method exploits several concepts derived from soft computing theory. In particular, Artifical Neural Networks have been exploited to classify magnetic variables useful to determine shape and position of the plasma with a reduced computational complexity. The proposed technique is used to analyze simulated databases of plasma equilibria based on ITER geometry configuration. As well as demonstrating the successful recovery of scalar equilibrium parameters, we show that the technique can yield practical advantages compares with earlier methods.

Keywords: Tokamak, sensors, artificial neural network.

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Authors: P.S. Prakash, S. Selvan

Abstract:

Quality of Service (QoS) Routing aims to find path between source and destination satisfying the QoS requirements which efficiently using the network resources and underlying routing algorithm and to fmd low-cost paths that satisfy given QoS constraints. One of the key issues in providing end-to-end QoS guarantees in packet networks is determining feasible path that satisfies a number of QoS constraints. We present a Optimized Multi- Constrained Routing (OMCR) algorithm for the computation of constrained paths for QoS routing in computer networks. OMCR applies distance vector to construct a shortest path for each destination with reference to a given optimization metric, from which a set of feasible paths are derived at each node. OMCR is able to fmd feasible paths as well as optimize the utilization of network resources. OMCR operates with the hop-by-hop, connectionless routing model in IP Internet and does not create any loops while fmding the feasible paths. Nodes running OMCR not necessarily maintaining global view of network state such as topology, resource information and routing updates are sent only to neighboring nodes whereas its counterpart link-state routing method depend on complete network state for constrained path computation and that incurs excessive communication overhead.

Keywords: QoS Routing, Optimization, feasible path, multiple constraints.

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Authors: Po-Hsieh Lin, Jyi-Tsong Lin

Abstract:

A vertical SOI-based MOSFET with trench body structure operated as 1T DRAM cell at various temperatures has been studied and investigated. Different operation temperatures are assigned for the device for its performance comparison, thus the thermal stability is carefully evaluated for the future memory device applications. Based on the simulation, the vertical SOI-based MOSFET with trench body structure demonstrates the electrical characteristics properly and possess conspicuous kink effect at various operation temperatures. Transient characteristics were also performed to prove that its programming window values and retention time behaviors are acceptable when the new 1T DRAM cell is operated at high operation temperature.

Keywords: SOI, 1T DRAM, thermal stability.

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Authors: Hyoup-Sang Yoon

Abstract:

The quality of video transmitted by mobile ad hoc networks (MANETs) can be influenced by several factors, including protocol layers; parameter settings of each protocol. In this paper, we are concerned with understanding the functional relationship between these influential factors and objective video quality in MANETs. We illustrate a systematic statistical design of experiments (DOE) strategy can be used to analyze MANET parameters and performance. Using a 2^k factorial design, we quantify the main and interactive effects of 7 factors on a response metric (i.e., mean opinion score (MOS) calculated by PSNR with Evalvid package) we then develop a first-order linear regression model between the influential factors and the performance metric.

Keywords: Evalvid, full factorial design, mobile ad hoc networks, ns-2.

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Authors: Kriangkrai Maneerat, Chutima Prommak

Abstract:

This paper presents an analysis of the localization accuracy of indoor positioning systems using Cramer-s rule via IEEE 802.15.4 wireless sensor networks. The objective is to study the impact of the methods used to convert the received signal strength into the distance that is used to compute the object location in the wireless indoor positioning system. Various methods were tested and the localization accuracy was analyzed. The experimental results show that the method based on the empirical data measured in the non line-of-sight (NLOS) environment yield the highest localization accuracy; with the minimum error distance less than 3 m.

Keywords: Indoor positioning systems, localization accuracy, wireless networks, Cramer's rule.

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Authors: Parvinder S. Sandhu, Roma Jaswal, Sandeep Khimta, Shailendra Singh

Abstract:

As the majority of faults are found in a few of its modules so there is a need to investigate the modules that are affected severely as compared to other modules and proper maintenance need to be done in time especially for the critical applications. As, Neural networks, which have been already applied in software engineering applications to build reliability growth models predict the gross change or reusability metrics. Neural networks are non-linear sophisticated modeling techniques that are able to model complex functions. Neural network techniques are used when exact nature of input and outputs is not known. A key feature is that they learn the relationship between input and output through training. In this present work, various Neural Network Based techniques are explored and comparative analysis is performed for the prediction of level of need of maintenance by predicting level severity of faults present in NASA-s public domain defect dataset. The comparison of different algorithms is made on the basis of Mean Absolute Error, Root Mean Square Error and Accuracy Values. It is concluded that Generalized Regression Networks is the best algorithm for classification of the software components into different level of severity of impact of the faults. The algorithm can be used to develop model that can be used for identifying modules that are heavily affected by the faults.

Keywords: Neural Network, Software faults, Software Metric.

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Authors: Isao Taguchi, Yasuo Sugai

Abstract:

This paper proposes an efficient learning method for the layered neural networks based on the selection of training data and input characteristics of an output layer unit. Comparing to recent neural networks; pulse neural networks, quantum neuro computation, etc, the multilayer network is widely used due to its simple structure. When learning objects are complicated, the problems, such as unsuccessful learning or a significant time required in learning, remain unsolved. Focusing on the input data during the learning stage, we undertook an experiment to identify the data that makes large errors and interferes with the learning process. Our method devides the learning process into several stages. In general, input characteristics to an output layer unit show oscillation during learning process for complicated problems. The multi-stage learning method proposes by the authors for the function approximation problems of classifying learning data in a phased manner, focusing on their learnabilities prior to learning in the multi layered neural network, and demonstrates validity of the multi-stage learning method. Specifically, this paper verifies by computer experiments that both of learning accuracy and learning time are improved of the BP method as a learning rule of the multi-stage learning method. In learning, oscillatory phenomena of a learning curve serve an important role in learning performance. The authors also discuss the occurrence mechanisms of oscillatory phenomena in learning. Furthermore, the authors discuss the reasons that errors of some data remain large value even after learning, observing behaviors during learning.

Keywords: data selection, function approximation problem, multistage leaning, neural network, voluntary oscillation.

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Authors: B. Golchin, N. Riahi

Abstract:

One of the most significant issues as attended a lot in recent years is that of recognizing the sentiments and emotions in social media texts. The analysis of sentiments and emotions is intended to recognize the conceptual information such as the opinions, feelings, attitudes and emotions of people towards the products, services, organizations, people, topics, events and features in the written text. These indicate the greatness of the problem space. In the real world, businesses and organizations are always looking for tools to gather ideas, emotions, and directions of people about their products, services, or events related to their own. This article uses the Twitter social network, one of the most popular social networks with about 420 million active users, to extract data. Using this social network, users can share their information and opinions about personal issues, policies, products, events, etc. It can be used with appropriate classification of emotional states due to the availability of its data. In this study, supervised learning and deep neural network algorithms are used to classify the emotional states of Twitter users. The use of deep learning methods to increase the learning capacity of the model is an advantage due to the large amount of available data. Tweets collected on various topics are classified into four classes using a combination of two Bidirectional Long Short Term Memory network and a Convolutional network. The results obtained from this study with an average accuracy of 93%, show good results extracted from the proposed framework and improved accuracy compared to previous work.

Keywords: emotion classification, sentiment analysis, social networks, deep neural networks

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Authors: Christian Endisch, Christoph Hackl, Dierk Schröder

Abstract:

This paper presents an exact pruning algorithm with adaptive pruning interval for general dynamic neural networks (GDNN). GDNNs are artificial neural networks with internal dynamics. All layers have feedback connections with time delays to the same and to all other layers. The structure of the plant is unknown, so the identification process is started with a larger network architecture than necessary. During parameter optimization with the Levenberg- Marquardt (LM) algorithm irrelevant weights of the dynamic neural network are deleted in order to find a model for the plant as simple as possible. The weights to be pruned are found by direct evaluation of the training data within a sliding time window. The influence of pruning on the identification system depends on the network architecture at pruning time and the selected weight to be deleted. As the architecture of the model is changed drastically during the identification and pruning process, it is suggested to adapt the pruning interval online. Two system identification examples show the architecture selection ability of the proposed pruning approach.

Keywords: System identification, dynamic neural network, recurrentneural network, GDNN, optimization, Levenberg Marquardt, realtime recurrent learning, network pruning, quasi-online learning.

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Authors: H. Khorashadi-Zadeh, M. R. Aghaebrahimi

Abstract:

A novel application of neural network approach to fault classification and fault location of Medium voltage cables is demonstrated in this paper. Different faults on a protected cable should be classified and located correctly. This paper presents the use of neural networks as a pattern classifier algorithm to perform these tasks. The proposed scheme is insensitive to variation of different parameters such as fault type, fault resistance, and fault inception angle. Studies show that the proposed technique is able to offer high accuracy in both of the fault classification and fault location tasks.

Keywords: Artificial neural networks, cable, fault location andfault classification.

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Authors: Kevin Fong-Rey Liu, Ken Yeh, Cheng-Wu Chen, Han-Hsi Liang

Abstract:

This paper proposes the use of Bayesian belief networks (BBN) as a higher level of health risk assessment for a dumping site of lead battery smelter factory. On the basis of the epidemiological studies, the actual hospital attendance records and expert experiences, the BBN is capable of capturing the probabilistic relationships between the hazardous substances and their adverse health effects, and accordingly inferring the morbidity of the adverse health effects. The provision of the morbidity rates of the related diseases is more informative and can alleviate the drawbacks of conventional methods.

Keywords: Bayesian belief networks, lead battery smelter factory, health risk assessment.

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Authors: A. Dashti, M. Eskandari, L. Farahmand, P. Parvin, A. Jafargholi

Abstract:

Breast Cancer is one of the most considerable diseases in the United States and other countries and is the second leading cause of death in women. Common breast cancer treatments would lead to adverse side effects such as loss of hair, nausea, and weakness. These complications arise because these cancer treatments damage some healthy cells while eliminating the cancer cells. In an effort to address these complications, laser radiation was utilized and tested as a targeted cancer treatment for breast cancer. In this regard, tissue engineering approaches are being employed by using an electrospun scaffold in order to facilitate the growth of breast cancer cells. Polycaprolacton (PCL) was used as a material for scaffold fabricating because of its biocompatibility, biodegradability, and supporting cell growth. The specific breast cancer cells have the ability to create a three-dimensional cell cluster due to the spontaneous accumulation of cells in the porosity of the scaffold under some specific conditions. Therefore, we are looking for a higher density of porosity and larger pore size. Fibers showed uniform diameter distribution and final scaffold had optimum characteristics with approximately 40% porosity. The images were taken by SEM and the density and the size of the porosity were determined with the Image. After scaffold preparation, it has cross-linked by glutaraldehyde. Then, it has been washed with glycine and phosphate buffer saline (PBS), in order to neutralize the residual glutaraldehyde. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromidefor (MTT) results have represented approximately 91.13% viability of the scaffolds for cancer cells. In order to create a cluster, Michigan Cancer Foundation-7 (MCF-7, breast cancer cell line) and Michigan Cancer Foundation-10A (MCF-10A, human mammary epithelial cell line) cells were cultured on the scaffold in 24 well plate for five days. Then, we have exposed the cluster to the laser diode 808 nm radiation to investigate the effect of laser on the tumor with different power and time. Under the same conditions, cancer cells lost their viability more than the healthy ones. In conclusion, laser therapy is a viable method to destroy the target cells and has a minimum effect on the healthy tissues and cells and it can improve the other method of cancer treatments limitations.

Keywords: Breast cancer, electrospun scaffold, polycaprolacton, laser diode, cancer treatment.

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Authors: P. Narrima, C. Y. Looi, M. A. Mohd, H. M. Ali

Abstract:

Persea declinata (Bl.) Kosterm is a member of the Lauraceae family, widely distributed in Southeast Asia. It is from the same genus with avocado (Persea americana Mill), which is widely consumed as food and for medicinal purposes. In the present study, we examined the anticancer properties of Persea declinata (Bl.) Kosterm bark methanolic crude extract (PDM). PDM exhibited a potent antiproliferative effect in MCF-7 human breast cancer cells, with an IC50 value of 16.68 .g/mL after 48h of treatment. We observed that PDM caused cell cycle arrest and subsequent apoptosis in MCF-7 cells, as exhibited by increased population at G0/G1 phase, higher lactate dehydrogenase (LDH) release, and DNA fragmentation. Mechanistic studies showed that PDM caused significant elevation in ROS production, leading to perturbation of mitochondrial membrane potential, cell permeability, and activation of caspases-3/7. On the other hand, real-time PCR and Western blot analysis showed that PDM treatment increased the expression of the proapoptotic molecule, Bax, but decreased the expression of prosurvival proteins, Bcl-2 and Bcl-xL, in a dose-dependent manner. These findings imply that PDM could inhibit proliferation in MCF-7 cells via cell cycle arrest and apoptosis induction, indicating its potential as a therapeutic agent worthy of further development.

Keywords: Antiproliferative, apoptosis, MCF-7 human breast cancer, Persea declinata.

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