Search results for: hydrogel network

Authors: Safak Baykal

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The purpose of this study is analyzing the relationship between self leadership and social capital of people with using Social Network Analysis. In this study, two aspects of social capital will be focused: bonding, homophilous social capital (BoSC) which implies better, strong, dense or closed network ties, and bridging, heterophilous social capital (BrSC) which implies weak ties, bridging the structural holes. The other concept of the study is Trust (Tr), namely interpersonal trust, willingness to ascribe good intentions to and have confidence in the words and actions of other people. In this study, the sample group, 61 people, was selected from a private firm from the defense industry. The relation between BoSC/BrSC and Tr is shown by using Social Network Analysis (SNA) and statistical analysis with Likert type-questionnaire. The results of the analysis show the Cronbach’s alpha value is 0.73 and social capital values (BoSC/BrSC) is highly correlated with Tr values of the people.

Keywords: bonding social capital, bridging social capital, trust, social network analysis (SNA)

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Authors: Ghorab Sara, Meziani Lila, Rubin Harvey Stuart

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The rapid growth of deep learning has led to intricate and resource-intensive deep neural networks widely used in computer vision tasks. However, their complexity results in high computational demands and memory usage, hindering real-time application. To address this, research focuses on model compression techniques. The paper provides an overview of recent advancements in compressing neural networks and categorizes the various methods into four main approaches: network pruning, quantization, network decomposition, and knowledge distillation. This paper aims to provide a comprehensive outline of both the advantages and limitations of each method.

Keywords: model compression, deep neural network, pruning, knowledge distillation, quantization, low-rank decomposition

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Authors: Mary Anne Roa

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Congestion in network occurs due to exceed in aggregate demand as compared to the accessible capacity of the resources. Network congestion will increase as network speed increases and new effective congestion control methods are needed, especially for today’s very high speed networks. To address this undeniably global issue, the study focuses on the development of a fuzzy-based congestion control model concerned with allocating the resources of a computer network such that the system can operate at an adequate performance level when the demand exceeds or is near the capacity of the resources. Fuzzy logic based models have proven capable of accurately representing a wide variety of processes. The model built is based on bandwidth, the aggregate incoming traffic and the waiting time. The theoretical analysis and simulation results show that the proposed algorithm provides not only good utilization but also low packet loss.

Keywords: congestion control, queue management, computer networks, fuzzy logic

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Authors: Alexander Hempfing

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The desire to understand business cycle fluctuations, trade interdependencies and co-movement has a long tradition in economic thinking. From input-output economics to business cycle theory, researchers aimed to find appropriate answers from an empirical as well as a theoretical perspective. This paper empirically analyses how the production structure of the global economy and several states developed over time, what their distributional properties are and if there are network specific metrics that allow identifying structurally important nodes, on a global, national and sectoral scale. For this, the World Input-Output Database was used, and different statistical methods were applied. Empirical evidence is provided that the importance of the Eastern hemisphere in the global production network has increased significantly between 2000 and 2014. Moreover, it was possible to show that the sectoral eigenvector centrality indices on a global level are power-law distributed, providing evidence that specific national sectors exist which are more critical to the world economy than others while serving as a hub within the global production network. However, further findings suggest, that global production cannot be characterized as a scale-free network.

Keywords: economic integration, industrial organization, input-output economics, network economics, production networks

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Authors: M. R. Martinez-Torres, S. L. Toral, M. Olmedilla

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Typically, virtual communities exhibit the well-known phenomenon of participation inequality, which means that only a small percentage of users is responsible of the majority of contributions. However, the sustainability of the community requires that the group of active users must be continuously nurtured with new users that gain expertise through a participation process. This paper analyzes the time evolution of Open Source Software (OSS) communities, considering users that join/abandon the community over time and several topological properties of the network when modeled as a social network. More specifically, the paper analyzes the role of those users rejoining the community and their influence in the global characteristics of the network.

Keywords: open source communities, social network Analysis, time series, virtual communities

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Authors: Younghyun Jeon, Seungjoo Maeng

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In the Ad-hoc network, the great interests regarding MIMO scheme leads to their combination, which is also utilized into its applicable network. We manage the field of the problem into Reverse-link MIMO Ad-hoc Network (RMAN) and propose the methodology to maximize the data rate with its power consumption using Node-Cooperative beamforming technique. Based on the result of mathematical optimization formulation, we design the algorithm to construct optimal orthogonal weight vector according to channel feedback and control its transmission power according to QoS-pricing value level. In simulation results, we show the validity of the proposed mathematical optimization result and algorithm which mean that the sum-rate of each link is converged into some point.

Keywords: ad-hoc network, MIMO, cooperative beamforming, transmit power

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Authors: Sultan Noman Qasem

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This paper presents an evolutionary algorithm for solving multi-objective optimization problems-based artificial neural network (ANN). The multi-objective evolutionary algorithm used in this study is genetic algorithm while ANN used is radial basis function network (RBFN). The proposed algorithm named memetic elitist Pareto non-dominated sorting genetic algorithm-based RBFNN (MEPGAN). The proposed algorithm is implemented on medical diseases problems. The experimental results indicate that the proposed algorithm is viable, and provides an effective means to design multi-objective RBFNs with good generalization capability and compact network structure. This study shows that MEPGAN generates RBFNs coming with an appropriate balance between accuracy and simplicity, comparing to the other algorithms found in literature.

Keywords: radial basis function network, hybrid learning, multi-objective optimization, genetic algorithm

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Authors: Widyani Fatwa Dewi, Subroto Athor

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In a telecommunication service provider, quantity and interval of customer demand often difficult to predict due to high dependency on customer expansion strategy and technological development. Demand arrives when a customer needs to add capacity to an existing site or build a network in a new site. Because demand is uncertain for each period, and sometimes there is a null demand for several equipments, it is categorized as intermittent. This research aims to improve demand forecast quality in Indonesia's telecommunication service providers by using Artificial Neural Network. In Artificial Neural Network, the pattern or relationship within data will be analyzed using the training process, followed by the learning process as validation stage. Historical demand data for 36 periods is used to support this research. It is found that demand forecast by using Artificial Neural Network outperforms the existing method if it is reviewed on two criteria: the forecast accuracy, using Mean Absolute Deviation (MAD), Mean of the sum of the Squares of the Forecasting Error (MSE), Mean Error (ME) and service level which is shown through inventory cost. This research is expected to increase the reference for a telecommunication demand forecast, which is currently still limited.

Keywords: artificial neural network, demand forecast, forecast accuracy, intermittent, service level, telecommunication

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Authors: Donya Ashtiani Haghighi, Amirali Baniasadi

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Coronavirus (COVID-19) disease has spread abruptly all over the world since the end of 2019. Computed tomography (CT) scans and X-ray images are used to detect this disease. Different Deep Neural Network (DNN)-based diagnosis solutions have been developed, mainly based on Convolutional Neural Networks (CNNs), to accelerate the identification of COVID-19 cases. However, CNNs lose important information in intermediate layers and require large datasets. In this paper, Capsule Network (CapsNet) is used. Capsule Network performs better than CNNs for small datasets. Accuracy of 0.9885, f1-score of 0.9883, precision of 0.9859, recall of 0.9908, and Area Under the Curve (AUC) of 0.9948 are achieved on the Capsule-based framework with hyperparameter tuning. Moreover, different dropout rates are investigated to decrease overfitting. Accordingly, a dropout rate of 0.1 shows the best results. Finally, we remove one convolution layer and decrease the number of trainable parameters to 146,752, which is a promising result.

Keywords: capsule network, dropout, hyperparameter tuning, classification

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Authors: Eu Tteum Ha, Seyoung Kim, Jeongmin Kim, Kwang Ryel Ryu

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The smart home environment backed up by IoT (internet of things) technologies enables intelligent services based on the awareness of the situation a user is currently in. One of the convenient sensors for recognizing the situations within a home is the smart meter that can monitor the status of each electrical appliance in real time. This paper aims at learning a Bayesian network that models the causal relationship between the user situations and the status of the electrical appliances. Using such a network, we can infer the current situation based on the observed status of the appliances. However, learning the conditional probability tables (CPTs) of the network requires many training examples that cannot be obtained unless the user situations are closely monitored by any means. This paper proposes a method for learning the CPT entries of the network relying only on the user feedbacks generated occasionally. In our case study with a robot vacuum cleaner, the feedback comes in whenever the user gives an order to the robot adversely from its preprogrammed setting. Given a network with randomly initialized CPT entries, our proposed method uses this feedback information to adjust relevant CPT entries in the direction of increasing the probability of recognizing the desired situations. Simulation experiments show that our method can rapidly improve the recognition performance of the Bayesian network using a relatively small number of feedbacks.

Keywords: Bayesian network, IoT, learning, situation -awareness, smart home

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Authors: J. D. Cabral, E. Murray, P. Turner, E. Hewitt, A. Ali, M. McConnell

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Worldwide demand for organ replacement and tissue regeneration is progressively increasing. Three-dimensional (3D) bioprinting, where a physical construct is produced using computer-aided design, is a promising tool to advance the tissue engineering and regenerative medicine fields. In this paper we describe two different approaches to developing 3D bioprinted constructs for use in tissue regeneration. Bioink development is critical in achieving the 3D biofabrication of functional, regenerative tissues. Hydrogels, cross-linked macromolecules that absorb large amounts of water, have received widespread interest as bioinks due to their relevant soft tissue mechanics, biocompatibility, and tunability. In turn, not only is bioink optimisation crucial, but the creation of vascularized tissues remains a key challenge for the successful fabrication of thicker, more clinically relevant bioengineered tissues. Among the various methodologies, cell-laden hydrogels are regarded as a favorable approach; and when combined with novel core/shell 3D bioprinting technology, an innovative strategy towards creating new vessel-like structures. In this work, we investigate this cell-based approach by using human umbilical endothelial cells (HUVECs) entrapped in a viscoelastic chitosan/dextran (CD)-based core hydrogel, printed simulataneously along with a gelatin methacrylate (GelMA) shell. We have expanded beyond our previously reported FDA approved, commercialised, post-surgical CD hydrogel, Chitogel®, by functionalizing it with cell adhesion and proteolytic peptides in order to promote bone marrow-derived mesenchymal stem cell (immortalized BMSC cell line, hTERT) and HUVECs growth. The biocompatibility and biodegradability of these cell lines in a 3D bioprinted construct is demonstrated. Our studies show that particular peptide combinations crosslinked within the CD hydrogel was found to increase in vitro growth of BMSCs and HUVECs by more than two-fold. These gels were then used as a core bioink combined with the more mechanically robust, UV irradiated GelMA shell bioink, to create 3D regenerative, vessel-like scaffolds with high print fidelity. As well, microporous MEW scaffolds made from milk proteins blended with PCL were found to show promising bioactivity, exhibiting a significant increase in keratinocyte (HaCaTs) and fibroblast (normal human dermal fibroblasts, NhDFs) cell migration and proliferation when compared to PCL only scaffolds. In conclusion, our studies indicate that a peptide functionalized CD hydrogel bioink reinforced with a GelMA shell is biocompatible, biodegradable, and an appropriate cell delivery vehicle in the creation of regenerative 3D constructs. In addition, a novel 3D printing technique, melt electrowriting (MEW), which allows fabrication of micrometer fibre meshes, was used to 3D print polycaprolactone (PCL) and bioactive milk protein, lactorferrin (LF) and whey protein (WP), blended scaffolds for potential skin regeneration applications. MEW milk protein/PCL scaffolds exhibited high porosity characteristics, low overall biodegradation, and rapid protein release. Human fibroblasts and keratinocyte cells were seeded on to the scaffolds. Scaffolds containing high concentrations of LF and combined proteins (LF+WP) showed improved cell viability over time as compared to PCL only scaffolds. This research highlights two scaffolds made using two different 3D printing techniques using a combination of both natural and synthetic biomaterial components in order to create regenerative constructs as potential chronic wound treatments.

Keywords: biomaterials, hydrogels, regenerative medicine, 3D bioprinting

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Authors: Oleg Vlasovets, Fabian Schaipp, Christian L. Mueller

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we conduct a network analysis and predict the sex of 1000 participants based on ”connectome” - pairwise Pearson’s correlation across 436 brain parcels. We solve the non-smooth convex optimization problem, known under the name of Graphical Lasso, where the solution includes a low-rank component. With this solution and machine learning model for a sex prediction, we explain the brain parcels-sex connectivity patterns.

Keywords: network analysis, neuroscience, machine learning, optimization

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Authors: Sifat Rahman Ahona, Rifat Tasnim, Naima Hassan

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Data aggregation is a helpful technique for reducing the data communication overhead in wireless sensor network. One of the important tasks of data aggregation is positioning of the aggregator points. There are a lot of works done on data aggregation. But, efficient positioning of the aggregators points is not focused so much. In this paper, authors are focusing on the positioning or the placement of the aggregation points in wireless sensor network. Authors proposed an algorithm to select the aggregators positions for a scenario where aggregator nodes are more powerful than sensor nodes.

Keywords: aggregation point, data communication, data aggregation, wireless sensor network

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Authors: Richard Draper

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A supercomputer can be constructed from identical building blocks which are small parallel processors connected by a network referred to as the local network. The routers have unused ports which are used to interconnect the building blocks. These connections are referred to as the global network. The address space has a global and a local component (g, l). The conventional way to connect the building blocks is to connect (g, l) to (g’,l). If there are K blocks, this requires K global ports in each router. If a block is of size M, the result is a machine with KM routers having diameter two. To increase the size of the machine to 2K blocks, each router connects to only half of the other blocks. The result is a larger machine but also one with greater diameter. This is a crude description of how the network of the CRAY XC® is designed. In this paper, a family of interconnection networks using routers with K global and M local ports is defined. Coordinates are (c,d, p) and the global connections are (c,d,p)↔(c’,p,d) which swaps p and d. The network is denoted D3(K,M) and is called a Swapped Dragonfly. D3(K,M) has KM2 routers and has diameter three, regardless of the size of K. To produce a network of size KM2 conventionally, diameter would be an increasing function of K. The family of Swapped Dragonflies has other desirable properties: 1) D3(K,M) scales linearly in K and quadratically in M. 2) If L < K, D3(K,M) contains many copies of D3(L,M). 3) If L < M, D3(K,M) contains many copies of D3(K,L). 4) D3(K,M) can perform an all-to-all exchange in KM2+KM time which is only slightly more than the time to do a one-to-all. This paper makes several contributions. It is the first time that a swap has been used to define a linearly scalable family of networks. Structural properties of this new family of networks are thoroughly examined. A synchronizing packet header is introduced. It specifies the path to be followed and it makes it possible to define highly parallel communication algorithm on the network. Among these is an all-to-all exchange in time KM2+KM. To demonstrate the effectiveness of the swap properties of the network of the CRAY XC® and D3(K,16) are compared.

Keywords: all-to-all exchange, CRAY XC®, Dragonfly, interconnection network, packet switching, swapped network, topology

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Authors: Xiaohan Bookman, Xiaoyan Zhu

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The outputs of non-linear feed-forward neural network are positive, which could be treated as probability when they are normalized to one. If we take Entropy-Based Principle into consideration, the outputs for each sample could be represented as the distribution of this sample for different clusters. Entropy-Based Principle is the principle with which we could estimate the unknown distribution under some limited conditions. As this paper defines two processes in Feed-Forward Neural Network, our limited condition is the abstracted features of samples which are worked out in the abstraction process. And the final outputs are the probability distribution for different clusters in the clustering process. As Entropy-Based Principle is considered into the feed-forward neural network, a clustering method is born. We have conducted some experiments on six open UCI data sets, comparing with a few baselines and applied purity as the measurement. The results illustrate that our method outperforms all the other baselines that are most popular clustering methods.

Keywords: feed-forward neural network, clustering, max-entropy principle, probabilistic models

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Authors: Zakaria Baka, Halima Alem

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Cancer is a major worldwide health problem that has caused around ten million deaths in 2020. In addition, efforts to develop new anti-cancer drugs still face a high failure rate. This is partly due to the lack of preclinical models that recapitulate in-vivo drug responses. Indeed conventional cell culture approach (known as 2D cell culture) is far from reproducing the complex, dynamic and three-dimensional environment of tumors. To set up more in-vivo-like cancer models, 3D bioprinting seems to be a promising technology due to its ability to achieve 3D scaffolds containing different cell types with controlled distribution and precise architecture. Moreover, the introduction of microfluidic technology makes it possible to simulate in-vivo dynamic conditions through the so-called “cancer-on-chip” platforms. Whereas several cancer types have been modeled through the cancer-on-chip approach, such as lung cancer and breast cancer, only a few works describing ovarian cancer models have been described. The aim of this work is to combine 3D bioprinting and microfluidic technics with setting up a 3D dynamic model of ovarian cancer. In the first phase, alginate-gelatin hydrogel containing SKOV3 cells was used to achieve tumor-like structures through an extrusion-based bioprinter. The desired form of the tumor-like mass was first designed on 3D CAD software. The hydrogel composition was then optimized for ensuring good and reproducible printability. Cell viability in the bioprinted structures was assessed using Live/Dead assay and WST1 assay. In the second phase, these bioprinted structures will be included in a microfluidic device that allows simultaneous testing of different drug concentrations. This microfluidic dispositive was first designed through computational fluid dynamics (CFD) simulations for fixing its precise dimensions. It was then be manufactured through a molding method based on a 3D printed template. To confirm the results of CFD simulations, doxorubicin (DOX) solutions were perfused through the dispositive and DOX concentration in each culture chamber was determined. Once completely characterized, this model will be used to assess the efficacy of anti-cancer nanoparticles developed in the Jean Lamour institute.

Keywords: 3D bioprinting, ovarian cancer, cancer-on-chip models, microfluidic techniques

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Authors: Monica Sharma, Harjit Pal Singh, Jasbinder Singh, Manju Bala

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In Voice over Internet Protocol (VoIP) system, the speech signal is degraded when passed through the network layers. The speech signal is processed through the best effort policy based IP network, which leads to the network degradations including delay, packet loss and jitter. The packet loss is the major issue of the degradation in the VoIP signal quality; even a single lost packet may generate audible distortion in the decoded speech signal. In addition to these network degradations, the quality of the speech signal is also affected by the environmental noises and coder distortions. The signal quality of the VoIP system is improved through the interleaving technique. The performance of the system is evaluated for various types of noises at different network conditions. The performance of the enhanced VoIP signal is evaluated using perceptual evaluation of speech quality (PESQ) measurement for narrow band signal.

Keywords: VoIP, interleaving, packet loss, packet size, background noise

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Authors: Silumpa Suboonsan, Suwat Pattaramalai

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In this paper, the performance of internet usage by using Vertical Handover (VHO) between cellular network and wireless local area network (WLAN) on Silom line Bangkok Mass Transit System (BTS) is evaluated. In the evaluation model, there is the WLAN on every BTS station and there are cellular base stations along the BTS path. The maximum data rates for cellular network are 7.2, 14.4, 42, and 100Mbps and for WLAN are 54, 150, and 300Mbps. The simulation are based on users using internet, watching VDOs and browsing web pages, on the BTS train from first station to the last station (full time usage) and on the BTS train for traveling some number of stations (random time). The results shows that VHO system has throughput a lot more than using only cellular network when the data rate of WLAN is more than one of cellular network. Lastly, the number of watching HD VDO and Full HD VDO is higher on VHO system on both regular time and rush hour of BTS travelling.

Keywords: vertical handover, WLAN, cellular, silom line BTS

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Authors: Raquel Galante, Marina Braga, Daniela Ghisleni, Terezinha J. A. Pinto, Rogério Colaço, Ana Paula Serro

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The sensitive nature of soft biomaterials, such as hydrogels, renders their sterilization a particularly challenging task for the biomedical industry. Widely used contact lenses are now studied as promising platforms for topical corneal drug delivery. However, to the best of the authors knowledge, the influence of sterilization methods on these systems has yet to be evaluated. The main goal of this study was to understand how different pairs drug-hydrogel would interact under an ozone-based sterilization method in comparison with two conventional processes (steam heat and gamma irradiation). For that, Si-Hy containing hydroxylethyl methacrylate (HEMA) and [tris(trimethylsiloxy)silyl]propyl methacrylate (TRIS) was produced and soaked in different drug solutions, commonly used for the treatment of ocular diseases (levofloxacin, chlorhexidine, diclofenac and timolol maleate). The drug release profiles and main material properties were evaluated before and after the sterilization. Namely, swelling capacity was determined by water uptake studies, transparency was accessed by UV-Vis spectroscopy, surface topography/morphology by scanning electron microscopy (SEM) and mechanical properties by performing tensile tests. The drug released was quantified by high performance liquid chromatography (HPLC). The effectiveness of the sterilization procedures was assured by performing sterility tests. Ozone gas method led to a significant reduction of drug released and to the formation of degradation products specially for diclofenac and levofloxacin. Gamma irradiation led to darkening of the loaded Si-Hys and to the complete degradation of levofloxacin. Steam heat led to smoother surfaces and to a decrease of the amount of drug released, however, with no formation of degradation products. This difference in the total drug released could be the related to drug/polymer interactions promoted by the sterilization conditions in presence of the drug. Our findings offer important insights that, in turn, could be a useful contribution to the safe development of actual products.

Keywords: drug delivery, silicone hydrogels, sterilization, gamma irradiation, steam heat, ozone gas

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Authors: Swapnil Singh, Sanjoy Das

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Mobile Ad-Hoc Network (MANET) is infrastructure less networks dynamically formed by autonomous system of mobile nodes that are connected via wireless links. Mobile nodes communicate with each other on the fly. In this network each node also acts as a router. The battery power and the bandwidth are very scarce resources in this network. The network lifetime and connectivity of nodes depends on battery power. Therefore, energy is a valuable constraint which should be efficiently used. In this paper, we survey various energy efficient routing protocol. The energy efficient routing protocols are classified on the basis of approaches they use to minimize the energy consumption. The purpose of this paper is to facilitate the research work and combine the existing solution and to develop a more energy efficient routing mechanism.

Keywords: delaunay triangulation, deployment, energy efficiency, MANET

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Authors: H. Ferfera-Harrar, T. Benhalima, D. Lerari

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Contamination of water, due to the discharge of untreated industrial wastewaters into the ecosystem, has become a serious problem for many countries. In this study, bioadsorbents based on chitosan-g-poly(acrylamide) and montmorillonite (MMt) clay (CTS-g-PAAm/MMt) hydrogel nanocomposites were prepared via free‐radical grafting copolymerization and crosslinking of acrylamide monomer (AAm) onto natural polysaccharide chitosan (CTS) as backbone, in presence of various contents of MMt clay as nanofiller. Then, they were hydrolyzed to obtain highly functionalized pH‐sensitive nanomaterials with uppermost swelling properties. Their structure characterization was conducted by X-Ray Diffraction (XRD) and Scanning Electron Microscopy (SEM) analyses. The adsorption performances of the developed nanohybrids were examined for removal of methylene blue (MB) cationic dye from aqueous solutions. The factors affecting the removal of MB, such as clay content, pH medium, adsorbent dose, initial dye concentration and temperature were explored. The adsorption process was found to be highly pH dependent. From adsorption kinetic results, the prepared adsorbents showed remarkable adsorption capacity and fast adsorption rate, mainly more than 88% of MB removal efficiency was reached after 50 min in 200 mg L^-1 of dye solution. In addition, the incorporating of various content of clay has enhanced adsorption capacity of CTS-g-PAAm matrix from 1685 to a highest value of 1749 mg g^-1 for the optimized nanocomposite containing 2 wt.% of MMt. The experimental kinetic data were well described by the pseudo-second-order model, while the equilibrium data were represented perfectly by Langmuir isotherm model. The maximum Langmuir equilibrium adsorption capacity (q_m) was found to increase from 2173 mg g⁻¹ until 2221 mg g⁻¹ by adding 2 wt.% of clay nanofiller. Thermodynamic parameters revealed the spontaneous and endothermic nature of the process. In addition, the reusability study revealed that these bioadsorbents could be well regenerated with desorption efficiency overhead 87% and without any obvious decrease of removal efficiency as compared to starting ones even after four consecutive adsorption/desorption cycles, which exceeded 64%. These results suggest that the optimized nanocomposites are promising as low cost bioadsorbents.

Keywords: chitosan, clay, dye adsorption, hydrogels nanocomposites

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Authors: Sun Zhe, Ruggero Micheletto

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As discussed extensively in many studies, noise in neural networks have an important role in the functioning and time evolution of the system. The mechanism by which noise induce stochastic resonance enhancing and influencing certain operations is not clarified nor is the mechanism of information storage and coding. With the present research we want to study the role of noise, especially focusing on the frequency peaks in a three variable Hindmarsh−Rose Small−World network. We investigated the behaviour of the network to external noises. We demonstrate that a variation of signal to noise ratio of about 10 dB induces an increase in membrane potential signal of about 15%, averaged over the whole network. We also considered the integral of the whole membrane potential as a paradigm of internal noise, the one generated by the brain network. We showed that this internal noise is attenuated with the size of the network or with the number of random connections. By means of Fourier analysis we found that it has distinct peaks of frequencies, moreover, we showed that increasing the size of the network introducing more neurons, reduced the maximum frequencies generated by the network, whereas the increase in the number of random connections (determined by the small-world probability p) led to a trend toward higher frequencies. This study may give clues on how networks utilize noise to alter the collective behaviour of the system in their operations.

Keywords: neural networks, stochastic processes, small-world networks, discrete Fourier analysis

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Authors: Yasmina Houda Bendahma, Tewfik Bouchaour, Meriem Merad, Ulrich Maschke

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The purpose of this work is to study retention of dye dissolved in distilled water, by an hydrophilic acrylic polymer network. The polymer network considered is Poly (2-hydroxyethyl methacrylate) (PHEMA): it is prepared by photo-polymerization under UV irradiation in the presence of a monomer (HEMA), initiator and an agent cross-linker. PHEMA polymer network obtained can be used in the retention of dye molecules present in the wastewater. The results obtained are interesting in the study of the kinetics of swelling and de-swelling of cross linked polymer networks PHEMA in colored aqueous solutions. The dyes used for retention by the PHEMA networks are eosin Y and Malachite Green, dissolved in distilled water. Theoretical conformational study by a simplified molecular model of system cross linked PHEMA / dye (eosin Y and Malachite Green), is used to simulate the retention phenomenon (or Docking) dye molecules in cavities in nano-domains included in the PHEMA polymer network.

Keywords: dye retention, molecular modeling, photochemically crosslinked polymer network, swelling deswelling, PHEMA, HEMA

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Authors: Rehab Abdulmajed, Amr Hamada, Ahmed Elsaid, Hisashi Hayakawa, Ayman Mahrous

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Solar emissions have a high impact on the Earth’s magnetic field, and the prediction of solar events is of high interest. Various techniques have been used in the prediction of solar wind using mathematical models, MHD models, and neural network (NN) models. This study investigates the coronal hole (CH) derived high-speed streams (HSSs) and their correlation to the CH area and create a neural network model to predict the HSSs. Two different algorithms were used to compare different models to find a model that best simulates the HSSs. A dataset of CH synoptic maps through Carrington rotations 1601 to 2185 along with Omni-data set solar wind speed averaged over the Carrington rotations is used, which covers Solar cycles (sc) 21, 22, 23, and most of 24.

Keywords: artificial neural network, coronal hole area, feed-forward neural network models, solar high speed streams

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Authors: Jakyoung Kim, Sungwook Jang

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The purpose of this study is to examine the research trends of life-long education for people with disabilities using a keyword network analysis. For this purpose, 151 papers were selected from 594 papers retrieved using keywords such as 'people with disabilities' and 'life-long education' in the Korean Education and Research Information Service. The Keyword network analysis was constructed by extracting and coding the keyword used in the title of the selected papers. The frequency of the extracted keywords, the centrality of degree, and betweenness was analyzed by the keyword network. The results of the keyword network analysis are as follows. First, the main keywords that appeared frequently in the study of life-long education for people with disabilities were 'people with disabilities', 'life-long education', 'developmental disabilities', 'current situations', 'development'. The research trends of life-long education for people with disabilities are focused on the current status of the life-long education and the program development. Second, the keyword network analysis and visualization showed that the keywords with high frequency of occurrences also generally have high degree centrality and betweenness centrality. In terms of the keyword network diagram, it was confirmed that research trends of life-long education for people with disabilities are centered on six prominent keywords. Based on these results, it was discussed that life-long education for people with disabilities in the future needs to expand the subjects and the supporting areas of the life-long education, and the research needs to be further expanded into more detailed and specific areas. 

Keywords: life-long education, people with disabilities, research trends, keyword network analysis

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Authors: Chaghoub Soraya, Zhang Xiaoyan

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This research presents the first constant approximation algorithm to the p-median network design problem with multiple cable types. This problem was addressed with a single cable type and there is a bifactor approximation algorithm for the problem. To the best of our knowledge, the algorithm proposed in this paper is the first constant approximation algorithm for the p-median network design with multiple cable types. The addressed problem is a combination of two well studied problems which are p-median problem and network design problem. The introduced algorithm is a random sampling approximation algorithm of constant factor which is conceived by using some random sampling techniques form the literature. It is based on a redistribution Lemma from the literature and a steiner tree problem as a subproblem. This algorithm is simple, and it relies on the notions of random sampling and probability. The proposed approach gives an approximation solution with one constant ratio without violating any of the constraints, in contrast to the one proposed in the literature. This paper provides a (21 + 2)-approximation algorithm for the p-median network design problem with multiple cable types using random sampling techniques.

Keywords: approximation algorithms, buy-at-bulk, combinatorial optimization, network design, p-median

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Authors: S. B. Kumbalavati, J. D. Mallapur, K. Y. Bendigeri

Abstract:

A mobile Ad-hoc network (MANET) is a multihop wireless network where nodes communicate each other without any pre-deployed infrastructure. There is no central administrating unit. Hence, MANET is generally prone to many of the attacks. These attacks may alter, release or deny data. These attacks are nothing but intrusions. Intrusion is a set of actions that attempts to compromise integrity, confidentiality and availability of resources. A major issue in the design and operation of ad-hoc network is sharing the common spectrum or common channel bandwidth among all the nodes. We are performing intrusion detection using game theory approach. Game theory is a mathematical tool for analysing problems of competition and negotiation among the players in any field like marketing, e-commerce and networking. In this paper mathematical model is developed using game theory approach and intruders are detected and removed. Bandwidth utilization is estimated and comparison is made between bandwidth utilization with intrusion detection technique and without intrusion detection technique. Percentage of intruders and efficiency of the network is analysed.

Keywords: ad-hoc network, IDS, game theory, sensor networks

Procedia PDF Downloads 387

Authors: Elmabruk Laias, Abdalla M. Hanashi, Mohammed Alnas

Abstract:

Worldwide Interoperability for Microwave Access (WiMAX) became one of the most challenging issues, since it was responsible for distributing available resources of the network among all users this leaded to the demand of constructing and designing high efficient scheduling algorithms in order to improve the network utilization, to increase the network throughput, and to minimize the end-to-end delay. In this study, the proposed algorithm focuses on an efficient mechanism to serve non-real time traffic in congested networks by considering channel status.

Keywords: WiMAX, Quality of Services (QoS), OPNE, Diff-Serv (DS).

Procedia PDF Downloads 286

Authors: Hussam Elias, Ninovic Perez, Holger Hirsch

Abstract:

The completion of the EMC regulations worldwide is growing steadily as the usage of electronics in our daily lives is increasing more than ever. In this paper, we introduce a novel method to perform the final phase of Electromagnetic compatibility (EMC) measurement and to reduce the required test time according to the norm EN 55032 by using a developed tool and the conventional neural network(CNN). The neural network was trained using real EMC measurements, which were performed in the Semi Anechoic Chamber (SAC) by CETECOM GmbH in Essen, Germany. To implement our proposed method, we wrote software to perform the radiated electromagnetic interference (EMI) measurements and use the CNN to predict and determine the position of the turntable that meets the maximum radiation value.

Keywords: conventional neural network, electromagnetic compatibility measurement, mean absolute error, position error

Procedia PDF Downloads 200

Authors: Vishnu Pratap Singh Kirar

Abstract:

The main aim of the communication system is to achieve maximum performance. In cognitive radio, any user or transceiver have the ability to sense best suitable channel, while the channel is not in use. It means an unlicensed user can share the spectrum of licensed user without any interference. Though the spectrum sensing consumes a large amount of energy and it can reduce by applying various artificial intelligent methods for determining proper spectrum holes. It also increases the efficiency of Cognitive Radio Network (CRN). In this survey paper, we discuss the use of different learning models and implementation of Artificial Neural Network (ANN) to increase the learning and decision-making capacity of CRN without affecting bandwidth, cost and signal rate.

Keywords: artificial neural network, cognitive radio, cognitive radio networks, back propagation, spectrum sensing

Procedia PDF Downloads 609