Search results for: photochemically crosslinked polymer network

Authors: Kevin D. Manalo, Jumelyn L. Torres, Noel B. Linsangan

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This paper focuses on a wireless myoelectric prosthesis of the upper-limb that uses a Multilayer Perceptron Neural network with back propagation. The algorithm is widely used in pattern recognition. The network can be used to train signals and be able to use it in performing a function on their own based on sample inputs. The paper makes use of the Neural Network in classifying the electromyography signal that is produced by the muscle in the amputee’s skin surface. The gathered data will be passed on through the Classification Stage wirelessly through Zigbee Technology. The signal will be classified and trained to be used in performing the arm positions in the prosthesis. Through programming using Verilog and using a Field Programmable Gate Array (FPGA) with Zigbee, the EMG signals will be acquired and will be used for classification. The classified signal is used to produce the corresponding Hand Movements (Open, Pick, Hold, and Grip) through the Zigbee controller. The data will then be processed through the MLP Neural Network using MATLAB which then be used for the surface myoelectric prosthesis. Z-test will be used to display the output acquired from using the neural network.

Keywords: field programmable gate array, multilayer perceptron neural network, verilog, zigbee

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Authors: Athena Nguyen, Rojin Belganeh

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Detecting and identifying differences in like polymer materials are key factors in deformulation, comparative analysis as well as reverse engineering. Pyrolysis-GC/MS is an easy solid sample introduction technique which expands the application areas of gas chromatography and mass spectrometry. The Micro-furnace pyrolyzer is directly interfaced with the GC injector preventing any potential of cold spot, carryover, and cross contamination. This presentation demonstrates the study of two similar polymers by performing different mode of operations in the same system: Evolve gas analysis (EGA), Flash pyrolysis, Thermal desorption analysis, and Heart-cutting analysis. Unknown polymer materials and their chemical compositions are identified.

Keywords: gas chromatography/mass spectrometry, pyrolysis, pyrolyzer, thermal desorption-GC/MS

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Authors: Nadia El Alami El Hassani, Soukaina Motia, Benachir Bouchikhi, Nezha El Bari

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Doxycycline (DXy) is a cycline antibiotic, most frequently prescribed to treat bacterial infections in veterinary medicine. However, its broad antimicrobial activity and low cost, lead to an intensive use, which can seriously affect human health. Therefore, its spread in the food products has to be monitored. The scope of this work was to synthetize a sensitive and very selective molecularly imprinted polymer (MIP) for DXy detection in honey samples. Firstly, the synthesis of this biosensor was performed by casting a layer of carboxylate polyvinyl chloride (PVC-COOH) on the working surface of a gold screen-printed electrode (Au-SPE) in order to bind covalently the analyte under mild conditions. Secondly, DXy as a template molecule was bounded to the activated carboxylic groups, and the formation of MIP was performed by a biocompatible polymer by the mean of polyacrylamide matrix. Then, DXy was detected by measurements of differential pulse voltammetry (DPV). A non-imprinted polymer (NIP) prepared in the same conditions and without the use of template molecule was also performed. We have noticed that the elaborated biosensor exhibits a high sensitivity and a linear behavior between the regenerated current and the logarithmic concentrations of DXy from 0.1 pg.mL⁻¹ to 1000 pg.mL⁻¹. This technic was successfully applied to determine DXy residues in honey samples with a limit of detection (LOD) of 0.1 pg.mL⁻¹ and an excellent selectivity when compared to the results of oxytetracycline (OXy) as analogous interfering compound. The proposed method is cheap, sensitive, selective, simple, and is applied successfully to detect DXy in honey with the recoveries of 87% and 95%. Considering these advantages, this system provides a further perspective for food quality control in industrial fields.

Keywords: doxycycline, electrochemical sensor, food control, gold nanoparticles, honey, molecular imprinted polymer

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Authors: Earleen Jane Fuentes, Regeene Melarese Lim, Franklin Benjamin Tapia, Alexis Pantola

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Mobile Ad-hoc Network (MANET) is an infrastructure-less network of mobile devices, also known as nodes. These nodes heavily rely on each other’s resources such as memory, computing power, and energy. Thus, some nodes may become selective in forwarding packets so as to conserve their resources. These nodes are called misleading nodes. Several reputation-based techniques (e.g. CORE, CONFIDANT, LARS, SORI, OCEAN) and acknowledgment-based techniques (e.g. TWOACK, S-TWOACK, EAACK) have been proposed to detect such nodes. These techniques do not appropriately punish misleading nodes. Hence, this paper addresses the limitations of these techniques using a system called MINDRA.

Keywords: acknowledgment-based techniques, mobile ad-hoc network, selfish nodes, reputation-based techniques

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Authors: Hyehyeon Lee, Juwon Yu, Juwon Kim, Raquel Kristina Leoni Tumiar, Taewon Kim, Juae Kim, Hongsuk Suh

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Recently, the development of photovoltaics is rapidly accelerating as one of green energy sources. So we designed pyrimidine-based polymers with 2-{3-[4,6-bis-(4-hexyl-thiophen-2-yl)-pyrimidin-2-yl]-phenyl}-thiazolo[5,4-b]pyridine (mPTP), as active layer substances for polymer solar cells. Polymers with push-pull types, mPTPBDT-12, mPTPBDT-EH, mPTPBDTT-EH and mPTPTTI, are comprised of electron pushing unit using benzo[1,2-b;3,4-b’]dithiophene (BDT) or 4,8-bis(5-thiophen-2-yl)benzo[1,2-b:4,5-b']dithiophene (BDTT) or 6-(2-thienyl)-4H-thieno[3,2-b]indole(TTI) and electron pulling unit using mPTP. The device including mPTPTTI-12 indicated a VOC of 0.67 V, a JSC of 2.16 mA/cm², and a fill factor (FF) of 0.30, giving a power conversion efficiency (PCE) of 0.43%. The device including mPTPBDT-EH indicated a VOC of 0.56 V, a JSC of 2.64 mA/cm², and an FF of 0.30, giving a PCE of 0.44%. The device including mPTPBDTT-EH indicated a VOC of 0.44 V, a JSC of 2.45 mA/cm², and an FF of 0.29, giving a PCE of 0.31%. The device including mPTPTTI indicated a VOC of 0.72 V, a JSC of 4.95 mA/cm², and an FF of 0.32, giving a PCE of 1.15%. Therefore, mPTPBDT-12, mPTPBDT-EH, mPTPBDTT-EH and mPTPTTI were fabricated by Stille polymerization. Their optical properties were measured and the results show that pyrimidine-based polymers have a great promise to act as donor of active layer.

Keywords: polymer solar cells, photovoltaics, thiazolopyridine, conjugated polymer

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Authors: Kazui Sasakii, Seira Mormune-Moriya, Hiroaki Tanahashi, Shigeji Kongaya

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Poly (3.4-ethylenedioxythiophene) doped with poly (4-styrene sulfonate) (PEDOT: PSS) attracted a great deal of attention because of its unique characteristics of flexibility, optical properties, heat resistance and colloidal dispersion in water. It is well known that when high boiling solvents such as ethylene glycol or dimethyl sulfoxide are added as a secondary dopant to the micellar structure, PEDOT microcrystallizes and becomes highly conductive. In previous study bis(4-hydroxyphenyl) sulfone (BPS) was used as a secondary dopant for PEDOT:PSS and the enhancement of the conductivity was revealed. However, ductility is one of the serious issues which limited the application of PEDOT:PSS/BPS. So far, the composition with polymer binders has been conducted, however, polymer binders decrease the conductivity of the materials. In this study, PEDOT: PSS composites with polyester (PEs) were prepared by a simple aqueous process using PEs emulsion. The structural studies revealed that PEDOT:PSS and PEs were homogeneously distributed in the composites. It was found that the properties of PEDOT:PSS were remarkably enhanced by the incorporation of PEs. According to the tensile test, the ductility of PEDOT:PSS was remarkably improved. Interestingly, the conductivity of PEDOT:PSS/PEs composites was higher than that of neat PEDOT:PSS. For example, the conductivity increased by 8% at PEs content of 25 wt%. Since PEDOT:PSS were homogeneously dispersed on the surface of PEs particles, it was assumed that the conductive pathway was constructed by PEs particles in the nanocomposites. Therefore, a significant increase in conductivity was achieved.

Keywords: polymer composites, conductivity, PEDOT:PSS, polyester

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Authors: Yang Xiong, Hua Cheng

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In this paper, for the basic problem of wireless sensor network topology control and deployment, the Roesser model in rectangular grid sensor networks is presented. In addition, a general constructive realization procedure will be proposed. The procedure enables a distributed implementation of linear systems on a sensor network. A non-trivial example is illustrated.

Keywords: grid sensor networks, Roesser model, state-space realization, multidimensional systems

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Authors: Frederic Jumelle, Kelvin So, Didan Deng

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In this paper, we are introducing a model of artificial general intelligence (AGI), the functional neural network (FNN), for modeling human decision-making processes. The FNN is composed of multiple artificial mirror neurons (AMN) racing in the network. Each AMN has a similar structure programmed independently by the users and composed of an intention wheel, a motor core, and a sensory core racing at a specific velocity. The mathematics of the node’s formulation and the racing mechanism of multiple nodes in the network will be discussed, and the group decision process with fuzzy logic and the transformation of these conceptual methods into practical methods of simulation and in operations will be developed. Eventually, we will describe some possible future research directions in the fields of finance, education, and medicine, including the opportunity to design an intelligent learning agent with application in AGI. We believe that FNN has a promising potential to transform the way we can compute decision-making and lead to a new generation of AI chips for seamless human-machine interactions (HMI).

Keywords: neural computing, human machine interation, artificial general intelligence, decision processing

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Authors: Hao-Wen Chang, Santhanamoorthi Nachimuthu, Jyh-Chiang Jiang

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All-solid-state lithium batteries (ASSLBs) are increasingly recognized as a safer and more reliable alternative to conventional lithium-ion batteries due to their non-flammable nature and enhanced safety performance. ASSLBs utilize a range of solid-state electrolytes, including solid polymer electrolytes (SPEs), inorganic solid electrolytes (ISEs), and composite polymer electrolytes (CPEs). SPEs are particularly valued for their flexibility, ease of processing, and excellent interfacial compatibility with electrodes, though their ionic conductivity remains a significant limitation. ISEs, on the other hand, provide high ionic conductivity, broad electrochemical windows, and strong mechanical properties but often face poor interfacial contact with electrodes, impeding performance. CPEs, which merge the strengths of SPEs and ISEs, represent a compelling solution for next-generation ASSLBs by addressing both electrochemical and mechanical challenges. Despite their potential, the mechanisms governing lithium-ion transport within these systems remain insufficiently understood. In this study, we designed CPEs based on argyrodite-type Li₆PS₅Cl (LPSC) combined with two distinct polymer matrices: poly(ethylene oxide) (PEO) with 24.5 wt% lithium bis(trifluoromethane)sulfonimide (LiTFSI) and polycaprolactone (PCL) with 25.7 wt% LiTFSI. Through density functional theory (DFT) calculations, we investigated the interfacial chemistry of these materials, revealing critical insights into their stability and interactions. Additionally, ab initio molecular dynamics (AIMD) simulations of lithium electrodes interfaced with LPSC layers containing polymers and LiTFSI demonstrated that the polymer matrix significantly mitigates LPSC decomposition, compared to systems with only a lithium electrode and LPSC layers. These findings underscore the pivotal role of CPEs in improving the performance and longevity of ASSLBs, offering a promising path forward for next-generation energy storage technologies.

Keywords: all-solid-state lithium-ion batteries, composite solid electrolytes, DFT calculations, Li-ion transport

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Authors: Han Bing, Yin Zhenjie

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In order to analyze the status of a diesel engine, as well as conduct fault prediction, a new prediction model based on a gray system is proposed in this paper, which takes advantage of the neural network and the genetic algorithm. The proposed GBPGA prediction model builds on the GM (1.5) model and uses a neural network, which is optimized by a genetic algorithm to construct the error compensator. We verify our proposed model on the diesel faulty simulation data and the experimental results show that GBPGA has the potential to employ fault prediction on diesel.

Keywords: fault prediction, neural network, GM(1, 5) genetic algorithm, GBPGA

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Authors: Abhisek Dash

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This study had three neural networks, one for number segmentation, one for number detection and one for number recognition all of which are coupled to one another. All networks were trained on the MNIST dataset and were convolutional. It was assumed that the images had lighter background and darker foreground. The segmentation network took 28x28 images as input and had sixteen outputs. Segmentation training starts when a dark pixel is encountered. Taking a window(7x7) over that pixel as focus, the eight neighborhood of the focus was checked for further dark pixels. The segmentation network was then trained to move in those directions which had dark pixels. To this end the segmentation network had 16 outputs. They were arranged as “go east”, ”don’t go east ”, “go south east”, “don’t go south east”, “go south”, “don’t go south” and so on w.r.t focus window. The focus window was resized into a 28x28 image and the network was trained to consider those neighborhoods which had dark pixels. The neighborhoods which had dark pixels were pushed into a queue in a particular order. The neighborhoods were then popped one at a time stitched to the existing partial image of the number one at a time and trained on which neighborhoods to consider when the new partial image was presented. The above process was repeated until the image was fully covered by the 7x7 neighborhoods and there were no more uncovered black pixels. During testing the network scans and looks for the first dark pixel. From here on the network predicts which neighborhoods to consider and segments the image. After this step the group of neighborhoods are passed into the detection network. The detection network took 28x28 images as input and had two outputs denoting whether a number was detected or not. Since the ground truth of the bounds of a number was known during training the detection network outputted in favor of number not found until the bounds were not met and vice versa. The recognition network was a standard CNN that also took 28x28 images and had 10 outputs for recognition of numbers from 0 to 9. This network was activated only when the detection network votes in favor of number detected. The above methodology could segment connected and overlapping numbers. Additionally the recognition unit was only invoked when a number was detected which minimized false positives. It also eliminated the need for rules of thumb as segmentation is learned. The strategy can also be extended to other characters as well.

Keywords: convolutional neural networks, OCR, text detection, text segmentation

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Authors: Miroslav Mrlik, Marketa Ilcikova, Martin Cvek, Josef Osicka, Michal Sedlacik, Vladimir Pavlinek, Jaroslav Mosnacek

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Magnetorheological elastomers (MREs) are a unique type of materials consisting of two components, magnetic filler, and elastomeric matrix. Their properties can be tailored upon application of an external magnetic field strength. In this case, the change of the viscoelastic properties (viscoelastic moduli, complex viscosity) are influenced by two crucial factors. The first one is magnetic performance of the particles and the second one is off-state stiffness of the elastomeric matrix. The former factor strongly depends on the intended applications; however general rule is that higher magnetic performance of the particles provides higher MR performance of the MRE. Since magnetic particles possess low stability properties against temperature and acidic environment, several methods how to improve these drawbacks have been developed. In the most cases, the preparation of the core-shell structures was employed as a suitable method for preservation of the magnetic particles against thermal and chemical oxidations. However, if the shell material is not single-layer substance, but polymer material, the magnetic performance is significantly suppressed, due to the in situ polymerization technique, when it is very difficult to control the polymerization rate and the polymer shell is too thick. The second factor is the off-state stiffness of the elastomeric matrix. Since the MR effectivity is calculated as the relative value of the elastic modulus upon magnetic field application divided by elastic modulus in the absence of the external field, also the tuneability of the cross-linking reaction is highly desired. Therefore, this study is focused on the controllable modification of magnetic particles using a novel monomeric system based on 2-(1H-pyrrol-1-yl)ethyl methacrylate. In this case, the short polymer chains of different chain lengths and low polydispersity index will be prepared, and thus tailorable stability properties can be achieved. Since the relatively thin polymer chains will be grafted on the surface of magnetic particles, their magnetic performance will be affected only slightly. Furthermore, also the cross-linking density will be affected, due to the presence of the short polymer chains. From the application point of view, such MREs can be utilized for, magneto-resistors, piezoresistors or pressure sensors especially, when the conducting shell on the magnetic particles will be created. Therefore, the selection of the pyrrole-based monomer is very crucial and controllably thin layer of conducting polymer can be prepared. Finally, such composite particle consisting of magnetic core and conducting shell dispersed in elastomeric matrix can find also the utilization in shielding application of electromagnetic waves.

Keywords: atom transfer radical polymerization, core-shell, particle modification, electromagnetic waves shielding

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Authors: John Gibbs, Benjamin Flanders, Dylan Pozorski, Weixuan Liu

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Weight Painting–adjusting the influence a skeletal joint has on a given vertex in a character mesh–is an arduous and time con- suming part of the 3D animation pipeline. This process generally requires a trained technical animator and many hours of work to complete. Our skiNNer plug-in, which works within Autodesk’s Maya 3D animation software, uses Machine Learning and data pro- cessing techniques to create a deep neural network model that can accomplish the weight painting task in seconds rather than hours for bipedal quasi-humanoid character meshes. In order to create a properly trained network, a number of challenges were overcome, including curating an appropriately large data library, managing an arbitrary 3D mesh size, handling arbitrary skeletal architectures, accounting for extreme numeric values (most data points are near 0 or 1 for weight maps), and constructing an appropriate neural network model that can properly capture the high frequency alter- ation between high weight values (near 1.0) and low weight values (near 0.0). The arrived at neural network model is a cross between a traditional CNN, deep residual network, and fully dense network. The resultant network captures the unusually hard-edged features of a weight map matrix, and produces excellent results on many bipedal models.

Keywords: 3d animation, animation, character, rigging, skinning, weight painting, machine learning, artificial intelligence, neural network, deep neural network

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Authors: Muhammad Azam, Micheal Olaolu Arowolo, Fei He, Mihail Popescu, Dong Xu

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The number of biological papers is growing quickly, which means that the number of biological pathway figures in those papers is also increasing quickly. Each pathway figure shows extensive biological information, like the names of genes and how the genes are related. However, manually annotating pathway figures takes a lot of time and work. Even though using advanced image understanding models could speed up the process of curation, these models still need to be made more accurate. To improve gene name recognition from pathway figures, we applied a Siamese network to map image segments to a library of pictures containing known genes in a similar way to person recognition from photos in many photo applications. We used a triple loss function and a triplet spatial pyramid pooling network by combining the triplet convolution neural network and the spatial pyramid pooling (TSPP-Net). We compared VGG19 and VGG16 as the Siamese network model. VGG16 achieved better performance with an accuracy of 93%, which is much higher than OCR results.

Keywords: biological pathway, image understanding, gene name recognition, object detection, Siamese network, VGG

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Authors: Henok Mezemr Besfat, Haftom Gebreslassie Gebregwergs

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wireless network is one of the most important mediums of transmission of information from one device to another devices. Wireless communication has a broad range of applications, including mobile communications through cell phones and satellites, Internet of Things (IoT) connecting several devices, wireless sensor networks for traffic management and environmental monitoring, satellite communication for weather forecasting and TV without requiring any cable or wire or other electronic conductors, by using electromagnetic waves like IR, RF, satellite, etc. This paper summarizes different wireless network technologies, applications of different wireless technologies and different types of wireless networks. Generally, wireless technology will further enhance operations and experiences across sectors with continued innovation. This paper suggests different strategies that can improve wireless networks and technologies.

Keywords: wireless senser, wireless technology, wireless network, internet of things

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Authors: Oluwaponmile David Alao

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Misdiagnosis has been the major problem in health sector. Heart attack has been one of diseases that have high level of misdiagnosis recorded on the part of physicians. In this paper, an intelligent system has been developed for diagnosis of heart attack in the health sector. Dataset of heart attack obtained from UCI repository has been used. This dataset is made up of thirteen attributes which are very vital in diagnosis of heart disease. The system is developed on the multilayer perceptron trained with back propagation neural network then simulated with feed forward neural network and a recognition rate of 87% was obtained which is a good result for diagnosis of heart attack in medical field.

Keywords: heart attack, artificial neural network, diagnosis, intelligent system

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Authors: İkbal Eski

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This investigation is researched on design of robust neural network predictors for analyzing vibration effects on moving parts of a drilling machine. Moreover, the research is divided two parts; first part is experimental investigation, second part is simulation analysis with neural networks. Therefore, a real time the drilling machine is used to vibrations during working conditions. The measured real vibration parameters are analyzed with proposed neural network. As results: Simulation approaches show that Radial Basis Neural Network has good performance to adapt real time parameters of the drilling machine.

Keywords: artificial neural network, vibration analyses, drilling machine, robust

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Authors: Jain Jyoti, Jain Shorab, Sinha Shishir

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In the current scenario, development of new biodegradable composites with the reinforcement of some plant derived natural fibers are in major research concern. Abundant quantity of these natural plant derived fibers including sisal, ramp, jute, wheat straw, pine, pineapple, bagasse, etc. can be used exclusively or in combination with other natural or synthetic fibers to augment their specific properties like chemical, mechanical or thermal properties. Among all natural fibers, wheat straw, bagasse, kenaf, pineapple leaf, banana, coir, ramie, flax, etc. pineapple leaf fibers have very good mechanical properties. Being hydrophilic in nature, pineapple leaf fibers have very less affinity towards all types of polymer matrixes. Not much work has been carried out in this area. Surface treatments like alkaline treatment in different concentrations were conducted to improve its compatibility towards hydrophobic polymer matrix. Pineapple leaf fiber epoxy composites have been prepared using hand layup method. Effect of variation in fiber loading up to 20% in epoxy composites has been studied for mechanical properties like tensile strength and flexural strength. Analysis of fiber morphology has also been studied using FTIR, XRD. SEM micrographs have also been studied for fracture surface.

Keywords: composite, mechanical, natural fiber, pineapple leaf fiber

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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: Talakokula Visalakshi

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Concrete is the most commonly used construction material across the globe, its usage is second only to water. It is prepared using ordinary Portland cement whose production contributes to 5-8% of total carbon emission in the world. On the other hand the fly ash by product from the power plants is produced in huge quantities is termed as waste and disposed in landfills. In order to address the above issues mentioned, it is essential that other forms of binding material must be developed in place of cement to make concrete. The geo polymer concrete is one such alternative developed by Davidovits in 1980’s. Geopolymer do not form calcium-silicate hydrates for matrix formation and strength but undergo polycondensation of silica and alumina precursors to attain structural strength. Its setting mechanism depends upon polymerization rather than hydration. As a result it is able to achieve its strength in 3-5 days whereas concrete requires about a month to do the same. The objective of this research is to assess the performance of geopolymer concrete under sulphate and acid attack. The assessment is done based on the experiments conducted on geopolymer concrete. The expected outcomes include that if geopolymer concrete is more durable than normal concrete, then it could be a competitive replacement option of concrete and can lead to significant reduction of carbon foot print and have a positive impact on the environment. Fly ash based geopolymer concrete offers an opportunity to completely remove the cement content from concrete thereby making the concrete a greener and future construction material.

Keywords: fly ash, geo polymer, geopolymer concrete, construction material

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Authors: Guler S., Guzel E., Gulen M.

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Fiber Reinforced Polymer (FRP) materials are commonly used in construction sector to enhance the strength and ductility capacities of structural elements. The equations on confined compressive strength of FRP wrapped concrete cylinders is described in the 7th chapter of the Turkish Earthquake Code (TEC-07) that enter into force in 2007. This study aims to evaluate the applicability of TEC-07 on confined compressive strengths of circular FRP wrapped concrete cylinders. To this end, a large number of data on circular FRP wrapped concrete cylinders are collected from the literature. It is clearly seen that the predictions of TEC-07 on circular FRP wrapped the FRP wrapped columns is not same accuracy for different ranges of concrete strengths.

Keywords: Fiber Reinforced Polymer (FRP), concrete cylinders, Turkish Earthquake Code, earthquake

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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: D. Bikiaris, M. Nerantzaki, I. Koliakou, A. Francone, N. Kehagias

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In the present study, the formation of structures in poly(lactic acid) (PLA) has been investigated with respect to producing areas of regular, superficial features with dimensions comparable to those of cells or biological macromolecules. Nanoimprint lithography, a method of pattern replication in polymers, has been used for the production of features ranging from tens of micrometers, covering areas up to 1 cm², down to hundreds of nanometers. Both micro- and nano-structures were faithfully replicated. Potentially, PLA has wide uses within biomedical fields, from implantable medical devices, including screws and pins, to membrane applications, such as wound covers, and even as an injectable polymer for, for example, lipoatrophy. The possibility of fabricating structured PLA surfaces, with structures of the dimensions associated with cells or biological macro- molecules, is of interest in fields such as cellular engineering. Imprint-based technologies have demonstrated the ability to selectively imprint polymer films over large areas resulting in 3D imprints over flat, curved or pre-patterned surfaces. Here, we compare nano-patterned with nano-patterned by nanoimprint lithography (NIL) PLA film. A silicon nanostructured stamp (provided by Nanotypos company) having positive and negative protrusions was used to pattern PLA films by means of thermal NIL. The polymer film was heated from 40°C to 60°C above its Tg and embossed with a pressure of 60 bars for 3 min. The stamp and substrate were demolded at room temperature. Scanning electron microscope (SEM) images showed good replication fidelity of the replicated Si stamp. Contact-angle measurements suggested that positive microstructuring of the polymer (where features protrude from the polymer surface) produced a more hydrophilic surface than negative micro-structuring. The ability to structure the surface of the poly(lactic acid), allied to the polymer’s post-processing transparency and proven biocompatibility. Films produced in this were also shown to enhance the aligned attachment behavior and proliferation of Wharton’s Jelly Mesenchymal Stem cells, leading to the observed growth contact guidance. The bacterial attachment patterns of some bacteria, highlighted that the nano-patterned PLA structure can reduce the propensity for the bacteria to attach to the surface, with a greater bactericidal being demonstrated activity against the Staphylococcus aureus cells. These biocompatible, micro- and nanopatterned PLA surfaces could be useful for polymer– cell interaction experiments at dimensions at, or below, that of individual cells. Indeed, post-fabrication modification of the microstructured PLA surface, with materials such as collagen (which can further reduce the hydrophobicity of the surface), will extend the range of applications, possibly through the use of PLA’s inherent biodegradability. Further study is being undertaken to examine whether these structures promote cell growth on the polymer surface.

Keywords: poly(lactic acid), nano-imprint lithography, anti-bacterial properties, PLA

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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: Asmaa M. Fahim

Abstract:

In this investigation, the (E)-2-cyano-3-(dimethylamino)-N-(2,4-dioxo-1,2,3,4-tetrahydropyrimidin-5-yl)acrylam-ide (4) which used TAM as a template which interacts with Methacrylic Acid (MAA) monomer, in the presence of CH₃CN as progen. The TAM-MMA complex interactions are dependent on stable hydrogen bonding interaction between the carboxylic acid group of TAM(Template) and the hydroxyl group of MMA(methyl methacrylate) with minimal interference of porogen CH₃CN. The physical computational studies were used to optimize their structures and frequency calculations. The binding energies between TAM with different monomers showed the most stable molar ratio of 1:4, which was confirmed through experimental analysis. The optimized polymers were investigated in industrial applications.

Keywords: molecular imprinted polymer, computational studies, SEM, spectral analysis, industrial applications

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Authors: Younghyun Jeon, Seungjoo Maeng

Abstract:

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

Abstract:

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

Abstract:

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

Procedia PDF Downloads 161