Search results for: polymer hybrid nanocomposites

Authors: Muthukumarasamy Govindarajan

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Detection of unwanted, unsolicited mails called spam from email is an interesting area of research. It is necessary to evaluate the performance of any new spam classifier using standard data sets. Recently, ensemble-based classifiers have gained popularity in this domain. In this research work, an efficient email filtering approach based on ensemble methods is addressed for developing an accurate and sensitive spam classifier. The proposed approach employs Naive Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA) as base classifiers along with different ensemble methods. The experimental results show that the ensemble classifier was performing with accuracy greater than individual classifiers, and also hybrid model results are found to be better than the combined models for the e-mail dataset. The proposed ensemble-based classifiers turn out to be good in terms of classification accuracy, which is considered to be an important criterion for building a robust spam classifier.

Keywords: accuracy, arcing, bagging, genetic algorithm, Naive Bayes, sentiment mining, support vector machine

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Authors: Ravindra Dastikop, G. S. Thyagaraju, U. P. Kulkarni

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Even though the number of context aware applications is increasing day by day along with the users, till today there is no generic programming paradigm for context aware applications. This situation could be remedied by design and developing the appropriate context modeling and programming paradigm for context aware applications. In this paper, we are proposing the static context model and metrics for validating the expressiveness and understandability of the model. The proposed context modeling is a way of describing a situation of user using context entities , attributes and relationships .The model which is an extended and hybrid version of ER model, ontology model and Graphical model is specifically meant for expressing and understanding the user situation in context aware environment. The model is useful for understanding context aware problems, preparing documentation and designing programs and databases. The model makes use of context entity attributes relationship (CEAR) diagram for representation of association between the context entities and attributes. We have identified a new set of graphical notations for improving the expressiveness and understandability of context from the end user perspective .

Keywords: user context, context entity, context entity attributes, situation, sensors, devices, relationships, actors, expressiveness, understandability

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Authors: Hossein Kh. Bisheh, Nan Wu

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Advanced composite materials have a great importance in engineering structures due to their high specific modulus and strength and low weight. These materials can be used in design and fabrication of automotive drive shafts to reduce the weight of the structure. Hence, an optimum design of a composite drive shaft satisfying the design criteria, can be an appropriate substitution of metallic drive shafts. The aim of this study is to design and analyze a composite automotive drive shaft with high specific strength and low weight satisfying the design criteria. Tsai-Wu criterion is chosen as the failure criterion. Various designs with different lay-ups and materials are investigated based on the design requirements and finally, an optimum design satisfying the design criteria is chosen based on the weight and cost considerations. The results of this study indicate that if the weight is the main concern, a shaft made of Carbon/Epoxy can be a good option, and if the cost is a more important parameter, a hybrid shaft made of aluminum and Carbon/Epoxy can be considered.

Keywords: Bending natural frequency, Composite drive shaft, Peak torque, Torsional buckling

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Authors: Nasser A. M. Barakat

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Membrane technology is the most promising process for oil-water separation operation if the hydrophilicity, fouling and reusability properties could be improved. In this study, novel effective and reusable membrane for oil-water separation process is introduced based on modification of polysulfone (PSF) electrospun nanofiber mats. The modification process was achieved by incorporation of NaOH nanoparticles inside the PSF nanofibers, and formation of a thin layer from a polyamide polymer on the surface of the electrospun mat. Typically, solutions composed of PSF and NaOH (twelve solutions were prepared based on different PSF concentrations; 15, 18 and 20 wt%, and various NaOH content; 1.5, 1.7 and 2.5 wt%) have been electrospun, then the dried nanofiber mats were treated by m-phenylenediamine and 1,3,5-benzenetricarbonyl chloride to form polyamide thin layer on the surface of the mats. The results indicated that incorporation of NaOH and the formed polyamide could decrease the water contact angle from ~ 130˚ to 13˚ for the nanofiber mats obtained from 20 wt% PSF solutions containing 1.7 wt% sodium hydroxide powders. Interestingly, the membrane having the lowest contact angle could separate oil-water mixture for three successive cycles and 100% removal of the oil with relatively high water flux; 5.5 m3/m2.day. Overall, simplicity of the manufacturing technique, and effectiveness and reusability of the produced nanofiber mats open new avenue for the introduced as promising membranes for the oil-water separation process.

Keywords: electrospinning, oil-water separation, hydrophilic membrane, nanofibers

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Authors: Stefano Zappacosta, Cristiano Bove, Maria Carmela Marinelli, Paola di Lauro, Katarina Spasenovic, Lorenzo Ostano, Giuseppe Aiello, Marco Pietrosanto

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This paper proposes a novel neural network approach for assessing flood hazard mapping. The core of the model is a machine learning component fed by frequency ratios, namely statistical correlations between flood event occurrences and a selected number of topographic properties. The proposed hybrid model can be used to classify four different increasing levels of hazard. The classification capability was compared with the flood hazard mapping River Basin Plans (PAI) designed by the Italian Institute for Environmental Research and Defence, ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale). The study area of Piemonte, an Italian region, has been considered without loss of generality. The frequency ratios may be used as a standalone block to model the flood hazard mapping. Nevertheless, the mixture with a neural network improves the classification power of several percentage points, and may be proposed as a basic tool to model the flood hazard map in a wider scope.

Keywords: flood modeling, hazard map, neural networks, hydrogeological risk, flood risk assessment

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Authors: Hamza Benyahia, Mostapha Tarfaoui, Ahmed El-Moumen, Djamel Ouinas

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Composite pipes are commonly used in the oil industry, and extreme flow of hot and cold gas fluid can cause degradation of their mechanical performance and properties. Therefore, it is necessary to consider thermomechanical behavior as an important parameter in designing these tubular structures. In this paper, an experimental study is conducted on composite glass/epoxy tubes, with a thickness of 6.2 mm and 86 mm internal diameter made by filament winding of (Փ = ± 55°), to investigate the effects of extreme thermal condition on their mechanical properties b over a temperature range from -40 to 80°C. The climatic chamber is used for the thermal aging and then, combine split disk system is used to perform tensile tests on these composite pies. Thermal aging is carried out for 8hr but each specimen was subjected to various temperature ranges and then, uniaxial tensile test is conducted to evaluate their mechanical performance. Experimental results show degradation in the mechanical properties of composite pipes with an increase in temperature. The rigidity of pipes increases progressively with a decrease in thermal load and results in a radical decrease in their elongation before fracture, thus, decreasing their ductility. However, with an increase in the temperature, there is a decrease in the yield strength and an increase in yield strain, which confirmed an increase in the plasticity of composite pipes.

Keywords: composite pipes, thermal-mechanical properties, filament winding, thermal degradation

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Authors: Feng Ju Chuang, Yu Wen Wang, Tai Jung Hsieh, Shyh Ming Kuo

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Polylactic acid is a synthetic polymer with good biocompatibility and degradability, is widely used in clinical applications. In this study, we utilized Polylactic acid particles as stimulants for macrophages and the collagen synthesis of co-cultured fibroblasts was evaluated. The results indicated that Polylactic acid particles were nontoxic to cells from 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide. No obvious inflammation effect was observed (under the PLLA concentration of 1 mg/mL) after 24-h co-culture of Raw264.7 and NIH3T3 cells (from TNF-α assay). The addition of PLLA particles to the Raw264.7 and NIH3T3 co-cultures increased the synthesis of collagen, the highest collagen synthesis from the fibroblast was the 0.2 mg/mL (approximately 60% increased as compared with without addition Polylactic acid particles). Moreover, a co-axial atomization delivery device was used to percutaneously introduce Polylactic acid particles into the dermis layer and stimulating macrophages to secrete growth factors promoting fibroblasts to produce collagen. The preliminary results demonstrated the synthesis of collagen was increased mildly after the introduction of Polylactic acid particles for 28-d post implantation. The Polylactic acid particles could be successfully introduced into the dermis layer from H&E staining examination, however, the optimum concentration of Polylactic acid particles and the time-period for collagen synthesis still need to be evaluated.

Keywords: collagen synthesis, macrophage, NIH3T3 cells, polylactic acid particles

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Authors: Hans Marquez Ramos, Cesar Hernandez, Ingrid Páez

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This paper develops a multiple channel assignment model, which allows to take advantage in most efficient way, spectrum opportunities in cognitive radio networks. Developed scheme allows make several available and frequency adjacent channel assignments, which require a bigger wide band, under an equality environment. The hybrid assignment model it is made by to algorithms, one who makes the ranking and select available frequency channels and the other one in charge of establishing an equality criteria, in order to not restrict spectrum opportunities for all other secondary users who wish to make transmissions. Measurements made were done for average bandwidth, average delay, as well fairness computation for several channel assignment. Reached results were evaluated with experimental spectrum occupational data from GSM frequency band captured. Developed model, shows evidence of improvement in spectrum opportunity use and a wider average transmit bandwidth for each secondary user, maintaining equality criteria in channel assignment.

Keywords: bandwidth, fairness, multichannel, secondary users

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Authors: C. Joseph Abou-Fayssal, K. Philippot, R. Poli, E. Manoury, A. Riisager

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The use of soluble polymer-supported metal nanoparticles (MNPs) has received significant attention for the ease of catalyst recovery and recycling. Of particular interest are MNPs that are supported on polymers that are either soluble or form stable colloidal dispersion in water, as this allows to combine of the advantages of the aqueous biphasic protocol with the catalytical performances of MNPs. The objective is to achieve good confinement of the catalyst in the nanoreactor cores and, thus, a better catalyst recovery in order to overcome the previously witnessed MNP extraction. Inspired by previous results, we are interested in the design of polymeric nanoreactors functionalized with ligands able to solidly anchor metallic nanoparticles in order to control the activity and selectivity of the developed nanocatalysts. The nanoreactors are core-crosslinked micelles (CCM) synthesized by reversible addition-fragmentation chain transfer (RAFT) polymerization. Varying the nature of the core-linked functionalities allows us to get differently stabilized metal nanoparticles and thus compare their performance in the catalyzed aqueous biphasic hydrogenation of model substrates. Particular attention is given to catalyst recyclability.

Keywords: biphasic catalysis, metal nanoparticles, polymeric nanoreactors, catalyst recovery, RAFT polymerization

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Authors: Shokooh Moghadam, Mohammad Taghi Khorasani, Sajjad Seifi Mofarah, M. Daliri

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Through the recent two decades, the use of magnetic-property materials with the aim of target cell’s separation and eventually cancer treatment has incredibly increased. Numerous factors can alter the efficacy of this method on curing. In this project, the effect of magnetic field on adhesion of PDL and L929 cells on nanocomposite of iron oxide/PHB with different density of iron oxides (1%, 2.5%, 5%) has been studied. The nanocamposite mentioned includes a polymeric film of poly hydroxyl butyrate and γ-Fe2O3 particles with the average size of 25 nanometer dispersed in it and during this process, poly vinyl alcohol with 98% hydrolyzed and 78000 molecular weight was used as an emulsion to achieve uniform distribution. In order to get the homogenous film, the solution of PHB and iron oxide nanoparticles were put in a dry freezer and in liquid nitrogen, which resulted in a uniform porous scaffold and for removing porosities a 100◦C press was used. After the synthesis of a desirable nanocomposite film, many different tests were performed, First, the particles size and their distribution in the film were evaluated by transmission electron microscopy (TEM) and even FTIR analysis and DMTA test were run in order to observe and accredit the chemical connections and mechanical properties of nanocomposites respectively. By comparing the graphs of case and control samples, it was established that adding nano particles caused an increase in crystallization temperature and the more density of γ-Fe2O3 lead to more Tg (glass temperature). Furthermore, its dispersion range and dumping property of samples were raised up. Moreover, the toxicity, morphologic changes and adhesion of fibroblast and cancer cells were evaluated by a variety of tests. All samples were grown in different density and in contact with cells for 24 and 48 hours within the magnetic fields of 2×10^-3 Tesla. After 48 hours, the samples were photographed with an optic and SEM and no sign of toxicity was traced. The number of cancer cells in the case of sample group was fairly more than the control group. However, there are many gaps and unclear aspects to use magnetic field and their effects in cancer and all diseases treatments yet to be discovered, not to neglect that there have been prominent step on this way in these recent years and we hope this project can be at least a minimum movement in this issue.

Keywords: nanocomposite, cell attachment, magnetic field, cytotoxicity

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Authors: Monica Racha, Siva Chandrasekaran, Alex Stojcevski

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The engineering programs focus on promoting students' personal and professional development by ensuring that students acquire technical and professional competencies during four-year studies. The traditional engineering laboratory provides an opportunity for students to "practice by doing," and laboratory facilities aid them in obtaining insight and understanding of their discipline. Due to rapid technological advancements and the current COVID-19 outbreak, the traditional labs were transforming into virtual learning environments. Aim: To better understand the limitations of the physical laboratory, this research study aims to use a Machine Learning (ML) algorithm that interfaces with the Augmented Reality HoloLens and predicts the image behavior to classify and detect the electronic components. The automated electronic components error classification and detection automatically detect and classify the position of all components on a breadboard by using the ML algorithm. This research will assist first-year undergraduate engineering students in conducting laboratory practices without any supervision. With the help of HoloLens, and ML algorithm, students will reduce component placement error on a breadboard and increase the efficiency of simple laboratory practices virtually. Method: The images of breadboards, resistors, capacitors, transistors, and other electrical components will be collected using HoloLens 2 and stored in a database. The collected image dataset will then be used for training a machine learning model. The raw images will be cleaned, processed, and labeled to facilitate further analysis of components error classification and detection. For instance, when students conduct laboratory experiments, the HoloLens captures images of students placing different components on a breadboard. The images are forwarded to the server for detection in the background. A hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm will be used to train the dataset for object recognition and classification. The convolution layer extracts image features, which are then classified using Support Vector Machine (SVM). By adequately labeling the training data and classifying, the model will predict, categorize, and assess students in placing components correctly. As a result, the data acquired through HoloLens includes images of students assembling electronic components. It constantly checks to see if students appropriately position components in the breadboard and connect the components to function. When students misplace any components, the HoloLens predicts the error before the user places the components in the incorrect proportion and fosters students to correct their mistakes. This hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm automating electronic component error classification and detection approach eliminates component connection problems and minimizes the risk of component damage. Conclusion: These augmented reality smart glasses powered by machine learning provide a wide range of benefits to supervisors, professionals, and students. It helps customize the learning experience, which is particularly beneficial in large classes with limited time. It determines the accuracy with which machine learning algorithms can forecast whether students are making the correct decisions and completing their laboratory tasks.

Keywords: augmented reality, machine learning, object recognition, virtual laboratories

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Authors: Daniel Joseph Whittaker

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This paper examines the broad array of historic sites from which Chicago common brick has emerged, and the methods this brick has been utilized within and around a new hybrid structure recently completed-and periodically opened to the public, as a private art, architecture, design, and social activism gallery space. Various technical aspects regarding the structural and aesthetic reuse methods of salvaged brick within the interior and exterior of this new Tadao Ando-designed building in Lincoln Park, Chicago, are explored. This paper expands specifically upon the multiple possible origins of Chicago common brick, as well as the extant brick currently composing the surrounding alley which is integral to demarcating the southern site boundary of the old apartment building now gallery. Themes encompassing Chicago’s archeological and architectural history, local resource extraction, and labor practices permeate this paper’s investigation into urban, social and architectural history and building construction technology advancements through time.

Keywords: masonry construction, history brickmaking, private museums, Chicago Illinois, Tadao Ando

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Authors: Nighat Zia-ud-Den, Shazia Anwer Bukhari

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In the present study, sunflower seeds were exposed to magnetic bio-stimulation at different milli Tesla, and their effects were studied. The present study addressed to establish the effectiveness of magnetic bio-stimulation on seed germination, growth, and other dynamics of crop growth. The changes in physiological characters, i.e. the growth parameters of seedlings (biomass, root and shoot length, fresh and dry weight of root shoot leaf and fruit, leaf area, the height of plants, number of leaves, and number of fruits per plant) and antioxidant activities were measured. The parameters related to germination and growth were measured under controlled conditions while they changed significantly compared with that of the control. These changes suggested that magnetic seed stimulator enhanced the inner energy of seeds, which contributed to the acceleration of the growth and development of seedlings. Moreover, pretreatment with a magnetic field was found to be a positive impact on sunflower seeds germination, growth, and other biochemical parameters.

Keywords: sunflower seeds, physical priming method, biochemical parameters, antioxidant activities

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Authors: H. Oudira, A. Saifi

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The therapeutic utility of certain Auger emitters such as iodine-125 depends on their position within the cell nucleus . Or diagnostically, and to maintain as low as possible cell damage, it is preferable to have radionuclide localized outside the cell or at least the core. One solution to this problem is to consider markers capable of conveying anticancer drugs to the tumor site regardless of their location within the human body. The objective of this study is to simulate the impact of a complex such as bleomycin on single and double strand breaks in the DNA molecule. Indeed, this simulation consists of the following transactions: - Construction of BLM -Fe- DNA complex. - Simulation of the electron’s transport from the metastable state excitation of Fe 57 by the Monte Carlo method. - Treatment of chemical reactions in the considered environment by the diffusion equation. For physical, physico-chemical and finally chemical steps, the geometry of the complex is considered as a sphere of 50 nm centered on the binding site , and the mathematical method used is called step by step based on Monte Carlo codes.

Keywords: concentration, yield, radical species, bleomycin, excitation, DNA

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Authors: M. Al-Farttoosi, M. Y. Rafiq, J. Summerscales, C. Williams

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Structures can be subjected to impact loading from various sources like earthquake, tsunami, missiles and explosions. The impact loading can cause different degrees of damage to concrete structures. The demand for strengthening and rehabilitation of damaged structures is increasing. In recent years, Car0bon Fibre Reinforced Polymer (CFRP) matrix composites has gain more attention for strengthening and repairing these structures. To study the impact behaviour of the reinforced concrete (RC) beams strengthened or repaired using CFRP, a heavy impact test machine was designed and manufactured .The machine included a newly designed support system for beams together with various instrumentation. This paper describes the support design configuration of the impact test machine, instrumentation and dynamic analysis of the concrete beams. To evaluate the efficiency of the new impact test machine, experimental impact tests were conducted on simple supported reinforced concrete beam. Different methods were used to determine the impact force and impact response of the RC beams in terms of inertia force, maximum deflection, reaction force and fracture energy. The manufactured impact test machine was successfully used in testing RC beams under impact loading and used successfully to test the reinforced concrete beams strengthened or repaired using CFRP under impact loading.

Keywords: beam, concrete, impact, machine

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Authors: Magdalena Hałupka-Bryl, Magdalena Bednarowicz, Ryszard Krzyminiewski, Yukio Nagasaki

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Due to their unique physical properties, superparamagnetic iron oxide nanoparticles are increasingly used in medical applications. They are very useful carriers for delivering antitumor drugs in targeted cancer treatment. Magnetic nanoparticles (PEG-PIONs/DOX) with chemotherapeutic were synthesized by coprecipitation method followed by coating with biocompatible polymer PEG-derivative (poly(ethylene glycol)-block-poly(4-vinylbenzylphosphonate). Complete physicochemical characterization was carried out (ESR, HRTEM, X-ray diffraction, SQUID analysis) to evaluate the magnetic properties of obtained PEG-PIONs/DOX. Nanoparticles were investigated also in terms of their stability, drug loading efficiency, drug release and antiproliferative effect on cancer cells. PEG-PIONs/DOX have been successfully used for the efficient delivery of an anticancer drug into the tumor region. Fluorescent imaging showed the internalization of PEG-PIONs/DOX in the cytoplasm. Biodistribution studies demonstrated that PEG-PIONs/DOX preferentially accumulate in tumor region via the enhanced permeability and retention effect. The present findings show that synthesized nanosystem is promising tool for potential magnetic drug delivery.

Keywords: targeted drug delivery, magnetic properties, iron oxide nanoparticles, biodistribution

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Authors: Wonkyung Na, Namhun Kim, Heeyeop Chae

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Quantum dots (QDs) are getting attentions due to their excellent optical properties in display, solar cell, biomolecule detection and lighting applications. Energy band gap can be easilty controlled by controlling their size and QDs are proper to apply in light-emitting-diode(LED) and lighting application, especially. Typically cadmium (Cd) containing QDs show a narrow photoluminescence (PL) spectrum and high quantum yield. However, Cd is classified as a hazardous materials and the use of Cd is being tightly regulated under 100ppm level in many countries.InP and CuInS2 (CIS) are being investigated as Cd-free QD materials and it is recently demonstrated that the performance of those Cd-free QDs is comparable to their Cd-based rivals.Due to a broad emission spectrum, CuInS2 QDs are also proper to be applied to white LED.4 For the lighting applications, the QD should be made in forms of color conversion films. Various film processes are reported with QDs in polymer matrixes. In this work, we synthesized the CuInS2 (CIS) QDs and QD embedded polystyrene color conversion films were fabricated for white color emission with electro-spinning process. As a result, blue light from blue LED is converted to white light with high color rendering index (CRI) of 72 by the color conversion films.

Keywords: CuInS2/ZnS, electro-spinning, color conversion films, white light emitting diodes

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Authors: Ghofrane Rehaiem, Hamza Gharsellaoui, Samir Benahmed

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In this study, Artificial Neural Networks (ANNs) were used for modeling the parameters that allow the real-time scheduling of embedded systems under resources constraints designed for real-time applications running. The objective of this work is to implement a neural networks based approach for real-time scheduling of embedded systems in order to handle real-time constraints in execution scenarios. In our proposed approach, many techniques have been proposed for both the planning of tasks and reducing energy consumption. In fact, a combination of Dynamic Voltage Scaling (DVS) and time feedback can be used to scale the frequency dynamically adjusting the operating voltage. Indeed, we present in this paper a hybrid contribution that handles the real-time scheduling of embedded systems, low power consumption depending on the combination of DVS and Neural Feedback Scheduling (NFS) with the energy Priority Earlier Deadline First (PEDF) algorithm. Experimental results illustrate the efficiency of our original proposed approach.

Keywords: optimization, neural networks, real-time scheduling, low-power consumption

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Authors: Pablo Vidal, Misael Martinez, Carlos Hernandez, Ananta R. Adhikari, Luis Materon, Yuanbing Mao, Karen Lozano

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Conjugated polymers are smart materials that show tremendous practical applications in diverse subjects. Polydiacetylenes are conjugated polymers with special optical properties. In response to the environmental changes such as pH and molecular binding, it changes its color. Such an interesting chromic and emissive behavior of polydiacetylenes make them a highly popular polymer in wide areas, including biomedicine such as a biosensor. In this research, we used polyvinyl butyrate as a matrix to fibrillate polydiacetylenes. We initially prepared polyvinyl butyrate/diacetylene matrix using forcespinning technique. They were then polymerized to form polyvinyl butyrate/polydiacetylene (PVB/PDA). These matrices then studied for their bio-sensing response to gram-positive and gram-negative bacteria. The sensing ability of the PVB/PDA biosensor was observed as early as 30 min in the presence of bacteria at 37°C. Now our effort is to decrease this effective temperature to room temperature to make this device applicable in the general daily life. These chromic biosensors will find extensive application not only alert the infection but also find other promising applications such as wearable sensors and diagnostic systems.

Keywords: smart material, conjugated polymers, biosensor, polyvinyl butyrate/polydiacetylene

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Authors: Debananda Mohapatra, Subramanya Badrayyana, Smrutiranjan Parida

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Carbon nano-onions (CNOs) are the spherical graphitic nanostructures composed of concentric shells of graphitic carbon can be hypothesized as the intermediate state between fullerenes and graphite. These are very important members in fullerene family also known as the multi-shelled fullerenes can be envisioned as promising supercapacitor electrode with high energy & power density as they provide easy access to ions at electrode-electrolyte interface due to their curvature. There is still very sparse report concerning on CNOs as electrode despite having an excellent electrodechemical performance record due to their unavailability and lack of convenient methods for their high yield preparation and purification. Keeping all these current pressing issues in mind, we present a facile scalable and straightforward flame synthesis method of pure and highly dispersible CNOs without contaminated by any other forms of carbon; hence, a post processing purification procedure is not necessary. To the best of our knowledge, this is the very first time; we developed an extremely simple, light weight, novel inexpensive, flexible free standing pristine CNOs electrode without using any binder element. Locally available daily used cotton wipe has been used for fabrication of such an ideal electrode by ‘dipping and drying’ process providing outstanding stretchability and mechanical flexibility with strong adhesion between CNOs and porous wipe. The specific capacitance 102 F/g, energy density 3.5 Wh/kg and power density 1224 W/kg at 20 mV/s scan rate are the highest values that ever recorded and reported so far in symmetrical two electrode cell configuration with 1M Na2SO4 electrolyte; indicating a very good synthesis conditions employed with optimum pore size in agreement with electrolyte ion size. This free standing CNOs electrode also showed an excellent cyclic performance and stability retaining 95% original capacity after 5000 charge –discharge cycles. Furthermore, this unique method not only affords binder free - freestanding electrode but also provide a general way of fabricating such multifunctional promising CNOs based nanocomposites for their potential device applications in flexible solar cells and lithium-ion batteries.

Keywords: binder-free, flame synthesis, flexible, carbon nano onion

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Authors: S. Neelima, P. S. Subramanyam

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The major tool for distribution planning is load forecasting, which is the anticipation of the load in advance. Artificial neural networks have found wide applications in load forecasting to obtain an efficient strategy for planning and management. In this paper, the application of neural networks to study the design of short term load forecasting (STLF) Systems was explored. Our work presents a pragmatic methodology for short term load forecasting (STLF) using proposed two-stage model of wavelet transform (WT) and artificial neural network (ANN). It is a two-stage prediction system which involves wavelet decomposition of input data at the first stage and the decomposed data with another input is trained using a separate neural network to forecast the load. The forecasted load is obtained by reconstruction of the decomposed data. The hybrid model has been trained and validated using load data from Telangana State Electricity Board.

Keywords: electrical distribution systems, wavelet transform (WT), short term load forecasting (STLF), artificial neural network (ANN)

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Authors: Phuc D. Nguyen, Kristy L. Hansen, Branko Zajamsek

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The existence of special characteristics of wind farm noise such as amplitude modulation (AM) contributes significantly to annoyance, which could ultimately result in sleep disturbance and other adverse health effects for residents living near wind farms. In order to detect and quantify this phenomenon, several methods have been developed which can be separated into three types: time-domain, frequency-domain and hybrid methods. However, due to a lack of systematic validation of these methods, it is still difficult to select the best method for identifying AM. Furthermore, previous comparisons between AM methods have been predominantly qualitative or based on synthesised signals, which are not representative of the actual noise. In this study, a comparison between methods for detecting and quantifying AM has been carried out. The results are based on analysis of real noise data which were measured at a wind farm in South Australia. In order to evaluate the performance of these methods in terms of detecting AM, an approach has been developed to select the most successful method of AM detection. This approach uses a receiver operating characteristic (ROC) curve which is based on detection of AM in audio files by experts.

Keywords: amplitude modulation, wind farm noise, ROC curve

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Authors: Pei-Wen Lin, Ta-I Yang

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Cancer has been one of the leading causes of human death for centuries. Considerable effort has been devoted to developing new treatments to reduce and control cancers. Magnetic particle hyperthermia and near-infrared photothermal therapy are the promising strategies to treat cancers due to its effectiveness with only mild side effects. This study focused on synthesizing magnetic Ag@Fe3O4 nanoparticles applicable for both of magnetic hyperthermia and near-infrared photothermal therapy. The hydrophilic poly(diallyldimethylammonium chloride) polymer was utilized to prepare superparamagnetic Fe3O4 clusters and to promote silver nanoparticles grown on Fe3O4 surfaces, obtaining Ag@Fe3O4 nanoparticles. The morphology (shape and dimension) of Ag nanoparticles was subsequently tailored using commercial LED lights. Therefore, the resulting Ag@Fe3O4 nanoparticles can absorb specific wavelength of light ranging from 400 nm to 800 nm by adjusting the wavelength of LED lights and the free silver ions in reaction solution. Heating performance tests confirmed that the synthesized Ag@Fe3O4 nanoparticles show appreciable heating capability for both of magnetic particle hyperthermia and near-infrared photothermal therapy. The findings in this study could provide new ideas to design functional materials to treat cancers.

Keywords: light-emitting diode assisted synthesis, magnetic particles, photothermal materials, hyperthermia

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Authors: W. Hashim, H. Burok, N. Ghazaly, H. Ahmad Nasir, N. Mohamad Anas, A. F. Ismail, K. L. Yau

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Future communication networks require devices that are able to work on a single platform but support heterogeneous operations which lead to service diversity and functional flexibility. This paper proposes two cognitive mechanisms termed cognitive hybrid function which is applied in multiple broadband user terminals in order to maintain reliable connectivity and preventing unnecessary interferences. By employing such mechanisms especially for future hyper-dense network, we can observe their performances in terms of optimized speed and power saving efficiency. Results were obtained from several empirical laboratory studies. It was found that selecting reliable network had shown a better optimized speed performance up to 37% improvement as compared without such function. In terms of power adjustment, our evaluation of this mechanism can reduce the power to 5dB while maintaining the same level of throughput at higher power performance. We also discuss the issues impacting future telecommunication standards whenever such devices get in place.

Keywords: dense network, intelligent network selection, multiple networks, transmit power adjustment

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Authors: M. Boudjerda, B. Touaibia, M. K. Mihoubi

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In Algeria agricultural irrigation is the primary water consuming sector followed by the domestic and industrial sectors. Economic development in the last decade has weighed heavily on water resources which are relatively limited and gradually decreasing to the detriment of agriculture. The research presented in this paper focuses on the optimization of irrigation water demand. Dynamic Programming-Neural Network (DPNN) method is applied to investigate reservoir optimization. The optimal operation rule is formulated to minimize the gap between water release and water irrigation demand. As a case study, Foum El-Gherza dam’s reservoir system in south of Algeria has been selected to examine our proposed optimization model. The application of DPNN method allowed increasing the satisfaction rate (SR) from 12.32% to 55%. In addition, the operation rule generated showed more reliable and resilience operation for the examined case study.

Keywords: water management, agricultural demand, dam and reservoir operation, Foum el-Gherza dam, dynamic programming, artificial neural network

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Authors: Cavidan Yakupoglu, Kurt Rohloff

Abstract:

In this study, we aim to provide a novel parameter selection model for the BFVrns scheme, which is one of the prominent FHE schemes. Parameter selection in lattice-based FHE schemes is a practical challenges for experts or non-experts. Towards a solution to this problem, we introduce a hybrid principles-based approach that combines theoretical with experimental analyses. To begin, we use regression analysis to examine the parameters on the performance and security. The fact that the FHE parameters induce different behaviors on performance, security and Ciphertext Expansion Factor (CEF) that makes the process of parameter selection more challenging. To address this issue, We use a multi-objective optimization algorithm to select the optimum parameter set for performance, CEF and security at the same time. As a result of this optimization, we get an improved parameter set for better performance at a given security level by ensuring correctness and security against lattice attacks by providing at least 128-bit security. Our result enables average ~ 5x smaller CEF and mostly better performance in comparison to the parameter sets given in [1]. This approach can be considered a semiautomated parameter selection. These studies are conducted using the PALISADE homomorphic encryption library, which is a well-known HE library. The abstract goes here.

Keywords: lattice cryptography, fully homomorphic encryption, parameter selection, LWE, RLWE

Procedia PDF Downloads 155

Authors: Deepika Christopher, Garima Anand

Abstract:

To determine the best model for churn prediction in the telecom industry, this paper compares 11 machine learning algorithms, namely Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, XGBoost, LightGBM, Cat Boost, AdaBoost, Extra Trees, Deep Neural Network, and Hybrid Model (MLPClassifier). It also aims to pinpoint the top three factors that lead to customer churn and conducts customer segmentation to identify vulnerable groups. According to the data, the Logistic Regression model performs the best, with an F1 score of 0.6215, 81.76% accuracy, 68.95% precision, and 56.57% recall. The top three attributes that cause churn are found to be tenure, Internet Service Fiber optic, and Internet Service DSL; conversely, the top three models in this article that perform the best are Logistic Regression, Deep Neural Network, and AdaBoost. The K means algorithm is applied to establish and analyze four different customer clusters. This study has effectively identified customers that are at risk of churn and may be utilized to develop and execute strategies that lower customer attrition.

Keywords: attrition, retention, predictive modeling, customer segmentation, telecommunications

Procedia PDF Downloads 57

Authors: El-Sayed Negim, Ainakulova D. T., Puteri S. M., Khaldun M. Azzam, Bekbayeva L. K., Arpit Goyal, Ganjian E.

Abstract:

Anticorrosion coatings protect metal surfaces from environmental factors including moisture, oxygen, and gases that caused corrosion to the metal. Various types of anticorrosion coatings are available, with different properties and application methods. Many researchers have been developing methods to prevent corrosion, and epoxy polymers are one of the wide methods due to their excellent adhesion, chemical resistance, and durability. In this study, synthesis reactive dilute based on glycidyl methacrylate (GMA) with each of 2-ethylhexyl acrylate (2-EHA) and butyl acrylate (BuA) to improve the performance of epoxy resin and anticorrosion coating. The copolymers were synthesized with composition ratio (5/5) by bulk polymerization technique using benzoyl peroxide as a catalyst and temperature at 85 oC for 2 hours and at 90 oC for 30 minutes to complete the polymerization process. The obtained copolymers were characterized by FTIR, viscosity and thixotropic index. The effect of copolymers as reactive dilute on the physical and mechanical properties of epoxy resin was investigated. Metal plates coated by the modified epoxy resins with different contents of copolymers were tested using alkali and salt test methods, and the copolymer based on GMA and BUA showed the best protection efficiency due to the barrier effect of the polymer layer.

Keywords: epoxy, coating, dilute, corrosion, reactive

Procedia PDF Downloads 52

Authors: Faci Youcef

Abstract:

In the present work, an experimental study is carried out using the digital image correlation (DIC) technique to analyze the damage and behavior of woven composite carbon/epoxy under tensile loading. The tension mechanisms associated with failure modes of bolted joints in advanced composites are studied, as well as displacement distribution and strain distribution. The evolution value of bolt angle inclination during tensile tests was studied. In order to compare the distribution of displacements and strains along the surface, figures of image mapping are made. Several factors that are responsible for the failure of fiber-reinforced polymer composite materials are observed. It was found that strain concentrations observed in the specimens can be used to identify full-field damage onset and to monitor damage progression during loading. Moreover, there is an interaction between laminate pattern, laminate thickness, fastener size and type, surface strain concentrations, and out-of-plane displacement. Conclusions include a failure analysis associated with bolt angle inclinations and supported by microscopic visualizations of the composite specimen. The DIC results can be used to develop and accurately validate numerical models.

Keywords: Carbone, woven, damage, digital image, bolted joint, the inclination of angle

Procedia PDF Downloads 80

Authors: Enver Kucukkulahli, Pakize Erdogmus, Kemal Polat

Abstract:

The 2D image segmentation is a significant process in finding a suitable region in medical images such as MRI, PET, CT etc. In this study, we have focused on 2D MRI images for image segmentation process. We have designed a GUI (graphical user interface) written in MATLABTM for 2D MRI images. In this program, there are two different interfaces including data pre-processing and image clustering or segmentation. In the data pre-processing section, there are median filter, average filter, unsharp mask filter, Wiener filter, and custom filter (a filter that is designed by user in MATLAB). As for the image clustering, there are seven different image segmentations for 2D MR images. These image segmentation algorithms are as follows: PSO (particle swarm optimization), GA (genetic algorithm), Lloyds algorithm, k-means, the combination of Lloyds and k-means, mean shift clustering, and finally BBO (Biogeography Based Optimization). To find the suitable cluster number in 2D MRI, we have designed the histogram based cluster estimation method and then applied to these numbers to image segmentation algorithms to cluster an image automatically. Also, we have selected the best hybrid method for each 2D MR images thanks to this GUI software.

Keywords: image segmentation, clustering, GUI, 2D MRI

Procedia PDF Downloads 377