Search results for: multi-layer elastomeric isolators

Authors: Daniela Istrati, Alina Morosan, Maria Stanca, Bogdan Purcareanu, Adrian Fudulu, Laura Olariu, Alice Buteica, Ion Mindrila, Rodica Cristescu, Dan Eduard Mihaiescu

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Our present work is related to the synthesis, characterization and applications of new nanocomposite materials based on silica mesoporous nanocompozites systems. The nanocomposite support was obtained by using a specific step–by–step multilayer structure buildup synthetic route, characterized by XRD (X-Ray Difraction), TEM (Transmission Electron Microscopy), FT-IR (Fourier Transform-Infra Red Spectrometry), BET (Brunauer–Emmett–Teller method) and loaded with Salvia officinalis plant extract obtained by a hydro-alcoholic extraction route. The sustained release of the target compounds was studied by a modified LC method, proving low release profiles, as expected for the high specific surface area support. The obtained results were further correlated with the in vitro / in vivo behavior of the nanocomposite material and recommending the silica mesoporous nanocomposites as good candidates for biomedical applications. Acknowledgements: This study has been funded by the Research Project PN-III-P2-2.1-PTE-2016-0160, 49-PTE / 2016 (PROZECHIMED) and Project Number PN-III-P4-ID-PCE-2016-0884 / 2017.

Keywords: biomedical, mesoporous, nanocomposites, natural products, sustained release

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Authors: Qian Zhu, Shidong Nie, Bo Yang, Gang Xiong, Guoxin Dai

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GJ steel is a new type of high-performance structural steel which has been increasingly adopted in practical engineering. Q460GJ structural steel has a nominal yield strength of 460 MPa, which does not decrease significantly with the increase of steel plate thickness like normal structural steel. Thus, Q460GJ structural steel is normally used in medium-walled welded sections. However, research works on the residual stress in GJ steel members are few though it is one of the vital factors that can affect the member and structural behavior. This article aims to investigate the residual stresses in welded I-shaped sections fabricated from Q460GJ structural steel plates by experimental tests. A total of four full scale welded medium-walled I-shaped sections were tested by sectioning method. Both circular curve correction method and straightening measurement method were adopted in this study to obtain the final magnitude and distribution of the longitudinal residual stresses. In addition, this paper also explores the interaction between flanges and webs. And based on the statistical evaluation of the experimental data, a multilayer residual stress model is proposed.

Keywords: Q460GJ structural steel, residual stresses, sectioning method, welded medium-walled I-shaped sections

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Authors: Naser Safdarian, Nader Jafarnia Dabanloo

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In this paper we used four features i.e. Q-wave integral, QRS complex integral, T-wave integral and total integral as extracted feature from normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ventricle of heart. In our research we focused on detection and localization of MI in standard ECG. We use the Q-wave integral and T-wave integral because this feature is important impression in detection of MI. We used some pattern recognition method such as Artificial Neural Network (ANN) to detect and localize the MI. Because these methods have good accuracy for classification of normal and abnormal signals. We used one type of Radial Basis Function (RBF) that called Probabilistic Neural Network (PNN) because of its nonlinearity property, and used other classifier such as k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP) and Naive Bayes Classification. We used PhysioNet database as our training and test data. We reached over 80% for accuracy in test data for localization and over 95% for detection of MI. Main advantages of our method are simplicity and its good accuracy. Also we can improve accuracy of classification by adding more features in this method. A simple method based on using only four features which extracted from standard ECG is presented which has good accuracy in MI localization.

Keywords: ECG signal processing, myocardial infarction, features extraction, pattern recognition

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Authors: Amir Andria Gad Shehata

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Minimizing the inventory cost, optimizing the inventory quantities, and increasing system operational availability are the main motivations to enhance forecasting demand of spare parts in a major power utility company in Medina. This paper reports in an effort made to optimize the orders quantities of spare parts by improving the method of forecasting the demand. The study focuses on equipment that has frequent spare parts purchase orders with uncertain demand. The pattern of the demand considers a lumpy pattern which makes conventional forecasting methods less effective. A comparison was made by benchmarking various methods of forecasting based on experts’ criteria to select the most suitable method for the case study. Three actual data sets were used to make the forecast in this case study. Two neural networks (NN) approaches were utilized and compared, namely long short-term memory (LSTM) and multilayer perceptron (MLP). The results as expected, showed that the NN models gave better results than traditional forecasting method (judgmental method). In addition, the LSTM model had a higher predictive accuracy than the MLP model.

Keywords: spare part, spare part inventory, inventory model, optimization, maintenanceneural network, LSTM, MLP, forecasting demand, inventory management

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Authors: Parisa Golbayani, Dan Wang, Ionut¸ Florescu

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In this work we implement machine learning techniques to financial statement reports in order to asses company’s credit rating. Specifically, the work analyzes the performance of four neural network architectures (MLP, CNN, CNN2D, LSTM) in predicting corporate credit rating as issued by Standard and Poor’s. The paper focuses on companies from the energy, financial, and healthcare sectors in the US. The goal of this analysis is to improve application of machine learning algorithms to credit assessment. To accomplish this, the study investigates three questions. First, we investigate if the algorithms perform better when using a selected subset of important features or whether better performance is obtained by allowing the algorithms to select features themselves. Second, we address the temporal aspect inherent in financial data and study whether it is important for the results obtained by a machine learning algorithm. Third, we aim to answer if one of the four particular neural network architectures considered consistently outperforms the others, and if so under which conditions. This work frames the problem as several case studies to answer these questions and analyze the results using ANOVA and multiple comparison testing procedures.

Keywords: convolutional neural network, long short term memory, multilayer perceptron, credit rating

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Authors: Ebenezer O. Olaniyi, Oyebade K. Oyedotun, Khashman Adnan

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In the food industry high quality production is required within a limited time to meet up with the demand in the society. In this research work, we have developed a model which can be used to replace the human operator due to their low output in production and slow in making decisions as a result of an individual differences in deciding the defective and healthy banana. This model can perform the vision attributes of human operators in deciding if the banana is defective or healthy for food production based. This research work is divided into two phase, the first phase is the image processing where several image processing techniques such as colour conversion, edge detection, thresholding and morphological operation were employed to extract features for training and testing the network in the second phase. These features extracted in the first phase were used in the second phase; the classification system phase where the multilayer perceptron using backpropagation neural network was employed to train the network. After the network has learned and converges, the network was tested with feedforward neural network to determine the performance of the network. From this experiment, a recognition rate of 97% was obtained and the time taken for this experiment was limited which makes the system accurate for use in the food industry.

Keywords: banana, food processing, identification system, neural network

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Authors: Cameron Hamilton, Walter Potter, Gerrit Hoogenboom, Ronald McClendon, Will Hobbs

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A model was constructed to predict the amount of solar radiation that will make contact with the surface of the earth in a given location an hour into the future. This project was supported by the Southern Company to determine at what specific times during a given day of the year solar panels could be relied upon to produce energy in sufficient quantities. Due to their ability as universal function approximators, an artificial neural network was used to estimate the nonlinear pattern of solar radiation, which utilized measurements of weather conditions collected at the Griffin, Georgia weather station as inputs. A number of network configurations and training strategies were utilized, though a multilayer perceptron with a variety of hidden nodes trained with the resilient propagation algorithm consistently yielded the most accurate predictions. In addition, a modeled DNI field and adjacent weather station data were used to bolster prediction accuracy. In later trials, the solar radiation field was preprocessed with a discrete wavelet transform with the aim of removing noise from the measurements. The current model provides predictions of solar radiation with a mean square error of 0.0042, though ongoing efforts are being made to further improve the model’s accuracy.

Keywords: artificial neural networks, resilient propagation, solar radiation, time series forecasting

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Authors: Sheena Christabel Pravin, M. Palanivelan

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Speech disfluencies are common in spontaneous speech. The primary purpose of this study was to distinguish linguistic disfluencies from stuttering disfluencies in bilingual Tamil–English (TE) speaking children. The secondary purpose was to determine whether their disfluencies are mediated by native language dominance and/or on an early onset of developmental stuttering at childhood. A detailed study was carried out to identify the prosodic and acoustic features that uniquely represent the disfluent regions of speech. This paper focuses on statistical modeling of repetitions, prolongations, pauses and interjections in the speech corpus encompassing bilingual spontaneous utterances from school going children – English and Tamil. Two classifiers including Hidden Markov Models (HMM) and the Multilayer Perceptron (MLP), which is a class of feed-forward artificial neural network, were compared in the classification of disfluencies. The results of the classifiers document the patterns of disfluency in spontaneous speech samples of school-aged children to distinguish between Children Who Stutter (CWS) and Children with Language Impairment CLI). The ability of the models in classifying the disfluencies was measured in terms of F-measure, Recall, and Precision.

Keywords: bi-lingual, children who stutter, children with language impairment, hidden markov models, multi-layer perceptron, linguistic disfluencies, stuttering disfluencies

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Authors: Manjit Singha, Sandeep Singh Sandhu, A. S. Shahi

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Stellite 6 is cobalt based superalloy used for protective coatings. It is used to improve the wear performance of stainless steel engineering components subjected to harsh environmental conditions. This paper reports the high temperature wear analysis of satellite 6 cladded on AISI 304 L substrate using SMAW process. Bead on plate experiment was carried out by varying current and electrode manipulation techniques to optimize the dilution and hardness. 80 Amp current and weaving technique was found to be the optimum set of parameters for overlaying which were further used for multipass multilayer cladding on two plates of AISI 304 L substrate. On the first plate, seven layers seven passes of stellite 6 was overlaid which was used in as cladded form and the second plate was overlaid with five layers five passes of satellite 6 with further TIG remelting. The wear performance was examined for normal temperature environmental condition and harsh temperature environmental condition. The satellite 6 coating with TIG remelting was found to be better in both the conditions even with lesser metal deposition due to its finer grain structure.

Keywords: surfacing, stellite 6, dilution, overlay, SMAW, high-temperature frictional wear, micro-structure, micro-hardness

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Authors: Gizelle K. Vianna, Gabriel V. Cunha, Gustavo S. Oliveira

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Foliage diseases in plants can cause a reduction in both quality and quantity of agricultural production. Intelligent detection of plant diseases is an essential research topic as it may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. This work investigates ways to recognize the late blight disease from the analysis of tomato digital images, collected directly from the field. A pair of multilayer perceptron neural network analyzes the digital images, using data from both RGB and HSL color models, and classifies each image pixel. One neural network is responsible for the identification of healthy regions of the tomato leaf, while the other identifies the injured regions. The outputs of both networks are combined to generate the final classification of each pixel from the image and the pixel classes are used to repaint the original tomato images by using a color representation that highlights the injuries on the plant. The new images will have only green, red or black pixels, if they came from healthy or injured portions of the leaf, or from the background of the image, respectively. The system presented an accuracy of 97% in detection and estimation of the level of damage on the tomato leaves caused by late blight.

Keywords: artificial neural networks, digital image processing, pattern recognition, phytosanitary

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Authors: Jiaqi Dong, Qing-Hua Qin, Yi Xiao

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Some of the most fundamental challenges of elastic metamaterials (EMMs) optimization can be attributed to the high consumption of computational power resulted from finite element analysis (FEA) simulations that render the optimization process inefficient. Furthermore, due to the inherent mesh dependence of FEA, minuscule geometry features, which often emerge during the later stages of optimization, induce very fine elements, resulting in enormously high time consumption, particularly when repetitive solutions are needed for computing the objective function. In this study, a surrogate modelling algorithm is developed to reduce computational time in structural optimization of EMMs. The surrogate model is constructed based on a multilayer feedforward artificial neural network (ANN) architecture, trained with prepopulated eigenfrequency data prepopulated from FEA simulation and optimized through regime selection with genetic algorithm (GA) to improve its accuracy in predicting the location and width of the primary elastic band gap. With the optimized ANN surrogate at the core, a Nelder-Mead (NM) algorithm is established and its performance inspected in comparison to the FEA solution. The ANNNM model shows remarkable accuracy in predicting the band gap width and a reduction of time consumption by 47%.

Keywords: artificial neural network, machine learning, mechanical metamaterials, Nelder-Mead optimization

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Authors: Aqsa Ali, Aleem Mushtaq, Attaullah Memon, Monna

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In this paper, we present a low cost design for a smart glove that can perform sign language recognition to assist the speech impaired people. Specifically, we have designed and developed an Assistive Hand Gesture Interpreter that recognizes hand movements relevant to the American Sign Language (ASL) and translates them into text for display on a Thin-Film-Transistor Liquid Crystal Display (TFT LCD) screen as well as synthetic speech. Linear Bayes Classifiers and Multilayer Neural Networks have been used to classify 11 feature vectors obtained from the sensors on the glove into one of the 27 ASL alphabets and a predefined gesture for space. Three types of features are used; bending using six bend sensors, orientation in three dimensions using accelerometers and contacts at vital points using contact sensors. To gauge the performance of the presented design, the training database was prepared using five volunteers. The accuracy of the current version on the prepared dataset was found to be up to 99.3% for target user. The solution combines electronics, e-textile technology, sensor technology, embedded system and machine learning techniques to build a low cost wearable glove that is scrupulous, elegant and portable.

Keywords: American sign language, assistive hand gesture interpreter, human-machine interface, machine learning, sensing glove

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Authors: Bhaumik Tyagi, Mandeep Kaur, Kanika Singla

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The advancement of blockchain has facilitated scholars to remodel e-voting systems for future generations. Server-side attacks like SQL injection attacks and DOS attacks are the most common attacks nowadays, where malicious codes are injected into the system through user input fields by illicit users, which leads to data leakage in the worst scenarios. Besides, quantum attacks are also there which manipulate the transactional data. In order to deal with all the above-mentioned attacks, integration of blockchain, convolutional neural network (CNN), and Quantum Key Distribution is done in this very research. The utilization of blockchain technology in e-voting applications is not a novel concept. But privacy and security issues are still there in a public and private blockchains. To solve this, the use of a hybrid blockchain is done in this research. This research proposed cryptographic signatures and blockchain algorithms to validate the origin and integrity of the votes. The convolutional neural network (CNN), a normalized version of the multilayer perceptron, is also applied in the system to analyze visual descriptions upon registration in a direction to enhance the privacy of voters and the e-voting system. Quantum Key Distribution is being implemented in order to secure a blockchain-based e-voting system from quantum attacks using quantum algorithms. Implementation of e-voting blockchain D-app and providing a proposed solution for the privacy of voters in e-voting using Blockchain, CNN, and Quantum Key Distribution is done.

Keywords: hybrid blockchain, secure e-voting system, convolutional neural networks, quantum key distribution, one-time pad

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Authors: Arkadiusz Urzędowski, Dorota Wójcicka-Migasiuk, Andrzej Sachajdak, Magdalena Paśnikowska-Łukaszuk

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Due to the impact of climate changes and inevitability to reduce greenhouse gases, the need to use low-carbon and sustainable construction has increased. In this work, it is investigated how texture of the surface building materials and radiative heat transfer phenomenon in flat multilayer can be correlated. Attempts to test the surface emissivity are taken however, the trustworthiness of measurement results remains a concern since sensor size and thickness are common problems. This paper presents an experimental method to studies surface emissivity with use self constructed thermal sensors and thermal imaging technique. The surface of building materials was modified by mechanical and chemical treatment affecting the reduction of the emissivity. For testing the shaping surface of materials and mapping its three-dimensional structure, scanning profilometry were used in a laboratory. By comparing the results of laboratory tests and performed analysis of 3D computer fluid dynamics software, it can be shown that a change in the surface coverage of materials affects the heat transport by radiation between layers. Motivated by recent advancements in variational inference, this publication evaluates the potential use a dedicated data processing approach, and properly constructed temperature sensors, the influence of the surface emissivity on the phenomenon of radiation and heat transport in the entire partition can be determined.

Keywords: heat transfer, surface roughness, surface emissivity, radiation

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Authors: Youlu Huang, Huanjun Jiang

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A large number of old masonry buildings built in the last century still remain in the city. It generates the problems of unsafety, obsolescence, and non-habitability. In recent years, many old buildings have been reconstructed through renovating façade, strengthening, and adding floors. However, most projects only provide a solution for a single problem. It is difficult to comprehensively solve problems of poor safety and lack of building functions. Therefore, a comprehensive functional renovation program of adding reinforced concrete frame story at the bottom via integrally lifting the building and then strengthening the building was put forward. Based on field measurement and YJK calculation software, the seismic performance of an actual three-story masonry structure in Shanghai was identified. The results show that the material strength of masonry is low, and the bearing capacity of some masonry walls could not meet the code requirements. The elastoplastic time history analysis of the structure was carried out by using SAP2000 software. The results show that under the 7 degrees rare earthquake, the seismic performance of the structure reaches 'serious damage' performance level. Based on the code requirements of the stiffness ration of the bottom frame (lateral stiffness ration of the transition masonry story and frame story), the bottom frame story was designed. The integral lifting process of the masonry building was introduced based on many engineering examples. The reinforced methods for the bottom frame structure strengthened by the steel-reinforced mesh mortar surface layer (SRMM) and base isolators, respectively, were proposed. The time history analysis of the two kinds of structures, under the frequent earthquake, the fortification earthquake, and the rare earthquake, was conducted by SAP2000 software. For the bottom frame structure, the results show that the seismic response of the masonry floor is significantly reduced after reinforced by the two methods compared to the masonry structure. The previous earthquake disaster indicated that the bottom frame is vulnerable to serious damage under a strong earthquake. The analysis results showed that under the rare earthquake, the inter-story displacement angle of the bottom frame floor meets the 1/100 limit value of the seismic code. The inter-story drift of the masonry floor for the base isolated structure under different levels of earthquakes is similar to that of structure with SRMM, while the base-isolated program is better to protect the bottom frame. Both reinforced methods could significantly improve the seismic performance of the bottom frame structure.

Keywords: old buildings, adding story, seismic strengthening, seismic performance

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Authors: Niklas Panten, Eberhard Abele

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This paper presents a novel approach for real-time and near-optimal control of industrial smart grids by deep reinforcement learning (DRL). To achieve highly energy-efficient factory systems, the energetic linkage of machines, technical building equipment and the building itself is desirable. However, the increased complexity of the interacting sub-systems, multiple time-variant target values and stochastic influences by the production environment, weather and energy markets make it difficult to efficiently control the energy production, storage and consumption in the hybrid industrial smart grids. The studied deep reinforcement learning approach allows to explore the solution space for proper control policies which minimize a cost function. The deep neural network of the DRL agent is based on a multilayer perceptron (MLP), Long Short-Term Memory (LSTM) and convolutional layers. The agent is trained within multiple Modelica-based factory simulation environments by the Advantage Actor Critic algorithm (A2C). The DRL controller is evaluated by means of the simulation and then compared to a conventional, rule-based approach. Finally, the results indicate that the DRL approach is able to improve the control performance and significantly reduce energy respectively operating costs of industrial smart grids.

Keywords: industrial smart grids, energy efficiency, deep reinforcement learning, optimal control

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Authors: Mostafa Mjahed

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Tilt Rotor UAVs (Unmanned Aerial Vehicles) are naturally unstable and difficult to maneuver. The purpose of this paper is to design controllers for the stabilization and trajectory tracking of this type of UAV. To this end, artificial intelligence methods have been exploited. First, the dynamics of this UAV was modeled using the Lagrange-Euler method. The conventional method based on Proportional, Integral and Derivative (PID) control was applied by decoupling the different flight modes. To improve stability and trajectory tracking of the Tilt Rotor, the fuzzy approach and the technique of multilayer neural networks (NN) has been used. Thus, Fuzzy Proportional Integral and Derivative (FPID) and Neural Network-based Proportional Integral and Derivative controllers (NNPID) have been developed. The meta-heuristic approach based on Particle Swarm Optimization (PSO) method allowed adjusting the setting parameters of NNPID controller, giving us an improved NNPID-PSO controller. Simulation results under the Matlab environment show the efficiency of the approaches adopted. Besides, the Tilt Rotor UAV has become stable and follows different types of trajectories with acceptable precision. The Fuzzy, NN and NN-PSO-based approaches demonstrated their robustness because the presence of the disturbances did not alter the stability or the trajectory tracking of the Tilt Rotor UAV.

Keywords: neural network, fuzzy logic, PSO, PID, trajectory tracking, tilt-rotor UAV

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Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

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Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: artificial neural network, data mining, electroencephalogram, epilepsy, feature extraction, seizure detection, signal processing

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Authors: Mohamed Habila, Ahmed Mohamed El-Toni, Mohamed Sheikh Moshab, Abdulrhman Al-Awadi, Zeid AL Othman

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Water pollution with dyes is a critical environmental issue because off the huge amount of dyes disbarred annually, which cause severe damage for the ecosystem and human life. The main raison for this severs pollution is the rapid industrial development which led to more production of harmful pollutants. on the other hand, the core shell based magnetic materials have showed amazing character for controlling the material synthesis with the targeted structure to enhance the adsorptive removal of pollutants. Herein, the Fe3O4core-meso SiO2/TiO2 double shell have been prepared for methylene blue dye adsorption. the preparation procedure is controlled to prepare the magnetic core with further coating layers from silica and titania. The prepared Fe3O4core-meso SiO2/TiO2 double shell showed adsorption capacity for methylene blue removal about 50 mg/g at pH 6 after 80 min contact time form 50 ppm methylene blue solution. The adsorption process of methylene blue onto Fe3O4core-meso SiO2/TiO2 double shell was well fitted with the pseudo-second-order kinetic model and freundlish isotherm, indicating a quick and multilayer adsorption mechanism.

Keywords: magnetic core, silica shell, titania shell, water treatment, methylene blue, solvo-thermal process, adsorption

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Authors: S. Paszkiewicz, A. Zubkiewicz, A. Szymczyk, D. Pawlikowska, I. Irska, E. Piesowicz, A. Linares, T. A. Ezquerra

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Over the last century, the world has become increasingly dependent on oil as its main source of chemicals and energy. Driven largely by the strong economic growth of India and China, demand for oil is expected to increase significantly in the coming years. This growth in demand, combined with diminishing reserves, will require the development of new, sustainable sources for fuels and bulk chemicals. Biomass is an attractive alternative feedstock, as it is widely available carbon source apart from oil and coal. Nowadays, academic and industrial research in the field of polymer materials is strongly oriented towards bio-based alternatives to petroleum-derived plastics with enhanced properties for advanced applications. In this context, 2,5-furandicarboxylic acid (FDCA), a biomass-based chemical product derived from lignocellulose, is one of the most high-potential biobased building blocks for polymers and the first candidate to replace the petro-derived terephthalic acid. FDCA has been identified as one of the top 12 chemicals in the future, which may be used as a platform chemical for the synthesis of biomass-based polyester. The aim of this study is to synthesize and characterize the multiblock copolymers containing rigid segments of poly(trimethylene 2,5-furanoate) (PTF) and soft segments of poly(tetramethylene oxide) (PTMO) with excellent elastic properties or aliphatic polyesters of polycaprolactone (PCL). Two series of PTF based copolymers, i.e., PTF-block-PTMO-T and PTF-block-PCL-T, with different content of flexible segments were synthesized by means of a two-step melt polycondensation process and characterized by various methods. The rigid segments of PTF, as well as the flexible PTMO/or PCL ones, were randomly distributed along the chain. On the basis of 1H NMR, SAXS and WAXS, DSC an DMTA results, one can conclude that both phases were thermodynamically immiscible and the values of phase transition temperatures varied with the composition of the copolymer. The copolymers containing 25, 35 and 45wt.% of flexible segments (PTMO) exhibited elastomeric property characteristics. Moreover, with respect to the flexible segments content, the temperatures corresponding to 5%, 25%, 50% and 90% mass loss as well as the values of tensile modulus decrease with the increasing content of aliphatic polyether or aliphatic polyester in the composition.

Keywords: furan based polymers, multiblock copolymers, supramolecular structure, functional properties

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Authors: Akinbo Razak Yinka, Adesanya Kehinde Kazeem, Oladokun Oluwagbenga Peter

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Time series data are sequences of observations collected over a period of time. Such data can be used to predict health outcomes, such as disease progression, mortality, hospitalization, etc. The Statistical approach is based on mathematical models that capture the patterns and trends of the data, such as autocorrelation, seasonality, and noise, while Neural methods are based on artificial neural networks, which are computational models that mimic the structure and function of biological neurons. This paper compared both parametric and non-parametric time series models of patients treated for malaria in Maternal and Child Health Centres in Lagos State, Nigeria. The forecast methods considered linear regression, Integrated Moving Average, ARIMA and SARIMA Modeling for the parametric approach, while Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Network were used for the non-parametric model. The performance of each method is evaluated using the Mean Absolute Error (MAE), R-squared (R2) and Root Mean Square Error (RMSE) as criteria to determine the accuracy of each model. The study revealed that the best performance in terms of error was found in MLP, followed by the LSTM and ARIMA models. In addition, the Bootstrap Aggregating technique was used to make robust forecasts when there are uncertainties in the data.

Keywords: ARIMA, bootstrap aggregation, MLP, LSTM, SARIMA, time-series analysis

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Authors: Wed Othman Alghamdi

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The current study aims to find a non-toxic, low density, hydrogen-rich material that can be used in aprons without causing health issues for nuclear medical workers that could hinder their work and negatively affect patients. Five samples were tested in terms of fast neutron removal cross-section(C21H25ClO5, C2H4, LiH,H3NBH3,MgH2) mathematically using computer program called Phy-x/PSD it is a computer program designed to calculate the fast neutron removal cross section, and it was obtained that ammonia borane (𝐻3𝑁𝐵𝐻3) with a density of 0.78 (g/ cm3) ,And it containment of the three most important elements that play a major role in protection shields, which are (hydrogen, boron, nitrogen), Hydrogen works as a moderator that slows neutrons and turn them into thermal neutrons, boron and nitrogen both have the largest neutron absorption cross section. Ammonia borane has the highest fast neutron removal cross-section with the value of (0.122959317985393cm-1) and the least for polyethylene (𝐶2𝐻4) with the value of (0.0838038707225853 cm-1) which made the ammonia borane a better candidate than polyethylene and other compounds that have been tasted in previous research for multi-layer aprons in nuclear medicine, and may approve a proper protection against the hazard radiations that its produced in nuclear medicine filed by several ways, due to it is low density and non-toxicity.

Keywords: aprons, radiation, non-toxic, nuclear medicine, neutrons

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Authors: Roxana Gheorghita, Gheorghe Gutt

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Environmental pollution due to non-degradation of packaging from the food and pharmaceutical industry is reaching increasingly alarming levels. The packaging used for food supplements is usually composed of successive layers of synthetic materials, conventional, glue, and paint. The situation is becoming more and more problematic as the population, according to statistics, uses food supplements more and more often. The solution can be represented by edible packaging, completely biodegradable, and compostable. The tested materials were obtained from biopolymers, agar, carrageenan, and alginate, in well-established quantities and plasticized with glycerol. Rosemary, thyme, and oregano essential oils have been added in varying proportions. The obtained films are completely water-soluble in hot liquids (with a temperature of about 80° C) and can be consumed with the product contained. The films were glossy, pleasant to the touch, thin (thicknesses between 32.8 and 52.8 μm), transparent, and with a pleasant smell, specific to the added essential oil. Tested for microbial evaluation, none of the films indicated the presence of E. coli, S. aureus, enterobacteria, coliform bacteria, yeasts, or molds. This aspect can also be helped by the low values of the water activity index (located between 0.546 and 0.576). The mechanical properties indicated that the material became more resistant with the addition of essential oil, the best values being recorded by the addition of oregano. The results obtained indicate the possibility of using biopolymer-based films with the addition of rosemary, thyme, and oregano essential oil, for wrapping food supplements, thus replacing conventional packaging, multilayer, impossible to sort and recycle.

Keywords: edible films, food supplements, oregano, rosemary, thyme

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Authors: Mohammad Shafiul Alam, Mineo Saito

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Graphene-based materials have attracted much attention because they are candidates for post silicon materials. Since controlling of impurities is necessary to achieve nano device, we study hydrogen impurity in multi-layer graphene. We perform local spin Density approximation (LSDA) in which the plane wave basis set and pseudopotential are used. Previously hydrogen monomer and dimer in graphene is well theoretically studied. However, hydrogen on multilayer graphene is still not clear. By using first-principles electronic structure calculations based on the LSDA within the density functional theory method, we studied hydrogen monomers and dimers in two-layer graphene. We found that the monomers are spin-polarized and have magnetic moment 1 µB. We also found that most stable dimer is much more stable than monomer. In the most stable structures of the dimers in two-layer graphene, the two hydrogen atoms are bonded to the host carbon atoms which are nearest-neighbors. In this case two hydrogen atoms are located on the opposite sides. Whereas, when the two hydrogen atoms are bonded to the same sublattice of the host materials, magnetic moments of 2 µB appear in two-layer graphene. We found that when the two hydrogen atoms are bonded to third-nearest-neighbor carbon atoms, the electronic structure is nonmagnetic. We also studied hydrogen monomers and dimers in three-layer graphene. The result is same as that of two-layer graphene. These results are very important in the field of carbon nanomaterials as it is experimentally difficult to show the magnetic state of those materials.

Keywords: first-principles calculations, LSDA, multi-layer gra-phene, nanomaterials

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Authors: Talha Khan, Surendhar Kumaran, Rajeev Nair

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The development of economical, efficient, and reliable next-generation thermal and water management systems to provide efficient cooling and water management technologies is being pursued application in compact and lightweight spacecraft. The elimination of liquid-vapor phase change-based thermal and water management systems is being done due to issues with the reliability and robustness of this technology. To achieve the motive of implementing the principle of using an innovative evaporator and condenser design utilizing bimodal wicks manufactured using a microsecond pulsed CO₂ laser has been proposed in this study. Cylin drical, multilayered capillary copper wicks with a substrate diameter of 39 mm are additively manufactured using a pulsed laser. The copper particles used for layer-by-layer addition on the substrate measure in a diameter range of 225 to 450 micrometers. The primary objective is to develop a novel, high-quality, fast turnaround, laser-based additive manufacturing process that will eliminate the current technical challenges involved with the traditional manufacturing processes for nano/micro-sized powders, like particle agglomeration. Raster-scanned, pulsed-laser sintering process has been developed to manufacture 3D wicks with controlled porosity and permeability.

Keywords: liquid-vapor phase change, bimodal wicks, multilayered, capillary, raster-scanned, porosity, permeability

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Authors: Sepehr Lajevardi Esfahani, Shohre Rouhani, Zahra Ranjbar

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Graphene has gained much attention owing to its unique optical and electrical properties. Charge carriers in graphene sheets (GS) carry out a linear dispersion relation near the Fermi energy and behave as massless Dirac fermions resulting in unusual attributes such as the quantum Hall effect and ambipolar electric field effect. It also exhibits nondispersive transport characteristics with an extremely high electron mobility (15000 cm²/(Vs)) at room temperature. Recently, several progresses have been achieved in the fabrication of single- or multilayer GS for functional device applications in the fields of optoelectronic such as field-effect transistors ultrasensitive sensors and organic photovoltaic cells. In addition to device applications, graphene also can serve as reinforcement to enhance mechanical, thermal, or electrical properties of composite materials. Electrophoretic deposition (EPD) is an attractive method for development of various coatings and films. It readily applied to any powdered solid that forms a stable suspension. The deposition parameters were controlled in various thicknesses. In this study, the graphene electrodeposition conditions were optimized. The results were obtained from SEM, Ohm resistance measuring technique and AFM characteristic tests. The minimum sheet resistance of electrodeposited reduced graphene oxide layers is achieved at conditions of 2 V in 10 s and it is annealed at 200 °C for 1 minute.

Keywords: electrophoretic deposition (EPD), graphene oxide (GO), electrical conductivity, electro-optical devices

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Authors: Sanjeev Kumar Raghuwanshi, Yadvendra Singh

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The objective of this paper is to develop and optimize the Fiber Bragg (FBG) grating based Surface Plasmon Resonance (SPR) sensor for monitoring the hazardous toxic chemicals and gases in underground mines or any industrial area. A fully cladded telecommunication standard FBG is proposed to develop to produce surface plasmon resonance. A thin few nm gold/silver film (subject to optimization) is proposed to apply over the FBG sensing head using e-beam deposition method. Sensitivity enhancement of the sensor will be done by adding a composite nanostructured Graphene Oxide (GO) sensing layer using the spin coating method. Both sensor configurations suppose to demonstrate high responsiveness towards the changes in resonance wavelength. The GO enhanced sensor may show increased sensitivity of many fold compared to the gold coated traditional fibre optic sensor. Our work is focused on to optimize GO, multilayer structure and to develop fibre coating techniques that will serve well for sensitive and multifunctional detection of hazardous chemicals. This research proposal shows great potential towards future development of optical fiber sensors using readily available components such as Bragg gratings as highly sensitive chemical sensors in areas such as environmental sensing.

Keywords: surface plasmon resonance, fibre Bragg grating, sensitivity, toxic gases, MATRIX method

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Authors: Maha M.Saber, Eitedal Daoud, Moetazza M. Alshafei, Lobna M. Abd El-Latif

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Children in the third world consumes many food products colored red like sweets and soft drink without knowing its effect on health or the type of color used in these products Erythrosine (ER,FD & C Red No.3) is one of the most common coloring agent used in these products and in coloring cherry in compotes. The possible adverse effect of erythrosine ER on the thyroid gland function is investigated in albino rats. Forty-five adult male albino rats were divided to three groups two groups will receive ER orally in doses 68 and I36mg/kg respectively. Third group will receive distilled water for three months Sections of thyroid glands were examined for histopathological, morphometric analysis and MIB-I Ki67 (proliferative marker). Serum concentration of triiodothyronine (T3), Thyroxin (T4) and thyrotrophin (TSH) were determined, results showed histological changes in the two treatment groups versus control group in the group with 68mg/kg dose show vaculation of the cytoplasm of follicular cells and pleomorphism of their nuclei. While the other treated group {136mg /kg} showed congestion of blood vessels, hyperplasia of the interstitial cells and increased multilayer of the follicular cells. Highly significant increase in the mean area of the thyroid follicles in both treated groups compared to control group.Erythrosine treated groups showed a very highly significant decrease (P < 0.001) in serum concentration of T3 and T 4 while TSH showed a very highly significant increase versus control.

Keywords: erythrosine, thyroid, morphometrics, proliferative marker

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Authors: Sanjeet Patra, Soham Roychowdhury

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In recent times, functionally graded dielectric elastomer (FGDE) has gained significant attention within the realm of soft actuation due to its dual capacity to exert highly localized stresses while maintaining its compliant characteristics on application of electro-mechanical loading. Nevertheless, the full potential of dielectric elastomer (DE) has not been fully explored due to their susceptibility to instabilities when subjected to electro-mechanical loads. As a result, study and analysis of such instabilities becomes crucial for the design and realization of dielectric actuators. Prismatic bifurcation is a type of instability that has been recognized in a DE tube. Though several studies have reported on the analysis for prismatic bifurcation in an isotropic DE tube, there is an insufficiency in studies related to prismatic bifurcation of FGDE tubes. Therefore, this paper aims to determine the onset of prismatic bifurcations on an incompressible FGDE tube when subjected to electrical loading across the thickness of the tube and internal pressurization. The analysis has been conducted by imposing two axial boundary conditions on the tube, specifically axially free ends and axially clamped ends. Additionally, the rigidity modulus of the tube has been linearly graded in the direction of thickness where the inner surface of the tube has a lower stiffness than the outer surface. The static equilibrium equations for deformation of the axisymmetric tube are derived and solved using numerical technique. The condition for prismatic bifurcation of the axisymmetric static equilibrium solutions has been obtained by using the linearized incremental constitutive equations. Two modes of bifurcations, corresponding to two different non-circular cross-sectional geometries, have been explored in this study. The outcomes reveal that the FGDE tubes experiences prismatic bifurcation before the Hessian criterion of failure is satisfied. It is observed that the lower mode of bifurcation can be triggered at a lower critical voltage as compared to the higher mode of bifurcation. Furthermore, the tubes with larger stiffness gradient require higher critical voltages for triggering the bifurcation. Moreover, with the increase in stiffness gradient, a linear variation of the critical voltage is observed with the thickness of the tube. It has been found that on applying internal pressure to a tube with low thickness, the tube becomes less susceptible to bifurcations. A thicker tube with axially free end is found to be more stable than the axially clamped end tube at higher mode of bifurcation.

Keywords: critical voltage, functionally graded dielectric elastomer, linearized incremental approach, modulus of rigidity, prismatic bifurcation

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Authors: S. E. Mohmad-Saberi, W. Song, N. Oliver, M. Adrian, T.C. Hsu, A. Darbyshire

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Biodegradable polyurethane (PU) has emerged as a potential material to promote repair and regeneration of damaged/diseased tissues in heart valve regeneration due to its excellent biomechanical profile. Understanding the effects of sterilization on their properties is vital since they are more sensitive and more critical of porous structures compared to bulk ones. In this study, the effects of low concentration of hydrogen peroxide (H₂O₂) solution sterilization has been investigated to determine whether the procedure would be efficient and non-destructive to porous three-dimensional (3D) elastomeric nanocomposite, polyhedral oligomeric silsesquioxane-terminated poly (ethylene-diethylene glycol succinate-sebacate) urea-urethane (POSS-EDSS-PU) scaffold. All the samples were tested for sterility following sterilization using phosphate buffer saline (PBS) as control and 5 % v/v H₂O₂ solution. The samples were incubated in tryptic soy broth for the cultivation of microorganisms under agitation at 37˚C for 72 hours. The effects of the 5 % v/v H₂O₂ solution sterilization were evaluated in terms of morphology, chemical and mechanical properties using scanning electron microscopy (SEM), Fourier transform infrared (FTIR) and tensile tester apparatus. Toxicity effects of the 5 % v/v H₂O₂ solution decontamination were studied by in vitro cytotoxicity test, where the cellular responses of human dermal fibroblast (HDF) were examined. A clear, uncontaminated broth using 5 % v/v H₂O₂ solution method indicated efficient sterilization after 3 days, while the non-sterilized control shows clouding broth indicated contamination. The morphology of 3D POSS-EDSS-PU scaffold appeared to have similar morphology after sterilization with 5 % v/v H₂O₂ solution regarding of pore size and surface. FTIR results show that the sterilized samples and non-sterilized control share the same spectra pattern, confirming no significant alterations over the surface chemistry. For the mechanical properties of the H₂O₂ solution-treated scaffolds, the tensile strain was not significantly decreased, however, become significantly stiffer after the sterilization. No cytotoxic effects were observed after the 5 % v/v H₂O₂ solution sterilization as confirmed by cell viability assessed by Alamar Blue assay. The results suggest that low concentration of 5 % v/v hydrogen peroxide solution can be used as an alternative method for sterilizing biodegradable 3D porous scaffold with micro/nano-architecture without structural deformation. This study provides the understanding of the sterilization effects on biomechanical profile and cell proliferation of 3D POSS-EDSS-PU scaffolds.

Keywords: biodegradable, hydrogen peroxide solution, POSS-EDSS-PU, sterilization

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