Search results for: disordered multilayer laminae

Authors: Yang Zhong, Mei-Jie Xu

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To study the dynamic mechanics response of asphalt pavement under the temperature load and vehicle loading, asphalt pavement was regarded as multilayered elastic half-space system, and theory analysis was conducted by regarding dynamic modulus of asphalt mixture as the parameter. Firstly, based on the dynamic modulus test of asphalt mixture, function relationship between the dynamic modulus of representative asphalt mixture and temperature was obtained. In addition, the analytical solution for thermal stress in the single layer was derived by using Laplace integral transformation and Hankel integral transformation respectively by using thermal equations of equilibrium. The analytical solution of calculation model of thermal stress in asphalt pavement was derived by transfer matrix of thermal stress in multilayer elastic system. Finally, the variation of thermal stress in pavement structure was analyzed. The result shows that there is an obvious difference between the thermal stress based on dynamic modulus and the solution based on static modulus. Therefore, the dynamic change of parameter in asphalt mixture should be taken into consideration when the theoretical analysis is taken out.

Keywords: asphalt pavement, dynamic modulus, integral transformation, transfer matrix, thermal stress

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Authors: Sujata Gupta Kedar, J. N. Tulika

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Early researchers suggested that imprisonment had negative psychological and physical effects on its inmates, leading to psychological deterioration. The term “prisons” in the Consensus Statement of WHO is intended to denote, as those institutions which hold people who have been sentenced to a period of imprisonment by the courts for offences against the law. Thus “prisons” if local circumstances justify it, may also be taken to include secure institutions holding on a compulsory basis on any of the following categories of people: remand prisoners; civil prisoners; juvenile detainees; immigration detainees; some categories of mentally disordered patients; asylum seekers; refugees; people detained pending expulsion, deportation, exile, exclusion or any other form of compulsory transfer to other countries or areas of the country; people detained in police cells; and any other compulsorily detained group. Prisons are aimed to cure the criminal and their behavior but their records are not encouraging. Instead the imprisonment affects all prisoners in different way. From withstanding the shock of entry to the new culture, which is very different from their own, prisoners must try to determine how to spend the time in prison, since the hours appears to be endless in prisons. There is also the fear of deterioration. This article aims to provide an overview of the psychological well being of female prisoners in the prison environment in five areas- satisfaction, efficiency, sociability, mental health and interpersonal relations. Research was done on two different types of imprisonment- under trial prisoner and convict. Total sample included 22 female prisoners of Nagaon Special Jail of Assam. The instrument used for the study was based on Psychological Well Being Scale. Statistical analysis was done with t-test and one way anova test. The result demonstrated that there is no significant difference in the psychological wellbeing of female prisoners in the prison and that there is no significant difference in the psychological well being of different types of female prisoners involved in different crimes but there is significant difference in the mental health of the female prisoners in prison.

Keywords: psychological effect, female prisoners, prison, well being of prisoners

Procedia PDF Downloads 377

Authors: Ş. Karabulut, A. Güllü, A. Güldaş, R. Gürbüz

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This study investigates the effects of the lead angle and chip thickness variation on surface roughness during the machining of compacted graphite iron using ceramic cutting tools under dry cutting conditions. Analytical models were developed for predicting the surface roughness values of the specimens after the face milling process. Experimental data was collected and imported to the artificial neural network model. A multilayer perceptron model was used with the back propagation algorithm employing the input parameters of lead angle, cutting speed and feed rate in connection with chip thickness. Furthermore, analysis of variance was employed to determine the effects of the cutting parameters on surface roughness. Artificial neural network and regression analysis were used to predict surface roughness. The values thus predicted were compared with the collected experimental data, and the corresponding percentage error was computed. Analysis results revealed that the lead angle is the dominant factor affecting surface roughness. Experimental results indicated an improvement in the surface roughness value with decreasing lead angle value from 88° to 45°.

Keywords: CGI, milling, surface roughness, ANN, regression, modeling, analysis

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Authors: Mikheil Kalmakhelidze

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Record linkage (RL) problem has become more and more important in recent years due to the growing interest towards big data analysis. The problem can be formulated in a very simple way: Given two entries a and b of a database, decide whether they represent the same object or not. There are two classical deterministic and probabilistic ways of solving the RL problem. Using simple Bayes classifier in many cases produces useful results but sometimes they show to be poor. In recent years several successful approaches have been made towards solving specific RL problems by neural network algorithms including single layer perception, multilayer back propagation network etc. In our work, we model the RL problem for specific dataset of student applications in fuzzy description logic (FDL) where linkage of specific pair (a,b) depends on the truth value of corresponding formula A(a,b) in a canonical FDL model. As a main result, we build neural network for deciding truth value of FDL formulas in a canonical model and thus link RL problem to machine learning. We apply the approach to dataset with 10000 entries and also compare to classical RL solving approaches. The results show to be more accurate than standard probabilistic approach.

Keywords: description logic, fuzzy logic, neural networks, record linkage

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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: L. Bernabeo, T. Loftus

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Many children often experience phases of picky eating, food aversions and/or avoidance. For families with children who have special needs, these experiences are often exacerbated, which can lead to feelings that negatively impact a caregiver’s relationship with their child. Within the scope of speech language pathology practice, knowledge of both emotional and feeding development is key. This paper will explore the significance of “messy play” within typical feeding development, and the challenges that may arise if a child does not have the opportunity to engage in this type of exploratory play. This paper will consider several contributing factors that can result in a “picky eater.” Further, research has shown that individuals with special needs, including autism, possess a neurological makeup that differs from that of a typical individual. Because autism is a disorder of relating and communicating due to differences in the limbic system, an individual with special needs may respond to a typical feeding experience as if it is a traumatic event. As a result, broadening one’s dietary repertoire may seem to be an insurmountable challenge. This paper suggests that introducing new foods through exploratory play can help broaden and strengthen diets, as well as improve the feeding experience, of individuals with autism. The DIRFloortimeⓇ methodology stresses the importance of following a child's lead. Within this developmental model, there is a special focus on a person’s individual differences, including the unique way they process the world around them, as well as the significance of therapy occurring within the context of a strong and motivating relationship. Using this child-centered approach, we can support our children in expanding their diets, while simultaneously building upon their cognitive and creative development through playful and respectful interactions that include exposure to foods that differ in color, texture, and smell. Further, this paper explores the importance of exploration, self-feeding and messy play on brain development, both in the context of typically developing individuals and those with disordered development.

Keywords: development, feeding, floortime, sensory

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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: 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: Laura Bickel

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This paper aims to investigate to what extent Merleau-Ponty’s philosophy of body memory serves as a viable resource for the enactive approach to cognitive science and its first-person experience-based research on ‘recurrent depressive disorder’ coded F33 in ICD-10. In pursuit of this goal, the analysis begins by revisiting the neuroreductive paradigm. This paradigm serves biological psychiatry to explain the condition of vital contact in terms of underlying neurophysiological mechanisms. It is demonstrated that the neuroreductive model cannot sufficiently account for the depressed person’s episodical withdrawal in causal terms. The analysis of the irregular loss of vital resonance requires integrating the body as the subject of experience and its phenomenological time. Then, it is shown that the enactive approach to depression as disordered sense-making is a promising alternative. The enactive model of perception implies that living beings do not register pre-existing meaning ‘out there’ but unfold ‘sense’ in their action-oriented response to the world. For the enactive approach, Husserl’s passive synthesis of inner time consciousness is fundamental for what becomes perceptually present for action. It seems intuitive to bring together the enactive approach to depression with the long-standing view in phenomenological psychopathology that explains the loss of vital contact by appealing to the disruption of the temporal structure of consciousness. However, this paper argues that the disruption of the temporal structure is not justified conceptually. Instead, one may integrate Merleau-Ponty’s concept of the past as the unconscious into the enactive approach to depression. From this perspective, the living being’s experiential and biological past inserts itself in the form of habit and bodily skills and ensures action-oriented responses to the environment. Finally, it is concluded that the depressed person’s withdrawal indicates the impairment of this application process. The person suffering from F33 cannot actualize sedimented meaning to respond to the valences and tasks of a given situation.

Keywords: depression, enactivism, neuroreductionsim, phenomenology, temporality

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Authors: Dounya Barrit, Ming-Chun Tang, Hoang Dang, Kai Wang, Detlef-M. Smilgies, Aram Amassian

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Several deposition methods have been developed for perovskite film preparation. The one-step spin-coating process has emerged as a more popular option thanks to its ability to produce films of different compositions, including mixed cation and mixed halide perovskites, which can stabilize the perovskite phase and produce phases with desired band gap. The two-step method, however, is not understood in great detail. There is a significant need and opportunity to adopt the two-step process toward mixed cation and mixed halide perovskites, but this requires deeper understanding of the two-step conversion process, for instance when using different cations and mixtures thereof, to produce high-quality perovskite films with uniform composition. In this work, we demonstrate using in situ investigations that the conversion of PbI₂ to perovskite is largely dictated by the state of the PbI₂ precursor film in terms of its solvated state. Using time-resolved grazing incidence wide-angle X-Ray scattering (GIWAXS) measurements during spin coating of PbI₂ from a DMF (Dimethylformamide) solution we show the film formation to be a sol-gel process involving three PbI₂-DMF solvate complexes: disordered precursor (P₀), ordered precursor (P₁, P₂) prior to PbI₂ formation at room temperature after 5 minutes. The ordered solvates are highly metastable and eventually disappear, but we show that performing conversion from P₀, P₁, P₂ or PbI₂ can lead to very different conversion behaviors and outcomes. We compare conversion behaviors by using MAI (Methylammonium iodide), FAI (Formamidinium Iodide) and mixtures of these cations, and show that conversion can occur spontaneously and quite rapidly at room temperature without requiring further thermal annealing. We confirm this by demonstrating improvements in the morphology and microstructure of the resulting perovskite films, using techniques such as in situ quartz crystal microbalance with dissipation monitoring, SEM and XRD.

Keywords: in situ GIWAXS, lead iodide, mixed cation, perovskite solar cell, sol-gel process, solvate phase

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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: Giulia Magi Meconi, Nicholas Ballard, José M. Asua, Ronen Zangi

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Experimental and computational studies are combined to elucidate the adsorption proprieties of ionic and nonionic surfactants on hydrophobic polymer surface such us poly(styrene). To present these two types of surfactants, sodium dodecyl sulfate and poly(ethylene glycol)-block-poly(ethylene), commonly utilized in emulsion polymerization, are chosen. By applying quartz crystal microbalance with dissipation monitoring it is found that, at low surfactant concentrations, it is easier to desorb (as measured by rate) ionic surfactants than nonionic surfactants. From molecular dynamics simulations, the effective, attractive force of these nonionic surfactants to the surface increases with the decrease of their concentration, whereas, the ionic surfactant exhibits mildly the opposite trend. The contrasting behavior of ionic and nonionic surfactants critically relies on two observations obtained from the simulations. The first is that there is a large degree of interweavement between head and tails groups in the adsorbed layer formed by the nonionic surfactant (PEO/PE systems). The second is that water molecules penetrate this layer. In the disordered layer, these nonionic surfactants generate at the surface, only oxygens of the head groups present at the interface with the water phase or oxygens next to the penetrating waters can form hydrogen bonds. Oxygens inside this layer lose this favorable energy, with a magnitude that increases with the surfactants density at the interface. This reduced stability of the surfactants diminishes their driving force for adsorption. All that is shown to be in accordance with experimental results on the dynamics of surfactants desorption. Ionic surfactants assemble into an ordered structure and the attraction to the surface was even slightly augmented at higher surfactant concentration, in agreement with the experimentally determined adsorption isotherm. The reason these two types of surfactants behave differently is because the ionic surfactant has a small head group that is strongly hydrophilic, whereas the head groups of the nonionic surfactants are large and only weakly attracted to water.

Keywords: emulsion polymerization process, molecular dynamics simulations, polymer surface, surfactants adsorption

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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: Sudhakara Naidu Neppalli, Tejas S. Bhosale

Abstract:

It is well known that the block copolymer (BCP) can form a complex molecule, through non-covalent bonds such as hydrogen bond, ionic bond and co-ordination bond, with low molecular weight compound as well as with macromolecules, which provide vast applications, includes the alteration of morphology and properties of polymers. Hence we covered the research that, the importance of non-covalent bonds in increasing the non-favourable segmental interactions of the blocks was well examined by attaching and detaching the bonds between the BCP and additive. We also monitored the phase transition of block copolymer and effective interaction parameter (χeff) for Li-doped polymers using small angle x-ray scattering and transmission electron microscopy. The effective interaction parameter (χeff) between two block components was evaluated using Leibler theory based on the incompressible random phase approximation (RPA) for ionized BCP in a disordered state. Furthermore, conductivity experiments demonstrate that the ionic conductivity in the samples quenched from the different structures is morphology-independent, while it increases with increasing ion salt concentration. Morphological transitions, interaction parameter, and thermal stability also examined in quarternized block copolymer. D-spacing was used to estimate effective interaction parameter (χeff) of block components in weak and strong segregation regimes of ordered phase. Metal-containing polymer has been the topic of great attention in recent years due to their wide range of potential application. Similarly, metal- ligand complex is used as a supramolecular linker between the polymers giving rise to a ‘Metallo-Supramolecule assembly. More precisely, functionalized polymer end capped with 2, 2’:6’, 2”- terpyridine ligand can be selectively complexed with wide range of transition metal ions and then subsequently attached to other terpyridine terminated polymer block. In compare to other supramolecular assembly, BCP involved metallo-supramolecule assembly offers vast applications such as optical activity, electrical conductivity, luminescence and photo refractivity.

Keywords: band gap, block copolymer, conductivity, interaction parameter, phase transition

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