Search results for: neural tube defects

Authors: M. A. Pawar, C. S. Patil

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Investigations were carried out to evaluate the persistent toxicity of imidacloprid, thiamethoxam and dimethoate to Aphis gossypii and Amrasca biguttula biguttula under laboratory condition during 2012. The experiment was conducted in a completely randomized block design with three replications in the glass house of department of Entomology M. P. K. V. Rahuri. Okra plants were raised in glass house following all recommended agronomic practices. The 21 days old plants were used for assessing the effect of insecticides on aphids and jassids. The insecticides were diluted with distilled water to make desired concentrations and used for foliar application. The insecticides included in the study were imidacloprid 17.8 SL, imidacloprid 70 WG, thiamethoxam 25 WG and dimethoate 30 EC. Untreated check was maintained by spraying with distilled water. The mortality of aphids and jassids on treated leaf were recorded at 1, 3, 5, 7, 9, 11, 13, 15, 17, 21, and 25 days after spray till zero per cent mortality observed for each treatment. Treated leaves from the glasshouse were brought to laboratory and were put in tube with moist cotton swab at the bottom of leaf and sucking apparatus was fit to the tube. Ten jassids were sucked in each tube from the plants in the field. Evaluated insecticides differed in their persistence and index of persistence toxicity against both insects of different treatments. Recommended dose of imidacloprid (25 g a.i/ha) persisted for 21 days against both aphids and jassids. However dimethoate, a conventional insecticide persisted for 11 days.

Keywords: Amrasca biguttula biguttula, Aphis gossypii, imidacloprid, persistent toxicity

Procedia PDF Downloads 181

Authors: Hamad Almohamadi, Nabeel Alharthi, Majed Alamoudi

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Biphasic electrodes featuring CuSx/NiSx electrodeposited on nickel foam have been investigated for their electrocatalytic activity in water splitting. The study investigates the impacts of an S-vacancy induced biphasic design on the overpotential and Tafel slope. According to the findings, the NiSx/CuSx/NF electrode with S-vacancy defects displays stronger oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) activity with lower overpotential and a steeper Tafel slope than the non-defect sample. NiSx/CuSx/NF exhibits the lowest overpotential value of 212 mV vs reversible hydrogen electrode (RHE) for OER and −109 mV vs RHE for HER at 10 mA cm−2. Tafel slope of 25.4 mV dec−1 for OER and −108 mV dec−1 for OER found of that electrode. The electrochemical surface area (ECSA) and diffusion impedance of the electrode is calculated. The maximum ECSA, lowest series resistance and lowest charge transfer resistance are found in the *NiSx/CuSx/NF sample with S-vacancy defects, showing increased electrical conductivity and quick charge transfer kinetics. The *NiSx/CuSx/NF electrode was found to be stable for 80 hours in pure water splitting and 20 hours in sea-water splitting. The investigation comes to the conclusion that the enhanced water splitting activity and electrical conductivity of the electrode are caused by S-vacancy defects resulting in improved water splitting performance.

Keywords: water splitting, electrocatalyst, biphasic design, electrodeposition

Procedia PDF Downloads 63

Authors: Sirilak Areerachakul, Nat Ployong, Supayothin Na Songkla

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This research’s objective is to select the model with most accurate value by using Neural Network Technique as a way to filter potential students who enroll in IT course by electronic learning at Suan Suanadha Rajabhat University. It is designed to help students selecting the appropriate courses by themselves. The result showed that the most accurate model was 100 Folds Cross-validation which had 73.58% points of accuracy.

Keywords: artificial neural network, classification, students, e-learning

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Authors: Ashkan Zakaryazad, Ekrem Duman

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A typical classification technique ranks the instances in a data set according to the likelihood of belonging to one (positive) class. A credit card (CC) fraud detection model ranks the transactions in terms of probability of being fraud. In fact, this approach is often criticized, because firms do not care about fraud probability but about the profitability or costliness of detecting a fraudulent transaction. The key contribution in this study is to focus on the profit maximization in the model building step. The artificial neural network proposed in this study works based on profit maximization instead of minimizing the error of prediction. Moreover, some studies have shown that the back propagation algorithm, similar to other gradient–based algorithms, usually gets trapped in local optima and swarm-based algorithms are more successful in this respect. In this study, we train our profit maximization ANN using the Migrating Birds optimization (MBO) which is introduced to literature recently.

Keywords: neural network, profit-based neural network, sum of squared errors (SSE), MBO, gradient descent

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Authors: Sheela Tiwari, R. Naresh, R. Jha

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The paper presents an investigation into the effect of neural network predictive control of UPFC on the transient stability performance of a multi-machine power system. The proposed controller consists of a neural network model of the test system. This model is used to predict the future control inputs using the damped Gauss-Newton method which employs ‘backtracking’ as the line search method for step selection. The benchmark 2 area, 4 machine system that mimics the behavior of large power systems is taken as the test system for the study and is subjected to three phase short circuit faults at different locations over a wide range of operating conditions. The simulation results clearly establish the robustness of the proposed controller to the fault location, an increase in the critical clearing time for the circuit breakers and an improved damping of the power oscillations as compared to the conventional PI controller.

Keywords: identification, neural networks, predictive control, transient stability, UPFC

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Authors: Kemka C. Ihemelandu, Chukwuemeka U. Ihemelandu

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The incidence of melanoma has been increasing rapidly over the past two decades, making melanoma a current public health crisis. Unfortunately, even as screening efforts continue to expand in an effort to ameliorate the death rate from melanoma, there is a need to improve diagnostic accuracy to decrease misdiagnosis. Artificial intelligence (AI) a new frontier in patient care has the ability to improve the accuracy of melanoma diagnosis. Convolutional neural network (CNN) a form of deep neural network, most commonly applied to analyze visual imagery, has been shown to outperform the human brain in pattern recognition. However, there are noted limitations with the accuracy of the CNN models. Our aim in this study was the optimization of convolutional neural network algorithms for the automated diagnosis of melanoma. We hypothesized that Optimal selection of the momentum and batch hyperparameter increases model accuracy. Our most successful model developed during this study, showed that optimal selection of momentum of 0.25, batch size of 2, led to a superior performance and a faster model training time, with an accuracy of ~ 83% after nine hours of training. We did notice a lack of diversity in the dataset used, with a noted class imbalance favoring lighter vs. darker skin tone. Training set image transformations did not result in a superior model performance in our study.

Keywords: melanoma, convolutional neural network, momentum, batch hyperparameter

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Authors: Dana M. Ragab, Jasim A. Ghaeb

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The three-phase power system suffers from different challenging problems, e.g. voltage unbalance conditions at the load side. The voltage unbalance usually degrades the power quality of the electric power system. Several techniques can be considered for load balancing including load reconfiguration, static synchronous compensator and static reactive power compensator. In this work an efficient neural network is designed to control the unbalanced condition in the Aqaba-Qatrana-South Amman (AQSA) electric power system. It is designed for highly enhanced response time of the reactive compensator for voltage balancing. The neural network is developed to determine the appropriate set of firing angles required for the thyristor-controlled reactor to balance the three load voltages accurately and quickly. The parameters of AQSA power system are considered in the laboratory model, and several test cases have been conducted to test and validate the proposed technique capabilities. The results have shown a high performance of the proposed Neural Network Control (NNC) technique for correcting the voltage unbalance conditions at three-phase load based on accuracy and response time.

Keywords: three-phase power system, reactive power control, voltage unbalance factor, neural network, power quality

Procedia PDF Downloads 188

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

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

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

Procedia PDF Downloads 261

Authors: Nilson Barbieri, Bruno Matos Martins, Gabriel de Sant'Anna Vitor Barbieri

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In more complex systems, such as automotive gearbox, a rigorous treatment of the data is necessary because there are several moving parts (gears, bearings, shafts, etc.), and in this way, there are several possible sources of errors and also noise. The basic objective of this work is the detection of damage in automotive gearbox. The detection methods used are the wavelet method, the bispectrum; advanced filtering techniques (selective filtering) of vibrational signals and mathematical morphology. Gearbox vibration tests were performed (gearboxes in good condition and with defects) of a production line of a large vehicle assembler. The vibration signals are obtained using five accelerometers in different positions of the sample. The results obtained using the kurtosis, bispectrum, wavelet and mathematical morphology showed that it is possible to identify the existence of defects in automotive gearboxes.

Keywords: automotive gearbox, mathematical morphology, wavelet, bispectrum

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Authors: Danilo López, Nelson Vera, Luis Pedraza

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This paper analyzes fundamental ideas and concepts related to neural networks, which provide the reader a theoretical explanation of Long Short-Term Memory (LSTM) networks operation classified as Deep Learning Systems, and to explicitly present the mathematical development of Backward Pass equations of the LSTM network model. This mathematical modeling associated with software development will provide the necessary tools to develop an intelligent system capable of predicting the behavior of licensed users in wireless cognitive radio networks.

Keywords: neural networks, multilayer perceptron, long short-term memory, recurrent neuronal network, mathematical analysis

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Authors: Moh Hanif Mubarok, Muhammad Rivai

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The public's response to quality rice is very high. So it is necessary to set minimum standards in checking the quality of rice. Most rice quality measurements still use manual methods, which are prone to errors due to limited human vision and the subjectivity of testers. So, a gas detection system can be a solution that has high effectiveness and subjectivity for solving current problems. The use of gas sensors in testing rice quality must pay attention to several parameters. The parameters measured in this research are the percentage of rice water content, gas concentration, output voltage, and measurement time. Therefore, this research was carried out to identify carbon dioxide (CO₂), nitrous oxide (N₂O) and methane (CH₄) gases in rice quality using a series of gas sensors using the Neural Network method.

Keywords: carbon dioxide, dinitrogen oxide, methane, semiconductor gas sensor, neural network

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Authors: Neeta Kumari, Gopal Pathak

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Artificial neural network has been proved to be an efficient tool for non-parametric modeling of data in various applications where output is non-linearly associated with input. It is a preferred tool for many predictive data mining applications because of its power , flexibility, and ease of use. A standard feed forward networks (FFN) is used to predict the groundwater fluoride content. The ANN model is trained using back propagated algorithm, Tansig and Logsig activation function having varying number of neurons. The models are evaluated on the basis of statistical performance criteria like Root Mean Squarred Error (RMSE) and Regression coefficient (R2), bias (mean error), Coefficient of variation (CV), Nash-Sutcliffe efficiency (NSE), and the index of agreement (IOA). The results of the study indicate that Artificial neural network (ANN) can be used for groundwater fluoride prediction in the limited data situation in the hard rock region like western parts of Jharkhand with sufficiently good accuracy.

Keywords: Artificial neural network (ANN), FFN (Feed-forward network), backpropagation algorithm, Levenberg-Marquardt algorithm, groundwater fluoride contamination

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Authors: Peng Wang, Tigang Jiang

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The computer based lip reading method of different languages cannot be universal. At present, for the research of Chinese lip reading, whether the work on data sets or recognition algorithms, is far from mature. In this paper, we study the Chinese lipreading method based on machine learning, and propose a Chinese Sentence-level lip-reading network (CNLipNet) model which consists of spatio-temporal convolutional neural network(CNN), recurrent neural network(RNN) and Connectionist Temporal Classification (CTC) loss function. This model can map variable-length sequence of video frames to Chinese Pinyin sequence and is trained end-to-end. More over, We create CNLRS, a Chinese Lipreading Dataset, which contains 5948 samples and can be shared through github. The evaluation of CNLipNet on this dataset yielded a 41% word correct rate and a 70.6% character correct rate. This evaluation result is far superior to the professional human lip readers, indicating that CNLipNet performs well in lipreading.

Keywords: lipreading, machine learning, spatio-temporal, convolutional neural network, recurrent neural network

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Authors: Gautam Kumar Saharia, Sagardeep Talukdar, Riki Dutta, Sudipta Nandy

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The physics-informed neural network (PINN) method opens up an approach for numerically solving nonlinear partial differential equations leveraging fast calculating speed and high precession of modern computing systems. We construct the PINN based on a strong universal approximation theorem and apply the initial-boundary value data and residual collocation points to weekly impose initial and boundary conditions to the neural network and choose the optimization algorithms adaptive moment estimation (ADAM) and Limited-memory Broyden-Fletcher-Golfard-Shanno (L-BFGS) algorithm to optimize learnable parameter of the neural network. Next, we improve the PINN with a weighted loss function to obtain both the bright and dark soliton solutions of the Fokas-Lenells equation (FLE). We find the proposed scheme of adjustable weight coefficients into PINN has a better convergence rate and generalizability than the basic PINN algorithm. We believe that the PINN approach to solve the partial differential equation appearing in nonlinear optics would be useful in studying various optical phenomena.

Keywords: deep learning, optical soliton, physics informed neural network, partial differential equation

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Authors: Pavlo Selyshchev, Tetiana Didenko

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Within the framework of the obstacle radiation hardening and the dislocation climb-glide model a theoretical approach is developed to describe peculiarities of transient plasticity of metal under irradiation. It is considered nonlinear dynamics of accumulation of point defects (vacancies and interstitial atoms). We consider metal under such stress and conditions of irradiation at which creep is determined by dislocation motion: dislocations climb obstacles and glide between obstacles. It is shown that the rivalry between vacancy and interstitial fluxes to dislocation leads to fractures of plasticity time dependence. Simulation and analysis of this phenomenon are performed. Qualitatively different regimes of transient plasticity under irradiation are found. The fracture time is obtained. The theoretical results are compared with the experimental ones.

Keywords: climb and glide of dislocations, fractures of transient plasticity, irradiation, non-linear feed-back, point defects

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Authors: Azadeh Bashiri

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Introduction: Lack of proper diagnosis and inadequate treatment of asthma leads to physical and financial complications. This study aimed to use data mining techniques and creating a neural network intelligent system for diagnosis of asthma. Methods: The study population is the patients who had visited one of the Lung Clinics in Tehran. Data were analyzed using the SPSS statistical tool and the chi-square Pearson's coefficient was the basis of decision making for data ranking. The considered neural network is trained using back propagation learning technique. Results: According to the analysis performed by means of SPSS to select the top factors, 13 effective factors were selected, in different performances, data was mixed in various forms, so the different models were made for training the data and testing networks and in all different modes, the network was able to predict correctly 100% of all cases. Conclusion: Using data mining methods before the design structure of system, aimed to reduce the data dimension and the optimum choice of the data, will lead to a more accurate system. Therefore, considering the data mining approaches due to the nature of medical data is necessary.

Keywords: asthma, data mining, Artificial Neural Network, intelligent system

Procedia PDF Downloads 266

Authors: Pitchaporn Purngpiputtrakul, Suttasinee Petsakul, Sunisa Chatmongkolchart

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Nasogastric tube (NGT) insertion in anaesthetized and intubated patients can be challenging even for experienced anesthesiologists. Various techniques have been proposed to facilitate NGT insertion in these patients. This study aimed to compare the success rate and time required for NGT insertion between the GlideScope visualization and neck flexion with lateral neck pressure techniques. This randomized clinical trial was performed at a teaching hospital on 86 adult patients undergoing abdominal surgery under relaxant general anaesthesia who required intraoperative NGT insertion. The patients were randomized into two groups, the GlideScope group (group G) and the neck flexion with lateral neck pressure group (group F). The success rate of first and second attempts, duration of insertion, and complications were recorded. The total success rate was 79.1% in Group G compared with 76.7% in Group F (P=1) The median time required for NGT insertion was significantly longer in Group G, for both first and second attempts (97 vs 42 seconds P<0.001) and (70 vs 48.5 seconds P=0.015), respectively. Complications were reported in 23 patients (53.5%) in group G and 13 patients (30.2%) in group F. Bleeding and kinking were the most common complications in both techniques. Using GlideScope visualization to facilitate NGT insertion was comparable to neck flexion with lateral neck pressure technique in degree of success rate of insertion, while neck flexion with lateral neck pressure technique had fewer complications and was less time-consuming.

Keywords: anaesthesia, nasogastric tube, GlideScope, intubation

Procedia PDF Downloads 156

Authors: Abir Hmida, Nihel Chekir, Ammar Ben Brahim

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In the present paper, we investigate the feasibility of a thermal solar driven cold room in Gabes, southern region of Tunisia. The cold room of 109 m³ is refrigerated using an ammonia absorption machine. It is destined to preserve dates during the hot months of the year. A detailed study of the cold room leads previously to the estimation of the cooling load of the proposed storage room in the operating conditions of the region. The next step consists of the estimation of the required heat in the generator of the absorption machine to ensure the desired cold temperature. A thermodynamic analysis was accomplished and complete description of the system is determined. We propose, here, to provide the needed heat thermally from the sun by using vacuum tube collectors. We found that at least 21m² of solar collectors are necessary to accomplish the work of the solar cold room.

Keywords: absorption, ammonia, cold room, solar collector, vacuum tube

Procedia PDF Downloads 166

Authors: Mohammad Izadkhah, Mojtaba Hoseini, Alireza Khalili Tehrani

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In this paper we presented a new method for tracking flying targets in color video sequences based on contour and kernel. The aim of this work is to overcome the problem of losing target in changing light, large displacement, changing speed, and occlusion. The proposed method is made in three steps, estimate the target location by particle filter, segmentation target region using neural network and find the exact contours by greedy snake algorithm. In the proposed method we have used both region and contour information to create target candidate model and this model is dynamically updated during tracking. To avoid the accumulation of errors when updating, target region given to a perceptron neural network to separate the target from background. Then its output used for exact calculation of size and center of the target. Also it is used as the initial contour for the greedy snake algorithm to find the exact target's edge. The proposed algorithm has been tested on a database which contains a lot of challenges such as high speed and agility of aircrafts, background clutter, occlusions, camera movement, and so on. The experimental results show that the use of neural network increases the accuracy of tracking and segmentation.

Keywords: video tracking, particle filter, greedy snake, neural network

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Authors: Y. L. Hor, H. S. Chu, V. P. Bui

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Composite material is widely used to replace conventional material, especially in the aerospace industry to reduce the weight of the devices. It is formed by combining reinforced materials together via adhesive bonding to produce a bulk material with alternated macroscopic properties. In bulk composites, degradation may occur in microscopic scale, which is in each individual reinforced fiber layer or especially in its matrix layer such as delamination, inclusion, disbond, void, cracks, and porosity. In this paper, we focus on the detection of defect in matrix layer which the adhesion between the composite plies is in contact but coupled through a weak bond. In fact, the adhesive defects are tested through various nondestructive methods. Among them, pulsed phase thermography (PPT) has shown some advantages providing improved sensitivity, large-area coverage, and high-speed testing. The aim of this work is to develop an efficient numerical model to study the application of PPT to the nondestructive inspection of weak bonding in composite material. The resulting thermal evolution field is comprised of internal reflections between the interfaces of defects and the specimen, and the important key-features of the defects presented in the material can be obtained from the investigation of the thermal evolution of the field distribution. Computational simulation of such inspections has allowed the improvement of the techniques to apply in various inspections, such as materials with high thermal conductivity and more complex structures.

Keywords: pulsed phase thermography, weak bond, composite, CFRP, computational modelling, optimization

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Authors: P. Selyshchev

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Construction materials for nuclear facilities are operated under extreme thermal and radiation conditions. First of all, they are nuclear fuel, fuel assemblies, and reactor vessel. It places high demands on the control of their state, stability of their state, and their operating conditions. An irradiated material is a typical example of an open non-equilibrium system with nonlinear feedbacks between its elements. Fluxes of energy, matter and entropy maintain states which are far away from thermal equilibrium. The links that arise under irradiation are inherently nonlinear. They form the mechanisms of feed-backs that can lead to instability. Due to this instability the temperature of the sample, heat transfer, and the defect density can exceed the steady-state value in several times. This can lead to change of typical operation and an accident. Therefore, it is necessary to take into account the thermal instability to avoid the emergency situation. The point is that non-thermal energy can be accumulated in materials because irradiation produces defects (first of all these are vacancies and interstitial atoms), which are metastable. The stored energy is about energy of defect formation. Thus, an annealing of the defects is accompanied by releasing of non-thermal stored energy into thermal one. Temperature of the material grows. Increase of temperature results in acceleration of defect annealing. Density of the defects drops and temperature grows more and more quickly. The positive feed-back is formed and self-reinforcing annealing of radiation defects develops. To describe these phenomena a theoretical approach to thermal instability is developed via formalism of complex systems. We consider system of nonlinear differential equations for different components of microstructure and temperature. The qualitative analysis of this non-linear dynamical system is carried out. Conditions for development of instability have been obtained. Points of bifurcation have been found. Convenient way to represent obtained results is a set of phase portraits. It has been shown that different regimes of material state under irradiation can develop. Thus degradation of irradiated material can be limited by means of choice appropriate kind of evolution of materials under irradiation.

Keywords: irradiation, material, non-equilibrium state, nonlinear feed-back, thermal instability

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Authors: Natalia Efremova, Sergey Tarasenko

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We propose a novel neural network architecture for visual saliency detections, which utilizes neuro physiologically plausible mechanisms for extraction of salient regions. The model has been significantly inspired by recent findings from neuro physiology and aimed to simulate the bottom-up processes of human selective attention. Two types of features were analyzed: color and direction of maximum variance. The mechanism we employ for processing those features is PCA, implemented by means of normalized Hebbian learning and the waves of spikes. To evaluate performance of our model we have conducted psychological experiment. Comparison of simulation results with those of experiment indicates good performance of our model.

Keywords: neural network models, visual saliency detection, normalized Hebbian learning, Oja's rule, psychological experiment

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Authors: Nishant Singh, Christian Huyck, Vaibhav Gandhi, Alexander Jones

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A robotic arm and hand controlled by simulated neurons is presented. The robot makes use of a biological neuron simulator using a point neural model. The neurons and synapses are organised to create a finite state automaton including neural inputs from sensors, and outputs to effectors. The robot performs a simple pick-and-place task. This work is a proof of concept study for a longer term approach. It is hoped that further work will lead to more effective and flexible robots. As another benefit, it is hoped that further work will also lead to a better understanding of human and other animal neural processing, particularly for physical motion. This is a multidisciplinary approach combining cognitive neuroscience, robotics, and psychology.

Keywords: cell assembly, force sensitive resistor, robot, spiking neuron

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Authors: J. I. Corcoles, J. D. Moya-Rico, A. Molina, J. F. Belmonte, J. A. Almendros-Ibanez

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One of the most common heat exchangers technology in engineering processes is the use of double-pipe heat exchangers (DPHx), mainly in the food industry. To improve the heat transfer performance, several passive geometrical devices can be used, such as the wall corrugation of tubes, which increases the wet perimeter maintaining a constant cross-section area, increasing consequently the convective surface area. It contributes to enhance heat transfer in forced convection, promoting secondary recirculating flows. One of the most extended tools to analyse heat exchangers' efficiency is the use of computational fluid dynamic techniques (CFD), a complementary activity to the experimental studies as well as a previous step for the design of heat exchangers. In this study, a double pipe heat exchanger behaviour with two different inner tubes, smooth and spirally corrugated tube, have been analysed. Hence, experimental analysis and steady 3-D numerical simulations using the commercial code ANSYS Workbench v. 17.0 are carried out to analyse the influence of geometrical parameters for spirally corrugated tubes at turbulent flow. To validate the numerical results, an experimental setup has been used. To heat up or cool down the cold fluid as it passes through the heat exchanger, the installation includes heating and cooling loops served by an electric boiler with a heating capacity of 72 kW and a chiller, with a cooling capacity of 48 kW. Two tests have been carried out for the smooth tube and for the corrugated one. In all the tests, the hot fluid has a constant flowrate of 50 l/min and inlet temperature of 59.5°C. For the cold fluid, the flowrate range from 25 l/min (Test 1) and 30 l/min (Test 2) with an inlet temperature of 22.1°C. The heat exchanger is made of stainless steel, with an external diameter of 35 mm and wall thickness of 1.5 mm. Both inner tubes have an external diameter of 24 mm and 1 mm thickness of stainless steel with a length of 2.8 m. The corrugated tube has a corrugation height (H) of 1.1 mm and helical pitch (P) of 25 mm. It is characterized using three non-dimensional parameters, the ratio of the corrugation shape and the diameter (H/D), the helical pitch (P/D) and the severity index (SI = H²/P x D). The results showed good agreement between the numerical and the experimental results. Hence, the lowest differences were shown for the fluid temperatures. In all the analysed tests and for both analysed tubes, the temperature obtained numerically was slightly higher than the experimental results, with values ranged between 0.1% and 0.7%. Regarding the pressure drop, the maximum differences between the values obtained numerically, and the experimental values were close to 16%. Based on the experimental and the numerical results, for the corrugated tube, it can be highlighted that the temperature difference between the inlet and the outlet of the cold fluid is 42%, higher than the smooth tube.

Keywords: corrugated tube, heat exchanger, heat transfer, numerical simulation

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Authors: Alexandre Barbosa de Almeida, Telma Woerle de Lima Soares

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Protein structure prediction is a challenging task in the bioinformatics field. The biological function of all proteins majorly relies on the shape of their three-dimensional conformational structure, but less than 1% of all known proteins in the world have their structure solved. This work proposes a deep learning model to address this problem, attempting to predict some aspects of the protein conformations. Throughout a process of multiobjective dominance, a recurrent neural network was trained to abstract the particular bias of each individual multiobjective algorithm, generating a heuristic that could be useful to predict some of the relevant aspects of the three-dimensional conformation process formation, known as protein folding.

Keywords: Ab initio heuristic modeling, multiobjective optimization, protein structure prediction, recurrent neural network

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Authors: Serdar Delice, Mehmet Isik, Nizami Hasanli, Kadir Goksen

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Researchers have donated great interest to ternary and quaternary semiconductor compounds especially with the improvement of the optoelectronic technology. The quaternary compound Tl2Ga2Se3S which was grown by Bridgman method carries the properties of ternary thallium chalcogenides group of semiconductors with layered structure. This compound can be formed from TlGaSe2 crystals replacing the one quarter of selenium atom by sulfur atom. Although Tl2Ga2Se3S crystals are not intentionally doped, some unintended defect types such as point defects, dislocations and stacking faults can occur during growth processes of crystals. These defects can cause undesirable problems in semiconductor materials especially produced for optoelectronic technology. Defects of various types in the semiconductor devices like LEDs and field effect transistor may act as a non-radiative or scattering center in electron transport. Also, quick recombination of holes with electrons without any energy transfer between charge carriers can occur due to the existence of defects. Therefore, the characterization of defects may help the researchers working in this field to produce high quality devices. Thermoluminescence (TL) is an effective experimental method to determine the kinetic parameters of trap centers due to defects in crystals. In this method, the sample is illuminated at low temperature by a light whose energy is bigger than the band gap of studied sample. Thus, charge carriers in the valence band are excited to delocalized band. Then, the charge carriers excited into conduction band are trapped. The trapped charge carriers are released by heating the sample gradually and these carriers then recombine with the opposite carriers at the recombination center. By this way, some luminescence is emitted from the samples. The emitted luminescence is converted to pulses by using an experimental setup controlled by computer program and TL spectrum is obtained. Defect characterization of Tl2Ga2Se3S single crystals has been performed by TL measurements at low temperatures between 10 and 300 K with various heating rate ranging from 0.6 to 1.0 K/s. The TL signal due to the luminescence from trap centers revealed one glow peak having maximum temperature of 36 K. Curve fitting and various heating rate methods were used for the analysis of the glow curve. The activation energy of 13 meV was found by the application of curve fitting method. This practical method established also that the trap center exhibits the characteristics of mixed (general) kinetic order. In addition, various heating rate analysis gave a compatible result (13 meV) with curve fitting as the temperature lag effect was taken into consideration. Since the studied crystals were not intentionally doped, these centers are thought to originate from stacking faults, which are quite possible in Tl2Ga2Se3S due to the weakness of the van der Waals forces between the layers. Distribution of traps was also investigated using an experimental method. A quasi-continuous distribution was attributed to the determined trap centers.

Keywords: chalcogenides, defects, thermoluminescence, trap centers

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Authors: Hamed Zolfaghari, Mojtaba Kord

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After feasibility study the design phase is started and the rest of other phases are highly dependent on this phase. forecasting the duration of design phase could do a miracle and would save a lot of time. This study provides a fast and accurate Machine learning (ML) and optimization framework, which allows a quick duration estimation of project design phase, hence improving operational efficiency and competitiveness of a design construction company. 3 data sets of three years composed of daily time spent for different design projects are used to train and validate the ML models to perform multiple projects. Our study concluded that Artificial Neural Network (ANN) performed an accuracy of 0.94.

Keywords: time estimation, machine learning, Artificial neural network, project design phase

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Authors: Bryan Crompton, Daniel Giger, Tanay Mehta, Apurva Mody

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The task of classifying radio frequency (RF) signals has seen recent success in employing deep neural network models. In this work, we present a combined signal processing and machine learning approach to signal classification for cochannel anomalous signals. The power spectral density and cyclostationary signal processing features of a captured signal are computed and fed into a neural net to produce a classification decision. Our combined signal preprocessing and machine learning approach allows for simpler neural networks with fast training times and small computational resource requirements for inference with longer preprocessing time.

Keywords: signal processing, machine learning, cyclostationary signal processing, signal classification

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Authors: Bahaa Eldin A. Fouda, Khaled N. Yossef, Mohamed Elhosseny, Ahmed Lotfy, Mohamed Salama, Mohamed Sobh

Abstract:

Developmental neurotoxicity (DNT) entails the toxic effects imparted by various chemicals on the brain during the early childhood period. As human brains are vulnerable during this period, various chemicals would have their maximum effects on brains during early childhood. Some toxicants have been confirmed to induce developmental toxic effects on CNS e.g. lead, however; most of the agents cannot be identified with certainty due the defective nature of predictive toxicology models used. A novel alternative method that can overcome most of the limitations of conventional techniques is the use of 3D neurospheres system. This in-vitro system can recapitulate most of the changes during the period of brain development making it an ideal model for predicting neurotoxic effects. In the present study, we verified the possible DNT of epoxomicin which is a naturally occurring selective proteasome inhibitor with anti-inflammatory activity. Rat neural progenitor cells were isolated from rat embryos (E14) extracted from placental tissue. The cortices were aseptically dissected out from the brains of the fetuses and the tissues were triturated by repeated passage through a fire-polished constricted Pasteur pipette. The dispersed tissues were allowed to settle for 3 min. The supernatant was, then, transferred to a fresh tube and centrifuged at 1,000 g for 5 min. The pellet was placed in Hank’s balanced salt solution cultured as free-floating neurospheres in proliferation medium. Two doses of epoxomicin (1µM and 10µM) were used in cultured neuropsheres for a period of 14 days. For proliferation analysis, spheres were cultured in proliferation medium. After 0, 4, 5, 11, and 14 days, sphere size was determined by software analyses. The diameter of each neurosphere was measured and exported to excel file further to statistical analysis. For viability analysis, trypsin-EDTA solution were added to neurospheres for 3 min to dissociate them into single cells suspension, then viability evaluated by the Trypan Blue exclusion test. Epoxomicin was found to affect proliferation and viability of neuropsheres, these effects were positively correlated to doses and progress of time. This study confirms the DNT effects of epoxomicin on 3D neurospheres model. The effects on proliferation suggest possible gross morphologic changes while the decrease in viability propose possible focal lesion on exposure to epoxomicin during early childhood.

Keywords: neural progentor cells, epoxomicin, neurosphere, medical and health sciences

Procedia PDF Downloads 418

Authors: Vishnu Pratap Singh Kirar

Abstract:

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

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

Procedia PDF Downloads 602