Search results for: gamma neural oscillation

Authors: Ilker Ali Ozkan

Abstract:

In this study, an artificial neural network model has been developed to estimate chronic kidney failure which is a common disease. The patients’ age, their blood and biochemical values, and 24 input data which consists of various chronic diseases are used for the estimation process. The input data have been subjected to preprocessing because they contain both missing values and nominal values. 147 patient data which was obtained from the preprocessing have been divided into as 70% training and 30% testing data. As a result of the study, artificial neural network model with 25 neurons in the hidden layer has been found as the model with the lowest error value. Chronic kidney failure disease has been able to be estimated accurately at the rate of 99.3% using this artificial neural network model. The developed artificial neural network has been found successful for the estimation of chronic kidney failure disease using clinical data.

Keywords: estimation, artificial neural network, chronic kidney failure disease, disease diagnosis

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Authors: Patarawan Sangnawakij, Sa-Aat Niwitpong

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In this paper, we proposed two new confidence intervals for the difference of coefficients of variation, CIw and CIs, in two independent gamma distributions. These proposed confidence intervals using the close form method of variance estimation which was presented by Donner and Zou (2010) based on concept of Wald and Score confidence interval, respectively. Monte Carlo simulation study is used to evaluate the performance, coverage probability and expected length, of these confidence intervals. The results indicate that values of coverage probabilities of the new confidence interval based on Wald and Score are satisfied the nominal coverage and close to nominal level 0.95 in various situations, particularly, the former proposed confidence interval is better when sample sizes are small. Moreover, the expected lengths of the proposed confidence intervals are nearly difference when sample sizes are moderate to large. Therefore, in this study, the confidence interval for the difference of coefficients of variation which based on Wald is preferable than the other one confidence interval.

Keywords: confidence interval, score’s interval, wald’s interval, coefficient of variation, gamma distribution, simulation study

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Authors: Aydin Teymourifar, Gurkan Ozturk

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In this paper, a new hybrid Hopfield neural network is proposed for the dynamic, flexible job shop scheduling problem. A new heuristic based and easy to implement energy function is designed for the Hopfield neural network, which penalizes the constraints violation and decreases makespan. Moreover, for enhancing the performance, several heuristics are integrated to it that achieve active, and non-delay schedules also, prevent early convergence of the neural network. The suggested algorithm that is designed as a generalization of the previous studies for the flexible and dynamic scheduling problems can be used for solving real scheduling problems. Comparison of the presented hybrid method results with the previous studies results proves its efficiency.

Keywords: dynamic flexible job shop scheduling, neural network, heuristics, constrained optimization

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Authors: Lu Zhao, Nadir Farhi, Yeltsin Valero, Zoi Christoforou, Nadia Haddadou

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In this paper, a long short-term memory (LSTM) neural network model is proposed to replicate simultaneously car-following and lane-changing behaviors in road networks. By combining two kinds of LSTM layers and three input designs of the neural network, six variants of the LSTM model have been created. These models were trained and tested on the NGSIM 101 dataset, and the results were evaluated in terms of longitudinal speed and lateral position, respectively. Then, we compared the LSTM model with a classical car-following model (the intelligent driving model (IDM)) in the part of speed decision. In addition, the LSTM model is compared with a model using classical neural networks. After the comparison, the LSTM model demonstrates higher accuracy than the physical model IDM in terms of car-following behavior and displays better performance with regard to both car-following and lane-changing behavior compared to the classical neural network model.

Keywords: traffic modeling, neural networks, LSTM, car-following, lane-change

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Authors: R. B. Mishra

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Hidden Markov Model (HMM) has been used in prediction and determination of states that generate different neural activations as well as mental working conditions. This paper addresses two applications of HMM; one to determine the optimal sequence of states for two neural states: Active (AC) and Inactive (IA) for the three emission (observations) which are for No Working (NW), Waiting (WT) and Working (W) conditions of human beings. Another is for the determination of optimal sequence of intentionality i.e. Believe (B), Desire (D), and Intention (I) as the states and three observational sequences: NW, WT and W. The computational results are encouraging and useful.

Keywords: hiden markov model, believe desire intention, neural activation, simulation

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Authors: B. Afsharipoor, E. Nazemi

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Artificial neural networks (ANNs) are powerful tool for prediction which can be trained based on a set of examples and thus, it would be useful for nonlinear image processing. The present paper reviews several paper regarding applications of ANN in image processing to shed the light on advantage and disadvantage of ANNs in this field. Different steps in the image processing chain including pre-processing, enhancement, segmentation, object recognition, image understanding and optimization by using ANN are summarized. Furthermore, results on using multi artificial neural networks are presented.

Keywords: neural networks, image processing, segmentation, object recognition, image understanding, optimization, MANN

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Authors: O. Maranci, A. B. Tugrul

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Scintigraphy is an imaging method of nuclear events provoked by collisions or charged current interactions with radiation. It is used for diagnostic test used in nuclear medicine via radiopharmaceuticals emitting radiation which is captured by gamma cameras to form two-dimensional images. Liver scintigraphy is widely used in nuclear medicine.Tc-99m and Cr-51 gamma radioisotopes can be used for this purpose. Cr-51 usage is more important for patients’ organ dose that has higher energy and longer half-life as compared to Tc-99m. In this study, it is aimed to determine the required dose for critical organs of patient through liver scintigraphy via Cr-51 gamma radioisotope. Experimental studies were conducted on patients even though conducting experimental studies on patients is extremely difficult for determination of critical organ doses. Torso phantom was utilized to simulate the liver scintigraphy by using 20 mini packages of Cr-51 that were placed on the organ. The radioisotope was produced by irradiation in central thimble of TRIGA MARK II Reactor at 250 KW power. As the results of the study, critical organ doses were determined and evaluated with different critic organs.

Keywords: critical organ doses, liver, scintigraphy, TRIGA Mark-II

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Authors: Li Kewen, Su Zhaoxin, Wang Xingmou, Zhu Jian Bing

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Conventional reservoir prediction methods ar not sufficient to explore the implicit relation between seismic attributes, and thus data utilization is low. In order to improve the predictive classification accuracy of reservoir lithology, this paper proposes a deep learning lithology prediction method based on ResNet (Residual Neural Network) and SENet (Squeeze-and-Excitation Neural Network). The neural network model is built and trained by using seismic attribute data and lithology data of Shengli oilfield, and the nonlinear mapping relationship between seismic attribute and lithology marker is established. The experimental results show that this method can significantly improve the classification effect of reservoir lithology, and the classification accuracy is close to 70%. This study can effectively predict the lithology of undrilled area and provide support for exploration and development.

Keywords: convolutional neural network, lithology, prediction of reservoir, seismic attributes

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Authors: Li Zhang, Yuehong Su

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Lightpipe as a mature solar light tube technique has been employed worldwide. Accurately assessing the performance of lightpipe and evaluate daylighting available has been a challenging topic. Previous research had used regression model and computational simulation methods to estimate the performance of lightpipe. However, due to the nonlinear nature of solar light transferring in lightpipe, the methods mentioned above express inaccurate and time-costing issues. In the present study, a neural network model as an alternative method is investigated to predict the transmittance of lightpipe. Four types of commercial lightpipe with bended angle 0°, 30°, 45° and 60° are discussed under clear, intermediate and overcast sky conditions respectively. The neural network is generated in MATLAB by using the outcomes of an optical software Photopia simulations as targets for networks training and testing. The coefficient of determination (R²) for each model is higher than 0.98, and the mean square error (MSE) is less than 0.0019, which indicate the neural network strong predictive ability and the use of the neural network method could be an efficient technique for determining the performance of lightpipe.

Keywords: neural network, bended lightpipe, transmittance, Photopia

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Authors: Naci Büyükkaracığan

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Artificial neural networks (ANN) is an electrical model based on the human brain nervous system and working principle. Artificial neural networks have been the subject of an active field of research that has matured greatly over the past 55 years. ANN now is used in many fields. But, it has been viewed that artificial neural networks give better results in particular optimization and control systems. There are requirements of optimization and control system in many of the area forming the subject of civil engineering applications. In this study, the first artificial intelligence systems are widely used in the solution of civil engineering systems were examined with the basic principles and technical aspects. Finally, the literature reviews for applications in the field of civil engineering were conducted and also artificial intelligence techniques were informed about the study and its results.

Keywords: artificial neural networks, civil engineering, Fuzzy logic, statistics

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Authors: Keethadath Arshad, Kappalli Sudha

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Mud crab, Scylla Serrata, a commercially important shellfish with high global demand appears to be the rich source of dietary fatty acids. Its increased production through aquaculture and highly perishable nature would necessitate improved techniques for their proper preservation. Optimized irradiation has been identified as an effective method to facilitate safety and extended shelf life for a broad range of the perishable food items including finfishes and shellfishes. The present study analyzed the effects of dose-dependent gamma irradiation on the fatty acid profile of the muscle derived from the candidate species (S. serrata) at both qualitative and quantitative levels. Wild grown, average sized, intermolt male S. Serrata were gamma irradiated (^60C, 3.8kGy/ hour) at the dosage of 0.5kGy, 1.0kGy and 2.0kGy using gamma chamber. Total lipid extracted by Folch method, after methylation, were analyzed for the presence fatty acids adopting Gas Chromatograph equipped with flame ionization detector by comparing with the authentic FAME reference standards. The tissue from non-irradiated S. serrata showed the presence of 12 SFA, 6 MUFA, 8PUFA and 2 TF; PUFA includes medicinally important ω-3 FA such as C18:3, C20:5 and C22:6 and ω-6 FA such as γ- C18:3 and C20:2. Dose-dependent gamma irradiation reduced the number of detectable fatty acids (10, 8 and 8 SFA, 6, 6 and 5MUFA, 7, 7, and 6 PUFA and 1, 1, and 0 TF in 0.5kGy, 1.0kGy and 2kGy irradiated samples respectively). Major fatty acids detected in both irradiated and non-irradiated samples were as follows: SFA- C16:0, C18:0, C22:0 and C14:0; MUFA - C18:1 and C16:1and PUFA- C18:2, C20:5, C20:2 and C22:6. Irradiation doses ranging from 1-2kGy substantially reduced the ω-6 C18:3 and ω-3 C18:3. However, the omega fatty acids such as C20:5, C22:6 and C20:2 could survive even after 2kGy irradiation. Significantly, trans fat like C18:2T and C18:1T were completely disappeared upon 2kGy irradiation. From the overall observations made from the present study, it is suggested that irradiation dose up to 1kGy is optimum to maintain the fatty acid profile and eradicate the trans fat of the muscle derived from S. serrata.

Keywords: fatty acid profile, food preservation, gamma irradiation, scylla serrata

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Authors: Magdi. M. Nabi, Ding-Li Yu

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Control of a semi-batch polymerization reactor using an adaptive radial basis function (RBF) neural network method is investigated in this paper. A neural network inverse model is used to estimate the valve position of the reactor; this method can identify the controlled system with the RBF neural network identifier. The weights of the adaptive PID controller are timely adjusted based on the identification of the plant and self-learning capability of RBFNN. A PID controller is used in the feedback control to regulate the actual temperature by compensating the neural network inverse model output. Simulation results show that the proposed control has strong adaptability, robustness and satisfactory control performance and the nonlinear system is achieved.

Keywords: Chylla-Haase polymerization reactor, RBF neural networks, feed-forward, feedback control

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Authors: Faezeh Aghazadeh, Mohammad Javad Sharifi

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The application of neural networks to model a full-scale industrial acetylene hydrogenation in olefin plant has been studied. The operating variables studied are the, input-temperature of the reactor, output-temperature of the reactor, hydrogen ratio of the reactor, [C₂H₂]input, and [C₂H₆]input. The studied operating variables were used as the input to the constructed neural network to predict the [C₂H₆]output at any time as the output or the target. The constructed neural network was found to be highly precise in predicting the quantity of [C₂H₆]output for the new input data, which are kept unaware of the trained neural network showing its applicability to determine the [C₂H₆]output for any operating conditions. The enhancement of [C₂H₆]output as compared with [C₂H₆]input was a consequence of low selective acetylene hydrogenation to ethylene.

Keywords: acetylene hydrogenation, Pd-Ag/Al₂O₃, artificial neural network, modeling, optimal design

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Authors: Mohammad Saleh Vahdatpour, Yanqing Zhang

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Understanding the neural mechanisms underlying motion detection in the human visual system has long been a fascinating challenge in neuroscience and artificial intelligence. This paper presents a spiking neural network model inspired by the processing of motion information in the primate visual system, particularly focusing on the Middle Temporal (MT) area. In our study, we propose a multi-layer spiking neural network model to perform motion detection tasks, leveraging the idea that synaptic delays in neuronal communication are pivotal in motion perception. Synaptic delay, determined by factors like axon length and myelin insulation, affects the temporal order of input spikes, thereby encoding motion direction and speed. Overall, our spiking neural network model demonstrates the feasibility of capturing motion detection principles observed in the primate visual system. The combination of synaptic delays, learning mechanisms, and shared weights and delays in SMD provides a promising framework for motion perception in artificial systems, with potential applications in computer vision and robotics.

Keywords: neural network, motion detection, signature detection, convolutional neural network

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Authors: Juliana V. Pereira, Adriana S. M. Batista, Jefferson P. Nascimento, Clascídia A. Furtado, Luiz O. Faria

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The combination of the properties of graphene oxide (OG) and PVDF homopolymer makes their combined composite materials as multifunctional systems with great potential. Knowledge of the molecular structure is essential for better use. In this work, the degradation of PVDF polymer exposed to gamma irradiation in oxygen atmosphere in high dose rate has been studied and compared to degradation of PVDF/OG composites. The samples were irradiated with a Co-60 source at constant dose rate, with doses ranging from 100 kGy to 1,000 kGy. In FTIR data shown that the formation of oxidation products was at the both samples with formation of carbonyl and hydroxyl groups amongst the most prevalent products in the pure PVDF samples. In the other hand, the composites samples exhibit less presence of degradation products with predominant formation of carbonyl groups, these results also seen in the UV-Vis analysis. The results show that the samples of composites may have greater resistance to the irradiation process, since they have less degradation products than pure PVDF samples seen by spectroscopic techniques.

Keywords: gamma irradiation, PVDF, PVDF/OG composites, spectroscopic techniques

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Authors: P. Karimi, A. H. Khedmati Bazkiaei

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The main goal of this study is to test differential neural network as a controller of smart structure and is to enumerate its advantages and disadvantages in comparison with other controllers. In this study, the smart structure has been considered as a Euler Bernoulli cantilever beam and it has been tried that it be under control with the use of vibration neural network resulting from movement. Also, a linear observer has been considered as a reference controller and has been compared its results. The considered vibration charts and the controlled state have been recounted in the final part of this text. The obtained result show that neural observer has better performance in comparison to the implemented linear observer.

Keywords: smart material, on-line differential artificial neural network, active control, finite element method

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Authors: Si Mon Kueh, Tom J. Kazmierski

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There are about 1% of the world population suffering from the hidden disability known as epilepsy and major developing countries are not fully equipped to counter this problem. In order to reduce the inconvenience and danger of epilepsy, different methods have been researched by using a artificial neural network (ANN) classification to distinguish epileptic waveforms from normal brain waveforms. This paper outlines the aim of achieving massive ANN parallelization through a dedicated hardware using bit-serial processing. The design of this bit-serial Neural Processing Element (NPE) is presented which implements the functionality of a complete neuron using variable accuracy. The proposed design has been tested taking into consideration non-idealities of a hardware ANN. The NPE consists of a bit-serial multiplier which uses only 16 logic elements on an Altera Cyclone IV FPGA and a bit-serial ALU as well as a look-up table. Arrays of NPEs can be driven by a single controller which executes the neural processing algorithm. In conclusion, the proposed compact NPE design allows the construction of complex hardware ANNs that can be implemented in a portable equipment that suits the needs of a single epileptic patient in his or her daily activities to predict the occurrences of impending tonic conic seizures.

Keywords: Artificial Neural Networks (ANN), bit-serial neural processor, FPGA, Neural Processing Element (NPE)

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Authors: J. K. Alhassan, C. S. Actsu

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This study is on the prediction of feature performance of undergraduate students with Artificial Neural Networks (ANN). With the growing decline in the quality academic performance of undergraduate students, it has become essential to predict the students’ feature academic performance early in their courses of first and second years and to take the necessary precautions using such prediction-based information. The feed forward multilayer neural network model was used to train and develop a network and the test carried out with some of the input variables. A result of 80% accuracy was obtained from the test which was carried out, with an average error of 0.009781.

Keywords: academic performance, artificial neural network, prediction, students

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Authors: Natasha Petrovska, Mirjana Pavlovic, Maria M. Larrondo-Petrie

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Neural stem cells (NSCs) are multi-potent, self-renewing cells that generate new neurons. Three subtypes of NSCs can be separated regarding the stages of NSC lineage: quiescent neural stem cells (qNSCs), activated neural stem cells (aNSCs) and neural progenitor cells (NPCs), but their gene expression signatures are not utterly understood yet. Single-cell examinations have started to elucidate the complex structure of NSC populations. Nevertheless, there is a lack of thorough molecular interpretation of the NSC lineage heterogeneity and an increasing need for tools to analyze and improve the efficiency and correctness of single-cell sequencing data. Feature selection and ordering can identify and classify the gene expression signatures of these subtypes and can discover novel subpopulations during the NSCs activation and differentiation processes. The aim here is to review the implementation of the feature selection technique on NSC subtypes and the classification techniques that have been used for the identification of gene expression signatures.

Keywords: feature selection, feature similarity, neural stem cells, genes, feature selection methods

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Authors: B. S.Akhmetov, S. T. Akhmetova, D. N. Nadeyev, V. Yu. Yegorov, V. V. Smogoonov

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Estimated probabilities of errors of the first and second kind for nonideal biometrics-neural transducers 256 outputs, the construction of nomograms based error probability of 'own' and 'alien' from the mathematical expectation and standard deviation of the normalized measures Hamming.

Keywords: modeling, errors, probability, biometrics, neural network, authentication

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Authors: Liu Zhiyuan, Sun Zongdi

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In this paper, the BP neural network model is established to predict the carbon trading price and carbon trading volume in Shanghai City. First of all, we find the data of carbon trading price and carbon trading volume in Shanghai City from September 30, 2015 to December 23, 2016. The carbon trading price and trading volume data were processed to get the average value of each 5, 10, 20, 30, and 60 carbon trading price and trading volume. Then, these data are used as input of BP neural network model. Finally, after the training of BP neural network, the prediction values of Shanghai carbon trading price and trading volume are obtained, and the model is tested.

Keywords: Carbon trading price, carbon trading volume, BP neural network model, Shanghai City

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Authors: Xiaohan Bookman, Xiaoyan Zhu

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Margin-Based Principle has been proposed for a long time, it has been proved that this principle could reduce the structural risk and improve the performance in both theoretical and practical aspects. Meanwhile, feed-forward neural network is a traditional classifier, which is very hot at present with a deeper architecture. However, the training algorithm of feed-forward neural network is developed and generated from Widrow-Hoff Principle that means to minimize the squared error. In this paper, we propose a new training algorithm for feed-forward neural networks based on Margin-Based Principle, which could effectively promote the accuracy and generalization ability of neural network classifiers with less labeled samples and flexible network. We have conducted experiments on four UCI open data sets and achieved good results as expected. In conclusion, our model could handle more sparse labeled and more high-dimension data set in a high accuracy while modification from old ANN method to our method is easy and almost free of work.

Keywords: Max-Margin Principle, Feed-Forward Neural Network, classifier, structural risk

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Authors: Baeza S. Roberto

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The knitted fabric suffers a deformation in its dimensions due to stretching and tension factors, transverse and longitudinal respectively, during the process in rectilinear knitting machines so it performs a dry relaxation shrinkage procedure and thermal action of prefixed to obtain stable conditions in the knitting. This paper presents a dry relaxation shrinkage prediction of Bordeaux fiber using a feed forward neural network and linear regression models. Six operational alternatives of shrinkage were predicted. A comparison of the results was performed finding neural network models with higher levels of explanation of the variability and prediction. The presence of different reposes are included. The models were obtained through a neural toolbox of Matlab and Minitab software with real data in a knitting company of Southern Guanajuato. The results allow predicting dry relaxation shrinkage of each alternative operation.

Keywords: neural network, dry relaxation, knitting, linear regression

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Authors: Ilkay Turk Cakir, Murat Altinli, Zekeriya Uysal, Abdulkadir Senol, Olcay Bolukbasi Yalcinkaya, Ali Yilmaz

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The Higgs boson was discovered by the ATLAS and CMS experimental groups in 2012 at the Large Hadron Collider (LHC). Production and decay properties of the Higgs boson, Standard Model (SM) couplings, and limits on effective scale of the Higgs boson’s couplings with other bosons are investigated at particle colliders. Deviations from SM estimates are parametrized by effective Lagrangian terms to investigate Higgs couplings. This is a model-independent method for describing the new physics. In this study, sensitivity to neutral gauge boson anomalous couplings with the Higgs boson is investigated using the parameters of the Large Hadron electron Collider (LHeC) and the Future Circular electron-hadron Collider (FCC-eh) with a model-independent approach. By using MadGraph5_aMC@NLO multi-purpose event generator with the parameters of LHeC and FCC-eh, the bounds on the anomalous Hγγ, HγZ and HZZ couplings in e− p → e− q H process are obtained. Detector simulations are also taken into account in the calculations.

Keywords: anomalos couplings, FCC-eh, Higgs, Z boson

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Authors: Alexander Goncharenko, Andrey Denisov, Sergey Alyamkin, Evgeny Terentev

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The neural network quantization is highly desired procedure to perform before running neural networks on mobile devices. Quantization without fine-tuning leads to accuracy drop of the model, whereas commonly used training with quantization is done on the full set of the labeled data and therefore is both time- and resource-consuming. Real life applications require simplification and acceleration of quantization procedure that will maintain accuracy of full-precision neural network, especially for modern mobile neural network architectures like Mobilenet-v1, MobileNet-v2 and MNAS. Here we present a method to significantly optimize training with quantization procedure by introducing the trained scale factors for discretization thresholds that are separate for each filter. Using the proposed technique, we quantize the modern mobile architectures of neural networks with the set of train data of only ∼ 10% of the total ImageNet 2012 sample. Such reduction of train dataset size and small number of trainable parameters allow to fine-tune the network for several hours while maintaining the high accuracy of quantized model (accuracy drop was less than 0.5%). Ready-for-use models and code are available in the GitHub repository.

Keywords: distillation, machine learning, neural networks, quantization

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Authors: Sorin Valcan, Mihail Gaianu

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Labeling is a very costly and time consuming process which aims to generate datasets for training neural networks in several functionalities and projects. For driver monitoring system projects, the need for labeled images has a significant impact on the budget and distribution of effort. This paper presents the modifications done to an algorithm used for the generation of ground truth data for 2D eyes location on infrared images with drivers in order to improve the quality of the data and performance of the trained neural networks. The algorithm restrictions become tougher, which makes it more accurate but also less constant. The resulting dataset becomes smaller and shall not be altered by any kind of manual label adjustment before being used in the neural networks training process. These changes resulted in a much better performance of the trained neural networks.

Keywords: labeling automation, infrared camera, driver monitoring, eye detection, convolutional neural networks

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Authors: S. Subramaniam, J. S. A. Rao, P. Ramdas, K. R. Selvaduray, N. M. Han, M. K. Kutty, A. K. Radhakrishnan

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Immune system is a complex system where the immune cells have the capability to respond against a wide range of immune challenges including cancer progression. However, in the event of cancer development, tumour cells trigger immunosuppressive environment via activation of myeloid-derived suppressor cells and T regulatory (Treg) cells. The Treg cells are subset of CD4+ T lymphocytes, known to have crucial roles in regulating immune homeostasis and promoting the establishment and maintenance of peripheral tolerance. Dysregulation of these mechanisms could lead to cancer progression and immune suppression. Recently, there are many studies reporting on the effects of natural bioactive compounds on immune responses against cancer. It was known that tocotrienol-rich-fraction consisting 70% tocotrienols and 30% α-tocopherol is able to exhibit immunomodulatory as well as anti-cancer properties. Hence, this study was designed to evaluate the effects of gamma-tocotrienol (G-T3) supplementation on T-reg cells in a syngeneic mouse model of breast cancer. In this study, female BALB/c mice were divided into two groups and fed with either soy oil (vehicle) or gamma-tocotrienol (G-T3) for two weeks followed by inoculation with tumour cells. All the mice continued to receive the same supplementation until day 49. The results showed a significant reduction in tumour volume and weight in G-T3 fed mice compared to vehicle-fed mice. Lung and liver histology showed reduced evidence of metastasis in tumour-bearing G-T3 fed mice. Besides that, flow cytometry analysis revealed T-helper cell population was increased, and T-regulatory cell population was suppressed following G-T3 supplementation. Moreover, immunohistochemistry analysis showed that there was a marked decrease in the expression of FOXP3 in the G-T3 fed tumour bearing mice. In conclusion, the G-T3 supplementation showed good prognosis towards breast cancer by enhancing the immune response in tumour-bearing mice. Therefore, gamma-T3 can be used as immunotherapy agent for the treatment of breast cancer.

Keywords: breast cancer, gamma tocotrienol, immune suppression, supplement

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Authors: K. Sahithya, I. Balasundar, Pritapant, T. Raghua

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Ni-based superalloy with a nominal composition Ni-14% Cr-11% Co-5.8% Mo-2.4% Ti-2.4% Nb-2.8% Al-0.26 % Fe-0.032% Si-0.069% C (all in wt %) is used as turbine discs in a variety of aero engines. Like any other superalloy, the primary processing of the as-cast superalloy poses a major challenge due to its complex alloy chemistry. The challenge was circumvented by characterizing the different phases present in the material, optimizing the homogenization treatment, identifying a suitable thermomechanical processing window using dynamic materials modeling. The as-cast material was subjected to homogenization at 1200°C for a soaking period of 8 hours and quenched using different media. Water quenching (WQ) after homogenization resulted in very fine spherical γꞌ precipitates of sizes 30-50 nm, whereas furnace cooling (FC) after homogenization resulted in bimodal distribution of precipitates (primary gamma prime of size 300nm and secondary gamma prime of size 5-10 nm). MC type primary carbides that are stable till the melting point of the material were found in both WQ and FC samples. Deformation behaviour of both the materials below (1000-1100°C) and above gamma prime solvus (1100-1175°C) was evaluated by subjecting the material to series of compression tests at different constant true strain rates (0.0001/sec-1/sec). An in-detail examination of the precipitate dislocation interaction mechanisms carried out using TEM revealed precipitate shearing and Orowan looping as the mechanisms governing deformation in WQ and FC, respectively. Incoherent/semi coherent gamma prime precipitates in the case of FC material facilitates better workability of the material, whereas the coherent precipitates in WQ material contributed to higher resistance to deformation of the material. Both the materials exhibited discontinuous dynamic recrystallization (DDRX) above gamma prime solvus temperature. The recrystallization kinetics was slower in the case of WQ material. Very fine grain boundary carbides ( ≤ 300 nm) retarded the recrystallisation kinetics in WQ. Coarse carbides (1-5 µm) facilitate particle stimulated nucleation in FC material. The FC material was cogged (primary hot working) 1120˚C, 0.03/sec resulting in significant grain refinement, i.e., from 3000 μm to 100 μm. The primary processed material was subjected to intensive thermomechanical deformation subsequently by reducing the temperature by 50˚C in each processing step with intermittent heterogenization treatment at selected temperatures aimed at simultaneous coarsening of the gamma prime precipitates and refinement of the gamma matrix grains. The heterogeneous annealing treatment carried out, resulted in gamma grains of 10 μm and gamma prime precipitates of 1-2 μm. Further thermo mechanical processing of the material was carried out at 1025˚C to increase the homogeneity of the obtained micro-duplex structure.

Keywords: superalloys, dynamic material modeling, nickel alloys, dynamic recrystallization, superplasticity

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Authors: John Kabuba

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The study investigated the implementation of the Neural Network (NN) techniques for prediction of the loading of Cu ions onto clinoptilolite. The experimental design using analysis of variance (ANOVA) was chosen for testing the adequacy of the Neural Network and for optimizing of the effective input parameters (pH, temperature and initial concentration). Feed forward, multi-layer perceptron (MLP) NN successfully tracked the non-linear behavior of the adsorption process versus the input parameters with mean squared error (MSE), correlation coefficient (R) and minimum squared error (MSRE) of 0.102, 0.998 and 0.004 respectively. The results showed that NN modeling techniques could effectively predict and simulate the highly complex system and non-linear process such as ion-exchange.

Keywords: clinoptilolite, loading, modeling, neural network

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Authors: Vimal O. Kumar, C. K. Muthukumaran, P. Rakesh

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Liquid rocket engine (LRE) combustion chamber is subjected to pressure oscillation during the combustion process. The combustion noise (acoustic noise) is a broad band, small amplitude, high frequency component pressure oscillation. They constitute only a minor fraction ( < 1%) of the entire combustion process. However, this high frequency oscillation is huge concern during the design phase of LRE combustion chamber as it would cause catastrophic failure of the chamber. Depends on the chamber geometry, certain frequencies form standing wave pattern, and they resonate with high amplitude and are known as Eigen modes. These Eigen modes could cause failures unless it is suppressed to be within safe limits. These modes are categorized into radial, tangential, and azimuthal modes, and their structure inside the combustion chamber is of interest to the researchers. In the present proposal, experimental as well as numerical simulation will be performed to obtain the frequency-amplitude characteristics of the model combustion chamber for different baffle configuration. The main objective of this study is to find effect of baffle configuration that would provide better suppression of acoustic modes. The experimental study aims at measuring the frequency amplitude characteristics at certain points in the chamber wall. The experimental measurement will be also used for scheme used in numerical simulation. In addition to experiments, numerical simulation would provide detailed structure of the Eigenmodes exhibited and their level of suppression with the aid of different baffle configurations.

Keywords: baffle, instability, liquid rocket engine, pressure response of chamber

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