Search results for: neural stem cells

Authors: Anita Kushwaha

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We propose a new evolutionary computational model called Artificial Reproduction System which is based on the complex process of meiotic reproduction occurring between male and female cells of the living organisms. Artificial Reproduction System is an attempt towards a new computational intelligence approach inspired by the theoretical reproduction mechanism, observed reproduction functions, principles and mechanisms. A reproductive organism is programmed by genes and can be viewed as an automaton, mapping and reducing so as to create copies of those genes in its off springs. In Artificial Reproduction System, the binding mechanism between male and female cells is studied, parameters are chosen and a network is constructed also a feedback system for self regularization is established. The model then applies Mendel’s law of inheritance, allele-allele associations and can be used to perform data analysis of imbalanced data, multivariate, multiclass and big data. In the experimental study Artificial Reproduction System is compared with other state of the art classifiers like SVM, Radial Basis Function, neural networks, K-Nearest Neighbor for some benchmark datasets and comparison results indicates a good performance.

Keywords: bio-inspired computation, nature- inspired computation, natural computing, data mining

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Authors: Tugce Talay, Kadir Erkan

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In this study, the necessary steps for the design of axial flow permanent magnet motors are shown. The design and analysis of the engine were carried out based on ANSYS Maxwell program. The design parameters of the ANSYS Maxwell program and the artificial neural network system were established in MATLAB and the most efficient design parameters were found with the trained neural network. The results of the Maxwell program and the results of the artificial neural networks are compared and optimal working design parameters are found. The most efficient design parameters were submitted to the ANSYS Maxwell 3D design and the cogging torque was examined and design studies were carried out to reduce the cogging torque.

Keywords: AFPM, ANSYS Maxwell, cogging torque, design optimisation, efficiency, NNTOOL

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Authors: Farhad Aalizadeh, Ali Moosavi

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When a blood vessel is injured, the cells of your blood bond together to form a blood clot. The blood clot helps you stop bleeding. Blood clots are made of a combination of blood cells, platelets(small sticky cells that speed up the clot-making process), and fibrin (protein that forms a thread-like mesh to trap cells). Doctors call this kind of blood clot a “thrombus.”We study the effects of different parameters on the deposition of Nanoparticles on the surface of a bump in the blood vessels by the magnetic field. The Maxwell and the flow equations are solved for this purpose. It is assumed that the blood is non-Newtonian and the number of particles has been considered enough to rely on the results statistically. Using MHD and its property it is possible to control the flow velocity, remove the fouling on the walls and return the system to its original form.

Keywords: MHD, fouling, in-vivo, blood clots, simulation

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Authors: Kishore K. Pochampally

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The success of a supply chain heavily relies on the efficiency of the suppliers involved. In this paper, we propose a neural network approach to evaluate the efficiency of a supplier, which is being considered for inclusion in a supply chain, using the available linguistic (fuzzy) data of suppliers that already exist in the supply chain. The approach is carried out in three phases, as follows: In phase one, we identify criteria for evaluation of the supplier of interest. Then, in phase two, we use performance measures of already existing suppliers to construct a neural network that gives weights (importance values) of criteria identified in phase one. Finally, in phase three, we calculate the overall rating of the supplier of interest. The following are the major findings of the research conducted for this paper: (i) linguistic (fuzzy) ratings of suppliers such as 'good', 'bad', etc., can be converted (defuzzified) to numerical ratings (1 – 10 scale) using fuzzy logic so that those ratings can be used for further quantitative analysis; (ii) it is possible to construct and train a multi-level neural network in order to determine the weights of the criteria that are used to evaluate a supplier; and (iii) Borda’s rule can be used to group the weighted ratings and calculate the overall efficiency of the supplier.

Keywords: fuzzy data, neural network, supplier, supply chain

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Authors: Ebenezer Obaloluwa Olaniyi

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In this paper, an intelligent system has been designed to grade apple based on either its defective or healthy for production in food processing. This paper is segmented into two different phase. In the first phase, the image processing techniques were employed to extract the necessary features required in the apple. These techniques include grayscale conversion, segmentation where a threshold value is chosen to separate the foreground of the images from the background. Then edge detection was also employed to bring out the features in the images. These extracted features were then fed into the neural network in the second phase of the paper. The second phase is a classification phase where neural network employed to classify the defective apple from the healthy apple. In this phase, the network was trained with back propagation and tested with feed forward network. The recognition rate obtained from our system shows that our system is more accurate and faster as compared with previous work.

Keywords: image processing, neural network, apple, intelligent system

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Authors: Xuewen Yu, Danhui Dan

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The identification of impact load and some hard-to-obtain system parameters is crucial for the activities of analysis, validation, and evaluation in the engineering field. This paper proposes a method that utilizes neural networks based on 1D-CNN to identify the impact load and partial system parameters from measured responses. To this end, forward computations are conducted to provide datasets consisting of the triples (parameter θ, input u, output y). Then neural networks are trained to learn the mapping from input to output, fu|{θ} : y → u, as well as from input and output to parameter, fθ : (u, y) → θ. Afterward, feeding the trained neural networks the measured output response, the input impact load and system parameter can be calculated, respectively. The method is tested on two simulated examples and shows sound accuracy in estimating the impact load (waveform and location) and system parameters.

Keywords: convolutional neural network, impact load identification, system parameter identification, inverse problem

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Authors: Widyani Fatwa Dewi, Subroto Athor

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In a telecommunication service provider, quantity and interval of customer demand often difficult to predict due to high dependency on customer expansion strategy and technological development. Demand arrives when a customer needs to add capacity to an existing site or build a network in a new site. Because demand is uncertain for each period, and sometimes there is a null demand for several equipments, it is categorized as intermittent. This research aims to improve demand forecast quality in Indonesia's telecommunication service providers by using Artificial Neural Network. In Artificial Neural Network, the pattern or relationship within data will be analyzed using the training process, followed by the learning process as validation stage. Historical demand data for 36 periods is used to support this research. It is found that demand forecast by using Artificial Neural Network outperforms the existing method if it is reviewed on two criteria: the forecast accuracy, using Mean Absolute Deviation (MAD), Mean of the sum of the Squares of the Forecasting Error (MSE), Mean Error (ME) and service level which is shown through inventory cost. This research is expected to increase the reference for a telecommunication demand forecast, which is currently still limited.

Keywords: artificial neural network, demand forecast, forecast accuracy, intermittent, service level, telecommunication

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Authors: T. Mahesh, M. K. Samanta

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Antibody dug conjugate (ADC’s) concept is a less explored and more trustable for the treatment of Type 1 diabetes (T1D). T1D is thought to arise from selective immunologically mediated destruction of the insulin- producing β-cells in the pancreatic islets of Langerhans with consequent insulin deficiency. It is evident that type 1 diabetes can be conquered, by 1) to stop immune destruction of βcells, 2) to replace or regenerate β-cells, and 3) to preserve β-cell function and mass. Many studies found that the regulatory T cells (Tregs) are crucial for the maintenance of immunological tolerance. Immune tolerance is liable for the activation of the Th1 response. The important role of Th1 response in pathology of T1D entails the depletion of CD4+ T cells, which initiated the use of anti-CD4 monoclonal antibodies (mAbs) against CD4+ T cells to interfere with induction of T1D.Insulin is regulated by Glucagon-Like Peptide-1 hormone (GLP-1) which also stimulates β-cells proliferation as the half-life of GLP-1 harmone is less due to rapid degradation by DPP-IV enzyme an alternative DPP-IV-inhibitors can increase the half-life of GLP-1 through which it conquers the replacement and reserve β-cells mass. Thus in the present study Anti-CD4 mAb was conjugated with Sitagliptin which is a DPP-IV inhibitor Drug loaded in Nanoparticles through Sulfo-MBS cross-linkers. The above study can be an effective approach for treatment to overcome the Passive subcutaneous insulin therapy.

Keywords: antibody drug conjugates, anti-CD4 Mab, DPP IV inhibitors, GLP-1

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Authors: Prasita Sooksamran, Wareerat Kaewurai

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STEM is an integrated teaching approach promoted by the Ministry of Education in Thailand. STEM Education is an integrated approach to teaching Science, Technology, Engineering, and Mathematics. It has been questioned by Thai teachers on the grounds of how to integrate STEM into the classroom. Therefore, the main objective of this study is to develop a science instructional model based on the STEM approach to enhance scientific mind and problem-solving skills for primary students. This study is participatory action research, and follows the following steps: 1) develop a model 2) seek the advice of experts regarding the teaching model. Developing the instructional model began with the collection and synthesis of information from relevant documents, related research and other sources in order to create prototype instructional model. 2) The examination of the validity and relevance of instructional model by a panel of nine experts. The findings were as follows: 1. The developed instructional model comprised of principles, objective, content, operational procedures and learning evaluation. There were 4 principles: 1) Learning based on the natural curiosity of primary school level children leading to knowledge inquiry, understanding and knowledge construction, 2) Learning based on the interrelation between people and environment, 3) Learning that is based on concrete learning experiences, exploration and the seeking of knowledge, 4) Learning based on the self-construction of knowledge, creativity, innovation and 5) relating their findings to real life and the solving of real-life problems. The objective of this construction model is to enhance scientific mind and problem-solving skills. Children will be evaluated according to their achievements. Lesson content is based on science as a core subject which is integrated with technology and mathematics at grade 6 level according to The Basic Education Core Curriculum 2008 guidelines. The operational procedures consisted of 6 steps: 1) Curiosity 2) Collection of data 3) Collaborative planning 4) Creativity and Innovation 5) Criticism and 6) Communication and Service. The learning evaluation is an authentic assessment based on continuous evaluation of all the material taught. 2. The experts agreed that the Science Instructional Model based on the STEM Education Approach had an excellent level of validity and relevance (4.67 S.D. 0.50).

Keywords: instructional model, STEM education, scientific mind, problem solving

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Authors: Saeedeh Shahbazkhany

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Cancer is a terrible disease that, if not diagnosed early, therapy can be difficult while it is easily medicable if it is diagnosed in early stages. So it is very important for cancer diagnosis that medical procedures are performed. In this paper we introduce a new method. In this method, we only need pictures of healthy cells and cancer cells. In fact, where we suspect cancer, we take a picture of cells or tissue in that area, and then take some pictures of the surrounding tissues. Then, fractal dimension of images are calculated and compared. Cancer can be easily detected by comparing the fractal dimension of images. In this method, we use Matlab software.

Keywords: Matlab software, fractal dimension, cancer, surrounding tissues, cells or tissue, new method

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Authors: Mohammad Y. Alfaifi

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Methidathion (MTD) (Trade name Supracide®) is a non-systemic organophosphorus insecticide used intensively worldwide including Saudi Arabia. However, there is a lack in published studies about it's genotoxicity. In this study we evaluated MTD toxicity in rat bone marrow cells (in vivo) and in lymphocytes (in vitro) using different doses based on LD50. MNNCE (Micronucleated normocromatic erythrocytes) and MNPCE (Micronucleated polychromatic erythrocytes), NDI (Nuclear division index) and NDCI (nuclear division cytotoxicity index), necrotic and apoptotic cells were recorded in rat's bone marrow samples. CA, MI (number of cells undergoing mitosis) necrotic, and apoptotic cells recorded in lymphocytes. Results showed that there was a slight increase in the frequency of micronucleated bone marrow cells. However, no structural chromosomal aberrations were detected in vivo or in vitro. On the other hand, the results showed significant increase in necrotic and apoptotic cells following MTD administration in a dose-dependent manner comparing to positive and negative control groups. In light of these results, MTD can be considered highly cytotoxic and moderate genotoxic, and precaution should be taken when using MTD.

Keywords: methidathion, micronucleus, NDI, NDCI, toxicity, chromosomal aberrations

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Authors: Mohammad Namazi, Mohammad Sadeghzadeh Maharluie

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Predicting earnings management is vital for the capital market participants, financial analysts and managers. The aim of this research is attempting to respond to this query: Is there a significant difference between the regression model and neural networks’ models in predicting earnings management, and which one leads to a superior prediction of it? In approaching this question, a Linear Regression (LR) model was compared with two neural networks including Multi-Layer Perceptron (MLP), and Generalized Regression Neural Network (GRNN). The population of this study includes 94 listed companies in Tehran Stock Exchange (TSE) market from 2003 to 2011. After the results of all models were acquired, ANOVA was exerted to test the hypotheses. In general, the summary of statistical results showed that the precision of GRNN did not exhibit a significant difference in comparison with MLP. In addition, the mean square error of the MLP and GRNN showed a significant difference with the multi variable LR model. These findings support the notion of nonlinear behavior of the earnings management. Therefore, it is more appropriate for capital market participants to analyze earnings management based upon neural networks techniques, and not to adopt linear regression models.

Keywords: earnings management, generalized linear regression, neural networks multi-layer perceptron, Tehran stock exchange

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Authors: Temitayo Abayomi Ewemoje, Isaac Olugbemiga Afolayan, Badmus Alao Tayo

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Researchers in developing countries have worked on improving cassava resistance to diseases and pests, high yielding and early maturity However, water management has received little or no attention as cassava cultivation in Sub-Saharan Africa depended on available precipitation (rain-fed condition). Therefore the need for water management in Agricultural crop production cannot be overemphasized. As other sectors compete with agricultural sector for fresh water (which is not readily available), there is need to increase water productivity in agricultural production. Experimentation was conducted to examine water use, growth and yield of improved cassava varieties under rain fed condition using Latin- square design with four replications. Four improved disease free stem cassava varieties TMS (30572, 980505, 920326 and 090581) were planted and growth parameters of the varieties were monitored for 90 and 120 days after planting (DAP). Effective rainfall useful for the plant growth was calculated using CROPWAT8 for Windows. Results indicated TMS090581 was having the highest tuber yield and plant height while TMS30572 had highest number of nodes. Tuber stem and leaf water productivities at 90 and 120 DAP of TMS (30572, 980505, 920326 and 090581) are (1.27 and 3.58, 1.44 and 2.35, 0.89 and 1.86, 1.64 and 3.77) kg/m3 (1.56 and 2.59, 1.95 and 2.02, 1.98 and 2.05, 1.95 and 2.18) kg/m3, and (1.34 and 2.32, 1.94 and 2.16, 1.57 and 1.40, 1.27 and 1.80) kg/m3 respectively. Based on tuber water productivity TMS090581 are recommended while TMS30572 are recommended based on leaf and stem productivity in water scarce regions.Experimentation was conducted to examine water use, growth and yield of improved cassava varieties under rain fed condition using Latin- square design with four replications. Four improved disease free stem cassava varieties TMS (30572, 980505, 920326 and 090581) were planted and growth parameters of the varieties were monitored for 90 and 120 days after planting (DAP). Effective rainfall useful for the plant growth was calculated using CROPWAT8 for Windows. Results indicated TMS090581 was having the highest tuber yield and plant height while TMS30572 had the highest number of nodes. Tuber, stem and leaf water productivities at 90 and 120 DAP of TMS (30572, 980505, 920326 and 090581) are (1.27 and 3.58, 1.44 and 2.35, 0.89 and 1.86, 1.64 and 3.77) kg/m3 (1.56 and 2.59, 1.95 and 2.02, 1.98 and 2.05, 1.95 and 2.18) kg/m3, and (1.34 and 2.32, 1.94 and 2.16, 1.57 and 1.40, 1.27 and 1.80) kg/m3 respectively. Based on tuber water productivity TMS090581 are recommended while TMS30572 are recommended based on leaf and stem productivity in water scarce regions

Keywords: improved TMS varieties, leaf productivity, rain-fed cassava production, stem productivity, tuber productivity

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Authors: Deepa Gandhi, Pravin K. Naoghare, Amit Bafana, Krishnamurthi Kannan, Saravanadevi Sivanesana

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Oxidative stress is known to depreciate normal functioning of osteoblast cells. Present study reports oxidative/inflammatory signatures in fluoride exposed human osteosarcoma (HOS) cells and its possible association with the genes involved in bone developmental pathway. Microarray analysis was performed to understand the possible molecular mechanisms of stress-mediated bone lose in HOS cells. Cells were chronically exposed with sub-lethal concentration of fluoride. Global gene expression is profiling revealed 34 up regulated and 2598 down-regulated genes, which were associated with several biological processes including bone development, osteoblast differentiation, stress response, inflammatory response, apoptosis, regulation of cell proliferation. Microarray data were further validated through qRT-PCR and western blot analyses using key representative genes. Based on these findings, it can be proposed that chronic exposure of fluoride may impair bone development via oxidative and inflammatory stress. The present finding also provides important biological clues, which will be helpful for the development of therapeutic targets against diseases related bone.

Keywords: bone, HOS cells, microarray, stress

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Authors: Nahid Nariman

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The prosperity of any nation depends on its ability to use human potential, in particular, to offer an education that builds learners' competencies to become effective workforce participants and true citizens of the world. Ever since the Second World War, the United States has been a dominant player in the world politically, economically, socially, and culturally. The rapid rise of technological advancement and consumer technologies have made it clear that science, technology, engineering, and math (STEM) play a crucial role in today’s world economy. Exploring the top qualities demanded from new hires in the industry—i.e., problem-solving skills, teamwork, dependability, adaptability, technical and communication skills— sheds light on the kind of path that is needed for a successful educational system to effectively support STEM. The focus of 21st century education has been to build student competencies by preparing them to acquire and apply knowledge, to think critically and creatively, to competently use information, be able to work in teams, to demonstrate intellectual and moral values as well as cultural awareness, and to be able to communicate. Many educational reforms pinpoint various 'ideal' pathways toward STEM that educators, policy makers, and business leaders have identified for educating the workforce of tomorrow. This study will explore how problem-based learning (PBL), an instructional strategy developed in the medical field and adopted with many successful results in K-12 through higher education, is the proper approach to stimulate underrepresented high school students' interest in pursuing STEM careers. In the current study, the effect of a problem-based STEM model on students' attitudes and career interests was investigated using qualitative and quantitative methods. The participants were 71 low-income, native Hawaiian high school students who would be first-generation college students. They were attending a summer STEM camp developed as the result of a collaboration between the University of Hawaii and the Upward Bound Program. The project, funded by the National Science Foundation's Innovative Technology Experiences for Students and Teachers (ITEST) program, used PBL as an approach in challenging students to engage in solving hands-on, real-world problems in their communities. Pre-surveys were used before camp and post-surveys on the last day of the program to learn about the implementation of the PBL STEM model. A Career Interest Questionnaire provided a way to investigate students’ career interests. After the summer camp, a representative selection of students participated in focus group interviews to discuss their opinions about the PBL STEM camp. The findings revealed a significantly positive increase in students' attitudes towards STEM disciplines and STEM careers. The students' interview results also revealed that students identified PBL to be an effective form of instruction in their learning and in the development of their 21st-century skills. PBL was acknowledged for making the class more enjoyable and for raising students' interest in STEM careers, while also helping them develop teamwork and communication skills in addition to scientific knowledge. As a result, the integration of PBL and a STEM learning experience was shown to positively affect students’ interest in STEM careers.

Keywords: problem-based learning, science education, STEM, underrepresented students

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Authors: Latef M. Ali, Farah A. Abed

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In this paper, the effect of the Barium fluoride (BaF2) layer on the absorption efficiency of GaAs/InAs nanowire solar cells was investigated using the finite difference time domain (FDTD) method. By inserting the BaF2 as antireflection with the dominant size of 10 nm to fill the space between the shells of wires on the Si (111) substrate. The absorption is significantly improved due to the strong reabsorption of light reflected at the shells and compared with the reference cells. The present simulation leads to a higher absorption efficiency (Qabs) and reaches a value of 97%, and the external quantum efficiencies (EQEs) above 92% are observed. The current density (Jsc) increases by 0.22 mA/cm2 and the open-circuit voltage (Voc) is enhanced by 0.11 mV.

Keywords: nanowire solar cells, absorption efficiency, photovoltaic, band structures, fdtd simulation

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Authors: Muhammad Mukhtar

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The aim of this study is to evaluate the antioxidant activity and acute toxicity of the extracts of Ficus iteophylla by reactions with 1, 1-diphenyl-2-picryhydrazyl radical (DPPH) and method developed by Lork 1983, respectively. Stem bark of Ficus iteophylla was collected, air dried, pulverized to fine powdered and sequentially extracted using acetone, methanol and water in order of increasing polarity. The result shows strong radical scavenging activity against DPPH for all the extracts when compared with ascorbic acid. The LD50 of 316 mg/kg was calculated for all the three extras, and the values were found to be within the practically toxic range, and therefore, care should be taken when using the plants in traditional medicine.

Keywords: antioxidant, acute toxicity, Ficus iteophylla

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Authors: An Chengrui, Yin Zi, Wu Bingbing, Ma Yuanzhu, Jin Kaixiu, Chen Xiao, Ouyang Hongwei

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Single cell RNA sequence (scRNA-seq) is one of the effective tools to study transcriptomics of biological processes. Recently, similarity measurement of cells is Euclidian distance or its derivatives. However, the process of scRNA-seq is a multi-variate Bernoulli event model, thus we hypothesize that it would be more efficient when the divergence between cells is valued with relative entropy than Euclidian distance. In this study, we compared the performances of Euclidian distance, Spearman correlation distance and Relative Entropy using scRNA-seq data of the early, medial and late stage of limb development generated in our lab. Relative Entropy is better than other methods according to cluster potential test. Furthermore, we developed KL-SNE, an algorithm modifying t-SNE whose definition of divergence between cells Euclidian distance to Kullback–Leibler divergence. Results showed that KL-SNE was more effective to dissect cell heterogeneity than t-SNE, indicating the better performance of relative entropy than Euclidian distance. Specifically, the chondrocyte expressing Comp was clustered together with KL-SNE but not with t-SNE. Surprisingly, cells in early stage were surrounded by cells in medial stage in the processing of KL-SNE while medial cells neighbored to late stage with the process of t-SNE. This results parallel to Heatmap which showed cells in medial stage were more heterogenic than cells in other stages. In addition, we also found that results of KL-SNE tend to follow Gaussian distribution compared with those of the t-SNE, which could also be verified with the analysis of scRNA-seq data from another study on human embryo development. Therefore, it is also an effective way to convert non-Gaussian distribution to Gaussian distribution and facilitate the subsequent statistic possesses. Thus, relative entropy is potentially a better way to determine the divergence of cells in scRNA-seq data analysis.

Keywords: Single cell RNA sequence, Similarity measurement, Relative Entropy, KL-SNE, t-SNE

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Authors: Mohammad Heidari

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In this paper a method for high strength steel is proposed of residual stresses in autofrettaged tubes by combination of artificial neural networks is presented. Many different thick walled cylinders that were subjected to different conditions were studied. At first, the residual stress is calculated by analytical solution. Then by changing of the parameters that influenced in residual stresses such as percentage of autofrettage, internal pressure, wall ratio of cylinder, material property of cylinder, bauschinger and hardening effect factor, a neural network is created. These parameters are the input of network. The output of network is residual stress. Numerical data, employed for training the network and capabilities of the model in predicting the residual stress has been verified. The output obtained from neural network model is compared with numerical results, and the amount of relative error has been calculated. Based on this verification error, it is shown that the radial basis function of neural network has the average error of 2.75% in predicting residual stress of thick wall cylinder. Further analysis of residual stress of thick wall cylinder under different input conditions has been investigated and comparison results of modeling with numerical considerations shows a good agreement, which also proves the feasibility and effectiveness of the adopted approach.

Keywords: thick walled cylinder, residual stress, radial basis, artificial neural network

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Authors: Tania Tzong, Chao-Yi Teng, Tzong-Yuan Wu

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Currently, Chikungunya virus (CHIKV) transmitted to humans by Aedes mosquitoes has distributed from Africa to Southeast Asia, South America, and South Europe. However, little is known about the antigenic targets for immunity, and there are no licensed vaccines or specific antiviral treatments for the disease caused by CHIKV. Baculovirus has been recognized as a novel vaccine vector with attractive characteristic features of an optional vaccine delivery vehicle. This approach provides the safety and efficacy of CHIKV vaccine. In this study, bi-cistronic recombinant baculoviruses vAc-CMV-CHIKV26S-Rhir-EGFP and vAc-CMV-pH-CHIKV26S-Lir-EGFP were produced. Both recombinant baculovirus can express EGFP reporter gene in insect cells to facilitate the recombinant virus isolation and purification. Examination of vAc-CMV-CHIKV26S-Rhir-EGFP and vAc-CMV-pH-CHIKV26S-Lir-EGFP showed that this recombinant baculovirus could induce syncytium formation in insect cells. Unexpectedly, the immunofluorescence assay revealed the expression of E1 and E2 of CHIKV structural proteins in insect cells infected by vAc-CMV-CHIKV26S-Rhir-EGFP. This result may imply that the CMV promoter can induce the transcription of CHIKV26S in insect cells. There are also E1 and E2 expression in mammalian cells transduced by vAc-CMV-CHIKV26S-Rhir-EGFP and vAc-CMV-pH-CHIKV26S-Lir-EGFP. The expression of E1 and E2 proteins of insect and mammalian cells was validated again by Western blot analysis. The vector construction with dual tandem promoters, which is polyhedrin and CMV promoter, has higher expression of the E1 and E2 of CHIKV structural proteins than the vector construction with CMV promoter only. Most of the E1 and E2 proteins expressed in mammalian cells were glycosylated. In the future, the expression of structural proteins of CHIKV in mammalian cells is expected can form virus-like particle, so it could be used as a vaccine for chikungunya virus.

Keywords: chikungunya virus, virus-like particle, vaccines, baculovirus expression vector system

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Authors: Hooshmand Kalayeh

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The human brain consists of reptilian, mammalian, and thinking brain. And mind consists of conscious, subconscious, and unconscious parallel neural-net programs. The primary objective of this paper is to propose a methodology for quantitative analysis of neural-nets associated with these mental activities in the neocortex. The secondary objective of this paper is to suggest a methodology for quantitative analysis of presence; the proposed methodologies can be used as a first-step to measure, monitor, and understand consciousness and presence. This methodology is based on Neural-Networks (NN), number of neuron in each NN associated with consciousness, subconsciouness, and unconsciousness, and number of neurons in neocortex. It is assumed that the number of neurons in each NN is correlated with the associated area and volume. Therefore, online and offline visualization techniques can be used to identify these neural-networks, and online and offline measurement methods can be used to measure areas and volumes associated with these NNs. So, instead of the number of neurons in each NN, the associated area or volume also can be used in the proposed methodology. This quantitative analysis and associated online and offline measurements and visualizations of different Neural-Networks enable us to rewire the connections in our brain for a more balanced living.

Keywords: brain, mind, consciousness, presence, sub-consciousness, unconsciousness, skills, concentrations, attention

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Authors: Devineni Vijay Bhaskar, Yendluri Raja

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We recommend a model for traffic sign detection stranded on Convolutional Neural Networks (CNN). We first renovate the unique image into the gray scale image through with support vector machines, then use convolutional neural networks with fixed and learnable layers for revealing and understanding. The permanent layer can reduction the amount of attention areas to notice and crop the limits very close to the boundaries of traffic signs. The learnable coverings can rise the accuracy of detection significantly. Besides, we use bootstrap procedures to progress the accuracy and avoid overfitting problem. In the German Traffic Sign Detection Benchmark, we obtained modest results, with an area under the precision-recall curve (AUC) of 99.49% in the group “Risk”, and an AUC of 96.62% in the group “Obligatory”.

Keywords: convolutional neural network, support vector machine, detection, traffic signs, bootstrap procedures, precision-recall curve

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Authors: Benjamin O. Opawale, Anthony K. Onifade, Ayodele O. Ogundare

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The phytochemical and antimicrobial activities of stem bark extracts (cold water, ethanol and acetone) of Lovoa trichiliodes and Trichilia heudelotii were investigated using standard methods. The percentage yield of the extracts ranged from 3.90 to 6.53% and 9.63 to 10.20% respectively for the plant materials. Phytochemical screening of the plant materials revealed the presence of alkaloids, saponins, tannins, phlobatanins, phenols, anthraquinones and glycosides. Terpenes, cardenolides and flavonoids were absent in the two plants. All the extracts remarkably inhibited the growth of Bacillus subtilis, Staphylococcus aureus, Escherichia coli, Enterococcus faecalis, Klebsiella pneumoniae, Salmonella typhii, Aspergillus flavus, Candida albicans and Candida glabrata. The mean diameter of the zone of inhibition exhibited by the extracts was between 8.00 and 22.33mm while the minimum inhibitory concentration (MIC) was between 2.5 and 200mg/ml. However, the cold water extracts of L. trichiliodes stem bark exhibited no inhibitory activity against the organisms. The results of this investigation confirmed the folkloric uses of these plants for the treatment of various infectious diseases.

Keywords: antimicrobial, infectious diseases, phytochemical, T. heudelotii

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Authors: Deepika Bhalla, Raj Kumar Bansal, Hari Om Gupta

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Dissolved Gas Analysis (DGA) has been widely used for fault diagnosis in a transformer. Artificial neural networks (ANN) have high accuracy but are regarded as black boxes that are difficult to interpret. For many problems it is desired to extract knowledge from trained neural networks (NN) so that the user can gain a better understanding of the solution arrived by the NN. This paper applies a pedagogical approach for rule extraction from function approximating neural networks (REFANN) with application to incipient fault diagnosis using the concentrations of the dissolved gases within the transformer oil, as the input to the NN. The input space is split into subregions and for each subregion there is a linear equation that is used to predict the type of fault developing within a transformer. The experiments on real data indicate that the approach used can extract simple and useful rules and give fault predictions that match the actual fault and are at times also better than those predicted by the IEC method.

Keywords: artificial neural networks, dissolved gas analysis, rules extraction, transformer

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Authors: Baladitya Swaika, Rahul Khatry

Abstract:

Computer Adaptive Tests (CATs) is one of the most efficient ways for testing the cognitive abilities of students. CATs are based on Item Response Theory (IRT) which is based on item selection and ability estimation using statistical methods of maximum information selection/selection from posterior and maximum-likelihood (ML)/maximum a posteriori (MAP) estimators respectively. This study aims at combining both classical and Bayesian approaches to IRT to create a dataset which is then fed to a neural network which automates the process of ability estimation and then comparing it to traditional CAT models designed using IRT. This study uses python as the base coding language, pymc for statistical modelling of the IRT and scikit-learn for neural network implementations. On creation of the model and on comparison, it is found that the Neural Network based model performs 7-10% worse than the IRT model for score estimations. Although performing poorly, compared to the IRT model, the neural network model can be beneficially used in back-ends for reducing time complexity as the IRT model would have to re-calculate the ability every-time it gets a request whereas the prediction from a neural network could be done in a single step for an existing trained Regressor. This study also proposes a new kind of framework whereby the neural network model could be used to incorporate feature sets, other than the normal IRT feature set and use a neural network’s capacity of learning unknown functions to give rise to better CAT models. Categorical features like test type, etc. could be learnt and incorporated in IRT functions with the help of techniques like logistic regression and can be used to learn functions and expressed as models which may not be trivial to be expressed via equations. This kind of a framework, when implemented would be highly advantageous in psychometrics and cognitive assessments. This study gives a brief overview as to how neural networks can be used in adaptive testing, not only by reducing time-complexity but also by being able to incorporate newer and better datasets which would eventually lead to higher quality testing.

Keywords: computer adaptive tests, item response theory, machine learning, neural networks

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Authors: Romisaa Ali

Abstract:

The Policy Gradient approach is one of the deep reinforcement learning families that combines deep neural networks (DNN) with reinforcement learning RL to discover the optimum of the control problem through experience gained from the interaction between the robot and its surroundings. In contrast to earlier policy gradient algorithms, which were unable to handle these two types of error because of over-or under-estimation introduced by the deep neural network model, this article will discuss the state-of-the-art SOTA policy gradient technique, trust region policy optimization (TRPO), by applying this method in various environments compared to another policy gradient method, the Proximal Policy Optimization (PPO), to explain their robust optimization, using this SOTA to gather experience data during various training phases after observing the impact of hyper-parameters on neural network performance.

Keywords: deep neural networks, deep reinforcement learning, proximal policy optimization, state-of-the-art, trust region policy optimization

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Authors: Christopher Lama, Alix Rieser, Aleksandra Molchanova, Charles Thangaraj

Abstract:

New technologies like Tesla’s Dojo have made high-performance embedded computing more available. Although automobile computing has developed and benefited enormously from these more recent technologies, the costs are still high, prohibitively high in some cases for broader adaptation, particularly for the after-market and enthusiast markets. This project aims to implement a Raspberry Pi-based low-power (under one hundred Watts) highly mobile computing cluster for a neural network. The computing cluster built from off-the-shelf components is more affordable and, therefore, makes wider adoption possible. The paper describes the design of the neural network, Raspberry Pi-based cluster, and applications the cluster will run. The neural network will use input data from sensors and cameras to project a live view of the road state as the user drives. The neural network will be trained to predict traffic behavior and generate warnings when potentially dangerous situations are predicted. The significant outcomes of this study will be two folds, firstly, to implement and test the low-cost cluster, and secondly, to ascertain the effectiveness of the predictive AI implemented on the cluster.

Keywords: CS pedagogy, student research, cluster computing, machine learning

Procedia PDF Downloads 94

Authors: Rohan Roberts

Abstract:

The purpose of this project was to collaborate with a group of high-functioning, more-able students (aged 15-18) to promote STEM Education and a love for science by bringing a cosmic perspective into the classroom and high school environment. This was done using 21st century science of learning, a focus on the latest research on Neuroeducation, and modern pedagogical methods based on Howard Gardner's theory of Multiple Intelligences, Bill Lucas’ theory of New Smarts, and Sir Ken Robinson’s recommendations on encouraging creativity. The result was an increased sense of passion, excitement, and wonder about science in general, and about the marvels of space and the universe in particular. In addition to numerous unique and innovative science-based initiatives, clubs, workshops, and science trips, this project also saw a marked rise in student-teacher collaboration in science learning and in student engagement with the general public through the press, social media, and community-based initiatives. This paper also outlines the practical impact that bringing a cosmic perspective into the classroom has had on the lives, interests, and future career prospects of the students involved in this endeavour.

Keywords: cosmic perspective, gifted and talented, neuro-education, STEM education

Procedia PDF Downloads 327

Authors: Megha Rao, Søren H. Jensen, Xiufu Sun, Anke Hagen, Mogens B. Mogensen

Abstract:

Solid oxide electrolysis cells have an immense potential in converting CO₂ and H₂O into syngas during co-electrolysis operation. The produced syngas can be further converted into hydrocarbons. This kind of technology is called power-to-gas or power-to-liquid. To produce hydrocarbons via this route, durability of the cells is still a challenge, which needs to be further investigated in order to improve the cells. In this work, various nickel-yttria stabilized zirconia (Ni-YSZ) fuel electrode supported or YSZ electrolyte supported cells, cerium gadolinium oxide (CGO) barrier layer, and an oxygen electrode are investigated for durability under co-electrolysis conditions in both galvanostatic and potentiostatic conditions. While changing the gas on the oxygen electrode, keeping the fuel electrode gas composition constant, a change in the gas concentration arc was observed by impedance spectroscopy. Measurements of open circuit potential revealed the presence of leaks in the setup. It is speculated that the change in concentration impedance may be related to the leaks. Furthermore, the cells were also tested under pressurized conditions to find an inter-play between the leak rate and the pressure. A mathematical modeling together with electrochemical and microscopy analysis is presented.

Keywords: co-electrolysis, durability, leaks, gas concentration arc

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Authors: Dandan Shen, Hui-zhu Wang, Xi Yu, LiLi Gui, Xiao Wei, Xiao-yan Jiang, Da-wei Wang, Min Hong

Abstract:

Yu-ping-feng-san (YPFS) is a clinical medicine for asthma and other allergic diseases, but the mechanism of YPFS on relapse of allergy is unclear. Currently, people come to realize the epithelial cells(EC) play a key role in stimulating and regulating local immune response. The study of thymic stromal lymphopoietin（TSLP derived from EC provides an important evidence that the EC can regulate immune response to stimulate allergic response. In this study, we observed the effect of YPFS on TSLP in vivo and in vitro. We established a method by using bronchial epithelial cells (16HBE) for screening potential bioactive components by HPLC-MS in YPFS and then analyzed the components in serum containing YPFS by UPLC-MS. The results showed that YPFS could decrease TSLP protein level in OVA-sensitized mice and 16HBE cells. Five components combing with the 16HBE cells were both detected in the serum.

Keywords: TSLP, bronchial epithelial cells, cell-binding, drug-containing serum

Procedia PDF Downloads 506