Search results for: neural activity

Authors: Girish J. Showkatramani, Arthi M. Krishna, Sashi Nareddi, Naresh Nula, Aaron Pepe, Glen Brown, Greg Gabel, Chris Doninger

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Trademark examination and approval is a complex process that involves analysis and review of the design components of the marks such as the visual representation as well as the textual data associated with marks such as marks' description. Currently, the process of identifying marks with similar visual representation is done manually in United States Patent and Trademark Office (USPTO) and takes a considerable amount of time. Moreover, the accuracy of these searches depends heavily on the experts determining the trademark design codes used to catalog the visual design codes in the mark. In this study, we explore several methods to automate trademark design code classification. Based on recent successes of convolutional neural networks in image classification, we have used several different convolutional neural networks such as Google’s Inception v3, Inception-ResNet-v2, and Xception net. The study also looks into other techniques to augment the results from CNNs such as using Open Source Computer Vision Library (OpenCV) to pre-process the images. This paper reports the results of the various models trained on year of annotated trademark images.

Keywords: trademark design code, convolutional neural networks, trademark image classification, trademark image search, Inception-ResNet-v2

Procedia PDF Downloads 232

Authors: Taylor Kolody, Farkhund Iqbal, Rabia Batool, Benjamin Fung, Mohammed Hussaeni, Saiqa Aleem

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The rising threat of climate change has led to an increase in public awareness and care about our collective and individual environmental impact. A key component of this impact is our use of cars and other polluting forms of transportation, but it is often difficult for an individual to know how severe this impact is. While there are applications that offer this feedback, they require manual entry of what transportation mode was used for a given trip, which can be burdensome. In order to alleviate this shortcoming, a data from the 2016 TRIPlab datasets has been used to train a variety of machine learning models to automatically recognize the mode of transportation. The accuracy of 89.6% is achieved using single deep neural network model with Gated Recurrent Unit (GRU) architecture applied directly to trip data points over 4 primary classes, namely walking, public transit, car, and bike. These results are comparable in accuracy to results achieved by others using ensemble methods and require far less computation when classifying new trips. The lack of trip context data, e.g., bus routes, bike paths, etc., and the need for only a single set of weights make this an appropriate methodology for applications hoping to reach a broad demographic and have responsive feedback.

Keywords: classification, gated recurrent unit, recurrent neural network, transportation

Procedia PDF Downloads 137

Authors: D. J. Madhu Kumar, D. Jagadeesh Prasad, Sana Sheik, E. P. Rejeesh

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A series of Mannich bases derived from 1,2,4-triazole(3a-k and 4a-k) are synthesized by treating a Schiff base with various substituted primary/secondary amines and formaldehyde. The Schiff base is prepared by treating 3-methyl-4-amino-5-mercapto-1,2,4-triazole with 3,4-dimethoxybenzaldehyde in the presence of acid catalyst. The triazole is prepared by treating acetic acid with thiocarbohydrazide at reflux temperature. All the synthesized samples are characterised by FT-IR, 1H-NMR, and LC-MASS spectral studies and screened for their anti-oxidant activity.

Keywords: mannich bases, anti-oxidant activity, schiff base, triazole

Procedia PDF Downloads 516

Authors: Shengbiao Wu, Weihua Cao, Min Wu, Can Liu

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This paper presents an intelligent tuning method of microwave filter based on complex neural network and improved space mapping. The tuning process consists of two stages: the initial tuning and the fine tuning. At the beginning of the tuning, the return loss of the filter is transferred to the passband via the error of phase. During the fine tuning, the phase shift caused by the transmission line and the higher order mode is removed by the curve fitting. Then, an Cauchy method based on the admittance parameter (Y-parameter) is used to extract the coupling matrix. The influence of the resonant cavity loss is eliminated during the parameter extraction process. By using processed data pairs (the amount of screw variation and the variation of the coupling matrix), a tuning model is established by the complex neural network. In view of the improved space mapping algorithm, the mapping relationship between the actual model and the ideal model is established, and the amplitude and direction of the tuning is constantly updated. Finally, the tuning experiment of the eight order coaxial cavity filter shows that the proposed method has a good effect in tuning time and tuning precision.

Keywords: microwave filter, scattering parameter, coupling matrix, intelligent tuning

Procedia PDF Downloads 311

Authors: I. Savitskaya, A. Kistaubayeva, A. Zhubanova, I. Blavachinskaiya, D. Ibrayeva, M. Abdulzhanova, A. Otarbay, A.Isabekova

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This study was conducted for the investigation of number of cellulolytic bacteria and their ability in decomposition. Seven samples surface soil were collected on cellulose Zailiskii Alatau slopes. Cellulolitic activity of new strains of Bacillus, isolated from soil is determined. Isolated cellulose degrading bacteria were screened for determination of the highest cellulose activity by quantitative assay using Congo red, gravimetric assay and colorimetric DNS method trough of the determination of the parameters of sugar reduction. Strains are assigned to: B.subtilis, B.licheniformis, B. cereus and, В. megaterium. Bacillus strains consisting of several different types of cellulases have broad substrate specificity of cellulase complexes formed by them. Cellulolitic bacteria were recorded to have highest cellulase activity and selected for optimization of cellulase enzyme production.

Keywords: cellulose-degrading bacteria, cellulase complex, foothills soil, screening

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Authors: Carlos Tarmeño Noriega, Igor Aguilar Alonso

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Fungal diseases are common in vineyards; they cause a decrease in the quality of the products that can be sold, generating distrust of the customer towards the seller when buying fruit. Currently, technology allows the classification of fruits according to their characteristics thanks to artificial intelligence. This study proposes the implementation of a control system that allows the identification of the main fungal diseases present in the Italia grape, making use of a convolutional neural network (CNN), OpenCV, and TensorFlow. The methodology used was based on a collection of 20 articles referring to the proposed research on quality control, classification, and recognition of fruits through artificial vision techniques.

Keywords: computer vision, convolutional neural networks, quality control, fruit market, OpenCV, TensorFlow

Procedia PDF Downloads 83

Authors: Nasibeh Mohammadpour, Ali Rajabzadeh, Adel Azar, Hamid Zargham Borujeni,

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Generally, various industries development depends on their stakeholders and beneficiaries supports. One of the most important stakeholders in tourism industry ( which has become one of the most important lucrative and employment-generating activities at the international level these days) are host communities in tourist destination which are affected and effect on this industry development. Recognizing host community and its segmentations can be important to get their support for future decisions and policy making. In order to identify these segments, in this study, clustering of the residents has been done by using some tools that are designed to encounter human complexities and have ability to model and generalize complex systems without any needs for the initial clusters’ seeds like classic methods. Neural networks can help to meet these expectations. The research have been planned to design neural networks-based mathematical model for clustering the host community effectively according to multi criteria, and identifies differences among segments. In order to achieve this goal, the residents’ segmentation has been done by demographic characteristics, their attitude towards the tourism development, the level of satisfaction and the type of their support in this field. The applied method is self-organized neural networks and the results have compared with K-means. As the results show, the use of Self- Organized Map (SOM) method provides much better results by considering the Cophenetic correlation and between clusters variance coefficients. Based on these criteria, the host community is divided into five sections with unique and distinctive features, which are in the best condition (in comparison other modes) according to Cophenetic correlation coefficient of 0.8769 and between clusters variance of 0.1412.

Keywords: Artificial Nural Network, Clustering , Resident, SOM, Tourism

Procedia PDF Downloads 183

Authors: Youssef. K. A. Abdalhafid, Ezaldin A. M. Mohammed, Masoud M. M. Zatout

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This study described the changes in the liver of Uromastyx acanthinura (Bell, 1825) males and females during hibernation and activity seasons. The results revealed that, hibernation causes increase fatty liver and pigment cells with abundant damage, comparing with nearly normal structure and less fatty liver after the hibernation with almost normal pattern. Genomic DNA showed apparent separation during hibernation. Also, caspase 3 and caspase 7 activity reached a high level in the liver tissue during hibernation comparing with activity season.

Keywords: histological liver, DNA fragmentation, hibernation, caspase 3 and caspase 7

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Authors: Jui-Chen Huang, Shou-Hsiung Cheng

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This study examined their willingness to use telecare for people who have had experience falling in the last three months in Taiwan. This study adopted convenience sampling and a structural questionnaire to collect data. It was based on the definition and the constructs related to the Health Belief Model (HBM). HBM is comprised of seven constructs: perceived benefits (PBs), perceived disease threat (PDT), perceived barriers of taking action (PBTA), external cues to action (ECUE), internal cues to action (ICUE), attitude toward using (ATT), and behavioral intention to use (BI). This study adopted Fuzzy Neural Network (FNN) to put forward an effective method. It shows the dependence of ATT on PB, PDT, PBTA, ECUE, and ICUE. The training and testing data RMSE (root mean square error) are 0.028 and 0.166 in the FNN, respectively. The training and testing data RMSE are 0.828 and 0.578 in the regression model, respectively. On the other hand, as to the dependence of ATT on BI, as presented in the FNN, the training and testing data RMSE are 0.050 and 0.109, respectively. The training and testing data RMSE are 0.529 and 0.571 in the regression model, respectively. The results show that the FNN method is better than the regression analysis. It is an effective and viable good way.

Keywords: fall, fuzzy neural network, health belief model, telecare, willingness

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Authors: Parthiban Srinivasan

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With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

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Authors: K. Padmavathi, K. Sri Ramakrishna

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This study describes a procedure for the detection of Left and Right Bundle Branch Block (LBBB and RBBB) ECG patterns using spectral Coherence(SC) technique and LM Neural Network. The Coherence function finds common frequencies between two signals and evaluate the similarity of the two signals. The QT variations of Bundle Blocks are observed in lead V1 of ECG. Spectral Coherence technique uses Welch method for calculating PSD. For the detection of normal and Bundle block beats, SC output values are given as the input features for the LMNN classifier. Overall accuracy of LMNN classifier is 99.5 percent. The data was collected from MIT-BIH Arrhythmia database.

Keywords: bundle block, SC, LMNN classifier, welch method, PSD, MIT-BIH, arrhythmia database

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Authors: Halimatu S. Abdullahi, Ray E. Sheriff, Fatima Mahieddine

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Agriculture is essential to the continuous existence of human life as they directly depend on it for the production of food. The exponential rise in population calls for a rapid increase in food with the application of technology to reduce the laborious work and maximize production. Technology can aid/improve agriculture in several ways through pre-planning and post-harvest by the use of computer vision technology through image processing to determine the soil nutrient composition, right amount, right time, right place application of farm input resources like fertilizers, herbicides, water, weed detection, early detection of pest and diseases etc. This is precision agriculture which is thought to be solution required to achieve our goals. There has been significant improvement in the area of image processing and data processing which has being a major challenge. A database of images is collected through remote sensing, analyzed and a model is developed to determine the right treatment plans for different crop types and different regions. Features of images from vegetations need to be extracted, classified, segmented and finally fed into the model. Different techniques have been applied to the processes from the use of neural network, support vector machine, fuzzy logic approach and recently, the most effective approach generating excellent results using the deep learning approach of convolution neural network for image classifications. Deep Convolution neural network is used to determine soil nutrients required in a plantation for maximum production. The experimental results on the developed model yielded results with an average accuracy of 99.58%.

Keywords: convolution, feature extraction, image analysis, validation, precision agriculture

Procedia PDF Downloads 316

Authors: Y. Zheng, M. Busi, A. F. Pedersen, M. A. Beltran, C. Gundlach

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X-ray phase contrast imaging has shown to yield a better contrast compared to conventional attenuation X-ray imaging, especially for soft tissues in the medical imaging energy range. This can potentially lead to better diagnosis for patients. However, phase contrast imaging has mainly been performed using highly brilliant Synchrotron radiation, as it requires high coherence X-rays. Many research teams have demonstrated that it is also feasible using a laboratory source, bringing it one step closer to clinical use. Nevertheless, the requirement of fine gratings and high precision stepping motors when using a laboratory source prevents it from being widely used. Recently, a random phase object has been proposed as an analyzer. This method requires a much less robust experimental setup. However, previous studies were done using a particular X-ray source (liquid-metal jet micro-focus source) or high precision motors for stepping. We have been working on a much simpler setup with just small modification of a commercial bench-top micro-CT (computed tomography) scanner, by introducing a piece of sandpaper as the phase analyzer in front of the X-ray source. However, it needs a suitable algorithm for speckle tracking and 3D reconstructions. The precision and sensitivity of speckle tracking algorithm determine the resolution of the system, while the 3D reconstruction algorithm will affect the minimum number of projections required, thus limiting the temporal resolution. As phase contrast imaging methods usually require much longer exposure time than traditional absorption based X-ray imaging technologies, a dynamic phase contrast micro-CT with a high temporal resolution is particularly challenging. Different reconstruction methods, including neural network based techniques, will be evaluated in this project to increase the temporal resolution of the phase contrast micro-CT. A Monte Carlo ray tracing simulation (McXtrace) was used to generate a large dataset to train the neural network, in order to address the issue that neural networks require large amount of training data to get high-quality reconstructions.

Keywords: micro-ct, neural networks, reconstruction, speckle-based x-ray phase contrast

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Authors: Yahia Massinissa, Benhouda Afaf, Benbia Souhila, Meddour Noura, Takellalet Karima, Zeroual Amina

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Introduction: chenopodiaceae family species are known for their important biological activity, in which Atriplex halimus belongs . However, the inflammatory effect of this plant leaves has not been studied. This work aimed at assessing the anti- inflammatory and hemostatic activities of the methanolic extract AHMeOH of Atriplex halimus’s leaves. Methods: The extract was obtained using sonication of leaves powder in 80 % methanol. The analysis of phenolic compounds was carried out using thin-layer chromatography (TLC).The anti-inflammatory activity was determined by studying the plasmical membrane stabilization and albumin denaturation inhibition, the hemostatic activity was evaluated by measuring the plasma in the blood. Results: Quantitative determination of total flavonoids reveals that AHMeOH is rich in flavonoids (16 ± 0.88 μg Q / mg extract) and polyphenols (20 ± 0.20 μg AG / mg extract). about anti-inflammatory activity, the tests show that AHMeOH has a significant effect (P≤0.05) of inhibiting hypotonic-induced hemolysis with concentrations (100 and 200 μg / ml) with 77.55 and 90% respectively, and heat-induced hemolysis with percentages 81.75% and 87.44% respectively with significant difference (P ≤0.05). The obtained results with this plant reveal that the inhibition of denaturation of albumin is dose dependent. The concentration of 400 μg / ml gives denaturation inhibition of 81.00 ± 17.70% and the concentration 600 μg / ml gives an effect of 82.95 ± 17.40%. Regarding the haemostatic activity our extract with the doses 10 mg / ml, 20 mg / ml and 30 mg / ml confer a decrease of the plasma recalcification time in the tube, these concentrations could prolong the time of coagulation significantly compared to the control (P≤0.001). This result is an interesting indication in favor of haemostatic activity of AHMeOH. Conclusion: Atriplex Halimus has a strong anti-inflammatory activity and constitutes a potential source for the development of new treatments.

Keywords: albumin, atriplex halimus, hemostatic activity, methanolic extract

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Authors: E. Yu. Yakovlev, A. V. Puchkov

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The study is devoted to experimental work on the assessment of emanation properties of kimberlites and host rocks of the Lomonosov diamond deposit of the Arkhangelsk diamondiferous province. The aim of the study is estimation the factors influencing on formation of the radon field over kimberlite pipes. For various types of rocks composing the kimberlite pipe and near-pipe space, the following parameters were measured: porosity, density, radium-226 activity, activity of free radon and emanation coefficient. The research results showed that the largest amount of free radon is produced by rocks of near-pipe space, which are the Vendian host deposits and are characterized by high values of the emanation coefficient, radium activity and porosity. The lowest values of these parameters are characteristic of vent-facies kimberlites, which limit the formation of activity of free radon in body of the pipe. The results of experimental work confirm the prospects of using emanation methods for prospecting of kimberlite pipes.

Keywords: emanation coefficient, kimberlites, porosity, radon volumetric activity

Procedia PDF Downloads 139

Authors: Jae Bok Heo, Yun Mi Lee, Hee Rang Yun

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Several GTPases are required for ribosome biogenesis and assembly. We recently characterized rice (Oryza sativa) nuclear/nucleolar GTPase 2 (OsNug2), belonging to the YlqF/YawG family of GTPases, as playing a role in pre-60S ribosomal subunit maturation. To investigate the potential factors involved in regulating the function of OsNug2, yeast two-hybrid screens were carried out using OsNug2 as bait. Rice serine/threonine kinase 1 (OsSTK1) was identified as a potential interacting protein candidate. In vitro pull down and bimolecular fluorescence complementation assays confirmed the interaction between OsNug2 and OsSTK1, and like green fluorescent protein-tagged OsNug2, green fluorescent protein-tagged OsSTK1 was targeted to the nucleus of Arabidopsis protoplasts. OsSTK1 was not found to affect the GTP-binding activity of OsNug2; however, when recombinant OsSTK1 was included in OsNug2 assay reaction mixtures, OsSTK1 increased the GTPase activity of OsNug2. To test whether OsSTK1 phosphorylates OsNug2 in vitro, a kinase assay was performed. OsSTK1 was found to have weak autophosphorylation activity and strongly phosphorylated serine 209 of OsNug2. Yeast complementation testing resulted in a GAL::OsNug2(S209N) mutant-harboring yeast strain exhibiting a growth-defective phenotype on galactose medium at 39°C, divergent from that of a yeast strain harboring GAL::OsNug2. The intrinsic GTPase activity of mutant OsNug2(S209N) was found to be similar to that of OsNug2, was not fully enhanced upon weak binding of OsSTK1. Our findings reported here indicate that OsSTK1 functions as a positive regulator protein of OsNug2 by enhancing the GTPase activity of OsNug2, and that the phosphorylation of serine 209 of OsNug2 is essential for the complete function of OsNug2 in ribosome biogenesis.

Keywords: OsSTK1, OsNug2, GTPase activity, GTP binding activity, phosphorylation

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Authors: Asima Shah, F. A. Masoodi, Adil Gani, Bilal Ahmad Ashwar

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The present study was designed to evaluate the effect of γ-rays on the antioxidant and antiproliferative potential of β-glucan isolated from oats. The β-glucan was irradiated with 0, 2, 6, and 10 kGy by gamma ray. The samples were characterized by FT-IR, GPC, and quantitative estimation by Megazyme β-glucan assay kit. The average molecular weight of non-irradiated β-glucan was 199 kDa that decreased to 70 kDa at 10 kGy. Both FT-IR spectrum and chemical analysis revealed that the extracted β-glucan was pure having minor impurities. Antioxidant activity was evaluated by DPPH, lipid peroxidation, reducing power, metal chelating ability and oxidative DNA damage assays. Results revealed that the antioxidant activity of β-glucan increased with the increase in irradiation dose. Irradiated β-glucan also exhibited dose dependent cancer cell growth inhibition with irradiation doses. The study revealed that low molecular weight β-glucan with enhanced antioxidant and antiproliferative activities can be produced by a simple irradiation method.

Keywords: γ-irradiation, antioxidant activity, antiproliferative activity, β-glucan, oats

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Authors: René A. Sørensen, Søren Skovsen, Peter Christiansen, Henrik Karstoft

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Autonomous agricultural machines act in stochastic surroundings and therefore, must be able to perceive the surroundings in real time. This perception can be achieved using image sensors combined with advanced machine learning, in particular Deep Learning. Deep convolutional neural networks excel in labeling and perceiving color images and since the cost of high-quality RGB-cameras is low, the hardware cost of good perception depends heavily on memory and computation power. This paper investigates the possibility of designing lightweight convolutional neural networks for semantic segmentation (pixel wise classification) with reduced hardware requirements, to allow for embedded usage in autonomous agricultural machines. Using compression techniques, a lightweight convolutional neural network is designed to perform real-time semantic segmentation on an embedded platform. The network is trained on two large datasets, ImageNet and Pascal Context, to recognize up to 400 individual classes. The 400 classes are remapped into agricultural superclasses (e.g. human, animal, sky, road, field, shelterbelt and obstacle) and the ability to provide accurate real-time perception of agricultural surroundings is studied. The network is applied to the case of autonomous grass mowing using the NVIDIA Tegra X1 embedded platform. Feeding case-specific images to the network results in a fully segmented map of the superclasses in the image. As the network is still being designed and optimized, only a qualitative analysis of the method is complete at the abstract submission deadline. Proceeding this deadline, the finalized design is quantitatively evaluated on 20 annotated grass mowing images. Lightweight convolutional neural networks for semantic segmentation can be implemented on an embedded platform and show competitive performance with regards to accuracy and speed. It is feasible to provide cost-efficient perceptive capabilities related to semantic segmentation for autonomous agricultural machines.

Keywords: autonomous agricultural machines, deep learning, safety, visual perception

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Authors: Tooba N. Shamsi, Romana Perveen, Sadaf Fatima

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Protease Inhibitors (PIs) are widespread in nature, produced by animals, plants and microorganisms. They play vital role in various biological activities by keeping a check on activity of proteases. Present study aims to investigate antioxidant and anti-inflammatory properties of PPI from Cajanus cajan (CCTI) and Phaseolus limensis (LBTI). PPI was purified from C. cajan (PUSA-992) by ammonium sulfate precipitation followed by ion exchange chromatography. The anti-oxidant activity was analyzed by two most common radical scavenging assays of FRAP (ferric reducing antioxidant power) and DPPH (1,1- diphenyl-2-picrylhydrazyl). Also, in-vitro anti-inflammatory activity was evaluated using albumin denaturation assay and membrane stabilization assay at different concentrations. Ascorbic acid and aspirin were used as a standards for antioxidant and anti-inflammatory assays respectively. The PPIs were also checked for antimicrobial activity against a number of bacterial strains. The CCTI and LBTI showed DPPH radical scavenging activity in a concentration–dependent manner with IC50 values 544 µg/ml and 506 µg/ml respectively comparative to ascorbic acid which was 258 µg/ml. Following FRAP assay, it was evaluated that LBTI had 87.5% and CCTI showed 84.4% antioxidant activity, taking value of standard ascorbic acid to be 100%. The PPIs also showed in-vitro anti‐inflammatory activity by inhibiting the heat induced albumin denaturation with IC50 values of 686 µg/ml and 615 µg/ml for CCTI and LBTI respectively compared to the standard (aspirin) which was 70.8 µg/ml. Red blood cells membrane stabilization with IC50 values of 641 µg/ml and 587 µg/ml for CCTI and LBTI respectively against aspirin which showed IC50 value of 70.4 µg/ml. PPIs showed antibacterial activity against 7 known strains while there was apparently no action against fungi.

Keywords: Cajanus cajan, Phaseolus limensis, Lima beans, protein protease inhibitor, antioxidant, anti-inflammatory, antimicrobial activity

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Authors: Hanieh Mohammadi, Sarah Fraser, Anil Nigam, Frederic Lesage, Louis Bherer

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A chronically higher cerebral pulsatility is thought to damage cerebral microcirculation, leading to cognitive decline in older adults. Although it is widely known that regular physical activity is linked to improvement in some cognitive domains, including executive functions, the mediating role of cerebral pulsatility on this link remains to be elucidated. This study assessed the impact of 6 months of regular physical activity upon changes in an optical index of cerebral pulsatility and the role of physical activity for the improvement of executive functions. 27 older adults (aged 57-79, 66.7% women) with cardiovascular risk factors (CVRF) were enrolled in the study. The participants completed the behavioral Stroop test, which was extracted from the Delis-Kaplan executive functions system battery at baseline (T0) and after 6 months (T6) of physical activity. Near-infrared spectroscopy (NIRS) was applied for an innovative approach to indexing cerebral pulsatility in the brain microcirculation at T0 and T6. The participants were at standing rest while a NIRS device recorded hemodynamics data from frontal and motor cortex subregions at T0 and T6. The cerebral pulsatility index of interest was cerebral pulse amplitude, which was extracted from the pulsatile component of NIRS data. Our data indicated that 6 months of physical activity was associated with a reduction in the response time for the executive functions, including inhibition (T0: 56.33± 18.2 to T6: 53.33± 15.7,p= 0.038)and Switching(T0: 63.05± 5.68 to T6: 57.96 ±7.19,p< 0.001) conditions of the Stroop test. Also, physical activity was associated with a reduction in cerebral pulse amplitude (T0: 0.62± 0.05 to T6: 0.55± 0.08, p < 0.001). Notably, cerebral pulse amplitude was a significant mediator of the link between physical activity and response to the Stroop test for both inhibition (β=0.33 (0.61,0.23),p< 0.05)and switching (β=0.42 (0.69,0.11),p <0.01) conditions. This study suggests that regular physical activity may support cognitive functions through the improvement of cerebral pulsatility in older adults with CVRF.

Keywords: near-infrared spectroscopy, cerebral pulsatility, physical activity, cardiovascular risk factors, executive functions

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Authors: Apu Kumar Saha, Mrinmoy Majumder

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The increase in the concentration of Green House gases all over the World has induced global warming phenomena whereby the average temperature of the world has aggravated to impact the pattern of climate in different regions. The frequency of extreme event has increased, early onset of season and change in an average amount of rainfall all are engrossing the conclusion that normal pattern of climate is changing. Sophisticated and complex models are prepared to estimate the future situation of the climate in different zones of the Earth. As hydro-energy is directly related to climatic parameters like rainfall and evaporation such energy resources will have to sustain the onset of the climatic abnormalities. The present investigation has tried to assess the impact of climatic abnormalities upon hydropower potential of different regions of the World. In this regard multi-criteria, decision making, and the neural network is used to predict the impact of the change cognitively by an index. The results from the study show that hydro-energy potential of Asian region is mostly vulnerable with respect to other regions of the world. The model results also encourage further application of the index to analyze the impact of climate change on the potential of hydro-energy.

Keywords: hydro-energy potential, neural networks, multi criteria decision analysis, environmental and ecological engineering

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Authors: Mathew Wakefield, Matthew Mitchell, Lisa Wise, Christopher McCarthy

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The properties of memory representations in artificial neural networks have cognitive implications. Distributed representations that encode instances as a pattern of activity across layers of nodes afford memory compression and enforce the selection of a single point in instance space. These encoding schemes also appear to distort the representational space, as well as trading off the ability to validate that input information is within the bounds of past experience. In contrast, a localist representation which encodes some meaningful information into individual nodes in a network layer affords less memory compression while retaining the integrity of the representational space. This allows the validity of an input to be determined. The validity (or familiarity) of input along with the capacity of localist representation for multiple instance selections affords a memory sampling approach that dynamically balances the bias-variance trade-off. When the input is familiar, bias may be high by referring only to the most similar instances in memory. When the input is less familiar, variance can be increased by referring to more instances that capture a broader range of features. Using this approach in a localist instance memory network, an experiment demonstrates a relationship between representational conflict, generalization performance, and memorization demand. Relatively small sampling ranges produce the best performance on a classic machine learning dataset of visual objects. Combining memory validity with conflict detection produces a reliable confidence judgement that can separate responses with high and low error rates. Confidence can also be used to signal the need for supervisory input. Using this judgement, the need for supervised learning as well as memory encoding can be substantially reduced with only a trivial detriment to classification performance.

Keywords: artificial neural networks, representation, memory, conflict monitoring, confidence

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Authors: Elizabeth Adebomi Akinlotan, Olukemi Odukoya

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INTRODUCTION: Physical Activity is defined as activity that involves bodily movement which is done as a part of daily activity in the form of working, playing, active transportation such as walking and also as a form of recreational activity. Physical inactivity has been identified as the fourth leading risk factor for global mortality and morbidity causing an estimated 3.2 million deaths globally and 5.5% of total deaths and it remains a pressing public health issue. There is a shift in the major causes of death from communicable to non-communicable diseases in many developed countries and this is fast becoming the case in developing countries. Physical activity is an important determinant of health and has been associated with lower mortality rates as it reduces the risk of developing chronic diseases such as diabetes mellitus, hypertension, stroke, cancer and osteoporosis. It improves musculoskeletal health, controls weight and reduces symptoms of depression. AIM: The aim is to study the knowledge, attitude and practices of physical activity among adults in Alimosho local government area. METHODOLOGY: This was a descriptive cross sectional survey designed to study the knowledge, attitude and practice of physical activity among adults in Alimosho Local Government Area. The study population were 250 adults aged 18-65 who were residents of the area of more than 6 months duration and had no chronic disease condition or physical disability. A multistage sampling method was used to select the respondents and data was collected using interviewer administered questionnaires. The data was analyzed with the use of EPI-info 2007 statistical software. Chi Square was thereafter used to test the association between selected variables. The level of statistical significance was set at 5% (p<0.05). RESULTS: In general, majority (61.6%) of the respondents had a good knowledge of what physical activity entails, 34.0% had fair knowledge and 4.4% had poor knowledge. There was a favorable attitude towards physical activity among the respondents with 82.4% having an overall positive attitude. Below a third of the respondents (26.4%) reported having a high physical activity (METS > 3001) while 40.0% had moderate (601-3000 METS) levels of activity and 33.6% were inactive (<600METS). There is statistical significance between the gender of the respondent and the levels of physical activity (p=0.0007); 75.2% males reached the minimum recommendations while 24.8% were inactive and 55.0% females reached the minimum recommendations while 45.0% were inactive. Results also showed that of 95 respondents who were satisfied with their levels of physical activity, 33.7% were insufficiently active while 66.3% were either minimally active or highly active and of 110 who were unsatisfied with their levels of physical activity, 72.0% were above the minimum recommendations while 38.0% were insufficiently active. CONCLUSION: In contrast to the high level of knowledge and favorable attitude towards physical activity, there was a lower level of practice of high or moderate physical activities. It is recommended that more awareness should be created on the recommended levels of physical activity especially for the vigorous intensity and moderate intensity physical activity.

Keywords: METS, physical activity, physical inactivity, public health

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Authors: Yun-An Chen, Hung-Jun Lin, Tai-Horng Young, Der-Zen Liu

Abstract:

Decellularized brain extracellular matrix had been shown that it has the ability to influence on cell proliferation, differentiation and associated cell phenotype. However, this scaffold is thought to have poor mechanical properties and rapid degradation, it is hard for cell recellularization. In this study, we used decellularized brain extracellular matrix combined with chitosan, which is naturally occurring polysaccharide and non-cytotoxic polymer, forming a 3-D scaffold for neural stem/precursor cells (NSPCs) regeneration. HE staining and DAPI fluorescence staining confirmed decellularized process could effectively vanish the cellular components from the brain. GAGs and collagen I, collagen IV were be showed a great preservation by Alcain staining and immunofluorescence staining respectively. Decellularized brain extracellular matrix was well mixed in chitosan to form a 3-D scaffold (DB-C scaffold). The pore size was approximately 50±10 μm examined by SEM images. Alamar blue results demonstrated NSPCs had great proliferation ability in DB-C scaffold. NSPCs that were cultured in this complex scaffold differentiated into neurons and astrocytes, as reveled by NSPCs expression of microtubule-associated protein 2 (MAP2) and glial fibrillary acidic protein (GFAP). In conclusion, DB-C scaffold may provide bioinformatics cues for NSPCs generation and aid for CNS injury functional recovery applications.

Keywords: brain, decellularization, chitosan, scaffold, neural stem/precursor cells

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Authors: Thohidul Islam, Md. Hamidul Haque, Robin Kumar Biswas

Abstract:

Floods are one of the deadliest natural disasters which are very complex to model; however, machine learning is opening the door for more reliable and accurate flood prediction. In this research, a multilayer perceptron neural network (MLP) is developed to model the rainfall-water level relation, in a subtropical monsoon climatic region of the Bangladesh-India border. Our experiments show promising empirical results to forecast the water level for 1 day lead time. Our best performing MLP model achieves 98.7% coefficient of determination with lower model complexity which surpasses previously reported results on similar forecasting problems.

Keywords: flood forecasting, machine learning, multilayer perceptron network, regression

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Authors: Abdelkrim Cheriti, Mebarka Belboukhari, Nasser Belboukhari

Abstract:

Launaea Cass. is a small genus of the family Asteraceae (tribe Lactuceae, subtribe Sonchinae), consisting of 54 species, of which 9 are presented in the flora of Algeria and is mainly distributed in the South Mediterranean, Africa and SW Asia. Plants in the Launaea genus have been used ethnobotanically as bitter stomachic, for treating diarrhea, gastrointestinal tracts, as anti-inflammatory, for skin diseases, treatment of infected wounds, hepatic pains, children fever, as soporific, lactagogue, diuretic and as insecticidal. Antioxidants are vital substances, which possess the ability to protect the body from damages caused by free radical induced oxidative stress. A variety of free radical scavenging antioxidants is found in a number of dietary sources. The main objective of this study focused on the screening of antioxidant activity of Launaea nudicaulis (Asteraceae) extracts. The in vitro antioxidant activity was investigated with DPPH radical scavenging assay. The quantitative evaluation of DPPH scavenging activity showed that n-BuOH and EtOAc extracts are the most active extracts with a percentage of antiradical activity of 89,62% and 71,57% respectively.

Keywords: Launaea, phytochemical, South Algeria, Sahara, endemic specie

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Authors: Dariush Jafari, S. Mostafa Nowee

Abstract:

In this study a ternary system containing sodium chloride as solute, water as primary solvent and ethanol as the antisolvent was considered to investigate the application of artificial neural network (ANN) in prediction of sodium solubility in the mixture of water as the solvent and ethanol as the antisolvent. The system was previously studied using by Extended UNIQUAC model by the authors of this study. The comparison between the results of the two models shows an excellent agreement between them (R2=0.99), and also approves the capability of ANN to predict the thermodynamic behavior of ternary electrolyte systems which are difficult to model.

Keywords: thermodynamic modeling, ANN, solubility, ternary electrolyte system

Procedia PDF Downloads 385

Authors: Min-Woo Kim, Ok-Kyun Na, Jun-Ho Byun, Jong-Hwan Park, Seung-Hwa Yang, Joon-Hong Park, Young-Chul Park

Abstract:

This study is focused on the comparative analysis and improvement to grasp the flow characteristic of the Anti-Splash Device located under the P/V Valve and new concept design models using the CFD analysis and Artificial Neural Network. The P/V valve located upper deck to solve the pressure rising and vacuum condition of inner tank of the liquid cargo ships occurred oil outflow accident by transverse and longitudinal sloshing force. Anti-Splash Device is fitted to improve and prevent this problem in the shipbuilding industry. But the oil outflow accidents are still reported by ship owners. Thus, four types of new design model are presented by study. Then, comparative analysis is conducted with new models and existing model. Mostly the key criterion of this problem is flux in the outlet of the Anti-Splash Device. Therefore, the flow and velocity are grasped by transient analysis. And then it decided optimum model and design parameters to develop model. Later, it needs to develop an Anti-Splash Device by Flow Test to get certification and verification using experiment equipment.

Keywords: anti-splash device, P/V valve, sloshing, artificial neural network

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Authors: Keunho Park, Sunghwan Jeong

Abstract:

The 4th Industrial Revolution, the next-generation industrial revolution, which is made up of convergence of information and communication technology (ICT), is no exception to the livestock industry, and various studies are being conducted to apply the livestock smart farm. In order to monitor livestock using sensors, it is necessary to drill holes in the organs such as the nose, ears, and even the stomach of the livestock to wear or insert the sensor into the livestock. This increases the stress of livestock, which in turn lowers the quality of livestock products or raises the issue of animal ethics, which has become a major issue in recent years. In this paper, we conducted a study to monitor livestock activity based on vision technology, effectively monitoring livestock activity without increasing animal stress and violating animal ethics. The movement rate was calculated based on the difference images between the frames, and the livestock activity was evaluated. As a result, the average F1-score was 96.67.

Keywords: barn monitoring, livestock, machine vision, smart farm

Procedia PDF Downloads 124

Authors: Homayun Ghasemi, Mojtaba Yousefirad, Mozhgan Farzamisepehr

Abstract:

Heavy metals are among soil pollutant resources that in case of accumulation in the soil and absorption by the plant, enter into the food chain and poison the plants or the people who consume those plants. This research was performed in order to examine the role of cadmium as a heavy metal in the activity of catalase and peroxidase as well as protein concentration in Trifolium resupinatum L. based on a randomized block design with three repetitions. The used treatments included consumption of Cd (NO3)2 at four levels, namely, 0, 100, 200, and 300 ppm. The plants under study were treated for 10 days. The results of the study showed that catalase activity decreased by the increase of cadmium. Moreover, peroxidase activity increased by an increase inthe consumption of cadmium. The analysis of protein level showed that plantlet protein decreased in high cadmium concentrations. The findings also demonstrated that cadmium concentration in roots was higher than in shoots.

Keywords: catalase, heavy metal, peroxidase, protein

Procedia PDF Downloads 248